Parviainen et al. (2014) adopted an anytime integer linear programming (ILP) Otherwise it returns a sub-optimal DAG with bounded treewidth. Nie et al. (2014) proposed an efficient anytime ILP approach with a polynomial number of constraints Nie et al. (2015) proposed the method S2.

We present a method for learning Bayesian networks from data sets containing thousands of variables without the need for structure constraints. Our approach is made of two parts. The first is a novel algorithm that effectively explores the space of possible parent sets of a node. It guides the exploration towards the most promising parent sets on the basis of an approximated score function that is computed in constant time. The second part is an improvement of an existing ordering-based algorithm for structure optimization. The new algorithm provably achieves a higher score compared to its original formulation. Our novel approach consistently outperforms the state of the art on very large data sets.

Abstract--The increasing demand for intelligent mobile applications has made multi-agent collaboration with Transformer-based large language models (LLMs) essential in mobile edge computing (MEC) networks. However, training LLMs in such environments remains challenging due to heavy computation, high end-to-end latency, and limited model generalization. We introduce CollaPipe, a hybrid distributed learning framework that integrates collaborative pipeline parallelism with federated aggregation to support self-evolving intelligent networks. In Col-laPipe, the encoder part is adaptively partitioned into variable-sized segments and deployed across mobile devices for pipeline-parallel training, while the decoder is deployed on edge servers to handle generative tasks. Then we perform global model update via federated aggregation. T o enhance training efficiency, we formulate a joint optimization problem that adaptively allocates model segments, micro-batches, bandwidth, and transmission power . We derive and use a closed-form convergence bound to design an Dynamic Segment Scheduling and Resource Allocation (DSSDA) algorithm based on Lyapunov optimization, ensuring system stability under long-term constraints. Extensive experiments on downstream tasks with Transformer and BERT models show that CollaPipe improves computation efficiency by up to 15.09%, reduces end-to-end latency by at least 48.98%, and cuts single device memory usage by more than half, enabling online learning in heterogeneous and dynamic communication environments. With the rapid development of artificial intelligence generated content (AIGC) technologies in mobile Internet of Things (IoT), AI agent systems powered by large language models (LLMs) are emerging as a critical enabler for next-generation intelligent applications in mobile edge computing (MEC) networks [1]-[3]. Jiewei Chen, Shaoyong Guo, and Xuesong Qiu are with the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China (e-mail: {chenjiewei, syguo, xsqiu}@bupt.edu.cn). Xiumei Deng is with the Singapore University of Technology and Design, Singapore (e-mail: xiumei_deng@sutd.edu.sg). Ze-hui Xiong is with the School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, United Kingdom (e-mail: z.xiong@qub.ac.uk).

As dawn spread over San Antonio on September 9, 2021, almond-colored smoke began to fill the sky above the city's Far West Side. The plumes were whorling off the top of a 132-foot-tall cell tower that overshadows an office park just north of SeaWorld. At a hotel a mile away, a paramedic snapped a photo of the spectacle and posted it to the r/sanantonio subreddit. "Cell tower on fire around 1604 and Culebra," he wrote. In typical Reddit fashion, the comments section piled up with corny jokes. "Blazing 5G speeds," quipped one user. "I hope no one inhales those fumes, the Covid transmission via 5G will be a lot more potent that way," wrote another, in a swipe at the conspiracy theorists who claim that radiation from 5G towers caused the Covid-19 pandemic. The wisecracks went on: "Can you hear me now?" "Great, some hero trying to save us from 5G." That self-styled hero was actually lurking in the comments. As he followed the thread on his phone, Sean Aaron Smith delighted in the sheer volume of attention the tower fire was receiving, even if most of it dripped with sarcasm. A lean, tattooed--and until recently, entirely apolitical--27-year-old, Smith had come to view 5G as the linchpin of a globalist plot to zombify humanity. To resist that supposed scheme, he'd spent the past five months setting Texas cell towers ablaze. Smith's crude and quixotic campaign against 5G was precisely the sort of security threat that was fast becoming one of the US government's top concerns in 2021.

Despite its status as a global tech hub, India lags far behind the likes of the US and China when it comes to homegrown AI. That gap has opened largely because India has chronically underinvested in R&D, institutions, and invention. Meanwhile, since no one native language is spoken by the majority of the population, training language models is far more complicated than it is elsewhere. So when the open-source foundation model DeepSeek-R1 suddenly outperformed many global peers, it struck a nerve. This launch by a Chinese startup prompted Indian policymakers to confront just how far behind the country was in AI infrastructure--and how urgently it needed to respond.

--Since the emergence of autonomous driving technology, it has advanced rapidly over the past decade. It is becoming increasingly likely that autonomous vehicles (A Vs) would soon coexist with human-driven vehicles (HVs) on the roads. Currently, safety and reliable decision-making remain significant challenges, particularly when A Vs are navigating lane changes and interacting with surrounding HVs. Therefore, precise estimation of the intentions of surrounding HVs can assist A Vs in making more reliable and safe lane change decision-making. This involves not only understanding their current behaviors but also predicting their future motions without any direct communication. However, distinguishing between the passing and yielding intentions of surrounding HVs still remains ambiguous. T o address the challenge, we propose a social intention estimation algorithm rooted in Directed Acyclic Graph (DAG), coupled with a decision-making framework employing Deep Reinforcement Learning (DRL) algorithms. T o evaluate the method's performance, the proposed framework can be tested and applied in a lane-changing scenario within a simulated environment. Furthermore, the experiment results demonstrate how our approach enhances the ability of A Vs to navigate lane changes safely and efficiently on roads. UTONOMOUS driving decision-making is a critical component of autonomous driving systems, aiming to make reasonable and safe driving decisions based on environmental perception [1]. The decision-making process not only needs to consider the kinematic and dynamic constraints of the vehicle but also needs to comply with traffic rules, evaluate potential risks, and coexist safely with other traffic participants in complex driving scenarios, such as executing lane changes on highways and navigating intersections, as illustrated in Figure 1. Executing lane changes on the highway remains a formidable challenge for A Vs in the real world, primarily due to environmental complexity and uncertainty. Jing Wang, Y an Jin are with the School of Mechanical and Aerospace Engineering, Queen's University Belfast, Belfast, United Kingdom (email: jwang61@qub.ac.uk, y.jin@qub.ac.uk)

In this paper, we present an end-to-end automated motion recognition (AutoMR) pipeline designed for multimodal datasets. The proposed framework seamlessly integrates data preprocessing, model training, hyperparameter tuning, and evaluation, enabling robust performance across diverse scenarios. Our approach addresses two primary challenges: 1) variability in sensor data formats and parameters across datasets, which traditionally requires task-specific machine learning implementations, and 2) the complexity and time consumption of hyperparameter tuning for optimal model performance. Our library features an all-in-one solution incorporating QuartzNet as the core model, automated hyperparameter tuning, and comprehensive metrics tracking. Extensive experiments demonstrate its effectiveness on 10 diverse datasets, achieving state-of-the-art performance. This work lays a solid foundation for deploying motion-capture solutions across varied real-world applications.

Large Language Models (LLMs) have shown remarkable potential in reasoning while they still suffer from severe factual hallucinations due to timeliness, accuracy, and coverage of parametric knowledge. Meanwhile, integrating reasoning with retrieval-augmented generation (RAG) remains challenging due to ineffective task decomposition and redundant retrieval, which can introduce noise and degrade response quality. In this paper, we propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP), enabling strategic and adaptive retrieval. By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step. Experiments show that DeepRAG improves retrieval efficiency while improving answer accuracy by 21.99%, demonstrating its effectiveness in optimizing retrieval-augmented reasoning.

Healthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional Machine Learning (ML) models excel at predicting outcomes, such as identifying high risk patients, they are limited in addressing what-if questions about interventions. This study introduces the Probabilistic Causal Fusion (PCF) framework, which integrates Causal Bayesian Networks (CBNs) and Probability Trees (PTrees) to extend beyond predictions. PCF leverages causal relationships from CBNs to structure PTrees, enabling both the quantification of factor impacts and simulation of hypothetical interventions. PCF was validated on three real-world healthcare datasets i.e. MIMIC-IV, Framingham Heart Study, and Diabetes, chosen for their clinically diverse variables. It demonstrated predictive performance comparable to traditional ML models while providing additional causal reasoning capabilities. To enhance interpretability, PCF incorporates sensitivity analysis and SHapley Additive exPlanations (SHAP). Sensitivity analysis quantifies the influence of causal parameters on outcomes such as Length of Stay (LOS), Coronary Heart Disease (CHD), and Diabetes, while SHAP highlights the importance of individual features in predictive modeling. By combining causal reasoning with predictive modeling, PCF bridges the gap between clinical intuition and data-driven insights. Its ability to uncover relationships between modifiable factors and simulate hypothetical scenarios provides clinicians with a clearer understanding of causal pathways. This approach supports more informed, evidence-based decision-making, offering a robust framework for addressing complex questions in diverse healthcare settings.

In this paper we present an approach to reduce hallucinations in Large Language Models (LLMs) by incorporating Knowledge Graphs (KGs) as an additional modality. Our method involves transforming input text into a set of KG embeddings and using an adapter to integrate these embeddings into the language model space, without relying on external retrieval processes. To facilitate this, we created WikiEntities, a dataset containing over 3 million Wikipedia texts annotated with entities from Wikidata and their corresponding embeddings from PyTorch-BigGraph. This dataset serves as a valuable resource for training Entity Linking models and adapting the described method to various LLMs using specialized adapters. Our method does not require fine-tuning of the language models themselves; instead, we only train the adapter. This ensures that the model's performance on other tasks is not affected. We trained an adapter for the Mistral 7B, LLaMA 2-7B (chat), and LLaMA 3-8B (instruct) models using this dataset and demonstrated that our approach improves performance on the HaluEval, True-False benchmarks and FEVER dataset. The results indicate that incorporating KGs as a new modality can effectively reduce hallucinations and improve the factual accuracy of language models, all without the need for external retrieval.