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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for machine Learning Tasks in Logic Synthesis

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

Hybrid Spiking Vision Transformer for Object Detection with Event Cameras (ICML 2025)

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arXiv 2025年

作者： Xu, Qi Deng, Jie Shen, Jiangrong Chen, Biwu Tang, Huajin Pan, Gang School of Computer Science and Technology Dalian University of Technology Dalian China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xian China National Key Lab of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University Xian China State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Shanghai Radio Equipment Research Institute Shanghai China College of Computer Science and Technology Zhejiang University Hangzhou China

Event-based object detection has gained increasing attention due to its advantages such as high temporal resolution, wide dynamic range, and asynchronous address-event representation. Leveraging these advantages, Spiking Neural Networks (SNNs) have emerged as a promising approach, offering low energy consumption and rich spatiotemporal dynamics. To further enhance the performance of event-based object detection, this study proposes a novel hybrid spike vision Transformer (HsVT) model. The HsVT model integrates a spatial feature extraction module to capture local and global features, and a temporal feature extraction module to model time dependencies and long-term patterns in event sequences. This combination enables HsVT to capture spatiotemporal features, improving its capability in handling complex event-based object detection tasks. To support research in this area, we developed and publicly released The Fall Detection Dataset as a benchmark for event-based object detection tasks. This dataset, captured using an event-based camera, ensures facial privacy protection and reduces memory usage due to the event representation format. We evaluated the HsVT model on GEN1 and Fall Detection datasets across various model sizes. Experimental results demonstrate that HsVT achieves significant performance improvements in event detection with fewer parameters. © 2025, CC BY-NC-SA.

关键词： Object detection

Escarcitys: A framework for enhancing medical image classification performance in scarcity of trainable samples scenarios

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Neural Networks 2025年 189卷 107573页

作者： Wang, Tianxiang Dai, Qun Xiong, Wei College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Computer Science China University of Geosciences Wuhan430078 China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China

In the field of healthcare, the acquisition and annotation of medical images present significant challenges, resulting in a scarcity of trainable samples. This data limitation hinders the performance of deep learning models, creating bottlenecks in clinical applications. To address this issue, we construct a framework (EScarcityS) aimed at enhancing the success rate of disease diagnosis in scarcity of trainable medical image scenarios. Firstly, considering that Transformer-based deep learning networks rely on a large amount of trainable data, this study takes into account the unique characteristics of pathological regions. By extracting the feature representations of all particles in medical images at different granularities, a multi-granularity Transformer network (MGVit) is designed. This network leverages additional prior knowledge to assist the Transformer network during training, thereby reducing the data requirement to some extent. Next, the importance maps of particles at different granularities, generated by MGVit, are fused to construct disease probability maps corresponding to the images. Based on these maps, a disease probability map-guided diffusion generation model is designed to generate more realistic and interpretable synthetic data. Subsequently, authentic and synthetical data are mixed and used to retrain MGVit, aiming to enhance the accuracy of medical image classification in scarcity of trainable medical image scenarios. Finally, we conducted detailed experiments on four real medical image datasets to validate the effectiveness of EScarcityS and its specific modules. © 2025 Elsevier Ltd

关键词： Diffusion generation model Medical image classification Scarcity of trainable samples Transformer

Computational neuroscience:a frontier of the 21st century

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National science Review 2020年第9期7卷 1418-1422页

作者： Xiao-Jing Wang Hailan Hu Chengcheng Huang Henry Kennedy Chengyu Tony Li Nikos Logothetis Zhong-Lin Lu Qingming Luo Mu-ming Poo Doris Tsao Si Wu Zhaohui Wu Xu Zhang Douglas Zhou Center for Neural Science New York University Center for Neuroscience Key Laboratory of Medical Neurobiology of the Ministry of Health of China School of Medicine Zhejiang University Department of Neuroscience and Department of Mathematics Center for the Neural Basis of Cognition University of Pittsburgh Université Claude Bernard Lyon 1 Inserm Stem Cell and Brain Research Institute U1208 Institute of Neuroscience State Key Laboratory of Neuroscience Chinese Academy of SciencesCAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Center for Brain Science and Brain-Inspired Technology Division of Arts and Sciences and NYU-ECNU Institute of Cognitive Neuroscience NYU Shanghai School of Biomedical Engineering Hainan University Huazhong University of Science and Technology-Suzhou Institute for Brainsmatics Division of Biology and Biological Engineering California Institute of Technology Howard Hughes Medical Institute School of Electronics Engineering and Computer Science IDG/Mc Govern Institute for Brain Research PKU-Tsinghua Center for Life SciencesPeking University College of Computer Science and Technology Zhejiang University Institute of Brain-Intelligence Science and Technology Zhangjiang Lab School of Mathematical Sciences MOE-LSCand Institute of Natural Sciences Shanghai Jiao Tong University

THEORY IN NEUROscience The human brain is a biological organ,weighing about three pounds or 1.4 kg,that determines our behaviors, thoughts,emotions and consciousness. Although comprising only 2%of the total body weigh...

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A Silk-Based Multifunctional and Bidirectional Neural Interface

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A Silk-Based Multifunctional and Bidirectional Neural Interf...

IEEE International Conference on Micro Electro Mechanical Systems

作者： Xiner Wang Yan Wang Yuxin Liu Xiaoling Wei Liuyang Sun Tiger H. Tao Zhitao Zhou 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Graduate Study University of Chinese Academy of Sciences Beijing China College of Chemistry and Materials Science Shanghai Normal University Shanghai China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China Neuroxess Co. Ltd Shanghai China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Guangdong Institute of Intelligence Science and Technology Zhuhai China Tianqiao and Chrissy Chen Institute for Translational Research Shanghai China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

We present the development of a silk-based multifunctional and bidirectional neural interface that integrates neural recording, temperature sensing, intracranial pressure (ICP) monitoring, electrical stimulation, and photothermal stimulation. Owing to the excellent mechanical properties of silk fibroins, this neural interface not only conforms seamlessly to the brain surface but also demonstrates robust mechanical stability. Moreover, the ease of functionalizing silk materials enables the interface to achieve exceptional temperature and pressure sensing capabilities, along with significant photothermal effects. This multifunctional interface can be incorporated into brain-machine interfaces (BMIs) to investigate and modulate neural activities and intracranial physiological states. Our innovation introduces a novel, flexible neural interface for multimodal recording and modulation, expanding opportunities for advanced brain research and offering potential applications in disease diagnostics and therapeutics.

关键词： Temperature sensors Electric potential Technological innovation Temperature Neural activity Modulation brain-computer interfaces Recording Sensors Monitoring

Low-frequency cortical activity reflects context-dependent parsing of word sequences

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iscience 2025年第6期28卷 112650页

作者： Chen, Honghua Zhang, Minhui Ye, Tianyi Wolpert, Max A. Ding, Nai Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Sciences Zhejiang University Hangzhou 310027 China Nanhu Brain-computer Interface Institute Hangzhou China Department of Linguistics Georgetown University Washington 20057 DC United States The State Key Lab of Brain-Machine Intelligence The MOE Frontier Science Center for Brain Science & Brain-machine Integration Zhejiang University Hangzhou 310027 China

During speech listening, it has been hypothesized that the brain builds representations of linguistic structures like sentences, which are tracked by neural activity entrained to the rhythm of these structures. Alternatively, others proposed that these sentence-tracking neural activities may reflect the predictability or syntactic properties of individual words. Here, to disentangle the neural responses to sentences and words, we design word sequences that are parsed into different sentences in different contexts. By analyzing neural activity recorded by magnetoencephalography, we find that low-frequency neural activity strongly depends on context—the difference between MEG responses to the same word sequence in two contexts yields a low-frequency signal, which precisely tracks sentences. The predictability and syntactic properties of words can partly explain the neural response in each context but not the difference between contexts. In summary, low-frequency neural activity encodes sentences and can reliably reflect how same-word sequences are parsed in different contexts. © 2025 The Author(s)

关键词： Cognitive neuroscience Neuroscience

DU-GAN: Generative adversarial networks with dual-domain U-net based discriminators for low-dose CT denoising

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arXiv 2021年

作者： Huang, Zhizhong Zhang, Junping Zhang, Yi Shan, Hongming The Shanghai Key Lab of Intelligent Information Processing The School of Computer Science Fudan University Shanghai200433 China The College of Computer Science Sichuan University Chengdu610065 China The Institute of Science and Technology for Brain-Inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China

Low-dose computed tomography (LDCT) has drawn major attention in the medical imaging field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing the radiation dose, however, decreases the quality of the reconstructed images, which consequently compromises the diagnostic performance. Over the past few years, various deep learning techniques, especially generative adversarial networks (GANs), have been introduced to improve the image quality of LDCT images through denoising, achieving impressive results over traditional approaches. GANs-based denoising methods usually leverage an additional classification network, i.e. discriminator, to learn the most discriminate difference between the denoised and normal-dose images and, hence, regularize the denoising model accordingly;it often focuses either on the global structure or local details. To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains. The merit of such a U-Net based discriminator is that it can not only provide the per-pixel feedback to the denoising network through the outputs of the U-Net but also focus on the global structure in a semantic level through the middle layer of the U-Net. In addition to the adversarial training in the image domain, we also apply another U-Net based discriminator in the image gradient domain to alleviate the artifacts caused by photon starvation and enhance the edge of the denoised CT images. Furthermore, the CutMix technique enables the per-pixel outputs of the U-Net based discriminator to provide radiologists with a confidence map to visualize the uncertainty of the denoised results, facilitating the LDCT-based screening and diagnosis. Extensive experiments on the simulated and real-world datasets demonstrate superior p

关键词： Generative adversarial networks

Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition

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arXiv 2021年

作者： Wu, Yihan Xia, Min Nie, Li Zhang, Yangsong Fan, Andong School of Computer Science and Technology Laboratory for Brain Science and Medical Artificial Intelligence Southwest University of Science and Technology Mianyang621010 China College of Mathematics and Physics Chengdu University of Technology Chengdu610059 China MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu China

In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field. The neuroscience researches indicate that the left and right brain hemispheres demonstrate activity differences under different emotional activities, which could be an important principle for designing deep learning (DL) model for emotion recognition. Besides, owing to the nonstationarity of EEG signals, using convolution kernels of a single size may not sufficiently extract the abundant features for EEG classification tasks. Based on these two angles, we proposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on convolutional neural network (CNN) structure. Evaluated on the public DEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, i.e., arousal, valence, dominance and liking, respectively. This study further demonstrated the promising potential to design the DL model from the multi-scale characteristics of the EEG data and the neural mechanisms of the emotion cognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Emotion Recognition

ZeroED: Hybrid Zero-shot Error Detection through Large Language Model Reasoning

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arXiv 2025年

作者： Ni, Wei Zhang, Kaihang Miao, Xiaoye Zhao, Xiangyu Wu, Yangyang Wang, Yaoshu Yin, Jianwei Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Department of Data Science City University of Hong Kong Hong Kong Software College Zhejiang University Ningbo China Shenzhen Institute of Computing Sciences Shenzhen China College of Computer Science Zhejiang University Hangzhou China

Error detection (ED) in tabular data is crucial yet challenging due to diverse error types and the need for contextual understanding. Traditional ED methods often rely heavily on manual criteria and labels, making them labor-intensive. Large language models (LLM) can minimize human effort but struggle with errors requiring a comprehensive understanding of data context. In this paper, we propose ZeroED, a novel hybrid error detection framework, which combines LLM reasoning ability with the machine learning pipeline via zero-shot prompting. ZeroED operates in four steps, i.e., feature representation, error labeling, training data construction, and detector training. Initially, to enhance error distinction, ZeroED generates rich data representations using LLM-driven error reason-aware binary features, pre-trained embeddings, and statistical features. Then, ZeroED employs LLM to holistically label errors through in-context learning, guided by a two-step LLM reasoning process for detailed ED guidelines. To reduce token costs, LLMs are applied only to representative data selected via clustering-based sampling. High-quality training data is constructed through in-cluster label propagation and LLM augmentation with verification. Finally, a classifier is trained to detect all errors. Extensive experiments on seven datasets demonstrate that, ZeroED outperforms state-of-the-art methods by a maximum 30% improvement in F1 score and up to 90% token cost reduction. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning