Search results for "LIBRARY"

A novel approach to integration by parts reduction

2015

Integration by parts reduction is a standard component of most modern multi-loop calculations in quantum field theory. We present a novel strategy constructed to overcome the limitations of currently available reduction programs based on Laporta's algorithm. The key idea is to construct algebraic identities from numerical samples obtained from reductions over finite fields. We expect the method to be highly amenable to parallelization, show a low memory footprint during the reduction step, and allow for significantly better run-times.

Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform

2012

Motivation The Burrows-Wheeler transform (BWT) is the foundation of many algorithms for compression and indexing of text data, but the cost of computing the BWT of very large string collections has prevented these techniques from being widely applied to the large sets of sequences often encountered as the outcome of DNA sequencing experiments. In previous work, we presented a novel algorithm that allows the BWT of human genome scale data to be computed on very moderate hardware, thus enabling us to investigate the BWT as a tool for the compression of such datasets. Results We first used simulated reads to explore the relationship between the level of compression and the error rate, the leng…

The FLUXCOM ensemble of global land-atmosphere energy fluxes

2019

Although a key driver of Earth’s climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat and their uncertainties. The resulting FLUXCOM database comprises 147 products in two setups: (1) 0.0833° resolution using MODIS remote sensing data (RS) and (2) 0.5° resolution using remote sensing and meteorological data (RS + METEO). Within each setup we use a full factorial design across machine learning methods, forcing datasets and energy balance closure corrections. For…

The DMT of Real and Quaternionic Lattice Codes and DMT Classification of Division Algebra Codes

2021

In this paper we consider the diversity-multiplexing gain tradeoff (DMT) of so-called minimum delay asymmetric space-time codes. Such codes are less than full dimensional lattices in their natural ambient space. Apart from the multiple input single output (MISO) channel there exist very few methods to analyze the DMT of such codes. Further, apart from the MISO case, no DMT optimal asymmetric codes are known. We first discuss previous criteria used to analyze the DMT of space-time codes and comment on why these methods fail when applied to asymmetric codes. We then consider two special classes of asymmetric codes where the code-words are restricted to either real or quaternion matrices. We p…

Appropriation et usages des TIC chez des " leaders " politiques en France et en Grande-Bretagne : pratiques et discours

2011

Facebook as a 2.0 ecological educational tool

2014

International audience

How, Why and with Whom Do Local Politicians Engage on Facebook?

2013

Part 2: Social Media and E-Participation; International audience; This article focuses on how, why and with whom local politicians engage on Facebook. Based on a literature review of the public sphere, eParticipation and research related to social media, we propose a theoretical framework that identifies thematic areas integral to understanding the nature of political participation. The explanatory potential of our ‘ENGAGE’ model (Exchange, Narcissist, Gather, Accented, General and Expense) is exemplified by conducting a qualitative case study focusing on politicians in a local municipality in southern Norway. The findings indicate various uses of Facebook among the respondents, and a disso…

Exprimer ses émotions en ligne : l'exemple des commentaires sur Facebook

2021

International audience

Fair Pairwise Learning to Rank

2020

Ranking algorithms based on Neural Networks have been a topic of recent research. Ranking is employed in everyday applications like product recommendations, search results, or even in finding good candidates for hiring. However, Neural Networks are mostly opaque tools, and it is hard to evaluate why a specific candidate, for instance, was not considered. Therefore, for neural-based ranking methods to be trustworthy, it is crucial to guarantee that the outcome is fair and that the decisions are not discriminating people according to sensitive attributes such as gender, sexual orientation, or ethnicity.In this work we present a family of fair pairwise learning to rank approaches based on Neur…

Avoiding strange attractors in efficient parametric families of iterative methods for solving nonlinear problems

2019

[EN] Searching zeros of nonlinear functions often employs iterative procedures. In this paper, we construct several families of iterative methods with memory from one without memory, that is, we have increased the order of convergence without adding new functional evaluations. The main aim of this manuscript yields in the advantage that the use of real multidimensional dynamics gives us to decide among the different classes designed and, afterwards, to select its most stable members. Moreover, we have found some elements of the family whose behavior includes strange attractors of different kinds that must be avoided in practice. In this sense, Feigenbaum diagrams have resulted an extremely …