Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose \ourmethod, a novel greedy MDL-based method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show \ourmethod is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values.

Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real world setting of outcome prediction, the seminal \'{E}l\H{o} update still remains, after more than 50 years, a valuable baseline which is difficult to improve upon, though in its original form it is a heuristic and not a proper statistical "model". Mathematically, the \'{E}l\H{o} rating system is very closely related to the Bradley-Terry models, which are usually used in an explanatory fashion rather than in a predictive supervised or on-line learning setting. Exploiting this close link between these two model classes and some newly observed similarities, we propose a new supervised learning framework with close similarities to logistic regression, low-rank matrix completion and neural networks. Building on it, we formulate a class of structured log-odds models, unifying the desirable properties found in the above: supervised probabilistic prediction of scores and wins/draws/losses, batch/epoch and on-line learning, as well as the possibility to incorporate features in the prediction, without having to sacrifice simplicity, parsimony of the Bradley-Terry models, or computational efficiency of \'{E}l\H{o}'s original approach. We validate the structured log-odds modelling approach in synthetic experiments and English Premier League outcomes, where the added expressivity yields the best predictions reported in the state-of-art, close to the quality of contemporary betting odds.

Why do so few women end up in physics, mathematics and other fields traditionally associated with "brilliance"? Part of the answer may lie in what happens to girls by the time they're out of kindergarten. A new study finds that 6-year-old girls are less likely than boys to think members of their own gender can be brilliant -- and they're more likely than boys to shy away from activities requiring that exceptional intelligence. That's a serious change from their attitudes at age 5, when they're just as likely as boys to think their own gender can be brilliant, and just as willing to take on those activities for brilliant children. The results, described in the journal Science, shows how early these gender stereotypes begin to affect the self-perception and behavior of girls -- which may limit their aspirations and careers into adulthood.

An illustrated artificial neural network (ANN) (CC BY SA 4.0 LearnDataSci via Wikimedia Commons) The system's'neural network' is advanced, but its abilities are being exaggerated by observers I have a fascination with translation, primarily because I have an interest in languages. I'm what I like to call "an aspiring polyglot," with the implication that I don't have time to practice (and reach complete fluency in) the few foreign languages I have some knowledge of, yet I give myself plenty of time to learn about said languages, how they are all different and by extension how they all work. As a technology- and startups-focused journalist, that makes the evermore popular topic of machine translation (MT) and "translation memory" fascinating, giving me the chance to cover companies like Austrian startup LingoHub (an essential service for apps) or Portuguese startup Unbabel (the next-level stuff they're doing is very cool). I can ask people how they communicate with lovers from other countries and report on developments like Google Translate's upgrade from "phrase-based machine translation" (PMT) with a "neural machine translation" (NMT). "Google Translate invented its own language to help it translate more effectively," wrote UX developer Gil Fewster on Medium, with the bold emphasis his own.

If there's one truth that the tech revolution has proven itself repeatedly, it's this: just because we can build something doesn't mean we should. Take, for instance, Google Glass, iTunes Ping, and Clippy (to say nothing of the smart fork.) Likewise, imagine an app that can take a snapshot of a student's homework assignment, chew on the questions using cloud-based artificial intelligence, and spit out the answers. In theory, this sounds great for students, yet terrible for learning -- that is, until you put Socratic to the test. "Every student today goes to the Internet, goes to Google, to ask all of their questions -- this is something that's happening anyway," says Shreyans Bhansali, the co-founder and head of engineering for the free homework-helping app that's currently topping Apple's App Store for education software. "We read the question, we figure out what they need to learn to answer it, and then we teach them that stuff."

The 1556 earthquake that killed an estimated 830,000 people in the Shaanxi Province is but the deadliest example of China's long history with the natural disaster. The 1920 Haiyuan quake killed 273,000; the 1976 Tangshan earthquake claimed about 232,000 lives. Whether or not they hold to the historic view that earthquakes indicate heaven's displeasure, the modern Chinese aren't sitting idle as the ground trembles. Starting in the mid-1960s, the country established a system to improve prediction capabilities, response training, and public communications to reduce the impact of calamities. They enlisted satellites to shape post-disaster responses, but since quakes have a habit of knocking out the ground-based systems that deliver their images to those who need them, a view from space isn't always much help.

"I returned, and saw under the sun, that the race is not to the swift, nor the battle to the strong, neither yet bread to the wise, nor yet riches to men of understanding, nor yet favour to men of skill; but time and chance happeneth to them all." Chance appears to name a single, unitary thing. But its genealogy, its family history, turns out to be a tangled one. One way to understand its branching origins is to turn to literature: We may look, in turn, to two very different novels. Anton Chigurh, the antagonist of Cormac McCarthy's novel No Country for Old Men, forces his victims to guess the outcome of a coin toss, taking their life if they guess in error. That chance is entirely contained, not in Chigurh, but in the toss--in nature itself. This is one source of uncertainty.

Smart machines and digitalization will boost rather than cut employment -- but people must get used to constant retraining to keep pace with change, the development chief of German software giant SAP tells Handelsblatt. SAP is the world's biggest supplier of business software and is shifting its business model towards cloud computing services. People are too negative about digitalization and artificial intelligence and should embrace new technologies as job creators rather than job killers, Bernd Leukert, the head of development at German business software giant SAP told Handelsblatt at the Davos World Economic Forum last week. Mr. Leukert, 49, who has worked at SAP since 1994 and joined the management board in 2014, said the German education system, the government and firms weren't doing enough to prepare people for a future in which there will be a constant need for new job training. "I think there's there's too much talk of negative scenarios in the discussion about digitalization and new technologies," he said.

Ever since 2007 with the explosion in the use of parallel hardware, the field of machine learning has become more exciting and more promising. It seems that the dream of true AI is finally just around the corner. Certainly, there are many companies that are starting to rely heavily on AI for their products. These include companies in search like Facebook, Google, as well as retailers and multimedia companies like Amazon and Netflix. But more recently many others in the health-care and cyber security industries are also interested in what AI and machine learning can do for them. Some of these technologies such as Tensorflow (which came about around 2015) are new and not widely understood.

School choice programs are implemented to give students/parents an opportunity to choose the public school the students attend. Controlled school choice programs need to provide choices for students/parents while maintaining distributional constraints on the composition of students, typically in terms of socioeconomic status. Previous works show that setting soft-bounds, which flexibly change the priorities of students based on their types, is more appropriate than setting hard-bounds, which strictly limit the number of accepted students for each type. We consider a case where soft-bounds are imposed and one student can belong to multiple types, e.g., financially-distressed and minority types. We first show that when we apply a model that is a straightforward extension of an existing model for disjoint types, there is a chance that no stable matching exists. Thus we propose an alternative model and an alternative stability definition, where a school has reserved seats for each type. We show that a stable matching is guaranteed to exist in this model and develop a mechanism called Deferred Acceptance for Overlapping Types (DA-OT). The DA-OT mechanism is strategy-proof and obtains the student-optimal matching within all stable matchings. Furthermore, we introduce an extended model that can handle both type-specific ceilings and floors and propose a extended mechanism DA-OT* to handle the extended model. Computer simulation results illustrate that DA-OT outperforms an artificial cap mechanism where we set a hard-bound for each type in each school. DA-OT* can achieve stability in the extended model without sacrificing students welfare.