Publications related to Mesure des paramètres macroscopiques de réseaux uranium-eau légère

Stable parameterization of continuous and piecewise-linear functions

Michaël Unser, Alexis Marie Frederic Goujon, Joaquim Gonçalves Garcia Barreto Campos

Rectified-linear-unit (ReLU) neural networks, which play a prominent role in deep learning, generate continuous and piecewise-linear (CPWL) functions. While they provide a powerful parametric representation, the mapping between the parameter and function s ...

2023

Memoryless Worker-Task Assignment with Polylogarithmic Switching Cost

Adam Teodor Polak

We study the basic problem of assigning memoryless workers to tasks with dynamically changing demands. Given a set of w workers and a multiset T ⊆ [t] of |T| = w tasks, a memoryless worker-task assignment function is any function ϕ that assigns the workers ...

Schloss Dagstuhl -- Leibniz-Zentrum fur Informatik2022

A New Channel-Base Lightning Current Formula With Analytically Adjustable Parameters

Farhad Rachidi-Haeri

The article provides a new channel-based current function, which aims at faithfully reproducing the salient parameters of lightning currents. All the model parameters can be set in a fully analytical way, guaranteeing the possibility of changing them (acco ...

2021

New insights on Drosophila antimicrobial peptide function in host defense and beyond

Bruno Lemaitre, Mark Austin Hanson

Since the first animal antimicrobial peptides (AMPs) were discovered in insects, Drosophila melanogaster has emerged as a powerful model for their characterization. Drosophila AMPs have been used extensively to monitor the activity of the Toll and Imd NF-κ ...

2019

Pairs of majority-decomposing functions

Giovanni De Micheli, Mathias Soeken, Eleonora Testa

We are interested in decompositions xnf1f2 of the majority function over n odd arguments x1, ...,xnsuch that f1and f2do not depend on xn. In this paper, we derive the conditions for f1and f2that satisfy the decomposition. Such decompositions play a centr ...

2018

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function

u

in multivariate polynomial spaces

P_\Lambda:=span\{y\mapsto y^\nu \,: \, \nu\in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma:=[-1,1]^d

, based on the s ...

2017

BDD Minimization for Approximate Computing

Mathias Soeken, Arun Chandrasekharan

We present Approximate BDD Minimization (ABM) as a problem that has application in approximate computing. Given a BDD representation of a multi-output Boolean function, ABM asks whether there exists another function that has a smaller BDD representa ...

IEEE2016

PostScript in IPython notebooks

Duncan Montana Forel

A python module containing the functionnalities for displaying LaTeX and PostScript code in an IPython notebook. ...

2016

Angular analysis and differential branching fraction of the decay B-s(0) -> phi mu(+)mu(-)

Jian Wang, Olivier Schneider, Yiming Li, Yi Zhang, Aurelio Bay, Guido Haefeli, Christoph Frei, Frédéric Blanc, Tatsuya Nakada, Michel De Cian, Luca Pescatore, Elena Graverini, Chitsanu Khurewathanakul, Renato Quagliani, Maria Vieites Diaz, Alessandro Mapelli, Zhirui Xu, Lei Zhang, Jessica Prisciandaro, Mark Tobin, Minh Tâm Tran, Niko Neufeld, Matthew Needham, Marc-Olivier Bettler, Greig Alan Cowan, Maurizio Martinelli, Donal Patrick Hill, Cédric Potterat, Liang Sun, Pietro Marino, Mirco Dorigo, Xiaoxue Han, Sebastiana Gianì, Liupan An, Federico Leo Redi, Ilya Komarov, Thibaud Humair, Maxime Schubiger, Guido Andreassi, Bastien Luca Muster, Violaine Bellée, Frédéric Guillaume Dupertuis, Julien Rouvinet, Olivier Göran Girard, Axel Kuonen, Hans Dijkstra, Gerhard Raven, Peter Clarke, Frédéric Teubert, Giovanni Carboni, Victor Coco, Adam Davis, Paolo Durante, Luis Alberto Granado Cardoso

An angular analysis and a measurement of the differential branching fraction of the decay B-s(0) -> phi mu(+)mu(-) are presented, using data corresponding to an integrated luminosity of 3.0 fb(-1) of pp collisions recorded by the LHCb experiment at root s ...

Springer2015

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function u in multivariate polynomial spaces

P_\Lambda := span\{y \mapsto y^\nu | \nu \in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma := [-1,1]^d

, based on the sa ...

MATHICSE2015

Mesure des paramètres macroscopiques de réseaux uranium-eau légère

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Stable parameterization of continuous and piecewise-linear functions

Memoryless Worker-Task Assignment with Polylogarithmic Switching Cost

A New Channel-Base Lightning Current Formula With Analytically Adjustable Parameters

New insights on Drosophila antimicrobial peptide function in host defense and beyond

Pairs of majority-decomposing functions

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

BDD Minimization for Approximate Computing

PostScript in IPython notebooks

Angular analysis and differential branching fraction of the decay B-s(0) -> phi mu(+)mu(-)

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Stable parameterization of continuous and piecewise-linear functions

Angular analysis and differential branching fraction of the decay B-s(0) -> phi mu(+)mu(-)

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Memoryless Worker-Task Assignment with Polylogarithmic Switching Cost

New insights on Drosophila antimicrobial peptide function in host defense and beyond

PostScript in IPython notebooks

Pairs of majority-decomposing functions

A New Channel-Base Lightning Current Formula With Analytically Adjustable Parameters

Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

BDD Minimization for Approximate Computing