Block CUR: Decomposing Matrices using Groups of Columns

A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the ability to sample specific individual rows or columns of the matrix is limited by either system constraints or cost. In this paper, we consider matrix approximation by sampling predefined \emph{blocks} of columns (or rows) from the matrix. We present an algorithm for sampling useful column blocks and provide novel guarantees for the quality of the approximation. This algorithm has application in problems as diverse as biometric data analysis to distributed computing. We demonstrate the effectiveness of the proposed algorithms for computing the Block CUR decomposition of large matrices in a distributed setting with multiple nodes in a compute cluster, where such blocks correspond to columns (or rows) of the matrix stored on the same node, which can be retrieved with much less overhead than retrieving individual columns stored across different nodes. In the biometric setting, the rows correspond to different users and columns correspond to users' biometric reaction to external stimuli, {\em e.g.,}~watching video content, at a particular time instant. There is significant cost in acquiring each user's reaction to lengthy content so we sample a few important scenes to approximate the biometric response. An individual time sample in this use case cannot be queried in isolation due to the lack of context that caused that biometric reaction. Instead, collections of time segments ({\em i.e.,} blocks) must be presented to the user. The practical application of these algorithms is shown via experimental results using real-world user biometric data from a content testing environment.Comment: shorter version to appear in ECML-PKDD 201

Solving kk-means on High-dimensional Big Data

In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little memory requirement. For the $k$-means problem, this has led to the development of several $(1+\varepsilon)$-approximations (under the assumption that $k$ is a constant), but also to the design of algorithms that are extremely fast in practice and compute solutions of high accuracy. However, when not only the length of the stream is high but also the dimensionality of the input points, then current methods reach their limits. We propose two algorithms, piecy and piecy-mr that are based on the recently developed data stream algorithm BICO that can process high dimensional data in one pass and output a solution of high quality. While piecy is suited for high dimensional data with a medium number of points, piecy-mr is meant for high dimensional data that comes in a very long stream. We provide an extensive experimental study to evaluate piecy and piecy-mr that shows the strength of the new algorithms.Comment: 23 pages, 9 figures, published at the 14th International Symposium on Experimental Algorithms - SEA 201

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

Peripheral electrical stimulation in Alzheimer's Disease: A randomized controlled trial on cognition and behavior

In a number of studies, peripheral electrical nerve stimulation has been applied to Alzheimer's disease (AD) patients who lived in a nursing home. Improvements were observed in memory, verbal fluency, affective behavior, activities of daily living and on the rest-activity rhythm and pupillary light reflex. The aim of the present, randomized, placebo-controlled, parallel-group clinical trial was to examine the effects of electrical stimulation on cognition and behavior in AD patients who still live at home. Repeated measures analyses of variance revealed no effects of the intervention in the verum group (n = 32) compared with the placebo group (n = 30) on any of the cognitive and behavioral outcome measures. However, the majority of the patients and the caregivers evaluated the treatment procedure positively, and applying the daily treatment at home caused minimal burden. The lack of treatment effects calls for reconsideration of electrical stimulation as a symptomatic treatment in A

Cell Cycle- and Cancer-Associated Gene Networks Activated by Dsg2: Evidence of Cystatin A Deregulation and a Potential Role in Cell-Cell Adhesion

This work was supported by grants from the National Institutes of Health (Mahoney, R01AR056067; Riobo, RO1 GM088256). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

cGAL, a temperature-robust GAL4–UAS system for Caenorhabditis elegans

The GAL4–UAS system is a powerful tool for manipulating gene expression, but its application in Caenorhabditis elegans has not been described. Here we systematically optimize the system's three main components to develop a temperature-optimized GAL4–UAS system (cGAL) that robustly controls gene expression in C. elegans from 15 to 25 °C. We demonstrate this system's utility in transcriptional reporter analysis, site-of-action experiments and exogenous transgene expression; and we provide a basic driver and effector toolkit

why do romanian universities fail to internalize quality assurance

Despite legal provisions in place since 2005, Romanian universities are considered to perform internal quality assurance only at a formal level, on paper, and usually in anticipation of external evaluations demanded by the government or other official institutions. This paper posits five hypotheses to explain this situation. We analyze 187 interviews with people in universities in order to evaluate these hypotheses. Only two hypotheses are confirmed by the data, allowing us to construct a narrative of policy failure. First, there are top-down failures resulting from unclear and inconsistent legal provisions that focus on multilayered evaluation procedures. Second, there are bottom-up failures related to the lack of ownership over internal quality assurance systems by the actors in the universities. The existing procedures are often seen as control-tools of government, and understood as disconnected from the universities' own goals and problems. Consequently, people on the ground passively try to subvert these tools by carrying them out in a ritualistic manner—which is why quality assurance cannot become internalized

Exchange hazards, relational reliability, and contracts in China: The contingent role of legal enforceability

Building on institutional and transaction cost economics, this article proposes that legal enforceability increases the use of contract over relational reliability (e.g., beliefs that the other party acts in a non-opportunistic manner) to safeguard market exchanges characterized by non-trivial hazards. The results of 399 buyer-supplier exchanges in China show that: (1) when managers perceive that the legal system can protect their firm's interests, they tend to use explicit contracts rather than relational reliability to safeguard transactions involving risks (i.e., asset specificity, environmental uncertainty, and behavioral uncertainty); and (2) when managers do not perceive the legal system as credible, they are less likely to use contracts, and instead rely on relational reliability to safeguard transactions associated with specialized assets and environmental uncertainty, but not those involving behavioral uncertainty. We further find that legal enforceability does not moderate the effect of relational reliability on contracts, but does weaken the effect of contracts on relational reliability. These results endorse the importance of prior experience (e.g., relational reliability) in supporting the use of explicit contracts, and alternatively suggest that, under conditions of greater legal enforceability, the contract signals less regarding one's intention to be trustworthy but more about the efficacy of sanctions. © 2010 Academy of International Business All rights reserved.postprin