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Kernel based learning is very popular in machine learning but often quite costly with at least quadratic runtime complexity. Random Fourier features and related techniques have been proposed to provide an explicit kernel expansion such that standard techniques with low runtime and memory complexity can be used. This strategy leads to rather high dimensional datasets which is a drawback in many cases...
Smart vision systems on a chip are promising for embedded applications. Currently, flexibility in the choice of integrated pre-processing tools is obtained at the expense of total silicon area and fill factor, which are otherwise optimized provided that the sensor performs a specific task. We propose a new architecture based on macropixel-level processing to improve the trade-off by using the same...
PIDs and PID Kernel Information, activities of the Research Data Alliance, have the potential to expand the utility and benefit of data provenance. The poster describes such expansion and outlines a study of the trade-offs of replacing the Research Object (RO) and OAI-ORE map solution of the SEAD publishing services with the PID Kernel Information approach.
Because of the rapid development of EtherCAT as the standard fieldbus in control and automation and the increasing demands of low cost portable main controllers, we aim to assess the performance of an EtherCAT network in handling real-time demands using open source EtherCAT master protocol on open hardware platforms. In comparison to that of commercial products, the performance of open platforms is...
The authors propose a variational level set image segmentation method for intensity inhomogeneous texture image. The method first extracts the main image structure by a relative total variation image decomposition method, which can better decompose the image into structural and textural parts. Then only uses the structural part as the input image for the variational level set segmentation. The intensity...
Infrared polarization imaging detection can be used to obtain not only the polarization state but also the radiation of target. With this method, the target that traditional photometry cannot detect can be settled. The degree and angle of polarization that used in polarization detection reflect different physical properties, and it is seriously redundancy along with intensity of images. A target detection...
To improve the adaptivity of drinking water treatment and stabilize the quality of treated water, raw water quality assessment based upon support vector machine (SVM) is developed in this study. Compared to existing raw water quality assessment methods, the assessment method studied herein can directly be used for the control of chemical (alum and ozone) dosing process of drinking water treatment...
This paper considers the modelling of scalar fields exhibiting non-stationary noise in the context of Gaussian Process (GP) regression. We show how a Heteroscedastic GP produces more accurate predictions of the variance of a process of this type compared to the standard Homoscedastic model. We present a parametric model for the noise process and derive analytical solutions to the Log Marginal Likelihood...
We develop a linear parameter-varying (LPV) spectral decomposition method, based on least-squares estimation and kernel expansions. Statistical properties of the estimator are analyzed and verified in simulations. The method is linear in the parameters, applicable to both the analysis and modeling problems and is demonstrated on both simulated signals as well as measurements of the torque in an electrical...
We consider two close ways of linearization for sublinear operator that takes compact convex values. The first way consists in a representation of given multioperator by the family of so called basis selectors that are single-valued linear bounded operators. The second way consists in linear extension of given multioperator from its values on some Hamel basis. Every of the ways above leads to its...
Regularization is used to find a solution that both fits the data and is sufficiently smooth, and thereby is very effective for designing and refining learning algorithms. But the influence of its exponent remains poorly understood. In particular, it is unclear how the exponent of the reproducing kernel Hilbert space (RKHS) regularization term affects the accuracy and the efficiency of kernel-based...
In this paper we describe a method for nonlinear class-specific discriminant learning that is based on Cholesky Decomposition. We show that the optimization problem solved in Class-Specific Kernel Discriminant Analysis is equivalent to that of Low-Rank Kernel Regression using training data independent target vectors. This connection allows us to devise a new Class-Specific Kernel Discriminant Analysis...
In this paper, we consider discrete-time dynamic games of the mean-field type with a finite number, N, of agents subject to an infinite-horizon discounted-cost optimality criterion. The state space of each agent is a locally compact Polish space. At each time, the agents are coupled through the empirical distribution of their states, which affects both the agents' individual costs and their state...
In this article, we address the problem of reproducibility of the blocked LU factorization on GPUs due to cancellations and rounding errors when dealing with floating-point arithmetic. Thanks to the hierarchical structure of linear algebra libraries, the computations carried within this operation can be expressed in terms of the Level-3 BLAS routines as well as the unblocked variant of the factorization,...
We compare the performance of pipelined and s-step GMRES, respectively referred to as l-GMRES and s-GMRES, on distributed multicore CPUs. Compared to standard GMRES, s-GMRES requires fewer all-reduces, while l-GMRES overlaps the all-reduces with computation. To combine the best features of two algorithms, we propose another variant, (l, t)-GMRES, that not only does fewer global all-reduces than standard...
Coalescent genealogy samplers are effective tools for the study of population genetics. They are used to estimate the historical parameters of a population based upon the sampling of present-day genetic information. A popular approach employs Markov chain Monte Carlo (MCMC) methods. While effective, these methods are very computationally intensive, often taking weeks to run. Although attempts have...
In this work, a new method for discrimination between normal and heart murmurs sound is presented. Statistical parameters, such as standard deviation (SD), are extracted from two datasets of heartbeats. Several classification technics, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), discriminative analysis, and classification tree, are used. Simulation results obtained...
Container technology has the potential to considerably simplify the management of the software stack of High Performance Computing (HPC) clusters. However, poor integration with established HPC technologies is still preventing users and administrators to reap the benefits of containers. Message Passing Interface (MPI) is a pervasive technology used to run scientific software, often written in Fortran...
Active scanning (AS) is a mechanism provided by the 802.11 standard by which WiFi clients proactively search for access points (APs) in their vicinity. To do so, a client sends out a probe request, in response to which all APs that hear the request send a probe response. Prior works have reported severe degradation in performance of WiFi networks due to excessive AS. In practice, a client triggers...
With increasing resolutions the volume of data generated by image processing applications is escalating dramatically. When coupled with real-time performance requirements, reducing energy con- sumption for such a large volume of data is proving challenging.
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