arXiv subjects

Xiaochun Zhang

Publications and source records attributed to Xiaochun Zhang.

8 recordsLinked to original sources

arXiv2026

SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms

Accurate time series forecasting underpins decision-making in many domains, yetconventional ML development often faces data scarcity, distribution shift, anddiminishing returns from manual iteration. We propose Self-Evolving Agent forTime Series Algorithms (SEATS), a framework that autonomously generates, val-idates, and optimizes forecasting algorithm code through an iterative self-evolutionloop. Our design combines three mechanisms: (1) Metric-Advantage MCTS(MA-MCTS), which replaces fixed rewards with a statistically normalized advan-tage score for search guidance, (2) code review with running prompt refinement,so every successfully executed solution is reviewed and the running prompt encodescorrective patterns for later iterations, and (3) global steerable reasoning, whichcompares each evaluated node to global best- and worst-performing solutions forcross-trajectory transfer. A MAP-Elites archive maintains architectural diversity.Across four datasets and two metrics, SEATS wins seven of eight comparisonsagainst strong baselines TimeMixer, Timer, and SEMixer

cs.AI↗

arXiv2026

Breaking the Communication-Accuracy Trade-off: A Sparsified Information Diffusion Framework for Multi-Agent Collaborative Perception

The growing relevance of multi-agent systems has drawn increasing focus on communication-efficient filters for collaborative perception to alleviate the system's communication burden. While the event-triggered (ET) mechanism can improve communication efficiency in collaborative state estimation, an inevitable trade-off exists between estimation accuracy and communication cost in ET filters. This paper proposes a fast and accurate ET diffusion-based filter for real-time multi-agent collaborative target tracking, aiming to reduce the system's data transmission without compromise in tracking performance. The proposed filter achieves improved tracking accuracy, reduced data transmission, and accelerated convergence using an error-minimized ET cubature information filter (CIF) for local estimation, and a correlation-aware diffusion strategy for global fusion. The experimental results confirm the scalability of the proposed EDC-CIF algorithm and demonstrate its efficacy in simultaneously reducing estimation error and computation time while significantly enhancing communication efficiency.

cs.MA↗

arXiv2026

An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules

Despite the high accuracy of 'black box' deep learning models, drug discovery still relies on protein-ligand interaction principles and heuristics. To improve interpretability of protein-small molecule binding predictions, we developed the PWRules framework, which applies binding affinity data to identify privileged small molecule fragments and subsequently defines complementary pairing rules between these fragments and protein words (semantic sequence units) through an interpretability module. The resulting word-fragment rules are then ranked by the PWScore function to prioritize active compounds. Evaluations on benchmark datasets show that PWScore achieves competitive performance comparable to the physics-based model (Glide) and the deep learning model (PSICHIC) and shows broad applicability for protein targets outside the training dataset, e.g., SARS-CoV-2 main protease. Notably, PWScore captures complementary interaction information, yielding superior enrichment performance when integrated with these established methods. Structural analysis of protein-ligand complexes indicates that learned word-fragment rules are significantly enriched near ligand-binding pockets, despite training without explicit structural guidance. By extracting and applying complementary pairing rules, PWRules provides an interpretable framework for drug discovery.

cs.LG↗

arXiv2025

Tuning Electrode Wettability to Optimize Nanobubble Nucleation and Reaction Rates in Electrochemical Gas-Evolving Reactions

Bubble formation in electrochemical system often hinders reaction efficiency by reducing active surface area and obstructing mass transfer, yet the mechanisms governing their nanoscale nucleation dynamics and impact remains unclear. In this study, we used molecular dynamics simulations to explore nanobubble nucleation and reaction rates during water electrolysis on planar- and nano-electrodes, with systematically tuning electrode wettability through water-electrode and gas-electrode interactions. We identified distinct nucleation regimes: gas layers, surface nanobubbles, bulk nanobubbles, and no nanobubbles, and revealed a volcano-shaped relationship between wettability and reaction rate, where optimal wettability strikes a balance between suppressing bubbles and ensuring sufficient reactant availability to maximize performance. Nanoelectrodes consistently exhibit higher current densities compared to planar electrodes with the same wettability, due to pronounced edge effects. Furthermore, moderate driving forces enhance reaction rates without triggering surface bubble formation, while excessive driving forces induce surface nanobubble nucleation, leading to suppressed reaction rates and complex dynamics driven by bubble growth and detachment. These findings highlight the importance of fine-tuning wettability and reaction driving forces to optimize gas-evolving electrochemical systems at the nanoscale and underscore the need for multiscale simulation frameworks integrating atomic-scale reaction kinetics, nanoscale bubble nucleation, and microscale bubble dynamics to fully understand bubble behavior and its impact on performance.

cond-mat.soft↗

arXiv2023

A Multi-day Needs-based Modeling Approach for Activity and Travel Demand Analysis

This paper proposes a multi-day needs-based model for activity and travel demand analysis. The model captures the multi-day dynamics in activity generation, which enables the modeling of activities with increased flexibility in time and space (e.g., e-commerce and remote working). As an enhancement to activity-based models, the proposed model captures the underlying decision-making process of activity generation by accounting for psychological needs as the drivers of activities. The level of need satisfaction is modeled as a psychological inventory, whose utility is optimized via decisions on activity participation, location, and duration. The utility includes both the benefit in the inventory gained and the cost in time, monetary expense as well as maintenance of safety stock. The model includes two sub-models, a Deterministic Model that optimizes the utility of the inventory, and an Empirical Model that accounts for heterogeneity and stochasticity. Numerical experiments are conducted to demonstrate model scalability. A maximum likelihood estimator is proposed, the properties of the log-likelihood function are examined and the recovery of true parameters is tested. This research contributes to the literature on transportation demand models in the following three aspects. First, it is arguably better grounded in psychological theory than traditional models and allows the generation of activity patterns to be policy-sensitive (while avoiding the need for ad hoc utility definitions). Second, it contributes to the development of needs-based models with a non-myopic approach to model multi-day activity patterns. Third, it proposes a tractable model formulation via problem reformulation and computational enhancements, which allows for maximum likelihood parameter estimation.

eess.SY↗

arXiv2021

Mechanisms behind high CO2/CH4 selectivity using ZIF-8 metal organic frameworks with encapsulated ionic liquids: a computational study

CO2/CH4 separation using ionic liquids (ILs) encapsulated metal-organic frameworks (MOFs), especially ZIF-8, has shown promise as a new technique for separating CO2 from CH4. However, the mechanisms behind the high CO2/CH4 selectivity of the method remains indistinct. Here we report the progress of understanding the mechanisms from examining the ZIF-8 aperture configuration variation using DFT and MD simulations. The results indicate that the pristine aperture configuration exhibits the best separation performance, and the addition of ILs prevents the apertures from large swing (i.e. configuration variation). Subsequently, the effect of IL viscosity on the layout variation was investigated. MD simulations also show that the pristine aperture configuration is more stabilized by ILs with large viscosity (0-87Cp). Further increase of IL viscosity above 87Cp did not result in noticeable changes in the aperture stability.

physics.chem-ph↗

arXiv2016

Climatic implications of a rapid wind/solar transition

A transition to a fully global renewable energy infrastructure is potentially possible in no more than a few decades, even using current wind/solar technologies. We demonstrate that at its completion this transition would terminate anthropogenic carbon emissions to the atmosphere derived from energy consumption in roughly 25 years as well as double current global energy production. This result would provide all human energy needs worldwide and additional energy required for climate adaptation as well as carbon sequestration from the atmosphere to bring down the atmospheric carbon dioxide (CO2) concentration to safer levels. The implementation of this energy transition in the near future would maximize the probability for achieving a less than 2 deg C, with a potential 1.5 deg C limit, increase to global temperature over the pre-industrial level by 2100. Our best case scenario utilizes less than 3% of current annual global energy consumption per year with an annual reinvestment of 10% of its growing renewable capacity to make more of itself.

physics.ao-ph↗

arXiv2011

Gaussian Affine Feature Detector

A new method is proposed to get image features' geometric information. Using Gaussian as an input signal, a theoretical optimal solution to calculate feature's affine shape is proposed. Based on analytic result of a feature model, the method is different from conventional iterative approaches. From the model, feature's parameters such as position, orientation, background luminance, contrast, area and aspect ratio can be extracted. Tested with synthesized and benchmark data, the method achieves or outperforms existing approaches in term of accuracy, speed and stability. The method can detect small, long or thin objects precisely, and works well under general conditions, such as for low contrast, blurred or noisy images.

cs.CV↗