Change detection (CD) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series. Despite having received significant attention, one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and the understanding of changepoints. We propose hierarchical quickest change detection (HQCD), a framework that formalizes the process of incorporating additional correlated sources for early changepoint detection. The core ideas behind HQCD are rooted in the theory of quickest detection and HQCD can be regarded as its novel generalization to a hierarchical setting. The sources are classified into targets and surrogates, and HQCD leverages this structure to systematically assimilate observed data to update changepoint statistics across layers. The decision on actual changepoints are provided by minimizing the delay while still maintaining reliability bounds. In addition, HQCD also uncovers interesting relations between changes at targets from changes across surrogates. We validate HQCD for reliability and performance against several state-of-the-art methods for both synthetic dataset (known changepoints) and several real-life examples (unknown changepoints). Our experiments indicate that we gain significant robustness without loss of detection delay through HQCD. Our real-life experiments also showcase the usefulness of the hierarchical setting by connecting the surrogate sources (such as Twitter chatter) to target sources (such as Employment related protests that ultimately lead to major uprisings).

Extremes play a special role in Anomaly Detection. Beyond inference and simulation purposes, probabilistic tools borrowed from Extreme Value Theory (EVT), such as the angular measure, can also be used to design novel statistical learning methods for Anomaly Detection/ranking. This paper proposes a new algorithm based on multivariate EVT to learn how to rank observations in a high dimensional space with respect to their degree of 'abnormality'. The procedure relies on an original dimension-reduction technique in the extreme domain that possibly produces a sparse representation of multivariate extremes and allows to gain insight into the dependence structure thereof, escaping the curse of dimensionality. The representation output by the unsupervised methodology we propose here can be combined with any Anomaly Detection technique tailored to non-extreme data. As it performs linearly with the dimension and almost linearly in the data (in O(dn log n)), it fits to large scale problems. The approach in this paper is novel in that EVT has never been used in its multivariate version in the field of Anomaly Detection. Illustrative experimental results provide strong empirical evidence of the relevance of our approach.

We introduce a data-driven order reduction method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction. The method rests on the assumption that the nonlinear system behaves linearly when lifted into a high (or infinite) dimensional feature space where balanced truncation may be carried out implicitly. This leads to a nonlinear reduction map which can be combined with a representation of the system belonging to a reproducing kernel Hilbert space to give a closed, reduced order dynamical system which captures the essential input-output characteristics of the original model. Empirical simulations illustrating the approach are also provided.

In many "smart city" applications, congestion arises in part due to the nature of signals received by individuals from a central authority. In the model of Marecek et al. [arXiv:1406.7639, Int. J. Control 88(10), 2015], each agent uses one out of multiple resources at each time instant. The per-use cost of a resource depends on the number of concurrent users. A central authority has up-to-date knowledge of the congestion across all resources and uses randomisation to provide a scalar or an interval for each resource at each time. In this paper, the interval to broadcast per resource is obtained by taking the minima and maxima of costs observed within a time window of length r, rather than by randomisation. We show that the resulting distribution of agents across resources also converges in distribution, under plausible assumptions about the evolution of the population over time.

BIO: Theophano Mitsa holds a Ph.D. degree in EE from the U. of Rochester and is the author of 47 publications, 11 U.S. patents and the book (CRC Press, 2010) "Temporal Data Mining". She has diverse academic and industrial experience, having served as a faculty member at the Universities of Iowa and Massachusetts and a Senior Software Engineer at GE HealthCare and Abiomed. Dr. Mitsa has received research awards from NSF, the Whitaker Foundation and HP. She is also a Fulbright scholar and the winner of the University of Rochester Eastman Medal. She is currently an independent Machine Learning and Analytics consultant.

The Times has assembled a panel of distinguished and diverse writers who will regularly contribute to the Books section. The 10 authors who make up the Los Angeles Times Cultural Critics At Large have published works of fiction, nonfiction and poetry. They have won dozens of prizes. A majority have deep connections to Southern California, even though they hail from four different nations. They will help expand the literary conversation, challenging ideas and broadening readers' understanding of literature and culture within the contemporary moment.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Machine Learning is the art of writing programs that get better at performing a task as they gain experience, without being explicitly programmed to do so. Feed your program more data, and it will get smarter at handling new situations. Some machine learning algorithms use fairly advanced math, but simple approaches can be surprisingly effective. In this session, we'll take a classic Machine Learning challenge from Kaggle.com, automatically recognizing hand-written digits (http://www.kaggle.com/c/digit-recognizer), So bring your laptop, and let's see how smart we can make our machines!

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Certara's regulatory and medical consultancy, Synchrogenix, introduced the artificial intelligence-enabled solution to meet new data transparency requirements released in January 2015 by the European Medicines Agency (EMA). The new transparency and disclosure rules, Policy 70, requires clinical study report publication for all successful marketing authorization applications submitted on or after that date. On March 2, 2016 the EMA published clarifications to Policy 70, which, as Synchrogenix President, Kelley Kendle, told us, expands on the type of clinical trial data to be published to include not just clinical study reports, but also patient narratives and other regulatory documents. However, in order to be compliant with Policy 70, sponsor companies will be required to redact patient information and confidential company information throughout these documents before publication. In response to the new regulations, Synchrogenix developed a knowledge-based technology system, which applies learned protocol to the redaction of such confidential information. The AI solution "applies machine learning that can't be duplicated by humans," explained Kendle.