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In the field of automated programming, large language models (LLMs) have demonstrated foundational generative capabilities when given detailed task descriptions. However, their current functionalities are primarily limited to function-level development, restricting their effectiveness in complex project environments and specific application scenarios, such as complicated image-processing tasks. This paper presents a multi-agent framework that utilises a hybrid set of LLMs, including GPT-4o and locally deployed open-source models, which collaboratively complete auto-programming tasks. Each agent plays a distinct role in the software development cycle, collectively forming a virtual organisation that works together to produce software products. By establishing a tree-structured thought distribution and development mechanism across project, module, and function levels, this framework offers a cost-effective and efficient solution for code generation. We evaluated our approach using benchmark datasets, and the experimental results demonstrate that VisionCoder significantly outperforms existing methods in image processing auto-programming tasks.

The rise of blockchain technologies has greatly accelerated the development and deployment of smart contracts. However, their inherent vulnerabilities and susceptibility to bugs have led to significant financial losses, underscoring the challenges in securing smart contracts. While traditional auditing methods are crucial, they often fall short in addressing the increasing complexity and volume of smart contracts. Recent advancements in Large Language Models (LLMs) offer promising solutions for enhancing software auditing by automatically identifying security vulnerabilities. Despite their potential, the practical application of these models is hindered by substantial computational demands. This paper investigates the feasibility of using smaller, fine-tuned models to achieve comparable or even superior results in smart contract auditing. We introduce the FTSmartAudit framework, which is designed to develop cost-effective, specialized models for smart contract auditing through the fine-tuning of LLMs. Our contributions include: (1) a single-task learning framework that streamlines data preparation, training, evaluation, and continuous learning; (2) a robust dataset generation method utilizing domain-special knowledge distillation to produce high-quality datasets from advanced models like GPT-4o; (3) an adaptive learning strategy to maintain model accuracy and robustness; (4) the proven effectiveness of fine-tuned models in detecting specific vulnerabilities and complex logical errors; and (5) a framework that can be extended to other domains requiring LLM solutions. Our experimental results demonstrate that smaller models can surpass state-of-the-art commercial models and tools in detecting vulnerabilities in smart contracts., Comment: 26 pages, 7 figures

The immutable nature of blockchain technology, while revolutionary, introduces significant security challenges, particularly in smart contracts. These security issues can lead to substantial financial losses. Current tools and approaches often focus on specific types of vulnerabilities. However, a comprehensive tool capable of detecting a wide range of vulnerabilities with high accuracy is lacking. This paper introduces LLM-SmartAudit, a novel framework leveraging the advanced capabilities of Large Language Models (LLMs) to detect and analyze vulnerabilities in smart contracts. Using a multi-agent conversational approach, LLM-SmartAudit employs a collaborative system with specialized agents to enhance the audit process. To evaluate the effectiveness of LLM-SmartAudit, we compiled two distinct datasets: a labeled dataset for benchmarking against traditional tools and a real-world dataset for assessing practical applications. Experimental results indicate that our solution outperforms all traditional smart contract auditing tools, offering higher accuracy and greater efficiency. Furthermore, our framework can detect complex logic vulnerabilities that traditional tools have previously overlooked. Our findings demonstrate that leveraging LLM agents provides a highly effective method for automated smart contract auditing., Comment: 14 pages, 5 figures, conference

The Poincar\'e gauge theory (PGT) of gravity provides a viable formulation of general relativity (Einstein-Cartan theory), and a popular model-building framework for modified gravity with torsion. Notoriously, however, the PGT terms which propagate vector torsion lead to strongly-coupled ghosts: the modern view is that only scalar torsion can propagate. To fix this, we revisit the concept of embedding explicit mass scales in scale-invariant theories, showing how the Klein-Gordon theory naturally leads to a slowly-rolling inflaton. We then show that the unique scale-invariant embedding of PGT leads to two new terms, one of which is the Maxwell term for vector torsion. We provide the full spectrum of quantum particles in the resulting theory. Our result means that every PGT is conformal and - after a two-decade hiatus - vector torsion is back on the menu., Comment: Fixed typos, added text around Eq. (18), added two refs

Kagome superconductors AV$_3$Sb$_5$ (A = K, Rb and Cs) have attracted much recent attention due to the coexistence of multiple exotic orders. Among them, the charge density wave (CDW) order has been shown to host various unconventional behaviors. Here, we investigate the CDW order by a combination of both bulk and surface doping methods. While element substitutions in bulk doping change both carriers and the crystal lattice, the surface doping primarily tunes the carrier concentration. As such, our results reveal a two-dimensional phase diagram of the CDW in doped CsV$_3$Sb$_5$. In the lightly bulk doped regime, the existence of CDW order is reversible by tuning the carrier concentration. But excessive bulk doping permanently destroys the CDW, regardless of the carrier doping level. These results provide insights to the origin of the CDW from both electronic and structural degrees of freedom. They also open an avenue for manipulating the exotic CDW order in Kagome superconductors., Comment: 14 pages, 4 figures

Blockchain smart contracts have emerged as a transformative force in the digital realm, spawning a diverse range of compelling applications. Since solidity smart contracts across various domains manage trillions of dollars in virtual coins, they become a prime target for attacks. One of the primary challenges is keeping abreast of the latest techniques and tools for developing secure smart contracts and examining those already deployed. In this paper, we seek to address these challenges from four aspects: (1) We begin by examining ten automatic tools, specifically focusing on their methodologies and their ability to identify vulnerabilities in solidity smart contracts. (2) We propose a novel criterion for evaluating these tools, based on the ISO/IEC 25010 standard. (3) To facilitate the evaluation of the selected tools, we construct a benchmark that encompasses two distinct datasets: a collection of 389 labelled smart contracts and a scaled set of 20,000 unique cases from real-world contracts. (4) We provide a comparison of the selected tools, offering insights into their strengths and weaknesses and highlighting areas where further improvements are needed. Through this evaluation, we hope to provide developers and researchers with valuable guidance on selecting and using smart contract analysis tools and contribute to the ongoing efforts to improve the security and reliability of smart contracts., Comment: 24 pages, 6 figure, IEEE Communications Surveys & Tutorials

With the increasing adoption of smart contracts, ensuring their security has become a critical concern. Numerous vulnerabilities and attacks have been identified and exploited, resulting in significant financial losses. In response, researchers have developed various tools and techniques to identify and prevent vulnerabilities in smart contracts. In this survey, we present a systematic overview of the quality assurance of smart contracts, covering vulnerabilities, attacks, defenses, and tool support. By classifying vulnerabilities based on known attacks, we can identify patterns and common weaknesses that need to be addressed. Moreover, in order to effectively protect smart contracts, we have created a labeled dataset to evaluate various vulnerability detection tools and compare their effectiveness., Comment: 35 pages, 6 figures, ACM Computing Surveys

Van Hove singularity (VHS) has been considered as a driving source for unconventional superconductivity. A VHS in two-dimensional (2D) materials consists of a saddle point connecting electron-like and hole-like bands. In a rare case, when a VHS appears at Fermi level, both electron-like and hole-like conduction can coexist, giving rise to an enhanced density of states as well as an attractive component of Coulomb interaction for unconventional electronic pairing. However, this van Hove scenario is often destroyed by an incorrect chemical potential or competing instabilities. Here, by using angle-resolved photoemission measurements, we report the observation of a VHS perfectly aligned with the Fermi level in a kagome superconductor CsV$_{3-x}$Ta$_x$Sb$_5$ (x~0.4), in which a record-high superconducting transition temperature is achieved among all the current variants of AV$_3$Sb$_5$ (A=Cs, Rb, K) at ambient pressure. Doping dependent measurements reveal the important role of van Hove scenario in boosting superconductivity, and spectroscopic-imaging scanning tunneling microscopy measurements indicate a distinct superconducting state in this system., Comment: 20 pages, 4 figures

In Kagome metal CsV$_3$Sb$_5$, multiple intertwined orders are accompanied by both electronic and structural instabilities. These exotic orders have attracted much recent attention, but their origins remain elusive. The newly discovered CsTi$_3$Bi$_5$ is a Ti-based Kagome metal to parallel CsV$_3$Sb$_5$. Here, we report angle-resolved photoemission experiments and first-principles calculations on pristine and Cs-doped CsTi$_3$Bi$_5$ samples. Our results reveal that the van Hove singularity (vHS) in CsTi$_3$Bi$_5$ can be tuned in a large energy range without structural instability, different from that in CsV$_3$Sb$_5$. As such, CsTi$_3$Bi$_5$ provides a complementary platform to disentangle and investigate the electronic instability with a tunable vHS in Kagome metals.

This study evaluated post traumatic stress disorder (PTSD) among frontline US physicians (treating COVID-19 patients) in comparison with second-line physicians (not treating COVID-19 patients), and identified the significance and patterns of factors associated with higher PTSD risk. A cross-sectional, web-based survey was deployed during August and September, 2020, to practicing physicians in the 18 states with the largest COVID-19 cases. Among 1,478 responding physicians, 1,017 completed the PTSD Checklist (PCL-5). First, the PCL-5 was used to compare symptom endorsement between the two physician groups. A greater percentage of frontline than second-line physicians had clinically significant endorsement of PCL-5 symptoms and higher PCL-5 scores. Second, logistic regression and seven nonlinear machine learning (ML) algorithms were leveraged to identify potential predictors of PTSD risk by analyzing variable importance and partial dependence plots. Predictors of PTSD risk included cognitive/psychological measures, occupational characteristics, work experiences, social support, demographics, and workplace characteristics. Importantly, the final ML model random forest, identified patterns of both damaging and protective predictors of PTSD risk among frontline physicians. Key damaging factors included depression, burnout, negative coping, fears of contracting/transmitting COVID-19, perceived stigma, and insufficient resources to treat COVID-19 patients. Protective factors included resilience and support from employers/friends/family/significant others. This study underscores the value of ML algorithms to uncover nonlinear relationships among protective/damaging risk factors for PTSD in frontline physicians, which may better inform interventions to prepare healthcare systems for future epidemics/pandemics., Comment: 34 pages, 5 Tables, 2 Figues, Under review with Journal of Psychiatric Research

The new two-dimensional (2D) kagome superconductor CsV$_3$Sb$_5$ has attracted much recent attention due to the coexistence of superconductivity, charge order, topology and kagome physics. A key issue in this field is to unveil the unique reconstructed electronic structure, which successfully accommodates different orders and interactions to form a fertile ground for emergent phenomena. Here, we report angle-resolved photoemission spectroscopy (ARPES) evidence for two distinct band reconstructions in CsV$_3$Sb$_5$. The first one is characterized by the appearance of new electron energy band at low temperature. The new band is theoretically reproduced when the three dimensionality of the charge order is considered for a band-folding along the out-of-plane direction. The second reconstruction is identified as a surface induced orbital-selective shift of the electron energy band. Our results provide the first evidence for the three dimensionality of the charge order in single-particle spectral function, highlighting the importance of long-range out-of-plane electronic correlations in this layered kagome superconductor. They also point to the feasibility of orbital-selective control of the band structure via surface modification, which would open a new avenue for manipulating exotic phenomena in this system, including superconductivity.

Disparity in suicide rates between urban and suburban/rural areas is growing, with rural areas typically witnessing higher suicide rates in the U.S. However, previous studies often ignored the effect of socio-environmental factors on the suicide rates and its regional disparity. To address these gaps, we propose a holistic data-driven framework to model the associations of social (demographic, socioeconomic) and environmental (climate) factors on suicide rates, and study the disparities across urban and suburban areas. Leveraging the county-level suicide data from 2000--2017 along with the socio-environmental features, we trained, tested and validated a suite of advanced statistical learning algorithms to identify, assess and predict the influence of key socio-environmental factors on suicide rates. Random forest outperformed all other models in terms of goodness-of-fit and predictive accuracy, and selected as the final model to make inferences. Our results indicate that population demographics is significantly associated with both urban and suburban suicide rates. We found that suburban population is more vulnerable to suicides compared to urban communities, with suburban suicide rate being particularly sensitive to unemployment rate and median household income. Our analysis revealed that suicide mortality is correlated to climate, showing that urban suicide rate is more sensitive to higher temperatures, seasonal-heating-degree-days and precipitation, while suburban suicide rate is sensitive to only seasonal-cooling-degree-days. This work provides deeper insights on interactions between key socio-environmental factors and suicides across different urbanized areas, and can help the public health agencies develop suicide prevention strategies to reduce the growing risk of suicides.

Purpose: Identify and examine the associations between health behaviors and increased risk of adolescent suicide attempts, while controlling for socioeconomic and demographic differences. Design: A data-driven analysis using cross-sectional data. Setting: Communities in the state of Montana from 1999 to 2017. Subjects: Selected 22,447 adolescents of whom 1,631 adolescents attempted suicide at least once. Measures: Overall 29 variables (predictors) accounting for psychological behaviors, illegal substances consumption, daily activities at schools and demographic backgrounds, were considered. Analysis: A library of machine learning algorithms along with the traditionally-used logistic regression were used to model and predict suicide attempt risk. Model performances (goodness-of-fit and predictive accuracy) were measured using accuracy, precision, recall and F-score metrics. Results: The non-parametric Bayesian tree ensemble model outperformed all other models, with 80.0% accuracy in goodness-of-fit (F-score:0.802) and 78.2% in predictive accuracy (F-score:0.785). Key health-behaviors identified include: being sad/hopeless, followed by safety concerns at school, physical fighting, inhalant usage, illegal drugs consumption at school, current cigarette usage, and having first sex at an early age (below 15 years of age). Additionally, the minority groups (American Indian/Alaska Natives, Hispanics/Latinos), and females are also found to be highly vulnerable to attempting suicides. Conclusion: Significant contribution of this work is understanding the key health-behaviors and health disparities that lead to higher frequency of suicide attempts among adolescents, while accounting for the non-linearity and complex interactions among the outcome and the exposure variables.