Tootfinder

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…

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…

Gini-based Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
Alexej Brauer, Paul Menzel
https://arxiv.org/abs/2510.04556 https://

Gini-based Model Monitoring: A General Framework with an Application to Non-life Insurance Pricing
In a dynamic landscape where portfolios and environments evolve, maintaining the accuracy of pricing models is critical. To the best of our knowledge, this is the first study to systematically examine concept drift in non-life insurance pricing. We (i) provide an overview of the relevant literature and commonly used methodologies, clarify the distinction between virtual drift and concept drift, and explain their implications for long-run model performance; (ii) review and formalize common perfo…

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…

Determining the Density of In-gap States in Organic Semiconductors: A Pitfall of Photoelectron Yield Spectroscopy
Ryotaro Nakazawa, Masaya Kitaoka, Ryota Kaimori, Manato Tateno, Runa Hoshikawa, Yuya Tanaka, Hisao Ishii
https://arxiv.org/abs/2510.00865

Determining the Density of In-gap States in Organic Semiconductors: A Pitfall of Photoelectron Yield Spectroscopy
Accurate determination of electronic states with low density in the bandgap (in-gap states) is crucial for understanding and optimizing the performance of organic optoelectronic devices. In the device research field, photoelectron yield spectroscopy (PYS) has been widely used to evaluate such states, and its derivative is commonly employed to estimate the density of states (DOS). However, low-energy photons in PYS can generate excitons and anions in organic semiconductors, raising questions abo…

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…

DSRAG: A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph
Mengzheng Yang, Yanfei Ren, David Osei Opoku, Ruochang Li, Peng Ren, Chunxiao Xing
https://arxiv.org/abs/2509.10467

DSRAG: A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph
Current general-purpose large language models (LLMs) commonly exhibit knowledge hallucination and insufficient domain-specific adaptability in domain-specific tasks, limiting their effectiveness in specialized question answering scenarios. Retrieval-augmented generation (RAG) effectively tackles these challenges by integrating external knowledge to enhance accuracy and relevance. However, traditional RAG still faces limitations in domain knowledge accuracy and context modeling.To enhance domain…

Operator Analysis of MACD
Yuelong Li
https://arxiv.org/abs/2509.21326 https://arxiv.org/pdf/2509.21326

Operator Analysis of MACD
This paper develops a rigorous functional-analytic framework for the MACD (Moving Average Convergence Divergence) indicator, a classical tool in technical analysis. We show that MACD, commonly defined as the difference between two moving averages, can be precisely interpreted as a phase-corrected, smoothed derivative operator. By analyzing nested and recursive moving averages, we establish that MACD is structurally equivalent to a band-pass filter and derive exact formulas expressing it as a fi…

Measures of Overlapping Multivariate Gaussian Clusters in Unsupervised Online Learning
Miha O\v{z}bot, Igor \v{S}krjanc
https://arxiv.org/abs/2508.15444 https://

Measures of Overlapping Multivariate Gaussian Clusters in Unsupervised Online Learning
In this paper, we propose a new measure for detecting overlap in multivariate Gaussian clusters. The aim of online learning from data streams is to create clustering, classification, or regression models that can adapt over time based on the conceptual drift of streaming data. In the case of clustering, this can result in a large number of clusters that may overlap and should be merged. Commonly used distribution dissimilarity measures are not adequate for determining overlapping clusters in 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…