The first golf major of the year is in the books, and we all know what that means. Yes, over the next couple of weeks, across the globe, countless amateur players are dusting down their clubs and hitting up their local course. And while this trend usually fizzles out (at least until the next major) once players are reminded of their own golfing limitations, the rise of shot trackers and swing analysers now mean it's easier than ever to actually break this seemingly never-ending cycle of mediocrity and improve into a solid regular. Whether it's through GPS watches or trackers from the likes of Garmin, or more dedicated club sensors from companies such as Game Golf and Arccos, receiving insights into your round has never been easier. But despite there being plenty of devices to choose from, few put together a complete tracking experience.

In the two months since Richard Watson strapped 200 remote-control-sized transmitters around his cows' necks, an artificial-intelligence system named Ida has pinged his phone with helpful alerts: when his cows are chewing the cud, when they're feeling sick, when they're ready for insemination. "There may be 10 animals out there that have a real problem, but could you pick them?" he said one morning, standing among a grazing herd of dairy cattle wearing what he calls "cow Fitbits." But on the neighboring pastures here in rural Georgia, other farmers say they aren't that impressed. When a cow's in heat, they know she'll start getting mounted by her bovine sisters, so they smear paint on the cows' backsides and then just look for the incriminating smudge. "I can spot a cow across a room that don't feel great just by looking in her eyes," said Mark Rodgers, a fourth-generation dairy farmer in Dearing, Georgia, whose dad still drives a tractor at 82. "The good Lord said, 'This is what you can do.' I can't draw, paint or anything else, but I can watch cows."

For most, the term Artificial Intelligence evokes thoughts of futuristic technologies, of C-3PO and Asimov's "I,Robot." We think of walking, sentient robots that look like us – or at least we imagine they appear like us. What we don't generally think of is a glorified speakerbox, or a supercomputer tasked to create recipes. But that's where AI starts – with small steps and incremental advances in technology. These small steps gradually lead to giant leaps forward; think of a narrow canyon off in the distance: you're pretty sure you can jump the canyon, but first you need to get there.

Decision making based on behavioral and neural observations of living systems has been extensively studied in brain science, psychology, and other disciplines. Decision-making mechanisms have also been experimentally implemented in physical processes, such as single photons and chaotic lasers. The findings of these experiments suggest that there is a certain common basis in describing decision making, regardless of its physical realizations. In this study, we propose a local reservoir model to account for choice-based learning (CBL). CBL describes decision consistency as a phenomenon where making a certain decision increases the possibility of making that same decision again later, which has been intensively investigated in neuroscience, psychology, etc. Our proposed model is inspired by the viewpoint that a decision is affected by its local environment, which is referred to as a local reservoir. If the size of the local reservoir is large enough, consecutive decision making will not be affected by previous decisions, thus showing lower degrees of decision consistency in CBL. In contrast, if the size of the local reservoir decreases, a biased distribution occurs within it, which leads to higher degrees of decision consistency in CBL. In this study, an analytical approach on local reservoirs is presented, as well as several numerical demonstrations. Furthermore, a physical architecture for CBL based on single photons is discussed, and the effects of local reservoirs is numerically demonstrated. Decision consistency in human decision-making tasks and in recruiting empirical data are evaluated based on local reservoir. In summary, the proposed local reservoir model paves a path toward establishing a foundation for computational mechanisms and the systematic analysis of decision making on different levels.

Abstract-- This paper aims to identify in a practical manner unknown physical parameters, such as mechanical models of actuated robot links, which are critical in dynamical robotic tasks. Key features include the use of an off-the-shelf physics engine and the Bayesian optimization framework. The task being considered is locomotion with a high-dimensional, compliant Tensegrity robot. A key insight, in this case, is the need to project the model identification challenge into an appropriate lower dimensional space for efficiency. Comparisons with alternatives indicate that the proposed method can identify the parameters more accurately within the given time budget, which also results in more precise locomotion control. I. INTRODUCTION This paper presents an approach for model identification by exploiting the availability of off-the-shelf physics engines used for simulating dynamics of robots and objects they interact with. There are many examples of popular physics engines that are becoming increasingly efficient [1]-[6].

We introduce our Distribution Regression Network (DRN) which performs regression from input probability distributions to output probability distributions. Compared to existing methods, DRN learns with fewer model parameters and easily extends to multiple input and multiple output distributions. On synthetic and real-world datasets, DRN performs similarly or better than the state-of-the-art. The field of regression analysis is largely established with methods ranging from linear least squares to multilayer perceptrons. However, the scope of the regression is mostly limited to real valued inputs and outputs (Fiori et al., 2015; Marquardt, 1963). In this paper, we perform distribution-to- distribution regression where one regresses from input probability distributions to output probability distributions. Distribution-to-distribution regression (see work by Oliva et al. (2013)) has not been as widely studied compared to the related task of functional regression (Ferraty & Vieu, 2006). Nevertheless, regression on distributions has many relevant applications. In the study of human populations, probability distributions capture the collective characteristics of the people.

A layered neural network is now one of the most common choices for the prediction of high-dimensional practical data sets, where the relationship between input and output data is complex and cannot be represented well by simple conventional models. Its effectiveness is shown in various tasks, however, the lack of interpretability of the trained result by a layered neural network has limited its application area. In our previous studies, we proposed methods for extracting a simplified global structure of a trained layered neural network by classifying the units into communities according to their connection patterns with adjacent layers. These methods provided us with knowledge about the strength of the relationship between communities from the existence of bundled connections, which are determined by threshold processing of the connection ratio between pairs of communities. However, it has been difficult to understand the role of each community quantitatively by observing the modular structure. We could only know to which sets of the input and output dimensions each community was mainly connected, by tracing the bundled connections from the community to the input and output layers. Another problem is that the finally obtained modular structure is changed greatly depending on the setting of the threshold hyperparameter used for determining bundled connections. In this paper, we propose a new method for interpreting quantitatively the role of each community in inference, by defining the effect of each input dimension on a community, and the effect of a community on each output dimension. We show experimentally that our proposed method can reveal the role of each part of a layered neural network by applying the neural networks to three types of data sets, extracting communities from the trained network, and applying the proposed method to the community structure.

With the development of big data technology, Gradient Boosting Decision Tree, i.e. GBDT, becomes one of the most important machine learning algorithms for its accurate output. However, the training process of GBDT needs a lot of computational resources and time. In order to accelerate the training process of GBDT, the asynchronous parallel sampling gradient boosting decision tree, abbr. asynch-SGBDT is proposed in this paper. Via introducing sampling, we adapt the numerical optimization process of traditional GBDT training process into stochastic optimization process and use asynchronous parallel stochastic gradient descent to accelerate the GBDT training process. Meanwhile, the theoretical analysis of asynch-SGBDT is provided by us in this paper. Experimental results show that GBDT training process could be accelerated by asynch-SGBDT. Our asynchronous parallel strategy achieves an almost linear speedup, especially for high-dimensional sparse datasets.

This paper investigates the problem of sparse signal recovery in the presence of additive impulsive noise. The heavytailed impulsive noise is well modelled with stable distributions. Since there is no explicit formulation for the probability density function of $S\alpha S$ distribution, alternative approximations like Generalized Gaussian Distribution (GGD) are used which impose $\ell_p$-norm fidelity on the residual error. In this paper, we exploit a Continuous Mixed Norm (CMN) for robust sparse recovery instead of $\ell_p$-norm. We show that in blind conditions, i.e., in case where the parameters of noise distribution are unknown, incorporating CMN can lead to near optimal recovery. We apply Alternating Direction Method of Multipliers (ADMM) for solving the problem induced by utilizing CMN for robust sparse recovery. In this approach, CMN is replaced with a surrogate function and Majorization-Minimization technique is incorporated to solve the problem. Simulation results confirm the efficiency of the proposed method compared to some recent algorithms in the literature for impulsive noise robust sparse recovery.

This paper describes the participation of Amobee in the shared sentiment analysis task at SemEval 2018. We participated in all the English sub-tasks and the Spanish valence tasks. Our system consists of three parts: training task-specific word embeddings, training a model consisting of gated-recurrent-units (GRU) with a convolution neural network (CNN) attention mechanism and training stacking-based ensembles for each of the sub-tasks. Our algorithm reached 3rd and 1st places in the valence ordinal classification sub-tasks in English and Spanish, respectively.