We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability. In addition, MPLSandbox also integrates both traditional and LLM-based code analysis tools, providing a comprehensive analysis of generated code. MPLSandbox can be effortlessly integrated into the training and deployment of LLMs to improve the quality and correctness of their generated code. It also helps researchers streamline their workflows for various LLM-based code-related tasks, reducing the development cost. To validate the effectiveness of MPLSandbox, we integrate it into training and deployment approaches, and also employ it to optimize workflows for a wide range of real-world code-related tasks. Our goal is to enhance researcher productivity on LLM-based code-related tasks by simplifying and automating workflows through delegation to MPLSandbox.

Document parsing is essential for converting unstructured and semi-structured documents--such as contracts, academic papers, and invoices--into structured, machine-readable data. Document parsing extract reliable structured data from unstructured inputs, providing huge convenience for numerous applications. Especially with recent achievements in Large Language Models, document parsing plays an indispensable role in both knowledge base construction and training data generation. This survey presents a comprehensive review of the current state of document parsing, covering key methodologies, from modular pipeline systems to end-to-end models driven by large vision-language models. Core components such as layout detection, content extraction (including text, tables, and mathematical expressions), and multi-modal data integration are examined in detail. Additionally, this paper discusses the challenges faced by modular document parsing systems and vision-language models in handling complex layouts, integrating multiple modules, and recognizing high-density text. It emphasizes the importance of developing larger and more diverse datasets and outlines future research directions.

I am honoured to be chairing the delivery of the inaugural International Scientific Report on Advanced AI Safety. I am proud to publish this interim report which is the culmination of huge efforts by many experts over the six months since the work was commissioned at the Bletchley Park AI Safety Summit in November 2023. We know that advanced AI is developing very rapidly, and that there is considerable uncertainty over how these advanced AI systems might affect how we live and work in the future. AI has tremendous potential to change our lives for the better, but it also poses risks of harm. That is why having this thorough analysis of the available scientific literature and expert opinion is essential. The more we know, the better equipped we are to shape our collective destiny.

The pursuit of realism in cinema has driven significant advancements in animatronics, where the integration of mechatronics, a multidisciplinary field that combines mechanical engineering, electronics, and computer science, plays a pivotal role in enhancing the functionality and realism of animatronics. This interdisciplinary approach facilitates smoother characters movements and enhances the sophistication of behaviors in animatronic creatures, thereby increasing their realism. This article examines the most recent developments in mechatronic technology and their significant impact on the art and engineering of animatronics in the filmmaking. It explores the sophisticated integration of system components and analyzes how these enhancements foster complexity and integration, crucial for achieving unprecedented levels of realism in modern cinema. Further, the article delves into in-depth case studies of well-known movie characters, demonstrating the practical applicability of these state-of-the-art mechatronic solutions in creating compelling, lifelike cinematic experiences. This paper aims to bridge the gap between the technical aspects of mechatronics and the creative demands of the film industry, ultimately contributing to the ongoing evolution of cinematic realism.

SAR images possess unique attributes that present challenges for both human observers and vision AI models to interpret, owing to their electromagnetic characteristics. The interpretation of SAR images encounters various hurdles, with one of the primary obstacles being the data itself, which includes issues related to both the quantity and quality of the data. The challenges can be addressed using generative AI technologies. Generative AI, often known as GenAI, is a very advanced and powerful technology in the field of artificial intelligence that has gained significant attention. The advancement has created possibilities for the creation of texts, photorealistic pictures, videos, and material in various modalities. This paper aims to comprehensively investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field and compare them with computer vision tasks, analyzing the similarity, difference, and general challenges of them. Then, an overview of the latest GenAI models is systematically reviewed, including various basic models and their variations targeting the general challenges. Additionally, the corresponding applications in SAR domain are also included. Specifically, we propose to summarize the physical model based simulation approaches for SAR, and analyze the hybrid modeling methods that combine the GenAI and interpretable models. The evaluation methods that have been or could be applied to SAR, are also explored. Finally, the potential challenges and future prospects are discussed. To our best knowledge, this survey is the first exhaustive examination of the interdiscipline of SAR and GenAI, encompassing a wide range of topics, including deep neural networks, physical models, computer vision, and SAR images. The resources of this survey are open-source at \url{https://github.com/XAI4SAR/GenAIxSAR}.

With its early roots in the development of calculus by Isaac Newton, Gottfried Wilhelm Leibniz, Pierre de Ferma t and others in the late 17th century, mathematical optimization has a rich his tory that involves contributions from numerous mathematicians, economists, eng ineers, and scientists. The birth of modern mathematical optimization is commonly c redited to George Dantzig, whose simplex algorithm developed in 1947 solves l inear optimization problems where ℓ is affine and X is a polyhedron ( Dantzig 1956). Subsequent milestones include the development of the rich theory of convex a nalysis ( Rockafellar 1970) as well as the discovery of polynomial-time solution metho ds for linear ( Khachiyan 1979, Karmarkar 1984) and broad classes of nonlinear convex optimization problems ( Nesterov and Nemirovskii 1994). Classical optimization problems are deterministic, that is, all problem data are assumed to be known with certainty. However, most decision pro blems encountered in practice depend on parameters that are corrupted by measu rement errors or that are revealed only after a decision must be determined and committed. A naïve approach to model uncertainty-affected decision problems a s deterministic optimization problems would be to replace all uncertain paramete rs with their expected values or with appropriate point predictions. However, it h as long been known and well-documented that decision-makers who replace an un certain parameter of an optimization problem with its mean value fall victim to th e'flaw of averages' ( Savage, Scholtes and Zweidler 2006, Savage 2012).

We present a novel methodology for convex optimization algorithm design using ideas from electric RLC circuits. Given an optimization problem, the first stage of the methodology is to design an appropriate electric circuit whose continuous-time dynamics converge to the solution of the optimization problem at hand. Then, the second stage is an automated, computer-assisted discretization of the continuous-time dynamics, yielding a provably convergent discrete-time algorithm. Our methodology recovers many classical (distributed) optimization algorithms and enables users to quickly design and explore a wide range of new algorithms with convergence guarantees.

Data sharing is a prerequisite for collaborative innovation, enabling organizations to leverage diverse datasets for deeper insights. In real-world applications like FinTech and Smart Manufacturing, transactional data, often in tabular form, are generated and analyzed for insight generation. However, such datasets typically contain sensitive personal/business information, raising privacy concerns and regulatory risks. Data synthesis tackles this by generating artificial datasets that preserve the statistical characteristics of real data, removing direct links to individuals. However, attackers can still infer sensitive information using background knowledge. Differential privacy offers a solution by providing provable and quantifiable privacy protection. Consequently, differentially private data synthesis has emerged as a promising approach to privacy-aware data sharing. This paper provides a comprehensive overview of existing differentially private tabular data synthesis methods, highlighting the unique challenges of each generation model for generating tabular data under differential privacy constraints. We classify the methods into statistical and deep learning-based approaches based on their generation models, discussing them in both centralized and distributed environments. We evaluate and compare those methods within each category, highlighting their strengths and weaknesses in terms of utility, privacy, and computational complexity. Additionally, we present and discuss various evaluation methods for assessing the quality of the synthesized data, identify research gaps in the field and directions for future research.

Large Language Models (LLMs) have recently garnered significant attention in various domains, including recommendation systems. Recent research leverages the capabilities of LLMs to improve the performance and user modeling aspects of recommender systems. These studies primarily focus on utilizing LLMs to interpret textual data in recommendation tasks. However, it's worth noting that in ID-based recommendations, textual data is absent, and only ID data is available. The untapped potential of LLMs for ID data within the ID-based recommendation paradigm remains relatively unexplored. To this end, we introduce a pioneering approach called "LLM for ID-based Recommendation" (LLM4IDRec). This innovative approach integrates the capabilities of LLMs while exclusively relying on ID data, thus diverging from the previous reliance on textual data. The basic idea of LLM4IDRec is that by employing LLM to augment ID data, if augmented ID data can improve recommendation performance, it demonstrates the ability of LLM to interpret ID data effectively, exploring an innovative way for the integration of LLM in ID-based recommendation. We evaluate the effectiveness of our LLM4IDRec approach using three widely-used datasets. Our results demonstrate a notable improvement in recommendation performance, with our approach consistently outperforming existing methods in ID-based recommendation by solely augmenting input data.

This paper presents a framework for extracting georeferenced vehicle trajectories from high-altitude drone footage, addressing key challenges in urban traffic monitoring and limitations of traditional ground-based systems. We employ state-of-the-art computer vision and deep learning to create an end-to-end pipeline that enhances vehicle detection, tracking, and trajectory stabilization. Conducted in the Songdo International Business District, South Korea, the study used a multi-drone experiment over 20 intersections, capturing approximately 12TB of 4K video data over four days. We developed a novel track stabilization method that uses detected vehicle bounding boxes as exclusion masks during image registration, which, combined with advanced georeferencing techniques, accurately transforms vehicle coordinates into real-world geographical data. Additionally, our framework includes robust vehicle dimension estimation and detailed road segmentation for in-depth traffic analysis. The framework produced two high-quality datasets: the Songdo Traffic dataset, comprising nearly 1 million unique vehicle trajectories, and the Songdo Vision dataset, containing over 5,000 human-annotated frames with about 300,000 vehicle instances in four classes. Comparisons between drone-derived data and high-precision sensor data from an instrumented probe vehicle highlight the accuracy and consistency of our framework's extraction in dense urban settings. By publicly releasing these datasets and the pipeline source code, this work sets new benchmarks for data quality, reproducibility, and scalability in traffic research. Results demonstrate the potential of integrating drone technology with advanced computer vision for precise, cost-effective urban traffic monitoring, providing valuable resources for the research community to develop intelligent transportation systems and improve traffic management strategies.