Tootfinder

This https://arxiv.org/abs/2404.03004 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Validation of the DESI 2024 Ly$α$ forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$α$ (Ly$α$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$α$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields usin…

Selecting active matter according to motility in an acoustofluidic setup: Self-propelled particles and sperm cells
V. R. Misko, L. Baraban, D. Makarov, T. Huang, P. Gelin, I. Mateizel, K. Wouters, N. De Munck, F. Nori, W. De Malsche
https://arxiv.org/abs/2404.05422

Selecting active matter according to motility in an acoustofluidic setup: Self-propelled particles and sperm cells
Active systems -- including sperm cells, living organisms like bacteria, fish, birds, or active soft matter systems like synthetic ''microswimmers'' -- are characterized by motility, i.e., the ability to propel using their own ''engine''. Motility is the key feature that distinguishes active systems from passive or externally driven systems. In a large ensemble, motility of individual species can vary in a wide range. Selecting active species according to their motility represents an exciting a…

Validation of the DESI 2024 Ly$\alpha$ forest BAO analysis using synthetic datasets
Andrei Cuceu, Hiram K. Herrera-Alcantar, Calum Gordon, Paul Martini, Julien Guy, Andreu Font-Ribera, Alma X. Gonzalez-Morales, M. Abdul Karim, J. Aguilar, S. Ahlen, E. Armengaud, A. Bault, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doel, K. Fanning, S. Ferraro, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, N. G. Kara\c{c}ayl{\i}, D. Kirkby, A. Krem…

Validation of the DESI 2024 Ly$α$ forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$α$ (Ly$α$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$α$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields usin…

Validation of the DESI 2024 Ly$\alpha$ forest BAO analysis using synthetic datasets
Andrei Cuceu, Hiram K. Herrera-Alcantar, Calum Gordon, Paul Martini, Julien Guy, Andreu Font-Ribera, Alma X. Gonzalez-Morales, M. Abdul Karim, J. Aguilar, S. Ahlen, E. Armengaud, A. Bault, D. Brooks, T. Claybaugh, A. de la Macorra, P. Doel, K. Fanning, S. Ferraro, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, N. G. Kara\c{c}ayl{\i}, D. Kirkby, A. Krem…

Validation of the DESI 2024 Ly$α$ forest BAO analysis using synthetic datasets
The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-$α$ (Ly$α$) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift $z=2.33$. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Ly$α$ forest BAO measurement. The synthetic data sets are based on Gaussian random fields usin…

This https://arxiv.org/abs/2402.15945 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management
This paper proposes an innovative Attention-GAN framework for enhancing cybersecurity, focusing on anomaly detection. In response to the challenges posed by the constantly evolving nature of cyber threats, the proposed approach aims to generate diverse and realistic synthetic attack scenarios, thereby enriching the dataset and improving threat identification. Integrating attention mechanisms with Generative Adversarial Networks (GANs) is a key feature of the proposed method. The attention mecha…

Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management
Mohammed Abo Sen
https://arxiv.org/abs/2402.15945 https://<…

Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management
This paper proposes an innovative Attention-GAN framework for enhancing cybersecurity, focusing on anomaly detection. In response to the challenges posed by the constantly evolving nature of cyber threats, the proposed approach aims to generate diverse and realistic synthetic attack scenarios, thereby enriching the dataset and improving threat identification. Integrating attention mechanisms with Generative Adversarial Networks (GANs) is a key feature of the proposed method. The attention mecha…

Non-reciprocal alignment induces asymmetric clustering in active repulsive mixtures
Kim L. Kreienkamp, Sabine H. L. Klapp
https://arxiv.org/abs/2403.19291 …

Non-reciprocal alignment induces asymmetric clustering in active repulsive mixtures
Heterogeneity is a ubiquitous feature in many biological and synthetic active matter systems that are inherently out of equilibrium. In addition to conservative interactions between active constituents, a non-equilibrium environment often induces effective non-reciprocal (NR) couplings. The full consequences, especially for systems with order parameters of different symmetries, still remain elusive. Here, we study a minimal active NR mixture exhibiting both, polar ordering and clustering using …