Publications associées à Distribution de Dirac

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

Polynomial-time universality and limitations of deep learning

Emmanuel Abbé

The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly-time ...

WILEY2023

Two-particle azimuthal correlations in γp interactions using pPb collisions at √sNN=8.16 TeV

Matthias Finger, Konstantin Androsov, Jan Steggemann, Qian Wang, Yiming Li, Varun Sharma, Xin Chen, Arvind Shah, Rakesh Chawla, Jian Wang

The first measurements of the Fourier coefficients (V-n Delta) of the azimuthal distributions of charged hadrons emitted from photon-proton (gamma p) interactions are presented. The data are extracted from 68.8nb(-1) of ultra-peripheral proton-lead (pPb) c ...

Elsevier2023

Incentive Mechanism in the Sponsored Content Market With Network Effects

Olga Fink, Mina Montazeri

We propose an incentive mechanism for the sponsored content provider (CP) market in which the communication of users can be represented by a graph, and the private information of the users is assumed to have a continuous distribution function. The CP stipu ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Thermal Evolution of Dirac Magnons in the Honeycomb Ferromagnet CrBr3

Andreas Martin Läuchli Herzig, Bruce Normand, Christian Rüegg

CrBr3 is an excellent realization of the two-dimensional honeycomb ferromagnet, which offers a bosonic equivalent of graphene with Dirac magnons and topological character. We perform inelastic neutron scattering measurements using state-of-the-art instrume ...

2022

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

Approximating delta invariants in the sense of plt complements

We show that delta invariant of a log Fano pair can be approximated by lc places of plt complements if it is no greater than one. Under the assumption that delta invariant (no greater than one) of a log Fano pair can be approximated by lc places of bounded ...

2022

Sparse non-negative super-resolution - simplified and stabilised

Armin Eftekhari, Hemant Tyagi

We consider the problem of non-negative super-resolution, which concerns reconstructing a non-negative signal x = Sigma(k )(i=1)a(i)delta(ti) from m samples of its convolution with a window function phi(s - t), of the form y(s(j)) = Sigma(k)(i=1) a(i) phi( ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021

SiML: Sieved Maximum Likelihood for Array Signal Processing

Paul Hurley, Matthieu Martin Jean-André Simeoni

Stochastic Maximum Likelihood (SML) is a popular direction of arrival (DOA) estimation technique in array signal processing. It is a parametric method that jointly estimates signal and instrument noise by maximum likelihood, achieving excellent statistical ...

2021

Experimental Observation and Spin Texture of Dirac Node Arcs in Tetradymite Topological Metals

Emmanouil Frantzeskakis, Ji Dai

We report the observation of a nontrivial spin texture in Dirac node arcs, i.e., novel topological objects formed when Dirac cones of massless particles extend along an open one-dimensional line in momentum space. We find that such states are present in al ...

AMER PHYSICAL SOC2021