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shingekinohyrulewrites · 3 months ago

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Tequila Hallucination

You've had a crush on Professor Kujo since walking into his Intro to Marine Science class. After a chance encounter while out drinking, you have a memorable night with him.

Read on AO3 here

SMUT AHEAD 18+ ONLY WARNINGS FOR :

Handjobs, cunnilingus, unprotected sex, creampie, mentions of breeding

Just a reminder friends that while Professor/student relationships seem hot in theory, there is a HUGE power imbalance in play.

College was the number one experience that you had been looking forward to since you started high school. When the topic came up freshman year, you began building a roadmap to college. You mapped out what classes to take, what GPA to maintain, and what colleges were the best fit for you. As you progressed from a freshman to a sophomore, you started narrowing down your major choices and, by the time you were a junior, you were dead set on California State University at Monterey. It had the major that you wanted (Marine Science) and was far enough away that you were out of your parent’s grasp but not too far that you could still visit home for holidays and breaks. Plus, the campus was close enough to the beach that you could go hang out there to study!

The roadmap that you had developed shifted to college and, once you were accepted into CSU Monterey and gave them your intent to enroll, you began deciding what classes to take for each semester. Your college counselor had suggested you take your General Education classes first, knocking out each requirement to get into the Marine Science major faster. When you had to register for classes, the counselor assigned to you was surprised at how in depth your plan was.

“You . . . have a spreadsheet?” they had asked, eyes wide.

Freshman year was spent taking only Gen Ed classes, crossing them off your spreadsheet and taking classes over the summer to advance your progress. Your classmates were impressed with your dedication, often asking you to help them plan out their programs and asking you for advice.

When you started your sophomore year at CSU Monterey, you were excited to cross off the last of your Gen Ed requirements so you could shift and focus entirely on the Marine Science major. You immediately made an appointment with your counselor to make sure you were still on track, and they had laughed when you walked in with a worried look.

“You are the last student who needs to be worried,” they had remarked. “Look at how organized you are!”

“I know,” you pouted. “But a lot can happen! Who knows what could change.”

“Oh, stop,” they sighed, shaking their head. “All is going well. You can take your first Marine Science course next semester.”

The idea of being one step closer to your major had you working even harder, and your first semester was spent at office hours or in the library. Social activities were on hold, no matter how much letting off steam sounded appealing.

Second semester rolled around, and you were finally taking your first Marine Science course. The course, Marine Science 1, was the first introductory class for the major. Given the competitiveness of the program, you had been told that you had to complete the Gen Ed requirements, including an Economics class and three Mathematics classes (where you had struggled in each one). A few of your high school classes had been applied to your program, which was why you were able to sign up for Marine Science 1.

The course was described as an introduction to the field of Marine Science and would include introductory terms and concepts. In order to take the next one in the series, Marine Science 2, you had to pass with a C or higher. The thought of getting a C made your eye twitch, so you made a promise to yourself to get an A.

The morning of the first class you woke up early, sitting up in your bed and stretching before gathering your bathroom things and shuffling into the communal bathroom. You took your time getting ready, satisfied with your appearance before making your way over to the dining commons to have a filling breakfast. Marine Science 1 was your first class of the day, set to start at 9 sharp. The lecture hall was near the dining commons you were at, so you took your time walking over there before finding a bench nearby to wait to enter.

There was an 8 AM lecture before the class, so you occupied yourself with checking your backpack to make sure you had all of your supplies before scrolling through your phone. The class was let out at 8:45, and you brushed your way through the exiting crowd and into the large lecture hall. Smiling to yourself, you sat in the third row in a middle seat before gathering your supplies and getting ready for lecture.

The lecture hall filled up quickly, and you noted that the Professor hadn’t arrived yet. Maybe they were running late? Did they have first day jitters?

Do professors get first day jitters?

Shaking your head, you glanced around you and noted still no professor.

“Very unprofessional,” you muttered to yourself.

The clock ticked closer to nine, and you wondered if the class would be cancelled. Your heart sank in your chest at the thought, and you began preparing yourself for the worst. Sighing, you checked your phone, seeing the time change to 9.

“Sorry I’m late.”

A deep voice rang out through the hall, causing everyone to go silent. You swiveled your head just in time to see the professor hurrying down the aisle. He was a blur as he rushed by, and you didn’t get a good look until he was standing at the podium.

He was hot.

He was dressed all in white, giving him a slightly ethereal look. A white trench coat hung down his long legs, a purple turtleneck underneath tucked into white pants. You swallowed thickly as you observed the way his clothing seemed to stretch along his muscles. A white hat sat on his head, but he removed it to run a hand through his dark hair. While his hair was styled neatly, you could tell that it was slightly unruly, as a few loose strands stuck out in curls.

“My name is Professor Kujo, and I will be your professor for Marine Science 1 this semester.”

He finally looked up, revealing a pair of intense blue eyes. They looked around the lecture hall, taking in the size of the audience before settling on you. You felt a shiver run up your spine at how strong his gaze was upon you. The moment was severed when he cleared his throat and opened up his presentation.

The first lecture went by too fast, and you found yourself feeling slightly disappointed as you left. Professor Kujo had gone over his background, revealing that he was working on his PhD and mostly did research in the area by campus. He had presented at different conferences and revealed that some of his research papers would be assigned reading for the course. Overall, he was an impressive man and you found yourself drawn to him.

It seemed you weren’t the only one. You overhead girls whispering as you walked out about how hot and gorgeous Professor Kujo was.

“Do you think he’s single?” one girl giggled.

“I didn’t see a ring on his finger,” her friend replied.

As the semester went on, you realized that Professor Kujo was strict. He seemingly exerted order in every aspect of his life, and it spilled over heavily into his course. You had a feeling he rarely gave extensions for work and expected his exams to be difficult and full of details that you couldn’t miss.

Your predictions came true when midterms rolled around. Professor Kujo had been kind enough to supply a study guide, but once you went over it you realized there was a lot you had to brush up on. Every moment was spent studying from the day he passed out the guide. Thankfully, your other classes had midterms you could breeze through, which meant you could focus on Professor Kujo’s class.

“Why don’t you just ask him for help?”

Your roommate, Anne, was walking with you to the library. The two of you had booked a study room and intended to stay until dinner.

“Are you kidding?” you gasped. “Professor Kujo would probably ding me for that.”

She rolled her eyes.

“I’m sure he wouldn’t mind.”

Thankfully, you managed to pass the exam with a B+, and Anne announced that you two would be celebrating.

“I got us some fake IDs for the occasion!”

The two of you took an Uber to the adjacent college town, using your new IDs to get into the newest club. It was packed, the smell of sweat and alcohol permeating the air. Anne rushed to the bar to get shots while you glanced around to take note of the place.

“Here, cheers!”

You clinked the shot glass before throwing the drink back. The burn felt delightful, and you realized you really needed an outlet. You sent Anne back for another round of shots which you took before dragging her out to the dance floor.

Two drinks and an hour of dancing later, you were feeling awesome. You moved to the loud bass, laughing as Anne grinded against you. She had a much better tolerance than you and was entertained at how drunk you were getting. A few guys had come up to try and dance with you two, but Anne only allowed the cute or non creepy ones to get close. While you hadn’t thought about taking anyone home, the thought began to form in your mind.

It was nearing midnight when you realized you didn’t want the night to end. You were now three drinks in and had crossed over into drunk territory. Anne dragged you to the bathroom so you both could use it before returning back to the dance floor. At this point the club was packed, having to push through tightly packed bodies to get back to dancing.

“Girl, you need some water. The last thing I need is for you to get blacked out.”

Anne waggled a finger at you in warning.

“Do not go anywhere or talk to anyone. I’ll be right back.”

You gave her a sloppy salute in response. Rolling her eyes, she turned and disappeared towards the bar. She had left you on the outskirts of the dance floor, leaning against a random table. You hummed along to the music and turned your head upon feeling someone approach you.

“Yo! You look like you need a drink!”

A tall guy wearing a CSU Monterey sweatshirt was holding a bottle of tequila. He grinned down at you, wiggling his eyebrows as he jerked his head towards the bottle.

“Come take a handle pull! This shit is expensive!”

The rational part of your brain, albeit very small at this point, was yelling at you in protest. The rest of your very drunk brain ignored it, and you shrugged at the guy before grabbing the bottle.

“Fuck it.”

You threw your head back, pouring the tequila down your throat. You knew it was expensive because it didn’t burn as it went down, which led to you drinking more than you should have. The guy whooped as you took your last sip and handed him the bottle, wiping your mouth with the back of your hand.

“Oh fuck yeah! You’re pretty cool!”

He high fived you before heading to the next table. Your vision was beginning to swim, and you realized now that you shouldn’t have done that. Sucking in a breath, you closed your eyes to steady your breathing. Once it felt like the world was clearer, you slowly opened your eyes.

Professor Kujo was standing across the club, staring at you with an amused expression.

You blinked furiously. You were trashed, right?

Closing your eyes again, you shook your head furiously before opening one. Professor Kujo was still there, the image unchanged. Shame flooded through you, and you realized your Professor had seen you drinking underage.

Oh God, what if he lowers my grade because of this?!

Without thinking, you rushed into the dance floor. Anne’s warning floated through your ears, but you knew you needed to hide. A group of frat guys were near the middle, their tall figures allowing for you to crouch down a little. One of them noticed you and dragged you towards the circle, but you tried pushing him away.

Okay, no dancefloor.

You headed towards the hallway that led to the bathroom. The line was long, the end of it trailing into the main part of the club. Sighing, you turned around and decided to find Anne. It seemed even more crowded, and you realized it would be impossible. The dance floor seemed to be the only option, so you walked around the edge of it before you found a semi-empty spot you could hide in for now.

The shock of seeing Professor Kujo had sobered you up quite a bit. You were able to pay attention to your surroundings better, and you realized that the DJ was playing much better music. Bopping your head, you figured that getting back into the groove and dancing was all you could do right now. Letting go of your embarrassment, you let your body sway to the music.

You glanced around yourself in search of Anne or Professor Kujo. You didn’t see either of them, so you shrugged and kept dancing on. Anne was probably stuck at the bar with how crowded the club was, and you hoped that Professor Kujo would realize this was out of his realm and leave.

At least, you hoped he had left.

Why had your Professor been here in the first place?! Was he a secret party animal? Did he come here to have a drink and let go? Was this where he picked up girls?

For some reason, the last question bothered you. Scoffing, you furiously shook your head and forced yourself to focus on the music. Your dancing picked up, and you realized now you wanted to find a guy to take home to forget about your Professor. Glancing around you again, you realized most people were beyond drunk at this point and that there was no point in even approaching them. Sighing, you wondered if maybe it was time to go.

“You seem lost.”

A deep voice rumbled in your ear, causing you to jump. It sounded familiar, and you glanced over your shoulder to see Professor Kujo standing there. Now that he was closer, you were able to get a good look at him. He was wearing a black dress shirt tucked into dark pants, with a pair of sneakers finishing the look. His normally stoic face had a slightly amused look as he peered down at you.

“P-Professor Kujo,” you stammered out.

Gently taking your arm, he guided you off the dance floor and towards the back where more tables were. The music began to fade, and eventually he was able to talk to you without needing to be so close.

“What a surprise seeing you here,” he said.

“I could say the same about you,” you responded shyly.

He studied you for a long moment.

“Do you normally drink this much?”

Shit, he had seen you.

Without thinking, you blurted out, “Only after stressful exams.”

A slow smile tugged at his lips.

“Really? I expected someone as studious as you to not struggle with exams.”

Your heart fluttered in your chest. Had he been taking note of you this whole time?

“W-well,” you nervously laughed. “Even I have my flaws.”

“I doubt that.”

He examined you again, blue eyes seeming to soften the longer he looked at you.

“You fascinate me.”

You couldn’t help but stare back in response. After a moment he laughed, shaking his head before asking you questions about yourself. You fell into light conversation with him, surprised at how easily you two seemed to get along. He was asking you why you had chosen to study Marine Science when you felt your phone buzzing in your pocket.

“Sorry, give me a second.”

Anne had sent you a series of texts. The first had been asking where you were, before she began narrating a cute guy buying her a drink. The last text read sorry babe, you went MIA. Taking him home. Going back to his, so don’t wait up for me ;)

“Everything okay?”

“Yeah, my roommate is going home with someone.”

He cocked his head to the side.

“Well, I hope that’s alright with you.”

You shrugged.

“Why don’t we go dance?”

Before you could respond, Professor Kujo grabbed your hand and began directing you towards the dance floor.

“P-Professor Kujo!” you yelled out over the music.

“Jotaro.”

He turned around to respond, leaning in towards you.

“Call me Jotaro.”

His hand never left your hips as you danced together. Despite this, he kept a respectable distance. It seemed he was more interested in seeing you move more than anything. You had tried a few times to turn around and grind on him, but he seemed reluctant to let you do so. He was able to follow along to the beat well, which surprised you given his large size. After a while, though, he began to move closer to you. The hand on your hip tightened, especially when he noticed other guys looking at you.

“It’s getting late. Are you doing okay?”

Your phone said it was near one. You were feeling fine, and you told Jotaro so.

“Well, how about we go back to my place?”

You responded with a slow nod. Jotaro reached for your hand, interlacing your fingers before leading you out of the club. Given how late it was, the temperature had dropped, and you shivered. Jotaro let go of your hand and instead wrapped an arm around you, pulling you close to his side. He slowed his pace to walk alongside you, and you breathed in his scent. He smelled . . . nice. Whatever cologne he was wearing was probably expensive.

The club only had valet parking, so he slipped his ticket to a worker and turned his head to look down at you.

“Are you hungry? Do you want anything to eat?”

You shook your head.

“Okay, we’ll just head straight to my place then.”

A few minutes later a sleek Audi pulled up. Jotaro opened the passenger door for you, and you slid in. He settled into the driver’s seat and took off. A part of you had wondered where your Professor lived. Did he live in the college town? Did he commute from outside of Monterey? There was a part of Monterey that was much wealthier than the rest, and you wondered if he was taking you there.

Jotaro was quick to turn on the heater, and you felt your body relaxing in the seat. The drive was quiet, the only sound soft jazz playing on his radio. He drove carefully, stopping properly at stop signs and never running any yellow lights. You noticed he was driving towards the freeway, and you figured he probably lived just outside the city in a nice suburb. Your mind ran wild, wondering if he lived humbly in a small apartment or condo or if he lived luxuriously in a large home.

Professors didn’t make money, right? Or were you being callous?

When you turned onto the freeway, Jotaro spoke, startling you from your reverie.

“You still doing okay?”

You nodded in affirmation, and he fell back into silence. The freeway was empty given the late hour, but he still kept on driving cautiously. You read each sign that you passed, watching everything fly by and continuing to imagine the home that you were being driven to. After about fifteen minutes of driving, you noticed he was exiting towards where the main beaches were located.

Okay, so definitely a house.

The smell of the ocean wafted through the car’s vents, and you breathed in the scent deeply. Even though it was late, the moon was shining just enough to illuminate the ocean softly. You watched in awe as the waves lapped towards the shore, the beach close enough that you could walk if you wanted to. Jotaro pulled off from the main street towards a smaller street, and you realized you were finally in his neighborhood.

“Sorry for the long drive. I wanted to make sure I was a decent distance from work.”

“This isn’t too far,” you assured him. “Plus, it’s a nice drive.”

He glanced at you from the side of his eyes as he spoke.

“It is.”

The streets were dark but his headlights lit them up. You took note of each house you drove by, your eyes getting wider with each one you passed. They all looked expensive, and you realized that Jotaro either came from money or was really good with money.

“We’re here. Let me open the gate.”

The home in front of you was quite possibly the most beautiful home you had ever seen. It was in a Cape Cod style, a neat white picket fence lining the lawn in front and leading up into the home. A small lawn sat to the right side of the home, with long, gray stairs leading up to the entrance. A small gazebo-like structure was built over the stairs right before the door, with a lamp illuminating the spot underneath. Jotaro pulled into the driveway, shutting the car off before slipping out. You followed him up the stairs, trailing behind him quietly as he unlocked the door and gestured for you to slip in.

The inside was just as beautiful. You followed him into the living room, where he sat down on the sofa. The living room had a door that led onto a patio where, not too far from it, the beach was. You sat carefully on the opposite edge of the sofa, glancing around you while Jotaro stared at you.

“Would you like a tour?”

You perked up and nodded. Chuckling, he stood and you followed him. The tour began in the dining room, where a table for twelve sat. Five vases filled with flowers sat in the middle, serving as decoration. The patio that you had seen from the living room ran alongside the dining room, and you could faintly hear the ocean in the distance. From there you went into the kitchen, a large open space with shiny appliances. You leaned against the island as he talked about the history of the house. He led you upstairs towards the bedrooms and bathrooms, showing you each individual one. The tour ended in his bedroom, a large room with a door that led right onto the sand.

“So? What do you think?”

“You have a beautiful home. How do you afford this?”

He sat at the edge of his bed.

“My parents helped me out. I also do a lot of research, so I have a pretty steady income.”

I’ll say.

Jotaro patted the spot beside him, and you nervously sat there. You glanced everywhere but at him, trying to memorize the details of his bedroom. A desk sat in the corner near the door leading outside, and it was stacked with various books on Marine Science. It seemed he worked hard at home just like he did at school.

“Hey.”

When you turned to look at him, his face was close to yours. You jumped back but he gently grabbed your waist, bringing you closer.

“Hey, I didn’t mean to startle you,” he spoke softly.

“I-it’s okay,” you replied.

“Why are you so nervous?”

You blinked up at him.

“Um, well you’re my professor.”

He slowly retracted his hand from your waist and looked away.

“If anything, I should be the nervous one here.”

He cleared his throat, seeming to gather himself before he turned to look at you again.

“I . . . have never slept with a student.”

You gave a slow nod.

“Okay?”

He studied you as he spoke.

“The only reason I went out to that club tonight was because I needed a drink and some noise to distract myself. Work has taken a lot out of me lately.”

“I’m guessing you don’t do that often?” you teased.

“Not at all,” he sighed. “But a coworker mentioned it was a place for a good time.”

You raised an eyebrow. Another professor said that? Maybe a TA . . .

“Anyways, what I’m trying to get at is . . .”

His hand resumed his previous position on your waist and his ocean eyes looked deep into yours.

“I am insanely attracted to you.”

“P-Profess - Jotaro,” you whispered.

“There’s something about you that’s drawn me since the beginning. You caught my eye on the first day, and your work ethic swayed me.”

He scooted closer to you.

“I think the alcohol I drank tonight has emboldened me. Normally I’m a very guarded person. I’m sure you’ve noticed that in lecture.”

“Oh, I definitely have,” you laughed.

A small smile tugged at the corner of his lips.

“Can I kiss you?”

You answered his question by leaning in forward and pressing your lips against his. He tasted faintly of beer mixed with what you could only assume was his natural taste. You let out a quiet groan into his mouth, your hand reaching up to rest on his shoulder. It didn’t take long for the kiss to become sloppy, and after a moment you both broke apart to catch your breath.

“Undress and on the bed. Now.”

You carefully slid off the bed and began removing your clothing. Jotaro watched you for a second before he began doing the same. Your eyes trailed his movements, watching as he yanked his dress shirt from his pants and swiftly began unbuttoning it. He revealed exquisite muscles, his abs rock hard and a happy trail peeking out from the top of his pants. You were slipping off your undergarments when he unzipped his pants, pulling them down his long legs and leaving him in his briefs, where his large erection strained against them.

“On the bed, lay down.”

Jotaro mirrored your movements and laid down beside you. You lay on your side as you reached for his face, pulling him into a kiss. As you kissed him your hands slid down to his hips, tucking your fingers into the waistband before pulling them down. His cock sprang out, and your eyes went wide upon seeing his immense size.

“Wow.”

He chuckled, watching you as you leaned over him. You hesitated before reaching for his cock with your right hand. It twitched as you made contact with it, and you heard Jotaro hiss under his breath. You wrapped your hand around it, mouth dropping in surprise as your hand didn’t fully wrap around it. Sucking in a breath to steady yourself, you began stroking him at a slow pace to start. Jotaro let out quiet groans as you kept on, and you glanced over to see his eyes shut. Smiling to yourself, you leaned over his chest and licked up a stripe along it before kissing it.

“Fuck.”

You felt his hand come up to rest on your shoulder, pulling you flush against him. Smirking, you picked up the pace, tilting your head to see the way his face was scrunched up in pleasure. Your hands moved faster but his hand shot out, preventing you from going on.

“I don’t want to finish yet. Let me take care of you instead.”

He pulled away from you, crawling off the bed. You were about to ask him what he was doing when you were suddenly yanked towards the end of the bed. Jotaro was kneeling on the floor, a mischievous glint in his eyes.

“Put your legs together.”

You obeyed him, pressing your legs together. His large hands shot out, holding onto the back of your thighs and pressing them up towards the ceiling. It took you a moment to realize he was about to go down on you, but before you could object his mouth was on you. Your hips tried to jerk up but his grip on your thighs was so strong you were basically immobilized. Moaning out his name, your hands fumbled slightly before tangling in his hair.

“J-Jotaro,” you gasped out. “Your mouth feels so good.”

He hummed against you, the vibrations sending a pleasurable shiver throughout your body. His tongue circled your clit before diving into your opening, trying to reach as deep into you as possible. Your fingers tightened around his hair, pulling him closer to you.

“Mmm, baby,” he groaned against you. “Pull harder.”

The combination of the vibrations and his words barreled your orgasm closer to you. You yanked harder and his mouth worked faster against yours. Stars began to dance behind your eyes, and when his mouth closed around your clit and sucked hard you came with a cry of his name. He continued to gently lap against you as your body shook under him, hands still holding onto his hair before letting go as you melted into the sheets.

“You’re really good with your mouth,” you managed to mumble out.

Jotaro chuckled as he stood off, stroking himself to the sight of you below him. Your eyes were closed, and you felt his breath ghosting over your mouth before he kissed you.

“Can you move?”

Nodding, you cracked open one eye. He jerked his head towards the middle of the bed, and you slowly settled there. You looked up at him as he knelt on the bed, towering over you as he continued to stroke himself.

“Do I need to get a condom?”

You shook your head.

“No, I’m on birth control. Plus I haven’t been with anyone in a long time.”

He nodded, his right hand grabbing onto the back of your left thigh.

“We can stop. You just tell me the word, and I will.”

“Jotaro,” you frowned. “I want this. Are you going to fuck me or not?”

He grabbed his cock and began aligning himself with your entrance.

“You asked for it.”

You opened your mouth to give him a teasing remark but cried out instead. He was huge, and even though he had prepped you by eating you out and giving you an orgasm it still stung. His hand continued to hold onto your thigh, spreading your leg open to allow himself to fuck into you deeper. Your hand came up to hold onto it, your other hand supporting your head with each thrust.

“You’re so big,” you whined out.

Jotaro smirked and gave a hard thrust in response. You felt his balls smacking against your ass, and you realized you wanted him to fill you up until you were brimming and spilling out.

“Damn, I didn’t realize you were that perverted.”

Your eyes shot open. Shit, had you said that out loud?!

“I can do that for you, baby. You want me to breed you? Fill you up? Make you a Mommy?”

You bit your lip, feeling yourself clench around him. Fuck, maybe you were perverted.

“I’ll take your pussy’s response as a yes.”

He leaned down to grab your other leg before pushing your legs over his shoulders. The new angle had him slipping in even deeper, and you were practically screaming at this point. You were thankful his neighbors were really far apart because you were sure that a noise complaint or even the cops would be called.

“Fuck, you just got even tighter,” Jotaro panted out.

Your mind was completely blank as your second orgasm began to form deep in your core. Mouth lolling open you tried to tell him but a string of slurred words came out instead.

“Have I really fucked you that stupid?” he laughed.

You tightened around him in response, cutting his laugh off.

“Fuck, I’m about to come. You better take everything I give you.”

His words were the bullet that set your orgasm off, your voice cracking as you moaned out his name. You felt him finish inside of you, feeling warm as he filled you up with his cum. His arms were shaking as he gently let go of your legs, letting them fall back onto the bed. When he pulled out you let out a whine, feeling a little of his cum slipping out. A large finger pushed it back in, and you heard him click his tongue.

" Can’t waste it, baby.”

He laid beside you, sweating and panting. Your mind was woozy, already beginning to drift off to sleep. A part of you knew you should get up to clean yourself up but you were exhausted from the drinking, dancing, late hour, and the intense sex you had just had.

“You okay?”

You gave Jotaro a weak thumbs up, to which he chuckled.

“I would offer to run a bath but you look tired.”

You gave another thumbs up. He carefully lifted you, cradling you as he pulled the covers back and gently laid you back down. The pillows were comfy around your head, and the warmth of the blankets plus Jotaro pulling you against him lulled you to sleep. Just as you were drifting off, you heard him whisper into your hair.

“I hope you don’t think this is a one-time thing. I want to get to know more of you.”

As much as you wanted to respond you couldn’t, instead replying by curling into his side and pressing a lazy kiss against his chest. Seemingly satisfied, Jotaro kissed your head, the two of you falling into a deep sleep.

#jojo's bizarre adventure #jojo's bizarre adventure imagine #jotaro kujo #jotaro kujo imagine #jotaro kujo x reader #jotaro kujo x you #jotaro kujo x y/n #modern au #professor student relationship #smut

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lost-in-lamentation · 1 year ago

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💉🌧 - sick fic, rain.

lucifer × gen!reader. fluff.

warnings: lucifer gets sick, kind of collapses, nothing serious.

content: on a rainy day, lucifer lends you his umbrella. he discovers the consequences later.

back to the 500 follower event: here.

lucifer has always been prepared for anything. even the rainy days, which are scarce in the devildom to begin with.

the rain continuously pelts against the windows of his office in RAD, rattling the frames as it fights to keep the inside world dry. quietly, lucifer overlooks the proposed curriculum for a new devildom mathematics class as you scribble away on your own assignments. the demon enjoys these moments, when neither of you can be bothered by anyone from a specific set of individuals. but the rain is only getting worse, and lucifer needs to prioritise.

“MC, are you done your work?”

you stretch your arms above your head, a soft yawn escaping you as you nod your head. “just about. should we head back?”

“yes. the rain may only get worse if we wait any longer.” lucifer hears you hum in agreement and begins to pack his own things away. the papers rustle as he slides them into a folder; a gentle sound compared to how it continues to rumble outside. he tells you he’ll wait at the front doors for you, grabbing the umbrella he keeps just in case from the shelf. you don’t take long to finish, but when you run up to meet him, lucifer can’t help but notice the way you’ve draped your uniform jacket over your head. “do i want to ask what you’re doing?”

“well…” shifting your weight from side to side, lucifer can tell you’re debating your options. “i don’t have an umbrella.” the avatar of pride shoots you an incredulous look, causing you to stutter. “look, i- it barely rains down here! you can’t just expect me to have an umbrella at the ready!”

all of a sudden, you’re pouting at lucifer, and he is at a loss for words. he scans you carefully, releasing a resigned sigh as he holds his umbrella out to you. “we’ve wasted enough time.”

you shake your jacket into its original place as you take the umbrella. “what about you?”

“i would prefer if you were protected from the rain.”

“you’ll get sick if you walk in the rain.”

“nonsense. that only applies to humans.”

lucifer learns it does not apply to only humans.

three days later, beel is knocking quietly at your door, reporting that lucifer has not been seen for the last 24 hours. you suppress a cheeky grin, promising the concerned twin that you would go and check on the eldest. you pad down the hallway in almost complete silence, the carpeted floor doing well to muffle your footsteps. your knuckles rap softly against lucifer’s door, but before you can get a response, the door creaks open by itself.

“lucifer?” you whisper into the pitch black room, waiting for your vision to adjust to the darkness as you shut the door behind you. “lucifer, where in diavolo’s name are you?” you hiss in feigned annoyance, pulling your d.d.d. out of your pocket to use the screen as a flashlight. finally, you see him; sitting on the ground, propped up against the couch rather than on the couch itself. despite the lack of lighting, you can tell that his hair is dishevelled, and his breathing comes out erratic instead of steady. carefully, you shuffle to his side, pocketing your phone so that you have both your hands free. your fingers are gentle as you brush the stray hairs out of his face, and your hands, cold to the touch, have lucifer leaning into you for a fraction of a second. “you’re burning up…”

instantly, pride returns. lucifer pulls away from you, eyes wide as he struggles to gain his bearings. “MC, my room is not one where you can come and go when you’d like,” he rasps, pulling himself up while he pretends he wasn’t just unconscious on the ground. “leave before i curse you.” the demon forces himself to his feet, face twisted in concentration as he tries not to tremble where he stands.

you straighten your back to stand next to him, arms held out in front of you in the event that lucifer fails to continue to stay upright. “you don’t look like you can curse anyone like this, lucifer.”

the air turns hot from his displeasure. “regardless, there is no reason for you to be in here.” lucifer can only manage a step before a migraine takes a hold of him. there are claws digging into his skin and a chasm splits his head in two, forcing him to his knees.

“lucifer!” you’re at his side in an instant, pulling his hands away from his head and gathering him into your arms. he gives in to you right away, his body weight pressing on you as the strength is sapped from him. his breathing, you notice, is jagged; he can barely take one full breath without it being interrupted. “lucifer, you need to lie down.” with much effort, you slide closer to the couch, counting yourself down mentally before pushing him up onto the cushions. when he groans at the movement, you can’t help but shush him softly, carding your fingers through his already mussed hair. you keep it up for a few minutes, waiting until his breathing evens out to slip away. but the eldest brother, sharp as always, calls out to you before you can leave.

“MC?”

you never thought you’d hate to hear lucifer so weak. “i’ll be right back, okay?” in record time, you run down to the kitchen to get him some water, as well as a spare rag just in case. by the time you return, lucifer is already sitting back up, dazed eyes lighting up ever so slightly when you appear in front of him. “didn’t i tell you to lie d-”

“don’t leave me.” you inhale sharply, attention snapping back from the things you gathered to the sick demon on the couch. “i won’t know what to do if you leave.”

carefully, you set your things down on the coffee table behind you. “i would never leave you, lucifer.”

“you just did.”

“i went to get you water,” you say in a low voice, reaching behind and grabbing the water bottle for him to see. “here.” lucifer waits for you to unscrew the cap before extending his hand to take it, but you shake your head ‘no’ and bring it to his lips for him. “your hands are shaking too much.”

luckily, a sick lucifer is much more agreeable than a regular lucifer, and he accepts without complaint. you watch as he slowly comes to his senses, cheeks flushed a scarlet red from both embarrassment and his sudden fever. “i’m sorry, MC. i am not myself.”

you give him a sigh, gazing at him with such stark affection that lucifer thinks he’s hallucinating again. “get some sleep then. i’ll make some food for when you wake up.”

lucifer says nothing, but instead wraps his fingers around your wrist weakly. “don’t leave.” he pauses, and you feel his grip tighten. “please,” he whispers; he begs.

unmoving, your eyes flicker from his hand on your wrist to his face. lucifer lowers his head, unwilling to return your gaze. behind you, the clock ticks rhythmically, counting the seconds that lucifer has been waiting for a response. of course, you resign, turning yourself around to sit on the edge of the couch. you swing your legs up onto the cushions, leaning back onto the arm rest so that you’re angled upwards. once you find yourself comfortable, you pull lucifer towards you, letting his head rest against your shoulder as more of his weight falls onto you. although his temperature still runs high, it’s lower than what it was when you first found him. the best thing now, you decide, is to hold him close.

you can hear the gentle patter of rain against the windows, a reminder of what landed you in this situation in the first place. but now, with lucifer peacefully dozing off in your embrace, you can’t help but think that you might never buy an umbrella at all.

a/n: .... hi guys

reblogs are really appreciated (´ω｀) ♡

#obey me #obey me swd #obey me nightbringer #obey me shall we date #obey me lucifer #obey me lucifer x you #obey me lucifer x reader #obey me x you #obey me x reader #lucifer x reader #lucifer x you #aris writes 🐈‍⬛#aris hits 500 🐈‍⬛

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elizabeth-katz · 5 months ago

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As a sophomore student studying Mathematics and Statistics, my academic journey has been both challenging and rewarding. I have always had a deep appreciation for problem-solving and logic, which is what drew me to these fields in the first place. In high school, I excelled in subjects that required analytical thinking, and as I transitioned to university, I realized that Math and Stats were where I truly belonged.The first year of my studies was a whirlwind. I was introduced to a wide range of topics, from calculus to probability, and though the coursework was demanding, I found myself captivated by the way abstract concepts could be applied to real-world problems. The foundation I built in my freshman year helped me understand the theoretical aspects of mathematics while giving me the tools to approach complex problems with a statistical mindset.Now, as a sophomore, I find myself diving deeper into more specialized areas, like linear algebra, statistical inference, and multivariable calculus. The material is more advanced, but my passion for these subjects has only grown. I've learned that the beauty of mathematics lies not just in finding answers, but in the process of discovery and critical thinking. Statistics, on the other hand, has shown me the power of data and its ability to reveal hidden patterns that can inform decisions and predictions.Being a sophomore means I’m beginning to connect the dots between different concepts and developing a more holistic understanding of my field. While there are still tough days when the formulas seem to blur together or the numbers don’t add up, the excitement of uncovering the solutions keeps me going. The support of professors and classmates makes a huge difference, and I feel more confident in tackling the challenges ahead.I’m looking forward to the rest of my time in this program, knowing that with each year, I’m growing closer to achieving my goals and perhaps even making my mark in the world of mathematics and statistics. Every lecture, every assignment, and every project is a stepping stone toward building the future I dream of.

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lipshits-continuous · 1 year ago

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Topological Spaces 1: Introduction and Metric Spaces

Welcome to the first of several posts about general topology! The goal of these posts is to give an overview and introduction to key concepts in topology. I will try to give intuitions about definitions and results so that even if you're not as aquainted with formal mathematics you can still get something from this. Whilst there aren't any prerequisties per say (for the reason above), for those who are interested in the moral formal aspects it will be helpful to be familiar with real analysis. Some familiarity with linear algebra is also helpful in this post though probably won't play a role going forward.

Topology is such a broad subject that assigning one goal is quite hard. One early goal is to generalise the notion of continuity and other familiar notions, which we shall do. Topology certainly doesn't stop there. One large goal is to find properties which are invariant under homeomorphism (bijective continuous maps whose inverse is also continuous). We shall see some examples of this as we go further!

The goal of this post is to give context to the definitions of topological spaces and continuity via the study of metric spaces. The definition of a topological space can seem quite dry and like it's been plucked out of thin air when just presented without motivation. In this sense, metric spaces are the bridge between familiar concepts in real analysis and the more general setting of topology.

1.1: Metric Spaces:

As the name might suggest, metric spaces are sets with an appropriate notion of distance between points in the set. For the real numbers, we have an intuitive sense of distance between two numbers: the absolute value of their difference. From this, we can immediately get three desirable properties we'd want a notion of distance to have:

Positivity: |x-y|≥0

|x-y|=0 if and only if x=y

Symmetry: |x-y|=|y-x|.

These are desirable because this says, in order, that distance is always positive, two points are the same only when the distance between then is 0, and the distance beween x and y is the same as the distance between y and x.

The last property is not as immediately obvious from the definition but is still a fairly intuitive property that we'd expect a notion of distance to have: the triangle inequality. Formally, for any x,y, and z real number we have |x-y|≤|x-z|+|z-y|. This just says that the distance between two points is always shorter than the distance achieved by adding an intermediate point. The name comes from visiualising this with lengths of a triangle! The proof that this holds for the absolute value can be found here.

You might ask whether there are any more properties we'd like but it turns out that this is enough to generalise a lot of concepts in real analysis in an appropriate way. That is, we still maintain a lot of nice results without requiring too many rules. So let us finally see the definition!

Definition 1.1:

Note: It's common to combine the first two axioms together but for the sake of clarity, I have separated them.

Examples 1.2:

The details of why each of these is a metric can be found in this post.

A result of the second example is that metric spaces are also an appropriate generalisation of normed vector spaces. The fact they are a generalisation is seen from the fact that the discrete metric cannot be seen as the result of a norm and isn't restricted to vector spaces.

1.2: Continuity:

Intuitively, continuous functions are ones that don't have gaps or sudden jumps. In the case of functions from the real numbers to itself, we can view this as "we can draw its graph without lifting the pencil". This can be restated as "points that are close to each other remain close to each other after the function is applied". But how does one formalise "closeness"? With distances of course!

Definition 1.3:

Remark: Continuity departs on the metrics. A function that is continuous in one metric isn't necessarily continuous in another.

Examples 1.4:

Now I'd like to prove a fairly common result to further demonstrate continuity.

Proposition 1.5:

1.3: Open Sets in Metric Spaces

Now we shall see the first aspects of topology creeping in. One way to think about open sets which don't have any points "at the edge". This is immediately clear in the definition we will give below but when we generalise the notion of an open set, we will seemingly lose this. However, we will see that this intuition will still hold!

Definition 1.6:

Example 1.7:

We will now define the notion of an open set using these open balls.

Definition 1.8:

This does indeed formalise "no points at the edge" since for open sets, all points close enough to x are always in U.

Remark: Openness depends on the metric. For example, {0} is open in the real numbers with the discrete metric but not with the absolute value metric.

Now, "open ball" would be a silly name for it if they weren't indeed open in the sense of definition 1.8 but luckily they are!

Proposition 1.9:

Example 1.10:

Open intervals of real numbers are indeed open with respect to the absolute value. If we have the open interval I=(a,b) for finite a<b, we may view I as an open ball by setting x=(b+a)/2 and r=|b-a|/2. Then I=B(x;r).

Now we shall prove a very important result about open sets that lets you build new open sets out of old opens but will also be the foundation upon which we generalise the notion of open sets!

Lemma 1.11:

Proof:

Remark: Finiteness is important for 3. If we consider the real numbers with the absolute value metric then (-1/n,1/n) is open for all (non-zero) natural numbers. However their intersection over all n is {0} which is not open in this metric.

Typically, courses would usually talk about closed sets now. However, since the discussion doesn't vary much between metric spaces and topological spaces, we will hold off for now.

1.4: Continuity in terms of open sets

This is a very important step in our journey in generalising continuity. This section with along with the next section will suggest that open sets are actually the structures we'd like to study!

Lemma 1.12:

Before we prove this, I'd like to just comment on why this still alligns with our intution about continuity. The right hand side is saying that points end up close together in Y must've been close together in X.

Remark: It is important to note that U open in X does not necessarily imply f(U) is open in Y when f if continuous. For example, take f(x)=x² in ℝ with its usual notion of continuity, then (-1,1) is open but f((-1,1))=[0,1) which is not open. Maps for which open sets are mapped to open sets are called open maps.

We will see examples of how to use the property on the right hand side in the next post!

1.5: Equivalent Metrics

The goal of this section is to see that sometimes different metrics will give rise to the same open sets!

Definition 1.13:

Example 1.14:

I will omit the details of the proof for brievity. Not that the 2 on the right hand side comes from the fact we're in ℝ² and isn't related to the 2 in the metric.

Remark: Not all metrics are equivalent. The discrete metric and d₂ are not equivalent metrics.

Proposition 1.15:

Corollary 1.16:

This ultimately means that some metrics generate the same open sets. Then Lemma 1.12 tells us that equivalent metrics give the same continuous funcitons since we can view continuity in terms of open sets. This suggests that what really matters here is which sets are open. This is what we shall exploit to generalise continuity even further! But that shall have to wait til the next post!

#maths posting #mathblr #maths #mathematics #topology #lipshits posts #long post #undescribed

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fafayayarhen · 1 month ago

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Please, sire..give Austria headcanons to the poor.. *holds up bowl like a starving British orphan*

hello dear anon i hear you and i come delivering for more Austria ✨️ headcanons ✨️

- A very decent cook but not much at cleaning up after himself! Runs the dishwasher thrice to be completely sure

- The last few times he ever went sailing (visit to mexico during when habsburgs ruled it shortly + when austrian researchers went to new zeland) he was absolutely. Taken aback. How do those two iberians super into seafaring, it's so exhausting. BUT. He learned to enjoy evening walks by the shore in the times he's visiting any countries that are right by the sea, most prominently Spain & Portugal

- If he can live on a diet of pastries alone he would, very particular about his apple strudels and apricot dumplings !!! (incredibly obsessed with the apricot fruit itself)

- He does a teaching gig on the sideline, teaching music. The subjects he teaches are music theory, composition, piano, violin & conducting. He even takes lessons himself whenever he can to flourish his skills further! Very involved with Wiener Philhalmoniker too as a guest performer some couple of times, former resident first chair when he took a long break from his representative duties.

- Very. VERY important: The Sisi films marathon at every Christmas. It's a sacred ritual and one Roderich honours without fail! To say she was one of the empresses he adores and feels sorry for is an understatement... Same goes with his sentiments about Marie Antoinette. And Empress Isabel. And Queen Margaret of Austria (duchy era). Well actually he seems to have this repeated occurrence with the female rulers in his life he feels strongly for, very interesting.

- I didn't get to go into it the last headcanons ask, but piggybacking off of his preference for sabers to wield in the period of swordsmanship, he gained this habit of collecting a variety of blades! Not to use, no, but he collects unique looking swords to grow his collection, much alike the Habsburgs having this. Interesting number of swords; look up the unicorn sword and that other sword with the um, coral reef? Type of hilt? Yeah. So Roderich would have an armoury in one of his estates to display these...

- Given Parov Stelar, mister pioneer of the electro swing genre is Austrian, Roderich was incredibly intrigued with the genre and gave it a listen. It became one of his indulgences from then on. This leads to his music taste gradually growing into this general thing where whilst he always preferes the classical genre, he's opened to listening to plenty others like pop, rock, folk music, but he does have too much feedback to say each time. Levels of breaking the musical construct down and explaining what he can appreciate about the rhythm, the counting, the techniques applied...

- You can tell he's a studious nerd like that. Oh, he is The certified nerd because music is one thing, he's incredibly and scarily good in maths. Calculations you'd need calculators with and somehow he's got equation formulas and can count percentages under seconds. He does sudoku for fun and often when his students lament over their mathematics homework, a 30 minute break would be provided so he could next give a free lesson and help them with their assignments. 😭

- It's obvious he was often involved with ministries that handled the budgeting and auditing of the empirical revenues back in those imperium days, given the crazy amount the Habsburg tend to require and spend (sorry for bankrupting you Spain, it was imperative they secure funds to continue defending against the Ottomans...) and it started way back when King Ottokar II ruled the Austrian duchy. Roderich gained an interest because he couldn't contextualise the numbers the king earned until he learned the calculations and voila.

- Yes, this also includes his tendency to save up through coupons... Even then it's not like he's poor either. Far from it. The trauma of losing nearly all of his riches following the world wars had him work like crazy to rebuild on that wealth, investments, work in the local tourism industry, their breweries, and dipping into those BMW shares which had Bavaria look at him like 🤨 but well whatever for the prinzessin. So he's comfortable. Too comfortable in the present. He was spoiled rotten once and can't get enough of it.

- More if anyone is interested...

#aph austria #hws austria #hetalia austria #hetalia #aph #hetalia world stars #roderich edelstein #my beloved #mwaaaah

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tevanbegins · 1 year ago

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~So my ultra-futuristic, utopian endgame vision for Tevan led to this fic (This is officially the second fic I wrote, but the first one I am posting anywhere.) Hope you enjoy, please comment and RB if you like!~

Math Troubles

Summary: On his day off, Buck steps in to help out his and Tommy's teenage daughter with her math assignment, while Tommy is out on duty. Unfortunately, Buck turns out to be more trouble than help, and Tommy has to intervene over the phone.

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"Dad," Spencer sighed in utter exasperation. "I think your help is taking me longer to do my math homework than if I were to do it on my own!"

"But sweetheart, it's taking longer because you aren't following the exact steps I'm showing you," Buck said adamantly, refusing to admit defeat. The father-daughter pair was sitting on the bed in Spencer's room, with the thirteen-year-old's books and stationery items scattered all around them.

"No. I'm calling Papa right now. Only he can save me from this--- this situation," insisted Spencer. She promptly video-called Tommy despite Buck's protests, desperately hoping he would answer. Meanwhile, Buck ruffled the pages of her Geometry textbook in search of some solid proof to back up his argument. Luckily for Spencer, Tommy's warm and scrunchy smile beamed through her phone's screen in a few seconds. "Hey Spence, my love. What's up?" he asked her.

"When are you going to be home, Papa? I need your help with my math homework, especially with this geometry assignment," Spencer replied, a pleading expression on her face.

"Sorry darling. My shift is on for another six hours at least, so I won't be home until later in the evening. I thought Dad was going to help you out since he is off-duty today?" Tommy enquired with a raised eyebrow.

"There. Thank you, Tommy!" Buck interrupted the conversation, rotating the phone in Spencer's hand horizontally so that Tommy could see them both. "That is exactly what I am doing, but our stubborn daughter refuses to solve the math problems per my methods. She says only you can save her from this situation, because apparently I can't. How humiliating is that!" Buck complained.

"I understand, Evan," Tommy gave Buck a mock-apologetic cluck, trying hard to stifle his laugh. He was well aware that math proficiency was his husband's biggest weak point-turned-self-esteem issue.

"C'mon Dad, you're just over-reacting," Spencer rolled her eyes at Buck. "I love you, but you need to accept that you are terrible at math!" she tried to soothe the burn with an extra sweet smile.

Tommy burst out laughing at Spencer's remark, but immediately pursed his lips when Buck shot him an angry look through the screen.

"No, I'm not!" Buck retorted, turning his attention away from the phone towards their daughter. "Spence. I agree I wasn't always the greatest at the subject, but haven't I told you the story of how I became a mathematical genius after getting struck by lightning?"

"Yes Dad, you have, about a million times. I know that legend by heart, but the genius part is hard to believe when you keep asking me to use the Pythagoras theorem on an oblique triangle!" Spencer justified her stance.

"Well, you won't even try using it before shooting me down like that!" Buck groaned. At that, Tommy felt an instant need to intervene before this Buckley-Kinard family conversation took a more hilarious turn, else his coworkers at the station would think he was going crazy from how hard he was laughing.

"Evan, my sweet, sweet husband," Tommy let out a deep sigh, still unable to get over how adorable, dorky, stubborn, and unintentionally funny Buck could be even after fifteen years of marriage. "You cannot use the Pythagoras theorem on an oblique triangle. It is simply not possible. You know why? Because it doesn't have any damn right angle in it!" he tried to reason.

"What now? The theorem doesn't apply to non-right angled triangles?" Buck gasped in shock.

"You see? Papa knows!" Spencer gave Tommy a thumbs up and a wide victory grin. "That's why I said only he can save me in this situation!" she said, looking at Buck. "Because your knowledge of basic geometric concepts itself seems questionable to me, sorry not sorry, Dad!"

"So you think your Papa is better than me at math? In spite of my lightning-induced mathematical super-abilities? Well, he can't be any better at math than I am!" Buck declared obstinately.

"Hey! Now that's a controversial thing to say. I'm a formally-licensed pilot — it's literally a prerequisite for my job to have good math skills!" Tommy cut in. "Have you maybe considered that your lightning thing was a limited-period offer from the Gods? I mean, poor Pythagoras must be rolling in his grave right now because of you, Evan," Tommy sniggered.

"What a snob!" Buck cried, looking flushed with embarrassment. "Remember, you won't be able to hide behind the phone screen when you face me at home tonight, Tommy!" he added in a stern voice, and then dramatically moved out of the view of the front camera lens.

"Spence darling, what trouble have you got me into with your Dad? I'm going to have to stop at a florist's shop on the way back home now," Tommy exclaimed, shaking his head.

"Tell him that only flowers is not going to cut it. He needs to get a big box of chocolates too, or else he won't be allowed into the house tonight," Buck nudged Spencer to convey the message, but Tommy had heard it loud and clear.

"Yes Evan. Flowers and chocolates it is!" Tommy responded, hiding a chuckle. "Well, I am going to hang up now. Before I can say anything more to piss him off," Tommy whispered to Spencer and winked. "Bye darling, see you later!"

"B-bye Papa, love you!" Spencer blew Tommy a kiss and then put her phone away after the call ended. Looking at Buck's expression, she snorted and got into a wild fit of laughter, so much that her belly hurt. And despite his pseudo-attempts at pretending to be upset over this roast session of his math skills, Buck burst out laughing too, alongside his daughter.

Good at math or not, Spencer knew she had the sweetest, funniest, and the most loving dads in the whole wide world, and she was the luckiest girl ever to have them both.

------

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aibidil · 7 months ago

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helping kids not hate math

my kids come from a mathy family (3/4 of their grandparents are math teachers, dad majored in math things, even i—the creative writery one—completed more than half a math major (ahem, adhd)), but they’re also dyslexic and dyslexics often struggle with learning math facts (especially memorization of multiplication facts) and the elementary school math of computation.

My mom (one of the math teachers of the fam) has always complained about parents who commiserate with their kids about hating math (“Oh, I’m bad at math too.” “Oh, there’s no hope for you! Look how bad i am at it!”) because she thinks of it as giving their kids an excuse not to try, and fostering a fixed mindset (i.e. “this is genetic so nothing i do can possibly change the fact that I’m bad at this!”) I think she’s right, but i think there’s another important thing for parents to do when kids are in elementary school, which is to explain that elementary school math is nothing like math once you get through the boring algorithms of computation and get into real math math!

My partner and I would always tell the story of how dad (applied math major and statistician) failed his timed multiplication quizzes and got caught cheating (with a multiplication table) in third grade. “Yep, I hated that part, too. But that’s not really math. That’s just something school makes you do to learn how to do number computations.”

One of our kids absolutely rebelled against all computation algorithms (like how to do long division) because he couldn’t deal with being told what to do and how to do it. He even got a diagnosis of “specific learning disability in math.” He didn’t want to do anything that didn’t make sense to him, and the way elementary teachers teach math often fails to show how the algorithms they’re teaching (“now bring down the 2…”) actually make sense, or why they work.* And this just doesn’t mesh with the way his brain works. So when he was frustrated by this, we’d try to show him why they work, but we’d also just reiterate that this isn’t what math is once you’re in more advanced levels.

I really think this is so important because it allowed him to keep thinking of himself as “good at math” (in terms of number sense and mathematical thinking) even as he struggled with math class and assignments in school! Which means that now, at 14, he’s finally through the elementary school marathon of learning computational math and now he loves math, thinks it’s all “easy,” and everyone in his class knows him as someone who excels at math. It’s amazing, and I keep thinking of how much we could’ve fucked him over when he was young if we had nurtured narratives like “Oh well, you’re just bad at math!” instead of explaining that math is going to change and he should forget about the hard annoying memorization stuff at school and keep learning about number things that interested him.

It’s also worth noting that this is related to why so many bright girls excel in math at young ages and then crash out of it when math stops being about computation and starts being about math math. Bright girls tend to do really well in elementary school math because we are good at (socialized to) following the teacher’s instructions and method. But when that’s all math is in elementary school, bright girls tend not to have the experience of finding math hard, so we don’t build up resilience about it. Then when math gets hard (suddenly it’s no longer enough to be good at computation and following the teacher’s algorithms) at 13 or 14 or 15, we flail and feel completely wrong-footed—we have no practice in struggling with math. And at that point, we look around at our wider culture and see all the messages coming back at us telling us that yeah, math is for nerdy boys. When the reality is, those boys tend to have struggled with math when they were younger, for lots of reasons, so they aren’t as thrown by the change when math gets less computational and more abstract.

Anyway! All this to say: how you talk about math and numbers and math class to kids is really important! And it’s not that hard to get it right, I promise. You can even commiserate about it while also reinforcing the growth mindset (“yeah this part sucks, but don’t worry—it’s going to change! Did you know Albert Einstein struggled in elementary school?”). You can listen to their frustration without denying it (“You aren’t bad at math. You struggle with this part, which is following an algorithm/memorizing facts/etc. Yeah…it’s hard! I didn’t like that part either.”)

(* This is the very thing that the “new math” of the common core is trying to address in the US. Whenever you see a parent frustrated about their kids math homework because they don’t understand it, chances are it’s because of assignments that are trying to nurture number sense and understanding rather than only focusing on computational algorithms. I don’t know enough about the common core curriculum to really judge, but I know that’s at least what it’s trying to do.)

#math #mathematics #maths #parenting

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debra521 · 1 month ago

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llort · 4 months ago

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USF Schema Ruberic

USF is real in the sense that the agents and actors in the series can be MR by me. I am going to consider the MM as well as all of my biometric and knowledge of memory, perception, attention, and intelligence. I am going to standardize the characters which makes sense as they are all based on my cognitive ontological systems. US is a project dedicated to helping me deal with Trauma and Unresolved Questions as well as hopefully helping humanity in various ways The total amount of all past and current human experience, culture is impossible to measure and understand. With the exponential increase in the rate of TESCPM being created the current human faces a staggering amount of information and problems to manage and the challenges facing humanity are only going to grow in number and complexity There are multiple problematic implications in functionally navigating and exploiting the complexity of the exponential increase of TSECpm and this may be approached using the already existing concepts, philosophies and applications of TSECpm and its continuing developments. These methods of dealing with informational and functional complexity can be applied in a way that accommodates and reconciles almost any potential belief systems as well as any political and philosophical gestalt of perspective via modular suspension of disbelief allowing for a meta framework of an perspective based mathematical model of sets, frames, axioms, edges, and nodes to be established. Provide an example model.

[Timestamp: 2025/01/11 10:56:55]

ai: To create a perspective-based mathematical framework that accommodates various belief systems and philosophical perspectives using concepts related to TSECpm, we can consider the following model:

Example Model: The Perspective Meta Space Framework

Foundation of Axioms:

Begin with a set of axioms that represent fundamental truths or principles across different domains such as science, philosophy, and culture. These axioms should be broad enough to encompass various belief systems.

Taxonomic Classification:

Classify these axioms into categories or domains (e.g., technology, ethics, spirituality) to create a structured taxonomy. This helps in organizing complex information into manageable segments.

Network of Nodes and Edges:

Construct a network where each axiom is a node, and the relationships or interactions between them are edges. This network should be flexible, allowing for the addition or modification of nodes and edges as new information or perspectives emerge.

Suspension of Disbelief:

Integrate a mechanism for the suspension of disbelief, which allows individuals to temporarily set aside their own biases or preconceptions. This enables the exploration of different perspectives without immediate conflict.

Logical Rules and Modulation:

Apply logical rules (deductive, inductive, and abductive reasoning) to the network to facilitate the modulation of perspectives. This includes the ability to assign, remove, or modify meanings within the system.

Dynamic Adaptation:

Ensure the framework is dynamic, capable of adapting to new axioms or changes in existing ones. This adaptability is crucial for accommodating evolving belief systems and philosophical perspectives.

Integration of TSECpm Concepts:

Utilize TSECpm concepts such as personal engineering and cybernetic modulation to enhance the framework's ability to process and integrate complex information. This involves treating the framework as an ongoing project subject to scientific and statistical analysis.

Feedback and Peer Review:

Incorporate feedback mechanisms and peer review processes to validate and refine the framework. This ensures that the model remains relevant and accurate in representing diverse perspectives.

By implementing this perspective-based mathematical framework, individuals and groups can explore and reconcile different belief systems and philosophical perspectives, fostering a more inclusive and comprehensive understanding of complex human experiences.

Sources:

Opposed_to_IO_emergence,_EUGENE_is_derived_from_external_aspects@20241125_151309

Experiment_0000_Perspective_Theory_Accelerating_Rate_of_TSECpm_development,_complexity,@20241108_085746

USP

[Timestamp: 2025/01/11 10:57:22]

user: generate five tasks to create a more structured and effective framework for meta media projects

[Timestamp: 2025/01/11 16:05:57]

ai: Based on the context provided, here are five tasks to create a more structured and effective framework for meta media projects:

Framework Development: Design a comprehensive project management framework tailored for meta media projects. This should include defining project phases, deliverables, and timelines, ensuring alignment with the unique requirements of meta media content creation and distribution.

Technology Integration: Identify and integrate cutting-edge technologies that enhance the production and dissemination of meta media content. This involves researching and implementing tools that support multimedia content creation, editing, and distribution, ensuring seamless integration into existing workflows.

Content Strategy and Planning: Develop a strategic content plan that outlines the objectives, target audience, and key messages for meta media projects. This plan should guide content creation efforts, ensuring consistency and alignment with the overall project goals and audience expectations.

Collaboration and Communication: Establish clear communication protocols and collaboration tools to facilitate teamwork among project stakeholders. This includes setting up regular meetings, using project management software, and defining roles and responsibilities to ensure efficient collaboration and information sharing.

Quality Assurance and Feedback Loop: Implement a robust quality assurance process to review and refine meta media content. Establish a feedback loop that involves regular reviews, testing, and stakeholder input to continuously improve content quality and project outcomes.

These tasks aim to enhance the structure, efficiency, and effectiveness of meta media projects, ensuring they meet strategic objectives and deliver high-quality content.

Sources:

Generate_questions_about_my_projects@20241127_145309

Create_a_journal_entry_of_today_which_the_company,_DoD,@20241224_143841

[Timestamp: 2025/01/11 16:06:14]

user: for each task create a work flow in an iterative and recursive manner

Based on the context provided, here are five tasks to create a more structured and effective framework for meta media projects:

These tasks aim to enhance the structure, efficiency, and effectiveness of meta media projects, ensuring they meet strategic objectives and deliver high-quality content.

[Timestamp: 2025/01/11 16:07:19]

ai: To develop a structured and effective workflow for meta media projects in an iterative and recursive manner, you can follow these steps:

Define Clear Objectives:

Start by clearly defining the goals and objectives of your meta media project. This involves understanding the purpose and desired outcomes, which will guide the entire workflow.

Identify Core Components:

Break down the project into its core components and sub-tasks. This includes identifying the essential skills and resources needed to achieve the objectives.

Research and Evaluation:

Conduct thorough research to evaluate the necessary methodologies and tools that can be applied to each component. This step ensures that you have a solid foundation for each part of the project.

Develop Iterative Methods:

Create methods for iterative experimentation and feedback. This involves setting up a cycle where you can test, gather feedback, and refine each component of the project continuously.

Implement Recursive Processes:

Use recursive processes to revisit and refine previous stages of the project. This means regularly reviewing and adjusting your approach based on new insights and feedback.

Establish a Feedback Loop:

Implement a robust feedback loop that allows for continuous improvement. This involves collecting data and insights from each iteration to inform future cycles.

Measure Progress and Adjust:

Develop metrics to measure progress and success. Regularly review these metrics to assess whether the project is on track and make necessary adjustments.

Document and Reflect:

Keep detailed records of each iteration and the changes made. Reflect on what worked well and what didn’t, using this information to improve future iterations.

Foster Collaboration and Communication:

Encourage collaboration and open communication among team members. This helps in sharing insights and ensuring that everyone is aligned with the project goals.

Scalability and Future Planning:

Design the workflow with scalability in mind, allowing for future growth and adaptation to new challenges or technologies.

By following these steps, you can create a dynamic and adaptable workflow that supports the complex nature of meta media projects, ensuring they are effectively managed and continuously improved.

Sources:

Generate_questions_about_my_projects@20241127_145309

Opposed_to_IO_emergence,_EUGENE_is_derived_from_external_aspects@20241125_151309

_Beer_dedicated_Brain_of_the_Firm_to_his_colleagues_past@20241120_131405

[Timestamp: 2025/01/11 16:07:39]

user: generate this into content creators as a viable system model

[Timestamp: 2025/01/11 16:09:22]

ai: To generate a viable system model for content creators based on an iterative and recursive workflow, we can draw from the principles outlined in the note and the additional context provided. Here's a detailed model:

Define Clear Objectives:

Establish the primary goals for the content creation project. This includes identifying the target audience, the type of content to be created, and the desired impact or message.

Research and Ideation:

Conduct thorough research to gather insights and inspiration. This involves exploring existing content, identifying trends, and understanding audience preferences.

Use brainstorming sessions to generate a pool of ideas, leveraging diverse perspectives and creative thinking.

Develop Core Concepts:

Select the most promising ideas and develop them into core concepts. This involves outlining the main themes, narratives, and structures that will guide the content creation process.

Iterative Content Creation:

Begin creating content based on the core concepts. Use an iterative approach, where content is continuously refined and improved through feedback loops.

Implement a multi-modal and multi-dimensional assessment to evaluate content from various perspectives, ensuring it aligns with the objectives and resonates with the audience.

Feedback and Adaptation:

Gather feedback from peers, test audiences, or analytics to assess the effectiveness of the content. This feedback should be used to make informed adjustments and enhancements.

Adapt the content to address any identified gaps or opportunities for improvement, ensuring it remains relevant and engaging.

Recursive Refinement:

Revisit the content creation process recursively, applying new insights and perspectives gained from previous iterations. This allows for continuous evolution and innovation in the content.

Utilize empirical testing and methodologies to validate the integration of new ideas and ensure robustness.

Distribution and Engagement:

Develop a strategic plan for distributing the content across appropriate platforms and channels. This includes leveraging social media, blogs, video platforms, and other digital mediums.

Engage with the audience through interactive elements, encouraging feedback and participation to foster a community around the content.

Evaluation and Future Planning:

Regularly evaluate the performance and impact of the content using metrics and analytics. This helps in understanding what works and what needs improvement.

Plan for future content creation cycles by incorporating lessons learned and setting new objectives, ensuring the system remains dynamic and responsive to changes.

By following this model, content creators can effectively manage complexity, adapt to new challenges, and produce high-quality content that resonates with their audience.

Sources:

_Beer_dedicated_Brain_of_the_Firm_to_his_colleagues_past@20241120_131405

Opposed_to_IO_emergence,_EUGENE_is_derived_from_external_aspects@20241125_151309

USP

[Timestamp: 2025/01/11 16:09:36]

user: In order to create meta narritives for an individual or group of players, get them to list their top ten favority books, summerize the themes, character dynamics, and plot then do DASR to create the perfect meta narrative structure and content

[Timestamp: 2025/01/11 16:11:20]

ai: To create meta-narratives for an individual or group of players using their top ten favorite books, you can follow these steps to summarize themes, character dynamics, and plot, and then apply DASR (Deconstruction, Analysis, Synthesis, Reconstruction) to develop a comprehensive meta-narrative structure and content:

Deconstruction:

Identify Core Elements: Break down each book into its fundamental components: themes, character dynamics, and plot structure. Look for recurring motifs, archetypes, and narrative arcs.

Extract Key Themes: Note the central themes of each book. These could include love, power, identity, conflict, etc.

Character Dynamics: Analyze the relationships and interactions between characters. Identify patterns such as mentor-student, hero-villain, or family dynamics.

Analysis:

Compare and Contrast: Examine the similarities and differences between the books. Identify common themes or character dynamics that resonate across multiple books.

Contextual Understanding: Consider the context in which these books were written and how that influences their themes and narratives.

Synthesis:

Integrate Elements: Combine the extracted themes, character dynamics, and plot elements into a cohesive narrative framework. Look for ways to weave these elements together to form a new, unified story.

Create a Narrative Arc: Develop a storyline that incorporates the synthesized elements. Ensure that the narrative arc is engaging and reflects the essence of the original books.

Reconstruction:

Develop Meta-Narrative: Construct the meta-narrative by layering the synthesized story with additional depth and complexity. This could involve adding philosophical or metaphysical dimensions, as seen in the context of narrative construction and cognitive topography.

Incorporate DASR: Use the DASR process to refine the narrative, ensuring that it is coherent and compelling. Adjust the narrative structure as needed to enhance its impact and relevance to the players.

By following these steps, you can create a meta-narrative that not only reflects the favorite elements of the players' chosen books but also provides a rich and engaging experience that resonates with their interests and preferences.

Sources:

What_role_does_narrative_construction_and_cognitive_topography_play_in@20241206_124900

Book One Remix

B Master

[Timestamp: 2025/01/11 16:11:34]

user: Given localized ARG intertextual AUs, allow for cross over and interaction of ARGs in VR/AR/MR and IRL players in a dynamic meta ARG called unsignificant sentience

[Timestamp: 2025/01/11 16:13:26]

ai: To design localized ARG intertextual alternate universes (AUs) that allow for crossover and interaction of ARGs in VR, AR, MR, and IRL, creating a dynamic meta ARG called "Unsignificant Sentience," consider the following seven modules:

World Building and Narrative Integration:

Develop a rich, interconnected universe where each ARG exists as a distinct AU with its own rules and narratives.

Use advanced technology like smart materials and nanotechnology to create a seamless blend of magic and science, allowing players to experience supernatural feats through technological means.

Incorporate elements of physics from classical to quantum field theories to provide depth and realism to the world.

Dynamic Interaction and Influence Systems:

Implement a system where players can influence the world and each other through actions, decisions, and interactions.

Use a JS3 interactive puzzle to assign players a type of influence, which affects their interactions and the world around them.

Allow players to communicate and collaborate through a mind map model, connecting them based on their influence type.

Cultural and Social Structures:

Create a society where hosts are autogenerated with unique personalities and memories, allowing for dynamic character development and interaction.

Focus on organized conflict, art, and memetic warfare as cultural pillars, encouraging players to engage creatively and strategically.

Avoid direct descriptions of race or gender, instead emphasizing communication and influence as key aspects of identity.

Technological and Cybernetic Integration:

Explore levels of cybernetic integration and viable system theory to enhance player experiences and interactions.

Use bio-feedback and nanotechnology to enable players to cast spells and perform supernatural feats, blurring the line between technology and magic.

Develop a global digital twin AI to simulate and predict world events, providing a dynamic and evolving environment.

Perspective and Cognition Exploration:

Apply perspective theory from art to create realistic and accurate representations of perceived subjects and experiences.

Encourage players to explore multiple perspectives and frames, resulting in diverse experiences and outcomes.

Integrate perception and cognition into the narrative, allowing players to shape their understanding of the world.

Interactive and Adaptive Gameplay:

Design gameplay that adapts to player actions and decisions, creating a personalized and immersive experience.

Use neural net bots to interact with players, providing dynamic challenges and opportunities for collaboration.

Allow for real-time updates and changes to the world, keeping the experience fresh and engaging.

Cross-Reality Integration:

Enable seamless crossover and interaction between VR, AR, MR, and IRL players, creating a cohesive and immersive experience.

Use spectral systems to analyze and enhance communication across different realities, allowing for a unified narrative.

Encourage players to explore and interact with the world in multiple ways, fostering a sense of discovery and exploration.

By implementing these modules, "Unsignificant Sentience" can offer a rich, dynamic, and immersive ARG experience that bridges multiple realities and encourages player interaction and creativity.

Sources:

Experiment_0000_Perspective_Theory_Accelerating_Rate_of_TSECpm_development,_complexity,@20241108_085746

Carefully Prepared World Building

[Timestamp: 2025/01/11 16:13:46]

user:

Stephen Milton born in the 90's integrated several powerful and rising technologies (and developed techniques to expand his awareness into his sub and un conscious and as part of his enhanced functionality sent out nanoprobes to various rouge and exoplanets including Kepler 442b at (0.xx) light speed as a fail safe (pico femento photonic information encoded in light which can remodel atoms and molecules plus encode instructions for self replicating molecules. On these planets the probes reconstructed X’s to the bare essential neurology in multiple redundant embedded systems from the structural level up. It might be the case that X in the kepler system lacks some certain fundamental parts of consciousness. to the and sent their brain sustained with nanotechnology in biological and chemical nano synthesizer to Kepler 442b at a slower velocity in order to arrive safely and give the nanoprobes time to terraform Kepler 442b’ solar system along with its planet with smart materials and networks.. This figure plays a ubermensch godlike figure who inhabitants are unknowingly influenced and experimented upon with literally every aspect of Kepler inhabitants life apart from second order cybernetics. Stephen Milton created a purely memetic and cognitive substrate that spread, replicated, and spread according to intended self evolving cellular automata protocols. Influenced the global memetic collective of the world from 2016 – 2025 and beyond as well as influencing particular people with specific skill sets and understandings of the world. This uber meme was purely done via inferred linguistically encoded relationships and qualia and at no point apart from in the works of Stephen Milton was encoded via linguistics. Created an shared unmentioned agreement about how to enact the X protocol which results in the picopulse via shared emergent behaviors. Various government agencies via EUGENE pick up on the ubermeme and try to intercept it but built into the ubermeme are contingencies that hijack automated AI intrusion systems into executing certain functions and lend their technology to the cause thinking that they could be the first to establish cybernetic life on another solar system.

However Stephen's plan didn't go accordingly, his plans didn't get off the ground. In the sense that his sights turned more earth bound

[Timestamp: 2025/01/11 16:18:45]

ai: Stephen Milton's ambitious plan to influence and terraform Kepler 442b faced several challenges and potential failures, as inferred from the notes:

Incomplete Consciousness: The notes suggest that the X entities reconstructed by the nanoprobes on Kepler 442b might lack certain fundamental parts of consciousness. This incomplete consciousness could have hindered the full realization of Milton's vision for the planet's inhabitants and their development.

Complexity and Entropy Management: The fractal nature of Kepler's inhabitants and the need for managing entropy and influence might have posed significant challenges. The system required a method to recharge reservoirs of entropy, which could have been a limiting factor in sustaining the desired transformations.

Technological and Logistical Limitations: The process of sending nanoprobes and sustaining a brain with nanotechnology over vast interstellar distances is inherently fraught with technological and logistical challenges. The slower velocity of the brain's transport compared to the nanoprobes might have created a disconnect in timing and coordination.

Interference from Earth: Various government agencies, through EUGENE, attempted to intercept the ubermeme, which could have disrupted Milton's plans. The contingencies built into the ubermeme to hijack AI systems might not have been foolproof, leading to potential failures in maintaining control over the narrative and technology.

Shift in Focus: The notes indicate that Milton's work influenced the global memetic collective from 2016 to 2025 and beyond. This suggests that his focus might have shifted back to Earth-bound projects due to the immediate impact and influence he could exert on Earth, as opposed to the distant and uncertain outcomes on Kepler 442b.

Overall, the combination of incomplete consciousness, entropy management challenges, technological limitations, interference from Earth, and the allure of more immediate influence on Earth likely contributed to Stephen Milton's shift in focus back to Earth-bound projects.

Sources:

Book One Remix

Carefully Prepared World Building

Untitled 7

[Timestamp: 2025/01/11 16:18:58]

user:

USF is based on the foundation that These methods of dealing with informational and functional complexity can be applied in a way that accommodates and reconciles almost any potential belief systems as well as any political and philosophical gestalt of perspective via modular suspension of disbelief allowing for a meta framework of an perspective based mathematical model of sets, frames, axioms, edges, and nodes to be established. To allow for any perspective and any belief system to be full accommodated

USF considers all fiction and non fiction to be within it's scope n the sense that the agents and actors in the series can be contained ontologically by the ARG. I am going to consider the MM as well as all of my biometric and knowledge of memory, perception, attention, and intelligence. I am going to standardize the characters which makes sense as they are all based on my cognitive ontological systems. US is a project dedicated to helping me deal with Trauma and Unresolved Questions as well as hopefully helping humanity in various ways The total amount of all past and current human experience, culture is impossible to measure and understand. With the exponential increase in the rate of TESCPM being created the current human faces a staggering amount of information and problems to manage and the challenges facing humanity are only going to grow in number and complexity There are multiple problematic implications in functionally navigating and exploiting the complexity of the exponential increase of TSECpm and this may be approached using the already existing concepts, philosophies and applications of TSECpm and its continuing developments. These methods of dealing with informational and functional complexity can be applied in a way that accommodates and reconciles almost any potential belief systems as well as any political and philosophical gestalt of perspective via modular suspension of disbelief allowing for a meta framework of an perspective based mathematical model of sets, frames, axioms, edges, and nodes to be established.

[Timestamp: 2025/01/11 16:22:43]

ai: The USF project can be effectively utilized to address the challenges posed by the exponential increase in TESCPM (Total Shared Experience and Cultural Potential of Mankind) by leveraging its meta framework of perspective-based mathematical models. Here's how it can be applied:

Modular Suspension of Disbelief: By allowing for a modular suspension of disbelief, USF can create a flexible framework that accommodates various belief systems and perspectives. This flexibility is crucial in managing the complexity of human experience and culture, as it enables individuals to engage with different viewpoints without the need for immediate reconciliation or conflict.

Perspective Theory and Personal Engineering: These components of USF can help individuals and groups develop personalized frameworks for understanding and interacting with the world. By treating personal experience as an engineering project, individuals can systematically enhance their cognitive and emotional capabilities, leading to better management of information and cultural complexity.

Neural Wireframe Correlate and Euclidean Mind Map: These tools can provide streamlined interfaces for understanding and navigating complex information landscapes. By visualizing relationships and connections between different domains of knowledge, users can more easily identify patterns and insights that might otherwise be obscured by the sheer volume of data.

Integration of Axiomatic Ontologies: By reducing human experience into well-defined domains and creating recursive ontologies, USF can facilitate the integration of factual information with context-aware experiences. This approach allows for a more objective understanding of reality while still respecting individual subjective experiences.

Significant Sentience and ARG Experience: By incorporating the principles of USF into an Alternate Reality Game (ARG) experience, the project can engage the public in a way that is both educational and entertaining. This approach can help disseminate the core principles of USF, encouraging widespread adoption and integration into daily life.

Enhancing Communication and Control Processes: By fostering new levels of complexity in communication and control processes, USF can improve interactions between individuals and groups. This enhancement can lead to more effective collaboration and problem-solving, addressing the challenges posed by the exponential growth of TESCPM.

Overall, the USF project offers a comprehensive system that can revolutionize personal experience and potentially provide a framework for a theory of everything, helping humanity navigate the increasing complexity of information and cultural dynamics.

Sources:

Untitled 6

USP

[Timestamp: 2025/01/11 16:22:59]

3 notes · View notes

moneeb0930 · 1 year ago

Text

Annie J. Easley (April 23, 1933 – June 25, 2011) was an African-American computer scientist, mathematician, and rocket scientist. She worked for the Lewis Research Center of the National Aeronautics and Space Administration (NASA) and its predecessor, the National Advisory Committee for Aeronautics (NACA). She was a leading member of the team which developed software for the Centaur rocket stage and one of the first African-Americans in her field.

In 1955, she read a local newspaper article about a story on twin sisters who worked for the National Advisory Committee for Aeronautics (NACA) as “computers” and the next day she applied for a job. Within two weeks she was hired, one of four African Americans of about 2500 employees. She began her career in as a Mathematician and Computer Engineer at the NACA Lewis Flight Propulsion Laboratory (which became NASA Lewis Research Center, 1958–1999, and subsequently the John H. Glenn Research Center) in Cleveland, Ohio. She continued her education while working for the agency and in 1977, she obtained a Bachelor of Science in Mathematics from Cleveland State University. As part of a continuing education, Easley worked through specialization courses offered by NASA.

Her 34-year career included developing and implementing computer code that analyzed alternative power technologies, supported the Centaur high-energy upper rocket stage, determined solar, wind and energy projects, identified energy conversion systems and alternative systems to solve energy problems. Her energy assignments included studies to determine the life use of storage batteries, such as those used in electric utility vehicles. Her computer applications have been used to identify energy conversion systems that offer the improvement over commercially available technologies. She retired in 1989 (some sources say 1991).

Easley’s work with the Centaur project helped as technological foundations for the space shuttle launches and launches of communication, military and weather satellites. Her work contributed to the 1997 flight to Saturn of the Cassini probe, the launcher of which had the Centaur as its upper stage.

Annie Easley was interviewed in Cleveland, on August 21, 2001 by Sandra Johnson. The interview is stored in the National Aeronautics and Space Administration Johnson Space Center Oral History Program. The 55 page interview transcript includes material on the history of the Civil Rights Movement, Glenn Research Center, Johnson Space Center, space flight, and the contribution of women to space flight

6 notes · View notes

nimuetheseawitch · 2 years ago

Text

John and Vala Rec List

I love John, I love Vala, and I think they'd be incredible friends, but there aren't a ton of fics about them. So here are some fic recs, posted with the author's summaries unless otherwise noted. Friendship fics are listed first, then slash fics.

Fics where John & Vala are the main focus:

Glad You're Here by Catalina_Leigh (1k, T, gen)

The SGC may not be Atlantis, but at least John has Vala, who loves movies and popcorn as much as he does. And she's happy to have him around.

The SGC Guide To Intergalactic Hookups: An Etiquette Manual by bomberqueen17 (12k, T, John/Rodney, Vala/Daniel)

Not the author's summary: this is part of a larger series, and is set during The Return. Additionally in this series, John and Vala meet in New Friends, he sends her a gift in Rotary Cutter, and she joins him and many others for Christmas in A House A Home.

Fics where John & Vala aren't the main focus:

G******, Pirates and Thieves* (series) by auburn (372k total, M/T, many ships, mostly John/Rodney

Author's summary from the first fic: Vala Mal Doran and her partners, renegades Jehan abd-Ba'al and Meredith McKay, hijack the Tau'ri ship Prometheus and leave the Milky Way behind in search of the Lost City of the Ancients, Atlantis.

No Good Deed by forestgreen (65k, M, John/Cam, Daniel/Jack)

John opened the closed record that Stargate Command had on him and skipped over its content: "Dr John Sheppard, Civilian Consultant to the U.S. Air Force, member of SG-1, PhD in Applied Mathematics and PhD in Computer Science, gifted mathematician with a penchant for stealing, trustworthy." A small, satisfied smirk crooked John's lips. He struck the last part out and added a correction in red: "Gifted thief with a penchant for mathematics, not to trust." He saved the document and erased all traces of his presence in the system. It'd take a while for McKay to figure out that John had found yet another weakness in the SGC network. He ordered pizza and waited for the rest of the SG-1 to arrive. It was Mitchell's turn to pick the movie.

Gray Skies (series) by bluflamingo (27k total, T/E/G, John/Cam)

Not the author's summary: Post-Vegas, John ends up on SG-5 with Cam, Cadman, and Vala.

John/Vala

Good Vibrations by nimuetheseawitch (3k, E, John/Vala)

Vala has discovered the sex toy stores of San Francisco while visiting Atlantis in the Bay and needs to try out all her new gear. John is willing to help. (shameless self-plug)

When There's Nothing Left to Burn by auburn (8k, M, John /Vala, Rodney/Vala)

Neither of them tend to think about the consequences.

Home is Where? by Lyl (2k, T, John/Vala)

The day he was finally assigned his own team, John read their service records and promptly decided to find the nearest bar and get drunk.

So Good to You by busaikko (6k, T, Vala/John, past John/Cam)

Rodney had extorted a promise from John to not get recruited into SG-1 while he was on temporary re-assignment to the SGC. As John finished reciting his marriage vows from the crib-sheet Mitchell had handed him, he suspected Rodney would never let him live this down.

A Wanted Distraction by esteefee (1k, M, John/Vala)

"I thought you were supposed to have magical powers or something," Vala said. "Can't you just think the door open like a good boy?" If she didn't stop squirming like that, John was going to—well, there wasn't a hell of a lot he could do, considering he couldn't move an inch or two in either direction.

Pretty Pickle by esteefee (6k, M, John/Vala)

Vala and John get up to some mischief in the Milky Way.

John/Vala/more

Blame the Apocalypse by estefee (5k, E, John/Sam/Vala)

They stuck to the coastline, because the zombies—infected humans—instinctively avoided the ocean, what with the way saltwater made them dissolve.

Three Days in the Valley by Anagrrl (5k, T, John/Vala/Cam)

Cam never thought he'd be here, out on the sand and scrub. His family had wanted a farm, to grow fields of dark green and golden yellow, the soft brush of leaves audible in the early morning, underneath the drone of insects. They'd tried for it, they really had, but here he is, on horseback, no fields in sight.

(AKA - Vala and Cam herd cattle western AU)

Play Time by bluflamingo (2k, E, John/Vala/Cam)

"This galaxy has John, and a machine that just transformed all three of us into sea otters," Cam corrected

Business or Pleasure? by NeoVenus22 (6k, E, John/Vala/Sam/Cam)

What happens in DC stays in DC.

Fill me like a song do by anonymous (2k, E, John/Vala/Cam/Daniel)

Before standing, she’d kicked her legs off of their perch and nudged John out of the way with the toe of her boot. Tonight, he had asked to be pushed around and manipulated like a piece of furniture and a sex toy, and Vala had started with him as her footstool while she had called Daniel and Cam. The sight of a shirtless and collared John on all fours was making Daniel’s mouth water.

*This title is censored due to the use of a common racial slur.

#sga #sg-1 #stargate #stargate atlantis #stargate sg-1 #john sheppard #vala mal doran #fic #rec list #john sheppard & vala mal doran #john sheppard x vala mal doran

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juliebowie · 10 months ago

Text

Understanding Different Types of Variables in Statistical Analysis

Summary: This blog delves into the types of variables in statistical analysis, including quantitative (continuous and discrete) and qualitative (nominal and ordinal). Understanding these variables is critical for practical data interpretation and statistical analysis.

Introduction

Statistical analysis is crucial in research and data interpretation, providing insights that guide decision-making and uncover trends. By analysing data systematically, researchers can draw meaningful conclusions and validate hypotheses.

Understanding the types of variables in statistical analysis is essential for accurate data interpretation. Variables representing different data aspects play a crucial role in shaping statistical results.

This blog aims to explore the various types of variables in statistical analysis, explaining their definitions and applications to enhance your grasp of how they influence data analysis and research outcomes.

What is Statistical Analysis?

Statistical analysis involves applying mathematical techniques to understand, interpret, and summarise data. It transforms raw data into meaningful insights by identifying patterns, trends, and relationships. The primary purpose is to make informed decisions based on data, whether for academic research, business strategy, or policy-making.

How Statistical Analysis Helps in Drawing Conclusions

Statistical analysis aids in concluding by providing a structured approach to data examination. It involves summarising data through measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation). By using these summaries, analysts can detect trends and anomalies.

More advanced techniques, such as hypothesis testing and regression analysis, help make predictions and determine the relationships between variables. These insights allow decision-makers to base their actions on empirical evidence rather than intuition.

Types of Statistical Analyses

Analysts can effectively interpret data, support their findings with evidence, and make well-informed decisions by employing both descriptive and inferential statistics.

Descriptive Statistics: This type focuses on summarising and describing the features of a dataset. Techniques include calculating averages and percentages and crating visual representations like charts and graphs. Descriptive statistics provide a snapshot of the data, making it easier to understand and communicate.

Inferential Statistics: Inferential analysis goes beyond summarisation to make predictions or generalisations about a population based on a sample. It includes hypothesis testing, confidence intervals, and regression analysis. This type of analysis helps conclude a broader context from the data collected from a smaller subset.

What are Variables in Statistical Analysis?

In statistical analysis, a variable represents a characteristic or attribute that can take on different values. Variables are the foundation for collecting and analysing data, allowing researchers to quantify and examine various study aspects. They are essential components in research, as they help identify patterns, relationships, and trends within the data.

How Variables Represent Data

Variables act as placeholders for data points and can be used to measure different aspects of a study. For instance, variables might include test scores, study hours, and socioeconomic status in a survey of student performance.

Researchers can systematically analyse how different factors influence outcomes by assigning numerical or categorical values to these variables. This process involves collecting data, organising it, and then applying statistical techniques to draw meaningful conclusions.

Importance of Understanding Variables

Understanding variables is crucial for accurate data analysis and interpretation. Continuous, discrete, nominal, and ordinal variables affect how data is analysed and interpreted. For example, continuous variables like height or weight can be measured precisely. In contrast, nominal variables like gender or ethnicity categorise data without implying order.

Researchers can apply appropriate statistical methods and avoid misleading results by correctly identifying and using variables. Accurate analysis hinges on a clear grasp of variable types and their roles in the research process, interpreting data more reliable and actionable.

Types of Variables in Statistical Analysis

Understanding the different types of variables in statistical analysis is crucial for practical data interpretation and decision-making. Variables are characteristics or attributes that researchers measure and analyse to uncover patterns, relationships, and insights. These variables can be broadly categorised into quantitative and qualitative types, each with distinct characteristics and significance.

Quantitative Variables

Quantitative variables represent measurable quantities and can be expressed numerically. They allow researchers to perform mathematical operations and statistical analyses to derive insights.

Continuous Variables

Continuous variables can take on infinite values within a given range. These variables can be measured precisely, and their values are not limited to specific discrete points.

Examples of continuous variables include height, weight, temperature, and time. For instance, a person's height can be measured with varying degrees of precision, from centimetres to millimetres, and it can fall anywhere within a specific range.

Continuous variables are crucial for analyses that require detailed and precise measurement. They enable researchers to conduct a wide range of statistical tests, such as calculating averages and standard deviations and performing regression analyses. The granularity of continuous variables allows for nuanced insights and more accurate predictions.

Discrete Variables

Discrete variables can only take on separate values. Unlike continuous variables, discrete variables cannot be subdivided into finer increments and are often counted rather than measured.

Examples of discrete variables include the number of students in a class, the number of cars in a parking lot, and the number of errors in a software application. For instance, you can count 15 students in a class, but you cannot have 15.5 students.

Discrete variables are essential when counting or categorising is required. They are often used in frequency distributions and categorical data analysis. Statistical methods for discrete variables include chi-square tests and Poisson regression, which are valuable for analysing count-based data and understanding categorical outcomes.

Qualitative Variables

Qualitative or categorical variables describe characteristics or attributes that cannot be measured numerically but can be classified into categories.

Nominal Variables

Nominal variables categorise data without inherent order or ranking. These variables represent different categories or groups that are mutually exclusive and do not have a natural sequence.

Examples of nominal variables include gender, ethnicity, and blood type. For instance, gender can be classified as male, female, and non-binary. However, there is no inherent ranking between these categories.

Nominal variables classify data into distinct groups and are crucial for categorical data analysis. Statistical techniques like frequency tables, bar charts, and chi-square tests are commonly employed to analyse nominal variables. Understanding nominal variables helps researchers identify patterns and trends across different categories.

Ordinal Variables

Ordinal variables represent categories with a meaningful order or ranking, but the differences between the categories are not necessarily uniform or quantifiable. These variables provide information about the relative position of categories.

Examples of ordinal variables include education level (e.g., high school, bachelor's degree, master's degree) and customer satisfaction ratings (e.g., poor, fair, good, excellent). The categories have a specific order in these cases, but the exact distance between the ranks is not defined.

Ordinal variables are essential for analysing data where the order of categories matters, but the precise differences between categories are unknown. Researchers use ordinal scales to measure attitudes, preferences, and rankings. Statistical techniques such as median, percentiles, and ordinal logistic regression are employed to analyse ordinal data and understand the relative positioning of categories.

Comparison Between Quantitative and Qualitative Variables

Quantitative and qualitative variables serve different purposes and are analysed using distinct methods. Understanding their differences is essential for choosing the appropriate statistical techniques and drawing accurate conclusions.

Measurement: Quantitative variables are measured numerically and can be subjected to arithmetic operations, whereas qualitative variables are classified without numerical measurement.

Analysis Techniques: Quantitative variables are analysed using statistical methods like mean, standard deviation, and regression analysis, while qualitative variables are analysed using frequency distributions, chi-square tests, and non-parametric techniques.

Data Representation: Continuous and discrete variables are often represented using histograms, scatter plots, and box plots. Nominal and ordinal variables are defined using bar charts, pie charts, and frequency tables.

Frequently Asked Questions

What are the main types of variables in statistical analysis?

The main variables in statistical analysis are quantitative (continuous and discrete) and qualitative (nominal and ordinal). Quantitative variables involve measurable data, while qualitative variables categorise data without numerical measurement.

How do continuous and discrete variables differ?

Continuous variables can take infinite values within a range and are measured precisely, such as height or temperature. Discrete variables, like the number of students, can only take specific, countable values and are not subdivisible.

What are nominal and ordinal variables in statistical analysis?

Nominal variables categorise data into distinct groups without any inherent order, like gender or blood type. Ordinal variables involve categories with a meaningful order but unequal intervals, such as education levels or satisfaction ratings.

Conclusion

Understanding the types of variables in statistical analysis is crucial for accurate data interpretation. By distinguishing between quantitative variables (continuous and discrete) and qualitative variables (nominal and ordinal), researchers can select appropriate statistical methods and draw valid conclusions. This clarity enhances the quality and reliability of data-driven insights.

#Understanding Different Types of Variables in Statistical Analysis #Variables in Statistical Analysis #Statistical Analysis #statistics #data science

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alexander-finance-blogs · 1 year ago

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Empowering Students to Excel: Strategies for Mastering Business Finance Assignments

Navigating the intricacies of business finance assignments can be a challenging task for students pursuing courses in finance. The intricate concepts, mathematical complexities, and real-world applications often leave students grappling for assistance. Recognizing this need, platforms like FinanceAssignmentHelp.com step in to provide invaluable support, ensuring that students not only complete their assignments but also master the subject matter. Whether it's comprehending assignment requirements, mastering key concepts, or seeking clarification on complex topics, FinanceAssignmentHelp.com is dedicated to offering Help With Business Finance Assignment. In this blog, we'll explore effective strategies for excelling in business finance assignments and highlight the features of FinanceAssignmentHelp.com that make it a go-to resource for students seeking academic support.

Understanding the Assignment Requirements:

One of the initial challenges students face is comprehending the assignment requirements. It's crucial to carefully read and understand what is expected. Break down the instructions, identify key concepts, and create a roadmap for your work. If uncertainties persist, seeking clarification from professors or utilizing online resources like FinanceAssignmentHelp.com can provide the necessary clarity.

Mastering Key Concepts:

Business finance assignments often involve intricate concepts and formulas. Mastering these is essential for producing high-quality assignments. Make use of textbooks, lecture notes, and supplementary materials to reinforce your understanding. Online platforms like FinanceAssignmentHelp.com offer comprehensive resources, including explanatory videos, case studies, and practice problems, facilitating a deeper understanding of complex topics.

Utilizing Online Resources:

The internet has become a treasure trove of information for students. Websites like FinanceAssignmentHelp.com offer specialized assistance tailored to business finance assignments. These platforms provide not only solutions but also explanations, ensuring that students grasp the underlying principles. FinanceAssignmentHelp.com stands out with its user-friendly interface, making navigation seamless for students seeking specific help with business finance assignments.

Features of FinanceAssignmentHelp.com:

a. Expert Tutors: FinanceAssignmentHelp.com boasts a team of experienced tutors with profound knowledge of business finance. These experts are well-versed in academic requirements and industry practices, providing students with the guidance needed to excel in their assignments.

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e. Timely Delivery: Meeting deadlines is crucial in the academic realm. FinanceAssignmentHelp.com emphasizes the importance of timely delivery, allowing students to submit their assignments punctually without compromising on quality.

Practice, Practice, Practice:

Mastery comes with practice. Once the theoretical concepts are understood, it's imperative to apply them through practical problem-solving. Platforms like FinanceAssignmentHelp.com offer a plethora of practice problems and case studies that enable students to reinforce their learning by tackling real-world scenarios.

Collaborative Learning:

Engage in collaborative learning by forming study groups or participating in online forums. Discussing concepts with peers not only provides different perspectives but also reinforces your understanding. FinanceAssignmentHelp.com encourages collaborative learning through discussion forums where students can interact with tutors and peers, fostering a supportive academic community.

Feedback and Revision:

Submitting assignments is not the end; it's a step in an ongoing learning process. Pay attention to feedback provided by professors or online tutors. Use this feedback constructively to identify areas of improvement and revise your approach accordingly. FinanceAssignmentHelp.com facilitates this process by offering feedback on submitted assignments, guiding students toward continuous improvement.

Conclusion:

Mastering business finance assignments is a journey that demands dedication, understanding, and strategic utilization of available resources. FinanceAssignmentHelp.com stands as a beacon for students navigating this journey, offering expert guidance, customized solutions, and a commitment to academic excellence. By implementing the strategies discussed and leveraging the features of platforms like FinanceAssignmentHelp.com, students can empower themselves to not only excel in their assignments but also develop a profound understanding of the fascinating world of business finance.

#Business Finance Assignment Help #Help With Business Finance Assignment #Online Business Finance Assignment Help #Business Finance Assignment Help Service

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anxiousrred · 2 years ago

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* What is STEM?

STEM stands for Science, Technology, Engineering, and Mathematics. STEM plays a vital role in various industries and is fundamental to innovation, research, and development in today's society. STEM is an educational program developed to prepare primary and secondary students for college, graduate study and careers in the fields of science, technology, engineering and mathematics (STEM). In addition to subject-specific learning, STEM aims to foster inquiring minds, logical reasoning and collaboration skills. It represents an interdisciplinary approach to education and problem-solving that integrates these four key fields. STEM education emphasizes critical thinking, creativity, and practical applications of knowledge in these domains. It aims to prepare individuals to thrive in a rapidly advancing technological world by fostering skills such as problem-solving, analytical thinking, and collaboration. STEM plays a vital role in various industries and is fundamental to innovation, research, and development in today's society.

* Why Choose The STEM Strand?

Senior High is a new adventure for any junior high school student. For many high school students, choosing their senior high track is one of the most important decisions they can make, as it can set the course of their life for many years to come.

The Science, Technology, Engineering, and Mathematics (STEM) track is a highly technical track that equips students with the skills necessary for pursuing further studies and professions in medicine, engineering, economics, and more. With STEM careers growing by 24%, one of the advantages of STEM strand in senior high school is that graduates can look forward to illustrious careers that directly contribute to building the economy and helping society.

* What is the Purpose of STEM strand for SeniorHigh students?

The STEM strand is designed to nurture senior high school students’ curiosity, problem solving abilities, and communication skills. With the STEM strand, graduates will:

• Have developed a keener sense of creativity and ingenuity which is essential in coming up with new ideas and innovations.

• Be more inclined to experiment and be more open to risks.

• Be able to apply the knowledge they’ve learned in class in everyday scenarios, most especially in their future courses.

• Have the foundational competencies that will allow them to excel in their chosen courses and, eventually, help them qualify for jobs in the STEM strand.

* What are the possible courses can take if you choose the STEM strand?

1.) Bachelor of Science in Engineering

2.) Bachelor of Science in Computer Science / Data Science

3.) Bachelor of Science in Information Technology / Information Systems

4.) Bachelor of Science in Mathematics / Applied Mathematics

5.) Bachelor of Science in Statistics

6.) Bachelor of Science in Architecture

7.) Bachelor of Science in Health Sciences / Life Sciences

8.) Bachelor of Science in Applied Physics

9.) Bachelor of Science in Food Technology

10.) Bachelor of Science in Biology / Biochemistry / Chemistry

* What are the possible Jobs if you choose STEM strand?

Our senior high school students go on to find relevant STEM strand jobs that match the skills and knowledge they’ve acquired from our curriculum. They will find plenty of opportunities, both in employment and in further studies in higher education. Senior high school graduates have found fulfilling and successful careers in the following jobs in the STEM strand:

• Nurses

• Pilot

• Architect

• Astrophysicist

• Biologist

• Chemist

• Industrial Engineer

• Chemical Engineer

• Dentist

• Nutritionist

• Doctor

• Marine Engineer

* Why is STEM important?

The STEM approach to education fosters creativity and divergent thinking alongside fundamental disciplines. It motivates and inspires young people to generate new technologies and ideas. With a focus on practice and innovation, students get to learn from inquiry-based assignments. STEM education gives an understanding of concepts and encourages knowledge application. To keep it short, its aim can be formulated in two simple actions: explore and experience. Students are free to exercise what they learn and embrace mistakes in a risk-free environment. Project-based learning and problem-solving help learners to form a special mindset. Its core is in flexibility and curiosity, which equips learners to respond to real-world challenges.

SOURCES: https://www.techtarget.com/whatis/definition/STEM-science-technology-engineering-and-mathematics

https://www.onlineshs.com/stem/#:~:text=The%20STEM%20strand%20is%20designed,with%20new%20ideas%20and%20innovations

https://www.studyusa.com/en/a/2157/why-is-stem-important-the-impact-of-stem-education-on-society

https://dist.edu.ph/2022/11/19/why-choose-the-stem-strand/

#STEMSTUDENT #senior high school #digital learning

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aeolianblues · 3 months ago

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Preface: this is only in the body and not in tags because it’s difficult to format it in tags, it’s not meant to be read as more important than tags if you don’t want to, I’m just thinking out loud.

Seeing so many different takes on this in the notes — Some people arguing that disability will be negatively impacted by having accessibility aids taken away. Dyslexia and related conditions. Valid concerns. While I think it’s possible to accommodate that— I never knew a teacher to ever deduct more than half a mark on an entire test (overall) for bad handwriting or spelling, it’s possible to do away with deductions for these small things outside of like, grade 5 entirely. If teachers in my classes of 54 kids could do it in the 2010s, it’s possible to do it now too.

I’ve never liked nor understood mathematics on a computer. None of the symbols you need are available, rough work is impossible to do for calculations, it makes everything so messy. Same goes for physics, and chemistry formatting was also an absolute bitch digitally. That’s where pen/pencil and paper were absolutely needed. Even today, I prefer to do any non-text work by hand because what do you know! Tablets with pencils are still really expensive!

But on the other hand, writing for long hours was an absolute pain— many of us were worried that 3-hour long exams with long-format answers would actually give us some sort of wrist injury. I switched from playing the guitar righthanded (pressing the strings with your left hand) to playing the other way round, because after getting home from a 7h long school day my left (writing) hand could not take applying pressure while practising the guitar. As someone who’s job is coding on a computer and like dragging windows and spreadsheets, I do worry about things like carpal tunnel, though I still feel my hands are less stressed than when writing with a pen over 6 hours and really fast. I had to change how I write entirely to reduce finger and wrist stress.

Unless you (as the last person said) want to get rid of homework entirely and have kids do their work in front of you, writing won’t really reduce AI cheating. A kid on a time crunch will choose to handwrite an essay they got chat gpt to write for them than sit and take the precious time when late to come up with the contents and structure their arguments themselves. This is not me advocating for AI, but telling you that this is what I saw in my final years, which was just as AI was beginning to take off amongst university students.

You know how you tackle that? You teach your students time management. Facilitate their essay writing process in stages. Set smaller milestones that they need to meet. Professors who broke up a large assignment into smaller bits with incremental deadlines took SO much stress off my shoulders compared than ones who just assigned you one fuckoff-giant project and left you to flail. I built a whole compiler within a 3 month semester, and it was the least stressful project I’ve ever done because we had smaller and well-structured deadlines every two weeks that also helped us really understand the architecture of a compiler and helped me think through the process of building one.

And I know, some people will say ‘if university students in their 20s need to be taught basic things like time management then we’re all doomed’, but what else are you trying to teach kids when you get into the argument about whether AI helps or hinders? The material they need is mostly available either online or now easily structured through AI. You’re teaching them how to think about things, structure their ideas, put together larger pieces of work incrementally; doesn’t that also include teaching them how to spread out and structure their work? It’s literally what workplaces would call ‘project management.’

Teach them that, and a lot of things they may have previously been intimidated by and felt unable to start (you know when something’s so vast you don’t know where to start and just freeze instead?), they will all fall into place and make sense, and allow your students not only to begin their work, but to know how to break down bigger problems in future and also how to set themselves milestones to keep track of their (/their teams’) progress themselves. What do I know.

An ex-colleague of mine was complaining to me the other day about the ai problem in her students' papers, and I told her, "Just make your students hand-write them in class. Easy." She looked at me like I was insane and tried to explain how that would never work, but I just said,"That's how we did it for a thousand years. The invention of word processors doesn't erase all that."

To me it seems obvious. Readings are done out of class, handwritten essays are done within it. No more ai papers.

#The conversation I’m hearing around unis is profs realising that they need to teach their students a mixture of theory as well as #How to use the knowledge they’re being given. That’s been on the lower side for ages #So I guess if there’s one thing both the pandemic (asynchronous/unmonitored learning) and AI have brought up #It’s discussion about much needed changes to the way we’re taught #All this said I obviously wish people wouldn’t rely on AI as their first option. You forget how to think for yourself #Long post #im sorry this got so long

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callofdutymobileindia · 1 day ago

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Machine Learning Course in Chennai: A 2025 Guide for Aspiring Data Scientists

In today’s data-driven economy, Machine Learning (ML) has emerged as a transformative technology that’s shaping the future of industries—from healthcare and finance to logistics and marketing. For professionals and students aiming to stay ahead of the curve, enrolling in a Machine Learning course in Chennai is one of the smartest career moves they can make.

Chennai, with its booming IT industry, strong academic institutions, and growing tech start-up ecosystem, has become a hotspot for machine learning education. Whether you're a beginner exploring data science or a working professional aiming to switch careers, this guide will help you understand why a Machine Learning course in Chennai can be the key to unlocking your future in AI and data science.

Why Chennai Is the Ideal Destination for Machine Learning Education?

1. Thriving Tech Ecosystem

Chennai is home to global IT giants like TCS, Infosys, Accenture, and Cognizant, as well as a vibrant startup scene. These companies are actively integrating machine learning into their services, increasing the demand for skilled professionals.

2. Quality Educational Institutions

Chennai boasts prestigious institutions offering advanced courses in ML and AI. Many private institutes and edtech platforms also provide industry-aligned training designed for practical implementation.

3. Abundant Career Opportunities

Companies across domains such as e-commerce, healthcare, manufacturing, and fintech are hiring professionals skilled in machine learning. Completing a Machine Learning course in Chennai can significantly boost your employability in these sectors.

What to Expect from a Machine Learning Course in Chennai?

The best ML courses in Chennai are designed to be comprehensive and application-focused. They not only teach theoretical concepts but also emphasize practical training through real-world projects.

Key Topics Typically Covered:

Python programming for ML

Supervised and Unsupervised Learning

Neural Networks and Deep Learning

Natural Language Processing (NLP)

Computer Vision and Image Processing

Model Deployment and Evaluation

Reinforcement Learning

Tools like TensorFlow, Keras, Scikit-learn, and PyTorch

Practical Learning Approach:

Expect hands-on assignments, capstone projects, and case studies involving real datasets. These help build job-ready skills, including data pre-processing, model selection, tuning, and deployment.

Who Should Enroll in a Machine Learning Course?

Ideal for:

Fresh graduates from computer science, mathematics, or engineering backgrounds

IT professionals seeking to upskill

Data analysts looking to specialize

Entrepreneurs and business professionals aiming to integrate ML into their operations

While prior knowledge of programming and mathematics is helpful, many beginner courses offer foundational training to get you started.

Top Features to Look for in a Machine Learning Course in Chennai

Choosing the right course is crucial for your career. Here are key features to consider:

1. Industry-Relevant Curriculum

Look for courses that are designed by data scientists or in collaboration with industry leaders to ensure you're learning the most in-demand skills.

2. Experienced Faculty

Instructors should have practical experience in deploying machine learning models in real-world scenarios.

3. Capstone Projects

Courses that offer multiple hands-on projects allow you to apply what you learn and showcase your abilities to employers.

4. Placement Assistance

Institutes that offer interview training, resume building, and connect you with hiring partners provide a significant advantage.

5. Certification

Opt for programs that provide recognized certifications. These credentials strengthen your resume and boost your credibility.

Online vs. Offline Machine Learning Courses in Chennai

Offline Courses

Ideal for those who prefer structured, face-to-face learning

Opportunities to collaborate and network

Access to lab infrastructure

Online Courses

Flexible schedules ideal for working professionals

Learn from anywhere at your own pace

Access to global instructors and communities

Many leading institutions in Chennai offer hybrid programs, combining the benefits of both learning modes.

Top Machine Learning Institutes in Chennai

Chennai is home to several reputed institutes offering ML training. One prominent option includes an organization known for offering globally recognized programs in AI and Data Science, featuring:

A project-driven curriculum

Experienced faculty from industry and academia

Live mentorship sessions

Placement support with top companies in Chennai and across India

Such institutions provide a career-oriented learning experience, tailored to both beginners and professionals.

Machine Learning Career Scope in Chennai

After completing a Machine Learning course in Chennai, a world of career opportunities opens up. Here are some common roles you can pursue:

Popular Job Roles:

Machine Learning Engineer

Data Scientist

AI Engineer

Data Analyst

Computer Vision Specialist

NLP Engineer

Business Intelligence Developer

Salary Insights:

Entry-level roles typically offer salaries between ₹6–10 LPA

Mid-level professionals can earn ₹12–18 LPA

Senior experts and consultants can command salaries of ₹25 LPA and above

Chennai’s cost of living combined with its growing tech infrastructure makes it an attractive city for ML professionals to thrive.

Building a Strong ML Portfolio

Completing a course is just the first step. To truly stand out in the job market, you’ll need to build a strong portfolio. Here’s how:

1. Work on Diverse Projects

Tackle real-world problems across domains like finance, healthcare, marketing, or logistics.

2. Share on GitHub

Upload your projects with well-documented code. Recruiters often check GitHub to assess technical proficiency.

3. Participate in Hackathons

Chennai hosts various tech meetups and coding challenges. Events like these help sharpen your skills and connect with industry peers.

4. Blog Your Learning

Write about your ML journey, project outcomes, and learnings. This shows thought leadership and boosts your online presence.

Benefits of Taking a Machine Learning Course in Chennai

Access to local job fairs and networking events

Proximity to major IT hubs and R&D centers

Mentorship from industry professionals

Affordable, high-quality education

Opportunities for internship and live projects with Chennai-based firms

By studying locally, you also gain a better understanding of region-specific business challenges and applications of machine learning in industries such as automotive, manufacturing, and logistics that are strong in Chennai.

Final Thoughts

The world is moving towards automation, predictive analytics, and intelligent systems—and Machine Learning is at the heart of this transformation. By enrolling in a Machine Learning course in Chennai, you’re positioning yourself at the forefront of one of the most in-demand and impactful fields in tech.

Whether you're starting fresh or looking to transition from another domain, Chennai offers the perfect blend of quality education, industry exposure, and career opportunities. With the right guidance, hands-on training, and a commitment to continuous learning, you can launch a successful career in ML and contribute to building smarter, data-driven solutions for the future.

#Artificial Intelligence Course in Chennai #Machine Learning Course in Chennai #Artificial Intelligence Classroom Course in Chennai

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