Tootfinder

Genus Zero Kashiwara-Vergne Solutions from Braids
Zsuzsanna Dancso, Iva Halacheva, Guillaume Laplante-Anfossi, Marcy Robertson, Chandan Singh
https://arxiv.org/abs/2507.16243

Genus Zero Kashiwara-Vergne Solutions from Braids
Using the language of moperads-monoids in the category of right modules over an operad-we reinterpret the Alekseev-Enriquez-Torossian construction of Kashiwara-Vergne (KV) solutions from associators. We show that any isomorphism between the moperad of parenthesized braids with a frozen strand and the moperad of chord diagrams gives rise to a family of genus zero KV solutions operadically generated by a single classical KV solution. We show that the Grothendieck-Teichmüller module groups act on…

Spatial Super-Infection and Co-Infection Dynamics in Networks
Alyssa Yu, Laura P. Schaposnik
https://arxiv.org/abs/2508.15740 https://arxiv.org/pdf/2508.15…

Spatial Super-Infection and Co-Infection Dynamics in Networks
Understanding interactions between the spread of multiple pathogens during an epidemic is crucial to assessing the severity of infections in human communities. In this paper, we introduce two new Multiplex Bi-Virus Reaction-Diffusion models (MBRD) on multiplex metapopulation networks: the super-infection model (MBRD-SI) and the co-infection model (MBRD-CI). These frameworks capture two-pathogen dynamics with spatial diffusion and cross-diffusion, allowing the prediction of infection clustering …

"If denial of collective self-determination of a people is a sin of the modern age, as Israeli defenders often repeat, what of the self-determination of Palestinians? "
The Most Important Book of 2025 | Portside
https://portside.org/2025-08-07/most-important-book-2025

🇺🇦 #NowPlaying on KEXP's #MiddayShow
The Flaming Lips:
🎵 Brainville
#TheFlamingLips
https://nonlips.bandcamp.com/track/brainville
https://open.spotify.com/track/4MfPridclsCChiN6ccmNkH

**Deadline Extended** (CFP: PAMLA 2025) Haunted Belonging: Memory, Erasure, and Identity in Diasporic Literatures
https://ift.tt/mVurDfg
updated: Tuesday, June 17, 2025 - 9:41amfull name / name of organization: Wenyuan Wang / Pacific…
via Input 4 RELCFP

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Alsharif Abuadbba, Chris Hicks, Kristen Moore, Vasilios Mavroudis, Burak Hasircioglu, Diksha Goel, Piers Jennings
https://arxiv.org/abs/2506.13434

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Large Language Models (LLMs) are set to reshape cybersecurity by augmenting red and blue team operations. Red teams can exploit LLMs to plan attacks, craft phishing content, simulate adversaries, and generate exploit code. Conversely, blue teams may deploy them for threat intelligence synthesis, root cause analysis, and streamlined documentation. This dual capability introduces both transformative potential and serious risks. This position paper maps LLM applications across cybersecurity fram…

MMBERT: Scaled Mixture-of-Experts Multimodal BERT for Robust Chinese Hate Speech Detection under Cloaking Perturbations
Qiyao Xue, Yuchen Dou, Ryan Shi, Xiang Lorraine Li, Wei Gao
https://arxiv.org/abs/2508.00760

MMBERT: Scaled Mixture-of-Experts Multimodal BERT for Robust Chinese Hate Speech Detection under Cloaking Perturbations
Hate speech detection on Chinese social networks presents distinct challenges, particularly due to the widespread use of cloaking techniques designed to evade conventional text-based detection systems. Although large language models (LLMs) have recently improved hate speech detection capabilities, the majority of existing work has concentrated on English datasets, with limited attention given to multimodal strategies in the Chinese context. In this study, we propose MMBERT, a novel BERT-based m…

A NICER view of the 1.4 solar-mass edge-on pulsar PSR J0614--3329
Lucien Mauviard, Sebastien Guillot, Tuomo Salmi, Devarshi Choudhury, Bas Dorsman, Denis Gonz\'alez-Caniulef, Mariska Hoogkamer, Daniela Huppenkothen, Christine Kazantsev, Yves Kini, Jean-Francois Olive, Pierre Stammler, Anna L. Watts, Melissa Mendes, Nathan Rutherford, Achim Schwenk, Isak Svensson, Slavko Bogdanov, Matthew Kerr, Paul S. Ray, Lucas Guillemot, Isma\"el Cognard, Gilles Theureau

A NICER view of the 1.4 solar-mass edge-on pulsar PSR J0614--3329
Four neutron star radius measurements have already been obtained by modeling the X-ray pulses of rotation-powered millisecond pulsars observed by the Neutron Star Interior Composition ExploreR (NICER). We report here the radius measurement of PSR J0614$-$3329 employing the same method with NICER and XMM-Newton data using Bayesian Inference. For all different models tested, including one with unrestricted inclination prior, we retrieve very similar non-antipodal hot regions geometries and radii.…

An Efficient Candidate-Free R-S Set Similarity Join Algorithm with the Filter-and-Verification Tree and MapReduce
Yuhong Feng, Fangcao Jian, Yixuan Cao, Xiaobin Jian, Jia Wang, Haiyue Feng, Chunyan Miao
https://arxiv.org/abs/2506.03893

An Efficient Candidate-Free R-S Set Similarity Join Algorithm with the Filter-and-Verification Tree and MapReduce
Given two different collections of sets, the exact set similarity R-S Join finds all set pairs with similarity no less than a given threshold, which has widespread applications. While existing algorithms accelerate large-scale R-S Joins using a two-stage filter-and-verification framework along with the parallel and distributed MapReduce framework, they suffer from excessive candidate set pairs, leading to significant I/O, data transfer, and verification overhead, and ultimately degrading the pe…

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Alsharif Abuadbba, Chris Hicks, Kristen Moore, Vasilios Mavroudis, Burak Hasircioglu, Diksha Goel, Piers Jennings
https://arxiv.org/abs/2506.13434

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Large Language Models (LLMs) are set to reshape cybersecurity by augmenting red and blue team operations. Red teams can exploit LLMs to plan attacks, craft phishing content, simulate adversaries, and generate exploit code. Conversely, blue teams may deploy them for threat intelligence synthesis, root cause analysis, and streamlined documentation. This dual capability introduces both transformative potential and serious risks. This position paper maps LLM applications across cybersecurity fram…