We present a new method for forecasting systems of multiple interrelated time series. The method learns the forecast models together with discovering leading indicators from within the system that serve as good predictors improving the forecast accuracy and a cluster structure of the predictive tasks around these. The method is based on the classical linear vector autoregressive model (VAR) and links the discovery of the leading indicators to inferring sparse graphs of Granger causality. We formulate a new constrained optimisation problem to promote the desired sparse structures across the models and the sharing of information amongst the learning tasks in a multi-task manner. We propose an algorithm for solving the problem and document on a battery of synthetic and real-data experiments the advantages of our new method over baseline VAR models as well as the state-of-the-art sparse VAR learning methods.

In this paper we introduce Bardo, a real-time intelligent system to automatically select the background music for tabletop role-playing games. Bardo uses an off-the-shelf speech recognition system to transform into text what the players say during a game session, and a supervised learning algorithm to classify the text into an emotion. Bardo then selects and plays as background music a song representing the classified emotion. We evaluate Bardo with a Dungeons and Dragons (D&D) campaign available on YouTube. Accuracy experiments show that a simple Naive Bayes classifier is able to obtain good prediction accuracy in our classification task. A user study in which people evaluated edited versions of the D&D videos suggests that Bardo's selections can be better than those used in the original videos of the campaign.

The ability to automatically recognize a person's behavioral context can contribute to health monitoring, aging care and many other domains. Validating context recognition in-the-wild is crucial to promote practical applications that work in real-life settings. We collected over 300k minutes of sensor data with context labels from 60 subjects. Unlike previous studies, our subjects used their own personal phone, in any way that was convenient to them, and engaged in their routine in their natural environments. Unscripted behavior and unconstrained phone usage resulted in situations that are harder to recognize. We demonstrate how fusion of multi-modal sensors is important for resolving such cases. We present a baseline system, and encourage researchers to use our public dataset to compare methods and improve context recognition in-the-wild.

A well-calibrated estimator for the conditional probabilities should obey this equation. Once we have derived a statistical classifier, we need to validate it on some test data. This data should be different from that used to train the classifier, otherwise skill scores will be unduly optimistic. This is known as cross-validation. The confusion matrix expresses everything about the accuracy of a discrete classifier over a given database and you can use it to compose any possible skill score. Here, we are going to cover two that are rarely seen in the literature, but are nonetheless important for reasons that will become clear.

Maybe a cautious approach to a bigger version of the Monolith (2km long) in a POD that is only 2m in diameter is advisable. What do we know about how Monoliths react to stimuli?

To avoid epidemics such as the 2014 Ebola outbreak, early detection and diagnosing of diseases is critical. Even in the more isolated cases, such as the development of cancer, early diagnosis can save lives. Big data can be used as a versatile tool to assist in the detection, monitoring, and diagnosis of bacterial diseases and cancer. Past efforts to combat outbreaks of disease typically focused on the collection of physical information from laboratory test results and public health records, to create predictive models of how the disease might spread. However, the big data model uses medical information, internet resources, social media, and other sources to enable real time tracking of disease outbreaks.

High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candidate hits. Most statistical tools, including the industry standard B-score method, work on individual compound plates and do not exploit cross-plate correlation or statistical strength among plates. We present a new statistical framework for high throughput screening of compounds based on Bayesian nonparametric modeling. The proposed approach is able to identify candidate hits from multiple plates simultaneously, sharing statistical strength among plates and providing more robust estimates of compound activity. It can flexibly accommodate arbitrary distributions of compound activities and is applicable to any plate geometry. The algorithm provides a principled statistical approach for hit identification and false discovery rate control. Experiments demonstrate significant improvements in hit identification sensitivity and specificity over the B-score method, which is highly sensitive to threshold choice. The framework is implemented as an efficient R extension package BHTSpack and is suitable for large scale data sets.

It turns out that Floyd Mayweather and Conor McGregor didn't have the biggest fight of all time after all. Their boxing match on Aug. 26 reportedly generated 4.4 million pay-per-view buys, falling just shy of the record-setting 4.6 PPVs sold by Mayweather and Manny Pacquiao on May 2, 2015. During the two-month build towards the fight, which included a four-city press tour in the middle of July, there was talk that the undefeated boxer and the UFC star might set a new mark and possibly approach five million buys. Mayweather and McGregor will have to settle for second-best, according to BoxingScene, and an official announcement could come later this week. Finishing at No.2 is an achievement, nonetheless, as the bout sits comfortably ahead of the third-best selling fight in history.

Layers are Python functions that take Tensors and configuration options as input and produce other tensors as output. Once the necessary Tensors have been composed, the user can convert the result into an Estimator via a model function. A scalar used to train a model via gradient descent.

People should stop using patterns to unlock their devices, researchers have warned. A new study has found that it's a lot easier for people who might be looking over your shoulder as you unlock your phone to memorise a pattern than a passcode. So-called "shoulder surfing attacks" can be easy for a criminal to plan and execute, but you can protect yourself by switching to a PIN code and increasing its length from four digits to six, the researchers say. They got over 1,000 volunteers to act as attackers, challenging them to memorise a range of unlocking authentications – four- and six-digit PINs, and four- and six-length pa tterns with and without tracing lines – by watching a victim over their shoulder from a variety of angles. The 5-inch Nexus 5 and 6-inch OnePlus One were the two handsets used in the study, as the researchers say they "are similar to a wide variety of displays and form factors available on the market today, for both Android and iPhone".