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🚧 Towards Modeling Road Access Deprivation in Sub-Saharan Africa Based on a New Accessibility Metric and Road Quality
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Towards Modeling Road Access Deprivation in Sub-Saharan Africa Based on a New Accessibility Metric and Road Quality
Access to motorable roads is a critical dimension of urban infrastructure, particularly in rapidly urbanizing regions such as Sub-Saharan Africa. Yet, many urban communities, especially those in informal settlements, remain disconnected from road networks. This study presents a road access deprivation model that combines a new accessibility metric, capturing how well buildings are connected to the road network, with road surface type data as a proxy for road quality. These two components togeth…

Modeling gamma-ray signatures of particle acceleration in stellar clusters from GeV to PeV
A. Inventar, S. Gabici, E. Peretti
https://arxiv.org/abs/2510.12562 https://

Modeling gamma-ray signatures of particle acceleration in stellar clusters from GeV to PeV
Young massive stellar clusters (YMSCs) have recently regained interest as PeVatron candidates, potentially accounting for the cosmic-ray (CR) knee as alternatives to isolated supernova remnants (SNRs). LHAASO's unique capability to detect photons above 0.1 PeV, hence tracing multi-PeV CRs, can provide critical constraints on galactic acceleration models when combined with H.E.S.S. and Fermi-LAT data. We investigate the transport of particles from YMSCs acceleration sites, namely wind terminatio…

DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
Yingyan Li, Shuyao Shang, Weisong Liu, Bing Zhan, Haochen Wang, Yuqi Wang, Yuntao Chen, Xiaoman Wang, Yasong An, Chufeng Tang, Lu Hou, Lue Fan, Zhaoxiang Zhang
https://arxiv.org/abs/2510.12796

DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
Scaling Vision-Language-Action (VLA) models on large-scale data offers a promising path to achieving a more generalized driving intelligence. However, VLA models are limited by a ``supervision deficit'': the vast model capacity is supervised by sparse, low-dimensional actions, leaving much of their representational power underutilized. To remedy this, we propose \textbf{DriveVLA-W0}, a training paradigm that employs world modeling to predict future images. This task generates a dense, self-supe…

Lowering Barriers to CAD Adoption: A Comparative Study of Augmented Reality-Based CAD (AR-CAD) and a Traditional CAD tool
Muhammad Talha, Abdullah Mohiuddin, Sehrish Javed, Ahmed Jawad Qureshi
https://arxiv.org/abs/2510.12146

Lowering Barriers to CAD Adoption: A Comparative Study of Augmented Reality-Based CAD (AR-CAD) and a Traditional CAD tool
The paper presents a comparative user study between an Augmented Reality-based Computer-Aided Design (AR-CAD) system and a traditional computer-based CAD modeling software, SolidWorks. Twenty participants of varying skill levels performed 3D modeling tasks using both systems. The results showed that while the average task completion time is comparable for both groups, novice designers had a higher completion rate in AR-CAD than in the traditional CAD interface, and experienced designers had a s…

Towards fully predictive gyrokinetic full-f simulations
A. C. D. Hoffmann, T. N. Bernard, M. Francisquez, G. W. Hammett, A. Hakim, J. Boedo, R. Rizkallah, C. K. Tsui, the TCV team
https://arxiv.org/abs/2510.11874

Towards fully predictive gyrokinetic full-f simulations
Designing economical magnetic confinement fusion power plants motivates computational tools that can estimate plasma behavior from engineering parameters without direct reliance on experimental measurement of the plasma profiles. In this work, we present full-$f$ global gyrokinetic (GK) turbulence simulations of edge and scrape-off layer turbulence in tokamaks that use only magnetic geometry, heating power, and particle inventory as inputs. Unlike many modeling approaches that employ free param…

Generalized Fokker-Planck equation for the active Brownian motion
Sanju S Pillai, M Muhsin, M Sahoo
https://arxiv.org/abs/2510.12240 https://arxiv.org/pdf/…

Generalized Fokker-Planck equation for the active Brownian motion
We investigate the dynamics of an inertial active Ornstein-Uhlenbeck particle suspended in a non-Markovian environment. The particle is additionally subjected to external forces, such as harmonic confinement and a magnetic field. Motivated by the importance of understanding the non-Markovian behavior of complex environments, we examine the impact of a viscoelastic medium by employing the Jeffrey fluid framework for modeling the particle motion, which effectively captures both viscous and elasti…

Modeling the TMD shape function in $J/\psi$ electroproduction
Miguel G. Echevarria, Raj Kishore, Samuel F. Romera
https://arxiv.org/abs/2510.11809 https://…

Modeling the TMD shape function in $J/ψ$ electroproduction
The next-to-leading order hard function for quarkonium electroproduction is calculated within the framework of transverse-momentum-dependent (TMD) factorization in the low-transverse-momentum regime. The structure of the TMD shape function in quarkonium leptoproduction is analyzed through its operator-level definition. Particular attention is given to the convolution of the unpolarized TMD gluon distribution with the TMD shape function, thereby illustrating the latter's phenomenological role. B…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/6]:
- Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks
Rizal Fathony, Igor Melnyk, Owen Reinert, Nam H. Nguyen, Daniele Rosa, C. Bayan Bruss

Reinforced Preference Optimization for Recommendation
Junfei Tan, Yuxin Chen, An Zhang, Junguang Jiang, Bin Liu, Ziru Xu, Han Zhu, Jian Xu, Bo Zheng, Xiang Wang
https://arxiv.org/abs/2510.12211

Reinforced Preference Optimization for Recommendation
Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is achieved by generating target items conditioned on historical interactions. Yet current generative recommenders still suffer from two core limitations: the lack of high-quality negative modeling and the reliance on implicit rewards. Reinforcement learning with verifiable rewards (RLVR) offers a natural solution by enabli…

A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning
Yang Xiang, Li Fan, Chenke Yin, Chengtao Ji
https://arxiv.org/abs/2510.12369 http…

A Hierarchical Quantized Tokenization Framework for Task-Adaptive Graph Representation Learning
Recent progress in language and vision foundation models demonstrates the importance of discrete token interfaces that transform complex inputs into compact sequences for large-scale modeling. Extending this paradigm to graphs requires a tokenization scheme that handles non-Euclidean structures and multi-scale dependencies efficiently. Existing approaches to graph tokenization, linearized, continuous, and quantized, remain limited in adaptability and efficiency. In particular, most current quan…