Electrocardiogram (ECG) is a diagnostic tool used to determine whether abnormalities exist in cardiac electrical activity. Supervised ECG analysis methods are limited in identifying rare abnormalities because training data cannot encompass all abnormal patterns. In contrast, Unsupervised Anomaly Detection (UAD) methods can identify anomalies solely by learning the characteristics of normal ECG signals. However, existing UAD approaches still exhibit notable deficiencies in recognizing local anomalies and addressing reconstruction instability caused by noise interference and uncertainty fluctuations. Accordingly, we propose an unsupervised ECG anomaly detection network-MedNet (A Medical Overview-Focus Network). MedNet simulates the diagnostic workflow of ECG specialists and adopts a single-branch "global overview-local focus" architecture. This architecture first extracts global rhythm features through OverviewNet and uses them as prior information to guide FocusNet in concentrating on potential abnormal regions, thereby enhancing the model's capability to detect subtle anomalies. Meanwhile, MedNet incorporates a Context-aware Feature Reweighting (CFR) mechanism, which dynamically adjusts feature weights to highlight discriminative information and further improve the model's sensitivity to anomalous signals. In addition, we propose an Uncertainty-Weighted Regularization Loss (UWR Loss), which uses model predicted uncertainty as a regulatory factor to jointly constrain reconstruction errors and non-physiological discontinuities resulting from noise and uncertainty fluctuations, thus enhancing reconstruction stability. Extensive experiments on two benchmark ECG datasets demonstrate that MedNet achieves performance comparable to existing methods across major metrics, showcasing its effectiveness and generalization ability in unsupervised ECG anomaly detection tasks.

In real industries, there often exist application scenarios where the target domain holds fault categories never observed in the source domain, which is an open-set domain adaptation (DA) diagnosis issue. Existing DA diagnosis methods under the assumption of sharing identical label space across domains fail to work. What is more, labeled samples can be collected from different sources, where multisource information fusion is rarely considered. To handle this issue, a multisource open-set DA diagnosis approach is developed. Specifically, multisource domain data of different operation conditions sharing partial classes are adopted to take advantage of fault information. Then, an open-set DA network is constructed to mitigate the domain gap across domains. Finally, a weighting learning strategy is introduced to adaptively weigh the importance on feature distribution alignment between known class and unknown class samples. Extensive experiments suggest that the proposed approach can substantially boost the performance of open-set diagnosis issues and outperform existing diagnosis approaches.

Medical Named Entity Recognition (NER) is a fundamental but challenging task due to the lack of specialized entity datasets like tumor entities, which are often overlapped and discontinuous. In this paper, we propose a novel Prompt Enhanced Generative Machine Reading Comprehension Framework (PGMRC) to improve the overlapped and discontinuous NER performance. Specifically, we formulate NER as a Machine Reading Comprehension (MRC) task and employ a pre-trained encoder-decoder module to generate entity span sequences according to their entity query. In this way, we adopt query to guide the model to focus on answer entities in context, which can naturally solve entity overlap and alleviate the exposure bias of the generative model. Then, we introduce continuous prompts to the self-attention mechanism in Transformer to reduce the dependence on manually constructed queries. In addition, we annotate 875 pathological documents of pancreatic cancer and construct a Chinese pathological NER dataset (PAN) containing overlapped and discontinuous entities. Finally, we conduct our experiments on three widely used benchmarks (GENIA, ACE04, ACE05) and our dataset PAN. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods.

In fifth-generation wireless communication system (5G), more connections are built between metaheuristics and electromagnetic equipment design. In this paper, we propose a self-adaptive grey wolf optimizer (SAGWO) combined with a novel optimization model of a 5G frequency selection surface (FSS) based on FSS unit nodes. SAGWO includes three improvement strategies, improving the initial distribution, increasing the randomness, and enhancing the local search, to accelerate the convergence and effectively avoid local optima. In benchmark tests, the proposed optimizer performs better than the five other optimization algorithms: original grey wolf optimizer (GWO), genetic algorithm (GA), particle swarm optimizer (PSO), improved grey wolf optimizer (IGWO), and selective opposition based grey wolf optimization (SOGWO). Due to its global searchability, SAGWO is suitable for solving the optimization problem of a 5G FSS that has a large design space. The combination of SAGWO and the new FSS optimization model can automatically obtain the shape of the FSS unit with electromagnetic interference shielding capability at the center operating frequency. To verify the performance of the proposed method, a double-layer ring FSS is designed with the purpose of providing electromagnetic interference shielding features at 28 GHz. The results show that the optimized FSS has better electromagnetic interference shielding at the center frequency and has higher angular stability. Finally, a sample of the optimized FSS is fabricated and tested.

Cell detection on immunohistochemistry stained (IHC-stained) images plays an essential role in computer assisted prediction of tumor progression and treatment response. Currently available cell detection datasets provide either point level or bounding box level annotations for deep object detection network training. And these widely used networks usually employ standard pyramid structured multi-scale feature fusion. However, we find that these methods have obvious limitation when facing large amounts of cells in similar scale with severe overlapping. To address this problem, we propose a novel CellDet network with (1) Scale Consistency Feature Fusion Module (SCFFM) and (2) Dual Task Detection Module to simultaneously exploit the complementary information from both point and bounding box annotations. In order to verify the effectiveness our proposed method, We make efforts to relabel the public SHIDC-B-Ki-67 dataset with bounding box annotations. Extensive experimental results show that the proposed CellDet outperforms other state-of-the-art cell detection methods with a remarkable margin. We will release our source code and dataset in https://github.com/JiweiMaster/celldet.

NCL X circuit is a very efficient way to implement the QDI circuit, which can get all the advantages of the asynchronous circuit, especially the average performance. But the NCL_X circuits suffer from its huge area overhead. To solve this problem, a method for optimizing the complete detection network in the NCL_X circuit has been introduced in this paper. Using this method can dramatically reduced the area of the NCL_X circuit, according to the experimental result, the area of the NCL_X circuit may be reduced more than 60%. We also use this optimized method to implement an asynchronous microprocessor pipeline (APC). Compared to the synchronous implementation, this NCL_X implementation can achieve higher performance because the NCL_X circuit can get the average performance.