IBM's Watson supercomputer discovers 5 new genes linked to ALS


IBM Watson is known for its work in identifying cancer treatments and beating contestants on Jeopardy! But now the computing system has expertise in a new area of research: neuroscience. Watson discovered five genes linked to ALS, sometimes called Lou Gehrig's disease, IBM announced on Wednesday. The tech company worked with researchers at the Barrow Neurological Institute in Phoenix, Arizona. The discovery is Watson's first in any type of neuroscience, and suggests that Watson could make discoveries in research of other neurological diseases.


AAAI Conferences

This paper critically examines the issues involved in computer-assisted and automated caregiving for patients with Alzheimer's disease and related dementias. First, the nature of the problems experienced by those who care for dementia patients is explicated. Next, a high-level overview of the ways in which computers might assist these caregivers, now and in the future, is presented. Finally, the ethical issues raised by the advent of automated caregiving are explored.

Two-block vs. Multi-block ADMM: An empirical evaluation of convergence Machine Learning

Alternating Direction Method of Multipliers (ADMM) has become a widely used optimization method for convex problems, particularly in the context of data mining in which large optimization problems are often encountered. ADMM has several desirable properties, including the ability to decompose large problems into smaller tractable sub-problems and ease of parallelization, that are essential in these scenarios. The most common form of ADMM is the two-block, in which two sets of primal variables are updated alternatingly. Recent years have seen advances in multi-block ADMM, which update more than two blocks of primal variables sequentially. In this paper, we study the empirical question: {\em Is two-block ADMM always comparable with sequential multi-block ADMM solving an equivalent problem?} In the context of optimization problems arising in multi-task learning, through a comprehensive set of experiments we surprisingly show that multi-block ADMM consistently outperformed two-block ADMM on optimization performance, and as a consequence on prediction performance, across all datasets and for the entire range of dual step sizes. Our results have an important practical implication: rather than simply using the popular two-block ADMM, one may considerably benefit from experimenting with multi-block ADMM applied to an equivalent problem.

Discriminative Feature Selection for Uncertain Graph Classification Machine Learning

Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objects are certain, and the binary edges within each graph represent the "presence" of linkages among the nodes. In many real-world applications, however, the linkage structure of the graphs is inherently uncertain. Therefore, existing measurements of interestingness based upon certain graphs are unable to capture the structural uncertainty in these applications effectively. In this paper, we study the problem of discriminative subgraph feature selection from uncertain graphs. This problem is challenging and different from conventional subgraph mining problems because both the structure of the graph objects and the discrimination score of each subgraph feature are uncertain. To address these challenges, we propose a novel discriminative subgraph feature selection method, DUG, which can find discriminative subgraph features in uncertain graphs based upon different statistical measures including expectation, median, mode and phi-probability. We first compute the probability distribution of the discrimination scores for each subgraph feature based on dynamic programming. Then a branch-and-bound algorithm is proposed to search for discriminative subgraphs efficiently. Extensive experiments on various neuroimaging applications (i.e., Alzheimer's Disease, ADHD and HIV) have been performed to analyze the gain in performance by taking into account structural uncertainties in identifying discriminative subgraph features for graph classification.

IATSL - Intelligent Assistive Technology and Systems Lab

AITopics Original Links

Welcome to the Intelligent Assistive Technology and Systems Lab (IATSL), located in the Department of Occupational Science and Occupational Therapy at the University of Toronto. We are a multi-disciplinary group of researchers with backgrounds in engineering, computer science, occupational therapy, speech-language pathology, and gerontology. Our goal is to develop zero-effort technologies that are adaptive, flexible, and intelligent, to enable users to participate fully in their daily lives. We have an opening for an enthusiastic post-doctoral fellow to work on computer vision, signal processing, and video analysis algorithms for applications in sleep monitoring and medical diagnosis. Please read more about this position here at