Tootfinder

A neural drift-plus-penalty algorithm for network power allocation and routing
Ahmed Rashwan, Keith Briggs, Chris Budd
https://arxiv.org/abs/2509.09637 https://

A neural drift-plus-penalty algorithm for network power allocation and routing
The drift-plus-penalty method is a Lyapunov optimisation technique commonly applied to network routing problems. It reduces the original stochastic planning task to a sequence of greedy optimizations, enabling the design of distributed routing algorithms which stabilize data queues while simultaneously optimizing a specified penalty function. While drift-plus-penalty methods have desirable asymptotic properties, they tend to incur higher network delay than alternative control methods, especiall…

Digital gazetteers: review and prospects for place name knowledge bases
Kalana Wijegunarathna, Kristin Stock, Christopher B. Jones
https://arxiv.org/abs/2507.08553

Digital gazetteers: review and prospects for place name knowledge bases
Gazetteers typically store data on place names, place types and the associated coordinates. They play an essential role in disambiguating place names in online geographical information retrieval systems for navigation and mapping, detecting and disambiguating place names in text, and providing coordinates. Currently there are many gazetteers in use derived from many sources, with no commonly accepted standard for encoding the data. Most gazetteers are also very limited in the extent to which th…

A Stable and Principled Loss Function for Direct Language Model Alignment
Yuandong Tan
https://arxiv.org/abs/2508.07137 https://arxiv.org/pdf/2508.07137

A Stable and Principled Loss Function for Direct Language Model Alignment
The alignment of large language models (LLMs) with human preferences is commonly achieved through Reinforcement Learning from Human Feedback (RLHF). Direct Preference Optimization (DPO) simplified this paradigm by establishing a direct mapping between the optimal policy and a reward function, eliminating the need for an explicit reward model. However, we argue that the DPO loss function is theoretically misaligned with its own derivation, as it promotes the indefinite maximization of a logits d…

The players I can't stop drafting in 2025 fantasy football... so far https://www.nytimes.com/athletic/6523703/2025/07/31/fantasy-football-2025-most-drafted-players-jake-ciely/

Assessing Universal Relations for Rapidly Rotating Neutron Stars: Insights from an Interpretable Deep Learning Perspective
Grigorios Papigkiotis, Georgios Vardakas, Nikolaos Stergioulas
https://arxiv.org/abs/2508.05850

Assessing Universal Relations for Rapidly Rotating Neutron Stars: Insights from an Interpretable Deep Learning Perspective
Relations between stellar properties independent of the nuclear equation of state offer profound insights into neutron star physics and have practical applications in data analysis. Commonly, these relations are derived from utilizing various realistic nuclear cold hadronic, hyperonic, and hybrid EoS models, each of which should obey the current constraints and cover a wide range of stiffnesses. Concurrently, the field of multimessenger astronomy has been significantly enhanced by the advent of…

eKalibr-Inertial: Continuous-Time Spatiotemporal Calibration for Event-Based Visual-Inertial Systems
Shuolong Chen, Xingxing Li, Liu Yuan
https://arxiv.org/abs/2509.05923 https:…

eKalibr-Inertial: Continuous-Time Spatiotemporal Calibration for Event-Based Visual-Inertial Systems
The bioinspired event camera, distinguished by its exceptional temporal resolution, high dynamic range, and low power consumption, has been extensively studied in recent years for motion estimation, robotic perception, and object detection. In ego-motion estimation, the visual-inertial setup is commonly adopted due to complementary characteristics between sensors (e.g., scale perception and low drift). For optimal event-based visual-inertial fusion, accurate spatiotemporal (extrinsic and tempor…

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
Dhruv S. Kushwaha, Zoleikha A. Biron
https://arxiv.org/abs/2508.09128 https://…

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
Reinforcement learning (RL) has proven to be particularly effective in solving complex decision-making problems for a wide range of applications. From a control theory perspective, RL can be considered as an adaptive optimal control scheme. Lyapunov and barrier functions are the most commonly used certificates to guarantee system stability for a proposed/derived controller and constraint satisfaction guarantees, respectively, in control theoretic approaches. However, compared to theoretical gua…

Data-driven generative simulation of SDEs using diffusion models
Xuefeng Gao, Jiale Zha, Xun Yu Zhou
https://arxiv.org/abs/2509.08731 https://arxiv.org/pdf…

Data-driven generative simulation of SDEs using diffusion models
This paper introduces a new approach to generating sample paths of unknown stochastic differential equations (SDEs) using diffusion models, a class of generative AI models commonly employed in image and video applications. Unlike the traditional Monte Carlo methods for simulating SDEs, which require explicit specifications of the drift and diffusion coefficients, our method takes a model-free, data-driven approach. Given a finite set of sample paths from an SDE, we utilize conditional diffusion…

Feasibility of Extracting Skin Nerve Activity from Electrocardiogram Recorded at A Low Sampling Frequency
Youngsun Kong, Farnoush Baghestani, I-Ping Chen, Ki Chon
https://arxiv.org/abs/2508.00494

Feasibility of Extracting Skin Nerve Activity from Electrocardiogram Recorded at A Low Sampling Frequency
Skin nerve activity (SKNA) derived from electrocardiogram (ECG) signals has been a promising non-invasive surrogate for accurate and effective assessment of the sympathetic nervous system (SNS). Typically, SKNA extraction requires a higher sampling frequency than the typical ECG recording requirement (> 2 kHz) because analysis tools extract SKNA from the 0.5-1 kHz frequency band. However, ECG recording systems commonly provide a sampling frequency of 1 kHz or lower, particularly for wearable de…

Nearly full-sky low-multipole CMB temperature anisotropy: III. CMB anomalies
Laura Herold, Graeme E. Addison, Charles L. Bennett, Hayley C. Nofi, J. L. Weiland
https://arxiv.org/abs/2509.03720

Nearly full-sky low-multipole CMB temperature anisotropy: III. CMB anomalies
Unexpected features have been observed in the cosmic microwave background (CMB) temperature on large scales. We revisit these CMB anomalies using new foreground-cleaned CMB temperature maps derived in a companion paper from WMAP and Planck data, which are tailored to low-resolution analysis and require only minimal masking of $1\%$ of the sky. These maps allow us to assess the impact of foreground-cleaning methods and the choice of sky cut on the significance of five commonly studied CMB anomal…