Joint peak detection is a central problem when comparing samples in genomic data analysis, but current algorithms for this task are unsupervised and limited to at most 2 sample types. We propose PeakSegJoint, a new constrained maximum likelihood segmentation model for any number of sample types. To select the number of peaks in the segmentation, we propose a supervised penalty learning model. To infer the parameters of these two models, we propose to use a discrete optimization heuristic for the segmentation, and convex optimization for the penalty learning. In comparisons with state-of-the-art peak detection algorithms, PeakSegJoint achieves similar accuracy, faster speeds, and a more interpretable model with overlapping peaks that occur in exactly the same positions across all samples.

This paper is concerned with the problems of interaction screening and nonlinear classification in a high-dimensional setting. We propose a two-step procedure, IIS-SQDA, where in the first step an innovated interaction screening (IIS) approach based on transforming the original $p$-dimensional feature vector is proposed, and in the second step a sparse quadratic discriminant analysis (SQDA) is proposed for further selecting important interactions and main effects and simultaneously conducting classification. Our IIS approach screens important interactions by examining only $p$ features instead of all two-way interactions of order $O(p^2)$. Our theory shows that the proposed method enjoys sure screening property in interaction selection in the high-dimensional setting of $p$ growing exponentially with the sample size. In the selection and classification step, we establish a sparse inequality on the estimated coefficient vector for QDA and prove that the classification error of our procedure can be upper-bounded by the oracle classification error plus some smaller order term. Extensive simulation studies and real data analysis show that our proposal compares favorably with existing methods in interaction selection and high-dimensional classification.

Data analysis and machine learning have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and providing insights about the problem. Yet, caution should avoid using machine learning as a black-box tool, but rather consider it as a methodology, with a rational thought process that is entirely dependent on the problem under study. In particular, the use of algorithms should ideally require a reasonable understanding of their mechanisms, properties and limitations, in order to better apprehend and interpret their results. Accordingly, the goal of this thesis is to provide an in-depth analysis of random forests, consistently calling into question each and every part of the algorithm, in order to shed new light on its learning capabilities, inner workings and interpretability. The first part of this work studies the induction of decision trees and the construction of ensembles of randomized trees, motivating their design and purpose whenever possible. Our contributions follow with an original complexity analysis of random forests, showing their good computational performance and scalability, along with an in-depth discussion of their implementation details, as contributed within Scikit-Learn. In the second part of this work, we analyse and discuss the interpretability of random forests in the eyes of variable importance measures. The core of our contributions rests in the theoretical characterization of the Mean Decrease of Impurity variable importance measure, from which we prove and derive some of its properties in the case of multiway totally randomized trees and in asymptotic conditions. In consequence of this work, our analysis demonstrates that variable importances [...].

Community detection, which aims to cluster $N$ nodes in a given graph into $r$ distinct groups based on the observed undirected edges, is an important problem in network data analysis. In this paper, the popular stochastic block model (SBM) is extended to the generalized stochastic block model (GSBM) that allows for adversarial outlier nodes, which are connected with the other nodes in the graph in an arbitrary way. Under this model, we introduce a procedure using convex optimization followed by $k$-means algorithm with $k=r$. Both theoretical and numerical properties of the method are analyzed. A theoretical guarantee is given for the procedure to accurately detect the communities with small misclassification rate under the setting where the number of clusters can grow with $N$. This theoretical result admits to the best-known result in the literature of computationally feasible community detection in SBM without outliers. Numerical results show that our method is both computationally fast and robust to different kinds of outliers, while some popular computationally fast community detection algorithms, such as spectral clustering applied to adjacency matrices or graph Laplacians, may fail to retrieve the major clusters due to a small portion of outliers. We apply a slight modification of our method to a political blogs data set, showing that our method is competent in practice and comparable to existing computationally feasible methods in the literature. To the best of the authors' knowledge, our result is the first in the literature in terms of clustering communities with fast growing numbers under the GSBM where a portion of arbitrary outlier nodes exist.

Hey Siri, turn off the kitchen light. The first "smart" home gadgets that can be controlled by Apple's voice-activated digital assistant are going on sale this week, just days after rival tech giant Google announced it's building its own software for Internet-connected home appliances and other gadgets. The new products could be an important step forward for the emerging industry of "smart" or "connected" homes, where appliances, thermostats and even door locks contain computer chips that communicate wirelessly. While a number of companies are working on similar products, analysts say Apple could persuade more consumers to try them by making it easy to control different products from a familiar device, such as the iPhone. Apple announced its "HomeKit" software project a year ago, but isn't making the new products.

Hey Siri, turn off the kitchen light. The first "smart" home gadgets that can be controlled by Apple's voice-activated digital assistant are going on sale this week, just days after rival tech giant Google announced it's building its own software for Internet-connected home appliances and other gadgets. The new products could be an important step forward for the emerging industry of "smart" or "connected" homes, where appliances, thermostats and even door locks contain computer chips that communicate wirelessly. While a number of companies are working on similar products, analysts say Apple could persuade more consumers to try them by making it easy to control different products from a familiar device, such as the iPhone. Apple announced its "HomeKit" software project a year ago, but isn't making the new products.

Network metrics form a fundamental part of the network analysis toolbox. Used to quantitatively measure different aspects of the network, these metrics can give insights into the underlying network structure and function. In this work, we connect network metrics to modern probabilistic machine learning. We focus on the centrality metric, which is used a wide variety of applications from web search to gene-analysis. First, we formulate an eigenvector-based Bayesian centrality model for determining node importance. Compared to existing methods, our probabilistic model allows for the assimilation of multiple edge weight observations, the inclusion of priors and the extraction of uncertainties. To enable tractable inference, we develop a variational lower bound (VBC) that is demonstrated to be effective on a variety of networks (two synthetic and five real-world graphs). We then bridge this model to sparse Gaussian processes. The sparse variational Bayesian centrality Gaussian process (VBC-GP) learns a mapping between node attributes to latent centrality and hence, is capable of predicting centralities from node features and can potentially represent a large number of nodes using only a limited number of inducing inputs. Experiments show that the VBC-GP learns high-quality mappings and compares favorably to a two-step baseline, i.e., a full GP trained on the node attributes and pre-computed centralities. Finally, we present two case-studies using the VBC-GP: first, to ascertain relevant features in a taxi transport network and second, to distribute a limited number of vaccines to mitigate the severity of a viral outbreak.

A fundamental limitation of the common machine learning methods is the cost incurred by the preparation of the large training samples required for good generalization. Multi-task learning (MTL) offers a potential remedy. Unlike common single task learning, MTL accomplishes tasks simultaneously with other related tasks, using a shared representation. One general assumption of multi-task learning is that all tasks should share some common structures, including a similarity metric matrix [3], a low ranksubspace [4, 5], parametersofBayesianmodels [6] oracommon set of features [7, 8, 9]. Improved generalization is achieved because what is learned from each task can help with the learning of other tasks [10]. MTL has been successfully applied to many applications such as stock selection [3], speech classification [11] and medical diagnoses [12]. While the majority of existing multi-task feature learning algorithms assume that the relevant features are shared by all tasks, some studies have begun to consider a more general case where features can be commonly shared only among most, but not necessarily all of them. In other word, they try to learn the features specific to each task as well as the common features shared among tasks [1]. In addition, MTL is commonly formulated as a convex regularization problem.

In two months, Microsoft will return the Start Menu to the world. Windows 10 is arriving for download on July 29 -- and it's a free download for Windows 7 and Windows 8 users. After July of 2016, Windows 7 users have to pay.) Watch CNET Update below to learn about the big highlights of Windows 10, including Cortana and new security protection perks. Also in Update: Google just put all privacy and security settings in one place, and Asus has a new smartwatch that may remind you of another well-known watch: CNET Update delivers the tech news you need in under three minutes.

The newest version of Microsoft Windows arrives July 29. Microsoft announced the launch date for Windows 10 on Monday. Upgrading to the major new edition of its operating system will be free for most consumers with a Windows 8 or Windows 7 machine. Microsoft didn't announce a price for those ineligible for a free upgrade. Nor did it say when the smartphone version would be available. Windows 10 represents Microsoft's first attempt to build an operating system that looks and feels the same regardless of the size of the screen or the type of device being used.