Nonparametric detection of existence of an anomalous structure over a network is investigated. Nodes corresponding to the anomalous structure (if one exists) receive samples generated by a distribution q, which is different from a distribution p generating samples for other nodes. If an anomalous structure does not exist, all nodes receive samples generated by p. It is assumed that the distributions p and q are arbitrary and unknown. The goal is to design statistically consistent tests with probability of errors converging to zero as the network size becomes asymptotically large. Kernel-based tests are proposed based on maximum mean discrepancy that measures the distance between mean embeddings of distributions into a reproducing kernel Hilbert space. Detection of an anomalous interval over a line network is first studied. Sufficient conditions on minimum and maximum sizes of candidate anomalous intervals are characterized in order to guarantee the proposed test to be consistent. It is also shown that certain necessary conditions must hold to guarantee any test to be universally consistent. Comparison of sufficient and necessary conditions yields that the proposed test is order-level optimal and nearly optimal respectively in terms of minimum and maximum sizes of candidate anomalous intervals. Generalization of the results to other networks is further developed. Numerical results are provided to demonstrate the performance of the proposed tests.

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. Demonstrating the breadth and power of our platform, we describe a set of example experiments that build and evaluate algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.

The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine natural phenomena through the computational lens, i.e., using algorithmic or information processing abstractions of the underlying processes; and our ability to acquire, share, integrate and analyze disparate types of data. However, there is a huge gap between our ability to acquire, store, and process data and our ability to make effective use of the data to advance discovery. Despite successful automation of routine aspects of data management and analytics, most elements of the scientific process currently require considerable human expertise and effort. Accelerating science to keep pace with the rate of data acquisition and data processing calls for the development of algorithmic or information processing abstractions, coupled with formal methods and tools for modeling and simulation of natural processes as well as major innovations in cognitive tools for scientists, i.e., computational tools that leverage and extend the reach of human intellect, and partner with humans on a broad range of tasks in scientific discovery (e.g., identifying, prioritizing formulating questions, designing, prioritizing and executing experiments designed to answer a chosen question, drawing inferences and evaluating the results, and formulating new questions, in a closed-loop fashion). This calls for concerted research agenda aimed at: Development, analysis, integration, sharing, and simulation of algorithmic or information processing abstractions of natural processes, coupled with formal methods and tools for their analyses and simulation; Innovations in cognitive tools that augment and extend human intellect and partner with humans in all aspects of science.

WASHINGTON – A Boeing company manager convicted of child pornography charges in December says he has a right to know what arguments the government used to obtain the warrant from the Foreign Intelligence Surveillance Court. Now, the Los Angeles case is testing a defendant's ability to access information about himself presented to the nation's secretive intelligence court, which issued the warrant that let agents scour his computers. At issue is how the government uses evidence derived through the Foreign Intelligence Surveillance Court and under what circumstances that information should be seen by defendants, particularly when it's repurposed for a routine criminal prosecution that has nothing to do with national security. Gartenlaub and his lawyers say they have a right to know the government's arguments that were used to obtain the warrant, and fight them. "You can't base a search on lies," the 47-year-old said in an interview with The Associated Press.

Nvidia's fastest GPU yet, the new Tesla P100, will be available in servers next year, the company said. Dell, Hewlett Packard Enterprise, Cray and IBM will start taking orders for servers with the Tesla P100 in the fourth quarter of this year, Nvidia CEO Jen-Hsun Huang said during a keynote at the GPU Technology Conference in San Jose, California. The servers will start shipping in the first quarter of next year, Huang said Tuesday. The GPU will also ship to companies designing hyperscale servers in-house and then to outsourced manufacturing shops. It will be available for in-house "cloud servers" by the end of the year, Huang said.

A study conducted by researchers at Stanford University and published in Science Daily found that humans get physiologically aroused by touching a robot's intimate areas. The researchers used Aldebaran Robotics' NAO, a humanoid robot, for their study. The robot instructed participants in the study to either touch or point at 13 different parts of its body. They then monitored the participants' responses as they carried out each command. The study found that participants more hesitant to touch more intimate parts of the robot, such as its eyes and buttocks, and they were physiologically aroused when touching these areas.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Nvidia's varied range of GPU developer tools have been spread out into specialized kits, but that's not the case any more. The company has announced the Nvidia SDK unified toolkit, which brings together its game development, supercomputing, virtual reality, automotive and drone and robot development tools into one package. The toolkit brings together essential tools and libraries necessary for GPU development, Jen-Hsun Huang, CEO of Nvidia, said during a keynote at the company's GPU Technology Conference in San Jose, California. The toolkit is tuned for Nvidia's latest Pascal GPU architecture, which the company is expected to detail at the show. Pascal contains many technological improvements that could trigger changes in the way applications are written for GPUs.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display