Wigner-space synthesis of discrete-time periodic signals

A procedure that facilitates at least-squares synthesis of periodic, discrete-time signals from Wigner quasi-distributions is proposed. The scheme is based on expanding the desired time sequence on a generally nonorthogonal, Gabor-type basis whose associated biorthogonal function presumably exists. The specific basis selection may crucially affect the efficiency and quality of the ensuing synthesis procedure. The cited basis type constitutes a considerable generalization over the standardly used orthogonal variety, thus creating previously unavailable degrees of freedom. Of primary significance is the acquired capability of generating time-frequency basis functions that are well localized. Localization is a highly desirable property that can advantageously serve in various applications. It is shown and numerically demonstrated that benefits of localization as well as the fact that achieving effective time-frequency basis localization renders a certain degree of oversampling unavoidable     

Competitive learning and soft competition for vector quantizerdesign

The authors provide a convergence analysis for the Kohonen learning algorithm (KLA) with respect to vector quantizer (VQ) optimality criteria and introduce a stochastic relaxation technique which produces the global minimum but is computationally expensive. By incorporating the principles of the stochastic approach into the KLA, a deterministic VQ design algorithm, the soft competition scheme (SCS), is introduced. Experimental results are presented where the SCS consistently provided better codebooks than the generalized Lloyd algorithm (GLA), even when the same computation time was used for both algorithms. The SCS may therefore prove to be a valuable alternative to the GLA for VQ design     

ACAT inhibitors derived from hetero-Diels-Alder cycloadducts of thioaldehydes

Acyl-CoA:cholesterol acyltransferase (ACAT) is the enzyme largely responsible for intracellular cholesterol esterification. A systemic inhibitor of ACAT is believed to be able to slow or even reverse the atherosclerotic process. Towards that goal, a series of cyclic sulfides, derived from the hetero-Diels-Alder reaction of thioaldehydes with 1,3-dienes, and bearing carboxamide substituents, were prepared and evaluated for in vitro (in several tissues and species) and ex vivo ACAT inhibition. Minor changes in subsequent structure were found to have a significant effect in optimization of the biological activity of this series of compounds.     

Kernel machines for current status data

Machine Learning - In survival analysis, estimating the failure time distribution is an important and difficult task, since usually the data is subject to censoring. Specifically, in this paper we...     

Potassium sorbate solutions as copper chemical mechanical planarization (CMP) based slurries

Copper depassivation and repassivation characteristics in potassium sorbate solutions, subsequent to mechanical abrading are reported. The identification of copper repassivation kinetics obtained subsequent to mechanical damage of copper protective films formed in sorbate based solutions is discussed. The repassivation rate of copper in sorbate based solutions was measured by means of a slurryjet system capable of measuring single particle impingments on microelectrodes. Copper repassivation rates measured by this slurryjet system in sulfate solutions containing 10 g L⁻¹ potassium sorbate were found to be in the range of 0.5-1.5 ms. An increase in the potassium sorbate concentration leads to a decrease in copper repassivation time at potentials ranging from 200 to 600 mV_Ag/AgCl. The impingement angle between the copper surface and a single abrasive particle has no impact on copper repassivation time nor peak current (I_max) values. XPS studies revealed that copper passivation in potassium based solution was due to the formation of a thin film which is constituted of: Cu₂O, Cu(OH)₂ and Cu(II)-sorbate, while copper(II)-sorbate is mainly present at the top levels of the passive film. It is therefore recommended that the use of potassium sorbate as a passivating component in conjunction with the addition of strong oxidizing agents in chemical mechanical planarization (CMP) slurry design should be considered.     

Assignment Games with Conflicts: Robust Price of Anarchy and Convergence Results via Semi-Smoothness

We study assignment games in which jobs select machines, and in which certain pairs of jobs may conflict, which is to say they may incur an additional cost when they are both assigned to the same machine, beyond that associated with the increase in load. Questions regarding such interactions apply beyond allocating jobs to machines: when people in a social network choose to align themselves with a group or party, they typically do so based upon not only the inherent quality of that group, but also who amongst their friends (or enemies) chooses that group as well. We show how semi-smoothness, a recently introduced generalization of smoothness, is necessary to find tight bounds on the robust price of anarchy, and thus on the quality of correlated and Nash equilibria, for several natural job-assignment games with interacting jobs. For most cases, our bounds on the robust price of anarchy are either exactly 2 or approach 2. We also prove new convergence results implied by semi-smoothness for our games. Finally we consider coalitional deviations, and prove results about the existence and quality of strong equilibrium.     

Fusing distributional and experiential information for measuring semantic relatedness

Models of semantic relatedness have usually focused on language-based distributional information without taking into account “experiential data” concerning the embodied sensorial source of the represented concepts. In this paper, we present an integrative cognitive model of semantic relatedness. The model – semantic family resemblance – uses a variation of the co-product as a mathematical structure that guides the fusion of distributional and experiential information. Our algorithm provides superior results in a set expansion task and a significant correlation with two benchmarks of human rated word-pair similarity datasets.     

Steward: Scaling Byzantine Fault-Tolerant Replication to Wide Area Networks

This paper presents the first hierarchical Byzantine fault-tolerant replication architecture suitable to systems that span multiple wide-area sites. The architecture confines the effects of any malicious replica to its local site, reduces message complexity of wide-area communication, and allows read-only queries to be performed locally within a site for the price of additional standard hardware. We present proofs that our algorithm provides safety and liveness properties. A prototype implementation is evaluated over several network topologies and is compared with a flat Byzantine fault-tolerant approach. The experimental results show considerable improvement over flat Byzantine replication algorithms, bringing the performance of Byzantine replication closer to existing benign fault-tolerant replication techniques over wide area networks.     

User assisted separation of reflections from a single image using a sparsity prior

When we take a picture through transparent glass the image we obtain is often a linear superposition of two images: the image of the scene beyond the glass plus the image of the scene reflected by the glass. Decomposing the single input image into two images is a massively ill-posed problem: in the absence of additional knowledge about the scene being viewed there are an infinite number of valid decompositions. In this paper we focus on an easier problem: user assisted separation in which the user interactively labels a small number of gradients as belonging to one of the layers. Even given labels on part of the gradients, the problem is still ill-posed and additional prior knowledge is needed. Following recent results on the statistics of natural images we use a sparsity prior over derivative filters. This sparsity prior is optimized using the terative reweighted least squares (IRLS) approach. Our results show that using a prior derived from the statistics of natural images gives a far superior performance compared to a Gaussian prior and it enables good separations from a modest number of labeled gradients.