Tootfinder

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…

Spirits in the Stacks: A Ghostly Tour at the Congregational Library & Archives
https://ift.tt/mb4RdeS
ANN: New Book on Matriculture, Shamanism, and the Authority of Women: The Powers That Be Linnéa…
via Input 4 RELCFP

Dynamics of Infection Spread and Hotspot Growth in Bi-Pathogen Networks
Alyssa Yu, Laura P. Schaposnik
https://arxiv.org/abs/2509.03374 https://arxiv.org/p…

Dynamics of Infection Spread and Hotspot Growth in Bi-Pathogen Networks
Understanding the spatio-temporal evolution of epidemics with multiple pathogens requires not only new theoretical models but also careful analysis of their practical consequences. Building on the Multiplex Bi-Virus Reaction-Diffusion framework (MBRD) introduced in our companion paper, we investigate how the super-infection model (MBRD-SI) and the co-infection model (MBRD-CI) behave under different epidemiological and network conditions. Through numerical experiments, we study the effects of pa…

Ah, ook binnen het CDA voldoende (te veel) Christofascisten. Maar laf, want anoniem.
Bah.
https://www.rtl.nl/nieuws/politiek/artikel/5516729/cda-asielwetten-kritiek-tegenstem-prominenten-fout-tweede-kamer

Gemor binnen CDA over tegenstem Tweede Kamerfractie over asielwetten
In het CDA klinkt gemor over het besluit van de CDA-Tweede Kamerfractie om tegen de wetten te stemmen die het asielbeleid strenger maken. "Een domme fout", moppert een voormalig CDA-bewindspersoon.

On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
Alberto Caron, Chris Hicks, Vasilios Mavroudis
https://arxiv.org/abs/2507.02639 htt…

On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
In this work, we address the challenge of data-efficient exploration in reinforcement learning by examining existing principled, information-theoretic approaches to intrinsic motivation. Specifically, we focus on a class of exploration bonuses that targets epistemic uncertainty rather than the aleatoric noise inherent in the environment. We prove that these bonuses naturally signal epistemic information gains and converge to zero once the agent becomes sufficiently certain about the environment…

Analysis of Publicly Accessible Operational Technology and Associated Risks
Matthew Rodda, Vasilios Mavroudis
https://arxiv.org/abs/2508.02375 https://arxi…

Analysis of Publicly Accessible Operational Technology and Associated Risks
Operational Technology (OT) is an integral component of critical national infrastructure, enabling automation and control in industries such as energy, manufacturing, and transportation. However, OT networks, systems, and devices have been designed and deployed prioritising functionality rather than security. This leads to inherent vulnerabilities in many deployed systems when operational misconfigurations expose them to the internet. This report provides an up-to-date overview of the OT threat…

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
Rifki Afina Putri
https://arxiv.org/abs/2507.01645

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
In this paper, we investigate the transferability of pre-trained language models to low-resource Indonesian local languages through the task of sentiment analysis. We evaluate both zero-shot performance and adapter-based transfer on ten local languages using models of different types: a monolingual Indonesian BERT, multilingual models such as mBERT and XLM-R, and a modular adapter-based approach called MAD-X. To better understand model behavior, we group the target languages into three categori…

Deadly 'Wet-Bulb' Temperatures Are Smothering the Eastern U.S.
https://gizmodo.com/deadly-wet-bulb-temperatures-are-smothering-the-eastern-u-s-2000636294

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…

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Shae McFadden, Myles Foley, Mario D'Onghia, Chris Hicks, Vasilios Mavroudis, Nicola Paoletti, Fabio Pierazzi
https://arxiv.org/abs/2508.18839

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanisms, struggle to maintain performance under concept drift in malware domains, as their supervised learning formulation cannot optimize when to defer decisions to manual labeling and adaptation. Modern malware detection pipelines combine classifiers with monthly active learning (AL) and rejection mechanisms to mitigate t…