Tootfinder

Adaptive Data-Knowledge Alignment in Genetic Perturbation Prediction
Yuanfang Xiang, Lun Ai
https://arxiv.org/abs/2510.00512 https://arxiv.org/pdf/2510.005…

Adaptive Data-Knowledge Alignment in Genetic Perturbation Prediction
The transcriptional response to genetic perturbation reveals fundamental insights into complex cellular systems. While current approaches have made progress in predicting genetic perturbation responses, they provide limited biological understanding and cannot systematically refine existing knowledge. Overcoming these limitations requires an end-to-end integration of data-driven learning and existing knowledge. However, this integration is challenging due to inconsistencies between data and know…

Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysis
Meihui Zhang, Liming Wang, Chi Zhang, Zhaojing Luo
https://arxiv.org/abs/2510.12642 https://…

Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysis
A growing trend in modern data analysis is the integration of data management with learning, guided by accuracy, latency, and cost requirements. In practice, applications draw data of different formats from many sources. In the meanwhile, the objectives and budgets change over time. Existing systems handle these applications across databases, analysis libraries, and tuning services. Such fragmentation leads to complex user interaction, limited adaptability, suboptimal performance, and poor exte…

Adaptive Semantic Communication for UAV/UGV Cooperative Path Planning
Fangzhou Zhao, Yao Sun, Jianglin Lan, Lan Zhang, Xuesong Liu, Muhammad Ali Imran
https://arxiv.org/abs/2510.06901

Adaptive Semantic Communication for UAV/UGV Cooperative Path Planning
Effective path planning is fundamental to the coordination of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems, particularly in applications such as surveillance, navigation, and emergency response. Combining UAVs' broad field of view with UGVs' ground-level operational capability greatly improve the likelihood of successfully achieving task objectives such as locating victims, monitoring target areas, or navigating hazardous terrain. In complex environments, UAVs nee…

A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0
Shufeng Chen, Mariat James Elizebeth, Robab Aghazadeh Chakherlou, Xingyu Zhao, Eric Barbier, Siddartha Khastgir, Paul Jennings
https://arxiv.org/abs/2510.00092

A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0
Assurance 2.0 is a modern framework developed to address the assurance challenges of increasingly complex, adaptive, and autonomous systems. Building on the traditional Claims-Argument-Evidence (CAE) model, it introduces reusable assurance theories and explicit counterarguments (defeaters) to enhance rigor, transparency, and adaptability. It supports continuous, incremental assurance, enabling innovation without compromising safety. However, limitations persist in confidence measurement, residu…

Characterizing Agent-Based Model Dynamics via $\epsilon$-Machines and Kolmogorov-Style Complexity
Roberto Garrone (University of Milano-Bicocca)
https://arxiv.org/abs/2510.12729

Characterizing Agent-Based Model Dynamics via $ε$-Machines and Kolmogorov-Style Complexity
We propose a two-level information-theoretic framework for characterizing the informational organization of Agent-Based Model (ABM) dynamics within the broader paradigm of Complex Adaptive Systems (CAS). At the macro level, a pooled $ε$-machine is reconstructed as a reference model that summarizes the system-wide informational regime. At the micro level, $ε$-machines are reconstructed for each caregiver-elder dyad and variable, and are complemented with algorithm-agnostic Kolmogorov-style mea…

Solving Fokker-Planck-Kolmogorov Equation by Distribution Self-adaptation Normalized Physics-informed Neural Networks
Yi Zhang, Yiting Duan, Xiangjun Wang, Zhikun Zhang
https://arxiv.org/abs/2510.09422

Solving Fokker-Planck-Kolmogorov Equation by Distribution Self-adaptation Normalized Physics-informed Neural Networks
Stochastic dynamical systems provide essential mathematical frameworks for modeling complex real-world phenomena. The Fokker-Planck-Kolmogorov (FPK) equation governs the evolution of probability density functions associated with stochastic system trajectories. Developing robust numerical methods for solving the FPK equation is critical for understanding and predicting stochastic behavior. Here, we introduce the distribution self-adaptive normalized physics-informed neural network (DSN-PINNs) fo…