The integration of artificial intelligence into engineering workflows has opened new frontiers for design automation and innovation. This paper explores an AI-driven approach to electrical engineering design, leveraging the capabilities of large language models (LLMs) such as GPT-4 to enhance both accuracy and efficiency. We propose a novel framework named ELEGANT (Electrical Engineering design Assisted by Natural-language Transformers), which utilizes LLMs to assist in key stages of electrical design, including schematic generation, component selection, and simulation parameter tuning. Unlike existing approaches, ELEGANT uniquely integrates a multi-stage natural language prompt parsing with domain-specific circuit simulation feedback, enabling adaptive and precise design automation. This leads to significant improvements in design speed and accuracy, while providing an intuitive user experience that bridges human intent with AI-driven execution. By aligning natural language prompts with engineering intent, our method enables intuitive interaction between human designers and AI systems, facilitating faster iteration and reduced design errors. We further introduce prompt engineering techniques and domain-specific adaptations to improve the performance of LLMs in electrical engineering tasks. Experimental results on several design scenarios demonstrate that our AI-assisted approach outperforms traditional methods in terms of design speed and correctness. This work highlights the transformative potential of LLMs in accelerating and augmenting electrical engineering design processes.

Industrial Anomaly Detection (IAD) is an indispensable quality control technology in modern production processes. Recently, on account of the outstanding visual comprehension and cross-domain knowledge transfer capabilities of multimodal large language models (MLLMs), existing studies have explored the application of MLLMs in the IAD domain and established some multimodal IAD datasets. However, although the latest datasets contain various fundamental IAD tasks, they formulate tasks in a general question-and-answer format lacking a rigorous reasoning process, and they are relatively limited in the diversity of scenarios, which restricts their reliability in practical applications. In this paper, we propose AnomalyCoT, a multimodal Chain-of-Thought (CoT) dataset for multi-scenario IAD tasks. It consists of 37,565 IAD samples with the CoT data and is defined by challenging composite IAD tasks. Meanwhile, the CoT data for each sample provides precise coordinates of anomaly regions, thereby improving visual comprehension of defects across different types. AnomalyCoT is constructed through a systematic pipeline and involves multiple manual operations. Based on AnomalyCoT, we conducted a comprehensive evaluation of various mainstream MLLMs and fine-tuned representative models in different ways. The final results show that Gemini-2.0-flash achieved the best performance in the direct evaluation with an accuracy rate of 59.6\%, while Llama 3.2-Vision achieves the best performance after LoRA fine-tuning with an accuracy rate of 94.0\%. Among all the fine-tuned models, the average accuracy improvement reaches 36.5\%, demonstrating the potential of integrating CoT datasets in future applications within the IAD field. The code and data are available at \url{https://github.com/Zhaolutuan/AnomalyCoT}.

Hyperspectral image multi-class change detection (HSI-MCD) based on deep learning (DL) rely significantly on the number of labeled data. Due to the high cost of manually labeling for hyperspectral images (HSIs), obtaining a large amount of labeled samples is difficult. Moreover, for multi-class change detection (MCD) tasks, there is the phenomenon of semantic cross-coupling of changes due to complex change scenarios. To solve the above problems, a cross-domain few-shot learning method based on fractional domain information for HSI-MCD (FrCFSL) is proposed. Firstly, a spectral-spatial-fractional information extraction module is proposed, which can extract spectral-spatial-fractional domain joint feature. Thus, the module can obtain more comprehensive and discriminative representations of land cover categories, alleviating the phenomenon of semantic cross-coupling between classes. Afterward, a cross-domain fewshot learning strategy is introduced, where it learns task-relevant category discrimination meta-knowledge from a pair of richly labeled very high-resolution optical images (VHRIs) dataset and transfers it to the bitemporal HSIs dataset. Thus, the model can achieve better MCD performance with a small number of labeled samples. Finally, to mitigate the domain distribution differences between VHRIs data and HSIs data, a topological structure alignment module is proposed to align the intrinsic topological relationships between land cover categories, thus narrowing the gap between the two domain distributions. Through experiments conducted on three HSI-MCD datasets and comparative analysis with six state-of-the-art methods, the validity and stability of the proposed method are indicated.

The application of hyperspectral image change detection (HSI-CD) in remote sensing is becoming increasingly widespread. However, due to the low spatial resolution of HSIs, conducting CD directly on the original HSIs does not effectively capture subtle changes. Therefore, this letter proposes an attention feature interaction CD method based on detail enhancement for dual-temporal HSIs (AIDECD). First, a detail enhancement module is designed to enhance the detail information of original HSIs. Second, considering the relationship between dual-temporal images, an attention interaction module is designed to achieve the interaction of temporal features between the dual-temporal images. Then, a multiscale feature extraction module is designed to capture features of different scales. The kappa coefficients obtained on three HSI datasets are 86.11%, 96.08%, and 97.54%, respectively. Compared with six other CD methods, this method has higher detection performance.

Chronic kidney disease (CKD) is a gradual loss of renal function over time, and it increases the risk of mortality, decreased quality of life, as well as serious complications. The prevalence of CKD has been increasing in the last couple of decades, which is partly due to the increased prevalence of diabetes and hypertension. To accurately detect CKD in diabetic patients, we propose a novel framework to learn sparse longitudinal representations of patients' medical records. The proposed method is also compared with widely used baselines such as Aggregated Frequency Vector and Bag-of-Pattern in Sequences on real EHR data, and the experimental results indicate that the proposed model achieves higher predictive performance. Additionally, the learned representations are interpreted and visualized to bring clinical insights.

Electronic health records (EHR) provide a rich source of temporal data that present a unique opportunity to characterize disease patterns and risk of imminent disease. While many data-mining tools have been adopted for EHR-based disease early detection, linear discriminant analysis (LDA) is one of the most commonly used statistical methods. However, it is difficult to train an accurate LDA model for early disease diagnosis when too few patients are known to have the target disease. Furthermore, EHR data are heterogeneous with significant noise. In such cases, the covariance matrices used in LDA are usually singular and estimated with a large variance. This article presents Daehr , an extension of the LDA framework using electronic health record data to address these issues. Beyond existing LDA analyzers, we propose Daehr to (1) eliminate the data noise caused by the manual encoding of EHR data and (2) lower the variance of parameter (covariance matrices) estimation for LDA models when only a few patients’ EHR are available for training. To achieve these two goals, we designed an iterative algorithm to improve the covariance matrix estimation with embedded data-noise/parameter-variance reduction for LDA. We evaluated Daehr extensively using the College Health Surveillance Network, a large, real-world EHR dataset. Specifically, our experiments compared the performance of LDA to three baselines (i.e., LDA and its derivatives) in identifying college students at high risk for mental health disorders from 23 U.S. universities. Experimental results demonstrate Daehr significantly outperforms the three baselines by achieving 1.4%--19.4% higher accuracy and a 7.5%--43.5% higher F1-score.

According to a 2014 Spring American College Health Association Survey, almost 50% of college students reported feeling things were hopeless and that it was difficult to function within the last 12 months. More than 80% reported feeling overwhelmed and exhausted by their responsibilities. This critical subpopulation of Americans is facing significant levels of mental health disorders, challenging colleges to provide accessible and high quality behavioral health care. However, psychiatric disorders are frequently unrecognized in primary care settings, posing physical, emotional, economic, and social burdens to patients and others. Towards the goal of earlier identification and treatment of mental health disorders, this paper proposes M-SEQ, an early detection framework for anxiety/depression using electronic health data from primary care visit sequences. Specifically, compared to existing methods that predict a future disease state using frequency of diagnoses in a patient's medical history, we hypothesize that future disease might also be correlated with the temporal orders of diagnoses. Thus, M-SEQ first discovers a set of diagnosis codes that are discriminative of anxiety/depression, and then extracts each diagnosis pair from each patient's health record to represent the temporal orders of diagnoses. Further, it incorporates the extracted temporal order information with the existing representation to predict whether a patient is at risk of anxiety/depression. We evaluate M-SEQ using the electronic health record (EHR) data of 213,112 college students from 10 schools participating in the College Health Surveillance Network (CHSN) from January 1, 2011 through December 31, 2014. The experimental results shows that our framework can detect a future diagnosis of anxiety and depression based on the primary care visit data up to 3 months in advance, with approximately 1%-4.5% higher accuracy, compared to baseline methods using frequency of diagnoses.

The analysis of large scale data logged from complex cyber-physical systems, such as microgrids, often entails the discovery of invariants capturing functional as well as operational relationships underlying such large systems. We describe a latent factor approach to infer invariants underlying system variables and how we can leverage these relationships to monitor a cyber-physical system. In particular we illustrate how this approach helps rapidly identify outliers during system operation.

We present a new sampling-based method for planning optimal, collision-free, curvature-constrained paths for nonholonomic robots to visit multiple goals in any order. Rather than sampling configurations as in standard sampling-based planners, we construct a roadmap by sampling circles of constant curvature and then generating feasible transitions between the sampled circles. We provide a closed-form formula for connecting the sampled circles in 2D and generalize the approach to 3D workspaces. We then formulate the multi-goal planning problem as finding a minimum directed Steiner tree over the roadmap. Since optimally solving the multi-goal planning problem requires exponential time, we propose greedy heuristics to efficiently compute a path that visits multiple goals. We apply the planner in the context of medical needle steering where the needle tip must reach multiple goals in soft tissue, a common requirement for clinical procedures such as biopsies, drug delivery, and brachytherapy cancer treatment. We demonstrate that our multi-goal planner significantly decreases tissue that must be cut when compared to sequential execution of single-goal plans.

In Chip Multiprocessors (CMPs) architecture, it is common that multiple cores share some on-chip cache. The sharing may cause cache thrashing and contention among co-running jobs. Job co-scheduling is an approach to tackling the problem by assigning jobs to cores appropriately so that the contention and consequent performance degradations are minimized. Job co-scheduling includes two tasks: the estimation of co-run performance, and the determination of suitable co-schedules. Most existing studies in job co-scheduling have concentrated on the first task but relies on simple techniques (e.g., trying different schedules) for the second. This paper presents a systematic exploration to the second task. The paper uncovers the computational complexity of the determination of optimal job co-schedules, proving its NP-completeness. It introduces a set of algorithms, based on graph theory and Integer/Linear Programming, for computing optimal co-schedules or their lower bounds in scenarios with or without job migrations. For complex cases, it empirically demonstrates the feasibility for approximating the optimal effectively by proposing several heuristics-based algorithms. These discoveries may facilitate the assessment of job co-schedulers by providing necessary baselines, as well as shed insights to the development of co-scheduling algorithms in practical systems.