Stability Selection was recently introduced by Meinshausen and B¨uhlmann (2010) as a very general technique designed to improve the performance of a variable selection algorithm. It is based on aggregating the results of ...

Many computational models of visual attention have been created from a wide variety of different approaches to predict where people look in images. Each model is usually introduced by demonstrating performances on new ...

In this paper we study the problem of building a connected dominating set with constant degree (CCDS) in the dual graph radio network model. This model includes two types of links: reliable links, which always deliver ...

The numerical calculation of integrals is central to many computer graphics algorithms such as Monte-Carlo Ray Tracing. We show that such methods can be studied using Fourier analysis. Numerical error is shown to correspond ...

CPHash is a concurrent hash table for multicore processors. CPHash partitions its table across the caches of cores and uses message passing to transfer lookups/inserts to a partition. CPHash's message passing avoids the ...

A number of approximate program transformations have recently emerged that enable transformed programs to trade accuracy of their results for increased performance by dynamically and nondeterministically modifying variables ...

Multi-scale manipulations are central to image editing but they are also prone to halos. Achieving artifact-free results requires sophisticated edgeaware techniques and careful parameter tuning. These shortcomings were ...

Modern systems require applications to balance competing goals, e.g. achieving high performance and low power. Achieving this balance places an unrealistic burden on application programmers who must understand the power ...

We consider the problem of leader election (LE) in single-hop radio networks with synchronized time slots for transmitting and receiving messages. We assume that the actual number n of processes is unknown, while the size ...

We present a novel technique, automatic input rectification, and a prototype implementation called SOAP. SOAP learns a set of constraints characterizing typical inputs that an application is highly likely to process ...

We study multi-category classification in the framework of computational learning theory. We show how a relaxation approach, which is commonly used in binary classification, can be generalized to the multi-class setting. ...

In this work we are interested in the problems of supervised learning and variable selection when the input-output dependence is described by a nonlinear function depending on a few variables. Our goal is to consider a ...

We consider the partially observable control problem where it is potentially necessary to perform complex information-gathering operations in order to localize state. One approach to solving these problems is to create ...

Learning by temporal association rules such as Foldiak's trace rule is an attractive hypothesis that explains the development of invariance in visual recognition. Consistent with these rules, several recent experiments ...

This thesis analyzes and discusses the effectiveness of social efforts to achieve collective action amongst Internet network operators in order to manage the growth of the Internet routing table. The size and rate of growth ...

This document describes 19 MOOS-IvP autonomy tools. uHelmScope provides a run-time scoping window into the state of an active IvP Helm executing its mission. pMarineViewer is a geo-based GUI tool for rendering marine ...

This document describes the IvP Helm - an Open Source behavior-based autonomy application for unmanned vehicles. IvP is short for interval programming - a technique for representing and solving multi-objective optimizations ...

Recent trends in OS research have shown evidence that there are performance benefits to running OS services on different cores than the user applications that rely on them. We quantitatively evaluate this claim in terms ...

This paper presents a scalable information theoretic approach to infer the state of an environment by distributively controlling robots equipped with sensors. The robots iteratively estimate the environment state using a ...

Kernel methods are among the most popular techniques in machine learning. From a frequentist/discriminative perspective they play a central role in regularization theory as they provide a natural choice for the hypotheses ...