Reconstructing components of a genomic mixture from data obtained by means of DNA sequencing is a challenging problem encountered in a variety of applications including single individual haplotyping and studies of viral communities. High-throughput DNA sequencing platforms oversample mixture components to provide massive amounts of reads whose relative positions can be determined by mapping the reads to a known reference genome; assembly of the components, however, requires discovery of the reads' origin -- an NP-hard problem that the existing methods struggle to solve with the required level of accuracy. In this paper, we present a learning framework based on a graph auto-encoder designed to exploit structural properties of sequencing data. The algorithm is a neural network which essentially trains to ignore sequencing errors and infers the posteriori probabilities of the origin of sequencing reads. Mixture components are then reconstructed by finding consensus of the reads determined to originate from the same genomic component. Results on realistic synthetic as well as experimental data demonstrate that the proposed framework reliably assembles haplotypes and reconstructs viral communities, often significantly outperforming state-of-the-art techniques.

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. MIDAS has the following properties: (a) it detects microcluster anomalies while providing theoretical guarantees about its false positive probability; (b) it is online, thus processing each edge in constant time and constant memory, and also processes the data 108-505 times faster than state-of-the-art approaches; (c) it provides 46%-52% higher accuracy (in terms of AUC) than state-of-the-art approaches.

Microsoft's big experiment in real-world augmented reality gaming, Minecraft Earth, is live now for players in North America, the U.K., and a number of other areas. The pocket-size AR game lets you collect blocks and critters wherever you go, undertake little adventures with friends, and of course build sweet castles. I played an early version of Minecraft Earth earlier this year, and found it entertaining and the AR aspect surprisingly seamless. The gameplay many were first introduced to in Pokemon GO is adapted here in a more creative and collaborative way. You still walk around your neighborhood, rendered in this case charmingly like a Minecraft world, and tap little icons that pop up around your character. These may be blocks you can use to build, animals you can collect, or events like combat encounters that you can do alone or with friends for rewards.

Microsoft's big experiment in real-world augmented reality gaming, Minecraft Earth, is live now for players in North America, the U.K., and a number of other areas. The pocket-size AR game lets you collect blocks and critters wherever you go, undertake little adventures with friends, and of course build sweet castles. I played an early version of Minecraft Earth earlier this year, and found it entertaining and the AR aspect surprisingly seamless. The gameplay many were first introduced to in Pokemon GO is adapted here in a more creative and collaborative way. You still walk around your neighborhood, rendered in this case charmingly like a Minecraft world, and tap little icons that pop up around your character. These may be blocks you can use to build, animals you can collect, or events like combat encounters that you can do alone or with friends for rewards.

Let's look at the latest face of automation - the good, the bad, and the ugly! It solves some of today's problems and is starting to create new ones. Find out if your job is at risk .of My books on Amazon: The Little Book of Fundamental Indicators: Hands-On Market Analysis with Python: Find Your Market Bearings with Python, Jupyter Notebooks, and Freely Available Data: https://amzn.to/2DERG3d Create Income Streams with Online Classes: Design Classes That Generate Long-Term Revenue: https://amzn.to/2VToEHK

Two of the largest drugmakers in the country are investing in a startup applying artificial intelligence in pathology. Boston-based PathAI said that it had closed its $75 million Series B financing round with funding from New York-based Bristol-Myers Squibb and the Merck Global Health Innovation Fund, part of Kenilworth, New Jersey-based Merck & Co. PathAI said it would use the money to bolster its clinical development capabilities. PathAI had announced its $60 million Series B financing its April, led by venture capital firms General Atlantic and General Catalyst, with participation from LabCorp, which the company said the latest investment follows and extends.

IBM has always believed that 100% of jobs will ultimately change due to the impact of AI. Recent empirical research conducted by the MIT-IBM Watson AI Lab provides insights into the prediction and explains how it's going to happen. The joint research by Massachusetts Institute of Technology and IBM scrutinized the probable applications of Machine Learning in 170 million online job postings between 2010 and 2017 and came up with a report "The Future of Work: How New Technologies Are Transforming Tasks." The research examined the impact of Artificial Intelligence on employment and inferred that the result will be a significant decrease in the number of tasks. It additionally stated that work that would require "soft skills" would be given more focus on.

Artificial Intelligence is a big part of the world we live in. If you have a digital presence, AI is already impacting your daily life in ways you can't imagine. Applied AI has made its way into applications such as Facebook, Amazon, and Netflix, and is profoundly influencing our choices in real-time. For marketers, that would probably sound familiar. Marketing to audiences across demographics at the click of a button is a modern-day marketer's dream come true.

Roundup Hello, here's a short but sweet round up of news from the world of machine learning beyond what we have already covered on El Reg. Microsoft funded an AI startup that spies on Palestinians: Microsoft invested in AnyVision, a company that supports a secret Israeli military project that surveils Palestinians travelling within the West Bank. Palestinians living in the contentious region occupied by Israel can only travel via designated checkpoints. The Israeli government also tracks their movements using CCTV cameras as they walk throughout eastern Jerusalem. The military project, supposedly codenamed "Google Ayosh," was carried out to search for specific people by matching the faces spotted on the cameras to a known database, according to NBC News.

One of my favorite things about deep reinforcement learning is that, unlike supervised learning, it really, really doesn't want to work. Throwing a neural net at a computer vision problem might get you 80% of the way there. Throwing a neural net at an RL problem will probably blow something up in front of your face -- and it will blow up in a different way each time you try. A lot of the biggest challenges in RL revolve around two questions: how we interact with the environment effectively (e.g. In this post, I want to explore a few recent directions in deep RL research that attempt to address these challenges, and do so with particularly elegant parallels to human cognition. This post will begin with a quick review of two canonical deep RL algorithms -- DQN and A3C -- to provide us some intuitions to refer back to, and then jump into a deep dive on a few recent papers and breakthroughs in the categories described above.