Tootfinder

Distributed Koopman Operator Learning from Sequential Observations
Ali Azarbahram, Shenyu Liu, Gian Paolo Incremona
https://arxiv.org/abs/2509.20071 https://

Distributed Koopman Operator Learning from Sequential Observations
This paper presents a distributed Koopman operator learning framework for modeling unknown nonlinear dynamics using sequential observations from multiple agents. Each agent estimates a local Koopman approximation based on lifted data and collaborates over a communication graph to reach exponential consensus on a consistent distributed approximation. The approach supports distributed computation under asynchronous and resource-constrained sensing. Its performance is demonstrated through simulati…

My phone being at 23% charge vs. finishing listening to the rest of this new very long, death doomy Perishing album. Which shall prevail
https://perishing.bandcamp.com/album/malicious-acropolis-unveiled

Mitigating Model Drift in Developing Economies Using Synthetic Data and Outliers
Ilyas Varshavskiy, Bonu Boboeva, Shuhrat Khalilbekov, Azizjon Azimi, Sergey Shulgin, Akhlitdin Nizamitdinov, Haitz Saez de Ocariz Borde
https://arxiv.org/abs/2510.09294

Mitigating Model Drift in Developing Economies Using Synthetic Data and Outliers
Machine Learning models in finance are highly susceptible to model drift, where predictive performance declines as data distributions shift. This issue is especially acute in developing economies such as those in Central Asia and the Caucasus - including Tajikistan, Uzbekistan, Kazakhstan, and Azerbaijan - where frequent and unpredictable macroeconomics shocks destabilize financial data. To the best of our knowledge, this is among the first studies to examine drift mitigation methods on financi…

Sea-ing Through Scattered Rays: Revisiting the Image Formation Model for Realistic Underwater Image Generation
Vasiliki Ismiroglou, Malte Pedersen, Stefan H. Bengtson, Andreas Aakerberg, Thomas B. Moeslund
https://arxiv.org/abs/2509.15011

Sea-ing Through Scattered Rays: Revisiting the Image Formation Model for Realistic Underwater Image Generation
In recent years, the underwater image formation model has found extensive use in the generation of synthetic underwater data. Although many approaches focus on scenes primarily affected by discoloration, they often overlook the model's ability to capture the complex, distance-dependent visibility loss present in highly turbid environments. In this work, we propose an improved synthetic data generation pipeline that includes the commonly omitted forward scattering term, while also considering a …

Immigration and Customs Enforcement (#ICE) will seine Fähigkeiten im #PredictivePolicing ausbauen und macht eine Ausschreibung für Datendienstleister:
"The contractor will research and analyze data from commercial and law enforcement databases as well as other publicly accessib…

Facts look different in different contexts...
Nigeria, Libya, Caspian Sea, and other oil patches in impoverished places are effectively without the concept of environmental regulation. They flare whatever wherever they want. In Nigeria the rampant theft of unrefined oil and "fire pit refining" is beyond anything done by regulated refiners in TX.
Not that we want to emulate Nigeria or Azerbaijan or Libya...

AgriEval: A Comprehensive Chinese Agricultural Benchmark for Large Language Models
Lian Yan, Haotian Wang, Chen Tang, Haifeng Liu, Tianyang Sun, Liangliang Liu, Yi Guan, Jingchi Jiang
https://arxiv.org/abs/2507.21773

AgriEval: A Comprehensive Chinese Agricultural Benchmark for Large Language Models
In the agricultural domain, the deployment of large language models (LLMs) is hindered by the lack of training data and evaluation benchmarks. To mitigate this issue, we propose AgriEval, the first comprehensive Chinese agricultural benchmark with three main characteristics: (1) Comprehensive Capability Evaluation. AgriEval covers six major agriculture categories and 29 subcategories within agriculture, addressing four core cognitive scenarios: memorization, understanding, inference, and genera…

Transportation Cyber Incident Awareness through Generative AI-Based Incident Analysis and Retrieval-Augmented Question-Answering Systems
Ostonya Thomas, Muhaimin Bin Munir, Jean-Michel Tine, Mizanur Rahman, Yuchen Cai, Khandakar Ashrafi Akbar, Md Nahiyan Uddin, Latifur Khan, Trayce Hockstad, Mashrur Chowdhury
https://arxiv.org/abs/2508.025…

Transportation Cyber Incident Awareness through Generative AI-Based Incident Analysis and Retrieval-Augmented Question-Answering Systems
Technological advancements have revolutionized numerous industries, including transportation. While digitalization, automation, and connectivity have enhanced safety and efficiency, they have also introduced new vulnerabilities. With 95% of data breaches attributed to human error, promoting cybersecurity awareness in transportation is increasingly critical. Despite numerous cyberattacks on transportation systems worldwide, comprehensive and centralized records of these incidents remain scarce. …

Correction of weighted and shifted seven-step BDF for parabolic equations with nonsmooth data
Minghua Chen, Jiankang Shi, Fan Yu, Zhi Zhou
https://arxiv.org/abs/2509.02307 https…

Correction of weighted and shifted seven-step BDF for parabolic equations with nonsmooth data
It is well known that the seven-step backward difference formula (BDF) is unstable for the parabolic equations, since it is not even zero-stable. However, a linear combination of two non zero-stable schemes, namely the seven-step BDF and its shifted counterpart, can yield $A(α)$ stable. Based on this observation, the authors [Akrivis, Chen, and Yu, IMA J. Numer. Anal., DOI:10.1093/imanum/drae089] propose the weighted and shifted seven-step BDF methods for the parabolic equations, which stabili…

Video-Based MPAA Rating Prediction: An Attention-Driven Hybrid Architecture Using Contrastive Learning
Dipta Neogi, Nourash Azmine Chowdhury, Muhammad Rafsan Kabir, Mohammad Ashrafuzzaman Khan
https://arxiv.org/abs/2509.06826

Video-Based MPAA Rating Prediction: An Attention-Driven Hybrid Architecture Using Contrastive Learning
The rapid growth of visual content consumption across platforms necessitates automated video classification for age-suitability standards like the MPAA rating system (G, PG, PG-13, R). Traditional methods struggle with large labeled data requirements, poor generalization, and inefficient feature learning. To address these challenges, we employ contrastive learning for improved discrimination and adaptability, exploring three frameworks: Instance Discrimination, Contextual Contrastive Learning, …