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arXiv 2022年

作者： Barbalau, Antonio Ionescu, Radu Tudor Georgescu, Mariana-Iuliana Dueholm, Jacob Ramachandra, Bharathkumar Nasrollahi, Kamal Khan, Fahad Shahbaz Moeslund, Thomas B. Shah, Mubarak Department of Computer Science University of Bucharest 14 Academiei Street Bucharest010014 Romania SecurifAI 21D Mircea Voda Bucharest030662 Romania MBZ University of Artificial Intelligence Abu Dhabi Masdar City United Arab Emirates Department of Architecture Design and Media Technology Aalborg University Rendsburggade 14 Aalborg9000 Denmark Milestone Systems Banemarksvej 50C Brøndby 2605 Denmark Geopipe Inc 460 W 51st New York CityNY10019 United States Linköping University Linköping581 83 Sweden University of Central Florida OrlandoFL32816 United States

A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature. Due to its highly accurate results, the method attracted the attention of many researchers. In this work, we revisit the self-supervised multi-task learning framework, proposing several updates to the original method. First, we study various detection methods, e.g. based on detecting high-motion regions using optical flow or background subtraction, since we believe the currently used pre-trained YOLOv3 is suboptimal, e.g. objects in motion or objects from unknown classes are never detected. Second, we modernize the 3D convolutional backbone by introducing multi-head self-attention modules, inspired by the recent success of vision transformers. As such, we alternatively introduce both 2D and 3D convolutional vision transformer (CvT) blocks. Third, in our attempt to further improve the model, we study additional self-supervised learning tasks, such as predicting segmentation maps through knowledge distillation, solving jigsaw puzzles, estimating body pose through knowledge distillation, predicting masked regions (inpainting), and adversarial learning with pseudo-anomalies. We conduct experiments to assess the performance impact of the introduced changes. Upon finding more promising configurations of the framework, dubbed SSMTL++v1 and SSMTL++v2, we extend our preliminary experiments to more data sets, demonstrating that our performance gains are consistent across all data sets. In most cases, our results on Avenue, ShanghaiTech and UBnormal raise the state-of-the-art performance bar to a new level. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Mixed reality architecture in space habitats 70

Mixed reality architecture in space habitats

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70th International Astronautical Congress, IAC 2019

作者： Basu, Tamalee Bannova, Olga Camba, Jorge D. Sasakawa International Center for Space Architecture University of Houston 4800 Calhoun Rd HoustonTX77004 United States Department of Computer Graphics Technology Purdue University 401 N Grant St West LafayetteIN47907 United States

From assisting with assembling the Orion capsule to using highly immersive virtual environments for astronaut training, MR technologies provide a powerful mechanism to alter the perception of the physical world and deliver realistic personalized visual stimuli to users. In this paper, we discuss a novel strategy to utilize MR technologies as a design element to enhance the interior architecture of the space habitat and enrich the inhabitants' personal experience. We discuss two scenarios that entail long-duration missions as well as a customized experience for space tourists in the Low Earth Orbit (LEO). A series of spacecraft volumetric studies of the ergonomics associated with the application of MR technologies are reported. Physical, virtual and combined experiences are mapped within the volumes with respect to crew ConOps. The experiences are then analyzed and translated to architectural design requirements that inform criteria for the development of personalized MR- based interventions. For the first scenario, NASA's 500 days on the surface of Mars mission is considered, which requires 600 additional days in microgravity transit inside the Deep Space Transfer vehicle, a 7.2 m wide hard-shell module. In this scenario, MR experiences are used as a stress countermeasure to help a crew of four to sustain psychological and behavioral health, maintain productivity, and stimulate teamwork and performance. This is accomplished by providing novelty in the habitat as well as designing content that can increase the volumetric perception of the environment. The second scenario is presented in the context of space tourism where habitats with minimum physical interior design elements can be transformed into comfortable habitable personal environments. Bigelow Space Operations' B330 was selected as a reference site for a 12-day LEO tourism mission. We discuss a design approach that provides tourists with a high level of comfort by using projection-based MR technologies to cus

关键词： Mixed reality

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Bandwidth-Aware Last-Level Caching: Efficiently Coordinating Off-Chip Read and Write Bandwidth

Bandwidth-Aware Last-Level Caching: Efficiently Coordinating...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Mainak Chaudhuri Jayesh Gaur Sreenivas Subramoney Department of Computer Science and Engineering Indian Institute of Technology Kanpur Processor Architecture Research Lab Intel Corporation

The last two decades have witnessed a large number of proposals on the last-level cache (LLC) replacement policy aiming to minimize the number of LLC read misses. Another independent large body of work has explored mechanisms to address the inefficiencies arising from the DRAM writes introduced by the LLC replacement policy. These DRAM scheduling proposals, however, leave the LLC replacement policy unchanged and, as a result, miss the opportunity of synergistically shaping and scheduling the DRAM write bandwidth demand. In this paper, we argue that DRAM read and write bandwidth demands must be coordinated carefully from the LLC side and hence, introduce bandwidth-awareness in the LLC policy. Our bandwidth-aware LLC policy proposal enables long uninterrupted stretches of DRAM reads while maintaining the efficiency of the last-level cache and controlling precisely when and for how long writes can demand DRAM bandwidth. Our proposal comfortably outperforms the state-of-the-art eager DRAM write scheduling proposals and bridges 75% of the performance gap between the baseline and a hypothetical system that deploys an unbounded DRAM write buffer.

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ICare Designer: A rule-driven layout co-designing system for elderly caring 23

ICare Designer: A rule-driven layout co-designing system for...

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23rd IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2019

作者： Sun, Yuling Zhou, Jie Yao, Dong He, Liang Department of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Izhaohu Co. Ltd. Shanghai China Department of Architecture Tongji University Shanghai China

ISBN: (纸本)9781728103501

In CSCW and related fields, computer supported collaborative design has been used in various fields. Yet, there has been little research focusing on the collaborative context design for elderly caring. For filling this gap, this paper explores a notion of rule-driven layout co-designing system for elderly caring. What makes it distinct from other layout designing system is that it employs hierarchically modularization to store all standardized components that are needed for the aging caring space, and embeds intelligence algorithm into the typical evidence-based design for improving design efficiency and reducing design costs. In this paper, we designed and implemented a prototype called iCare Designer, and conducted a preliminary study to understand how this notion works in reality. While findings of the preliminary study suggest promises of the notion of rule-driven space layout co-designing system, it reveals some challenges this kind of systems to truly work in practices, mainly reflecting in its flexibility and perception to ambient environment. An in-depth discussion is provided in the end. © 2019 IEEE.

关键词： Modular construction

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The static parallel distribution algorithms for hybrid density-functional calculations in HONPAS package

arXiv

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arXiv 2020年

作者： Qin, Xinming Shang, Honghui Xu, Lei Hu, Wei Yang, Jinlong Li, Shigang Zhang, Yunquan 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

Hybrid density-functional calculation is one of the most commonly adopted electronic structure theory used in computational chemistry and materials science because of its balance between accuracy and computational cost. Recently, we have developed a novel scheme called NAO2GTO to achieve linear scaling (Order-N) calculations for hybrid density-functionalsShang et al. (2011). In our scheme, the most time-consuming step is the calculation of the electron repulsion integrals (ERIs) part. So how to create an even distribution of these ERIs in parallel implementation is an issue of particular importance. Here, we present two static scalable distributed algorithms for the ERIs computation. Firstly, the ERIs are distributed over ERIs shell pairs. Secondly, the ERIs is distributed over ERIs shell quartets. In both algorithms, the calculation of ERIs is independent of each other, so the communication time is minimized. We show our speedup results to demonstrate the performance of these static parallel distributed algorithms in the Hefei Order-N packages for ab initio simulations (HONPAS)Qin et al. (2014). Copyright © 2020, The Authors. All rights reserved.

关键词： Calculations

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The dynamic parallel distribution algorithm for hybrid density-functional calculations in HONPAS package

arXiv

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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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