A Graphical Model (GM) describes a probability distribution over a set of random variables which factorizes over the edges of a graph. It is of interest to recover the structure of GMs from random samples. The graphical structure contains valuable information on the dependencies between the random variables. In fact, the neighborhood of a random variable is the minimal set that provides us maximum information about this variable. Unsurprisingly, GM reconstruction plays an important role in various fields such as the study of gene expression [1], protein interactions [2], neuroscience [3], image processing [4], sociology [5] and even grid science [6, 7]. The origin of the GM reconstruction problem is traced back to the seminal 1968 paper by Chow and Liu [8], where the problem was posed and resolved for the special case of tree-structured GMs. In this special tree case the maximum likelihood estimator is tractable and is tantamount to finding a maximum weighted spanning-tree. However, it is also known that in the case of general graphs with cycles, maximum likelihood estimators are intractable as they require computation of the partition function of the underlying GM, with notable exceptions of the Gaussian GM, see for instance [9], and some other special cases, like planar Ising models without magnetic field [10]. 1 A lot of efforts in this field has focused on learning Ising models, which are the most general GMs over binary variables with pairwise interaction/factorization. Early attempts to learn the Ising model structure efficiently were heuristic, based on various mean-field approximations, e.g.

Redescription mining is a field of knowledge discovery that aims at finding different descriptions of similar subsets of instances in the data. These descriptions are represented as rules inferred from one or more disjoint sets of attributes, called views. As such, they support knowledge discovery process and help domain experts in formulating new hypotheses or constructing new knowledge bases and decision support systems. In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences. Construction of large and heterogeneous redescription set relies on CLUS-RM algorithm and a novel, conjunctive refinement procedure that facilitates generation of larger and more accurate redescription sets. The work also introduces the variability of redescription accuracy when missing values are present in the data, which significantly extends applicability of the method. Crucial part of the framework is the redescription set extraction based on heuristic multi-objective optimization procedure that allows user to define importance levels towards one or more redescription quality criteria. We provide both theoretical and empirical comparison of the novel framework against current state of the art redescription mining algorithms and show that it represents more efficient and versatile approach for mining redescriptions from data.

"I'm sorry Dave, I'm afraid I can't do that," rings as a warning to mankind when we yield too much power to machines. HAL, the tyrannical computer in Stanley Kubrick's movie, 2001: A Space Odyssey, was cleverly a one-letter shift from IBM. Fast-forward 50 years, and NASA's researchers are working with IBM to develop a Watson system that could serve as a flight operations advisor (soundsfamiliar?). According to Rob High, chief technical officer for IBM Watson, over the next five to 10 years, cognitive computing systems like Watson will "be the dominant form of computing," especially for NASA's flight command centers. Watson will not be alone on its mission because NASA is building an arsenal of robots in advance of human explorers to colonize MARS. This month, DARPA will put its robot crew through a series of rigorous tests.

As more of our lives move into the digital sphere, data has become incredibly valuable. There's so much digital information floating around that commentators have hailed the beginning of an era of'big data'. This basically refers to huge datasets that are much larger than traditional collections of information. This info has been generated by growing digitisation, especially from online financial transactions and social media. It's a never-ending paradox – the more digital society becomes, the more data there is. . .

A change in political leadership with Donald Trump's presidential victory and GOP control of Congress has raised expectation of policy shifts that could affect the regulatory compliance process. The incoming administration is promising to work to "dismantle the Dodd-Frank Act and replace it with new policies to encourage economic growth and job creation." This scenario would have plusses and minuses. On one hand, bank stocks are on the rise because of Trump's promise to lessen regulation. On the other hand, a complete dismantling of Dodd-Frank would mean that banks would have to overhaul the compliance processes that they have spent billions of dollars to put in place over the past six years.

Humanoid robot bartender "Carl" prepares a drink for a guest at the Robots Bar and Lounge in the eastern German town of Ilmenau. NEXT time you stop for petrol at a self-serve pump, say hello to the robot in front of you. Its life story can tell you a lot about the robot economy roaring toward us like an EF5 tornado on the prairie. Yeah, your automated petrol pump killed a lot of jobs over the years, but its biography might give you hope that the coming wave of automation driven by artificial intelligence (AI) will turn out better for almost all of us than a lot of people seem to think. The first crude version of an automated petrol-delivering robot appeared in 1964 at a station in Westminster, Colorado.

President-elect Donald Trump says the Chinese government should be told "we don't want the drone they stole back" and "let them keep it!" Trump's tweet Saturday evening came after U.S. officials confirmed that they "secured an understanding" for the return of the U.S. Navy unmanned underwater glider, which China seized in the South China Sea. The comments may extend one of the most serious incidents between the American and the Chinese militaries in years. The Chinese navy seized the drone on Thursday; the Pentagon said it was being operated by civilian contractors to conduct oceanic research. The U.S. lodged a formal diplomatic complaint and demanded the drone back.

A flight over farmlands could be part of a future Uber ride. Recently there's been a lot of ink and pixels declaring 2016 the year of humanity's discontent. That's tough to deny in the realm of geopolitics, between awful conflicts in places like Syria, acts of terror worldwide and contentious elections in the UK and US. But the world went on, and so did important work in science and innovation. If you sweep the ugly parts of 2016 under the rug and then check the place out, it's not too shabby.

Hamas claims it has both surveillance and attack drones. During the 2014 Gaza-Israel conflict, Hamas released aerial footage of parts of Israel.

It speaks volumes about the plight of rabble-rousers in China today that Ai Weiwei, the country's most famous dissident artist, has decided that working there is too dangerous -- so he wants to go to Syria. Ai, who received his passport back from Chinese authorities last year, is turning his attention to Syrian refugees. For the artist, who spent 81 days in Chinese detention in 2011 and then was blocked from traveling for four years, it is a way of remaining relevant without landing in jail. Ai reflected on his situation during a trip to New York last month. He said he does not want his 7-year-old son to experience the same difficulties as he did as a child when his father, the acclaimed Chinese poet Ai Qing, was purged after he fell out with former leader Mao Tse-tung and was exiled.