Last week, NVIDIA released its first-quarter fiscal 2017 report, which included some impressive results. The graphics-processor maker posted Q1 revenue of 1.3 billion, an increase of 13% year over year, and non-GAAP earning per share were up 39% from the year-ago quarter to 0.46. Wall Street analysts had been expecting revenue of around 1.26 billion and EPS of 0.41. NVIDIA co-founder and CEO Jen-Hsun Huang said the revenue and earnings growth were spurred on by all of the key segments of its business. "We are enjoying growth in all of our platforms -- gaming, professional visualization, datacenter and auto," Huang said in a press release.

Law school, the default location for America's brightest unemployed Liberal Arts graduates and the worst decision a 20-something can make in the modern era just became an even worse bargain, if that's possible. We're not talking about a surge in LSAT applicants, beginning in 2008, that led to the creation of InfiLaw Corporation scam-law-schools like Charlotte, Florida Coastal, or Arizona Summit, the latter of which boasts an average bar passage rate of below 31% and an average cost of attendance exceeding 250,000. We are not even speaking about a profession that requires the completion of at least seven years of college,followed by an expensive test and an invasive background check, just for the benefit of staring down the barrel of a 15.5 percent unemployment rate and roughly 200,000 in non-dischargeable student debt. One might even guess that we're talking about how recent studies show that over 40% of law school students suffer from clinical depression, with those unhappy figures only becoming worse as graduates enter a profession with a suicide rate nearly ten times above the national norm. Instead, we are talking about the latest and greatest idea among the old-timers; those old ones who have already won the legal profession lottery, allowing them to seize an upper-middle class lifestyle after matriculating from law schools referred to as'third-tier toilets,' or attending bastions of prestigious opportunity, like Harvard and Yale.

We review the task of Sentence Pair Scoring, popular in the literature in various forms - viewed as Answer Sentence Selection, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a component of Memory Networks. We argue that all such tasks are similar from the model perspective and propose new baselines by comparing the performance of common IR metrics and popular convolutional, recurrent and attention-based neural models across many Sentence Pair Scoring tasks and datasets. We discuss the problem of evaluating randomized models, propose a statistically grounded methodology, and attempt to improve comparisons by releasing new datasets that are much harder than some of the currently used well explored benchmarks. We introduce a unified open source software framework with easily pluggable models and tasks, which enables us to experiment with multi-task reusability of trained sentence model. We set a new state-of-art in performance on the Ubuntu Dialogue dataset.

We present a method based on the orthogonal symmetric non-negative matrix tri-factorization of the normalized Laplacian matrix for community detection in complex networks. While the exact factorization of a given order may not exist and is NP hard to compute, we obtain an approximate factorization by solving an optimization problem. We establish the connection of the factors obtained through the factorization to a non-negative basis of an invariant subspace of the estimated matrix, drawing parallel with the spectral clustering. Using such factorization for clustering in networks is motivated by analyzing a block-diagonal Laplacian matrix with the blocks representing the connected components of a graph. The method is shown to be consistent for community detection in graphs generated from the stochastic block model and the degree corrected stochastic block model. Simulation results and real data analysis show the effectiveness of these methods under a wide variety of situations, including sparse and highly heterogeneous graphs where the usual spectral clustering is known to fail. Our method also performs better than the state of the art in popular benchmark network datasets, e.g., the political web blogs and the karate club data.

Automatic modulation classification (AMC) is an important task for modern communication systems; however, it is a challenging problem when signal features and precise models for generating each modulation may be unknown. We present a new biologically-inspired AMC method without the need for models or manually specified features --- thus removing the requirement for expert prior knowledge. We accomplish this task using regularized stacked sparse denoising autoencoders (SSDAs). Our method selects efficient classification features directly from raw in-phase/quadrature (I/Q) radio signals in an unsupervised manner. These features are then used to construct higher-complexity abstract features which can be used for automatic modulation classification. We demonstrate this process using a dataset generated with a software defined radio, consisting of random input bits encoded in 100-sample segments of various common digital radio modulations. Our results show correct classification rates of > 99% at 7.5 dB signal-to-noise ratio (SNR) and > 92% at 0 dB SNR in a 6-way classification test. Our experiments demonstrate a dramatically new and broadly applicable mechanism for performing AMC and related tasks without the need for expert-defined or modulation-specific signal information.

Dictionary learning has recently received significant attention due to the increased importance of finding sparse representations of signals/data. In dictionary learning, the goal is to construct an overcomplete basis using input signals such that each signal can be described by a small number of atoms (columns) [1]. Although the existing literature has focused on one-dimensional data, many signals in practice are multidimensional and have a tensor structure: examples include 2-dimensional images and 3-dimensional signals produced via magnetic resonance imaging or computed tomography systems. In traditional dictionary learning techniques, multidimensional data are processed after vectorizing of signals. This can result in poor sparse representations as the structure of the data is neglected [2]. In this paper we provide fundamental limits on learning dictionaries for multidimensional data with tensor structure: we call such dictionaries Kronecker-structured (KS). Several algorithms have been proposed to learn KS dictionaries [2]-[7] but there has been little work on the theoretical guarantees of such algorithms. The lower bounds we provide on the minimax risk of learning a KS dictionary give a measure to evaluate the performance of the existing algorithms.

Last week, NVIDIA (NASDAQ:NVDA) released its first-quarter fiscal 2017 report, which included some impressive results. The graphics-processor maker posted Q1 revenue of 1.3 billion, an increase of 13% year over year, and non-GAAP earning per share were up 39% from the year-ago quarter to 0.46. Wall Street analysts had been expecting revenue of around 1.26 billion and EPS of 0.41. NVIDIA co-founder and CEO Jen-Hsun Huang said the revenue and earnings growth were spurred on by all of the key segments of its business. "We are enjoying growth in all of our platforms -- gaming, professional visualization, datacenter and auto," Huang said in a press release.

The experiment, developed by physicists from The Australian National University (ANU) and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from the ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero. They could be used for mineral exploration or navigation systems as they are extremely sensitive to external disturbances, which allows them to make very precise measurements such as tiny changes in the Earth's magnetic field or gravity.

The innovations protected by the new patent provide users of the Arria NLG Platform with an added level of control to query their data to achieve a deeper understanding. Users can direct the report output by changing the data parameters to refine its focus and apply the embedded subject matter expertise to quickly interrogate their data at the click of a mouse. For example, financial analyst who receive an NLG report analysing an entire year of sales data can use an interface to focus the report on the most recent quarter or on one or a combination of geographic regions, or by using any other search criteria that they specify. With one click, the NLG Platform searches the data afresh, conducts pre-programmed analysis and produces an amended report responding to the new query. This, Arria's eighth patent, covers flexible modification of search parameters not only for reports that may be generated with Arria NLG's advanced artificial intelligence technology, but also with more rudimentary, template based NLG systems offered by several of Arria NLG's competitors.

Sure announced today that it has introduced the world's first on-demand insurance app powered by artificial intelligence, marking an important step for the company as it drives rapid adoption of mobile, on-demand insurance products. An industry first, Sure uses data-driven approaches to empower consumers with the ability to personalize insurance needs on the go. In doing so, it eliminates the built-in limitations of an industry driven largely by one-size-fits-all recommendations from brokers. Sure continues to pioneer Episodic Insurance in the insurance market by providing consumers with simple options based on the context and information gathered through his or her mobile device with permission. Sure uses data-intelligence and advanced analytics to identify needs and provide products for easy, on-demand purchase. For the first time, customers can buy the type of insurance they need, for exactly the duration they need it.