Tootfinder

🇺🇦 #NowPlaying on KEXP's #MorningShow
Röyksopp:
🎵 Senior Living
#Röyksopp
https://open.spotify.com/track/05qwbMIAzCcfYTCWjLdk7Z

Electromagnetic probes as signatures for a first-order QCD phase transition
Mohamad Lukman Aidid Mohd Yusoff, Norhasliza Yusof, Hasan Abu Kassim, Jan Steinheimer, Marcus Bleicher, Apiwit Kittiratpattana, Ayut Limphirat, Christoph Herold
https://arxiv.org/abs/2510.05565

Electromagnetic probes as signatures for a first-order QCD phase transition
We investigate dimuon production in the context of a first-order phase transition in QCD matter using a chiral fluid dynamics model. This approach incorporates non-equilibrium effects such as entropy production and reheating, which emerge during the dynamical evolution through a first-order phase transition. By comparing equilibrium and non-equilibrium scenarios across a range of beam energies ($\sqrt{s_{NN}}=2.2-6.2$~GeV), we analyze the resulting invariant mass spectra. Our results reveal a s…

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Yousef Emami, Seyedsina Nabavirazavi, Jingjing Zheng, Hao Zhou, Miguel Gutierrez Gaitan, Kai Li, Luis Almeida
https://arxiv.org/abs/2510.05698

Joint Communication Scheduling and Velocity Control for Multi-UAV-Assisted Post-Disaster Monitoring: An Attention-Based In-Context Learning Approach
Recently, Unmanned Aerial Vehicles (UAVs) are increasingly being investigated to collect sensory data in post-disaster monitoring scenarios, such as tsunamis, where early actions are critical to limit coastal damage. A major challenge is to design the data collection schedules and flight velocities, as unfavorable schedules and velocities can lead to transmission errors and buffer overflows of the ground sensors, ultimately resulting in significant packet loss. Meanwhile, online Deep Reinforcem…

A Kolmogorov-Arnold Network for Interpretable Cyberattack Detection in AGC Systems
Jehad Jilan, Niranjana Naveen Nambiar, Ahmad Mohammad Saber, Alok Paranjape, Amr Youssef, Deepa Kundur
https://arxiv.org/abs/2509.05259

Computational complexity of the homology problem with orientable filtration: MA-completeness
Ryu Hayakawa, Casper Gyurik, Mahtab Yaghubi Rad, Vedran Dunjko
https://arxiv.org/abs/2510.07014

Computational complexity of the homology problem with orientable filtration: MA-completeness
We show the existence of an MA-complete homology problem for a certain subclass of simplicial complexes. The problem is defined through a new concept of orientability of simplicial complexes that we call a "uniform orientable filtration", which is related to sign-problem freeness in homology. The containment in MA is achieved through the design of new, higher-order random walks on simplicial complexes associated with the filtration. For the MA-hardness, we design a new gadget with which we can …

Beyond More Context: How Granularity and Order Drive Code Completion Quality
Uswat Yusuf, Genevieve Caumartin, Diego Elias Costa
https://arxiv.org/abs/2510.06606 https://…

Beyond More Context: How Granularity and Order Drive Code Completion Quality
Context plays an important role in the quality of code completion, as Large Language Models (LLMs) require sufficient and relevant information to assist developers in code generation tasks. However, composing a relevant context for code completion poses challenges in large repositories: First, the limited context length of LLMs makes it impractical to include all repository files. Second, the quality of generated code is highly sensitive to noisy or irrelevant context. In this paper, we present…

Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments
Jiahui Yang, Jason Jingzhou Liu, Yulong Li, Youssef Khaky, Kenneth Shaw, Deepak Pathak
https://arxiv.org/abs/2509.06953

Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments
Generating collision-free motion in dynamic, partially observable environments is a fundamental challenge for robotic manipulators. Classical motion planners can compute globally optimal trajectories but require full environment knowledge and are typically too slow for dynamic scenes. Neural motion policies offer a promising alternative by operating in closed-loop directly on raw sensory inputs but often struggle to generalize in complex or dynamic settings. We propose Deep Reactive Policy (DRP…

Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis
Gholamali Aminian, Idan Shenfeld, Amir R. Asadi, Ahmad Beirami, Youssef Mroueh
https://arxiv.org/abs/2507.05913

Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis
A simple yet effective method for inference-time alignment of generative models is Best-of-$N$ (BoN), where $N$ outcomes are sampled from a reference policy, evaluated using a proxy reward model, and the highest-scoring one is selected. While prior work argues that BoN is almost optimal in reward vs KL tradeoffs, the effectiveness of BoN depends critically on the quality of the proxy reward model used for selection. For this purpose, we study BoN through a smooth version known as Soft Best-of-N…

🇺🇦 #NowPlaying on KEXP's #MorningShow
Quivers:
🎵 You're Not Always On My Mind
#Quivers
https://quiversss.bandcamp.com/album/youre-not-always-on-my-mind-when-it-breaks-me-in-honey-r-e-m-cover
https://open.spotify.com/track/08yZSZvNR0eYt8Bx8xkPmA

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- InstaGeo: Compute-Efficient Geospatial Machine Learning from Data to Deployment
Yusuf, Houndayi, Oualha, Cherif, Panford-Quainoo, Pretorius