Wolfram Blog » John Moore http://blog.wolfram.com News, views, and ideas from the front lines at Wolfram Research. Thu, 20 Sep 2018 13:49:39 +0000 en hourly 1 http://wordpress.org/?v=3.2.1 Books from around the (Wolfram) World! http://blog.wolfram.com/2017/07/07/books-from-around-the-wolfram-world/ http://blog.wolfram.com/2017/07/07/books-from-around-the-wolfram-world/#comments Fri, 07 Jul 2017 16:40:03 +0000 John Moore http://blog.internal.wolfram.com/?p=37181 We’re always excited to see what new things people have created using Wolfram technologies. As the broad geographical distribution of Wolfram Community contributors illustrates, people all over the world are doing great things with the Wolfram Language. In this vein, today we want to highlight some recent books written in languages other than English that utilize Wolfram technologies. From engineering to statistics, these books provide valuable information for those looking to dig a little deeper into scientific applications of the Wolfram Language.

Hans Benker provides a brief and accessible introduction to Mathematica and shows its applications in problems of engineering mathematics, discussing the construction, operation and possibilities of the Wolfram Language in detail. He explores Mathematica usage for matrices and differential and integral calculus. The last part of the book is devoted to the advanced topics of engineering mathematics, including differential equations, transformations, optimization, probability and statistics. The calculations are all presented in detail and are illustrated by numerous examples.

This book explores mathematical models that are traditionally studied in courses on differential equations, but from a unique perspective. The authors analyze models by modifying their initial parameters, transforming them into problems that would be practically impossible to solve in an analytical way. Mathematica provides an essential computational platform for solving these problems, particularly when they are graphical in nature.

Svein Olav Nyberg provides an undergraduate-level statistical formulary with support for Mathematica. This volume includes basic formulas for Bayesian techniques, as well as for general basic statistics. It is an essential primer for Norwegian-language students working in statistical analysis.

Computational thinking is an increasingly necessary technique for problem solving in a range of disciplines, and Mathematica and the Wolfram Language equip students with a powerful computational tool. Approaching calculus from this perspective, K. V. Titov and N. D. Gorelov’s textbook provides a helpful introduction to using the Wolfram Language in the mathematics classroom.

Another textbook from K. V. Titov, Kompyuternaya matematika: uchebnoe posobie emphasizes the use of computer technologies for mathematical analyses and offers practical solutions for numerous problems in various fields of science and technology, as well as their engineering applications. Titov discusses methodological approaches to problem solving in order to promote the development and application of online resources in education and to help integrate computer mathematics in educational technology.

These titles are just a sampling of the many books that explore applications of the Wolfram Language. You can find more Wolfram technologies books, both in English and other languages, by visiting the Wolfram Books site.

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Recent Wolfram Technology Books http://blog.wolfram.com/2017/01/09/recent-wolfram-technology-books/ http://blog.wolfram.com/2017/01/09/recent-wolfram-technology-books/#comments Mon, 09 Jan 2017 17:48:35 +0000 John Moore http://blog.internal.wolfram.com/?p=34497 We’re always excited to see new books that explore new ways to use Wolfram technologies. Authors continue to find inventive ways to think with the Wolfram Language. A variety of new Wolfram technology books have been published over the past few months. We hope that you’ll find something on this list to support your new year’s resolution to upgrade your skills. (Update: also look for the newly released Chinese translation of Stephen Wolfram’s An Elementary Introduction to the Wolfram Language.)

Toolbox for the Mathematica Programmers

This new guide from Viktor Aladjev and V. A. Vaganov outlines a modular approach to programming with the Wolfram Language. Providing over 800 tools that can be incorporated into a variety of projects, Toolbox for the Mathematica Programmers will be useful for students and seasoned programmers alike.

Option Valuation under Stochastic Volatility II: With Mathematica Code

In this second volume of his series about quantitative finance, Alan L. Lewis’s Option Valuation under Stochastic Volatility II: With Mathematica Code expands his original focus to include jump diffusions. The finance industry is increasingly relying on computational analysis to model risk and track customer data. Lewis’s volume is a welcome addition to the literature of the field, of interest for both researchers and investors/traders looking to learn more about computational thinking. Topics covered include spectral theory for jump diffusions, boundary behavior for short-term interest rate models, modeling VIX options, inference theory and discrete dividends.

CRC Standard Curves and Surfaces with Mathematica

The third edition of the popular CRC Standard Curves and Surfaces with Mathematica is an indispensable reference text for anyone who works with curves and surfaces, from engineers to graphic designers. With new illustrations in almost every chapter, the updated version contains nearly 1,000 visualizations, depicting nearly every geometrical figure used today. It also includes a CD with a series of interactive Computable Document Format (CDF) files.

Butterworth & Bessel Filters

T. D. McGlone provides a useful introduction to Butterworth and Bessel (aka Thomson) filter functions. With an overview of mathematical functions, topology choices and component selection based on sensitivity criteria, Butterworth & Bessel Filters will be particularly useful for engineers.

Automation of Finite Element Methods

Another text for engineers, Automation of Finite Element Methods provides an introduction to developing virtual prediction techniques. New finite elements need to be created for individual purposes, which can be time-consuming. Authors Jože Korelc and Peter Wriggers outline an approach to automating this process through Wolfram Language programming.

Computational Proximity: Excursions in the Topology of Digital Images

Based on James F. Peters’s popular graduate course on the topology of digital images, Computational Proximity: Excursions in the Topology of Digital Images introduces the concept of computational proximity as an algorithmic approach to finding nonempty sets of points that are either close to each other or far apart. Peters discusses the applications of this concept in computer vision, multimedia, brain activity, biology, social networks and cosmology.

 Now available as well is the Chinese translation of Stephen Wolfram’s An Elementary Introduction to the Wolfram Language: Wolfram 语言入门. The translated edition includes all of the material that made the English edition popular with anyone wanting to learn to program in the Wolfram Language. Look out for translations into additional languages in the future!
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It’s been a busy year here at the Wolfram Blog. We’ve written about ways to avoid the UK’s most unhygienic foods, exciting new developments in mathematics and even how you can become a better Pokémon GO player. Here are some of our most popular stories from the year.

### Today We Launch Version 11!

In August, we launched Version 11 of Mathematica and the Wolfram Language. The result of two years of development, Version 11 includes exciting new functionality like the expanded map generation enabled by satellite images. Here’s what Wolfram CEO Stephen Wolfram had to say about the new release in his blog post:

OK, so what’s the big new thing in Version 11? Well, it’s not one big thing; it’s many big things. To give a sense of scale, there are 555 completely new functions that we’re adding in Version 11—representing a huge amount of new functionality (by comparison, Version 1 had a total of 551 functions altogether). And actually that function count is even an underrepresentation—because it doesn’t include the vast deepening of many existing functions.

### Finding the Most Unhygienic Food in the UK

Using the Wolfram Language, John McLoone analyzes government data about food safety inspections to create visualizations of the most unhygienic food in the UK. The post is a treasure trove of maps and charts of food establishments that should be avoided at all costs, and includes McLoone’s greatest tip for food safety: “If you really care about food hygiene, then the best advice is probably just to never be rude to the waiter until after you have gotten your food!”

### Finding Pokémon GO’s Shortest Tour to Compute ’em All!

Bernat Espigulé-Pons creates visualizations of Pokémon across multiple generations of the game and then uses WikipediaData, GeoDistance and FindShortestTour to create a map to local Pokémon GO gyms. If you’re a 90s kid or an avid gamer, Espigulé-Pons’s Pokémon genealogy is perfect gamer geek joy. If you’re not, this post might just help to explain what all those crowds were doing in your neighborhood park earlier this year.

### Behind Wolfram|Alpha’s Mathematical Induction-Based Proof Generator

Connor Flood writes about creating “the world’s first online syntax-free proof generator using induction,” which he designed using Wolfram|Alpha. With a detailed explanation of the origin of the concept and its creation from development to prototyping, this post provides a glimpse into the ways that computational thinking applications are created.

### An Exact Value for the Planck Constant: Why Reaching It Took 100 Years

Wolfram|Alpha Chief Scientist Michael Trott returns with a post about the history of the discovery of the exact value of the Planck constant, covering everything from the base elements of superheroes to the redefinition of the kilogram.

### Launching the Wolfram Open Cloud: Open Access to the Wolfram Language

In January of 2016, we launched the Wolfram Open Cloud to—as Stephen Wolfram says in his blog post about the launch—“let anyone in the world use the Wolfram Language—and do sophisticated knowledge-based programming—free on the web.” You can read more about this integrated cloud-based computing platform in his January post.

### On the Detection of Gravitational Waves by LIGO

In February, the Laser Interferometer Gravitational-Wave Observatory (LIGO) announced that it had confirmed the first detection of a gravitational wave. Wolfram software engineer Jason Grigsby explains what gravitational waves are and why the detection of them by LIGO is such an exciting landmark in experimental physics.

### Computational Stippling: Can Machines Do as Well as Humans?

Silvia Hao uses Mathematica to recreate the renaissance engraving technique of stippling: a kind of drawing style using only points to mimic lines, edges and grayscale. Her post is filled with intriguing illustrations and is a wonderful example of the intersection of math and illustration/drawing.

### Newest Wolfram Technologies Books Cover Range of STEM Topics

In April, we reported on new books that use Wolfram technology to explore a variety of STEM topics, from data analysis to engineering. With resources for teachers, researchers and industry professionals and books written in English, Japanese and Spanish, there’s a lot of Wolfram reading to catch up on!

### Announcing Wolfram Programming Lab

The year 2016 also saw the launch of Wolfram Programming Lab, an interactive online platform for learning to program in the Wolfram Language. Programming Lab includes a digital version of Stephen Wolfram’s 2016 book, An Elementary Introduction to the Wolfram Language, as well as Explorations for programmers already familiar with other languages and numerous examples for those who learn best by experimentation.

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Making Wikipedia Knowledge Visible http://blog.wolfram.com/2016/11/23/making-wikipedia-knowledge-visible/ http://blog.wolfram.com/2016/11/23/making-wikipedia-knowledge-visible/#comments Wed, 23 Nov 2016 17:38:12 +0000 John Moore http://blog.internal.wolfram.com/?p=33892

Over the past few months, Wolfram Community members have been exploring ways of visualizing the known universe of Wikipedia knowledge. From Bob Dylan’s networks to the persistence of “philosophy” as a category, Wolfram Community has been asking: “What does knowledge actually look like in the digital age?”

Mathematician Marco Thiel explored this question by modeling the “Getting to Philosophy” phenomenon on Wikipedia. “If you start at a random Wikipedia page, click on the first link in the main body of the article and then iterate, you will (with a probability of over 95%) end up at the Wikipedia article on philosophy,” Thiel explains. Using WikipediaData, he demonstrates how you can generate networks that describe this phenomenon.

He is able to document that about 94% of all Wikipedia articles lead to the “Philosophy” page if one follows the links as instructed, generating in the process some mesmerizing and elegant visualizations of the way that we categorize information.

University student Andres Aramburo also touched on the theme of Wikipedia categories by developing a method for clustering Wikipedia articles by topic. He began by taking a random sample of Wikipedia articles using a Wolfram Language function that he created for this specific task. He then used the links in and out of these articles to generate a graph of the relationships between them. “It’s not a trivial task” to determine if two articles are related to one another, he notes, since “there are several things that can affect the meaning of a sentence, semantics, synonyms, etc.” His visualizations include radial plots of the relationships between articles and word clouds listing shared words for related articles.

One final thread worth highlighting is Community’s celebration of the decision to award Bob Dylan the Nobel Prize in Literature. Wolfram’s own Vitaliy Kaurov created the visualization of the “Universe of Bob Dylan” featured at the top of this post. Alan Joyce (Wolfram|Alpha) generated a graph that compares the lengths of Dylan’s songs (in seconds) to the years in which they were recorded.

And first-time Wolfram Community participant Amy Friedman uploaded her submission from the 2016 Wolfram One-Liner Competition, an amusing word cloud of the poet’s songs in the shape of a guitar.

What new ways of visualizing Wikipedia knowledge can you dream up? With built-in functions like WikipediaData and WikipediaSearch, the Wolfram Language is the perfect tool for exploring Wikipedia data. Show us what you can do with those functions and more on Wolfram Community. We can’t wait to see what you create!

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Less Than Five Lines of Code http://blog.wolfram.com/2016/10/27/less-than-five-lines-of-code/ http://blog.wolfram.com/2016/10/27/less-than-five-lines-of-code/#comments Thu, 27 Oct 2016 16:42:47 +0000 John Moore http://blog.internal.wolfram.com/?p=33602 Software engineer and longtime Mathematica user Chad Slaughter uses the Wolfram Language to facilitate interdepartmental communication during software development. While most programming languages are designed to do one thing particularly well, developers like Slaughter often find that the Wolfram Language is more versatile: “With traditional C++, in order to develop a program, it’s going to take several hundred lines of code to do anything interesting. With Mathematica, I can do something interesting in less than five lines of code.”

When he was working at Enova, Slaughter used the Wolfram Language to build Colossus, an analytics engine that provides Enova’s clients in the financial services industry with instantaneous risk and credit analysis. Slaughter’s team was looking for a programming language that would allow them to deploy software changes without involving the entire engineering team in each new change. The Wolfram Language streamlines the process and saves countless hours of development work by communicating more effectively across teams involved in the development process, prototyping and deploying ideas quickly, and avoiding the use of multiple systems to process internal and external data.

In a talk at the 2015 Wolfram Technology Conference, Slaughter’s colleague Vinod Cheriyan explained that streamlining the production process enables Colossus to significantly outperform its predecessor. Colossus can deploy a model to production in just one and a half to two weeks, where its predecessor would typically take one to one and a half months.

Slaughter’s team also used Mathematica to efficiently manage Enova’s large database of XML credit reports. Credit agencies give Enova reports as XMLs with metadata that is packaged as a PDF or Word document. Slaughter’s team replaced a slower procedural approach for merging data with the Wolfram Language’s functional approach, where pattern matching and accelerating rules allowed them to achieve the same result two orders of magnitude faster.

When we talked with Slaughter about why he prefers the Wolfram Language, he mentioned its power both as a programming language and as a computation engine. By using the Wolfram Language, he is able to dramatically streamline his team’s workflow, bringing testing and production into one efficient system.

Be sure to check out other Wolfram Language stories like Chad Slaughter’s on our Customer Stories pages.

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Wolfram Data Summit 2016 http://blog.wolfram.com/2016/10/18/wolfram-data-summit-2016/ http://blog.wolfram.com/2016/10/18/wolfram-data-summit-2016/#comments Tue, 18 Oct 2016 14:21:54 +0000 John Moore http://blog.internal.wolfram.com/?p=33423 Wolfram continues to be at the forefront of data science innovation. We invite you to check out the latest here.

This past September, we hosted our annual Wolfram Data Summit in Fairfax, Virginia. Over the past seven years, the Data Summit has come to occupy a central place at the nexus of data, computation and business. This high-level gathering of data innovators brings together people from many backgrounds and provides them the opportunity to share their challenges and breakthroughs in analyzing, managing and disseminating data.

With its emphasis on cross-pollination, the Wolfram Data Summit has emerged as an exciting place to share insight into the subtle differences and unique challenges presented by data in different domains. New and unexpected points of commonality emerge from these conversations, allowing participants to trade solutions to emergent data problems.

This question of what to do with data was taken up by Lei Wu (Ancestry.com) and Cinzia Perlingieri (Center for Digital Archaeology), both of whom curate large data collections that preserve cultural memory. At Ancestry.com, Wu and his team of data scientists are reimagining digital genealogy as social networking. We use Facebook for friends and LinkedIn for professional relationships. What if we had a network—what he calls a “big tree”—for our shared genealogical history as well? This big tree engages users in the process of turning data into knowledge.

Like Wu, Perlingieri emphasized the importance of combining human insight with machine learning when it comes to doing things with data. Perlingieri and her team are searching for scalable methodologies that preserve cultural heritage. “We need a redefinition of data as a concept inclusive of alternative narratives and community-driven contributions to heritage,” she said.

Though Ancestry.com and the Center for Digital Archaeology serve very different clienteles, they share a user-focused approach to data curation and interpretation. Users create knowledge by interacting with archives, generating new connections between data elements in the process. These types of connections were explored by each of the Summit’s presenters, including Anthony Scriffignano, chief data scientist at Data Summit co-sponsor Dun & Bradstreet, whose talk delved into some of the “Things We Forget to Think About.”

Because we recognize the promise of computational knowledge for every industry, we will continue to expand the Wolfram Data Summit. The Wolfram Data Summit develops understanding of data at the level of software, hardware and technical processes. But at its core, the Wolfram Data Summit is about how we create and structure data, what kinds of insights can be derived from it and how we apply computational thinking. What kind of computational thinking do professionals use in different domains? How might computational thinking be applied across those industry boundaries? As the Wolfram Data Summit turns eight, we continue to search for answers to these questions and more.

Watch more videos from the 2016 Wolfram Data Summit, including Stephen Wolfram’s keynote address, here.

This post has been updated to include video of Anthony Scriffignano’s talk.

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