We elaborate a quantum model for the meaning associated with corpora of written documents, like the pages forming the World Wide Web. To that end, we are guided by how physicists constructed quantum theory for microscopic entities, which unlike classical objects cannot be fully represented in our spatial theater. We suggest that a similar construction needs to be carried out by linguists and computational scientists, to capture the full meaning carried by collections of documental entities. More precisely, we show how to associate a quantum-like 'entity of meaning' to a 'language entity formed by printed documents', considering the latter as the collection of traces that are left by the former, in specific results of search actions that we describe as measurements. In other words, we offer a perspective where a collection of documents, like the Web, is described as the space of manifestation of a more complex entity - the QWeb - which is the object of our modeling, drawing its inspiration from previous studies on operational-realistic approaches to quantum physics and quantum modeling of human cognition and decision-making. We emphasize that a consistent QWeb model needs to account for the observed correlations between words appearing in printed documents, e.g., co-occurrences, as the latter would depend on the 'meaning connections' existing between the concepts that are associated with these words. In that respect, we show that both 'context and interference (quantum) effects' are required to explain the probabilities calculated by counting the relative number of documents containing certain words and co-ocurrrences of words.

A recently proposed clustering method, called the Nearest Descent (ND), can organize the whole dataset into a sparsely connected graph, called the In-tree. This ND-based Intree structure proves able to reveal the clustering structure underlying the dataset, except one imperfect place, that is, there are some undesired edges in this In-tree which require to be removed. Here, we propose an effective way to automatically remove the undesired edges in In-tree via an effective combination of the In-tree structure with affinity propagation (AP). The key for the combination is to add edges between the reachable nodes in In-tree before using AP to remove the undesired edges. The experiments on both synthetic and real datasets demonstrate the effectiveness of the proposed method.

We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. Our approach attempts to parametrize all solutions of the equations using Gr\"obner bases. If successful, a push forward Gaussian process along the paramerization is the desired prior. We consider several examples, among them the full inhomogeneous system of Maxwell's equations. By bringing together stochastic learning and computeralgebra in a novel way, we combine noisy observations with precise algebraic computations.

Marketing analytics is a diverse field, with both academic researchers and practitioners coming from a range of backgrounds including marketing, operations research, statistics, and computer science. This paper provides an integrative review at the boundary of these three areas. The topics of visualization, segmentation, and class prediction are featured. Links between the disciplines are emphasized. For each of these topics, a historical overview is given, starting with initial work in the 1960s and carrying through to the present day. Recent innovations for modern large and complex "big data" sets are described. Practical implementation advice is given, along with a directory of open source R routines for implementing marketing analytics techniques.

Dear colleagues, We are looking for two Postdoctoral Research Associates with a background in electrical engineering, physics, statistics or computer science to work on a research project involving the application of machine learning methods to structural Magnetic Resonance Imaging data. The project is a collaboration between King's College London, UK (Dr. Vince Calhoun) and the Universidade Federal do ABC, Brazil (Prof João Sato). The post holders will be based at the Institute of Psychiatry, Psychology & Neuroscience (King's College London). I would be happy to answer any queries from prospective applicants.

The first WWE pay-per-view is also among the year's biggest. Royal Rumble 2018 kicks off the road to WrestleMania Sunday night in Philadelphia to determine two of the wrestlers that will challenge for titles in New Orleans on April 8. As one of the "Big Four" pay-per-views, the Royal Rumble begins earlier than usual with a 7 p.m. EST start time. The kickoff show starts at 5 p.m. EST on WWE Network, which costs subscribers $9.99 per month, followed by the Royal Rumble itself. Fans can watch the Royal Rumble on PPV for $54.99.

Google's artificial intelligence unit DeepMind engaged in "highly questionable" practices when it struck a 2015 deal to access years' worth of UK hospital patient records held by the National Health Service, says a paper published March 16 in the journal "Health and Technology." The paper, written by Cambridge University law academic Julia Powles and Economist journalist Hal Hodson, is the first piece of scholarship to analyze the terms by which 1.6 million patient records from three London hospitals that are part of the NHS Royal Free London trust were shared with DeepMind. That agreement was replaced by a 2016 deal that the authors will analyze in future. The earlier agreement is currently being investigated by two UK regulatory bodies. One of those investigations, by the Information Commissioner's Office (ICO), is "close to conclusion," the ICO says. The paper argues that both DeepMind and the hospital administrations, in their eagerness to take advantage of national data-sets, were too lax in the way the data was shared.

Artificial intelligence approached the summit of hype at this year's World Economic Forum. AI was on the lips of seemingly every corporate chief and policy maker attending this year's conference in Davos, Switzerland. On Wednesday, the term "artificial intelligence" appeared in more than 20 headlines and stories Bloomberg ran about the gathering. British Prime Minister Theresa May announced a new government-funded center to advise on ethical use of AI and French President Emmanuel Macron launched a 10 billion euro innovation fund aimed at new technologies like AI. AI is going to be more important than humanity's mastery of fire or electricity, Google CEO Sundar Pichai said. Alibaba founder Jack Ma warned that AI and robots are going to "kill a lot of jobs" and could start a third world war.

I think it's safe to assume that Shogo Shimada, a thoracic and cardiac surgeon at the University of Tokyo Hospital, did not start his career thinking he might one day be implanting robots into living animals. Yet that is just what Shimada and a team of surgeons and roboticists from around the world have now achieved. Their work is the latest in a long line of attempts to solve problems in biology using robots. When I hear about this sort of research, it usually involves micro-robots. And then there's talk of smaller, nano-size robots that can swim through the bloodstream and deliver drugs or make repairs, as referenced in the 1966 sci-fi film "Fantastic Voyage."

Bisimulation provides structural conditions to characterize indistinguishability from an external observer between nodes on labeled graphs. It is a fundamental notion used in many areas, such as verification, graph-structured databases, and constraint satisfaction. However, several current applications use graphs where nodes also contain data (the so called "data graphs"), and where observers can test for equality or inequality of data values (e.g., asking the attribute 'name' of a node to be different from that of all its neighbors). The present work constitutes a first investigation of "data aware" bisimulations on data graphs. We study the problem of computing such bisimulations, based on the observational indistinguishability for XPath ---a language that extends modal logics like PDL with tests for data equality--- with and without transitive closure operators. We show that in general the problem is PSpace-complete, but identify several restrictions that yield better complexity bounds (coNP, PTime) by controlling suitable parameters of the problem, namely the amount of non-locality allowed, and the class of models considered (graphs, DAGs, trees). In particular, this analysis yields a hierarchy of tractable fragments.