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.

Connectivity studies using resting-state functional magnetic resonance imaging are increasingly pooling data acquired at multiple sites. While this may allow investigators to speed up recruitment or increase sample size, multisite studies also potentially introduce systematic biases in connectivity measures across sites. In this work, we measure the inter-site effect in connectivity and its impact on our ability to detect individual and group differences. Our study was based on real, as opposed to simulated, multisite fMRI datasets collected in N=345 young, healthy subjects across 8 scanning sites with 3T scanners and heterogeneous scanning protocols, drawn from the 1000 functional connectome project. We first empirically show that typical functional networks were reliably found at the group level in all sites, and that the amplitude of the inter-site effects was small to moderate, with a Cohen's effect size below 0.5 on average across brain connections. We then implemented a series of Monte-Carlo simulations, based on real data, to evaluate the impact of the multisite effects on detection power in statistical tests comparing two groups (with and without the effect) using a general linear model, as well as on the prediction of group labels with a support-vector machine. As a reference, we also implemented the same simulations with fMRI data collected at a single site using an identical sample size. Simulations revealed that using data from heterogeneous sites only slightly decreased our ability to detect changes compared to a monosite study with the GLM, and had a greater impact on prediction accuracy. Taken together, our results support the feasibility of multisite studies in rs-fMRI provided the sample size is large enough.

The lending ecosystem has witnessed momentous changes in last five years, from fintechs disrupting the industry by leveraging technology and offering ease and speed in process, to evolution of stringent regulations post the 2008 meltdown. Technology has played a significant role in the rapid evolution of the lending industry. One such technology, machine learning, is beginning to create new avenues in the lending market. Machine learning, in simple terms, is an extension of artificial intelligence that enables computers or robots with the ability to learn, analysse and predict, using algorithms that iteratively learn from data. Machine learning therefore empowers the system to learn and adapt itself.

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words. Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it's vital we understand this new language, and what it's increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it. The language is a new class of machine learning known as deep learning, and the "whispered word" was a computer's use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat.

For many, the holiday season is a time of reflection, both from a personal and professional standpoint. I won't get too deep into politics and pop culture, but can't reflect on 2016 without thinking about the 2016 US presidential election and the Chicago Cub's finally winning the world series after 108 years. In my professional life I recall the challenges faced and overcome, the triumphs and even missed opportunities. Hopefully there were more triumphs than missed opportunities in your professional life, but reflecting on both can help prepare and potentially create your own opportunities in the new year – particularly if you work in digital commerce. If you're a digital commerce professional, either an online retailer or a B2B seller, last year saw a few milestones that are sure to impact your business in 2017 and beyond.

Artificially intelligent computer systems are now able to recognise skin cancer just as well as human scientists, according to a new report. Researchers from Stanford University used the internet to gather 130,000 pictures of skin lesions, representing over 2,000 different diseases. They then used these pictures to train an artificial intelligence algorithm - originally developed by Google to differentiate between cats and dogs - to tell the difference between cancerous and non-cancerous lesions. After training the algorithm, they tested it on 370 high-quality, biopsy-confirmed images, and compared its responses with those of 21 professional dermatologists. In its diagnoses of the skin lesions, which represented the most common and deadliest skin cancers, the algorithm matched the performance of the dermatologists.

A week hasn't even passed since the inauguration, but television news is saturated with the flurry of activity from President Donald Trump's administration. Trump, via Twitter, promised to launch an investigation into illegal voting and threatened to "send in the Feds" if Chicago police can't fix the "carnage." And that was just between Tuesday and Wednesday. This heightened scrutiny compelled the Internet Archive, a repository of everything posted on the web, to launch its Trump Archive in early January. You, perhaps, digitally time-traveled with the Internet Archive's Wayback Machine, or checked out free books, movies and software.

Medical diagnosis has been identified as one of the areas where artificial intelligence and machine learning could have most impact – but how should the NHS proceed? The likes of University College London Hospitals NHS Foundation Trust, Royal Free NHS Trust and Moorfields Eye Hospital have teamed up with Google DeepMind to work on better patient outcomes for those suffering from certain cancers, or for those who have had sight loss. According to Orlando Agrippa, deputy CIO at Barts Health NHS Trust, the NHS could benefit tenfold if it leveraged AI. "It could be used to increase accuracy over things like prescriptions, interventions and early diagnosis," he told BusinessCloud. The Commons Science and Technology Committee suggested last year that supercomputers assisting doctors with medical diagnoses could be one of the key impact areas of AI, but that government leadership in the fields of robotics and AI had been lacking.

London-based startup Digital Genius is targeting the contact centre with its deep learning technology. The Digital Genius neural network helps agents respond to common customer service queries quicker by first automatically sorting and labelling the metadata and then generating three potential responses, each with a level of certainty attached. Organisations can then set a threshold for automation, so responses the system views as 90 percent accurate could be automatically sent out - like a chatbot - and anything below is sent to the agent to review, for example. The startup claims it can reduce the average handling time for cases by 20 percent. This is especially important as organisations receive queries from more and more digital channels, including social media.