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arXiv 2020年

作者： Shang, Honghui Xu, Lei Wu, Baodong Qin, Xinming Zhang, Yunquan Yang, Jinlong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Hefei National Laboratory for Physical Sciences at Microscale Department of Chemical Physics Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China

This work presents a dynamic parallel distribution scheme for the Hartree-Fock exchange (HFX) calculations based on the real-space NAO2GTO framework. The most time-consuming electron repulsion integrals (ERIs) calculation is perfectly load-balanced with 2-level master-worker dynamic parallel scheme, the density matrix and the HFX matrix are both stored in the sparse format, the network communication time is minimized via only communicating the index of the batched ERIs and the final sparse matrix form of the HFX matrix. The performance of this dynamic scalable distributed algorithm has been demonstrated by several examples of large scale hybrid density-functional calculations on Tianhe-2 supercomputers, including both molecular and solid states systems with multiple dimensions, and illustrates good scalability. Copyright © 2020, The Authors. All rights reserved.

关键词： Density functional theory

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Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification 23rd

Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classi...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Dong, Qinglin Qiang, Ning Lv, Jinglei Li, Xiang Liu, Tianming Li, Quanzheng Center for Advanced Medical Computing and Analysis Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA United States School of Physics and Information Technology Shaanxi Normal University Xian China School of Biomedical Engineering and Sydney Imaging Brain and Mind Centre The University of Sydney Camperdown Australia Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center The University of Georgia AthensGA United States MGH & BWH Center for Clinical Data Science BostonMA United States

ISBN: (纸本)9783030597276

It has been of great interest in the neuroimaging community to model spatiotemporal brain function and disorders based on resting state functional magnetic resonance imaging (rfMRI). A variety of spatiotemporal methods have been proposed for rfMRI so far, including deep learning models such as convolution networks (CNN) and recurrent networks (RNN). However, the dominant models fail to capture the long-distance dependency (LDD) due to their sequential nature, which becomes critical at longer sequence lengths due to memory limit. Inspired by human brains extraordinary ability of long-term memory and attention, the attention mechanism is designed for machine translation to draw global dependencies and achieved state-of-the-art. In this paper, we propose a spatiotemporal attention autoencoder (STAAE) to discover global features that address LDDs in rfMRI. STAAE encodes the information throughout the rfMRI sequence and reveals resting state networks (RSNs) that characterize spatial and temporal properties of the data. Considering that the rfMRI is measured without external tasks, an unsupervised classification framework is developed based on the connectome generated with STAAE. This framework has been evaluated on 281 children with ADHD and 266 normal control children from 4 sites of ADHD200 datasets. The proposed STAAE reveals the global functional interaction in the brain and achieves a state-of-the-art classification accuracy from 59.5% to 77.2% on multiple sites. It is evident that the proposed attention-based model provides a novel approach towards better understanding of human brain. © 2020, Springer Nature Switzerland AG.

关键词： Brain

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Discovering Functional Brain Networks with 3D Residual Autoencoder (ResAE) 23rd

Discovering Functional Brain Networks with 3D Residual Autoe...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

ISBN: (纸本)9783030597276

Functional MRI has attracted increasing attention in cognitive neuroscience and clinical mental health research. Towards understanding how brain give rises to mental phenomena, deep learning has been applied to functional MRI (fMRI) dataset to discover the physiological basis of cognitive process. Considering the unsupervised nature of fMRI due to the complex intrinsic brain activities, an encoder-decoder structure is promising to model hidden structure of latent signal sources. Inspired by the success of deep residual learning, we propose a 68-layer 3D residual autoencoder (3D ResAE) to model deep representations of fMRI in this paper. The proposed model is evaluated on the fMRI data under 3 cognitive tasks in Human Connectome Project (HCP). The experimental results have shown that the temporal representations learned by the encoder matches the task design and the spatial representations can be interpreted to be meaningful functional brain networks (FBNs), which not only include tasks based FBNs, but also intrinsic FBNs. The proposed model also outperforms a 3-layer autoencoder, showing the key factor for the performance improvement is depth. Our work demonstrates the feasibility and success of adopting 2D advanced deep residual networks in computer vision into 3D fMRI volume modeling. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Self-Adaptive Address Mapping Mechanism for Access Pattern Awareness on DRAM

Self-Adaptive Address Mapping Mechanism for Access Pattern A...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Chundian Li Mingzhe Zhang Zhiwei Xu Xianhe Sun SKL of Computer Architecture Institute of Computing Technology Beijing China Department of Computer Science Illinois Institute of Technology Chicago USA University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

As DRAM is a considerably slow storage compared to CPU, the long access latency becomes a serious issue and affects the whole execution if fetching data is on the critical path. It is benefical if the data layout on DRAM, which is decided by address mapping, can serve data accesses with either great locality or bank-level parallelism. However for some cases, there exists a huge mismatch between access patterns and data layout of applications, which introduces the difficulty in obtaining locality or parallelism and current general address mapping cannot resolve it well. In an effort to overcome this challenge, we present an self-adaptive address mapping mechanism in memory controller to be aware of different access patterns on DRAM without any prior knowledge of applications. Moreover, there is nearly no modification on softwares, including applications, libraries and OS. We take several versions of matrix multiplication as an early verification, since their patterns are regular and simple to control. Impressively, the results reveal that memory performance of naive and tiling versions is improved up to 3.4× and 2.7× at most, 2.1× and 1.7× on average respectively. The whole execution time is reduced by up to 24% and 8% averagely. Even for highly-optimized implementations, execution time is decreased by 7% and memory performance speeds up to 1.6× on average.

关键词： Random access memory Layout Parallel processing Organizations Libraries Programming Optimization

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An Extension Network of Dendritic Neurons

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Computational intelligence and neuroscience 2023年第1期2023卷 7037124页

作者： Qianyi Peng Shangce Gao Yirui Wang Junyan Yi Gang Yang Yuki Todo Faculty of Engineering University of Toyama Toyama-shi 930-8555 Japan. Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang 315211 China. Department of Computer Science & Technology Beijing University of Civil Engineering and Architecture Beijing 100044 China. School of Information Renmin University of China Beijing China. Faculty of Electrical Information and Communication Engineering Kanazawa University Kanazawa Ishikawa 9201192 Japan.

Deep learning (DL) has achieved breakthrough successes in various tasks, owing to its layer-by-layer information processing and sufficient model complexity. However, DL suffers from the issues of both redundant model complexity and low interpretability, which are mainly because of its oversimplified basic McCulloch-Pitts neuron unit. A widely recognized biologically plausible dendritic neuron model (DNM) has demonstrated its effectiveness in alleviating the aforementioned issues, but it can only solve binary classification tasks, which significantly limits its applicability. In this study, a novel extended network based on the dendritic structure is innovatively proposed, thereby enabling it to solve multiple-class classification problems. Also, for the first time, an efficient error-back-propagation learning algorithm is derived. In the extensive experimental results, the effectiveness and superiority of the proposed method in comparison with other nine state-of-the-art classifiers on ten datasets are demonstrated, including a real-world quality of web service application. The experimental results suggest that the proposed learning algorithm is competent and reliable in terms of classification performance and stability and has a notable advantage in small-scale disequilibrium data. Additionally, aspects of network structure constrained by scale are examined.

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Mitigating urban heat islands: Enhancing outdoor comfort with retro-reflective building materials through computational-fluid-dynamics analysis

Next Research

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Next Research 2025年第2期2卷

作者： Jihui Yuan Da Yan Xiong Xiao Yasuhiro Shimazaki Yupeng Wang Zhichao Jiao Zhengsong Lin Marko Bizjak Craig Farnham Kazuo Emura Department of Living Environment Design Grad. School of Human Life and Ecology Osaka Metropolitan University Osaka Japan Department of Architecture and Civil Eng. Grad. School of Eng. Toyohashi University of Technology Aichi Japan Building Energy Research Center School of Architecture Tsinghua University Beijing China School of Human Settlements and Civil Engineering Xi'an Jiaotong University No.28 Xianning West Road Xi'an Shaanxi Province China College of Art & Design Nanjing Forestry University Nanjing 210037 China Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 SI 2000 Maribor Slovenia

This study aims to delineate the origin, purpose, process, and results of the research, focusing on investigating the impact of different exterior wall materials on outdoor thermal indicators in Nagoya, Japan. The ultimate goal is to mitigate the Urban Heat Island (UHI) effect and improve urban outdoor thermal comfort. Computational Fluid Dynamics (CFD) is employed to evaluate two types of exterior wall materials: diffuse highly reflective (DHR) and retro-reflective (RR) materials. The research process involves assessing various outdoor thermal indices, such as wet-bulb globe temperature (WBGT), standard effective temperature (SET*), and universal thermal climate index (UTCI), in order to comprehend the thermal performance of each material. The results of the study indicate that the RR material consistently outperforms the DHR material in terms of outdoor thermal comfort, despite having slightly lower solar reflectance. This is supported by lower surface temperatures and surface heat flow associated with the RR material, suggesting reduced indoor cooling loads and significant energy savings for cooling purposes. These findings highlight the potential of RR material in mitigating the UHI effect and enhancing urban outdoor thermal conditions. By establishing a critical relationship between exterior wall materials and outdoor thermal indicators, this study proposes the adoption of RR materials for urban building exteriors as a sustainable and economically viable solution to improve urban outdoor thermal comfort and enhance the quality of life for residents.

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Eliminating NB-IoT interference to LTE system: A sparse machine learning based approach

arXiv

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arXiv 2020年

作者： Liu, Sicong Xiao, Liang Han, Zhu Tang, Yuliang Department of Communication Engineering Key Laboratory of Digital Fujian on IoT Communication Architecture and Security Technology Xiamen University Xiamen361005 China University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

—Narrowband internet-of-things (NB-IoT) is a competitive 5G technology for massive machine-type communication scenarios, but meanwhile introduces narrowband interference (NBI) to existing broadband transmission such as the long term evolution (LTE) systems in enhanced mobile broadband (eMBB) scenarios. In order to facilitate the harmonic and fair coexistence in wireless heterogeneous networks, it is important to eliminate NB-IoT interference to LTE systems. In this paper, a novel sparse machine learning based framework and a sparse combinatorial optimization problem is formulated for accurate NBI recovery, which can be efficiently solved using the proposed iterative sparse learning algorithm called sparse cross-entropy minimization (SCEM). To further improve the recovery accuracy and convergence rate, regularization is introduced to the loss function in the enhanced algorithm called regularized SCEM. Moreover, exploiting the spatial correlation of NBI, the framework is extended to multiple-input multiple-output systems. Simulation results demonstrate that the proposed methods are effective in eliminating NB-IoT interference to LTE systems, and significantly outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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A novel FPGA-based high throughput accelerator for binary search trees

arXiv

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arXiv 2019年

作者： Melikoglu, Oyku Ergin, Oguz Salami, Behzad Pavon, Julian Unsal, Osman Cristal, Adrian Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey Computer Architecture for Parallel Paradigms Group Barcelona Supercomputing Center Barcelona Spain

This paper presents a deeply pipelined and massively parallel Binary Search Tree (BST) accelerator for Field Programmable Gate Arrays (FPGAs). Our design relies on the extremely parallel on-chip memory, or Block RAMs (BRAMs) architecture of FPGAs. To achieve significant throughput for the search operation on BST, we present several novel mechanisms including tree duplication as well as horizontal, duplicated, and hybrid (horizontal-vertical) tree partitioning. Also, we present efficient techniques to decrease the stalling rates that can occur during the parallel tree search. By combining these techniques and implementations on Xilinx Virtex-7 VC709 platform, we achieve up to 8X throughput improvement gain in comparison to the baseline implementation, i.e., a fully-pipelined FPGA-based accelerator. Copyright © 2019, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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A Novel FPGA-Based High Throughput Accelerator For Binary Search Trees

A Novel FPGA-Based High Throughput Accelerator For Binary Se...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Oyku Melikoglu Oguz Ergin Behzad Salami Julian Pavon Osman Unsal Adrian Cristal Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey Computer Architecture for Parallel Paradigms Group Barcelona Supercomputing Center Barcelona Spain

ISBN: (数字)9781728144849

ISBN: (纸本)9781728144856

关键词： Registers Throughput Field programmable gate arrays Vegetation Pipeline processing Binary search trees Random access memory

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MV-Sketch: A Fast and Compact Invertible Sketch for Heavy Flow Detection in Network Data Streams

MV-Sketch: A Fast and Compact Invertible Sketch for Heavy Fl...

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IEEE Conference on computer Communications

作者： Lu Tang Qun Huang Patrick P. C. Lee Department of Computer Science and Engineering The Chinese University of Hong Kong State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781728105161

Fast detection of heavy flows (e.g., heavy hitters and heavy changers) in massive network traffic is challenging due to the stringent requirements of fast packet processing and limited resource availability. Invertible sketches are summary data structures that can recover heavy flows with small memory footprints and bounded errors, yet existing invertible sketches incur high memory access overhead that leads to performance degradation. We present MV-Sketch, a fast and compact invertible sketch that supports heavy flow detection with small and static memory allocation. MV-Sketch tracks candidate heavy flows inside the sketch data structure via the idea of majority voting, such that it incurs small memory access overhead in both update and query operations, while achieving high detection accuracy. We present theoretical analysis on the memory usage, performance, and accuracy of MV-Sketch. Trace-driven evaluation shows that MVSketch achieves higher accuracy than existing invertible sketches, with up to 3.38× throughput gain. We also show how to boost the performance of MV-Sketch with SIMD instructions.

关键词： Sketches Reversibility Heavy Packet Processing traffic network degradation of performance Fasting

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