#L1 Regularization

[ingoampt]

Understanding Regularization in Deep Learning - day 47

Understanding Regularization in Deep Learning Understanding Regularization in Deep Learning – A Mathematical and Practical Approach Introduction One of the most compelling challenges in machine learning, particularly with deep learning models, is overfitting. This occurs when a model performs exceptionally well on the training data but fails to generalize to unseen data. Regularization offers…

[juliebowie]

An Introduction to Regularization in Machine Learning

Summary: Regularization in Machine Learning prevents overfitting by adding penalties to model complexity. Key techniques, such as L1, L2, and Elastic Net Regularization, help balance model accuracy and generalization, improving overall performance.

Introduction

Regularization in Machine Learning is a vital technique used to enhance model performance by preventing overfitting. It achieves this by adding a penalty to the model's complexity, ensuring it generalizes better to new, unseen data. 

This article explores the concept of regularization, its importance in balancing model accuracy and complexity, and various techniques employed to achieve optimal results. We aim to provide a comprehensive understanding of regularization methods, their applications, and how to implement them effectively in machine learning projects.

What is Regularization?

Regularization is a technique used in machine learning to prevent a model from overfitting to the training data. By adding a penalty for large coefficients in the model, regularization discourages complexity and promotes simpler models. 

This helps the model generalize better to unseen data. Regularization methods achieve this by modifying the loss function, which measures the error of the model’s predictions.

How Regularization Helps in Model Training

In machine learning, a model's goal is to accurately predict outcomes on new, unseen data. However, a model trained with too much complexity might perform exceptionally well on the training set but poorly on new data. 

Regularization addresses this by introducing a penalty for excessive complexity, thus constraining the model's parameters. This penalty helps to balance the trade-off between fitting the training data and maintaining the model's ability to generalize.

Key Concepts

Understanding regularization requires grasping the concepts of overfitting and underfitting.

Overfitting occurs when a model learns the noise in the training data rather than the actual pattern. This results in high accuracy on the training set but poor performance on new data. Regularization helps to mitigate overfitting by penalizing large weights and promoting simpler models that are less likely to capture noise.

Underfitting happens when a model is too simple to capture the underlying trend in the data. This results in poor performance on both the training and test datasets. While regularization aims to prevent overfitting, it must be carefully tuned to avoid underfitting. The key is to find the right balance where the model is complex enough to learn the data's patterns but simple enough to generalize well.

Types of Regularization Techniques

Regularization techniques are crucial in machine learning for improving model performance by preventing overfitting. They achieve this by introducing additional constraints or penalties to the model, which help balance complexity and accuracy. 

The primary types of regularization techniques include L1 Regularization, L2 Regularization, and Elastic Net Regularization. Each has distinct properties and applications, which can be leveraged based on the specific needs of the model and dataset.

L1 Regularization (Lasso)

L1 Regularization, also known as Lasso (Least Absolute Shrinkage and Selection Operator), adds a penalty equivalent to the absolute value of the coefficients. Mathematically, it modifies the cost function by adding a term proportional to the sum of the absolute values of the coefficients. This is expressed as:

where λ is the regularization parameter that controls the strength of the penalty.

The key advantage of L1 Regularization is its ability to perform feature selection. By shrinking some coefficients to zero, it effectively eliminates less important features from the model. This results in a simpler, more interpretable model. 

However, it can be less effective when the dataset contains highly correlated features, as it tends to arbitrarily select one feature from a group of correlated features.

L2 Regularization (Ridge)

L2 Regularization, also known as Ridge Regression, adds a penalty equivalent to the square of the coefficients. It modifies the cost function by including a term proportional to the sum of the squared values of the coefficients. This is represented as:

L2 Regularization helps to prevent overfitting by shrinking the coefficients of the features, but unlike L1, it does not eliminate features entirely. Instead, it reduces the impact of less important features by distributing the penalty across all coefficients. 

This technique is particularly useful when dealing with multicollinearity, where features are highly correlated. Ridge Regression tends to perform better when the model has many small, non-zero coefficients.

Elastic Net Regularization

Elastic Net Regularization combines both L1 and L2 penalties, incorporating the strengths of both techniques. The cost function for Elastic Net is given by:

where λ1​ and λ2 are the regularization parameters for L1 and L2 penalties, respectively.

Elastic Net is advantageous when dealing with datasets that have a large number of features, some of which may be highly correlated. It provides a balance between feature selection and coefficient shrinkage, making it effective in scenarios where both regularization types are beneficial. 

By tuning the parameters λ1​ and λ2, one can adjust the degree of sparsity and shrinkage applied to the model.

Choosing the Right Regularization Technique

Selecting the appropriate regularization technique is crucial for optimizing your machine learning model. The choice largely depends on the characteristics of your dataset and the complexity of your model.

Factors to Consider

Dataset Size: If your dataset is small, L1 regularization (Lasso) can be beneficial as it tends to produce sparse models by zeroing out less important features. This helps in reducing overfitting. For larger datasets, L2 regularization (Ridge) may be more suitable, as it smoothly shrinks all coefficients, helping to control overfitting without eliminating features entirely.

Model Complexity: Complex models with many features or parameters might benefit from L2 regularization, which can handle high-dimensional data more effectively. On the other hand, simpler models or those with fewer features might see better performance with L1 regularization, which can help in feature selection.

Tuning Regularization Parameters

Adjusting regularization parameters involves selecting the right value for the regularization strength (λ). Start by using cross-validation to test different λ values and observe their impact on model performance. A higher λ value increases regularization strength, leading to more significant shrinkage of the coefficients, while a lower λ value reduces the regularization effect.

Balancing these parameters ensures that your model generalizes well to new, unseen data without being overly complex or too simple.

Benefits of Regularization

Regularization plays a crucial role in machine learning by optimizing model performance and ensuring robustness. By incorporating regularization techniques, you can achieve several key benefits that significantly enhance your models.

Improved Model Generalization: Regularization techniques help your model generalize better by adding a penalty for complexity. This encourages the model to focus on the most important features, leading to more robust predictions on new, unseen data.

Enhanced Model Performance on Unseen Data: Regularization reduces overfitting by preventing the model from becoming too tailored to the training data. This leads to improved performance on validation and test datasets, as the model learns to generalize from the underlying patterns rather than memorizing specific examples.

Reduced Risk of Overfitting: Regularization methods like L1 and L2 introduce constraints that limit the magnitude of model parameters. This effectively curbs the model's tendency to fit noise in the training data, reducing the risk of overfitting and creating a more reliable model.

Incorporating regularization into your machine learning workflow ensures that your models remain effective and efficient across different scenarios.

Challenges and Considerations

While regularization is crucial for improving model generalization, it comes with its own set of challenges and considerations. Balancing regularization effectively requires careful attention to avoid potential downsides and ensure optimal model performance.

Potential Downsides of Regularization:

Underfitting Risk: Excessive regularization can lead to underfitting, where the model becomes too simplistic and fails to capture important patterns in the data. This reduces the model’s accuracy and predictive power.

Increased Complexity: Implementing regularization techniques can add complexity to the model tuning process. Selecting the right type and amount of regularization requires additional experimentation and validation.

Balancing Regularization with Model Accuracy:

Regularization Parameter Tuning: Finding the right balance between regularization strength and model accuracy involves tuning hyperparameters. This requires a systematic approach to adjust parameters and evaluate model performance.

Cross-Validation: Employ cross-validation techniques to test different regularization settings and identify the optimal balance that maintains accuracy while preventing overfitting.

Careful consideration and fine-tuning of regularization parameters are essential to harness its benefits without compromising model accuracy.

Frequently Asked Questions

What is Regularization in Machine Learning?

Regularization in Machine Learning is a technique used to prevent overfitting by adding a penalty to the model's complexity. This penalty discourages large coefficients, promoting simpler, more generalizable models.

How does Regularization improve model performance?

Regularization enhances model performance by preventing overfitting. It does this by adding penalties for complex models, which helps in achieving better generalization on unseen data and reduces the risk of memorizing training data.

What are the main types of Regularization techniques?

The main types of Regularization techniques are L1 Regularization (Lasso), L2 Regularization (Ridge), and Elastic Net Regularization. Each technique applies different penalties to model coefficients to prevent overfitting and improve generalization.

Conclusion

Regularization in Machine Learning is essential for creating models that generalize well to new data. By adding penalties to model complexity, techniques like L1, L2, and Elastic Net Regularization balance accuracy with simplicity. Properly tuning these methods helps avoid overfitting, ensuring robust and effective models.

[machinelearningsite]

Understanding Regularization in Machine Learning: Ridge, Lasso, and Elastic Net

Struggling with overfitting in your machine learning models? Have a look at this complete guide on Ridge, Lasso, and Elastic Net regularization. Learn these regularization techniques to improve accuracy and simplify your models for better performance.

A machine learning model learns over the data it is trained and should be able to generalize well over it. When a new data sample is introduced, the model should be able to yield satisfactory results. In practice, a model sometimes performs too well on the training set, however, it fails to perform well on the validation set. This model is then said to be overfitting. Contrarily, if the model…

[autistic-mandalorian]

I would love to hear this

Oh sure! Fair warning, this gets long, so it's under a cut:

So I have looked carefully at Maul post-bisection, specifically at where his abdomen ends and his prosthesis begins, and I believe that he was bisected between the L3 and L5 vertebrae, or just above his pelvic bone. Here is a diagram I drew on of where he was cut:

Image Description: The first image is a screenshot of Maul with his prosthetic legs from TCW. The screenshot is annotated to note where exactly Maul is divided between flesh and prosthesis. The second image is two diagrams side-by-side, one of the human body focusing on organs, and the other of the spine. Both have a line drawn around where the belly button is to note where Maul was bisected. End ID.

So in terms of what he lost, it was a LOT. Not just his legs, but most of his intestines, his bladder, his pelvis, his gonads, half his bones, most of his blood volume, and a lot of his abdominal and back muscles (as well as their attachment points, making the remaining muscles limited in their usefulness).

Image description: A diagram of the human musculature, from the ventral and dorsal sides. The diagram has a line drawn across it to show where Maul was bisected.

Fortunately for him, most of the organs in humanoids are located in the chest cavity (because the intestines need a LOT of room to work), so he kept his kidneys, liver, stomach, lungs, hearts, pancreas, gallbladder, etc etc. However, his intestines are interesting in that by getting chopped in half, his small intestine was actually disconnected from his large intestine. The small intestine connects to the large through the ileocecal valve, which is located on the left inferior side of the abdominal cavity. He got chopped right through the middle of the abdominal cavity, so he lost his entire cecum, the majority (if not all) of his ileum, and the valve that connected them. This means that anything he digested would just ooze into his abdominal cavity even after the giant wound repaired itself, unless he got surgery to reconnect them. We will say for the sake of the story that he fixed it with The Force while living in his trash hole.

Now, it is possible for people to be bisected like he was and survive, just only in a medical environment. It's an extremely rare and radical surgery called a hemicorporectomy. It's the last of the last resorts, because it leaves you with a lot of problems. Here are some of them:

Maul would need both a colostomy and urostomy bag, since his rectum and bladder are both gone. These would need to be regularly cleaned and emptied.

His missing intestines would also result in his not digesting most of his food fully, so he would need supplemental nutrients to help combat malnutrition. He obviously does not get these for most of his life (if ever) so he is almost certainly malnourished.

Due to his newfound Nightmare Castration, he would need regular doses of hormones or would risk osteoporosis. Which hormones is up to the reader (I nominate estrogen)

His spinal cord is, thankfully, fine--- it doesn't actually extend past L1-L2. However, he did lose the filum terminale, meaning his spinal cord is kinda unanchored in his spine and floating around, which isn't great and could lead to nerve issues down the line. Some of the nerves that were cut in his lumbar spine (specifically, the L4 lumbar nerve supplying the quadratus lumborum muscle) could also cause partial paralysis in his back, as well as some wicked back pain.

Shoutout to @necropocene for inspiration as well as the following headcanons:

Maul's lungs and other organs are constricted by his intestines being forced upward into his chest cavity, reducing his lung capacity

Maul suffers from chronic nausea

Maul's prosthesis needs to be very well-cushioned because the waist is not a load-bearing structure (too squishy!)

Now onto my specific headcanons for his prosthetics and mobility devices:

The thing about pelvises is not only do they let you use legs, they also allow your organs and muscles to attach to something rigid. For this reason, I think Maul should have two pelvises: one internal, being more like a metal frame that his abdominal and back muscles attach to, and one external and connected to his legs.

The lumbar spine and sacrum are what allow the spine to connect to the pelvis, so in order to use his prosthetic legs, I think it would be prudent to give Maul a prosthetic spine, Borg Queen-style. Now, this would admittedly be a pretty big infection risk (piece of metal sticking through the skin and all) but I think it's cool so I am invoking The Rule of Cool on this one.

Maul's legs are not something I spent much time on, because his canon ones are fine.

I do have headcanons for a wheelchair, though!

His wheelchair wouldn't be designed like your average wheelchair, because those are generally designed to accommodate people who have pelvises. His would probably look more like a plant pot or a baby bjorn, imo? It would have to support him without putting too much pressure on his torso, so I think a sort of foam well with a backrest, attached to wheels would be a good design.

I also think that his prosthetic spine should be able to dock with the wheelchair so that he can control it as an extension of his body, like the prosthetic legs.

Image description: Three pencil drawings on notebook paper. One is of Maul post-bisection, with each of his organs labeled and colostomy, urostomy, and gastronomy ports. The next two are of his wheelchair, which follows the description previously given. End ID.

And yeah, those are my headcanons! Thanks for asking :) I love talking about fantasy biology!

[culiehua]

Jem's ancestral origin

bc @jaimrennnn pointed it out again, here are my thoughts on Jem's maternal ancestry bc it only now occurs to me that the following are not just regular thoughts normal non chinese people have 😭:

Literary review: Jem speaks Mandarin and not Shanghainese, it's assumed he is from the north. But where? Why?

Now this is where it gets interesting:

一 The language(s)

Nothern Chinese dialects include quite a few different dialects; there are over 300 languages in China in total – and most are not mutually intelligible. At all.

(As someone who is also fluent in Mandarin and wouldn't understand a thing in ancient Shanghai... I feel his pain)

Mandarin is the official language in all of China TODAY, however, the Beijing dialect based Mandarin as we know it today did not undergo nationwide codification until the early 20th century.

And it was only in the 1800s that a shift to Beijing based Mandarin happened. Previously, and into the 1800s, Nanjing based Mandarin was the official Standard during the Ming (1368-1644) and part of the Qing dynasty (1644-1911).

While both would be plausible Mandarin varieties that he could have spoken given the time period, what we see Jem use though is the Beijing based version of Mandarin. While Nanjingese is semi-intellegible for speakers of Beijing Mandarin, it is notably different. So you are right: He does not use any other variety in the written text we see, therefore Mandarin being his only Chinese language means he or rather his family are likely not only from Northern China but speak a dialect that, at least in unintonated pinyin form, is exactly the same (note: we are assuming they don't have a notably different one bc dialect speakers are very particular about keeping their dialects alive as they do not really exist in written Chinese).

But there's more! The northern Mandarin dialects (not just Mandarin the official language) may belong to the same language family and some can be vaguely understood amongst each other (that's why it is also common to have instances where people speak with each another in different dialects, each understanding the other but unable to speak the other dialect) but 1) not all provinces above the Yangtze are Mandarin variety dominant: Zhejiang and south Jiangsu for example are predominantly Wu-dialect heavy (think Shanghainese) which are... not understandable as a Mandarin speaker lol while some dialects from Sichuan or Shandong for example may be easier to understand (remember there are many dialects, even from different language families, in each province).

and b) there's also the fact that even Mandarin varieties don't... necessarily understand each other. You can definitely hear the difference in speech even if they aren't different in grammar or vocabulary!

What do we know about Jem tho?

Jem speaks in a very... classic kind of way I suppose? There are no real regional colloquialisms, he sounds very Standard Mandarin (pronunciation not evaluable) but he does use some literary words that I'd never use in my life (separate post on that) which makes his Mandarin very textbook elegant. He doesn't have the classic /r/ marker at the end that many northeners have either. He doesn't have the Dongbei Mandarin accent either, the Xiajing dialect is something else, etc etc.

He does use the the Wade-Giles system of romanization (based on the Beijing dialect, was the anglophone system of transcription in the 20th century) when speaking English though (I Ching, Kwan Yin) which amuses (and...confuses) me bc he speaks Chinese in perfect Hanyu pinyin? So I'd say he englishifies Chinese terms when speaking English.

(There's also the problem that he was young when he lost contact to the language environment and that he had two L1s growing up resulting in crosslinguistic interference anyway. Mixed linguistic backgrounds do often result in the development of a "clearer" accent, or lack therof...)

二 The Ke family name

I've talked about it before, but Chinese family names have meaning. In the past last names were location bound, i.e. you'd have wider clan names for all the people from one region (which is what put Ke on the top 100 most common last names list in the first place. Yes that list exists). So you can look up where these names originate from or rather where they are common.

So far, I'd narrow the name origin down to these places: Shandong and Hubei (Wu dialectal regions excluded bc we assume he has no other background dialect; nomadic tribes also excluded bc traces unclear).

Why they would make sense:

Hubei the hometown of bronze and birthplace of Chinese modern industry for Steel works (haha... "they shatter even the strength of iron or of bronze". But that was after Jem). It's basically in the heart of China, a lot of cultural history, and the weather is very hot (they are called one of the three furnaces...) it's said that people there are louder and easier riled up bc of the weather. But the Hubei dialect does sound pretty different from the Standard. So there's that.

Shandong is famous for being a cultural and religious center for Taoism, Buddhism and Confucianism (Hometown of Confucius!!) ... and also for having the tallest people in the country. The people here are also very bold and straightforward but also very hospitable with a generally good reputation, the common hero personality. It is also an agricultural center (ahem, Ke ≈ tree, remember). Character wise, it fits. There are a lot of Shandong people who moved to Beijing a long time ago since it's very close.

Given that neither Jem nor Wenyu seem like hot-headed people, but rather practical, calm, honest and are believers of buddhism and chinese philosophy (reincarnation wheel + with how often Jem refers to that thousands years old book you'd believe he has it printed behind his eyelids...), I'm rather inclined to guess their ancestors are from Shandong originally, i.e. the chill ones. (Yes you cannot generalize people but we have nothing on them 😭). However, I believe they have lived in Beijing for generations.

What is also interesting is that the average victorian Englishman stood at 167cm. As of now, the English average height is 175±6(m) and 162(w). In Shandong, the average height now is 175(m) and 169(w). But in the Qing dynasty the average northern man was this tall already. So I'd go on the taller spectrum for the nephilim and say Wenyu, as a northerner, is atleast 170. Jem is considered tall, somewhere between 178 and 180cm. Calculating this into the height, Jonah is max. 178cm. Since the Carstairs weren't tiny either, this fits. (Shandong people are the tallest people in China, followed by Beijing people. Tall girl Wenyu supremacy!!). I think Shadowhunters are taller bc of exercise that's why you have Will at the same height but Jem could definitely not do as much physically, having to rely more on efficiency and genes so I believe he has tall!genes but he definitely did not get that primarily from the victorians.

三 Wenyu's pre-Shanghai residence

From what we also know, the Ke family ran the Beijing Institute as well (Jia, née Ke, and her hubby). It wouldn't be far fetched to say that they've been running it since way back. After all, the Ke's are also still in charge of Shanghai and the Herondale's ran London well into the 1990s.

What we also know is that Jem's mom, Wenyu, was sent as an ambassador of the nephilim to the emperor for a meeting at the Yuanming Yuan, burned down around a decade and a half prior to Jem mentioning it, so the visit occured before Jem could ever remember it.

Historically, from mundane history, we know this: Emperor Xianfeng ruled from 1850-61, the Garden was burned down by Europeans in 1860 during the 2nd Opium War.

Wenyu was born in 1839, so she probably went there as a late teenager. She had Jem at 21 and also moved to Shanghai with Jonah years later. Now why would China's nephilim send some random teenage girl to Beijing to talk to the ruler? Why would they send her from perhaps angel knows where thousand miles away? Unless... she was sent from the Beijing Institute? Who knows. Chaos breeds demons. (It's also odd that Cassie changed the timeline to this happening when Jem was 2 or 3. Was this supposed to mean that he might have witnessed it? Idk).

Beijing was the capital, and if the nephilim, as a race, would send an ambassador, it would make the most sense to send one from the local institute. Jem did not grow up learning another local differing variety either, leading me to believe both Wenyu and Jem were born in Beijing and learned court Mandarin.

四 Conclusion

I believe that Jem's maternal family originates from the Shandong/Beijing area but lived in Beijing for centuries before taking over Shanghai too, since they are not shown to know or speak any other distinctive dialect and the Shandong dialect, due to it's close similarity to the Standard variety, could have lost it's accent over time there with the surrounding Beijing dialect, the court dialect and their complete mutual intelligibility. Clan origin, height and personality also hint at this, as well as Wenyu's travel log. Jem's Chinese is very standard and does not deviate; he seems sophisticated and has a clear pronunciation. Beijing born and raised!

[the-areph]

AGENCY FOR THE RECOVERY OF ESCAPED PET HUMANS

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OS - @bluemoonscape

Here at AREPH, our main goal is find and secure any and all loose, missing, and escaped pet humans. If you have a pet human who has run away, contact us through our inbox and we'll get on it! We rehouse abandoned pet humans as well, so if you're looking for a new pet for a low price, get in touch!

We often work closely with associates at ANAKT, helping make sure all of the pets at the Garden are where they are supposed to be.

We have staff members that are human, but rest assured that they are monitored and checked frequently for any misconduct. They also are not allowed any clearance above Level 3. Do not pry into the file of Eddy.

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TAG DIRECTORY:

#OS REPORTS - Lore content, original posts

#OS FINDINGS - Reblogs

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#AREPH INTERCOM - Any in-lore announcements

#OS SPEAKING - Mod/Blue speaking

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FONTS:

- Name plaque: ABNES REGULAR

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All are downloadable for free through DaFont on IbisPaint

FACULTY UNITS:

UNIT: HOSPITAL [TRAINEE, L1, OS]

UNIT: LP HOLDING [L1, OS]

UNIT: STRATEGICS [L2, OS]

UNIT: RETRIEVAL [L3, OS]

UNIT: MILITARY [L4, OS]

UNIT: HP HOLDING AND TESTING [L5, OS]

PERMITTED UNITS FOR PATIENT ADMITTANCE:

UNIT: HOSPITAL

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CLEARANCE LEVELS:

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OVERSEER [OS]

formatting and assets were all done by me lol (zen @verdantlights ) hi hehe i made the profile templates :tism:

[pinkyjulien]

🟨 Appearance Creator Mod Infos Dump

With the release of ACM (Appearance Creator Mod) I wanted to dump some information that might be useful, especially for non-modders that want to play around with it!

(I had no involvement with the making of this mod at all, nor I have any tie with AMM development, just throwing my two cents as a regular user, player and modder)

You'll find some How To, Tutos and other useful heads-up under read more, if you plan on using this mod as a modding tool (sketching up outfits ideas before modding them as custom appearance for your blorbo for example) or simply want to play blorbo dress up!

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First, You might want to get familiar with these suffix, as you will need to identify what part of a character's outfit you wanna swap

▶ Garments Components Names

h0 - Character's head mesh he - Eyes mesh heb - Eyebrows mesh ht - Teeth mesh hx - Head's details meshes (pimples, makeup...) hh - Hair mesh t0 - Character's Body mesh t1 - Torso Inner Garment (T-shirt, Tank top...) t2 - Torso Outer Garment (Vest, Jacket, Hood...) a0 - Character's Arms mesh (Cyberarms...) l1 - Legs Garment (Pants...) s1 - Feet Garment (Shoes, Socks...) i1 - Accessory / Item Garment (Cyberware, Belt, Necklace...)

▶ Garments Rigs Names

ma - Man Average mb - Man Big mc - Man Child mf - Man Fat mm - Man Massive wa - Woman Average waf - Woman Average Fat

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⚠ It's important to note that Garments are NOT automatically refited to the character! You can use a WA garment on a Male NPC but it won't magically fit his shape

⚠ Johnny and Kerry, despite using the MA rig, both have a thinner bodies and most of the garment will float and appear larger on them

⚠ A lot of Garments have their own RIGs and physics, and might T-pose / break upon being swapped on your NPC of choice! For this reason, Hair with physics will automatically break as well

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AMC is a standalone window in game, and not included in AMM main window!

After spawning a NPC, the AMC window should fill up with the spawned entity's garment components (and yes it can get crowded really quick, especially on smaller monitors and resolution)

Yes, It also works on NPVs and NPC+! 🔥

Everything on these windows can be overwhelming especially if you're not familiar with modding, so I'll throw a little example here using Scorpion

First I want to get rid of his hood and vest, without using AMM's custom appearance, using only ACM

In the list, I look for his Vest and his Hood component

We can easily turn them invisible by clicking the "Toggle" button Make sure to also disable any decals, stickers, and additional component (his vest also has a decal component, see in the screenshot)

The little icon next to a component's name indicate it has been edited in some way

I'll now switch his tank top for something else

Click on "Select From List" next to the "Mesh Path" of the desired component (in Scorpion's case, t1_060_ma_tank__corset) Open up the "Torso" list, and you'll be able to click on any of the listed t1 and t2 garments!

⚠ As mentioned above, garments doesn't magically refits on characters; Scorpion use the Man Average body and rig, anything that isn't a "ma" garment won't fit him correctly!

⚠ Another thing to be aware of is that a lot of garments's might break around the collar area! However if you plan on using this same mesh in an outfit, don't let that scare you, as it's easily fixable in blender

Export your desired mesh and look for the "NeckCollar_JNT" bone and simply rename it to "Neck" (blender will put some numbers next to it but that's fine, ignore that)

I'm going to switch to Valentin here to showcase how to deal with potential clipping 🤏 Since Valentin's body is customized, bit chubbier, he always needs refiting

Let's say you swapped your character's top for something else but part of his torso clips out

To put my boys titas away, I'll look for his body component (t0)

Click on "Chunk Mask" to open the garment's submeshes list

Garment's meshes are broken down in multiple submeshes that can be easily chunkmasked (disabled) at will to deal with clipping! This is why NPC usually don't even have a full body mesh when taking off their clothes, they are chunkmasked to avoid clipping :D

I will disable the Submesh 1 by unchecking the "1" box

Don't be afraid to click on multiple box and see what gets chunkmasked on different meshes! You won't break anything 👌

aaand for now that's about all I can think of that might be confusing for users- tho if you have question don't hesitate to ask on this post, to reblog or to come into DMs!

[crippled-peeper]

Hey so today I learned that there is a multi-level fusion at some point in my not so distant future (L1-L2 and L5-S1) since I'm told the NHS doesn't cover replacements, and I just wanted to know... is it common for fusions like those to cause other damage long term? Is that just kind of an inevitability of having one early in life?

I know you're not a spine expert but you have way more experience than anyone else I can think of

It’s really unfortunate that the NHS only covers spinal fusions. there are other, less invasive, newer surgeries that can preserve motion in your spine and cause less biomechanical stress on your other discs, so you’re less likely to develop painful, long-term complications. Spinal fusion complications are difficult to address because fusions are supposed to be permanent. As far as I know, there is no way to remove them, so often they get extended & replaced as discs break down above & below them.

The condition of disc degeneration around a spinal fusion or fused segment is referred to adjacent segment degeneration (ASD) by researchers & doctors. it’s a complication of spinal fusion surgery that used to be considered “rare” but more recently, it’s actually been found to be a common long term complication of spinal fusions. I developed severe ASD nearly 10 years after my 10-level fusion. My fusion was in 2014 and my follow up disc arthroplasty surgery was in 2022

here’s a good article explaining adjacent segment degeneration more in depth

I have flatback syndrome in addition to ASD because my lumbar and cervical spine were straightened beyond what is natural after my 10 level fusion (in my thoracic and lumbar spine). I couldn’t find any statistics on how common this is after spinal fusions, but similar to ADS, the risk is greater the longer your fusion is and how “straight” your surgeon made your bars. In my case I lost both the upper and lower curves, but “flatback syndrome” can refer to either or both curves being absent. flatback is very common following for example scoliosis surgeries because the fusions tend to be long. It can also occur in shorter fusions in the lumbar area like how you described though, so I thought it was worth mentioning

here’s a good article defining & describing flatback syndrome

These two painful complications are why, if you have a fusion, it’s really important to maintain the muscles around your spine and back through regular gentle exercise. To make a fusion last and to put off more surgery as long as you can, you must take care of your whole spine. I personally think having a good physical therapist for preparing for & recovering from surgery is just as important as having a good surgeon. They should be able to tell you which exercises are safe and how to strengthen the muscles around your spine both before & after surgery

this post is super long but a couple people have asked me similar questions about having spinal fusions so I thought I’d try to summarize what I would look out for & consider if I were to do it all over again

I hope your doctors are good and kind to you and that if you have surgery your recovery is very boring and complication-free. If you/anyone has any other questions I’ll try to answer them also :) 🖤

[h34vybottom]

Oda's normal hold being a regular shotgun blast is so useful. Hold chaining into combos so fluidly just works marvels.

Oda feels like a different character despite being mostly unchanged. I'm very curious to see how much more ridiculous he gets. What the fuck will the L1+Triangle be lmao

[spidersonicbatl0]

My playstation all stars 2 dream roster

Spider-Man moveset + extras

Before, I explain spider man moveset. I want to say for spidey can do.

Spider man can web swinging if the stage is indoors or something that spidey can stick his webs to (hold x)

wall crawling is use by pushed L stick and x button next to a wall (can be use to lead into a wall attack like his series by pressing square. But overused it will cause the wall to be covered in a slippery substance making it unable to wall crawl).

Finally there Spider sense! Warning of attacks from opponents or stage hazards when an icon appears on spider man head (or head on the hud icon). But you can’t dodge automatically. You still have to pushed the L stick and hold L1 to dodge. But before you think “this is so broke” 1. Spider man has 200 health and low defense & 2. You’re fighting spider man! Course he should be hard to hit.

Moves

Spider sting (four hit combo kicks attack) (neutral square), uppercut launch (spidey launch his opponent into the air by uppercut them) (up square), under dodge (spider man slide under the opponent. Before kicked them forward from behind) (down square), air Spider sting (spidey hit the opponent three times with his fists before finishing with a kick) (air neutral square) swing kick (spidey shoot a web line and swing from it. Hitting any opponents in his way with a double kick) (air side square), ground strike (spider man shoot two web lines at the floor before putting himself down. Created a shockwave that damaged opponents) (air down square)

web strike (spidey shoot a web line on an opponent and pull himself towards them and punch them) (neutral triangle), web shooters (spider man fire webs balls that will tangle the opponent in webs. Making the other player or cpu opponent stuck and unable to attack, block, jump or use items. Human players have to mash buttons to break free from the webs. While CPUs just break free after a while. Webs shooters has an ammunition count of 10 tho (indicate on spidey hub icon) but will slowly replenish when a shot is fire. Abusing spider man webs will also make them easier to break out of.) (side triangle), upshot (spider man use a gadget that shoot 3 shots of energy that launch opponents in the air) (up triangle), sonic blast (spider man shoot a grenade that stun the opponent with sound) (down triangle),

spider barrage (a flurry of hits with the spider arms) (mashed circle), spider rush (spider man launch himself forward deals damaged. Hold down the button to determine how you go.) (hold side triangle), spider whiplash (launch a opponent into the air with the spider arms) (up circle), spider shock (shoot electricity web lines that stun a opponent) (down circle).

Each of spidey custom level 3 supers change his circle moves along with unique stats change attributes. These are anti venom suit (increase speed and agility), black suit (increase power and defense. Also change spider man dialogue to be more aggressive) and anti oct suit (turn his circle moves into the gadgets from marvel spider man 1 and increase web toughness). Spidey will reverse to his regular circle moves if he lose a live. So what are the replaced moves for each super?

Anti venom moves change

Anti venom bomb (spidey throw a explosive projectile ball of symbiote goop) (neutral circle), anti venom strike (spidey cover himself in tentacles and rush toward opponents. grabbing them and launched them in the air) (side circle), anti venom blast (multiple symbiote spikes come out of spidey suit in all directions) (down circle), anti venom tempest (spider man shoot two tentacles from his arms and twirl into the air. Any opponent caught by the tentacles are lift into the air with spidey too) (up circle).

Black suits moves change

Venom punch (spidey shoot a tentacle out of his arm that send opponents flying) (neutral circle), venom strike (exact same as anti venom strike) (side circle), venom blast (exact same as anti venom blast but it up circle this time) (up circle), venom yank (spider man grab opponents with symbiote tentacles. The suit pull them up and slam them back down) (down circle).

Anti oct suit moves change

Impact webs (projectile that instantly cover the opponent in webs and knock them back) (neutral circle), web bomb (cover multiple opponents in web if caught in the blast) (side circle), spider drones (just like mr zurkon from the first game) (up circle), trip mine (mine that web a opponent if step on) (down circle)

Custom supers

level 1: finisher (only works when close to the opponent. spidey recreates an finisher animation from his games. It deal minor damage this time tho ), ricochet webs (spider man shoot a web projectile that ricochet around the stage and web up anyone who get hit by it), iron arms suit power (more powerful version of spider barrage)

Level 2, concussive blast (spider man a blast of air that knock opponents back), web blossom (spidey jump into the air and shoot multiple webs in every directions. Any opponents caught in the area of spidey super are web up), web grabber (spider man shoot a gadget that grab opponents and items, smashed them into each other)

LEVEL 3, anti venom suit, black suit and anti ock suit!

Stage

Fisk construction site

The fight takes place on a steel beam being carried by a Cain. The battle begins on the ground, but after 10 seconds of the match started. The steel beam start to move up. Tho the steel beam will randomly stop at certain points next to other buildings parts (aka temporary performers) before continuing up.

The stage mashup is with the Arkham series. After the steel beam finally stop and is welded into place. Created a performer at the top with bottom the being full of construction equipment. Poison ivy show up in the background, in her giant plant armor thing from Arkham Asylum. Attacks the stage with vines that grab fighters stunned them and shoot deadly plants seeds. At the end of the battle. The human touch will show up and burn the giant plant armor thing.

Alt skins

Advanced suit 2.0 (default)

Advanced suit 1.0 (if the black suit super is use when wearing this skin. Advanced suit 1.0 will change color to ‘suit style 3’ from marvel spider man 2)

Classic suit (if the black suit super is use when wearing this skin. The skin will change to the classic black suit skin)

Classic black suit

Spider man 2099

Spider man noir

Scarlet spider

Spider armor mk1

DLC skins

Webbed suit (if the black suit super is use when wearing this skin. The colors will change to match the black suit in spider man 3. Webbed suit is free btw)

Tasm2 suit

Final swing suit (please let this be mcu spidey main suit gonna forward)

Bombastic bagman (including ‘kick me’ paper on the back)

Big time suit

Comic iron spider (used spider arms attacks when wearing this skin. Will change the arms colors to gold)

Electro proof suit

Vintage comic book suit (redesign to look more like Steve dikko version of the classic suit. Including under arm webs)

Last stand suit

Peter b. Parker suit (wearing this skin make spider man moves like the characters in spider verse)

Spider uk

Mangaverse spider man

Superior spider man suit (used spider arms attacks when wearing this skin. Will change the arms colors to red)

Spider punk

Life story 2010s

Life story 70s

Life story 80s

Life story 90s

Life story 2000s

Captain universe suit

Zombie spider man

Ben Reilly suit

Alex Ross concept design suit

Spider man 1602

60s cartoon spider man suit

90s the amazing spider man cartoon suit

Spectacular spider man cartoon suit (this is my most wanted suit for marvel spider man 3 btw)

Japan spider man

Music

Eight years in the making

The golden age

All the king man

City of hope

Stone cold

Chasing down the devil

International support

Negative view

Renewed rivalries

Destroying your own creation

Behind the mask (battle remix)

Web launch

Laying down the hammer

Greater together (battle remix)

A second chance

East river mayhem

Battling your inner demons

Fighting back (remix)

At last

Healing the world

Swing (battle remix)

Who am I sure you want to know? (Dandy elfman main theme from spider man 2002 movie. Come with the Webbed suit)

Main theme / tasm2 (come with the tasm2 suit)

Does what ever a spider can (theme of 67 spider man cartoon. Come with the 60 cartoon spider man suit)

Radioactive spider man! (theme of 94 spider man cartoon. Come with the 94 the amazing spider man suit)

Living on the edge fighting crime and spinning webs (theme of 08 spider man cartoon. Come with the spectacular spider man cartoon suit)

Change leopardon (theme of Japan spider man live action show. Come with the Japan spider man suit)

[shiorimakibawrites]

Daredevil Story Banners

Decided to make some story banners as inspiration and motivation to actually write some of these. All are Matt Murdock x Reader.

UPDATE: Now with added summaries.

THE PHANTOM

Image Credits: kissthemgoodbye.net / Zac Ong (Unsplash) / Biswapati Acharya (Unsplash)

Summary: Reader is a ghost. Not literally but she might as well be. Her old life is dead and because of them, she cannot make a new one. Then one day, in search of food and a shower, she enters an apartment and discovers a dangerous secret. The secret identity of Daredevil.

will be told in multiple installments

hurt/comfort angst

slow burn romance and eventual smut

HAPPY LITTLE ACCIDENT

Image Credits: kissthemgoodbye.net / Taelynn Christopher (Unsplash) / Anna Kolosyuk (Unsplash)

Summary: Reader is a klutz. She is pretty used to tripping over nothing and embarrassing herself. But this time has to be the worse. Because this time, she has gotten paint splattered all over Matt Murdock. Her handsome neighbor that she has an enormous crush on.

supposed to be a one-shot

COZY CORNERS

Image Credits: kissthemgoodbye.net / Greta Punch (Unsplash) / Stephanie Harvey (Unsplash)

Summary: Reader has a problem. She has feelings for two men. The first is blind attorney Matt Murdock, a regular at her cafe Cozy Corners, whom she has been pinning over since he first walked through her door. The second is Daredevil, the vigilante who saved her life during a mugging.

Will be told in multiple installments

Identity shenanigans

ARACHNE

Image Credits: kissthemgoodbye.net / Andre Benz (Unsplash) / Nicolas Picard (Unsplash)

Summary: Law school is hard enough without developing superpowers. Too bad nobody asked Reader who was bitten by a very weird spider. Fortunately, she isn’t the only weird L1 at Columbia Law School.

told in multiple installments

college era Matt

friends to lovers

together they fight crime.

[hotcocoabombb]

Wanted to give my thoughts on the Epic Mickey ReBrushed demo real quick

First, I wanna say that the original Epic Mickey is like a vague memory from when I was a kid. I owned it on Wii but never got very far. All I really remembered going in was the basic mechanics and the opening scene so this felt very new to me.

First off, the game looks amazing. I played this on PS5 and I do wanna sample it on Series S (since I have no storage on PS5 so I might get it on Xbox lol) but on PS5 the game runs amazing. There's no graphics pre-set either so it's just 1 mode and it's honestly great. The opening cutscene looks incredible. I went back and watched the Wii version, and while it's good there, this just blows it out of the water. In-game environments look amazing and the art-style is just perfect for a game like this. And the 2d cutscenes are amazing, with a great artstyle that I honestly wish I could watch a whole movie of. Overall, very nice graphically

Gameplay was also very fun from what I've played. Overall Mickey controls very well, although I do wish he was just a bit faster. The regular place is way too slow for my liking and the run pace, while better, could stand to be a bit faster. It's not bad though, just a personal thing. Controls are mapped well, although I did have 1 small issue again. Paint/thinner is mapped to the triggers. That's fine, but if you do a short press, Mickey does a shotgun type blast at short range instead of just a quick stream of paint. I would've preferred if the shotgun blast was mapped to L1/R1. Again, that's a small thing though and I got used to it eventually. (Also confirm is on triangle instead of x and while that's not a huge deal it did trip me up a bit). Overall, controls and button mapping was great with just 2 small complaints. Level design complimented the controls well. I'm not a game designer or anything so I'm not really gonna go in-depth with the level design, but just know it is fun from what the demo gives you.

Overall, I really enjoyed this demo. It was nostalgic and fresh at the same time and that's a strange feeling. I just wish it was longer, but hey, the full game is coming soon so ¯⁠\⁠_⁠(⁠ツ⁠)⁠_⁠/⁠¯

I'd say the demo was an 8/10 if that means anything to you. Really good, and I can't wait for the finished product.

[y2klostandfound]

Hot New Releases - Wipeout 3 on Game Players Vol.121(Video game magazine)(Hong Kong)(26/02/2000)

Translation in English:

Playstation 1

Manufacturer: SONY COMPUTER ENTERTAINMENT Price: 5800 Yen Memory: at least 1 BLOCK Release date: on sale Capacity: CD-ROM 1-2 Players / Compatible with DUALSHOCK/Compatible with NEGCON

The fastest race in human history

The original future racing game "WIPE OUT" has been released for three episodes, The graphics and fluency of the game are definitely not inferior to other PS racing games. Maybe because the background of the story is the future, some functions can be saved. As for the quality of the game itself is not bad, and you can use weapons to attack opponents, readers who like this kind of future racing may wish to play.

Operation method (for hand button)

As this game is not a normal racing car, so the operation is a little different from the normal racing car (in fact, just use a few more buttons), and now will introduce its operation method:

← Turn left → Turn right ↑ Up ↓ Down □ Use Weapons △ Change the viewpoint X speed up O Give up weapons L1 Rear Viewpoint R1 HYPERTHRUST L2 LEFT AIRBRAKE R2 RIGHT AIRBRAKE

Game play and Tips

The gameplay is basically not much different from that of regular racing, but the game has weapons, energy (that is HP) and HYPERTHRUST, which are relatively rare (or almost non-existent) in regular racing games, so there are obviously more things to pay attention to. The way to get weapons is to drive through the floor of those special colors, if the floor is purple, the weapons obtained are used for attack (such as MINES, ROCKETS, etc.), yellow is the auxiliary system (such as HP absorption, automatic control, shields, etc.), the appearance is random, so if you are not satisfied, you can press 〇 to give it up and take it again (you can also press the button to release it in use). As for blue, you won't get any weapons, but it will increase the speed of the car (for a short time), and you can move at high speed for a short time with HYPERTHRUST. HYPERTHRUST is a HP-consuming acceleration method, so do not use it recklessly, or the car will be easily crashed.

Weapons List

AUTO-PILOT: It can be operated automatically within a certain period of time after starting, and can be used to handle troublesome corners.

FORCE WALL: Creates a force field that resists magnetic enemies on the track, which can be destroyed.

CLOAK: It will not be LOCK-ON by enemy vehicles within a certain period of time after starting.

ROCKET: A missile fired forward.

GRAVITY SHIELD: It will not take damage for a certain period of time after activation.

MULTI-MISSLES Shoots guided missiles that can be LOCK-ON.

REFLECTOR: It can reflect the attack of enemy vehicles for a certain period of time.

QUAKE DISRUPTOR: The track shakes, but the car in front is injured, and the destructible objects can be destroyed at the same time.

ENERGY DRAIN: Absorb energy from nearby enemy vehicles.

PLASMA BOLT: Hitting an enemy vehicle immediately destroys it, and destructible objects can be destroyed at the same time.

MINES: Releases floating mines behind the car, which can be destroyed.

Basic game mode introduction

SINGLE RACE Players compete with other cars on the track. The number of enemy cars can be freely adjusted in the OPTION (optional). If the CHECKPOINT is not reached within the time limit, it will be GAME OVER. If the energy is insufficient, it can be replenished at the PIT.

TIME TRIAL There is only one opponent in this mode, and no weapons will appear at the same time, but it is the same as SINGLE MODE and reach CHECKPOINT within the time limit. Like similar games, this mode has a "ghost car", but the conditions for it to appear are other than completing this mode , but also use 2 BLOCK to record data.

[vishnudevnb]

Lung Cancer Treatment in Palakkad: Comprehensive Care by Dr. Ponraj, Medical Oncologist

Lung cancer continues to be one of the most prevalent and deadliest cancers worldwide. In Palakkad, the rising burden of this disease—driven by environmental exposure, smoking, and lack of early screening—has made it more crucial than ever to access expert medical care. For those searching for lung cancer treatment in Palakkad, Dr. Ponraj, a highly experienced medical oncologist at Thangam Hospital, offers personalized, evidence-based care tailored to each patient’s needs.

Why Early Detection of Lung Cancer Matters

Lung cancer often develops silently, with symptoms like persistent cough, breathlessness, chest pain, or unexplained weight loss appearing only in advanced stages. Unfortunately, this delay in diagnosis can significantly impact treatment success.

Dr. Ponraj emphasizes early screening using low-dose CT scans and other diagnostic tools, enabling early detection and improving treatment outcomes. Through regular awareness campaigns and community outreach, his team is committed to increasing public understanding of lung cancer symptoms and risk factors in Palakkad.

Common Causes and Risk Factors

The main contributors to lung cancer in Palakkad include:

Smoking & Secondhand Smoke

Air Pollution and Urban Exposure

Occupational Hazards (e.g., asbestos, silica dust)

Genetic Factors

Preventive efforts—such as smoking cessation, protective gear in high-risk jobs, and regular screenings—are central to Dr. Ponraj’s lung cancer care model.

Advanced Diagnostic Services in Palakkad

At Thangam Hospital, lung cancer diagnosis is done using state-of-the-art tools:

Low-Dose CT Scans (LDCT) for early detection

Bronchoscopy & Biopsy for tissue sampling

Molecular Testing (EGFR, ALK, KRAS, etc.) to guide targeted therapy

Multidisciplinary Tumor Board reviews for every case

These diagnostics ensure precise cancer staging and enable Dr. Ponraj to recommend the most appropriate treatment pathway for each patient.

Multidisciplinary Lung Cancer Treatment in Palakkad

Dr. Ponraj leads a collaborative oncology team that includes:

Medical Oncologists

Radiation Oncologists

Thoracic Surgeons

Pulmonologists

Palliative Care Experts

Each case is reviewed by the team to create a personalized, patient-centric plan. This integrated approach ensures every treatment decision—be it chemotherapy, surgery, radiation, or immunotherapy—is based on clinical evidence and patient-specific data.

Treatment Options Offered by Dr. Ponraj

Chemotherapy Traditional cancer-fighting drugs, tailored to cancer type and patient health.

Targeted Therapy Precision medicine that blocks specific mutations (e.g., EGFR, ALK).

Immunotherapy Drugs like PD-1/PD-L1 inhibitors that activate the immune system to fight cancer.

Surgery In collaboration with thoracic surgeons, options like lobectomy and video-assisted thoracoscopic surgery (VATS) are considered for early-stage cases.

Radiation Therapy Advanced techniques like SBRT (Stereotactic Body Radiotherapy) for localized tumors or palliative treatment.

Supportive & Palliative Care Services

Lung cancer treatment is not just about attacking the tumor—it’s about holistic healing. Dr. Ponraj’s team also provides:

Pain management

Nutritional support

Emotional and psychological counseling

Rehabilitation and survivorship programs

Patients and their families receive continuous support throughout their treatment journey.

Why Choose Dr. Ponraj for Lung Cancer Treatment in Palakkad?

Over 15 years of oncology experience Personalized treatment plans using genetic and molecular profiling Access to the latest therapies and clinical trials Modern infrastructure at Thangam Hospital Compassionate care that treats both body and mind

Dr. Ponraj combines global standards in cancer treatment with the local expertise that Palakkad residents trust.

Frequently Asked Questions (FAQ)

1. What are the early warning signs of lung cancer? Common symptoms include a persistent cough, chest pain, shortness of breath, coughing up blood, unexplained weight loss, and fatigue. Early detection significantly improves treatment success, so any of these signs should prompt medical evaluation.

2. How is lung cancer diagnosed at Dr. Ponraj’s clinic in Palakkad? Diagnosis involves advanced imaging (like low-dose CT scans), bronchoscopy, biopsy, and molecular/genetic testing. These tools help accurately determine the cancer type, stage, and best treatment options.

3. What treatment options are available for lung cancer at your center? Treatment may include chemotherapy, targeted therapy, immunotherapy, radiation, and surgery, depending on the cancer stage and molecular profile. All plans are personalized by Dr. Ponraj and the multidisciplinary team.

4. Do you offer minimally invasive surgery for lung cancer? Yes. When surgery is recommended, we collaborate with thoracic surgeons experienced in minimally invasive techniques, such as video-assisted thoracoscopic surgery (VATS), which offers faster recovery and fewer complications.

5. Is genetic or molecular testing available for lung cancer patients? Absolutely. We use advanced molecular diagnostics like next-generation sequencing (NGS) to identify mutations such as EGFR, ALK, ROS1, and KRAS—helping to match patients with effective targeted treatments.

6. Can I access immunotherapy at Dr. Ponraj’s clinic? Yes. Immunotherapy is available for eligible patients, especially those with advanced non-small cell lung cancer. We assess PD-L1 levels and other markers to determine suitability.

7. Are clinical trials available for lung cancer patients in Palakkad? Select clinical trials may be available depending on eligibility and trial phase. Dr. Ponraj’s team provides guidance on trial participation as part of a comprehensive treatment plan.

8. What supportive care services do you provide? Our supportive care includes pain management, nutritional support, psychological counseling, and palliative care—ensuring patients receive holistic care alongside curative treatments.

9. How often should I follow up after lung cancer treatment? Follow-up frequency depends on your treatment plan and response. Typically, follow-ups are scheduled every 3–6 months initially, with periodic imaging and lab tests to monitor for recurrence.

10. Do you offer online consultations for outstation patients? Yes. Virtual consultations are available to discuss reports, symptoms, or treatment options, offering convenience and access to expert care for patients outside Palakkad.

[djibanladesh]

Top 5 Ways DJI Drones Are Used in Agriculture and Surveying

In today’s fast-paced world, the agricultural and surveying industries are rapidly evolving with the help of drone technology. DJI, the world’s leading drone manufacturer, has revolutionized how farmers and surveyors operate. With innovative drone models and high-precision cameras, DJI drones are transforming traditional methods into efficient, data-driven processes. In this blog, we will explore the Top 5 Ways DJI Drones are Used in Agriculture and Surveying, with a special focus on how DJI Camera Bangladesh is helping professionals unlock the full potential of this technology.

1. Precision Agriculture with Multispectral Imaging

DJI drones, like the DJI Phantom 4 Multispectral, provide farmers with real-time insights into crop health. These drones use advanced multispectral cameras to detect plant stress, identify disease, and monitor crop growth.

With the help of DJI Camera Bangladesh, farmers across the region are now accessing cutting-edge imaging solutions to make informed decisions, reduce chemical usage, and boost yield.

2. Efficient Land Surveying and Mapping

Surveyors are replacing traditional land surveying tools with drones like the DJI Matrice 300 RTK paired with L1 or P1 payloads. These high-end drones create accurate 2D and 3D maps using photogrammetry and LiDAR technology.

In Bangladesh, DJI Camera Bangladesh provides tailored drone solutions for engineers and GIS professionals to complete topographic surveys, construction planning, and infrastructure mapping faster and more accurately than ever.

3. Crop Spraying with Agras Drones

The DJI Agras series, including Agras T20 and T30, are revolutionizing pesticide and fertilizer spraying. These drones cover large areas with precision, minimizing waste and maximizing efficiency.

Through DJI Camera Bangladesh, farmers are getting access to professional spraying drones, customized training, and support—making smart farming more accessible and profitable.

4. Soil and Field Analysis

Before planting, DJI drones can perform soil analysis to determine field conditions, moisture levels, and soil composition. This data helps in planning seed distribution and irrigation strategies.

Agricultural experts working with DJI Camera Bangladesh utilize this technology to offer field analysis services that reduce guesswork and optimize farming results.

5. Monitoring and Inspection

For large agricultural lands and infrastructure projects, regular monitoring is critical. DJI drones provide real-time aerial footage and time-lapse data to monitor crop progress or inspect survey sites for any irregularities or damages.

DJI Camera Bangladesh supplies drones like the DJI Mavic 3 Enterprise, which are ideal for long-range observation and thermal inspections, helping both farmers and engineers maintain project timelines and ensure safety.

Conclusion

DJI drones are reshaping agriculture and surveying across the globe, and DJI Camera Bangladesh is playing a key role in this transformation. Whether it’s for precision farming, crop spraying, or land mapping, DJI’s advanced drone technology offers efficiency, accuracy, and productivity like never before.

If you’re a farmer, surveyor, or business owner in Bangladesh looking to leverage drone technology, DJI Camera Bangladesh has the expertise and products to guide your journey.

[xaltius]

What Are the Regression Analysis Techniques in Data Science?

In the dynamic world of data science, predicting continuous outcomes is a core task. Whether you're forecasting house prices, predicting sales figures, or estimating a patient's recovery time, regression analysis is your go-to statistical superpower. Far from being a single technique, regression analysis encompasses a diverse family of algorithms, each suited to different data characteristics and problem complexities.

Let's dive into some of the most common and powerful regression analysis techniques that every data scientist should have in their toolkit.

1. Linear Regression: The Foundation

What it is: The simplest and most widely used regression technique. Linear regression assumes a linear relationship between the independent variables (features) and the dependent variable (the target you want to predict). It tries to fit a straight line (or hyperplane in higher dimensions) that best describes this relationship, minimizing the sum of squared differences between observed and predicted values.

When to use it: When you suspect a clear linear relationship between your variables. It's often a good starting point for any regression problem due to its simplicity and interpretability.

Example: Predicting a student's exam score based on the number of hours they studied.

2. Polynomial Regression: Beyond the Straight Line

What it is: An extension of linear regression that allows for non-linear relationships. Instead of fitting a straight line, polynomial regression fits a curve to the data by including polynomial terms (e.g., x2, x3) of the independent variables in the model.

When to use it: When the relationship between your variables is clearly curved.

Example: Modeling the trajectory of a projectile or the growth rate of a population over time.

3. Logistic Regression: Don't Let the Name Fool You!

What it is: Despite its name, Logistic Regression is primarily used for classification problems, not continuous prediction. However, it's often discussed alongside regression because it predicts the probability of a binary (or sometimes multi-class) outcome. It uses a sigmoid function to map any real-valued input to a probability between 0 and 1.

When to use it: When your dependent variable is categorical (e.g., predicting whether a customer will churn (Yes/No), if an email is spam or not).

Example: Predicting whether a loan application will be approved or denied.

4. Ridge Regression (L2 Regularization): Taming Multicollinearity

What it is: A regularization technique used to prevent overfitting and handle multicollinearity (when independent variables are highly correlated). Ridge regression adds a penalty term (proportional to the square of the magnitude of the coefficients) to the cost function, which shrinks the coefficients towards zero, but never exactly to zero.

When to use it: When you have a large number of correlated features or when your model is prone to overfitting.

Example: Predicting housing prices with many highly correlated features like living area, number of rooms, and number of bathrooms.

5. Lasso Regression (L1 Regularization): Feature Selection Powerhouse

What it is: Similar to Ridge Regression, Lasso (Least Absolute Shrinkage and Selection Operator) also adds a penalty term to the cost function, but this time it's proportional to the absolute value of the coefficients. A key advantage of Lasso is its ability to perform feature selection by driving some coefficients exactly to zero, effectively removing those features from the model.

When to use it: When you have a high-dimensional dataset and want to identify the most important features, or to create a more parsimonious (simpler) model.

Example: Predicting patient recovery time from a vast array of medical measurements, identifying the most influential factors.

6. Elastic Net Regression: The Best of Both Worlds

What it is: Elastic Net combines the penalties of both Ridge and Lasso regression. It's particularly useful when you have groups of highly correlated features, where Lasso might arbitrarily select only one from the group. Elastic Net will tend to select all features within such groups.

When to use it: When dealing with datasets that have high dimensionality and multicollinearity, offering a balance between shrinkage and feature selection.

Example: Genomics data analysis, where many genes might be correlated.

7. Support Vector Regression (SVR): Handling Complex Relationships

What it is: An adaptation of Support Vector Machines (SVMs) for regression problems. Instead of finding a hyperplane that separates classes, SVR finds a hyperplane that has the maximum number of data points within a certain margin (epsilon-tube), minimizing the error between the predicted and actual values.

When to use it: When dealing with non-linear, high-dimensional data, and you're looking for robust predictions even with outliers.

Example: Predicting stock prices or time series forecasting.

8. Decision Tree Regression: Interpretable Branching

What it is: A non-parametric method that splits the data into branches based on feature values, forming a tree-like structure. At each "leaf" of the tree, a prediction is made, which is typically the average of the target values for the data points in that leaf.

When to use it: When you need a model that is easy to interpret and visualize. It can capture non-linear relationships and interactions between features.

Example: Predicting customer satisfaction scores based on multiple survey responses.

9. Ensemble Methods: The Power of Collaboration

Ensemble methods combine multiple individual models to produce a more robust and accurate prediction. For regression, the most popular ensemble techniques are:

Random Forest Regression: Builds multiple decision trees on different subsets of the data and averages their predictions. This reduces overfitting and improves generalization.

Gradient Boosting Regression (e.g., XGBoost, LightGBM, CatBoost): Sequentially builds trees, where each new tree tries to correct the errors of the previous ones. These are highly powerful and often achieve state-of-the-art performance.

When to use them: When you need high accuracy and are willing to sacrifice some interpretability. They are excellent for complex, high-dimensional datasets.

Example: Predicting highly fluctuating real estate values or complex financial market trends.

Choosing the Right Technique

The "best" regression technique isn't universal; it depends heavily on:

Nature of the data: Is it linear or non-linear? Are there outliers? Is there multicollinearity?

Number of features: High dimensionality might favor regularization or ensemble methods.

Interpretability requirements: Do you need to explain how the model arrives at a prediction?

Computational resources: Some complex models require more processing power.

Performance metrics: What defines a "good" prediction for your specific problem (e.g., R-squared, Mean Squared Error, Mean Absolute Error)?

By understanding the strengths and weaknesses of each regression analysis technique, data scientists can strategically choose the most appropriate tool to unlock valuable insights and build powerful predictive models. The world of data is vast, and with these techniques, you're well-equipped to navigate its complexities and make data-driven decisions.