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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shibo Liu Xuan Zhang Anlei Jiao Shiwei Sun Longyang Dian Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China State Key Laboratory of Microbial Technology Shandong University Qingdao China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI’s 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC).

关键词： Representation learning Deep learning Accuracy Databases Source coding Ionization Predictive models Mass spectroscopy Electrons Software development management

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How to Train Your Neural Network for Molecular Structure Generation from Mass Spectra?

How to Train Your Neural Network for Molecular Structure Gen...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kai Zhao Yanmin Liu Longyang Dian Shiwei Sun Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China State Key Laboratory of Microbial Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Mass spectrometry serves as a pivotal tool for the analysis of small molecules through an examination of their mass-to-charge ratios. Recent advancements in deep learning have markedly enhanced the analysis of mass spectrometric data, facilitating the prediction of novel small molecule structures without the necessity of extensive databases. Nonetheless, the paucity of annotated datasets impedes the efficacious training of molecular generation models predicated on MS 2 spectra. To mitigate this limitation, we introduce ctMSNovelist, an avant-garde method that amalgamates pre-training, fine-tuning, and co-training techniques to construct a more precise model for the generation of molecular structures from tandem mass spectrometry data. This novel approach augments both the training regimen and the predictive accuracy of the MSNovelist model, thereby surmounting the obstacle of limited data. The methodology commences with the pretraining of a Variational Autoencoder (VAE) to generate molecular fingerprints derived from SMILES strings. Subsequently, it undergoes fine-tuning to emulate noisy fingerprints originating from mass spectrometry (MS) data. Concurrently, MSNovelist is co-trained utilizing these simulated fingerprints, inclusive of the highly noisy variants produced in the early stages of VAE training. The incorporation of a substantial volume of noisy data serves to enhance model accuracy and avert overfitting. We evaluated ctMSNovelist using the GNPS dataset and attained a SMILES prediction accuracy of 48.8%, representing a 4.1% enhancement over MSNovelist. It is pertinent to note that the sole distinction between ctMSNovelist and MSNovelist in this experiment was the training process. The code and models are publicly available at https://***/xfcui/ctMSNovelist.

关键词： Training Solid modeling Accuracy Biological system modeling Fingerprint recognition Predictive models Mass spectroscopy Data models Noise measurement Overfitting

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EdgeCross: Cloud Scale Traffic Management at Peering Edges

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Proceedings of the ACM on Networking 2024年第CONEXT4期2卷 1-23页

作者： Xiaoliang Wang Penghui Mi Yong Zhu Baoyi An Yinhua Wang Lixiang Wang Xuezhi Yu Qiong Xie Xiang Huang Mingliang Yin Chaoyang Ji Wei Sun Yihang Lv Yuhang Chen Cam-Tu Nguyen Chen Tian Xiaoming Fu State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Huawei Cloud Computing Technologies Co. Ltd. Shenzhen China Institute of Computer Science University of Göttingen Göttingen Germany

Cloud providers deployed dozens of PoPs and data centers globally to serve billions of geo-distributed users. The traffic management at peering edges has become a key capability of cloud network operators to meet the diverse demands of users. With the rapid growth of cloud applications, users have recently announced new performance requirements, e.g., achieving latency as low as possible instead of maintaining a specified delay. The conventional inter-domain bandwidth allocation approach, which aims to reduce the high operating expenditures of bandwidth usage, fails to meet these new requirements. We further reveal that the flow scheduling among PoPs may fail due to the limited link capacity hidden by the cloud private backbone network controller. Therefore we seek a new traffic management at peering *** propose a new controller framework, EdgeCross, that satisfies not only users' emerging demands but maintains low operating costs. The large number of fine-grain application-aware flows and the consideration of backbone links' capacity lead to very high complexity of routing computation and verification for the controller. EdgeCross introduces a two-phase operation that first achieves the low-expense bandwidth allocation according to the standard 95th percentile billing model and then allocates specified flows to peering edges based on users' requirements. EdgeCross further reduces large memory consumption by proposing an effective routing table compression approach. The evaluation based on a production network with 16 PoPs has shown that EdgeCross can successfully process the routes of 1 billion flows in 10 seconds, reduce the average delay for performance-sensitive flows by 2 milliseconds compared to traditional BGP, and is able to save the bandwidth cost by 10-26% compared to the state-of-the-art Cascara.

关键词： cloud network flow scheduling peering edges sdn controller

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Wave-particle duality relation with a quantum N-path beamsplitter

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Chinese Physics B 2021年第5期30卷 220-228页

作者： Dong-Yang Wang Jun-Jie Wu Yi-Zhi Wang Yong Liu An-Qi Huang Chun-Lin Yu Xue-Jun Yang Institute for Quantum Information&State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Information and Communication National University of Defense TechnologyXi'an 710006China China Greatwall Quantum Laboratory China Greatwall Technology Group CO.LTD.Changsha 410073China

The wave-particle duality relation derived by Englert sets an upper bound of the extractable information from wave and particle properties in a two-path ***,previous studies demonstrated that the introduction of a quantum beamsplitter in the interferometer could break the limitation of this upper bound,due to interference between wave and particle *** the other line,a lot of efforts have been made to generalize this relation from the two-path setup to the N-path ***,it is an interesting question that whether a quantum N-path beamsplitter can break the limitation as *** paper systemically studies the model of a quantum N-path beamsplitter,and finds that the generalized wave-particle duality relation between interference visibility and path distinguishability is also broken in certain *** further study the maximal extractable information's reliance on the interference between wave and particle properties,and derive a quantitative *** then propose an experimental methodology to verify the break of the *** work reflects the effect of quantum superposition on wave-particle duality,and exhibits a new aspect of the relation between visibility and path distinguishability in N-path ***,it implies the observer's influence on wave-particle duality.

关键词： wave-particle duality interference visibility path distinguishability quantum N-path beamsplitter

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AutoQNN: An End-to-End Framework for Automatically Quantizing Neural Networks

arXiv

引用

arXiv 2023年

作者： Gong, Cheng Lu, Ye Dai, Surong Qian, Deng Du, Chenkun Li, Tao College of Software Nankai University Tianjin300350 China College of Computer Science Nankai University Tianjin300350 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Exploring the expected quantizing scheme with suitable mixed-precision policy is the key point to compress deep neural networks (DNNs) in high efficiency and accuracy. This exploration implies heavy workloads for domain experts, and an automatic compression method is needed. However, the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios. In this paper, we propose an end-to-end framework named AutoQNN, for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor. AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques: quantizing scheme search (QSS), quantizing precision learning (QPL), and quantized architecture generation (QAG). QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search, and then uses the differentiable neural architecture search (DNAS) algorithm to seek the layer- or model-desired scheme from the set. QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes, to the best of our knowledge. QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint. QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention, to facilitate end-to-end neural network quantization. We have implemented AutoQNN and integrated it into Keras. Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization. For 2-bit weight and activation of AlexNet and ResNet18, AutoQNN can achieve the accuracy results of 59.75% and 68.86%, respectively, and obtain accuracy improvements by up to 1.65% and 1.74%, respectively, compared

关键词： Deep neural networks

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BuildEnVR: An Immersive Analysis System for Environmental Field

BuildEnVR: An Immersive Analysis System for Environmental Fi...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhenghan Zhou Kebin Liu Yantong Xie Hangxu Jin Shi Liu Ruiqing Wang Haitian Zhao Borong Lin Xiaofang Mu Hui Qi Global Innovation Exchange Tsinghua University Fuzhou Fuyao Institute for Advanced Study School of Architecture Carnegie Mellon University School of Architecture Tsinghua University College of Computer Science and Technology Taiyuan Normal University Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Amidst global warming and escalating extreme weather events, indoor environmental quality’s impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR’s intuitiveness and precision, and it is suitable for both experts and general users.

关键词： Wireless communication Wireless sensor networks Three-dimensional displays Buildings Psychology Data visualization Cognitive load Real-time systems Space heating Meteorology Climate change Global warming Environmental factors Immersive learning

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TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data

arXiv

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arXiv 2024年

作者： Liu, Niqi Liu, Fang Ji, Wenqi Du, Xinxin Liu, Xu Zhao, Guozhen Mu, Wenting Liu, Yong-Jin BNRist Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China State Key Laboratory of Media Convergence and Communication Communication University of China China Multimodal Sensing and Computing Laboratory Beijing China CAS Key Laboratory of Behavioral Science Institute of Psychology China Department of Psychology Tsinghua University China

The robust generalization of deep learning models in the presence of inherent noise remains a significant challenge, especially when labels are subjective and noise is indiscernible in natural settings. This problem is particularly pronounced in many practical applications. In this paper, we address a special and important scenario of monitoring suicidal ideation, where time-series data, such as photoplethysmography (PPG), is susceptible to such noise. Current methods predominantly focus on image and text data or address artificially introduced noise, neglecting the complexities of natural noise in time-series analysis. To tackle this, we introduce a novel neural network model tailored for analyzing noisy physiological time-series data, named TNANet, which merges advanced encoding techniques with confidence learning, enhancing prediction accuracy. Another contribution of our work is the collection of a specialized dataset of PPG signals derived from real-world environments for suicidal ideation prediction. Employing this dataset, our TNANet achieves the prediction accuracy of 63.33% in a binary classification task, outperforming state-of-the-art models. Furthermore, comprehensive evaluations were conducted on three other well-known public datasets with artificially introduced noise to rigorously test the TNANet's capabilities. These tests consistently demonstrated TNANet's superior performance by achieving an accuracy improvement of more than 10% compared to baseline methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Energy-efficient NTT Design with One-bank SRAM and 2-D PE Array

Energy-efficient NTT Design with One-bank SRAM and 2-D PE Ar...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jianan Mu Huajie Tan Jiawen Wu Haotian Lu Chip-Hong Chang Shuai Chen Shengwen Liang Jing Ye Huawei Li Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences CASTEST Tianjin University Nanyang Technological University Rock-Solid Security Lab Fiberhome

In Number Theoretic Transform (NTT) operation, more than half of the active energy consumption stems from memory accesses. Here, we propose a generalized design method to improve the energy efficiency of NTT operation by considering the effect of processing element (PE) geometry and memory organization on the data flow between PEs and memory. To decrease the number of data bits that are required to be accessed from the memory, a two-dimensional (2-D) PE array architecture is used. A pair of ping-pong buffers are proposed to transposed swap the coefficients to enable a single bank of memory to be used with the 2-D PE array to reduce the average memory bit access energy without compromising the throughput. Our experimental results show that this design method can produce NTT accelerators with up to 69.8% saving in average energy consumption compared with the existing designs based on multi-bank SRAM and one-bank SRAM with one-dimensional PE array with the same number of PEs and total memory size.

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Memory Access Optimization of High-Order CFD Stencil Computations on GPU 1

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21st International Conference on Parallel and Distributed computing, Applications, and Technologies, PDCAT 2020

作者： Wang, Shengxiang Li, Zhuoqian Che, Yonggang Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

Stencils computations are a class of computations commonly found in scientific and engineering applications. They have relatively lower arithmetic intensity. Therefore, their performance is greatly affected by memory access. This paper studies the issue of memory access optimization for the key stencil computations of a high-order CFD program on the NVidia GPU. Two methods are used to optimize the performance. First, we use registers to cache the data used by the stencil computations in the kernel. We use the CUDA warp shuffle functions to exchange data between neighboring grid points, and adjust the thread computation granularity to increase the data reuse. Second, we use the shared memory to buffer the grid data used by the stencil computations in the kernel, and utilize loop tiling to reduce redundant accesses to the global memory. Performance evaluation is done on an NVidia Tesla K80 GPU. The results show that compared to the original implementation that only uses the global memory, the optimized implementation that utilizes the registers achieves a maximum speedup of 2.59 and 2.79 relatively for 15M and 60M grids, and the optimized implementation that utilizes the shared memory achieves a maximum speedup of 3.51 and 3.36 relatively for 15M and 60M grids. © 2021, Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Radio Modulation Recognition Based on Attention-based Convolutional Neural Network and Time-frequency Analysis 2

Radio Modulation Recognition Based on Attention-based Convol...

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2nd International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2021

作者： Li, Xinlong Huang, Da Yang, Wenjing Li, Xueqiong Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781665412964

Radio modulation recognition is the key link of modern electronic warfare. This paper applies the idea of deep learning to radio modulation recognition. Since the modulation type is the most important information about the subtle features of the radio signal, we use short time Fourier transform (STFT) to convert the radio signal into a time-frequency distribution map to form a joint distribution of frequency and time. The attention-based convolutional neural network is used to classify and recognize the modulation types of time-frequency distribution maps. The simulation results show that time-frequency analysis can improve the accuracy of recognition, and adding attention can effectively improve the performance of the network. © 2021 IEEE.

关键词： Convolutional neural networks

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