Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings. Methods: The proposed approach used a combination of Convolution Neural Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and we selected the final prediction for classification. Results were cross-validated on the Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG recordings lasting from 9s to just over 60s. Results: Using the proposed structure and no explicit feature selection, 10-fold stratified cross-validation gave an overall F-measure of 0.83.10 0.015 on the held-out test data (mean standard deviation over all folds) and 0.80 on the hidden dataset of the Challenge entry server.

Structural and topological information play a key role in modeling flow and transport through fractured rock in the subsurface. Discrete fracture network (DFN) computational suites such as dfnWorks are designed to simulate flow and transport in such porous media. Flow and transport calculations reveal that a small backbone of fractures exists, where most flow and transport occurs. Restricting the flowing fracture network to this backbone provides a significant reduction in the network's effective size. However, the particle tracking simulations needed to determine the reduction are computationally intensive. Such methods may be impractical for large systems or for robust uncertainty quantification of fracture networks, where thousands of forward simulations are needed to bound system behavior. In this paper, we develop an alternative network reduction approach to characterizing transport in DFNs, by combining graph theoretical and machine learning methods. We consider a graph representation where nodes signify fractures and edges denote their intersections. Using random forest and support vector machines, we rapidly identify a subnetwork that captures the flow patterns of the full DFN, based primarily on node centrality features in the graph. Our supervised learning techniques train on particle-tracking backbone paths found by dfnWorks, but run in negligible time compared to those simulations. We find that our predictions can reduce the network to approximately 20% of its original size, while still generating breakthrough curves consistent with those of the original network.

Dear colleagues, We are looking for two Postdoctoral Research Associates with a background in electrical engineering, physics, statistics or computer science to work on a research project involving the application of machine learning methods to structural Magnetic Resonance Imaging data. The project is a collaboration between King's College London, UK (Dr. Vince Calhoun) and the Universidade Federal do ABC, Brazil (Prof João Sato). The post holders will be based at the Institute of Psychiatry, Psychology & Neuroscience (King's College London). I would be happy to answer any queries from prospective applicants.

Bisimulation provides structural conditions to characterize indistinguishability from an external observer between nodes on labeled graphs. It is a fundamental notion used in many areas, such as verification, graph-structured databases, and constraint satisfaction. However, several current applications use graphs where nodes also contain data (the so called "data graphs"), and where observers can test for equality or inequality of data values (e.g., asking the attribute 'name' of a node to be different from that of all its neighbors). The present work constitutes a first investigation of "data aware" bisimulations on data graphs. We study the problem of computing such bisimulations, based on the observational indistinguishability for XPath ---a language that extends modal logics like PDL with tests for data equality--- with and without transitive closure operators. We show that in general the problem is PSpace-complete, but identify several restrictions that yield better complexity bounds (coNP, PTime) by controlling suitable parameters of the problem, namely the amount of non-locality allowed, and the class of models considered (graphs, DAGs, trees). In particular, this analysis yields a hierarchy of tractable fragments.

In the case of revenge porn, people often ask: If the photos weren't taken in the first place, how could ex-partners, or hackers who steal nude photos, post them? We are now in the age of fake porn. Fake, as in, famous people's faces – or, for that matter, anybody's face – near-seamlessly stitched onto porn videos. As Motherboard reports, you can now find actress Jessica Alba's face on porn performer Melanie Rios' body, actress Daisy Ridley's face on another porn performer's body and Emma Watson's face on an actress's nude body, all on Celeb Jihad – a celebrity porn site that regularly posts celebrity nudes, including stolen/hacked ones. The word "appears" is key.

We consider a decision-making problem where the environment varies both in space and time. Such problems arise naturally when considering e.g., the navigation of an underwater robot amidst ocean currents or the navigation of an aerial vehicle in wind. To model such spatiotemporal variation, we extend the standard Markov Decision Process (MDP) to a new framework called the Time-Varying Markov Decision Process (TVMDP). The TVMDP has a time-varying state transition model and transforms the standard MDP that considers only immediate and static uncertainty descriptions of state transitions, to a framework that is able to adapt to future time-varying transition dynamics over some horizon. We show how to solve a TVMDP via a redesign of the MDP value propagation mechanisms by incorporating the introduced dynamics along the temporal dimension. We validate our framework in a marine robotics navigation setting using spatiotemporal ocean data and show that it outperforms prior efforts.

When a mosquito infected with malaria parasites bites someone, it transfers the parasites into their bloodstream via its saliva. These parasites work their way into the liver, where they mature and reproduce. After a few days, the parasites leave the liver and hijack red blood cells, where they continue to multiply, spreading around the body and causing symptoms, including potentially life-threatening complications. Malaria kills over half a million people each year, predominantly in Africa and south-east Asia. While a number of medicines are used to treat the disease, malaria parasites are growing increasingly resistant to these drugs, raising the spectre of untreatable malaria in the future.

Stunning drone footage has revealed the incredible progress on the Large Synoptic Survey Telescope – a massive instrument that will one day produce the'deepest, widest image of the universe' ever captured. Construction on the telescope in Chile began in 2015, with plans for it to begin operations around 2022. Now, nearly three years later, the new video shows how the enormous mountaintop facility has begun to take shape. Stunning drone footage has revealed the incredible progress on the Large Synoptic Survey Telescope – a massive instrument that will one day produce the'deepest, widest image of the universe' ever captured The video, published this week by the LSST team, was submitted by Assembly Integration Verification Manager Jacques Sebag, who captured the amazing view using a drone. At the time, the team was working with subcontractor Besalco to move the facility's mobile roof to a flatter area on the north side of the building.

The world's top 32 drone pilots will compete Saturday in Las Vegas for the world champion title in the International Drone Racing Assn.'s top challenge. Semi-professionals wearing virtual reality headgear compete for a $50,000 cash prize in the Challengers Cup Final on Friday and Saturday at the South Point hotel-casino at 9777 S. Las Vegas Blvd. Competitors qualified during 2017 races that began in Buenos Aires, Argentina, and concluded in Manila, the Philippines. Visitors can buy tickets to watch for $20. You'll be admitted to Friday's practice runs and the competition on Saturday afternoon. The elimination round will get underway at 12:30 p.m. with the finals set for 3:20 p.m. Saturday.

Australian-based TrademarkVision developed a deep learning-based reverse visual search platform that protects your brand by identifying similar trademarks from around the world. Simply upload your image to the platform, and their image recognition technology will compare it against other trademarked logos – making it much easier to identify IP infringements than the previous time-consuming and costly text-based search process. "Our technology not only makes it easy for an entrepreneur with a new design to ensure it is unique, but also enables the largest of companies to monitor for infringement," explains Cameron Mitchell, the Chief Operations Officer of TrademarkVision. The young startup has already integrated their technology with the intellectual property departments in the EU, Australia, Chile and more. Most recently, they launched a visual search for industrial designs.