#Linear Transformation Assignment Homework Help

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Investigating the Interplay Between Math and Computation

Mathematics and computing have been companions for a long time, impacting disciplines ranging from engineering and artificial intelligence to finance and scientific research. An understanding of how maths and computing overlap can go a long way in fostering problem-solving capacity, analytical powers, and scholastic excellence. From unraveling complex equations to constructing algorithms to discovering data science, the interaction between maths and computation is inevitable. Students in need of math assignment help typically find that maintaining a background in both fields results in improved efficiency and precision in their assignments.

This piece discusses the close relationship between computation and mathematics, providing students with useful tips on how to better understand and excel in both disciplines. With the assistance of computational thinking, mathematical modeling, and applications, this guide provides the information necessary to excel in both fields.

The Relationship Between Mathematics and Computation

Mathematics is the foundation of computation, and computation facilitates mathematical discovery and problem-solving. Computation refers to the process of carrying out calculations, which can be done manually, with a calculator, or using sophisticated programming methods. Computational software such as Python, and Wolfram Alpha has transformed the way mathematical problems are solved in modern education. Assignment helpers often recommend the use of these tools to enhance problem-solving efficiency and accuracy in mathematical tasks.

Areas Where Mathematics and Computation Intersect

Algebra and Algorithm Design – Algebraic algorithms are the most common, ranging from solving linear equations to function optimization.

Calculus in Computational Simulations – Differential equations have a wide range of applications in physics, engineering, and computer graphics and are often solved numerically.

Statistics and Data Science – Statistical analysis is highly dependent on computational methods for handling large datasets, identifying patterns, and making predictions.

Cryptography and Number Theory – Techniques of cryptography in cybersecurity are based on number theory and computational methods.

Machine Learning and Artificial Intelligence – Both are based on mathematical principles such as matrices, probability, and optimization, with the help of computational models.

Knowledge of such connections enables students to apply both fields to achieve maximum efficiency and problem-solving potential in mathematical applications.

Computational Thinking in Mathematics

Computational thinking is a problem-solving process that includes breaking down complicated problems, recognizing patterns, and step-by-step building of solutions. It is one of the major aspects of mathematics, especially when solving abstract problems or a high volume of calculations.

Basic Principles of Computational Thinking

Decomposition – Reducing a complicated problem into small, manageable pieces.

Pattern Recognition – Identifying recurring patterns in mathematical problems.

Abstraction – Choosing key details and ignoring irrelevant information.

Algorithmic Thinking – Developing logical step-by-step procedures for solving problems.

Students of mathematics who use computational thinking in assignments achieve the work comfortably, leading to satisfactory academic performance. Tutors of homework recommend students do this sort of technique practice in a bid to improve problem-solving capacity.

How Computation Helps Mathematical Education

Since the time computer programs were invented, computation has become a tool of inevitable requirement in mathematical study. From programming and simulation to math packages, computational techniques offer students an interactive platform to understand theoretical concepts.

Advantages of Computational Packages in Mathematics

Visual Representation of Problems – Graph utilities allow visual representation of functions, equations, and mappings in geometry. Automation of Tedious Calculations – Computer calculations aid in saving time spent on tiresome and redundant computations. Prompt Feedback – Immediate feedback allows students to make errors and learn. Real-World Application – Models and simulations base mathematical concepts more.

GeoGebra, Wolfram Alpha, and Python libraries such as NumPy and SymPy allow students to play with mathematical concepts, improving understanding and retention.

Applications of Computation and Mathematics in the Real World

Mathematics and computation not only meet in school but also have an impact on different industries and inventions.

Fields Where Computation and Mathematics Play a Central Role

Engineering – Bridges, airplanes, and circuits are designed using computational models.

Finance and Economics – Stock market predictions, risk calculation, and economic forecasting are all computationally based.

Medicine and Healthcare – Computational biology and data analysis help in medical diagnosis and research.

Artificial Intelligence – Machine learning models employ sophisticated mathematical computation to enhance decision-making.

Cybersecurity and Cryptography – Cryptographic methods provide mathematical solutions to data security.

Gaining an understanding of how mathematics and computation work together allows students to acquire transferable skills that can be used in numerous career options. Assignment helpers and writers typically ask students to do real-life case studies in the expectation of enhancing their competence and improving their performance in studies.s.

Understanding Computation and Mathematics Strategies

To succeed in computation and mathematics, students need to use correct study habits that ensure understanding and application.

Practical Strategies for Success

Learn Programming – Python and MATLAB programming languages make mathematical problem-solving easy. Use Internet Resources – Experiential and visual learning through online websites. Practice Daily – Daily practice of mathematical problems enhances computational skills. Solve Challenging Problems – Problem-solving breaks concepts down into easy-to-grasp bits. Use Maths for Real-Life Scenarios – Practical application of theories to real life enhances understanding.

With the incorporation of such methods, students will learn computational efficiency when solving mathematical problems.

Conclusion: Best Learning with Mathematics and Computation

The interaction between mathematics and computation provides students with an excellent model for solving intricate problems in most fields. From designing algorithms to data science and engineering, the interaction between the two subjects is seen in both learning and actual applications.

By employing computational thinking, using digital resources, and applying math to real life, students develop helpful skills to ensure academic as well as workplace success. Mathematics assignment help seekers can gain significant benefits through their comprehension of how computational devices enable math study.

Under the professional tutelage of Assignment in Need, students are able to expand their understanding, improve grades, and begin enjoying math and computation rather than hating them.

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Top 3 Tips for Machine Learning IoT Courses

When we think about excelling in Machine Learning IoT courses, it's clear that a few key strategies can really enhance our experience and outcomes. First, we need to establish a strong mathematical foundation, as it underpins our understanding of algorithms. Next, engaging in hands-on projects helps us apply our knowledge to real-world scenarios. Finally, staying informed about the latest trends and ethical considerations keeps us relevant in this fast-paced field. But what exactly should we focus on first to maximize our effectiveness in these areas?

Build Strong Foundations

When it comes to diving into machine learning, especially within the context of IoT, having a solid foundation is essential for software engineers and scientists.

We need to ensure that we possess a robust mathematical background, equivalent to at least one semester of linear algebra, calculus, probability, and statistics. Completing an advanced, proof-based math course is highly recommended, along with a solid understanding of data structures and algorithms.

Familiarity with Python is crucial since most homework assignments will require it. The Bloomberg course offers a comprehensive set of lectures, practical assignments, and essential textbooks like "The Elements of Statistical Learning."

By mastering these foundational concepts, we'll be well-equipped to tackle more specialized areas in machine learning.

Best For: Software engineers and scientists seeking to build a solid foundation in machine learning concepts and techniques, particularly in the context of IoT applications.

Pros:

Offers a comprehensive curriculum that includes both theoretical knowledge and practical assignments.

Provides access to essential textbooks and resources, enhancing the learning experience.

Emphasizes hands-on experience, allowing learners to apply concepts in real-world scenarios.

Cons:

Requires a strong mathematical background, which may be a barrier for some learners.

Python programming skills are necessary, potentially excluding those without programming experience.

The course is primarily lecture-based, which may not suit all learning styles.

Gain Practical Experience

Having built a solid foundation in machine learning concepts, we must now focus on gaining practical experience to truly apply our knowledge in real-world scenarios. By engaging in hands-on projects, we can solidify our understanding and learn how to tackle real problems effectively.

Here's a quick guide to help us through the process:

We should identify a relevant project that challenges us while ensuring it meets real-world needs. Next, we need to gather quality data that aligns with our objectives, avoiding the temptation to fit our problem to available datasets. Finally, a meticulous approach to data preprocessing will help us achieve accurate results. Engaging in this cycle of practical experience will sharpen our skills and prepare us for future challenges in the IoT landscape.

Stay Updated on Trends

To effectively navigate the rapidly evolving landscape of Machine Learning and IoT, we must stay updated on the latest trends and technologies shaping these fields. The integration of multimodal and customized AI offers exciting opportunities, allowing us to develop interactive applications that mimic human sensory processing.

By understanding these advancements, we can tailor our learning and projects to meet specific business needs while enhancing our creativity.

Moreover, the rise of agentic AI signifies a shift toward proactive systems that can autonomously set goals. This transformation not only enables non-technical workers to leverage AI for design and coding tasks but also drives the demand for new skill sets.

As we engage with these trends, reskilling and upskilling become crucial.

We should also pay attention to the ethical implications of AI development. Open source initiatives promote responsible innovation, and understanding governance structures ensures we can create solutions that prioritize transparency and user trust.

Frequently Asked Questions

How Machine Learning Can Be Used in Iot?

We can leverage machine learning in IoT to enhance predictive maintenance, detect anomalies in real-time, and optimize resource allocation. By analyzing data, we improve decision-making, operational efficiency, and ultimately enrich user experiences across various applications.

How Can I Learn Machine Learning Perfectly?

To learn machine learning perfectly, we should build a strong foundation, engage in hands-on projects, and stay updated on industry trends. Let's practice consistently and collaborate with others to enhance our understanding and skills.

How Can I Become Best in Machine Learning?

To become the best in machine learning, we should focus on building a strong foundation, engaging in hands-on projects, and continuously learning about emerging trends. Together, we can master this exciting field and apply our knowledge effectively.

How Machine Learning Can Be Used in Iot?

Machine learning transforms IoT by analyzing data in real-time, enhancing efficiency, and automating processes. Together, we can leverage these technologies to improve predictive maintenance, anomaly detection, and personalized user experiences across various applications.

Conclusion

In conclusion, by building strong foundations in math, gaining practical experience through hands-on projects, and staying updated on trends in machine learning and IoT, we can set ourselves up for success in this exciting field. Let's commit to continuous learning and adapting as technology evolves. Together, we can tackle real-world challenges and stay ahead of the curve, ensuring our skills remain relevant and impactful in the rapidly changing landscape of AI and IoT.

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How to Interpret Logit Regression Results in STATA Assignments

Logit regression is one of the fundamental tools in econometrics for modeling binary outcome variables. This article is primarily helpful for students learning STATA to interpret the results of a logit regression, especially in the context of assignments as well as coursework. We will use a practical example as well as provide coding illustrations for making the process clear and engaging. 

Introduction to Logit Regression

Logit regression, also known as logistic regression, is in use when your dependent variable is binary (e.g., yes/no, 1/0). Thus, instead of predicting a continuous outcome, it also models the probability of a certain outcome that is occurring. This is done by transforming the results from a linear regression into probabilities using the logistic function.

Understanding the Basics

Logits and Odds Ratios: This indicates that the basic form of logit regression does not actually predict probabilities.  Instead, it models the log-odds of an event happening.  The log-odds are then exponentiated to find the odds ratio to get more interpretations.  The odds ratio can be understood to provide a measure of how much the odds of your outcome either increase or decrease with a one-unit change of your predictor variable.

Coefficients: From STATA software, the change arising from a unit increase in your predictor as described in the logit regression output is presented as Coef.  When the coefficient sign is positive, it means odds of the outcome are higher.  When the coefficient sign is negative, it means odds of the outcome are lower. 

At the end of this guide, you should be able to comprehend various elements of logit regression analysis, especially applied when interpreting results obtained from analyzing econometrics data typically given in your course work assignments.  Ok, let’s do an example now and do the coding side a bit and keep it as simple as we want.

How to Perform a Logit Regression in STATA

Let us walk through how to perform a logit regression in STATA using a practical example. We will examine the relationship between education level and support for gay marriage using the dataset GSS2016.DTA.

First, make sure your dependent variable is binary. In our case, the dependent variable is support for gay marriage. If this variable isn’t binary, you’ll need to recode it.

recode marhomo (1/2=1 "Favor")(3/5=0 "Neutral or oppose"), gen(marhomo_r) label variable marhomo_r "Favorable view toward gay marriage"

To estimate a logit regression, use the following command:

logit marhomo_r educ, nolog

Also, Read our blog on Linear Regression in STATA for one-of-a-kind assignment help.

Menu Method:

Click on "Statistics" > "Binary outcomes" > "Logistic regression".

Fill in the dependent and independent variables.

Click on the "Reporting" tab to choose "Report estimated coefficients".

Click "OK".

Interpreting the Output

Here is a sample output:

Coefficients of the model. For educ, the coefficient is 0.153, indicating the log-odds of supporting gay marriage increase by 0.153 for each extra year of education.

Converting to Odds Ratios

Log-odds are not intuitive. Convert coefficients to odds ratios using the or option:

logit marhomo_r educ, nolog or

Odds Ratio: For educ, the odds ratio is 1.165, meaning each extra year of education increases the odds of supporting gay marriage by nearly 16.5%.

To control for additional variables like age and gender, extend the model:

logit marhomo_r educ age female, nolog or

Interpreting the output:

educ: Still significant with an odds ratio of 1.165. 

age: Odds ratio of 0.977, indicating older individuals are less likely to support gay marriage. 

female: Not significant in this model.

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Are you a student learning STATA in your econometrics coursework and finding it challenging to keep up with homework? Do complex statistical analyses and logit regressions leave you feeling overwhelmed? Our professional STATA homework help is here to support you every step of the way. Here’s why you should choose us for your STATA assignment needs: 

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Additional Resources

Books: 

• "Logistic Regression Using STATA" by Scott Long and Jeremy Freese. 

• "An Introduction to Statistics and Data Analysis Using STATA" by Lisa Daniels and Nicholas Minot. 

• Stata Documentation: The logit documentation is your official reference. 

• Statisticshelpdesk.com for help with Stata assignments.

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Statistics homework assignments often pose formidable challenges, requiring a blend of theoretical knowledge and practical application. Whether you're grappling with hypothesis testing, ANOVA, or time series analysis, the journey towards mastery can be fraught with obstacles. However, with the right tools and guidance, conquering these challenges becomes a feasible endeavor.

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Introduction

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Linear Transformation Assignment Homework Help

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Are You Having Trouble With Your Probability Assignment?

The likelihood of an event occurring is defined as Probability, also known as chance. It is typically expressed as a number between 0 and 1. In this case, 1 denotes uncertainty, and 0 denotes impossibility. Uncertainty and randomness are not uncommon in our daily lives. As a result, we require a solid understanding of Probability to make sound decisions in the face of uncertainty.

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  Conditional Probability

This is the Probability of a specific situation occurring given that another event has occurred. A variety of Data Science techniques employ the Bayes' theorem to update hypothesis probabilities while taking into account available evidence.

  Random Variable

These are collections of possible outcomes from a random event. A random variable may also be referred to as a stochastic variable or an aleatory variable. It can be both continuous and discrete. Continuous random variables can take any value, whereas discrete random variables can only take a limited number of values.

  Probability Distributions

In the case of a random variable, the Probability distribution describes the distribution of probabilities over the random variable's values. A Probability distribution can be classified into two types. The binomial distribution, which has n (n= number of trials) repeated trials with only two possible outcomes (success or failure), and the normal distribution, also known as the Gaussian distribution, which is symmetric about the mean. This means that data near the mean appear more frequently than data far from the mean.

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Ringing effect in time domain reconstructed signal after filtering

Hi all,

I'm trying to band pass filter (9Hz to 70Hz) an ECG signal

which is sampled at 256 Hz using MATLAB's 'butter' followed by 'filter'. I'm using an order of 10, just being greedy about making the pass band near perfect. Filtering happens alright but the time domain filtered signal has sort of ringing effect.

 I then reduce the order to 2 and perform the filtering and now the ringing is not there. What's this theoretically? Is this a manifestation of Gibb's phenomenon? 

Any help would be greatly appreciated.

ANSWER

Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.

Not sure exactly what you did, but in general, in Linear Systems Theory (Fourier Filtering), multiplication in one domain is equivalent to convolution in the other domain. A band pass filter is a rect function and the Fourier Transform of a rect function is a sinc function. So when you multiply by a rect function in the Fourier domain to do band pass filtering, it's like you convolve with a sinc function in the time domain. Since the sinc function has ripples, so will your output signal. Yes this is the Gibbs phenomenon. And since wider in one domain means narrower in the other domain, a wider rect will give a narrow sinc (faster ripples) while a narrow rect will give a wider sinc and slower ripples.

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What is R Programming?

R is a programming language and free software environment for statistical computing and graphics, developed by Ross Ihaka and Robert Gentleman in 1993. The R language is widely used among data miners and statisticians for developing statistical software and data analysis.

It includes machine learning algorithms, linear regression, time series, statistical inference to name a few. Most of the libraries are written in R, but for the heavy computational task, C codes are preferred. R is not only preferred by academics, but many large companies also use R programming language, including Facebook, Google Uber and so on.     

Data analysis with R is done in a sequence of steps;

Program: R is a clear and accessible programming tool.

Transform: R is made up of a collection of libraries specially designed for data science.

Discover: This involves investigation of the data, refine your hypothesis and analyze them.

Model: R provides a wide range of tools to conquer the right model for your data.

Communicate: Integrate codes, graphs, and outputs to a report with R markdown or create shiny apps to share with the world.

R is used for Statistical inference, data analysis, and machine learning algorithms. It is used in the healthcare industry and academics.

 Easy guide to R Programming

Detail analysis like clustering, correlation, and data reduction are done with R.

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[thesupergamercorpus]

Today I wanted to write about one of my ‘heroes’ or inspirations. The way I look at them is somewhat like this quote:

“Trophies and great men are not only to be gazed upon, but also inspire to do the same.”

I will write down one of the best stories I have read about John von Neumann. But first of all, this is the list he is known for. I will make the ones I think are most popular bold:

Abelian von Neumann algebra, Affiliated operator, Amenable group, Arithmetic logic unit, Artificial viscosity, Axiom of regularity, Axiom of limitation of size, Backward induction, Blast wave (fluid dynamics), Bounded set (topological vector space), Carry-save adder, Cellular automata, Class (set theory), Computer virus, Commutation theorem, Continuous geometry, Coupling constants, Decoherence theory (quantum mechanics), Density matrix, Direct integral, Doubly stochastic matrix, Duality Theorem, Durbin–Watson statistic, EDVAC, Ergodic theory, Explosive lenses, Game theory, Hilbert’s fifth problem, Hyperfinite type II factor, Inner model, Inner model theory, Interior point method, Koopman–von Neumann classical mechanics, Lattice theory, Lifting theory, Merge sort, Middle-square method, Minimax theorem, Monte, Carlo method, Mutual assured destruction, Normal-form game, Operation Greenhouse, Operator theory, Pointless topology, Polarization identity, Pseudorandomness, Pseudorandom number generator, Quantum logic, Quantum mutual information, Quantum statistical mechanics, Radiation implosion, Rank ring, Self-replication, Software whitening, Sorted array, Spectral theory, Standard probability space, Stochastic computing, Stone–von Neumann theorem, Subfactor, Ultrastrong topology, Von Neumann algebra, Von Neumann architecture, Von Neumann bicommutant theorem, Von Neumann cardinal assignment, Von Neumann cellular automaton, Von Neumann interpretation, Von Neumann measurement scheme, Von Neumann ordinals, Von Neumann universal constructor, Von Neumann entropy, Von Neumann Equation, Von Neumann neighborhood, Von Neumann paradox, Von Neumann regular ring, Von Neumann–Bernays–Gödel set theory, Von Neumann universe, Von Neumann spectral theorem, Von Neumann conjecture, Von Neumann ordinal, Von Neumann’s inequality, Von Neumann’s trace inequality, Von Neumann stability analysis, Von Neumann extractor, Von Neumann ergodic theorem, Von Neumann–Morgenstern utility theorem, ZND detonation model

Cellular automata → this one appears to function and replicate like DNA. Cellular automata preceded the discovery of the structure of DNA.

Decoherence theory (quantum mechanics) → quantum states get continuously ‘pushed around’ by external influences (like being observed e.g. the double-slit experiment), which can change their original state. A quantum state resides in a ‘superposition’. Superposition simply means a state where two or more ‘states’ are combined, like an up and down state simultaneously. When that is the case, a quantum system resides in coherence. When observing that system, decoherence, or wave function collapse happens e.g. the original quantum system both had an up and down state simultaneously, but after being observed, now only has either an up state or a down state.

Merge sort → see the chapter 08/31/2019—Top-down, bottom-up thinking, sorting algorithms, and working memory where I discuss this computer sorting algorithm and combine it with top-down and bottom-up thinking.

Self-replication → a machine replicating itself. If machines are also able to upgrade themselves with each replication, a so-called technological singularity can occur (Google it).

Von Neumann architecture → essentially how our computers are built.

Now onto some stories of him. Most information is taken from Wikipedia.

Examination and Ph.D.

He graduated as a chemical engineer from ETH Zurich in 1926 (although Wigner says that von Neumann was never very attached to the subject of chemistry), and passed his final examinations for his Ph.D. in mathematics simultaneously with his chemical engineering degree, of which Wigner wrote, “Evidently a Ph.D. thesis and examination did not constitute an appreciable effort.”

Mastery of mathematics

Stan Ulam, who knew von Neumann well, described his mastery of mathematics this way: “Most mathematicians know one method. For example, Norbert Wiener had mastered Fourier transforms. Some mathematicians have mastered two methods and might really impress someone who knows only one of them. John von Neumann had mastered three methods.” He went on to explain that the three methods were:

A facility with the symbolic manipulation of linear operators;

An intuitive feeling for the logical structure of any new mathematical theory;

An intuitive feeling for the combinatorial superstructure of new theories.

Edward Teller wrote that “Nobody knows all science, not even von Neumann did. But as for mathematics, he contributed to every part of it except number theory and topology. That is, I think, something unique.”

Cognitive abilities

As a six-year-old, he could divide two eight-digit numbers in his head and converse in Ancient Greek. When he was sent at the age of 15 to study advanced calculus under analyst Gábor Szegő, Szegő was so astounded with the boy’s talent in mathematics that he was brought to tears on their first meeting.

Hans Bethe on von Neumann

Nobel Laureate Hans Bethe said “I have sometimes wondered whether a brain like von Neumann’s does not indicate a species superior to that of man”, and later Bethe wrote that “von Neumann’s brain indicated a new species, an evolution beyond man”.

Edward Teller

Edward Teller admitted that he “never could keep up with John von Neumann.”

Teller also said “von Neumann would carry on a conversation with my 3-year-old son, and the two of them would talk as equals, and I sometimes wondered if he used the same principle when he talked to the rest of us.”

George Dantzig

George Dantzig is the mathematician who thought that two problems on the blackboard were homework. He solved them and handed them, albeit a bit later, so he thought they were overdue.

Here’s the plot twist: They were two famous unsolved problems in statistics with which the mathematics community struggled for decades.

When George Dantzig brought von Neumann an unsolved problem in linear programming “as I would to an ordinary mortal”, on which there had been no published literature, he was astonished when von Neumann said “Oh, that!” before offhandedly giving a lecture of over an hour, explaining how to solve the problem using the hitherto unconceived theory of duality.

Johnny as a student

George Pólya, whose lectures at ETH Zürich von Neumann attended as a student, said “Johnny was the only student I was ever afraid of. If in the course of a lecture I stated an unsolved problem, the chances were he’d come to me at the end of the lecture with the complete solution scribbled on a slip of paper.”

Nobel Prizes

Peter Lax wrote, “To gain a measure of von Neumann’s achievements, consider that had he lived a normal span of years, he would certainly have been a recipient of a Nobel Prize in economics. And if there were Nobel Prizes in computer science and mathematics, he would have been honored by these, too. So the writer of these letters should be thought of as a triple Nobel laureate or, possibly, a ​3 1⁄2-fold winner, for his work in physics, in particular, quantum mechanics”.

von Neumann as a teacher

Von Neumann was the subject of many dotty professor stories. He supposedly had the habit of simply writing answers to homework assignments on the board (the method of solution being, of course, obvious). One time one of his students tried to get more helpful information by asking if there was another way to solve the problem. Von Neumann looked blank for a moment, thought, and then answered, “Yes.”

Henry Ford

Henry Ford had ordered a dynamo for one of his plants. The dynamo didn’t work, and not even the manufacturers could figure out why. A Ford employee told his boss that von Neumann was “the smartest man in America,” so Ford called von Neumann and asked him to come out and take a look at the dynamo.

Von Neumann came, looked at the schematics, walked around the dynamo, then took out a pencil. He marked a line on the outside casing and said, “If you’ll go in and cut the coil here, the dynamo will work fine.”

They cut the coil, and the dynamo did work fine. Ford then told von Neumann to send him a bill for the work. Von Neumann sent Ford a bill for $5,000. Ford was astounded – $5,000 was a lot in the 1950s – and asked von Neumann for an itemised account. Here’s what he submitted:

Drawing a line with the pencil: $1

Knowing where to draw the line with the pencil: $4,999

Ford paid the bill.

David Blackwell

Blackwell did a year of postdoctoral research as a fellow at the Institute for Advanced Study in 1941 after receiving a Rosenwald Fellowship. There he met John von Neumann, who asked Blackwell to discuss his Ph.D. thesis with him. Blackwell, who believed that von Neumann was just being polite and not genuinely interested in his work, did not approach him until von Neumann himself asked him again a few months later. According to Blackwell, “He (von Neumann) listened to me talk about this rather obscure subject and in ten minutes he knew more about it than I did.”

von Neumann was the only genius

Von Neumann entered the Lutheran Fasori Evangélikus Gimnázium in 1911. This was one of the best schools in Budapest, part of a brilliant education system designed for the elite. Under the Hungarian system, children received all their education at the one gymnasium. Despite being run by the Lutheran Church, the majority of its pupils were Jewish. The school system produced a generation noted for intellectual achievement. Wigner was a year ahead of von Neumann at the Lutheran School. When asked why the Hungary of his generation had produced so many geniuses, Wigner, who won the Nobel Prize in Physics in 1963, replied that von Neumann was the only genius.”

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[starstruckautumn]

WEEKLY HOROSCOPES 3/19-3/26 - RISK

Hello My Star Babies! This week is hot fire, because we’ve got 4 planets in Aries, spring has officially begun and we are feeling alive. This week can bring us a little bit more weirdness on the love front due to Venus in retrograde tangoing with the Sun. When this happens, it looks like Venus has disappeared from the skies…almost as if Venus has taken a step back.

It’s cool since behind the Sun’s rays Mercury is linked up with Venus in a sweet way. It’s about expressing love this week, as well what it is that you are hankering for. You might think that playing it cool is the way to go, but trust me…it’s best to be sure about what you want and to ask for it.

HOW TO MACK THIS WEEK’S ENERGY:

ARIES SUN/RISING: You are feeling some kind of way about yourself and your place in the world at large. You are chomping at the bit for something to shift; in your head you know that you’ve been so patient and you’ve paid your dues! This week presents some shift, THE shift you’ve been waiting for. If you avoid power games and other non-hero behavior you’ll begin to shift the way that you need to.

TAURUS SUN/RISING: Who knew you could be such a little bad ass in the realms of work and promoting yourself? It’s almost seamless….but the thing that I want to bring to your attention this week is that you need to make sure that you kill the ghosts of lovers past. In regards to love and romance and money you need to make sure that you are authentic, and avoiding any possible stealth mode love entanglements that can destroy your hard won peace.

GEMINI SUN/RISING: You are done with the season of fog…that space of uncertainty, too much whiskey or indecision. This week you are waking up, and seeing that there are so many opportunities surrounding you for whatever it is that you want to create. Do you know what you want to create? You are figuring that out…it’s going to be grim work but you are taking on this necessary task like a champ.

CANCER SUN/RISING: Your mind is on your business, and on your next move. Your ideas are blooming and taking form. You know what you have to offer, and this time around you aren’t bogged down by the old energy siphon of unhealthy power plays from family, lovers, friends and co-workers/bosses. Ok, basically by anything. Something big is shifting for you. Very, very big.

LEO SUN/RISING:  “All She wants to do is Dance” is your internal theme song these days. No, really. You really want to play since you’ve been deprived of very day joys for what feels like an eternity. It’s not a desire to play that you are experiencing; it’s the joy of bizarre expansion that shoves you out of your comfort zone. It’s just what you needed, and you know it.

VIRGO SUN/RISING: Like most Mutable signs, you may struggle with keeping healthy boundaries. However this week your motto could very well be something along the lines of “Not my Circus, Not my Monkeys”. Seriously, you are so done with the ridiculous drains on your resources, your energy and your heart. You are seeking something darker and richer now. It’s like switching out Lemon Meringue Pie for Flourless Chocolate Cake. Your tastes have just gotten a little bit more complicated, and it’s good.

LIBRA SUN/RISING: You are performing the world’s most sexy, innovative and metaphysical strip tease of all time. You are on hot fire this week, and discovering something magical. What is this new spell that you are mastering? What new magicks are you conjuring? It is this very simple, very liberating truth: YOU ARE ALLOWED TO CHANGE YOUR MIND. About what? That’s your homework assignment, and what will become part of your incantation.

SCORPIO SUN/RISING: Right now you are in a prime space to make some powerful partnerships, be it for bedroom or boardroom games. Don’t worry about that Venus RX; other celestial factors can make sure that things work out in your favor. Don’t look a gift horse in the mouth and be smart and sassy, not suspect.

SAGITTARIUS SUN/RISING: Who knew that you had a Holly Homemaker lurking around inside of you? Well, this week you’ll be able to tap into that part of yourself in a way that feels almost warrior-esq…like Xena combined with Betty Crocker. It’s this shift that can help you look at your current money woes as a chance for a resurrection of a super power that you didn’t even know had faded.

CAPRICORN SUN/RISING: This week you’ll be tempted to knock over chairs, set things on fire…to be the satyr you are and not the master of the boardroom that the world wants you to be. I think getting in touch with that wildness inside of you will serve you well. You’ve run tame too long; it’s time to grab what you want, and hustle hard.

AQUARIUS SUN/RISING: It’s all endless talk talk talk for you this week….BUT IT’S SO NECESSARY. Every conversation will feel cathartic in one way or another. You are able to absorb things about the world at warp speed, and you are able to truly process everything into a fantastic database inside your brain, psyche and heart. Let all of this information transform you.

PISCES SUN/RISING: You are normally the kind of person who prefers to experience life as it happens; past, present and future are all the same to you since you really understand that time is not linear, but cyclical. However this week you’ll dive into your paper chase to create some foundation for your life, and for what you want to create.