In healthcare applications, temporal variables that encode movement, health status and longitudinal patient evolution are often accompanied by rich structured information such as demographics, diagnostics and medical exam data. However, current methods do not jointly optimize over structured covariates and time series in the feature extraction process. We present ShortFuse, a method that boosts the accuracy of deep learning models for time series by explicitly modeling temporal interactions and dependencies with structured covariates. ShortFuse introduces hybrid convolutional and LSTM cells that incorporate the covariates via weights that are shared across the temporal domain. ShortFuse outperforms competing models by 3% on two biomedical applications, forecasting osteoarthritis-related cartilage degeneration and predicting surgical outcomes for cerebral palsy patients, matching or exceeding the accuracy of models that use features engineered by domain experts.

Massive public resume data emerging on the WWW indicates individual-related characteristics in terms of profile and career experiences. Resume Analysis (RA) provides opportunities for many applications, such as talent seeking and evaluation. Existing RA studies based on statistical analyzing have primarily focused on talent recruitment by identifying explicit attributes. However, they failed to discover the implicit semantic information, i.e., individual career progress patterns and social-relations, which are vital to comprehensive understanding of career development. Besides, how to visualize them for better human cognition is also challenging. To tackle these issues, we propose a visual analytics system ResumeVis to mine and visualize resume data. Firstly, a text-mining based approach is presented to extract semantic information. Then, a set of visualizations are devised to represent the semantic information in multiple perspectives. By interactive exploration on ResumeVis performed by domain experts, the following tasks can be accomplished: to trace individual career evolving trajectory; to mine latent social-relations among individuals; and to hold the full picture of massive resumes' collective mobility. Case studies with over 2500 online officer resumes demonstrate the effectiveness of our system. We provide a demonstration video.

We propose a set of convex low rank inducing norms for a coupled matrices and tensors (hereafter coupled tensors), which shares information between matrices and tensors through common modes. More specifically, we propose a mixture of the overlapped trace norm and the latent norms with the matrix trace norm, and then, we propose a new completion algorithm based on the proposed norms. A key advantage of the proposed norms is that it is convex and can find a globally optimal solution, while existing methods for coupled learning are non-convex. Furthermore, we analyze the excess risk bounds of the completion model regularized by our proposed norms which show that our proposed norms can exploit the low rankness of coupled tensors leading to better bounds compared to uncoupled norms. Through synthetic and real-world data experiments, we show that the proposed completion algorithm compares favorably with existing completion algorithms.

Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. However, ANNs remain challenging to use for non-expert users. In this paper, we present NeuroNER, an easyto-use named-entity recognition tool based on ANNs. Users can annotate entities using a graphical web-based user interface (BRAT): the annotations are then used to train an ANN, which in turn predict entities' locations and categories in new texts. NeuroNER makes this annotationtraining-prediction flow smooth and accessible to anyone.

DETROIT – In just a few years, well-mannered self-driving robotaxis will share the roads with reckless, law-breaking human drivers. The prospect is causing migraines for the people developing the robotaxis. A self-driving car would be programmed to drive at the speed limit. Humans routinely exceed it by 10 to 15 mph (16 to 24 kph) - just try entering the New Jersey Turnpike at normal speed. Self-driving cars wouldn't dare cross a double yellow line; humans do it all the time.

Why do you want to understand it? How does it matter to your life? If not a master of it, why do you at least need to understand the basics of it? The answer to all these questions is very simple. It's because machine learning on a day to day basis is becoming bigger, and it is knowingly or unknowingly part of our life; so it is important to know what it is. We'll try to cover the topic and machine learning concepts including terminology in a form of series.

"China has a fairly deep awareness of what's happening in the English-speaking world, but the opposite is not true," says Ng. He points out that Baidu has rolled out neural network-based machine translation and achieved speech recognition accuracy that surpassed humans--but when Google and Microsoft, respectively, did so, the American companies got a lot more publicity. "The velocity of work is much faster in China than in most of Silicon Valley," says Ng Approved by the National Development and Reform Commission in January, the lab, based in China University of Science and Technology (USTC), aims to develop a brain-like computing paradigm and applications. The university, known for its leading role in developing quantum communication technology, hosts the national lab in collaboration with a number of the country's top research bodies such as Fudan University, Shenyang Institute of Automation of the Chinese Academy of Sciences as well as Baidu, operator of China's biggest online search engine. Wan Lijun, president of USTC and chairman of the national lab, said the ability to mimic the human brain's ability in sorting out information will help build a complete AI technology development paradigm.

In honor of Mother's day, celebrated in many countries on Sunday, May 14, 2017, we revisit KDnuggets "Mother of All Data" cartoon. Big Data Predicted that 67.53% of you would remember! Here are other KDnuggets Big Data, Data Mining, and Data Science Cartoons. Recent KDnuggets Cartoons: Cartoon: Perfect Valentine's Dates Found With Data Analysis Cartoon: When Self-Driving Car Machine Learning takes you too far ... Cartoon: Thanksgiving, Big Data, and Turkey Data Science. Cartoon: Data Scientist - the sexiest job of the 21st century until ... Cartoon: Facebook data science experiments and Cats Cartoon: It all started with the iPhone answering my email Cartoon: Where humans are still ahead of Deep Learning Cartoon: A solution for Data Scientists allergies caused by Big Data Cartoon: Perfect Valentine's Dates Found With Data Analysis Cartoon: When Self-Driving Car Machine Learning takes you too far ... Cartoon: Thanksgiving, Big Data, and Turkey Data Science.

Apple has recently acquired Lattice Data for around $200 million. The firm basically transforms the unstructured "dark data" like text and images and turns it into structured data. The latter can then be handled with conventional data analysis tools. The news comes from TechCrunch and essentially, the Cupertino giant has confirmed the acquisition by issuing a statement on the subject. So lets dive in to see some more details on the subject and how might Apple use the firm for its own set of purposes.

Japanese carmaker Toyota has announced its backing for a group of engineers who are developing a flying car. It will give 40 million yen (£274, 000) to the Cartivator group that operates outside Toyota city in central Japan. The Nikkei Asian Review reports Toyota and its group companies have agreed in principle to support the project. So far crowdfunding has paid for development of the so-called Skydrive car, which uses drone technology and has three wheels and four rotors. Measuring 9.5ft (2.9m) by 4.3ft (1.3m), Skydrive claims to be the world's smallest flying car.