Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common examples of such diseases, where the mutational load in the cancerous/viral population increases over time. In these cases, selective pressures are often observed along with competition, cooperation and parasitism among distinct cellular clones. Recently, we presented a mathematical framework to model these phenomena, based on a combination of Bayesian inference and Suppes' theory of probabilistic causation, depicted in graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). SBCNs are generative probabilistic graphical models that recapitulate the potential ordering of accumulation of such DNA changes during the progression of the disease. Such models can be inferred from data by exploiting likelihood-based model-selection strategies with regularization. In this paper we discuss the theoretical foundations of our approach and we investigate in depth the influence on the model-selection task of: (i) the poset based on Suppes' theory and (ii) different regularization strategies. Furthermore, we provide an example of application of our framework to HIV genetic data highlighting the valuable insights provided by the inferred.

In Machine Learning, Cross-validation is a resampling method used for model evaluation to avoid testing a model on the same dataset on which it was trained. This is a common mistake, especially that a separate testing dataset is not always available. However, this usually leads to inaccurate performance measures (as the model will have an almost perfect score since it is being tested on the same data it was trained on). To avoid this kind of mistakes, cross validation is usually preferred. The concept of cross-validation is actually simple: Instead of using the whole dataset to train and then test on same data, we could randomly divide our data into training and testing datasets.

Many complex ecosystems, such as those formed by multiple microbial taxa, involve intricate interactions amongst various sub-communities. The most basic relationships are frequently modeled as co-occurrence networks in which the nodes represent the various players in the community and the weighted edges encode levels of interaction. In this setting, the composition of a community may be viewed as a probability distribution on the nodes of the network. This paper develops methods for modeling the organization of such data, as well as their Euclidean counterparts, across spatial scales. Using the notion of diffusion distance, we introduce diffusion Frechet functions and diffusion Frechet vectors associated with probability distributions on Euclidean space and the vertex set of a weighted network, respectively. We prove that these functional statistics are stable with respect to the Wasserstein distance between probability measures, thus yielding robust descriptors of their shapes. We apply the methodology to investigate bacterial communities in the human gut, seeking to characterize divergence from intestinal homeostasis in patients with Clostridium difficile infection (CDI) and the effects of fecal microbiota transplantation, a treatment used in CDI patients that has proven to be significantly more effective than traditional treatment with antibiotics. The proposed method proves useful in deriving a biomarker that might help elucidate the mechanisms that drive these processes.

Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model student dropout using the largest known dataset on higher education attrition, which tracks over 32,500 students' demographics and transcript records at one of the nation's largest public universities. Our results highlight several early indicators of student attrition and show that dropout can be accurately predicted even when predictions are based on a single term of academic transcript data. These results highlight the potential for machine learning to have an impact on student retention and success while pointing to several promising directions for future work.

WrestleMania 33 is less than a month away, but there is still one more pay-per-view before WWE's biggest event of 2017. WWE Fastlane will feature the top wrestlers from "Monday Night Raw" at Milwaukee's Bradley Center Sunday night. The final PPV before WrestleMania is often uneventful, but that likely won't be the case this year. Not only are a few titles expected to change hands, but a couple of WWE superstars could make surprise returns. Goldberg will challenge Kevin Owens for the WWE Universal Championship, which Owens has held for more than six months.

This paper studies a new Bayesian algorithm for the joint reconstruction and classification of reflectance confocal microscopy (RCM) images, with application to the identification of human skin lentigo. The proposed Bayesian approach takes advantage of the distribution of the multiplicative speckle noise affecting the true reflectivity of these images and of appropriate priors for the unknown model parameters. A Markov chain Monte Carlo (MCMC) algorithm is proposed to jointly estimate the model parameters and the image of true reflectivity while classifying images according to the distribution of their reflectivity. Precisely, a Metropolis-whitin-Gibbs sampler is investigated to sample the posterior distribution of the Bayesian model associated with RCM images and to build estimators of its parameters, including labels indicating the class of each RCM image. The resulting algorithm is applied to synthetic data and to real images from a clinical study containing healthy and lentigo patients.

A secretive Canadian startup called Kindred AI is teaching robots how to perform difficult dexterous tasks at superhuman speeds by pairing them with human "pilots" wearing virtual-reality headsets and holding motion-tracking controllers. The technology offers a fascinating glimpse of how humans might work in synchronization with machines in the future, and it shows how tapping into human capabilities might amplify the capabilities of automated systems. For all the worry over robots and artificial intelligence eliminating jobs, there are plenty of things that machines still cannot do. The company demonstrated the hardware to MIT Technology Review last week, and says it plans to launch a product aimed at retailers in the coming months. The long-term ambitions are far grander.

The biggest holdup in making the boxing match between Conor McGregor and Floyd Mayweather a reality appears to be money. Both fighters are looking to make nine-figure paydays for what would undoubtedly become one of the highest-selling pay-per-views in history. Mayweather wants a guaranteed paycheck of $100 million to come out of retirement, and there is a precedent for the boxer earning such a large purse. It's how much he was paid to face Manny Pacquiao in 2015, and he eventually made more than twice that amount after receiving a cut of the PPV revenue. McGregor has asked for a $100 million payday, as well, but Mayweather isn't ready to agree to those terms. While McGregor is the UFC's biggest star, he's never had a purse of more than $3 million or even come close to approaching a nine-figure payday when factoring in his cut of the PPV revenue.

In this paper we introduce a new classification algorithm called Optimization of Distributions Differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as possible to one another while the gravity centers of these classes are as far as possible from one another. This aim is formulated as a multiobjective optimization problem that is solved by a hybrid of an evolutionary strategy and the Quasi-Newton method. The choice of the transformation function is flexible and could be any continuous space function. We experiment with a linear and a non-linear transformation in this paper. We show that the algorithm can outperform 6 other state-of-the-art classification methods, namely naive Bayes, support vector machines, linear discriminant analysis, multi-layer perceptrons, decision trees, and k-nearest neighbors, in 12 standard classification datasets. Our results show that the method is less sensitive to the imbalanced number of instances comparing to these methods. We also show that ODD maintains its performance better than other classification methods in these datasets, hence, offers a better generalization ability.

With WrestleMania 33 just a month away, it's been clear for a few weeks what WWE's top superstars will be doing at the biggest pay-per-view of 2017. A match between Brock Lesnar and Goldberg has been confirmed, while The Undertaker is likely to face Roman Reigns. The future of AJ Styles, however, hasn't been as clear. Aside from various rumors, the longest-reigning WWE Champion of the past year didn't have an obvious opponent for WrestleMania 33. The latest edition of "SmackDown Live," however, has provided the spark for Styles' next feud.