Spike sorting is a fundamental preprocessing step in neuroscience that is central to access simultaneous but distinct neuronal activities and therefore to better understand the animal or even human brain. But numerical complexity limits studies that require processing large scale datasets in terms of number of electrodes, neurons, spikes and length of the recorded signals. We propose in this work a novel active set algorithm aimed at solving the Lasso for a classical convolutional model. Our algorithm can be implemented efficiently on parallel architecture and has a linear complexity w.r.t. the temporal dimensionality which ensures scaling and will open the door to online spike sorting. We provide theoretical results about the complexity of the algorithm and illustrate it in numerical experiments along with results about the accuracy of the spike recovery and robustness to the regularization parameter.

In this article we investigate the outcomes of the standard Least Angle Regression (LAR) algorithm in high dimensions under the Gaussian noise assumption. We give the exact law of the sequence of knots conditional on the sequence of variables entering the model, i.e., the post-selection law of the knots of the LAR. Based on this result, we prove an exact of the False Discovery Rate (FDR) in the orthogonal design case and an exact control of the existence of false negatives in the general design case. First, we build a sequence of testing procedures on the variables entering the model and we give an exact control of the FDR in the orthogonal design case when the noise level can be unknown. Second, we introduce a new exact testing procedure on the existence of false negatives when the noise level can be unknown. This testing procedure can be deployed after any support selection procedure that will produce an estimation of the support (i.e., the indexes of nonzero coefficients) for any designs. The type~$I$ error of the test can be exactly controlled as long as the selection procedure follows some elementary hypotheses, referred to as admissible selection procedures. These support selection procedures are such that the estimation of the support is given by the $k$ first variables entering the model where the random variable $k$ is a stopping time. Monte-Carlo simulations and a real data experiment are provided to illustrate our results.

In this paper, we study the spectrum and the eigenvectors of radial kernels for mixtures of distributions in $\mathbb{R}^n$. Our approach focuses on high dimensions and relies solely on the concentration properties of the components in the mixture. We give several results describing of the structure of kernel matrices for a sample drawn from such a mixture. Based on these results, we analyze the ability of kernel PCA to cluster high dimensional mixtures. In particular, we exhibit a specific kernel leading to a simple spectral algorithm for clustering mixtures with possibly common means but different covariance matrices. We show that the minimum angular separation between the covariance matrices that is required for the algorithm to succeed tends to $0$ as $n$ goes to infinity.

Artificial intelligence (AI) is a technology which is increasingly being utilised in society and the economy worldwide, and its implementation is planned to become more prevalent in coming years. AI is increasingly being embedded in our lives, supplementing our pervasive use of digital technologies. But this is being accompanied by disquiet over problematic and dangerous implementations of AI, or indeed, even AI itself deciding to do dangerous and problematic actions, especially in fields such as the military, medicine and criminal justice. These developments have led to concerns about whether and how AI systems adhere, and will adhere to ethical standards. These concerns have stimulated a global conversation on AI ethics, and have resulted in various actors from different countries and sectors issuing ethics and governance initiatives and guidelines for AI. Such developments form the basis for our research in this report, combining our international and interdisciplinary expertise to give an insight into what is happening in Australia, China, Europe, India and the US.

The most famous single-player card game is 'Klondike', but our ignorance of its winnability percentage has been called "one of the embarrassments of applied mathematics". Klondike is just one of many single-player card games, generically called 'patience' or 'solitaire' games, for which players have long wanted to know how likely a particular game is to be winnable for a random deal. A number of different games have been studied empirically in the academic literature and by non-academic enthusiasts. Here we show that a single general purpose Artificial Intelligence program, called "Solvitaire", can be used to determine the winnability percentage of approximately 30 different single-player card games with a 95\% confidence interval of +/- 0.1\% or better. For example, we report the winnability of Klondike as 81.956% +/- 0.096% (in the 'thoughtful' variant where the player knows the location of all cards), a 30-fold reduction in confidence interval over the best previous result. Almost all our results are either entirely new or represent significant improvements on previous knowledge.

Anticipatory thinking is a complex cognitive process for assessing and managing risk in many contexts. Humans use anticipatory thinking to identify potential future issues and proactively take actions to manage their risks. In this paper we define a cognitive systems approach to anticipatory thinking as a metacognitive goal reasoning mechanism. The contributions of this paper include (1) defining anticipatory thinking in the MIDCA cognitive architecture, (2) operationalizing anticipatory thinking as a three step process for managing risk in plans, and (3) a numeric risk assessment calculating an expected cost-benefit ratio for modifying a plan with anticipatory actions.

Two of the most prominent public knowledge graphs, DBpedia [16] and YAGO [18], build rich taxonomies using Wikipedia's infoboxes and category graph, respectively. They describe more than five million entities and contain multiple hundred millions of triples [27]. When it comes to relation assertions (RAs), however, we observe - even for basic properties - a rather low coverage: More than 50% of the 1.35 million persons in DBpedia have no birthplace assigned; even more than 80% of birthplaces are missing in YAGO. At the same time, type assertions (TAs) are not present as well for many instances - for example, there are about half a million persons in DBpedia not explicitly typed as such [23]. Missing knowledge in Wikipedia-based knowledge graphs can be attributed to absent information in Wikipedia, but also to the extraction procedures of knowledge graphs. DBpedia uses infobox mappings to extract RAs for individual instances, but it does not explicate any information implicitly encoded in categories. YAGO uses manually defined patterns to assign RAs to entities of matching categories. For example, they extract a person's year of birth by

The stretch of summer between E3 and the holiday season is a hard time to be a video game fan. The industry tantalized us with all the wonderful games it's making, but the problem is that most of those new and amazing-sounding games won't come out until at least November. But that just means you've got plenty of time to catch up on all the amazing video games 2019 has already gifted us. Here are the best video games of 2019 so far, including a few that may have slipped under your radar. Come for the incredible visuals, stay for the profound and moving story.

Hitachi has entered into a definitive contract for the acquisition of the robotic system integrator business mainly operated by an American headquartered company, JR Automation. Hitachi will conduct the acquisition with Crestview Partners. Subject to the terms and conditions of the contract, Hitachi will acquire JR Automation, which builds production lines and logistics systems using industrial robots. As a result of this acquisition, Hitachi will enter the robotic systems integrator business in North America, which is a region that is expected to see a high rate of growth. The acquisition is expected to be executed by the end of 2019, subject to the satisfaction of certain regulatory and other customary closing conditions.

Product suggestions are an ingrained part of the ecommerce experience. With the up- and cross-selling opportunities that a good system can provide, a thoughtful ecommerce experience is invaluable, as Amazon's paid search and display advertising strategy has shown. For consumers, suggested products should bring real value. Rather than being haunted for weeks by a product they searched for once, consumers should experience helpful product suggestions which complement their purchases. Often, this is the case.