In this paper, we propose a communication-efficient penalized regression algorithm for high-dimensional sparse linear regression models with massive data. This approach incorporates an optimized distributed system communication algorithm, named CESDAR algorithm, based on the Enhanced Support Detection and Root finding algorithm. The CESDAR algorithm leverages data distributed across multiple machines to compute and update the active set and introduces the communication-efficient surrogate likelihood framework to approximate the optimal solution for the full sample on the active set, resulting in the avoidance of raw data transmission, which enhances privacy and data security, while significantly improving algorithm execution speed and substantially reducing communication costs. Notably, this approach achieves the same statistical accuracy as the global estimator. Furthermore, this paper explores an extended version of CESDAR and an adaptive version of CESDAR to enhance algorithmic speed and optimize parameter selection, respectively. Simulations and real data benchmarks experiments demonstrate the efficiency and accuracy of the CESDAR algorithm. Introduction The rapid development of data collection techniques has led to unprecedented growth and expansion in both the volume and dimensionality of data. The massive high-dimensional datasets entail high computational costs and memory constraints. Numerous methods have been utilized for variable selection and parameter estimation in the research domain, including LASSO [1], adaptive LASSO [2], the smoothly clipped absolute deviation (SCAD) penalty [3], the minimax concave penalty (MCP) [4] and so on.

These embeddings can be leveraged in various downstream tasks, including node classification, link prediction, clustering, exploratory data analysis, and network visualization. The method has found broad application across diverse domains, such as fraud detection in financial networks (van Belle et al. 2023), friendship recommendation and bot detection in social networks (Saxena et al. 2022; Dehghan et al. 2023), knowledge discovery in knowledge graphs (Egami et al. 2023; Liu et al. 2023), analysis of biological networks (Jiang et al. 2021; Pasquier et al. 2023), and fake review detection on online platforms (Zaki et al. 2024). A key challenge in Node Embedding is developing a scalable method for preserving the structural properties of nodes suitable for the required structural patterns of a downstream application task. The type of structural patterns in which a node is embedded within the graph can vary depending on the role or function of the node in a specific application task. For instance, fraudulent activities such as money laundering can be embedded in particular money flow patterns among illicit entities, resulting in characteristic structural patterns within the financial transaction network, such as suspicious cyclic transaction chains (Granados Vargas 2022). These structural patterns differ significantly from those observed in social networks, where specific roles such as bridge and core nodes define the network's connectivity and influence (Huang et al. 2014). As Node Embedding methods cannot preserve all types of structural patterns simultaneously, they must align with the requirements of a specific application task when defining types of structural identities.

Successful defense against dynamically evolving cyber threats requires advanced and sophisticated techniques. This research presents a novel approach to enhance real-time cybersecurity threat detection and response by integrating large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems with continuous threat intelligence feeds. Leveraging recent advancements in LLMs, specifically GPT-4o, and the innovative application of RAG techniques, our approach addresses the limitations of traditional static threat analysis by incorporating dynamic, real-time data sources. We leveraged RAG to get the latest information in real-time for threat intelligence, which is not possible in the existing GPT-4o model. We employ the Patrowl framework to automate the retrieval of diverse cybersecurity threat intelligence feeds, including Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), Exploit Prediction Scoring System (EPSS), and Known Exploited Vulnerabilities (KEV) databases, and integrate these with the all-mpnet-base-v2 model for high-dimensional vector embeddings, stored and queried in Milvus. We demonstrate our system's efficacy through a series of case studies, revealing significant improvements in addressing recently disclosed vulnerabilities, KEVs, and high-EPSS-score CVEs compared to the baseline GPT-4o. This work not only advances the role of LLMs in cybersecurity but also establishes a robust foundation for the development of automated intelligent cyberthreat information management systems, addressing crucial gaps in current cybersecurity practices.

--Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how dif - ferent regions approach AI development, balancing technological progress with ethical and regulatory considerations. This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia, focusing on three key dimensions: generative AI, ethical oversight, and industrial applications. The US prioritizes market -driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk -based framework emphasizing ethical safeguards, and Asia employs state -guided AI strategies that balance rapid deployment with regulatory oversight. Although these approaches reflect different economic models and policy priorities, their divergence poses challenges to international collaboration, regulatory harmonization, and the development of global AI standards. To address these challenges, this paper synthesizes regional strengths to propose an adaptive AI governance framework that integrates risk -tiered oversight, innovation accelerators, and strategic alignment mechanisms. By bridging governance gaps, this study offers actionable insights for fostering responsible AI development while ensuring a balance between technological progress, ethical imperatives, and regulatory coherence. Artificial intelligence (AI) has emerged as a transformative force in the 21st century, reshaping industries, governance structures, and societal interactions at an unprecedented pace. From generative AI creating human - like text and images to autonomous systems revolutionizing healthcare, finance, and manufacturing, AI's influence is profound and far - reaching.

Retrieval-Augmented Language Models boost task performance, owing to the retriever that provides external knowledge. Although crucial, the retriever primarily focuses on semantics relevance, which may not always be effective for generation. Thus, utility-based retrieval has emerged as a promising topic, prioritizing passages that provides valid benefits for downstream tasks. However, due to insufficient understanding, capturing passage utility accurately remains unexplored. This work proposes SCARLet, a framework for training utility-based retrievers in RALMs, which incorporates two key factors, multi-task generalization and inter-passage interaction. First, SCARLet constructs shared context on which training data for various tasks is synthesized. This mitigates semantic bias from context differences, allowing retrievers to focus on learning task-specific utility for better task generalization. Next, SCARLet uses a perturbation-based attribution method to estimate passage-level utility for shared context, which reflects interactions between passages and provides more accurate feedback. We evaluate our approach on ten datasets across various tasks, both in-domain and out-of-domain, showing that retrievers trained by SCARLet consistently improve the overall performance of RALMs.

Chinese Grammatical Error Correction (CGEC) is a critical task in Natural Language Processing, addressing the growing demand for automated writing assistance in both second-language (L2) and native (L1) Chinese writing. While L2 learners struggle with mastering complex grammatical structures, L1 users also benefit from CGEC in academic, professional, and formal contexts where writing precision is essential. This survey provides a comprehensive review of CGEC research, covering datasets, annotation schemes, evaluation methodologies, and system advancements. We examine widely used CGEC datasets, highlighting their characteristics, limitations, and the need for improved standardization. We also analyze error annotation frameworks, discussing challenges such as word segmentation ambiguity and the classification of Chinese-specific error types. Furthermore, we review evaluation metrics, focusing on their adaptation from English GEC to Chinese, including character-level scoring and the use of multiple references. In terms of system development, we trace the evolution from rule-based and statistical approaches to neural architectures, including Transformer-based models and the integration of large pre-trained language models. By consolidating existing research and identifying key challenges, this survey provides insights into the current state of CGEC and outlines future directions, including refining annotation standards to address segmentation challenges, and leveraging multilingual approaches to enhance CGEC.

Number of Distinct Values (NDV) estimation of a multiset/column is a basis for many data management tasks, especially within databases. Despite decades of research, most existing methods require either a significant amount of samples through uniform random sampling or access to the entire column to produce estimates, leading to substantial data access costs and potentially ineffective estimations in scenarios with limited data access. In this paper, we propose leveraging semantic information, i.e., schema, to address these challenges. The schema contains rich semantic information that can benefit the NDV estimation. To this end, we propose PLM4NDV, a learned method incorporating Pre-trained Language Models (PLMs) to extract semantic schema information for NDV estimation. Specifically, PLM4NDV leverages the semantics of the target column and the corresponding table to gain a comprehensive understanding of the column's meaning. By using the semantics, PLM4NDV reduces data access costs, provides accurate NDV estimation, and can even operate effectively without any data access. Extensive experiments on a large-scale real-world dataset demonstrate the superiority of PLM4NDV over baseline methods. Our code is available at https://github.com/bytedance/plm4ndv.

Compute nodes on modern heterogeneous supercomputing systems comprise CPUs, GPUs, and high-speed network interconnects (NICs). Parallelization is identified as a technique for effectively utilizing these systems to execute scalable simulation and deep learning workloads. The resulting inter-process communication from the distributed execution of these parallel workloads is one of the key factors contributing to its performance bottleneck. Most programming models and runtime systems enabling the communication requirements on these systems support GPU-aware communication schemes that move the GPU-attached communication buffers in the application directly from the GPU to the NIC without staging through the host memory. A CPU thread is required to orchestrate the communication operations even with support for such GPU-awareness. This survey discusses various available GPU-centric communication schemes that move the control path of the communication operations from the CPU to the GPU. This work presents the need for the new communication schemes, various GPU and NIC capabilities required to implement the schemes, and the potential use-cases addressed. Based on these discussions, challenges involved in supporting the exhibited GPU-centric communication schemes are discussed.

Large language models have revolutionized natural language processing with their surprising capability to understand and generate human-like text. However, many of these models inherit and further amplify the biases present in their training data, raising ethical and fairness concerns. The detection and mitigation of such biases are vital to ensuring that LLMs act responsibly and equitably across diverse domains. This work investigates Knowledge Graph-Augmented Training (KGAT) as a novel method to mitigate bias in LLM. Using structured domain-specific knowledge from real-world knowledge graphs, we improve the understanding of the model and reduce biased output. Public datasets for bias assessment include Gender Shades, Bias in Bios, and FairFace, while metrics such as demographic parity and equal opportunity facilitate rigorous detection. We also performed targeted mitigation strategies to correct biased associations, leading to a significant drop in biased output and improved bias metrics. Equipped with real-world datasets and knowledge graphs, our framework is both scalable and effective, paving the way toward responsible deployment in sensitive and high-stakes applications.

Financial institutions are required by regulation to report suspicious financial transactions related to money laundering. Therefore, they need to constantly monitor vast amounts of incoming and outgoing transactions. A particular challenge in detecting money laundering is that money launderers continuously adapt their tactics to evade detection. Hence, detection methods need constant fine-tuning. Traditional machine learning models suffer from catastrophic forgetting when fine-tuning the model on new data, thereby limiting their effectiveness in dynamic environments. Continual learning methods may address this issue and enhance current anti-money laundering (AML) practices, by allowing models to incorporate new information while retaining prior knowledge. Research on continual graph learning for AML, however, is still scarce. In this review, we critically evaluate state-of-the-art continual graph learning approaches for AML applications. We categorise methods into replay-based, regularization-based, and architecture-based strategies within the graph neural network (GNN) framework, and we provide in-depth experimental evaluations on both synthetic and real-world AML data sets that showcase the effect of the different hyperparameters. Our analysis demonstrates that continual learning improves model adaptability and robustness in the face of extreme class imbalances and evolving fraud patterns. Finally, we outline key challenges and propose directions for future research.