Publications associées à Gather/scatter (vector addressing)

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

Modern data management systems aim to provide both cutting-edge functionality and hardware efficiency. With the advent of AI-driven data processing and the post-Moore Law era, traditional memory-bound scale-up data management operations face scalability ch ...

2023

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Parviz Ghaderi

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of dens ...

FRONTIERS MEDIA SA2022

Variably Scaled Kernels Improve Classification of Hormonally-Treated Patient-Derived Xenografts

Cathrin Brisken, Fabio De Martino, Marie Shamseddin, Francesco Marchetti

Little is known about the biological functions which are exerted by hormone receptors in physiological conditions. Here, we made use of the Mouse INtraDuctal (MIND) model, an innovative patient-derived xenograft (PDX) model, to characterize global gene exp ...

IEEE2020

Exploring The Borderlands Of The Gathering Problem

Rachid Guerraoui, El Mahdi El Mhamdi, Alexandre David Olivier Maurer, Vladislav Tempez

Problems of pattern formation have been extensively studied in distributed computing. One of this problems is the gathering problem: agents must gather at a same position in a distributed manner. When gathering is not possible, a close problem is the conve ...

2019

The Disclosure Power of Shared Objects

Rachid Guerraoui, Mihail Igor Zablotchi, Julien Michel Stainer, Peva François Blanchard

Shared objects are the means by which processes gather and exchange information about the state of a distributed system. Objects that disclose more information about the system are therefore more desirable. In this paper, we propose the schedule reconstruc ...

2017

Modified group delay feature based total variability space modelling for speaker recognition

In this paper, modified group delay (MODGD) features are used to model target speakers in the Total Variability Space (TVS) framework for speaker recognition. MODGD based features have been shown to improve speaker recognition performance owing to the abil ...

2015

Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing

David Atienza Alonso, Martino Ruggiero, Karim Kanoun

In the last years the process of examining large amounts of different types of data, or Big-Data, in an effort to uncover hidden patterns or unknown correlations has become a major need in our society. In this context, stream mining applications are now wi ...

IEEE Press2014

Example-based Support Vector Machine for Drug Concentration Analysis

Giovanni De Micheli, Wenqi You Dubout

Machine learning has been largely applied to ana- lyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

On-line independent support vector machines

Barbara Caputo

Support vector machines (SVMs) are one of the most successful algorithms for classification. However, due to their space and time requirements, they are not suitable for on-line learning, that is, when presented with an endless stream of training observati ...

2010

Computers in Cardiology / Physionet Challenge 2009: Predicting Acute Hypotensive Episodes

Jean-Marc Vesin, Florian Jousset, Mathieu Lemay

The goal of the Computers in Cardiology / Physionet Challenge 2009 is to predict which patients will experience acute hypotensive episode within a forecast window of one hour. In our study, statistically robust features extracted from the supplied training ...

2009

Prior knowledge in Kernel methods

Alexei Pozdnoukhov

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical task ...

EPFL2006