There are many statistical tests that verify the null hypothesis: the variable of interest has the same distribution among k-groups. But once the null hypothesis is rejected, how to present the structure of dissimilarity between groups? In this article, we introduce The Merging Path Plot -- a methodology, and factorMerger -- an R package, for exploration and visualization of k-group dissimilarities. Comparison of k-groups is one of the most important issues in exploratory analyses and it has zillions of applications. The classical solution is to test a null hypothesis that observations from all groups come from the same distribution. If the global null hypothesis is rejected, a more detailed analysis of differences among pairs of groups is performed. The traditional approach is to use pairwise post hoc tests in order to verify which groups differ significantly. However, this approach fails with a large number of groups in both interpretation and visualization layer. The Merging Path Plot methodology solves this problem by using an easy-to-understand description of dissimilarity among groups based on Likelihood Ratio Test (LRT) statistic.

ABSTRACT A convolution neural network (CNN) based classification method for broadband DOA estimation is proposed, where the phase component of the short-time Fourier transform coefficients of the received microphone signals are directly fed into the CNN and the features required for DOA estimation are learned during training. Since only the phase component of the input is used, the CNN can be trained with synthesized noise signals, thereby making the preparation of the training data set easier compared to using speech signals. Through experimental evaluation, the ability of the proposed noise trained CNN framework to generalize to speech sources is demonstrated. In addition, the robustness of the system to noise, small perturbations in microphone positions, as well as its ability to adapt to different acoustic conditions is investigated using experiments with simulated and real data. Index Terms-- source localization, convolution neural networks, supervised learning, DOA estimation 1. INTRODUCTION Many applications such as hands-free communication, teleconferencing, and distant speech recognition require information on the location of a sound source in the acoustic environment.

We study two procedures (reverse-mode and forward-mode) for computing the gradient of the validation error with respect to the hyperparameters of any iterative learning algorithm such as stochastic gradient descent. These procedures mirror two methods of computing gradients for recurrent neural networks and have different trade-offs in terms of running time and space requirements. Our formulation of the reverse-mode procedure is linked to previous work by Maclaurin et al. [2015] but does not require reversible dynamics. The forward-mode procedure is suitable for real-time hyperparameter updates, which may significantly speed up hyperparameter optimization on large datasets. We present experiments on data cleaning and on learning task interactions. We also present one large-scale experiment where the use of previous gradient-based methods would be prohibitive.

This paper aims at theoretically and empirically comparing two standard optimization criteria for Reinforcement Learning: i) maximization of the mean value and ii) minimization of the Bellman residual. For that purpose, we place ourselves in the framework of policy search algorithms, that are usually designed to maximize the mean value, and derive a method that minimizes the residual $\|T_* v_\pi - v_\pi\|_{1,\nu}$ over policies. A theoretical analysis shows how good this proxy is to policy optimization, and notably that it is better than its value-based counterpart. We also propose experiments on randomly generated generic Markov decision processes, specifically designed for studying the influence of the involved concentrability coefficient. They show that the Bellman residual is generally a bad proxy to policy optimization and that directly maximizing the mean value is much better, despite the current lack of deep theoretical analysis. This might seem obvious, as directly addressing the problem of interest is usually better, but given the prevalence of (projected) Bellman residual minimization in value-based reinforcement learning, we believe that this question is worth to be considered.

The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue). Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks. In this situation, damage recovery can be seen as a Reinforcement Learning (RL) problem. However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously. In addition, most of the RL methods for robotics do not scale well with complex robots (e.g., walking robots) and either cannot be used at all or take too long to converge to a solution (e.g., hours of learning). In this paper, we introduce a novel learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks the complexity by pre-generating hundreds of possible behaviors with a dynamics simulator of the intact robot, and (2) allows complex robots to quickly recover from damage while completing their tasks and taking the environment into account. We evaluate our algorithm on a simulated wheeled robot, a simulated six-legged robot, and a real six-legged walking robot that are damaged in several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and whose objective is to reach a sequence of targets in an arena. Our experiments show that the robots can recover most of their locomotion abilities in an environment with obstacles, and without any human intervention.

The survey includes data from over 650 professionals across five regions and tells us that A.I will be hugely disruptive to many market sectors. But the data also reveals that there will both job loss and job creation to come. Nick McQuire from CCS Insight said: "Employees are drowning in a sea of data and digital technology." So the shift from manual to automation is inevitable and will only be embraced by companies who want to develop and grow in the future. He also added: "Rather than destroying jobs, AI is seen as a tool that could help us work smarter and better in the future." So which jobs are at risk? Whilst the spin on jobs and A.I are positive for some employees and employers who believe that there will be new / more roles available in the future, it's wise to note that not all professions will have the same opportunities post robotics. Many websites now offer statistical data to help workers work out the percentage possibility of their job being taken over by robots. Sites such as WillRobotsTakeMyJob.com return a % result based on any job title.

Police in the Netherlands may have been a tad too hasty in testing a squadron of drone-catching eagles. NOS has learned that Dutch law enforcement officials are retiring the birds (they're going to new homes) and winding down the program. Not surprisingly, the decision is a response to both actual demand as well as the performance of the birds themselves. To start, there just isn't much need for the eagles -- they were support to thwart terrorists and potential accidents, but there thankfully hasn't been much of a problem. And, like you might guess, even birds trained from youth to intercept drones won't always do what they're told.

Ask anyone what Google is and the most likely answer will be that it's a search engine, which is pretty hard to argue with. The tech giant's most important platform is called Google Search, where people type in queries and get search results in return. Except the nature of search is drastically changing, as Google further integrates machine learning and artificial intelligence into everything it does. We now get personalised search results based on our location, user history and content preferences. We get content recommendations popping up on our phones and alerts for the latest news, sports results and travel updates.

It hasn't been an easy couple of years for algorithms. Increasingly populated with content decried as'fake news', 'clickbait', today's highly personalized social media feeds are coming under increasing fire for being filter bubbles, culminating in Mark Zuckerberg's highly public apology to Facebook's users at the start of this month. These shifts in media mirror concurrent developments in spheres as diverse as customer service and support, financial trading, healthcare, and more. Today, though, tech is fighting back – thanks to AI. After partnering with Automated Insights in 2014 – a natural language generation start-up – the Associated Press became one of the earliest adopters of AI within the media space.

Inference, prediction and control of complex dynamical systems from time series is important in many areas, including financial markets, power grid management, climate and weather modeling, or molecular dynamics. The analysis of such highly nonlinear dynamical systems is facilitated by the fact that we can often find a (generally nonlinear) transformation of the system coordinates to features in which the dynamics can be excellently approximated by a linear Markovian model. Moreover, the large number of system variables often change collectively on large time- and length-scales, facilitating a low-dimensional analysis in feature space. In this paper, we introduce a variational approach for Markov processes (VAMP) that allows us to find optimal feature mappings and optimal Markovian models of the dynamics from given time series data. The key insight is that the best linear model can be obtained from the top singular components of the Koopman operator. This leads to the definition of a family of score functions called VAMP-r which can be calculated from data, and can be employed to optimize a Markovian model. In addition, based on the relationship between the variational scores and approximation errors of Koopman operators, we propose a new VAMP-E score, which can be applied to cross-validation for hyper-parameter optimization and model selection in VAMP. VAMP is valid for both reversible and nonreversible processes and for stationary and non-stationary processes or realizations.