Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographics and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms state-of-the-art models significantly.

The boundaries between smart materials, artificial intelligence, embodiment, biology, and robotics are blurring. Smart materials largely cover the same set of physical properties (stiffness, elasticity, viscosity) as biological tissue and state-of-the-art soft robotic technologies that have the potential to deliver this capability. We can foresee smart skins, assist and medical devices, biodegradable and environmental robots or intelligent soft robots. Ultimately wearable assist devices will make conventional assist devices redundant.

Bruce Ratner, The Significant StatisticianTM, is President and Founder of DM STAT-1 Consulting, the ensample for Statistical Modeling, Analysis and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in all standard statistical techniques, and methods using machine-learning/statistics algorithms, such as its patented GenIQ Model, to achieve its clients' goals - across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, Risk Management, and Nonprofit Fundraising. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.

Abbott Analytics, provides data mining consulting, knowledge transfer, and training for direct marketing, fraud detection, bioinformatics, and scientific computing. Algoritmica, providing consultancy and customized predictive analytics solutions for a number of international companies. Altius, specializes in the design and building of business-critical information systems that enhance business intelligence (BI) and performance management. Analytica, a consulting and IT firm serving US public and private sector enterprises focused on national security, law enforcement, health care and financial services. Analytics Advisory Group, offers services to improve your business outcomes by providing advisory, consulting, and training services anchored in Analytics. Analytical People offers a range of services, and resources, to any organisations who are looking to deploy Data Mining, Predictive Analytics or Statistical Analysis tools or methods. Anderson Analytics, focuses on helping clients gain the "Information Advantage" via quantitative and qualitative solutions to challenging marketing problems. Anthem Marketing Solutions, marketing and media strategists armed with the analytical capabilities and product solutions you need to deliver on your goals. Apteco, consultation and advice on the use of Faststats data mining to improve business insight and marketing campaigns. ASID Analytics provides data science consulting services, Tulsa, OK, USA. Austin Provider Solution, healthcare and managed care business intelligence solutions, including DSS for Hedis. Bayesia, providing consulting and customized solutions for computer-aided decision making, specializing in Bayesian Networks. Bentley University Center for Quantitative Analysis, provides professional analytical consulting services in support of fundamental and applied business research. Beyond the Arc, Inc., a strategic consultancy specializing in Voice of the Customer; uses analytics and text mining to translate customer data into knowledge, making customer experience more meaningful.

TANYA OTT: The future of artificial intelligence: what we know, what we don't know, and what it all means for you. I'm Tanya Ott, and this is the Press Room, Deloitte University Press's podcast on the issues and ideas that matter to your business today. When you think of artificial intelligence, you probably don't think of this: [sound of hiking through the woods]. It's the South, so it's hot--but the tree cover is helping a bit. This park is part of Red Mountain, here in the rolling foothills of the Appalachians. The trail is dotted with the remnants of 19th-century iron-ore mines. Back in the 1980s, you could walk into any outdoor store and find these big paper green and pink topographical maps. The US Geological Survey, which is part of the Department of Interior, made them. Hikers and hunters and other outdoor enthusiasts used these topo maps to make sure they didn't get lost in the wilderness. But by the 1990s, GPS and other similar technologies started getting broader adoption, and all of a sudden those maps, which had been painstakingly drawn by hand, started being produced on computers using a digital process. Suddenly, lots of cartographers were out of work.

This question originally appeared on Quora. This means if you were to remove everything you can see and interact with in the visible universe (people, food, home appliances, planets, stars, galaxies, nebulae, etc.), there would still be 95% of the universe remaining! The result is that there is more dark matter and dark energy in the room you are currently in than normal matter. It is percolating through your body as you read this answer! The general rule is that dark matter holds galaxies together and dark energy drives the expansion of the universe. It is the ultimate tug of war. At the beginning of the universe, dark matter was much more powerful than dark energy, which is what allowed early galaxies to form. But dark energy has now taken over and is causing distant galaxies to recede from us at a rate faster than the speed of light. As you may know, no object with mass can exceed the speed of light, however there is no limit on the speed at which the spacetime medium (which ...

Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. The majority of existing structural presentation schemes rely on the designer's understanding of specific material systems to identify geometric and statistical features, which could be biased and insufficient for reconstructing physically meaningful microstructures of complex material systems. In this paper, we develop a feature learning mechanism based on convolutional deep belief network to automate a two-way conversion between microstructures and their lower-dimensional feature representations, and to achieves a 1000-fold dimension reduction from the microstructure space. The proposed model is applied to a wide spectrum of heterogeneous material systems with distinct microstructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy, Fontainebleau sandstone, and Spherical colloids, to produce material reconstructions that are close to the original samples with respect to 2-point correlation functions and mean critical fracture strength. This capability is not achieved by existing synthesis methods that rely on the Markovian assumption of material microstructures.

Even a simple chemical reaction can be surprisingly complicated. That's especially true for reactions involving catalysts, which speed up the chemistry that makes fuel, fertilizer and other industrial goods. In theory, a catalytic reaction may follow thousands of possible paths, and it can take years to identify which one it actually takes so scientists can tweak it and make it more efficient. Now researchers at the Department of Energy's SLAC National Accelerator Laboratory and Stanford University have taken a big step toward cutting through this thicket of possibilities. They used machine learning – a form of artificial intelligence – to prune away the least likely reaction paths, so they can concentrate their analysis on the few that remain and save a lot of time and effort.

Steel production is an area that has been studied for decades, and as such the industry has remained very conservative. Despite the big data revolution beginning in the early 2000s, "old-school" industries like steel-making have largely shunned any form of data-driven applications. Fortunately, things change, and here's an example of how data analytics technologies, born within the internet industry, can be applied to an offline practice like turning pig iron into steel. When we began work with Magnitogorsk Iron and Steel Works (MMK), one of the world's largest steel producers and a leading steel company in Russia, a lot of time was spent looking for a challenge that if solved, could (a) positively impact business revenues, and (b) be completed in reasonable time.The challenge that was eventually uncovered and able to meet these criteria, is one well-known to all metallurgists: how much of each ferroalloy to add during steel-making process in order to ensure the required chemistry of the steel at the lowest possible cost. This chemistry is dictated by the international standards for steel – a list of required ranges for the amounts of each element in the final mix.

The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining tool-kit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.