Tootfinder

TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning
Songze Li, Mingxuan Zhang, Oubo Ma, Kang Wei, Shouling Ji
https://arxiv.org/abs/2506.09562

TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning
Deep reinforcement learning (DRL) has achieved remarkable success in a wide range of sequential decision-making domains, including robotics, healthcare, smart grids, and finance. Recent research demonstrates that attackers can efficiently exploit system vulnerabilities during the training phase to execute backdoor attacks, producing malicious actions when specific trigger patterns are present in the state observations. However, most existing backdoor attacks rely primarily on simplistic and heu…

Nou daar zijn we dan, een jaar #vangaslos balansventilatie, luchtwarmtepomp voor het warme water, bodemwarmtepomp voor de verwarming. wat zijn de resultaten? Kijk zelf, de drie belangrijkste grafieken:
de electriciteitsstromen in het huishouden
de vergelijking van het energieverbruik voor warm water en verwarming in het laatste jaar gasgebruik en het eerste gasloze jaar
het cumu…

MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed
Jiaqi Samantha Zhan, Crystina Zhang, Shengyao Zhuang, Xueguang Ma, Jimmy Lin
https://arxiv.org/abs/2506.09409

MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed
Effective video retrieval remains challenging due to the complexity of integrating visual, auditory, and textual modalities. In this paper, we explore unified retrieval methods using OmniEmbed, a powerful multimodal embedding model from the Tevatron 2.0 toolkit, in the context of the MAGMaR shared task. Evaluated on the comprehensive MultiVENT 2.0 dataset, OmniEmbed generates unified embeddings for text, images, audio, and video, enabling robust multimodal retrieval. By finetuning OmniEmbed wit…

On the interplay between the BFKL resummation and high-energy factorization in Mueller-Navelet dijet production
A. A. Chernyshev, M. A. Nefedov, V. A. Saleev
https://arxiv.org/abs/2506.10458

On the interplay between the BFKL resummation and high-energy factorization in Mueller-Navelet dijet production
We study the interplay between the high-energy resummation based on the BFKL equation and initial state radiation effects, taken into account within the framework of the high-energy factorization, in the production of Mueller-Navelet dijets at hadron colliders. We use the solution of the NLO BFKL equation built out of eigenfunctions perturbatively constructed up to NLO to avoid the need for a special renormalization scale setting. We demonstrate that the various data sets from the FNAL Tevatron…

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Siouxsie and the Banshees:
🎵 Dear Prudence
#SiouxsieandtheBanshees
https://cleopatrarecords.bandcamp.com/track/dear-prudence-la-scaltra
https://open.spotify.com/track/34tFidHrETMAjsiK87WVVW

🇺🇦 #NowPlaying on #BBC6Music's #HuwStephens
Whitney:
🎵 Darling
#Whitney
https://tobydarling.bandcamp.com/track/whitneys-farewell
https://open.spotify.com/track/0WTyIytT4Yi0nHtWI29PVZ