This volume offers a holistic understanding of the interconnections of language, specifically English, scholarly publishing, and the knowledge production and circulation through a sociolinguistic lens...

Are art and science separated by an unbridgeable divide? Can they find common ground? In this new book, neuroscientist Eric R. Kandel, whose remarkable scien... | CUP

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, yet their application to graph structure analysis, particularly in community search, remains underexplored. Community search, a fundamental task in graph analysis, aims to identify groups of nodes with dense interconnections, which is crucial for understanding the macroscopic structure of graphs. In this paper, we propose GraphCS, a comprehensive benchmark designed to evaluate the performance of LLMs in community search tasks. Our experiments reveal that while LLMs exhibit preliminary potential, they frequently fail to return meaningful results and suffer from output bias. To address these limitations, we introduce CS-Agent, a dual-agent collaborative framework to enhance LLM-based community search. CS-Agent leverages the complementary strengths of two LLMs acting as Solver and Validator. Through iterative feedback and refinement, CS-Agent dynamically refines initial results without fine-tuning or additional training. After the multi-round dialogue, Decider module selects the optimal community. Extensive experiments demonstrate that CS-Agent significantly improves the quality and stability of identified communities compared to baseline methods. To our knowledge, this is the first work to apply LLMs to community search, bridging the gap between LLMs and graph analysis while providing a robust and adaptive solution for real-world applications.

Recently, there have been notable advancements in large language models (LLMs), demonstrating their growing abilities in complex reasoning. However, existing research largely overlooks a thorough and systematic comparison of these models' reasoning processes and outputs, particularly regarding their self-reflection pattern (also termed "Aha moment") and the interconnections across diverse domains. This paper proposes a novel framework for analyzing the reasoning characteristics of four cutting-edge large reasoning models (GPT-o1, DeepSeek-R1, Kimi-k1.5, and Grok-3) using keywords statistic and LLM-as-a-judge paradigm. Our approach connects their internal thinking processes with their final outputs. A diverse dataset consists of real-world scenario-based questions covering logical deduction, causal inference, and multi-step problem-solving. Additionally, a set of metrics is put forward to assess both the coherence of reasoning and the accuracy of the outputs. The research results uncover various patterns of how these models balance exploration and exploitation, deal with problems, and reach conclusions during the reasoning process. Through quantitative and qualitative comparisons, disparities among these models are identified in aspects such as the depth of reasoning, the reliance on intermediate steps, and the degree of similarity between their thinking processes and output patterns and those of GPT-o1. This work offers valuable insights into the trade-off between computational efficiency and reasoning robustness and provides practical recommendations for enhancing model design and evaluation in practical applications. We publicly release our project at: https://github.com/ChangWenhan/FromThinking2Output

I am writing to express my deep concern regarding recent cybersecurity incidents involving the social media platform X and DOGE, particularly the potential interconnections between these events. On March 10, 2025, X experienced a significant cyberattack, which owner Elon Musk described as “a massive cyberattack” potentially orchestrated by “a large, coordinated group and/or a country.” The hacker group known as the Dark Storm Team has publicly claimed responsibility for this attack. Recent developments have raised alarms about DOGE’s internal security protocols: • Unauthorized Permissions: In an affidavit, Joseph Gioeli III, Deputy Commissioner of Transformation and Modernization at the Treasury Department’s Bureau of the Fiscal Service, revealed that Marco Elez was mistakenly granted “read/write permissions instead of read-only,” leading to unauthorized access to sensitive data. • Public Exposure of Sensitive Tools: During late February, DOGE staffer Jordan Wick, associated with the Executive Office of the President and embedded in the GSA, posted code on his public GitHub page. These tools included one designed to automatically download direct messages from Twitter accounts and another aimed at collecting sensitive data from government agency organizational charts. Such tools could potentially be used to gather data for AI models, raising concerns about privacy and misuse of information. Wick has since set his GitHub account to private. The convergence of these events is troubling: • Data Security Risks: The unauthorized access to sensitive government data by DOGE, combined with the cyberattack on X, raises concerns about potential vulnerabilities and the safeguarding of critical information. With that in mind, please let me know the following; 1. Investigation Status: Has a formal investigation been launched into the cyberattack on X and the Dark Storm Team’s claim of responsibility to insure no government was involved? 2. Impact on Government Accounts: Given Musk’s statement about the cyberattack’s scale and sophistication, is there evidence to suggest that DOGE’s official X accounts or any associated government accounts were compromised during this incident? 3. Internal Security Measures: What steps are being taken to address the internal security lapses within DOGE, such as the unauthorized access to sensitive government data and the public exposure of potentially sensitive tools by staff members? 4. Data Protection Protocols: How is DOGE ensuring the protection of sensitive government data, especially in light of these recent security concerns? 5. Security of Wick’s Activities: Has an investigation been initiated to assess whether Jordan Wick’s uploading of potentially sensitive tools to his public GitHub account compromised any government data, especially considering the possibility that he may have used the same computer for both official and personal activities?

I am writing to express my deep concern regarding recent cybersecurity incidents involving the social media platform X and DOGE, particularly the potential interconnections between these events. On March 10, 2025, X experienced a significant cyberattack, which owner Elon Musk described as “a massive cyberattack” potentially orchestrated by “a large, coordinated group and/or a country.” The hacker group known as the Dark Storm Team has publicly claimed responsibility for this attack. Recent developments have raised alarms about DOGE’s internal security protocols: • Unauthorized Permissions: In an affidavit, Joseph Gioeli III, Deputy Commissioner of Transformation and Modernization at the Treasury Department’s Bureau of the Fiscal Service, revealed that Marco Elez was mistakenly granted “read/write permissions instead of read-only,” leading to unauthorized access to sensitive data. • Public Exposure of Sensitive Tools: During late February, DOGE staffer Jordan Wick, associated with the Executive Office of the President and embedded in the GSA, posted code on his public GitHub page. These tools included one designed to automatically download direct messages from Twitter accounts and another aimed at collecting sensitive data from government agency organizational charts. Such tools could potentially be used to gather data for AI models, raising concerns about privacy and misuse of information. Wick has since set his GitHub account to private. The convergence of these events is troubling: • Data Security Risks: The unauthorized access to sensitive government data by DOGE, combined with the cyberattack on X, raises concerns about potential vulnerabilities and the safeguarding of critical information. With that in mind, please let me know the following; 1. Investigation Status: Has a formal investigation been launched into the cyberattack on X and the Dark Storm Team’s claim of responsibility to insure no government was involved? 2. Impact on Government Accounts: Given Musk’s statement about the cyberattack’s scale and sophistication, is there evidence to suggest that DOGE’s official X accounts or any associated government accounts were compromised during this incident? 3. Internal Security Measures: What steps are being taken to address the internal security lapses within DOGE, such as the unauthorized access to sensitive government data and the public exposure of potentially sensitive tools by staff members? 4. Data Protection Protocols: How is DOGE ensuring the protection of sensitive government data, especially in light of these recent security concerns? 5. Security of Wick’s Activities: Has an investigation been initiated to assess whether Jordan Wick’s uploading of potentially sensitive tools to his public GitHub account compromised any government data, especially considering the possibility that he may have used the same computer for both official and personal activities?

Alternating current (AC)/multi-terminal direct current (MTDC) hybrid power systems (HPSs) play a crucial role in enabling long-distance power transmission and flexible interconnections between AC grids. However, the challenges that HPSs encountered are numerous, with stability and harmonic issues being particularly prominent. Traditional electromagnetic transient (EMT) tools have struggled to accommodate small-signal stability problems and the potential issues of the optimal interactions among converters. To address this gap, HARMONY ("HARMONic stabilitY assessment of PE-penetrated power systems") has been developed for the advanced simulation and analysis of interconnected AC/MTDC HPSs as a comprehensive mathematical framework based on C++ programming language. The primary goals of Harmony are to provide faster and trusted stability analyses, and address the analytical difficulties associated with converter control dynamics, converter-driven stability, and interoperability in HPSs. This framework is intended to be open source, therefore broadening collaboration for researchers, and to contribute to the community of power systems engineers. In this paper, we demonstrate two core functionalities featured in HARMONY, that are optimal power flow (OPF) and harmonic stability analyses (HAS). The underlying analysis models and computational methodologies for both functionalities are presented in detail to help future readers and users gain a clear understanding of mathematical fundamentals of HARMONY. Furthermore, we introduce the integrated framework of OPF and HAS designed in HARMONY, along with representative printed analysis results, to demonstrate the appealing capabilities of HARMONY.

PJM Interconnection Expedites Generation Interconnection Process The PJM Interconnection is implementing a new accelerated process to address reliability concerns stemming from increased electricity demand, particularly from data centers, within its service area. Recognizing the urgent need for additional electricity generation capacity, PJM had previously estimated a requirement of approximately 15,000 megawatts (MW) to address shortfalls.

“It’s been clear for many, many, many years that this is absolutely critical and it would have certainly helped also in this context,” Miguel Stilwell told analysts in a conference call to present the...

In a groundbreaking exploration of the enduring scars left by childhood maltreatment, new research published in BMC Psychiatry reveals complex interconnections between early traumatic experiences, posttraumatic stress disorder (PTSD), and physical health outcomes, particularly those related to metabolic syndrome (MetS). While the psychological consequences of childhood maltreatment, such as PTSD, have been well documented, this comprehensive study shifts focus to the less understood somatic aftermaths, offering fresh insights into how early life stressors reverberate far beyond mental health.

The dynamics of water supply systems are becoming increasingly complex, particularly in the context of climate change and evolving human demands. A recent study conducted by Zhao and colleagues dives deep into the intricate relationship between inter-basin water transfer, land use changes, and the resultant effects on water yield service supply–demand risks. As global populations burgeon and urbanization accelerates, understanding these interconnections is more vital than ever.