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Abstract Industrial environments demand accurate detection of anomalies to maintain product quality and ensure operational safety. Traditional industrial anomaly detection (IAD) methods often lack the flexibility and adaptability needed in dynamic production settings, where new defect types and operational changes continually emerge. Recent advancements in multimodal large language models (MLLMs) have shown promise by combining visual and textual processing capabilities, yet they are often limited by their lack of domain-specific expertise, particularly regarding industry-standard defect tolerances. To overcome limitations, we introduce Echo, a novel multi-expert framework designed to enhance MLLM performance for IAD. Echo integrates four specialized modules: the Reference Extractor retrieves similar normal images to establish contextual baselines; the Knowledge Guide provides critical, industry-specific insights; the Reasoning Expert enables structured, stepwise analysis for complex queries; and the Decision Maker synthesizes information from the preceding modules to deliver precise, context-aware responses. Evaluations on the MMAD benchmark reveal that Echo significantly improves adaptability, precision, and robustness compared to conventional approaches. Our results demonstrate that guided MLLMs, when augmented with expert modules, can effectively bridge the gap between general visual understanding and the specialized requirements of industrial anomaly detection, paving the way for more reliable and interpretable inspection systems. 

With the increasing integration of machine learning into IoT devices, managing energy consumption and data transmission has become a critical challenge. Many IoT applications depend on complex computations performed on server-side infrastructure, necessitating efficient methods to reduce unnecessary data transmission. One promising solution involves deploying compact machine learning models near sensors, enabling intelligent identification and transmission of only relevant data frames. However, existing near-sensor models lack adaptability, as they require extensive pre-training and are often rigidly configured prior to deployment. This paper proposes a novel framework that fuses online learning, active learning, and knowledge distillation to enable adaptive, resource-efficient near-sensor intelligence. Our approach allows near-sensor models to dynamically fine-tune their parameters post-deployment using online learning, eliminating the need for extensive pre-labeling and training. Through a sequential training and execution process, the framework achieves continuous adaptability without prior knowledge of the deployment environment. To enhance performance while preserving model efficiency, we integrate knowledge distillation, enabling the transfer of critical insights from a larger teacher model to a compact student model. Additionally, active learning reduces the required training data while maintaining competitive performance. We validated our framework on both benchmark data from the MS COCO dataset and in a simulated IoT environment. The results demonstrate significant improvements in energy efficiency and data transmission optimization, highlighting the practical applicability of our method in real-world IoT scenarios. 

Link prediction is a crucial task in network analysis, but it has been shown to be prone to biased predictions, particularly when links are unfairly predicted between nodes from different sensitive groups. In this paper, we study the fair link prediction problem, which aims to ensure that the predicted link probability is independent of the sensitive attributes of the connected nodes. Existing methods typically incorporate debiasing techniques within graph embeddings to mitigate this issue. However, training on large real-world graphs is already challenging, and adding fairness constraints can further complicate the process. To overcome this challenge, we proposeFairLink, a method that learns a fairness-enhanced graph to bypass the need for debiasing during the link predictor's training.FairLinkmaintains link prediction accuracy by ensuring that the enhanced graph follows a training trajectory similar to that of the original input graph. Meanwhile, it enhances fairness by minimizing the absolute difference in link probabilities between node pairs within the same sensitive group and those between node pairs from different sensitive groups. Our extensive experiments on multiple large-scale graphs demonstrate thatFairLinknot only promotes fairness but also often achieves link prediction accuracy comparable to baseline methods. Most importantly, the enhanced graph exhibits strong generalizability across different GNN architectures.FairLinkis highly scalable, making it suitable for deployment in real-world large-scale graphs, where maintaining both fairness and accuracy is critical. 

Processing In-Memory (PIM) is a data-centric computation paradigm that performs computations inside the memory, hence eliminating the memory wall problem in traditional computational paradigms used in Von-Neumann architectures. The associative processor, a type of PIM architecture, allows performing parallel and energy-efficient operations on vectors. This architecture is found useful in vector-based applications such as Hyper-Dimensional (HDC) Reinforcement Learning (RL). HDC is rising as a new powerful and lightweight alternative to costly traditional RL models such as Deep Q-Learning. The HDC implementation of Q-Learning relies on encoding the states in a high-dimensional representation where calculating Q-values and finding the maximum one can be done entirely in parallel. In this article, we propose to implement the main operations of a HDC RL framework on the associative processor. This acceleration achieves up to\(152.3\times\)and\(6.4\times\)energy and time savings compared to an FPGA implementation. Moreover, HDRLPIM shows that an SRAM-based AP implementation promises up to\(968.2\times\)energy-delay product gains compared to the FPGA implementation.