Papers

在企业预算约束下，数字化投入与绿色化投入往往竞争同一笔资源，二者能否形成协同取决于技术收益能否跨部门兑现。本文基于企业层面面板数据，检验人工智能对数字化-绿色化协同转型的影响。固定效应结果显示，人工智能系数为0.0158061（p<0.01）；工具变量2SLS结果为0.0188387（p<0.01），第一阶段F值1864.52。异质性结果表明，效应主要出现在公平竞争程度较高地区和非龙头企业。机制检验显示，人工智能通过缓解信息不对称、降低融资约束和提升组织适应能力促进协同，同时提高数字风险暴露和金融化倾向。拓展结果显示，人工智能还能提升绿色创新、企业韧性与全要素生产率。本文据此提出“技术扩散-竞争治理-风险约束”协同治理框架。

本文在朱梁真理函数定理 3.0–3.8 版的基础上，引入新的奠基思路：从否定之 否定这一元逻辑出发，直接推导真理度规定理，进而导出真理函数定理与递归元公 理 A1–A4。这一新思路比原有从元事实出发的路径更彻底、更清晰，将整个理论 奠基于递归演化的内在逻辑之上。我们同时保留元事实路径作为历史背景与辅助阐 释，两条路径最终汇合于统一的朱梁渡劫递归元范式。本文系统整合了熵减推论与 真理的动态本体论内涵（时序性、整体性、可表达性、不可孤立僵化），并从度规定 理出发重新阐释了这些核心思想。文中对渡劫公理 A5 进行了严格的数学构造，证 明了递归元范畴的正合性、劫数对象的存在性及其自同构群的同构性。这些工作将 真理从静态函数提升为动态的渡劫递归元，揭示了真理在代谢过程中的矛盾消解机 制。本文所有证明均为完整形式，不依赖于外部引用。

This paper investigates a hierarchical approach to the optimal scheduling of flexibility offered as transactive energy by thermostatically controlled loads (TCLs). The two-stage scheduling framework includes the lower stage in which TCLs are aggregated as a virtual battery. The aggregated TCL power can offer the required flexibility for the upper stage with significant impacts on power system scheduling as transactive energy. Comparisons are also made between the virtual battery model of TCLs and a conventional battery model. At the lower stage, a transactive control strategy is also employed to regulate TCLs for preserving the end-user's information privacy. At the upper stage, a transactive energy market is developed in which peer-to-peer trading of the available TCL flexibility is considered among aggregators. Accordingly, TCL scheduling at power system and device levels are coordinated to regulate TCLs in a distributed fashion. The simulation results demonstrate that the scalability concerns of traditionally centralized operations are addressed by the proposed distributed alternative solution. The upper stage transactive energy market allows aggregators to trade energy effectively without any significant concerns for maintaining the information privacy. The results also point out that the lower stage virtual battery model can accurately characterize the TCL flexibility where TCLs can be effectively regulated in the proposed energy trading model.

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the 'where' and 'how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs.

Network Intrusion Detection Systems (NIDS) play a vital role in protecting digital infrastructures against increasingly sophisticated cyber threats. In this paper, we extend ODXU, a Neurosymbolic AI (NSAI) framework that integrates deep embedded clustering for feature extraction, symbolic reasoning using XGBoost, and comprehensive uncertainty quantification (UQ) to enhance robustness, interpretability, and generalization in NIDS. The extended ODXU incorporates score-based methods (e.g., Confidence Scoring, Shannon Entropy) and metamodel-based techniques, including SHAP values and Information Gain, to assess the reliability of predictions. Experimental results on the CIC-IDS-2017 dataset show that ODXU outperforms traditional neural models across six evaluation metrics, including classification accuracy and false omission rate. While transfer learning has seen widespread adoption in fields such as computer vision and natural language processing, its potential in cybersecurity has not been thoroughly explored. To bridge this gap, we develop a transfer learning strategy that enables the reuse of a pre-trained ODXU model on a different dataset. Our ablation study on ACI-IoT-2023 demonstrates that the optimal transfer configuration involves reusing the pre-trained autoencoder, retraining the clustering module, and fine-tuning the XGBoost classifier, and outperforms traditional neural models when trained with as few as 16,000 samples (approximately 50% of the training data). Additionally, results show that metamodel-based UQ methods consistently outperform score-based approaches on both datasets.

Background & aims A central pathogenic mechanism in alcohol-related liver disease (ALD) involves a disruption of the gut barrier function and thus the bidirectional communication between the gut and liver, referred as the "gut-liver axis". While gamma-aminobutyric acid (GABA) and type A GABA receptors (GABAARs) are present in the intestinal epithelium, their role in the gut-liver axis and contribution to the pathogenesis of ALD, remains poorly understood. Methods A Gao-binge mouse model of ALD, as well as Gabra1IEC-KO mice and intestinal cell lines were employed as approaches to assess the role of GABAARs-mediated signaling in ethanol-induced liver injury. Results Administration of GABA markedly improved liver function and intestinal barrier integrity in ALD mice. This improvement was associated with a downregulation of intestinal cytochrome P450 2E1 (CYP2E1) expression and a reduction of oxidative stress. These beneficial effects of GABA on intestinal integrity and liver function were substantially diminished by the GABAAR antagonist, picrotoxin. In addition, picrotoxin blocked GABA’s effect on CYP2E1 expression,, thereby preventing the attentuation of oxidative stress in the intestines of ALD mice. Moreover, when ethanol-stimulated cell models were subjected to pharmacological or genetic inhibition of CYP2E1, GABA treatment failed to produce any decrease in ROS levels. Finally, results from the intestinal epithelial-specific Gabra1 knockout mouse model demonstrated that the benifical effect of GABA on liver function in ALD is mediated by its activation of intestinal epithelial GABAARs. Remarkably, the pivotal role of intestinal CYP2E1 was robustly validated by confirming its dysregulated expression in patient-derived clinical samples. Conclusions Our results suggest that activation of intestinal GABAAR-mediated signaling reduces intestinal CYP2E1 expression and oxidative stress, thereby improving intestinal barrier function and alleviating ethanol-induced liver injury. Such findings suggest that intestinal GABA signaling offers a promising avenue for the development of novel strategies in the treatment of ALD. Powered

2024 年，可计算离散整体几何结构实验室发起了一场覆盖全国多所高校及科研机构的巡回艺术展。展览内容聚焦前沿几何拓扑理论与概念，尤其凸显各类整体几何结构。 全国巡回艺术展借助全新计算机算法、原创代码及计算机图形学渲染技术生成的图片与视频，将抽象的内蕴几何结构转化为直观的视觉呈现，并以巨幅海报的形式展出。这些展览内容的创新之处在于体现了数学家近几十年来发展的内蕴整体几何拓扑概念。目前，这场巡回艺术展已走进十余所高校，且仍在持续推进中，整个巡回展览预期将历时十年，100所高校。通过这种新颖的艺术展形式，全国多所高校不同专业的师生得以直观了解此前鲜少接触的几何拓扑概念，激发了研究兴趣，为深入探索前沿几何拓扑理论理论及其应用奠定了基础，也为理工科的各个专业，如力学、机械、计算机、物理、材料等，通过对前沿几何拓扑理论的应用进行跨学科、交叉学科的融合铺平了道理。通过此次全国巡回艺术展也就在艺术领域开拓了一个全新的“整体几何结构 几何拓扑艺术”流派。

Large language model (LLM) agents have evolved towards greater autonomy with the advancement of model context protocols. Self-controlled agents, such as Codex and Claude Code, highlight the need for novel organizational frameworks that facilitate agent-level autonomy. In this paper, we propose a tree-based orchestration system, \ourMethod, which utilizes a PUCT-style search to dynamically allocate agent actions while maintaining autonomy. This approach offers three key benefits: (i) full agent autonomy for critical tasks like planning and tool use, (ii) a generalized mechanism for inter-agent experience sharing, and (iii) scalability as the number of agents increases. We demonstrate the system’s effectiveness through the general matrix multiplication kernel optimization, achieving 80\% of the performance of the cuBLAS code. Additionally, the system exhibits a scaling phenomenon as the number of agents increases. Our approach provides a solution for organizing increasingly autonomous agents.

Machine Learning as a Service (MLaaS) facilitates the deployment and accessibility of medical models, yet concurrently exposes proprietary models to potential adversaries. Attackers may exploit model stealing attacks (MSAs) to replicate these models illicitly, leading to loss of training investment and privacy vulnerabilities. While existing research has mainly focused on MSAs in the context of 2D natural image classification, this work presents the first investigation into stealing 3D medical segmentation models. We introduce collaborative dual-model 3D medical segmentation stealing (CDMSS-3D), which decomposes the model stealing objective into two complementary aspects: stealing accuracy and stealing robustness. With our adversarial proxy training, CDMSS-3D achieves superior model stealing performance. Furthermore, we incorporate a dual-model discrepancy sampling strategy, which enhances the fidelity of the substitute model by prioritizing uncertain samples. Extensive experiments on four 3D medical segmentation datasets demonstrate that CDMSS-3D consistently outperforms adapted baselines.