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中科院计算所-计算机技术研究所计算技术研究所发表的相关论文

DWM: A Decomposable Winograd Method for Convolution Acceleration

Di Huang, Xishan Zhang, Rui Zhang, He Deyuan, Jiaming Guo, Chang Liu, Tian Zhi,Qi Guo, Du Zidong,Shaoli Liu, Tianshi Chen,Yunji Chen

national conference on artificial intelligence, (2020)

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Rubik's Cube+: A self-supervised feature learning framework for 3D medical image analysis

Jiuwen Zhu, Yuexiang Li, Yifan Hu, Kai Ma, S Kevin Zhou,Yefeng Zheng

Medical Image Anal., (2020): 101746

Methods

Conclusion

We proposed a novel self-supervised learning pretext task, involving three transformations— cube ordering, rotating and masking, to pre-train 3D neural networks for volumetric medical images

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Multi-task Scheduling for PIM-based Heterogeneous Computing System

Dawen Xu, Cheng Chu, Cheng Liu,Ying Wang, Xianzhong Zhou,Lei Zhang,Huaguo Liang,Huawei Li

GLSVLSI '20: Great Lakes Symposium on VLSI 2020 Virtual Event China September,..., pp.457-462, (2020)

Processing-in-Memory (PIM) or Near-Data Processing has been recognized as the most potential solution to resolve the ever-aggravating memory wall especially as the thrive of memory-intensive scale-out workloads such as graph computing and data analytics. However, when the future ...

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Bounding Maps for Universal Lesion Detection

Han Li,Hu Han, S. Kevin Zhou

medical image computing and computer assisted intervention, pp.417-428, (2020)

(ULD) in computed tomography plays an essential role in computer-aided diagnosis systems. Many detection approaches achieve excellent results for ULD using possible bounding boxes (or anchors) as proposals. However, empirical evidence shows that using anchor-based proposals leads...

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Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection

Qingsong Yao, Zecheng He, Hu Han, S. Kevin Zhou

medical image computing and computer-assisted intervention, pp.692-702, (2020)

Recent methods in multiple landmark detection based on deep convolutional neural networks (CNNs) reach high accuracy and improve traditional clinical workflow. However, the vulnerability of CNNs to adversarial-example attacks can be easily exploited to break classification and se...

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CNT-Cache - an Energy-Efficient Carbon Nanotube Cache with Adaptive Encoding

Dawen Xu, Kexin Chu, Cheng Liu,Ying Wang,Lei Zhang,Huawei Li

DATE, pp.963-966, (2020)

Carbon Nanotubu field-effect transistor (CNFET) that promises both higher clock speed and energy efficiency becomes an attractive alternative to the conventional power-hungry CMOS cache. We observe that the CNFET-based cache constructed with typical SRAM cells has distinct energy...

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Learning A Self-Inverse Network for Bidirectional Mri Image Synthesis

Zengming Shen, Yifan Chen, Kevin S. Zhou,Bogdan Georgescu, Xuqi Liu,Thomas S. Huang

ISBI, pp.1765-1769, (2020)

Methods

Conclusion

We have presented an approach for learning a CNN for bidirectional Magnetic Resonance Imaging image synthesis

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Fixed-Point Back-Propagation Training

Xishan Zhang,Shaoli Liu, Rui Zhang, Chang Liu, Di Huang, Shiyi Zhou, Jiaming Guo,Qi Guo,Zidong Du, Tian Zhi,Yunji Chen

CVPR, pp.2327-2335, (2020)

Methods

Conclusion

11Xeon Gold 6154 can only support multiplication between equal bitwidth fixed-point numbers, so in this experiment int16 × int8 is implemented as int16 × int16

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One-to-one Mapping for Unpaired Image-to-image Translation

Zengming Shen, Yifan Chen,Thomas S. Huang, S. Kevin Zhou,Bogdan Georgescu, Xuqi Liu

WACV, pp.1159-1168, (2020)

Methods

Conclusion

We propose a self-inverse network learning approach for unpaired image-to-image translation

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You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design

Chen Weiwei,Wang Ying, Yang Shuang, Liu Chen, Zhang Lei

DATE, pp.1283-1286, (2020)

DNN/Accelerator co-design has shown great potential in improving QoR and performance. Typical approaches separate the design flow into two-stage: (1) designing an application-specific DNN model with high accuracy; (2) building an accelerator considering the DNN specific charact...

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High-Resolution Chest X-ray Bone Suppression Using Unpaired CT Structural Priors

Han Li,Hu Han, Zeju Li, Lei Wang, Zhe Wu, Jingjing Lu, S Kevin Zhou

IEEE Transactions on Medical Imaging, no. 10 (2020): 3053-3063

Methods

Conclusion

We conduct experiments and clinical evaluations based on two benchmarking Chest X-rays databases and demonstrate that the performances of real clinical diagnosis and automatic lung disease classification are boosted with the aid of the bone suppressed image

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The Moving-Grid Effect in the Harmonic Vibrational Frequency Calculations with Numeric Atom-Centered Orbitals

Honghui Shang,Jinlong Yang

The journal of physical chemistry. A, (2020)

When using atom-centered integration grids, the portion of the grid that belongs to a certain atom also moves when this atom is displaced. In the paper, we investigate the moving-grid effect in the calculation of the harmonic vibrational frequencies when using all-electron full-p...

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DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior

Zhou Bo,Zhou S. Kevin

CVPR, pp.4272-4281, (2020)

Methods

Conclusion

We present a dual domain recurrent network for fast MRI reconstruction with T1 prior embedded

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SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis

Peng Cheng,Lin Wei-An,Liao Haofu,Chellappa Rama,Zhou Shaohua Kevin

CVPR, pp.7747-7756, (2020)

Conclusion

We introduce a Spatially Aware Interpolation NeTwork for medical slice synthesis to alleviate the memory constraint that volumetric data poses

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ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

Haofu Liao,Wei-An Lin,S Kevin Zhou,Jiebo Luo

IEEE transactions on medical imaging, no. 3 (2019): 634-643

Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately simulate the underlying physical mechanisms of CT ...

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Panthera: holistic memory management for big data processing over hybrid memories

Chenxi Wang,Huimin Cui, Ting Cao, John Zigman,Haris Volos,Onur Mutlu, Fang Lv,Xiaobing Feng,Guoqing Harry Xu

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp.347-362, (2019)

Methods

Conclusion

We present Panthera, the first memory management technique for managed Big Data processing over hybrid memories

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A Survey on Graph Processing Accelerators: Challenges and Opportunities

Chuangyi Gui,Long Zheng,Bingsheng He,Cheng Liu,Xinyu Chen,Xiaofei Liao,Hai Jin

J. Comput. Sci. Technol., no. 2 (2019): 339-371

Methods

Conclusion

The significant gap between the unique feature of graph processing and the hardware features of general-purpose architectures limits the further improvement of performance and energy efficiency

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OpenKMC: a KMC design for hundred-billion-atom simulation using millions of cores on Sunway Taihulight

Kun Li, Honghui Shang,Yunquan Zhang,Shigang Li, Baodong Wu, Dong Wang, Libo Zhang, Fang Li, Dexun Chen, Zhiqiang Wei

Proceedings of the International Conference for High Performance Computing, Networking, Storage and ..., pp.68, (2019)

With more attention attached to nuclear energy, the formation mechanism of the solute clusters precipitation within complex alloys becomes intriguing research in the embrittlement of nuclear reactor pressure vessel (RPV) steels. Such phenomenon can be simulated with atomic kineti...

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A Data-Driven Approach to Client-Transparent Access Selection of Dual-Band WiFi

Jun Zhang,Guangxing Zhang,Qinghua Wu, Binbin Liao,Gaogang Xie

IEEE Transactions on Network and Service Management, no. 1 (2019): 321-333

Conclusion

After regression we evaluate the model by mean absolute error, mean square error and R-squared

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TCP Stalls at the Server Side: Measurement and Mitigation

Jianer Zhou,Zhenyu Li,Qinghua Wu,Peter Steenkiste,Steve Uhlig, Jun Li,Gaogang Xie

IEEE/ACM Transactions on Networking, no. 1 (2019): 272-287

Conclusion

It is noteworthy that while smart-retransmission time out seems more aggressive than ER and Tail Loss Probe, the retransmission rate is still kept small as we show through experiments

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IJCAI 2020

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