Papers

Recently developed fused-ring organic electron-acceptors such as Y6 have strong oscillator strength, good charge-carrier transport and low bandgaps. They therefore have enormous current technical application to optoelectronic devices, such as solar cells. Due to the large number of atoms involved in representative aggregates of these materials, we need an efficient electronic structure method to model them. Standard density functional theory poorly describe charge-transfer states, and were developed for vacuum calculations of individual molecules. In this work we tune a range-separated hybrid functional for Y6. We characterise representative dimers of the solid-state and show that Y6 dimers show the extensive solvatochromic effects are due, in part, to oscillator strength borrowing. We provide an explanation for the short optimally tuned range-separation parameter, based in the Penn model for the frequency dependent dielectric of a semiconductor. We caution that standard range-separated hybrids are less accurate than global hybrids for these, and similar, materials. We show how reducing the range-separation length improves the accuracy of standard functionals, without an involved tuning process.

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。

数字经济的深度发展与AIGC技术的爆发式迭代，推动劳动形态、生产资料属性、价值创造与分配机制发生了根本性变革，也让传统劳动价值论面临四大核心理论困境：一是静态劳动计量框架无法适配数字技能的高频迭代与快速折旧；二是用户微劳动的“微观近乎为零、宏观形成巨额价值”的加总困境无法得到合理解释；三是平台算法劳动的二重性与价值运动规律缺乏系统拆解；四是无效劳动的界定存在被平台垄断滥用的风险。 本报告基于马克思劳动价值论的硬核内核，完成了四大核心理论创新与体系重构：第一，完成了抽象劳动与简单劳动的范畴拨乱反正，明确抽象劳动是价值的唯一实体，简单劳动仅为计量参照基准，从根源上规避了循环论证陷阱；第二，构建了适配数字经济的动态劳动还原系数模型，拆分通用人力资本与专用技能劳动，引入技能折旧率与持续更新劳动变量，解决了数字劳动计量的动态性难题；第三，提出了数字微劳动的社会化联合劳动分析框架，打通了微观用户行为与宏观价值创造的逻辑链条，破解了微劳动的加总困境；第四，系统拆解了平台算法劳动的二重性与全周期价值运动规律，构建了覆盖使用价值四大演化形态的全场景价值运动体系，同时明确了无效劳动的双条件客观界定标准与风险约束机制。 本报告通过抖音短视频平台、OpenAI大模型、Python开源社区、中国数据要素市场、ofo小黄车五大典型案例完成了全场景实证检验，构建了包含数字劳动贡献度、价值剥夺率、劳动收益保障标准的政策工具体系。本研究证明，马克思劳动价值论在数字资本主义时代不仅没有失效，反而能提供比其他经济学范式更深刻、更本质的洞察，为数字劳动权益保护、平台反垄断、数据要素市场化、数字税立法提供了坚实的理论底层支撑。

针对技术迭代加速背景下传统劳动价值论面临的异质劳动衡量、价值价格割裂、技术与劳动对立、产业变迁价值运动模糊四大核心难题，本报告基于马克思劳动价值论的硬核内核，重构了可量化、可实证、非机械唯物主义的理论分析框架。本报告首先明确了「技术的经济学本质是活劳动」的核心论断，厘清了活劳动与物化劳动的功能边界；其次构建了二重性社会必要劳动时间框架，解决了异质劳动的可操作衡量难题；再次区分了冗余的基数价值量与必要的相对价值量基准态，破解了李嘉图以来「不变价值尺度」的百年理论困境；最终构建了「技术活劳动→使用价值体系重构→行业兴衰→价值跨行业转移→全周期定价逻辑反转」的完整动态演化体系。本报告通过胶卷行业、MP3行业两大完整产业周期的实证案例，验证了理论框架的有效性，同时明确了理论的适用边界与局限性。本研究为技术进步、产业变迁、价值分配与价格运动提供了统一的分析框架，回应了主流经济学对劳动价值论的核心质疑，拓展了马克思主义政治经济学的现实应用场景。

基于“引流品-利润品”（““利润转出-利润转入”）二元结构，附加某些假设，提出若干收敛性，（看起来）可用于多产品寡头或垄断竞争市场的结构分析与收敛性分析。

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。

Abstract The trial-and-error paradigm of traditional materials discovery, fundamentally constrained by its inherent high entropy, is proving inadequate for designing complex intelligent matter. Here, we propose a new scientific paradigm: Artificial Intelligence as an ‘Anti-Entropy’ Engine, transforming research from passive understanding to active design. By systematically injecting informational negative entropy across perception, planning, and execution loops, AI guides material systems from disorder to pre-defined functional order. We demonstrate this through empirical advances—such as the GNoME model discovering 2.2 million stable crystals—and construct a unified ‘Perception-Planning-Execution’ framework enabling inverse design across scales. This paradigm extends beyond static structures to dynamic non-equilibrium systems and life-like chemical networks. We prospectively map future frontiers using a ‘Ladder of Intelligence’ and address ethical governance, systemic risk, and sustainability. Ultimately, this marks a fundamental transition for humanity, from being passive observers of nature to becoming active ‘anti-entropy’ designers in the evolution of matter. This review not only synthesizes these advances but also provides a unifying conceptual framework and a clear roadmap for the field, aiming to catalyze the transition towards this fifth paradigm of scientific discovery.

我就是“盛京小先锋” ——基于辽宁红色“六地”文化的“矩阵式”学程设计（最终版） （注：加粗部分为相对于初稿的所有修改和新增内容） 一、 设计背景：回应时代课题，深化育人实践 （一） 时代的课题：培养有根的时代新人 辽宁“六地”精神是宝贵的精神财富。在当前信息快速更迭的背景下，教育的重心正从“记住知识”转向“形成素养”，特别是培养学生在复杂情境中解决问题的能力和社会情感能力（SEL）。引导学生深入理解历史脉络、建立文化自信、涵养健全人格、培养社会责任感，是落实立德树人根本任务的关键所在。 （二） 育人的挑战：从“被动接受”到“主动建构” 厚重的红色历史与当代小学生之间存在天然的距离感。传统的“课程”模式下，孩子容易成为被动的“听众”。我们的挑战在于：如何把宏大的“六地”精神转化为孩子可亲可感的学习体验？如何激发学生的内在动机，让他们在真实的任务情境中，从知识的接收者转变为意义的主动建构者？更关键的是，如何精准识别每个学生的特点（学习者画像），并设计一个灵活、包容的体系，支持全校学生（1-6年级）根据自身发展水平，选择适切的学习路径？ （三） 实践的基础：依托红色沃土，探索学程转型 沈阳市盛京小学创建了“盛京小先锋”德育品牌，构建了“三红阶梯”育人体系。学校在校本读物、家校社协同等方面的扎实工作，为从“课程”走向“学程”提供了坚实的土壤。本设计旨在对现有实践进行系统化升级，构建一个整合的、动态的、支持个性化成长的红色育人新生态。

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.