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Shape matching has been a long-studied problem for the computer graphics and vision community. The objective is to predict a dense correspondence between meshes that have a certain degree of deformation. Existing methods either consider the…

Computer Vision and Pattern Recognition · Computer Science 2022-02-04 Mahdi Saleh , Shun-Cheng Wu , Luca Cosmo , Nassir Navab , Benjamin Busam , Federico Tombari

We suppose that overall strangeness production in both high energy elementary and heavy ion collisions can be described within the framework of an equilibrium statistical model in which the effective degrees of freedom are constituent…

High Energy Physics - Phenomenology · Physics 2009-11-07 F. Becattini , G. Pettini

The chameleon is a theorised scalar field that couples to matter and possess a screening mechanism, which weakens observational constraints from experiments performed in regions of higher matter density. One consequence of this screening…

General Relativity and Quantum Cosmology · Physics 2023-08-03 Chad Briddon , Clare Burrage , Adam Moss , Andrius Tamosiunas

We study string fragmentation in high multiplicity proton-proton collisions in a model where the string tension fluctuates. These fluctuations produce exponential pion spectra which are fitted to the transverse momentum distributions of…

High Energy Physics - Phenomenology · Physics 2020-06-30 H. J. Pirner , B. Z. Kopeliovich , K. Reygers

The systematics of strangeness enhancement is calculated using the HIJING and VENUS models and compared to recent data on $\,pp\,$, $\,pA\,$ and $\,AA\,$ collisions at CERN/SPS energies ($200A\,\, GeV\,$). The HIJING model is used to…

Nuclear Theory · Physics 2008-11-26 V. Topor Pop , M. Gyulassy , X. N. Wang , A. Andrighetto , M. Morando , F. Pellegrini , R. A. Ricci , G. Segato

Machine learning for the parameterization of subgrid-scale processes in climate models has been widely researched and adopted in a few models. A key challenge in developing data-driven parameterization schemes is how to properly represent…

Atmospheric and Oceanic Physics · Physics 2024-02-29 L. Minah Yang , Edwin P. Gerber

Tuning parameters is an important step for the application of metaheuristics to problem classes of interest. In this work we present a tuning framework based on the sequential optimization of perturbed regression models. Besides providing…

Neural and Evolutionary Computing · Computer Science 2019-12-02 Áthila R. Trindade , Felipe Campelo

Numerical simulations including magnetic fields have become important in many fields of astrophysics. Evolution of magnetic fields by the constrained transport algorithm preserves magnetic divergence to machine precision, and thus…

Astrophysics · Physics 2009-11-13 Jason Maron , Mordecai-Mark Mac Low , Jeffrey Oishi

We study the propagation of non-strange and strange meson modes in hadronic matter considering both isospin and strangeness mixings induced by quantum fluctuations in the medium. Baryons are described using the Quark Meson Coupling model…

Nuclear Theory · Physics 2009-11-10 R. M. Aguirre , A. L. De Paoli

Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system. How to choose the right tuning parameter is a fundamental and difficult problem in learning the sparse system. In this paper, by…

Methodology · Statistics 2019-01-18 Moo K. Chung , Jamie L. Hanson , Jieping Ye , Richard J. Davidson , Seth D. Pollak

We present recent results on the production, spectra and elliptic flow of strange particles in dynamic simulations employing hadronic degrees of freedom and from recombination models. The main focus will be on the Ultra-relativistic…

This talk is devoted to review the field of strangeness production in (ultra-)relativistic heavy ion collisions within our present theoretical understanding. Historically there have been (at least) three major ideas for the interest in the…

Nuclear Theory · Physics 2008-11-26 Carsten Greiner

Semiconductor superlattices are interesting for two distinct reasons: the possibility to design their structure (band-width(s),doping, etc.) gives access to a large parameter space where different physical phenomena can be explored.…

Condensed Matter · Physics 2022-09-21 A. P. Jauho , A. Wacker , A. A. Ignatov

Noise-assisted transport phenomena highlight the nontrivial interplay between environmental effects and quantum coherence in achieving maximal efficiency. Due to the complexity of biochemical systems and their environments, effective open…

In this work, we investigate an optimization problem over adapted couplings between pairs of real valued random variables, possibly describing random times. We relate those couplings to a specific class of causal transport plans between…

Probability · Mathematics 2022-10-18 Rémi Lassalle

To account for the randomness of propagation channels and interference levels in hierarchical spectrum sharing, a novel approach to multihop routing is introduced for cognitive random access networks, whereby packets are randomly routed…

Optimization and Control · Mathematics 2012-07-05 Emiliano Dall'Anese , Georgios B. Giannakis

Machine learning (ML) techniques have recently enabled enormous gains in sensitivity to new phenomena across the sciences. In particle physics, much of this progress has relied on excellent simulations of a wide range of physical processes.…

Data Analysis, Statistics and Probability · Physics 2025-10-20 Malte Algren , Tobias Golling , Francesco Armando Di Bello , Christopher Pollard

After a brief survey of the remarkable accomplishments of the current heavy ion collision experiments up to 200A GeV, we address in depth the role of strange particle production in the search for new phases of matter in these collisions. In…

Nuclear Theory · Physics 2021-05-24 Johann Rafelski , Jean Letessier , Ahmed Tounsi

Optical communication systems are always evolving to support the need for ever-increasing transmission rates. This demand is supported by the growth in complexity of communication systems which are moving towards ultra-wideband transmission…

We investigate the training of sparse layers that use different parameters for different inputs based on hashing in large Transformer models. Specifically, we modify the feedforward layer to hash to different sets of weights depending on…

Machine Learning · Computer Science 2021-07-21 Stephen Roller , Sainbayar Sukhbaatar , Arthur Szlam , Jason Weston