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Proton-recoil spectrometer for fast neutron spectrum based on GEM gas detector 49

Proton-recoil spectrometer for fast neutron spectrum based o...

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49th EPS conference on Plasma Physics, EPS 2023

作者： Scholz, M. Dąbrowski, W. Wiącek, U. Jardin, A. Lach, B. Drozdowicz, K. Woźnicka, U. Kurowski, A. Kulińska, A. Mazon, D. KrakowPL-31-342 Poland AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland CEA IRFM Saint-Paul-lez-DuranceF-13108 France

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Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segmentation Network Based on Local Attention and Mamba 27th

Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segment...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Zhang, Feifei Shi, Fei Ren, Dayong Jia, Zhenhong Wang, Jianyi School of Computer Science and Technology Xinjiang University Urumqi Xinjiang830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang830046 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

ISBN: (纸本)9783031781032

Brain tissue segmentation is critical for diagnosing and treating brain diseases. While Mamba-based models excel in the medical field, they face performance bottlenecks with high-resolution MRI images, often losing local feature information in complex texture structures. To address these challenges and enable deployment in resource-limited settings, we propose Dual-MambaNet, a lightweight segmentation model based on Mamba. In Dual-MambaNet, we introduce the Outlook attention module to capture local complex textures and structures in brain MRI images. Subsequently, we combined it with the Mamba block to construct a feature extractor (FE) encoder layer to couple local and global features. Additionally, we integrate dual decoder branches and a multi-level pixel contrastive loss function(MPCL) to better integrate local and global features. This method optimizes global feature representation by refining local complex textures and structural details, effectively capturing multi-level features in MRI images. Experimental results on public brain MRI datasets OASIS1 and MRBrainS13 demonstrate that Dual-MambaNet achieves high segmentation accuracy with minimal parameters and computational complexity, making it suitable for deployment in resource-limited medical environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic resonance imaging

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Cluster-Mined Negative Samples for Enhanced Unsupervised Sentence Representation Learning 27th

Cluster-Mined Negative Samples for Enhanced Unsupervised Sen...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Zhang, Yuhang Zhang, Wenjie Hua, Yang Wang, Zun Song, Xiaoning Wu, Xiao-Jun School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Biotechnology Co. Ltd. Suzhou215000 China

ISBN: (纸本)9783031783944

Negative samples selection for contrastive learning is considerable in the field of sentence representation, especially for semantic textual similarity. Traditional in-batch negative sampling methods not only lack hard negative samples but also ignore potential false negative samples. Despite numerous methods trying to improve traditional sampling strategies, the challenge of consistently generating high-quality negative samples remains untackled. To address this pivotal issue, we propose the Cluster-Mined Negative Samples for Enhanced Unsupervised Sentence Representation Learning (CMNS) framework. Specifically, dynamic queues are utilized to store the K-means cluster samples, enabling the most appropriate selection of clusters to serve as negative samples. Additionally, we generate noise-based negative samples via stored clusters while simultaneously constraining potential false negative samples. Above all, CMNS employs clustering techniques to efficiently mine sufficient quantity of high-quality negative samples from unlabeled datasets. Extensive experiments illustrate that our approach not only overcomes the inherent limitations of traditional sampling methods but also improves the performance of sentence representations in downstream tasks, demonstrating measurable advancements over current methodologies. (codes and models are available at https://***/hamrain/CMNS). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Speech Spoofing Detection Based on Graph Attention Networks with Spectral and Temporal Information 5

Speech Spoofing Detection Based on Graph Attention Networks ...

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5th ACM International conference on Multimedia in Asia, MMAsia 2023

作者： Zhang, Peng Chen, Yida Li, Meijuan Zhao, Hui Zhang, Jianqiang Wang, Fuqiang Wu, Xiaoming Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798400702051

Automatic speaker verification (ASV) systems are vulnerable to synthetic speech attacks. Synthetic algorithms usually introduce artifacts in specific sub-bands or time segments. However, under unknown spoofing attacks, it is challenging to choose the right domain for effective detection. In this paper, we propose a speech spoofing detection method based on graph attention networks with spectral and temporal information. First, high-level features of raw audio are extracted using SENet channel attention to enhance the spatial correlation between speech frames. Then, spectral graph and temporal graph are constructed for the high-level features using graph attention networks. Finally, we design a new heterogeneous multi-domain co-graph attention module to process the information from different domains for effective speech spoofing detection. The proposed model was evaluated on the ASVspoof 2019 dataset and obtains a min t-DCF of 0.0264 and an EER of 0.94%, exhibiting competitive performance. Experiments also show its effectiveness when detecting unknown types of attacks. © 2023 Copyright held by the owner/author(s).

关键词： Speech recognition

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Towards Fast Crash-Consistent Cluster Checkpointing

Towards Fast Crash-Consistent Cluster Checkpointing

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IEEE high performance Extreme Computing Virtual conference (HPEC)

作者： Wood, Andrew Hershcovitch, Moshik Ennmouri, Ilias Zong, Weiyu Chennuri, Saurav Cohen, Sarel Sundararaman, Swaminathan Waddington, Daniel Chin, Peter Boston Univ Boston MA USA IBM Res Boston MA USA Hasso Plattner Inst Acad Coll Tel Aviv Yaffo Tel Aviv Germany Dartmouth Coll Hanover NS USA

ISBN: (数字)9781665497862

ISBN: (纸本)9781665497862

Machine Learning models are expensive to train: they require expensive high-compute hardware and have long training times. Therefore, models are extra sensitive to program faults or unexpected system crashes, which can erase hours if not days worth of work. While there are plenty of strategies designed to mitigate the risk of unexpected system downtime, the most popular strategy in machine learning is called checkpointing: periodically saving the state of the model to persistent storage. Checkpointing is an effective strategy, however, it requires carefully balancing two operations: how often a checkpoint is made (the checkpointing schedule), and the cost of creating a checkpoint itself. In this paper, we leverage Python Memory Manager (PyMM), which provides Python support for Persistent Memory and emerging Persistent Memory technology (Optane DC) to accelerate the checkpointing operation while maintaining crash consistency. We first show that when checkpointing models, PyMM with persistent memory can save from minutes to days of checkpointing runtime. We then further optimize the checkpointing operation with PyMM and demonstrate our approach with the KMeans and Gaussian Mixture Model algorithms on two real-world datasets: MNIST and MusicNet. Through evaluation, we show that these two algorithms achieve a checkpointing speedup of a factor between 10 and 75x for KMeans and over 3x for GMM against the current state-of-the-art checkpointing approaches. We also verify that our solution recovers from crashes, while traditional approaches cannot.

关键词： Checkpointing Training Schedules Runtime Memory management Machine learning computer crashes

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Design and implementation of greenhouse environment monitoring system 8

Design and implementation of greenhouse environment monitori...

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8th IEEE Information Technology and Mechatronics Engineering conference, ITOEC 2025

作者： Jiang, Feng Wang, Qing Lu, Meiqi Wang, Wenshuai Luo, Yaoming Yu, Chengcheng Nantong Institute of Technology School of Information Engineering Nantong China Nantong Institute of Technology School of Electrical and Energy Engineering Nantong China

ISBN: (纸本)9798331517915

Agricultural modernization has become an inevitable trend of global agricultural development. The traditional greenhouse monitoring system has limitations, which cannot detect and deal with the abnormal environment in the greenhouse in time. In response to the above problems, this paper designs a greenhouse environment monitoring system based on STM32. The hardware part of the system uses STM32 as the main control module. The LD3322 speech recognition sensor is used in the speech module. Temperature and humidity sensor detection module using DHT11 digital sensor;Soil moisture using waterproof DS18B20 digital sensor;The actuator is controlled by a relay module. In addition, the LORA module is used in the wireless communication module, so that users can remotely monitor the situation of the greenhouse environment. In the light acquisition part of the software, the Kalman filter algorithm is used to analyze and process the data, and the serial communication protocol is used to transmit the collected data to the host computer. Through experimental debugging, the system realizes the function of speech recognition control and environmental temperature and humidity detection. The experimental results show that the greenhouse environmental monitoring and control system has high stability and reliability, can effectively solve the problems existing in the traditional greenhouse monitoring, and has certain application prospects and promotion value. © 2025 IEEE.

关键词： program debugging

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Importance Weighted Variational Cardiac MRI Registration Using Transformer and Implicit Prior 26th

Importance Weighted Variational Cardiac MRI Registration Usi...

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26th International conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Xu, Kangrong Huang, Qirui Yang, Xuan Shenzhen Univ Shenzhen 518060 Guangdong Peoples R China Guangdong Prov Key Lab Popular High Performance C Shenzhen Guangdong Peoples R China Shenzhen Key Lab Serv Comp & Applicat Shenzhen Guangdong Peoples R China

ISBN: (纸本)9783031439988;9783031439995

The variational registration model takes advantage of explaining uncertainties of registration results. However, most existing variational registration models are based on convolutional neural networks (CNNs), which cannot capture distant information in images. Besides, the evidence lower bound (ELBO) and the commonly used standard prior cannot close the gap between the real posterior and the variational posterior in the vanilla variational registration model. This paper proposes a network in a variational image registration model for cardiac motion estimation to effectively capture the spatial correspondence of long-distance images and solve the shortcomings of CNNs. Our proposed network comprises a Transformer with a T2T module and the cross attention between the moving and the fixed images. To close the gap between the real posterior and the variational posterior, the importance-weighted evidence lower bound (iwELBO) is introduced into the variational registration model with an implicit prior. The coefficients of a parametric transformation using multi-supports CSRBFs are latent variables in our variational registration model, which improve registration accuracy significantly. Experimental results show that the proposed method outperforms state-of-arts research on public cardiac datasets.

关键词： Variational inference Transformer Cross attention Compact support radial basis function (CSRBF)

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libcrpm: Improving the Checkpoint performance of NVM 22

libcrpm: Improving the Checkpoint Performance of NVM

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59th ACM/IEEE Design Automation conference (DAC) - From Chips to Systems - Learn Today, Create Tomorrow

作者： Ren, Feng Chen, Kang Wu, Yongwei Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRis Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781450391429

libcrpm is a new programming library to improve the checkpoint performance for applications running in NVM. It proposes the failure-atomic differential checkpointing protocol, which addresses two problems simtdtaneously that exist in the current NVM-based checkpoint-recovery libraries: (1) high write amplification when page-granularity incremental checkpointing is used, and (2) high persistence costs from excessive memory fence instructions when line-grained undo-log or copy-on-write is used. Evaluation results show that libcrpm reduces the checkpoint overhead in realistic workloads. For MPI-based parallel applications such as LULESH, the checkpoint overhead of libcrpm is only 44.78% of FTI, an application-level checkpoint-recovery library.

关键词： computer programming

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Nonuniformly Spaced Control Points Based on Variational Cardiac Image Registration 26th

Nonuniformly Spaced Control Points Based on Variational Card...

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26th International conference on Medical Image Computing and computer-Assisted Intervention (MICCAI)

作者： Su, Haosheng Yang, Xuan Shenzhen Univ Shenzhen 518060 Guangdong Peoples R China Guangdong Prov Key Lab Popular High Performance C Shenzhen Guangdong Peoples R China Shenzhen Key Lab Serv Comp & Applicat Shenzhen Guangdong Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Non-uniformly spaced control points located on the interface of different objects are beneficial for constructing an accurate displacement field for image registration. However, extracting features of non-uniformly spaced control points in images is challenging for convolutional neural networks (CNNs). We extend a probabilistic image registration model using uniformed-spaced control points by employing non-uniformly-spaced control points. We construct a network to extract the image and spatial features of non-uniformly-spaced control points. Moreover, a variational Bayesian (VB) model using a factorized prior is employed to estimate the distribution of latent variables. In theory, we analyze the KL divergence between the posterior and the two separated priors. We found that the factorized prior has the advantage of decreasing the KL divergence, but too more factorized priors, such as the standard normal, might deteriorate registration accuracy. Moreover, we analyze the relationship between the uncertainty of the displacement field and the spatial distribution of control points. Experimental results on four public datasets show that our network outperforms the state-of-arts registration networks and can provide registration uncertainty.

关键词： Cardiac image registration Variational Bayesian Non-uniformly-spaced control points

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Parallel Optimization of SLIC Algorithm for New-Generation Sunway Processors 23

Parallel Optimization of SLIC Algorithm for New-Generation S...

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7th International conference on high performance Compilation, Computing and Communications, HP3C 2023

作者： Xia, Zhuozhao Zhang, Qiang Zhou, Xinhang Zhao, Hongjie School of Computer Science Southwest Petroleum University Chengdu China

ISBN: (纸本)9781450399883

This study aims to explore optimization methods for superpixel segmentation algorithms in large-scale image processing. Due to the significant time consumption of existing superpixel segmentation algorithms when dealing with large-scale image data, this study focuses on parallel optimization of the SLIC superpixel segmentation algorithm on the new-generation Sunway manycore processor SW26010-Pro. The research analyzes the characteristics of the SW26010-Pro heterogeneous manycore processor, existing superpixel image segmentation algorithms, and the SLIC superpixel segmentation algorithm. The study primarily analyzes hot-spot functions and improves the algorithm's running speed by 6-7 times through optimizing processes such as RGB-to-CIELab color space conversion, cluster updating, and seed point updating. It effectively presents a parallel execution scheme for the SLIC superpixel algorithm on the SW26010-Pro. © 2023 ACM.

关键词： Superpixels

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