Automotive chipmaker Renesas Electronics Corp last week said it would suspend production at some plants for up to two months as it braces for China's growth to slow further. In recent months, other big companies such as factory-robot makers Yaskawa Electric Corp and Fanuc Corp; Mitsubishi Electric Corp, trading house Mitsui & Co and toilet giant Toto Ltd have blamed China as they cut profit forecasts.

We introduce the isoperimetric loss as a regularization criterion for learning the map from a visual representation to a semantic embedding, to be used to transfer knowledge to unknown classes in a zero-shot learning setting. We use a pre-trained deep neural network model as a visual representation of image data, a Word2Vec embedding of class labels, and linear maps between the visual and semantic embedding spaces. However, the spaces themselves are not linear, and we postulate the sample embedding to be populated by noisy samples near otherwise smooth manifolds. We exploit the graph structure defined by the sample points to regularize the estimates of the manifolds by inferring the graph connectivity using a generalization of the isoperimetric inequalities from Riemannian geometry to graphs. Surprisingly, this regularization alone, paired with the simplest baseline model, outperforms the state-of-the-art among fully automated methods in zero-shot learning benchmarks such as AwA and CUB. This improvement is achieved solely by learning the structure of the underlying spaces by imposing regularity.

Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models. To address this, we propose a probabilistic programming approach to affective computing, which models psychological-grounded theories as generative models of emotion, and implements them as stochastic, executable computer programs. We first review probabilistic approaches that integrate reasoning about emotions with reasoning about other latent mental states (e.g., beliefs, desires) in context. Recently-developed probabilistic programming languages offer several key desidarata over previous approaches, such as: (i) flexibility in representing emotions and emotional processes; (ii) modularity and compositionality; (iii) integration with deep learning libraries that facilitate efficient inference and learning from large, naturalistic data; and (iv) ease of adoption. Furthermore, using a probabilistic programming framework allows a standardized platform for theory-building and experimentation: Competing theories (e.g., of appraisal or other emotional processes) can be easily compared via modular substitution of code followed by model comparison. To jumpstart adoption, we illustrate our points with executable code that researchers can easily modify for their own models. We end with a discussion of applications and future directions of the probabilistic programming approach.

In the present scenario of domestic flights in USA, there have been numerous instances of flight delays and cancellations. In the United States, the American Airlines, Inc. have been one of the most entrusted and the world's largest airline in terms of number of destinations served. But when it comes to domestic flights, AA has not lived up to the expectations in terms of punctuality or on-time performance. Flight Delays also result in airline companies operating commercial flights to incur huge losses. So, they are trying their best to prevent or avoid Flight Delays and Cancellations by taking certain measures. This study aims at analyzing flight information of US domestic flights operated by American Airlines, covering top 5 busiest airports of US and predicting possible arrival delay of the flight using Data Mining and Machine Learning Approaches. The Gradient Boosting Classifier Model is deployed by training and hyper-parameter tuning it, achieving a maximum accuracy of 85.73%. Such an Intelligent System is very essential in foretelling flights'on-time performance.

We propose a novel ranking model that combines the Bradley-Terry-Luce probability model with a nonnegative matrix factorization framework to model and uncover the presence of latent variables that influence the performance of top tennis players. We derive an efficient, provably convergent, and numerically stable majorization-minimization-based algorithm to maximize the likelihood of datasets under the proposed statistical model. The model is tested on datasets involving the outcomes of matches between 20 top male and female tennis players over 14 major tournaments for men (including the Grand Slams and the ATP Masters 1000) and 16 major tournaments for women over the past 10 years. Our model automatically infers that the surface of the court (e.g., clay or hard court) is a key determinant of the performances of male players, but less so for females. Top players on various surfaces over this longitudinal period are also identified in an objective manner.

A $k$-junta function is a function which depends on only $k$ coordinates of the input. For relatively small $k$ w.r.t. the input size $n$, learning $k$-junta functions is one of fundamental problems both theoretically and practically in machine learning. For the last two decades, much effort has been made to design efficient learning algorithms for Boolean junta functions, and some novel techniques have been developed. However, in real world, multi-labeled data seem to be obtained in much more often than binary-labeled one. Thus, it is a natural question whether these techniques can be applied to more general cases about the alphabet size. In this paper, we expand the Fourier detection techniques for the binary alphabet to any finite field $\mathbb{F}_q$, and give, roughly speaking, an $O(n^{0.8k})$-time learning algorithm for $k$-juntas over $\mathbb{F}_q$. Note that our algorithm is the first non-trivial (i.e., non-brute force) algorithm for such a class even in the case where $q=3$ and we give an affirmative answer to the question posed in [MOS04]. Our algorithm consists of two reductions: (1) from learning juntas to LDME which is a variant of the learning with errors (LWE) problems introduced by [Reg05], and (2) from LDME to the light bulb problem (LBP) introduced by [Val88]. Since the reduced problem (i.e., LBP) is a kind of binary problem regardless of the alphabet size of the original problem (i.e., learning juntas), we can directly apply the techniques for the binary case in the previous work such as in [Val15, KKK18].

In a discounted reward Markov Decision Process (MDP) the objective is to find the optimal value function, i.e., the value function corresponding to an optimal policy. This problem reduces to solving a functional equation known as the Bellman equation and a fixed point iteration scheme known as the value iteration is utilized to obtain the solution. In [1], a successive over-relaxation based value iteration scheme is proposed to speed up the computation of the optimal value function. They propose a modified Bellman equation and prove faster convergence to the optimal value function. However, in many practical applications, the model information is not known and we resort to Reinforcement Learning (RL) algorithms to obtain optimal policy and value function. One such popular algorithm is Q-Learning. In this paper, we propose Successive Over Relaxation (SOR) Q-Learning. We first derive a fixed point iteration for optimal Q-values based on [1] and utilize stochastic approximation to derive a learning algorithm to compute the optimal value function and an optimal policy. We then prove the convergence of the SOR Q-Learning to optimal Q-values. Finally, through numerical experiments, we show that SOR Q-Learning is faster compared to the standard Q-Learning algorithm.

WASHINGTON - The top U.S. general said on Thursday that the Chinese military was benefiting from the work Alphabet Inc.'s Google was doing in China, where the technology giant has long sought to have a bigger presence. "The work that Google is doing in China is indirectly benefiting the Chinese military," Marine Gen. Joseph Dunford, chairman of the Joint Chiefs of Staff, said during a Senate Armed Services Committee hearing. "We watch with great concern when industry partners work in China knowing that there is that indirect benefit," he said. "Frankly, 'indirect' may be not a full characterization of the way it really is, it is more of a direct benefit to the Chinese military."

China has committed to becoming the world leader in AI by 2030, with goals to build a domestic artificial intelligence industry worth nearly $150 billion (according to this CNN article). Prompted by these efforts, the Semantic Scholar team at the Allen AI Institute analyzed over two million academic AI papers published through the end of 2018. Our analysis shows that China has already surpassed the US in published AI papers. If current trends continue, China is poised to overtake the US in the most-cited 50% of papers this year, in the most-cited 10% of papers next year, and in the 1% of most-cited papers by 2025. Citation counts are a lagging indicator of impact, so our results may understate the rising impact of AI research originating in China.

China has committed to becoming the world leader in AI by 2030, with goals to build a domestic artificial intelligence industry worth nearly $150 billion (according to this CNN article). Prompted by these efforts, the Semantic Scholar team at the Allen AI Institute analyzed over two million academic AI papers published through the end of 2018. Our analysis shows that China has already surpassed the US in published AI papers. If current trends continue, China is poised to overtake the US in the most-cited 50% of papers this year, in the most-cited 10% of papers next year, and in the 1% of most-cited papers by 2025. Citation counts are a lagging indicator of impact, so our results may understate the rising impact of AI research originating in China.