arXiv subjects

Jiacheng Ma

Publications and source records attributed to Jiacheng Ma.

3 recordsLinked to original sources

arXiv2026

CWF: A Collaborative Writing Framework for Personalized and Reliable Popular Science Writing

We introduce Personalized and Reliable Popular Science Writing, a novel task that requires adapting scientific explanations to audiences with different cognitive levels while preserving factual accuracy. However, improving personalization often introduces simplifications that increase the risk of hallucination and factual distortion. To address these challenges, we first construct a dataset of 39,134 entries and a reader-centric Personalized Science Communication Benchmark (PSCB) that jointly evaluates audience adaptation and factual accuracy. To reduce data and computational requirements while improving generalization across domains and audiences, we introduce DA-MoE, which explicitly decouples audience adaptation from domain knowledge through separate modeling. To enable robust verification and revision in evidence-scarce scenarios, a multi-agent fact-checking mechanism that augments limited evidence with role-specific agent debate and propagates confidence over a graph is proposed. Experiments on PSCB show that our approach achieves state-of-the-art performance. Our code is open-sourced at https://github.com/DPInnovationWorks/CWF.

cs.AI↗

arXiv2024

Frost/Defrost Models for Air-Source Heat Pumps with Retained Water Refreezing Considered

Cyclic frosting and defrosting operations constitute a common characteristic of air-source heat pumps in cold climates during winter. Simulation models that can capture simultaneous heat and mass transfer phenomena associated with frost/defrost behaviors and their impact on the overall heat pump system performance are of critical importance to improved controls of heat delivery and frost mitigation. This paper presents a novel frost formulation using an enthalpy method to systematically capture all phase-change behaviors including frost formation and melting, retained water refreezing and melting, and water drainage during cyclic frosting and defrosting operations. A Fuzzy modeling approach is proposed to smoothly switch source terms when evaluating the dynamics of frost and water mediums for numerical robustness. The proposed frost/defrost model is incorporated into a flat-tube outdoor heat exchanger model of an automotive heat pump system model to investigate system responses under cyclic operations of frosting and reverse-cycle defrosting.

eess.SY↗

arXiv2021

Towards Understanding the Generative Capability of Adversarially Robust Classifiers

Recently, some works found an interesting phenomenon that adversarially robust classifiers can generate good images comparable to generative models. We investigate this phenomenon from an energy perspective and provide a novel explanation. We reformulate adversarial example generation, adversarial training, and image generation in terms of an energy function. We find that adversarial training contributes to obtaining an energy function that is flat and has low energy around the real data, which is the key for generative capability. Based on our new understanding, we further propose a better adversarial training method, Joint Energy Adversarial Training (JEAT), which can generate high-quality images and achieve new state-of-the-art robustness under a wide range of attacks. The Inception Score of the images (CIFAR-10) generated by JEAT is 8.80, much better than original robust classifiers (7.50).

cs.LG↗