Visualizing Nanoscale Structures in Real Time

 Visualizing Nanoscale Structures in Real Time

University of Michigan News

James Lynch, August 18, 2022

A beta version of open-source three-dimensional (3D) data visualization software developed by University of Michigan (U-M)-led researchers can render nanoscale visuals of structures in minutes. The enhanced tomviz tool also allows researchers to view and manipulate 3D visualizations in real time, which could dramatically accelerate materials research. U-M's Robert Hovden said the software pulls data directly from an electron microscope as it is generated, and displays results immediately. Previously, researchers had to capture hundreds of two-dimensional projection images of nanomaterial from various angles and feed them to tomviz, which would take hours to produce 3D visualizations from them. Hovden said thanks to the augmented software, "You can start interpreting and doing science before you're even done with an experiment."  ... '

Pushing the Frontiers of Mathematical Research

My earliest work did this kind of research

Pushing the Frontiers of Mathematical Research,   By Allyn Jackson

Commissioned by CACM Staff, September 8, 2022

For decades, mathematicians have turned to computers for help with tasks like big numerical calculations and visualizing complex geometric objects. Like a blackboard, the computer has been a handy tool that picks up where human capacity to juggle numbers, symbols, and pictures drops off.

Today, however, computers are playing an entirely new role: they are learning modern mathematics.

A loose-knit international group is using computer proof assistants, originally developed to check formal software correctness, to create online libraries of mathematical theorems and proofs. The theorems housed in these libraries can then be called upon as building blocks for proofs of new mathematical results. The hope is that the libraries one day will encompass the entirety of mathematical knowledge.

"It's a completely new way to do mathematics that is very satisfying," said mathematician Kevin Buzzard of the U.K.'s Imperial College London.

Buzzard discussed this work in one of the most-watched lectures at the 2022 International Congress of Mathematicians in July this year. (The Congress, originally scheduled to be held in Saint Petersburg, Russia, was transformed into an entirely online event after that country's invasion of Ukraine.) Around the same time, a paper appeared on the arXiv containing what might be called a "proof assistant manifesto," laying out progress achieved and describing challenges ahead. Buzzard is one of the paper's 10 authors, along with ACM A.M. Turing Award recipient Leslie Lamport.

During the course of his career in number theory and algebraic geometry, Buzzard has worked on pure mathematics with no obvious applications. He describes his latest work with computer proof assistants as also "100% blue-sky research," explaining, "It's interesting, important, and beautiful. But I can't see the future. I don't know what killer app might come out of this work."

Verifying Proofs Down to the Axioms

Computer proof assistants, also called interactive theorem provers, began to have a significant impact in mathematics starting with the work of Thomas Hales of the University of Pittsburgh.  In the late 1990s, Hales announced a solution to a venerable sphere-packing problem called the Kepler conjecture. While his approach was generally accepted as correct, Hales' use of a computer program to sort through a huge number of possible packings left many skeptical.

Hales then embarked on a multi-year project to validate his solution by recasting it as a "formal proof," a thoroughgoing version of the proof in which every logical inference is checked, down to the fundamental axioms of mathematics. Formal proofs, far too verbose and tedious for humans to read, are tailor-made for computer proof assistants. In 2014, Hales and 20 collaborators completed a computer verification of the Kepler proof. 

In parallel with this work, during the early 2000s, a few mathematicians began to use proof assistants to formalize proofs of several classic mathematical results. The work was slow and painstaking, and the technology rather off-putting. As a result, formal proofs remained something of a niche area, far from the frontier of mathematics.

Buzzard got into the act after listening to a 2017 lecture in which Hales described his experiences using proof assistants. "That lecture changed my life," said Buzzard, as he realized that proof assistants could handle the theorems that mathematicians are working on at the frontier of research today. "Computer scientists were figuring out how to write the software, to make it useful," Buzzard said. "They've done that now. Now it's our turn."

The True Value of Proof Assistants

As the name suggests, a computer proof assistant is very good at verifying proofs, but its true value in mathematics lies elsewhere, in its ability to store mathematical results and the fundamental logic supporting them. Every theorem, lemma, and proposition that was used or proved in the course of verifying, for example, Hales's solution of the Kepler conjecture, now sits in the memory of a proof assistant, ready to be called upon as raw material to build proofs of new theorems.  ... ' 

Network Visualizations for Data Archive

Visualization always useful.  

Network Visualization Tool Maps Information Spread

By Indiana University Bloomington, July 29, 2022

Network Tool visualization

Network Tool visualizatons draw on a data archive with over 30 billion public tweets from the past three years.

Indiana University Bloomington's Observatory on Social Media (OSoMe) and Center for Complex Networks and Systems Research have launched an updated network visualization tool that shows journalists, scientists, and the public how information propagates.

The revamped Network Tool generates an interactive 3D map of information spreading across Twitter. Users can visualize who is retweeting or citing whom on a specific subject, or see which hashtags are being used with other hashtags, and all the data is now exportable.

OSoMe's tools leverage approximately 50 million tweets daily, equivalent to about 10% of public tweets, which are analyzed and indexed for use. Users can visualize data from any given month from the previous three years. ... 

From Indiana University Bloomington

View Full Article    

Archaeological Artifact Visualization

Thinking of some other uses of creation and identification.

 DIY Digital Archaeology: Methods for Visualizing Small Objects, Artifacts

Max Planck Institute for the Science of Human History, April 13, 2022

Researchers at Germany's Max Planck Institute for the Science of Human History (SHH), the U.K.'s University of Exeter, and Japanese videogame developer Cygames collaborated on new techniques for visualizing small artifacts. The Small Object and Artifact Photography (SOAP) protocol guides users through the process of photographing small objects and artifacts. The High Resolution Photogrammetry (HRP) protocol is a manual for developing high-resolution three-dimensional models by combining methods applied in academic and computer graphic fields. The researchers developed the techniques using Adobe Camera Raw, Adobe Photoshop, RawDigger, DxO Photolab, and RealityCapture, leveraging native functions and tools that simplify and accelerate image capture and processing. "By clearly explaining every step of the process, including theoretical and practical considerations, these methods will allow users to produce high-quality, publishable two- and three-dimensional visualizations of their archaeological artifacts independently," said SHH's Jacopo Niccolò Cerasoni. .... ' 

Opening Genomics Data to All

Boon to future research by opening data. 

Cloud-Based Platform Opens Genomics Data to All

By Johns Hopkins University, January 19, 2022

A team co-led by a Johns Hopkins University computer scientist has created a cloud-based platform that grants researchers easy access to one of the world's largest genomics databases.

Known as AnVIL, the platform gives any researcher with an Internet connection access to thousands of analysis tools, patient records, and more than 300,000 genomes. The work is described in "Inverting the Model of Genomics Data Sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-Space,"  published in the journal Cell Genomics.

"AnVIL is inverting the model of genomics data sharing," says project co-leader Michael Schatz, Bloomberg Distinguished Professor of computer science and biology at Johns Hopkins. Instead of having researchers download massive amounts of data from centralized warehouses to their own data centers, "we allow researchers to effortlessly move to the data in the cloud," he says.

AnVIL is currently built on the Google Cloud Platform to enable massive scalability and capacity for users within a robustly established security perimeter authorized for the storage and analysis of controlled access datasets.

From Johns Hopkins University

View Full Article 

Future of New, Bigger Graphics

 Future of New, Bigger Graphics

and a short simplified video look

Digital Product Development from Visualization

And forward to product design and development, from visualization. 

Visualization Adds Transparency to Digital Product Development

Fraunhofer-Gesellschaft (Germany),  January 4, 2021

Visualization software developed by researchers at Germany's Fraunhofer Institute for Computer Graphics Research (IGD) and Austria's Linz Center of Mechatronics could embed transparency within digital product development. The Fraunhofer IGD team applied this tool to the development of electric motors, while its Linz partner produced technical data and mathematically modeled all product parameters. The visualization covers all criteria relevant for motor development, and portrays the interactions of individual parameters, relaying what occurs when any value is changed and the impact on other criteria, in real time. Fraunhofer IGD's Lena Cibulski said, "The software is an ideal choice whenever dealing with many design options and a number of incompatible quality criteria that require a compromise to be made."

Holographic Video Display for Mobile Phones

 New directions for display on mobile phone, multi dimensional?  Implications for typical phone tasks?  Advanced marketing?Sounds impressive.

Thin Holographic Video Display for Mobile Phones

IEEE Spectrum, Charles Q. Choi

At South Korea's Samsung Advanced Institute of Technology, researchers have invented a method for creating a thin holographic video display that may eventually enable 4K three-dimensional videos on mobile phones. The display has a special backlight with a beam deflector that can tilt the angles of coherent light beams from laser diodes, expanding the viewing angle 30-fold without increasing the number of pixels required. The Samsung researchers also used a slim geometric phase lens to gather scattered light from the pixels, shrinking the optical components' thickness to just 1 centimeter. The display’s single-chip holographic video processor can perform about 140 billion operations per second to generate 4K holographic color images at a speed of 30 frames per second.  ... 

Visualizing Covid-19 Creating Digital Brains for Analysis and Action

Join Us Tomorrow!

Visualizing Covid-19
Creating Digital Brains for Analysis and Action

A Special Big Thinker Presentation Featuring:

Visualizing Covid-19
This Thursday, April 23, 2020
11:00 am Pacific Time, 2:00 pm Eastern Time

Register Now  https://register.gotowebinar.com/register/8728385502294360844

We are in unprecedented times. We wake up and go to sleep digesting a sea of information on the Covid-19 pandemic.

It’s critical for everyone to shape their own perspective of world events and TheBrain enables this. Join highly acclaimed Brain architects: Jerry Michalski and Dr. Mark Trexler. Both master Brain creators will demo their own Brains on Covid-19. Shelley Hayduk and Matt Caton from TheBrain will also debut their Covid-19 TeamBrain, as well as make it available for download to all attendees.

The session provides a broad range of perspectives and expertise on Brain creation for Covid-19 to help you get started on visualizing your own perspective.

Topics Include:

Crafting an information landscape that reflects your perspective
Strategies for mind mapping complex information networks
Visualizing world events, news items and scientific data
Aggregating disparate information sources from online news to documents and twitter feeds
Creating an all-encompassing Brain or mini content-focused Brains
Visualizing information geographically
Also features interactive Q&A with Jerry Michalski and Dr. Mark Trexler

Visualizing Covid-19
Brains for This Session
Now Available!  https://www.thebrain.com/covid 

Get a sneak peek at the different Brains covered during this session. View online and get the sample Brain now.

Fairness Indicators for AI Machine Learning

Brought to my attention, dealing with a potential solution for a project at hand.   Good technical and non technical coverage.  The approach is now available in Beta. Examining.

Fairness Indicators: Scalable Infrastructure for Fair ML Systems
Wednesday, December 11, 2019
Posted by Catherina Xu and Tulsee Doshi, Product Managers, Google Research

While industry and academia continue to explore the benefits of using machine learning (ML) to make better products and tackle important problems, algorithms and the datasets on which they are trained also have the ability to reflect or reinforce unfair biases. For example, consistently flagging non-toxic text comments from certain groups as “spam” or “high toxicity” in a moderation system leads to exclusion of those groups from conversation.

In 2018, we shared how Google uses AI to make products more useful, highlighting AI principles that will guide our work moving forward. The second principle, “Avoid creating or reinforcing unfair bias,” outlines our commitment to reduce unjust biases and minimize their impacts on people.

As part of this commitment, at TensorFlow World, we recently released a beta version of Fairness Indicators, a suite of tools that enable regular computation and visualization of fairness metrics for binary and multi-class classification, helping teams take a first step towards identifying unjust impacts. Fairness Indicators can be used to generate metrics for transparency reporting, such as those used for model cards, to help developers make better decisions about how to deploy models responsibly. Because fairness concerns and evaluations differ case by case, we also include in this release an interactive case study with Jigsaw’s Unintended Bias in Toxicity dataset to illustrate how Fairness Indicators can be used to detect and remediate bias in a production machine learning (ML) model, depending on the context in which it is deployed. Fairness Indicators is now available in beta for you to try for your own use cases.  ... "

Tableau Conference Nov 13-15

Particularly interested in seeing how they are integrating with Salesforce, providing new AI methods,  New kinds of data handling, Better visualization and prescriptive methods for decision makers.  I will be there. 

Via DSC: See the Tableau conference, free from anywhere.

Tableau Conference Livestream

November 13 - 15, 2019 | 8:30 AM - 6:30 PM PST daily
REGISTER NOW 

Get notified when the event goes LIVE

Don't miss a moment from Tableau Conference

Tableau Conference is right around the corner, and we are ready to get the data party started. If you aren't able to attend in person, we've got great news. We're bringing the excitement, inspiration, and learning to you, LIVE — Wednesday, Nov. 13 through Friday, Nov. 15, 8:30AM - 6:30PM daily. Access our amazing keynotes as they happen, including Iron Viz and Devs on Stage, along with our most popular sessions. And be sure to check out Tableau Conversations to take a deep dive into some of our newest features and offerings. You won't want to miss it. Register for our free Livestream and get notified by email when we go LIVE. Prepare to geek out over all things data.  ... "

Fortnite Gaming Powers Business Technology

We examined examples of game engines for analytic applications early on.  Now seeing the addition of VR and other immersion techniques.   Management still needs some help understanding this.  Best way is to start with currently used analytic methods and then extend from there to show augmentation.  Some good examples in this article of use.

Engine Behind 'Fortnite' Powers Business Technology 
The Wall Street Journal
By Agam Shah

Businesses are tapping the gaming-engine software of "Fortnite" and other videogames for applications ranging from car design to drug discovery. Gaming engines exploit complex calculations performed by hardware like graphics processing units to create photorealistic three-dimensional visualization and simulation, in addition to physics models. In conjunction with virtual reality (VR) technology, gaming engines let designers and engineers invent products, visualize sophisticated designs, and structure worker-training programs. Designers at McLaren Automotive are using Epic Games' Unreal Engine to create, assess, and refine digital car models, expediting a process that formerly relied on drawings and modeling clay. Meanwhile, the U.K.'s C4X Discovery Holdings uses gaming engines to discover new disease-fighting molecules, with chemists employing VR headsets and in-house software to configure virtual molecules for more precise targeting.   ... "

Best Visualization Libraries

Lots new to me here, well worth a look.

Best Visualization Libraries  from KD Nuggets.

There are plenty of library options out there to make great visualizations. We outline five of the best, complete with code examples and explanations, that will enable you to create and build interactive visualizations. text   By Lio Fleishman, Sisense.

As a Front-End Engineer at Sisense, I need to build multiple components using data visualization libraries. Visualization libraries exist to help us understand complex ideas. There are plenty of library options to make visualizations on the web. Each of which has their own positives and negatives. Here are, in my opinion, five of the best visualization libraries out there now.

To begin with, I assume you already know what React is. If you’re unfamiliar with it, the short answer is that React is a JavaScript library to build user interfaces. If you want the long answer, you can check out a bit more here. React is supported by Facebook and is the most popular Javascript library to build UIs today.  .... " 

Measuring Document and Chart Complexity

Measuring Document Complexity, I assume to provide feedback to the authoring and generation of the same?   Can it also provide direct suggestions as to how they should be updated for differing goals?

New Data Science Method Makes Charts Easier to Read at a Glance   By Columbia University 

Medical doctors reading EEGs in emergency rooms, first responders looking at multiple screens showing live data feeds from sensors in a disaster zone, brokers buying and selling financial instruments all need to make informed decisions very quickly. Visualization complexity can complicate decision-making when one is looking at data on a chart. When timing is critical, it is essential that a chart be easy to read and interpret.

To help decision-makers in scenarios like these, computer scientists at Columbia Engineering and Tufts University have developed a new method—"Pixel Approximate Entropy"—that measures the complexity of a data visualization and can be used to develop easier to read visualizations. Eugene Wu, assistant professor of computer science, and Gabriel Ryan, a Ph.D. student at Columbia, will describe their findings in "At a Glance: Pixel Approximate Entropy as a Measure of Line Chart Complexity," to be presented at the IEEE VIS 2018 conference in Berlin, Germany.  .... "

Diffbot : Foundation of Knowledge Graphs

A massive public database and graph.  How might it be integrated with private data? 

In Datanami, a description of the Diffbot Graph
The Graph That Knows the World   By Alex Woodie

Somewhere in a data center in Fremont, California, exists a large computer cluster that’s hoovering up every piece of data it can find from the Web and using machine learning algorithms to find connections among them. It’s arguably the largest known graph database in existence, encompassing 10 billion entities and 10 trillion edges.

No, it’s not some secret government project to catalog the world’s information. In fact, the graph was created and is run by a private company called Diffbot, and in fact you can get access to it for as little as $300 per month.

You can’t accuse Mike Tung, the founder and CEO of Diffbot, of thinking small, or beating around the bush for that matter. During an interview last week, he got right to the point. “The purpose of our company,” he tells Datanami, “is to build the first comprehensive map of all human knowledge.”

That might sound like a crazy thing to do, in 2018, a quarter century after the Web went mainstream, after the first dot-com crash, the rise of Web 2.0, the emergence of e-commerce 3.0, and the forthcoming industry 4.0 wave that’s projected to shake it all lose again. Haven’t we done this already? And isn’t that what Google and Wikipedia are for?


Diffbot CEO and founder Mike Tung graduated from Stanford University with a master’s degree in AI

Not according to Tung, who started work on the Diffbot graph while at Stanford University in 2008 and then started the Diffbot company in 2011. While it’s true that Google and Wikipedia are creating large knowledge graphs, they’re not as useful as one might think, Tung says.

“Our knowledge base is not only larger, deeper and more accurate [than Google’s and Wikipedia’s] but it’s accessible and more useful,” Tung says. “We hope that this is the first step in creating a future where…you have almost infinite access to knowledge.”

AI Crawlers

Tung says that what makes Diffbot unique, apart from its size and public nature, is how it’s assembled. While Google and Wikipedia rely largely on human labor to curate the information that goes into their graphs – and Facebook relies on its 2 billion users to create its knowledge graph —  the Diffbot graph is created automatically  — autonomously, really — through a variety of machine learning techniques, including computer vision, natural language processing (NLP), and others.

The Diffbot knowledge base currently has 10 billion vertices, which correspond to entities, including people, places and things. Connecting those 10 billion entities are 10 trillion edges, which are facts that can be searched through an API or DQL, the SQL-like Diffbot Query Language. ... " 

Sisense to Understand and Visualize Data

Of interest. A challenge for the enterprise.  Will this solve it?  I like the idea of making the results broadly available in the enterprise.  Both for use of the data and understanding what you have available.  Now could it also intelligently suggest which data you need?   Point out the holes in your data, perhaps by industry context?

See also past posts on how Sisense can link with Amazon Echo ...

Sisense hauls in $80M investment as data analytics business matures  In TechCrunch By Ron Miller  @ron_miller

Sisense, a company that helps customers understand and visualize their data across multiple sources, announced an $80 million Series E investment today led by Insight Venture Partners. They also announced that Zack Urlocker, former COO at Duo Security and Zendesk, has joined the organization’s board of directors.

The company has attracted a prestigious list of past investors, who also participated in the round, including Battery Ventures, Bessemer Venture Partners, DFJ Venture Capital, Genesis Partners and Opus Capital. Today’s investment brings the total raised to close to $200 million.

CEO Amir Orad says investors like their mission of simplifying complex data with analytics and business intelligence and delivering it in whatever way makes sense. That could be on screens throughout the company, desktop or smartphone, or via Amazon Alexa. “We found a way to make accessing data extremely simple, mashing it together in a logical way and embedding it in every logical place,” he explained.   ..."

Thinking About Virtual Worlds

Even a simple mirror can create a virtual world.  I remember when we experimented with data immersion using VR in virtual worlds, it was remarkable to see how hard navigation was.  To the point that you often had to go back to the expected flat world to make sense of it.   This article hints at why.

The Physics of a Mirror Creates a Virtual World in Wired.
Human eyes are sort of dumb—but you can trick them into being smart ... " 

Visualizing Uncertainty

Much of this is well known in the data visualization space, but like to see it in one place.  Should be taught this way.  Uncertainty should always be included in early analysis of data.   Includes pros and Cons of approaches.

Visualizing the Uncertainty in Data  by Nathan Yau

Feature Visualization

In the Google Blog, they provide an overview and pointer to deeper information.  An image rich and informative article.

Feature Visualization
Posted by Christopher Olah, Research Scientist, Google Brain Team and Alex Mordvintsev, Research Scientist, Google Research

Have you ever wondered what goes on inside neural networks? Feature visualization is a powerful tool for digging into neural networks and seeing how they work.

Our new article, published in Distill, does a deep exploration of feature visualization, introducing a few new tricks along the way!

Building on our work in DeepDream, and lots of work by others since, we are able to visualize what every neuron a strong vision model (GoogLeNet [1]) detects. Over the course of multiple layers, it gradually builds up abstractions: first it detects edges, then it uses those edges to detect textures, the textures to detect patterns, and the patterns to detect parts of objects….   " 

" .... There is a growing sense that neural networks need to be interpretable to humans. The field of neural network interpretability has formed in response to these concerns. As it matures, two major threads of research have begun to coalesce: feature visualization and attribution. ... " 

How to Lie with Analytics

Article in Internet Citizen.  Good piece.    I remember reading the classic 'How to Lie with Statistics', by Darrell Huff.   It was not a 'how to', but rather a how not to get fooled by simple statistics and numerical arguments.  Though simplistic, it still reads well.   This article makes a similar case, but takes it into the realm of big data and deep learning.  In a world with much more available data. So biases are much easier to support today.  And they can be supported not only at the basic statistical level, but also at a much deeper algorithmic level, that is harder to explain or validate than a bar chart.