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THE LAST DAYS OF THE POLYMATH

THE LAST DAYS OF THE POLYMATH
People who know a lot about a lot have long been an exclusive club, but now they are an endangered species. Edward Carr tracks some down ... From INTELLIGENT LIFE Magazine, Autumn 2009 CARL DJERASSI can remember the moment when he became a writer. It was 1993, he was a professor of chemistry at Stanford University in California and he had already written books about science and about his life as one of the inventors of the Pill. Now he wanted to write a literary novel about writers’ insecurities, with a central character loosely modelled on Norman Mailer, Philip Roth and Gore Vidal. His wife, Diane Middlebrook, thought it was a ridiculous idea. Even at 85, slight and snowy-haired, Djerassi is a det­ermined man. Eventually Djerassi got the bound galleys of his book. Diane Middlebrook died of cancer in 2007 and, as Djerassi speaks, her presence grows stronger. Carl Djerassi is a polymath. The word “polymath” teeters somewhere between Leo­nardo da Vinci and Stephen Fry.

Power law An example power-law graph, being used to demonstrate ranking of popularity. To the right is the long tail, and to the left are the few that dominate (also known as the 80–20 rule). In statistics, a power law is a functional relationship between two quantities, where a relative change in one quantity results in a proportional relative change in the other quantity, independent of the initial size of those quantities: one quantity varies as a power of another. For instance, considering the area of a square in terms of the length of its side, if the length is doubled, the area is multiplied by a factor of four.[1] Empirical examples of power laws[edit] Properties of power laws[edit] Scale invariance[edit] One attribute of power laws is their scale invariance. , scaling the argument by a constant factor causes only a proportionate scaling of the function itself. That is, scaling by a constant simply multiplies the original power-law relation by the constant . and A power-law only if Universality[edit]

Bite the Bullet Point - Magazine Posted Oct 1, 2010 1:49 AM CDT By Dennis Kennedy Illustration by Jim Frazier I’ve recently been in the audience for a lot of PowerPoint presentations, and some of the uses have made me wonder if recent news articles asking “Is PowerPoint killing presentations?” are right on target. In the right hands, PowerPoint or any other presentation program can literally and figuratively make a presentation sing. But they can also drain out all the energy, passion and interest. The standard approach to PowerPoint for lawyers involves text-dense, bullet-pointed slides on conservative, firm-branded backgrounds with minimal, often simplistic clip art. Snore. The biggest problem I see is that people have moved the focus from the speech and the speaker to the slides. Does this mean it’s time for lawyers to abandon PowerPoint for presentations? For most of us, however, PowerPoint can enhance our presentations and help us get our message across. 1) Are slides even needed? 5) Get the details right.

Table of mathematical symbols When reading the list, it is important to recognize that a mathematical concept is independent of the symbol chosen to represent it. For many of the symbols below, the symbol is usually synonymous with the corresponding concept (ultimately an arbitrary choice made as a result of the cumulative history of mathematics), but in some situations a different convention may be used. For example, depending on context, the triple bar "≡" may represent congruence or a definition. Further, in mathematical logic, numerical equality is sometimes represented by "≡" instead of "=", with the latter representing equality of well-formed formulas. In short, convention dictates the meaning. Each symbol is shown both in HTML, whose display depends on the browser's access to an appropriate font installed on the particular device, and in TeX, as an image. Guide[edit] This list is organized by symbol type and is intended to facilitate finding an unfamiliar symbol by its visual appearance. Basic symbols[edit]

Facebook in Online Privacy Breach; Applications Transmitting Identifying Information Primality Proving 2.1: Finding very small primes For finding all the small primes, say all those less than 10,000,000,000; one of the most efficient ways is by using the Sieve of Eratosthenes (ca 240 BC): Make a list of all the integers less than or equal to n (greater than one) and strike out the multiples of all primes less than or equal to the square root of n, then the numbers that are left are the primes. (See also our glossary page.) For example, to find all the odd primes less than or equal to 100 we first list the odd numbers from 3 to 100 (why even list the evens?) The first number is 3 so it is the first odd prime--cross out all of its multiples. This method is so fast that there is no reason to store a large list of primes on a computer--an efficient implementation can find them faster than a computer can read from a disk. Bressoud has a pseudocode implementation of this algorithm [Bressoud89, p19] and Riesel a PASCAL implementation [Riesel94, p6]. To find individual small primes trial division works well.

What’s the difference between Taxonomies and Ontologies? - Ask Dr. Search A Reader Asks: What’s the difference between Taxonomies and Ontologies? And do I even need to care !? Editor’s Note: For basic definitions of the terms in this article please see our online glossary of terms. Dr. Wow, that’s a great question! I’d summarize the similarities and differences this way: For casual users, these are very similar concepts. Beyond academic precision, ontologies try to represent knowledge in a form so carefully that even computers can derive meaning by traversing the various relationships. Taxonomies can also be read and used in computer software, for example Verity’s Topic Sets were a form of taxonomy, and could be loaded into a profiler to classify incoming documents; many other companies have had this idea as well. Why this Matters? And as to your question “… and do I even need to care?” Are you considering Taxonomies for an upcoming project? Other terms associated with Taxonomies A “Knowledge Base” or “KBase” may also refer to a taxonomy or ontology. In Closing:

Numbers: Facts, Figures & Fiction Click on cover for larger image Numbers: Facts, Figures & Fiction by Richard Phillips. Published by Badsey Publications. See sample pages: 24, 82, 103. Order the book direct from Badsey Publications price £12. In Australia it is sold by AAMT and in the US by Parkwest. For those who need a hardback copy, a limited number of the old 1994 hardback edition are still available. Have you ever wondered how Room 101 got its name, or what you measure in oktas? This new edition has been updated with dozens of new articles, illustrations and photographs. Some press comments – "This entertaining and accessible book is even more attractive in its second edition..." – Jennie Golding in The Mathematical Gazette "...tangential flights into maths, myth and mystery..." – Vivienne Greig in New Scientist ... and on the first edition – Contents –

The Art of Storytelling in Presentations – Connecting In late March I’ll be speaking at Hook: The Presentation Conference about The Art of Storytelling in Effective Presentations. As we lead up to that event, which you can register for here, I wanted to write a few articles about the different aspects of storytelling (in no particular order) that I’ll be addressing. I wanted to start with Connecting, which I believe is why including storytelling in your presentations is so important. As Nancy Duarte said in her most recent book “Resonate” (which is a must buy): Stories are the emotional glue that connects an audience to your idea. As a presenter trying to convey an idea, whether it’s to educate, persuade, encourage action or otherwise, it’s imperative that you make a connection with your audience. If this was a sales presentation, and you are up against various other vendors, how are you going to win the business if they forget everything you said? Stories are different from facts, figures and features. Connect: Authored by: Jon Thomas

Computational complexity theory Computational complexity theory is a branch of the theory of computation in theoretical computer science and mathematics that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other. A computational problem is understood to be a task that is in principle amenable to being solved by a computer, which is equivalent to stating that the problem may be solved by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used. Closely related fields in theoretical computer science are analysis of algorithms and computability theory. Computational problems[edit] A traveling salesman tour through Germany’s 15 largest cities. Problem instances[edit] A computational problem can be viewed as an infinite collection of instances together with a solution for every instance. Representing problem instances[edit]

P versus NP problem Diagram of complexity classes provided that P≠NP. The existence of problems within NP but outside both P and NP-complete, under that assumption, was established by Ladner's theorem.[1] The P versus NP problem is a major unsolved problem in computer science. Informally, it asks whether every problem whose solution can be quickly verified by a computer can also be quickly solved by a computer. It was essentially first mentioned in a 1956 letter written by Kurt Gödel to John von Neumann. Consider the subset sum problem, an example of a problem that is easy to verify, but whose answer may be difficult to compute. An answer to the P = NP question would determine whether problems that can be verified in polynomial time, like the subset-sum problem, can also be solved in polynomial time. Context[edit] In such analysis, a model of the computer for which time must be analyzed is required. Is P equal to NP? NP-complete[edit] Main article: NP-complete Harder problems[edit] for some constant c.

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