This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived which leads to low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of surveillance video are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed CS system outperforms existing CS systems.

Google on Friday announced its "biggest leap forward" in years in its search algorithm, offering an unusually detailed public explanation of its secret formula. The world's most popular internet search engine said its latest refinement uses machine learning to improve how it handles conversationally phrased English-language requests. "We're making a significant improvement to how we understand queries, representing the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of search," Google search vice president Pandu Nayak said in an online post. The California-based internet company last year debuted a neural network-based technique for processing "natural language." The company said the new effort is based on what it calls Bidirectional Encoder Representations from Transformers (BERT), which seeks to understand query words in the context of sentences for insights, according to Nayak.

SAN FRANCISCO – Google on Friday announced its "biggest leap forward" in years in its search algorithm, offering an unusually detailed public explanation of its secret formula. The world's most popular internet search engine said its latest refinement uses machine learning to improve how it handles conversationally phrased English-language requests. "We're making a significant improvement to how we understand queries, representing the biggest leap forward in the past five years, and one of the biggest leaps forward in the history of search," Google search vice president Pandu Nayak said in an online post. The California-based internet company last year debuted a neural network-based technique for processing "natural language." The company said the new effort is based on what it calls Bidirectional Encoder Representations from Transformers (BERT), which seeks to understand query words in the context of sentences for insights, according to Nayak.

Neural style transfer was developed in 2015 by Leon Gatys and colleagues at the University of Tubingen in Germany. It comes about from a fascinating insight into the way neural networks learn to recognize images of different kinds. Neural networks consist of layers that analyze an image at different scales. The first layer might recognize broad features like edges, the next layer sees how these edges form simple shapes like circles, the next layer recognizes patterns of shapes, such as two circles close together, and yet another layer might label these pairs of circles as eyes. This kind of network would be able to recognize eyes in paintings in a wide variety of styles, from Leonardo da Vinci to Van Gogh to Picasso.

Mobile Artificial Intelligence (AI) Market report also covers very important aspect which is competitive intelligence and with this businesses can gain competitive advantage to thrive in the market. The data and the information regarding the industry has been derived from the consistent sources. Worldwide mobile AI market report additionally contains the drivers and restrains for the mobile AI market that are derived from SOWT analysis. Global Mobile AI market is expected to reach USD 17.79 billion by 2025 from USD 5.14 billion in 2017 and is projected to grow at a CAGR of 28.43 % in the forecast period of 2018 to 2025. The Mobile Artificial Intelligence (AI) market report contains data for historic year 2016, the base year of calculation is 2017 and the forecast period is 2018 to 2025 (Current Year Statistic Will Be Provided In Report).

We study the safe reinforcement learning problem with nonlinear function approximation, where policy optimization is formulated as a constrained optimization problem with both the objective and the constraint being nonconvex functions. For such a problem, we construct a sequence of surrogate convex constrained optimization problems by replacing the nonconvex functions locally with convex quadratic functions obtained from policy gradient estimators. We prove that the solutions to these surrogate problems converge to a stationary point of the original nonconvex problem. Furthermore, to extend our theoretical results, we apply our algorithm to examples of optimal control and multi-agent reinforcement learning with safety constraints.

We present a framework based on neural networks to extract music scores directly from polyphonic audio in an end-to-end fashion. Most previous Automatic Music Transcription (AMT) methods seek a piano-roll representation of the pitches, that can be further transformed into a score by incorporating tempo estimation, beat tracking, key estimation or rhythm quantization. Unlike these methods, our approach generates music notation directly from the input audio in a single stage. For this, we use a Convolutional Recurrent Neural Network (CRNN) with Connectionist Temporal Classification (CTC) loss function which does not require annotated alignments of audio frames with the score rhythmic information. We trained our model using as input Haydn, Mozart, and Beethoven string quartets and Bach chorales synthesized with different tempos and expressive performances. The output is a textual representation of four-voice music scores based on **kern format. Although the proposed approach is evaluated in a simplified scenario, results show that this model can learn to transcribe scores directly from audio signals, opening a promising avenue towards complete AMT.

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We are a unique social enterprise providing a vital service for the world's 500 million smallholder farmers who live and work without internet access. This pioneering, peer-to-peer platform enables farmers to access crowdsourced information by SMS, creating social impact on a groundbreaking scale and generating a game-changing data feed through the use of cutting edge AI techniques. In just one year WeFarm has scaled to more than 72,000 farmers across Kenya, Uganda and Peru, has facilitated over 11.5 million interactions and featured in the FT, Forbes, Wired.co.uk, as well as winning awards from Google's Impact Challenge, The Venture and the European Commission's Ideas From Europe. Would you like to change the world and create social impact on a global scale? Do you want every LOC you write to save livelihoods?

Brazil's mining major Vale is set to start applying machine learning to identify new drilling targets at its Coleman nickel mine. Coleman Mine, which is the flagship asset of Vale in Ontario, Canada, is part of the company's base metals operations. Vale has selected technology company GoldSpot Discoveries to examine and analyse the vast amount of data acquired by it over decades of mining at Coleman. GoldSpot Discoveries' team of geologists and data scientists will also discover previously unrecognised data trends, which may point to unknown areas of in-depth mineralisation. By using its geoscience and machine science expertise, GoldSpot Discoveries' team will clean, unify and analyse exploration data from Vale's Coleman Mine.