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Random graph generation is an important tool for studying large complex networks. Despite abundance of random graph models, constructing models with application-driven constraints is poorly understood. In order to advance state-of-the-art…

Data Structures and Algorithms · Computer Science 2018-01-01 Mohsen Bayati , Andrea Montanari , Amin Saberi

Event cameras produce asynchronous event streams that are spatially sparse yet temporally dense. Mainstream event representation learning algorithms typically use event frames, voxels, or tensors as input. Although these approaches have…

Computer Vision and Pattern Recognition · Computer Science 2025-11-27 Futian Wang , Fan Zhang , Xiao Wang , Mengqi Wang , Dexing Huang , Jin Tang

In these proceedings, we report on our progress in developing the $\texttt{history}$ framework, which aims to implement the fully-local Nested Soft-Collinear infrared subtraction scheme for the automated phase-space integration of…

High Energy Physics - Phenomenology · Physics 2025-12-01 Lukas Simon , Sven Yannick Klein

This paper discusses the latest generation of the MONARC (MOdels of Networked Analysis at Regional Centers) simulation framework, as a design and modelling tool for large scale distributed systems applied to HEP experiments. A…

Distributed, Parallel, and Cluster Computing · Computer Science 2011-06-28 Ciprian Dobre , Corina Stratan

The current and upcoming generation of Very Large Volume Neutrino Telescopes---collecting unprecedented quantities of neutrino events---can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the…

Data Analysis, Statistics and Probability · Physics 2019-12-05 IceCube Collaboration , M. G. Aartsen , M. Ackermann , J. Adams , J. A. Aguilar , M. Ahlers , M. Ahrens , I. Al Samarai , D. Altmann , K. Andeen , T. Anderson , I. Ansseau , G. Anton , C. Argüelles , T. C. Arlen , J. Auffenberg , S. Axani , H. Bagherpour , X. Bai , A. Balagopal V. , J. P. Barron , I. Bartos , S. W. Barwick , V. Baum , R. Bay , J. J. Beatty , J. Becker Tjus , K. -H. Becker , S. BenZvi , D. Berley , E. Bernardini , D. Z. Besson , G. Binder , D. Bindig , E. Blaufuss , S. Blot , C. Bohm , M. Bohmer , M. Börner , F. Bos , S. Böser , O. Botner , E. Bourbeau , J. Bourbeau , F. Bradascio , J. Braun , M. Brenzke , H. -P. Bretz , S. Bron , J. Brostean-Kaiser , A. Burgman , R. S. Busse , T. Carver , E. Cheung , D. Chirkin , A. Christov , K. Clark , L. Classen , G. H. Collin , J. M. Conrad , P. Coppin , P. Correa , D. F. Cowen , R. Cross , P. Dave , M. Day , J. P. A. M. de André , C. De Clercq , J. J. DeLaunay , H. Dembinski , S. De Ridder , P. Desiati , K. D. de Vries , G. de Wasseige , M. de With , T. DeYoung , J. C. Díaz-Vélez , V. di Lorenzo , H. Dujmovic , J. P. Dumm , M. Dunkman , M. A. DuVernois , E. Dvorak , B. Eberhardt , T. Ehrhardt , B. Eichmann , P. Eller , R. Engel , J. J. Evans , P. A. Evenson , S. Fahey , A. R. Fazely , J. Felde , K. Filimonov , C. Finley , S. Flis , A. Franckowiak , E. Friedman , A. Fritz , T. K. Gaisser , J. Gallagher , A. Gartner , L. Gerhardt , R. Gernhaeuser , K. Ghorbani , W. Giang , T. Glauch , T. Glüsenkamp , A. Goldschmidt , J. G. Gonzalez , D. Grant , Z. Griffith , C. Haack , A. Hallgren , F. Halzen , K. Hanson , J. Haugen , A. Haungs , D. Hebecker , D. Heereman , K. Helbing , R. Hellauer , F. Henningsen , S. Hickford , J. Hignight , G. C. Hill , K. D. Hoffman , B. Hoffmann , R. Hoffmann , T. Hoinka , B. Hokanson-Fasig , K. Holzapfel , K. Hoshina , F. Huang , M. Huber , T. Huber , T. Huege , K. Hultqvist , M. Hünnefeld , R. Hussain , S. In , N. Iovine , A. Ishihara , E. Jacobi , G. S. Japaridze , M. Jeong , K. Jero , B. J. P. Jones , P. Kalaczynski , O. Kalekin , W. Kang , D. Kang , A. Kappes , D. Kappesser , T. Karg , A. Karle , T. Katori , U. Katz , M. Kauer , A. Keivani , J. L. Kelley , A. Kheirandish , J. Kim , M. Kim , T. Kintscher , J. Kiryluk , T. Kittler , S. R. Klein , R. Koirala , H. Kolanoski , L. Köpke , C. Kopper , S. Kopper , J. P. Koschinsky , D. J. Koskinen , M. Kowalski , C. B. Krauss , K. Krings , M. Kroll , G. Krückl , S. Kunwar , N. Kurahashi , T. Kuwabara , A. Kyriacou , M. Labare , J. L. Lanfranchi , M. J. Larson , F. Lauber , D. Lennarz , K. Leonard , M. Lesiak-Bzdak , A. Leszczynska , M. Leuermann , Q. R. Liu , E. Lohfink , J. LoSecco , C. J. Lozano Mariscal , L. Lu , J. Lünemann , W. Luszczak , J. Madsen , G. Maggi , K. B. M. Mahn , S. Mancina , S. Mandalia , S. Marka , Z. Marka , R. Maruyama , K. Mase , R. Maunu , K. Meagher , M. Medici , M. Meier , T. Menne , G. Merino , T. Meures , S. Miarecki , J. Micallef , G. Momenté , T. Montaruli , R. W. Moore , M. Moulai , R. Nahnhauer , P. Nakarmi , U. Naumann , G. Neer , H. Niederhausen , S. C. Nowicki , D. R. Nygren , A. Obertacke Pollmann , M. Oehler , A. Olivas , A. O'Murchadha , E. O'Sullivan , A. Palazzo , T. Palczewski , H. Pandya , D. V. Pankova , L. Papp , P. Peiffer , J. A. Pepper , C. Pérez de los Heros , T. C. Petersen , D. Pieloth , E. Pinat , J. L. Pinfold , M. Plum , P. B. Price , G. T. Przybylski , C. Raab , L. Rädel , M. Rameez , L. Rauch , K. Rawlins , I. C. Rea , R. Reimann , B. Relethford , M. Relich , M. Renschler , E. Resconi , W. Rhode , M. Richman , M. Riegel , S. Robertson , M. Rongen , C. Rott , T. Ruhe , D. Ryckbosch , D. Rysewyk , I. Safa , T. Sälzer , S. E. Sanchez Herrera , A. Sandrock , J. Sandroos , P. Sandstrom , M. Santander , S. Sarkar , S. Sarkar , K. Satalecka , H. Schieler , P. Schlunder , T. Schmidt , A. Schneider , S. Schoenen , S. Schöneberg , F. G. Schröder , L. Schumacher , S. Sclafani , D. Seckel , S. Seunarine , M. H. Shaevitz , J. Soedingrekso , D. Soldin , S. Söldner-Rembold , M. Song , G. M. Spiczak , C. Spiering , J. Stachurska , M. Stamatikos , T. Stanev , A. Stasik , R. Stein , J. Stettner , A. Steuer , T. Stezelberger , R. G. Stokstad , A. Stößl , N. L. Strotjohann , T. Stuttard , G. W. Sullivan , M. Sutherland , I. Taboada , A. Taketa , H. K. M. Tanaka , J. Tatar , F. Tenholt , S. Ter-Antonyan , A. Terliuk , S. Tilav , P. A. Toale , M. N. Tobin , C. Tönnis , S. Toscano , D. Tosi , M. Tselengidou , C. F. Tung , A. Turcati , C. F. Turley , B. Ty , E. Unger , M. Usner , J. Vandenbroucke , W. Van Driessche , D. van Eijk , N. van Eijndhoven , S. Vanheule , J. van Santen , D. Veberic , E. Vogel , M. Vraeghe , C. Walck , A. Wallace , M. Wallraff , F. D. Wandler , N. Wandkowsky , A. Waza , C. Weaver , A. Weindl , M. J. Weiss , C. Wendt , J. Werthebach , S. Westerhoff , B. J. Whelan , K. Wiebe , C. H. Wiebusch , L. Wille , D. R. Williams , L. Wills , M. Wolf , J. Wood , T. R. Wood , E. Woolsey , K. Woschnagg , G. Wrede , S. Wren , D. L. Xu , X. W. Xu , Y. Xu , J. P. Yanez , G. Yodh , S. Yoshida , T. Yuan

We derive new discrete event simulation algorithms for marked time point processes. The main idea is to couple a special structure, namely the associated local independence graph, as defined by Didelez arXiv:0710.5874, with the activity…

Computation · Statistics 2021-03-05 Cyrille Mascart , Alexandre Muzy , Patricia Reynaud-bouret

We develop threshold resummation for single-particle inclusive cross sections in hadron-hadron collisions to the level of next-to-next-to-leading logarithm, up to full matching with two-loop hard functions. We define and calculate all…

High Energy Physics - Phenomenology · Physics 2019-04-03 Patriz Hinderer , Felix Ringer , George Sterman , Werner Vogelsang

Modern neutrino-nucleus cross section predictions need to incorporate sophisticated nuclear models to achieve greater predictive precision. However, the computational complexity of these advanced models often limits their practicality for…

High Energy Physics - Experiment · Physics 2025-05-19 Mathias El Baz , Federico Sánchez , Natalie Jachowicz , Kajetan Niewczas , Ashish Kumar Jha , Alexis Nikolakopoulos

This is the user's manual of MC@NLO 3.4. This package is a practical implementation, based upon the HERWIG event generator, of the MC@NLO formalism, which allows one to incorporate NLO QCD matrix elements consistently into a parton shower…

High Energy Physics - Phenomenology · Physics 2008-12-04 Stefano Frixione , Bryan R. Webber

We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate…

High Energy Physics - Phenomenology · Physics 2020-04-22 Christina Gao , Stefan Hoeche , Joshua Isaacson , Claudius Krause , Holger Schulz

We investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatiotemporally has many practical applications including crime prediction, social media analysis, and traffic forecasting. In…

Machine Learning · Computer Science 2021-02-17 Fatih Ilhan , Suleyman Serdar Kozat

Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains, typically by employing agentic workflows that follow detailed instructions and operational sequences. However, constructing…

Graph generation generally aims to create new graphs that closely align with a specific graph distribution. Existing works often implicitly capture this distribution through the optimization of generators, potentially overlooking the…

Machine Learning · Computer Science 2024-07-19 Song Wang , Zhen Tan , Xinyu Zhao , Tianlong Chen , Huan Liu , Jundong Li

Markov-chain Monte Carlo algorithms rely on trial moves that are either rejected or accepted based on certain criteria. Here, we provide an efficient algorithm to generate random rotation matrices in four dimensions (4D) covering an…

Computational Physics · Physics 2023-02-14 Jakob Tómas Bullerjahn , Balázs Fábián , Gerhard Hummer

We propose a method for matching the next-to-leading order (NLO) calculation of a given QCD process with a parton shower Monte Carlo (MC) simulation. The method has the following features: fully exclusive events are generated, with…

High Energy Physics - Phenomenology · Physics 2010-11-11 S. Frixione , B. R. Webber

We propose a methodology for generating time-dependent turbulent inflow data with the aid of machine learning (ML), which has a possibility to replace conventional driver simulations or synthetic turbulent inflow generators. As for the ML…

Fluid Dynamics · Physics 2019-06-19 Kai Fukami , Yusuke Nabae , Ken Kawai , Koji Fukagata

In this paper the current release of the Monte Carlo event generator Sherpa, version 1.1, is presented. Sherpa is a general-purpose tool for the simulation of particle collisions at high-energy colliders. It contains a very flexible…

High Energy Physics - Phenomenology · Physics 2009-02-12 T. Gleisberg , S. Hoeche , F. Krauss , M. Schoenherr , S. Schumann , F. Siegert , J. Winter

In the electric system, extreme weather events can cause trips or physical damage to transmission lines, leading to large-scale load shedding. To mitigate power shedding, we propose a framework that pre-positions the commitment of…

Optimization and Control · Mathematics 2026-04-07 Yongzheng Dai , Antonio J. Conejo , Feng Qiu

We describe a new parallel approach to the evaluation of phase space for Monte-Carlo event generation, implemented within the framework of the WHIZARD package. The program realizes a twofold self-adaptive multi-channel parameterization of…

High Energy Physics - Phenomenology · Physics 2019-04-18 Simon Braß , Wolfgang Kilian , Jürgen Reuter

The automated generation of arbitrary exclusive final states produced via photon fusion in ultraperipheral high-energy collisions of protons and/or nuclei is implemented in the MadGraph5_aMC@NLO and HelacOnia Monte Carlo codes. Cross…

High Energy Physics - Phenomenology · Physics 2022-10-19 Hua-Sheng Shao , David d'Enterria
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