检索结果-内蒙古大学图书馆

Address register promotion method based on feedbacks

学校读者我要写书评

暂无评论

Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2009年第4期46卷 698-704页

作者： Zhang, Chao Lü, Fang Wang, Lei Feng, Xiaobing Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

In processor architectures such as MIPS, ALPHA, SPARC and PowerPC, indirect addressing mode is always adopted to access global variables and static ones. Since the addresses of these variables and the corresponding values are in different data sections in the corresponding binary file, the data locality of the program will be very poor. As a result, accessing the read only addresses of these variables every time tends to result in non-trivial redundant data cache miss memory accesses. Moreover, such indirect addressing mode will generate two sequential load instructions which have data dependences between them. As a result, the amount of instruction level parallelism (ILP) of the program will be decreased. The authors present an address register promotion method based on feedbacks (ARPF) to solve the above problems. ARPF algorithm reduces the redundant accesses to the read only addresses of the global variables and static ones, increases the amount of instruction level parallelism of a program, and avoids the performance declines due to the increase in register pressure caused by register promotion. The algorithm has been implemented in the Loongson compiler for MIPS architecture. Experiments on SPEC CPU2000INT benchmarks are conducted to show that ARPF can improve the performance of all benchmarks by 1%-6%.

关键词： Benchmarking

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Liao, Hao Liu, Ming-Kai Mariani, Manuel Sebastian Zhou, Mingyang Wu, Xingtong National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China URPP Social Networks Universität Zürich CH-8050 Switzerland

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag strongly affects the metrics' reconstruction accuracy, depending on the network's clustering coefficient and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

Under-determined Blind Speech Separation via the Convolutive Transfer Function and lp Regularization

学校读者我要写书评

暂无评论

Under-determined Blind Speech Separation via the Convolutive...

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Liu Yang Junjie Yang Yi Guo School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT (GDUT) Guangzhou China Centre for Research in Mathematics School of Computing Engineering and Mathematics Western Sydney University Parramatta Australia

ISBN: (纸本)9781665406697

Convolutive blind speech separation (CBSS) attempts to recover speech sources from recorded mixtures transmitted by room impulse response filters (RIRs). The existing BSS methods are mainly based on a multiplicative narrowband approximation (MNA) model in the short-time Fourier transform (STFT) domain. However, the MNA model may result in serious system transformation error under the situation of a strongly reverberant environment due to its sensitivity to RIRs with large lengths. Moreover, the solution of source reconstruction is not unique when the system is underdetermined even if the RIRs are given in advance. To tackle these problems, a convolutive narrowband approximation (CNA) model is explored in the proposed approach to constrain the system transformation error. Then an $l p(0\lt p\leq 1)$ regularization is provided to reconstruct the sparse sources in which p can be dynamically selected to achieve the best performance. The experimental results demonstrate the robustness of the proposed method to room reverberation under various speech separation cases in comparison to conventional methods.

关键词： Sensitivity Fourier transforms Transfer functions Reconstruction algorithms Robustness Sensors Reverberation

A 128kb Stochastic computing Chip based on RRAM Flicker Noise with High Noise Density and Nearly Zero Autocorrelation on 28-nm CMOS Platform

学校读者我要写书评

暂无评论

A 128kb Stochastic Computing Chip based on RRAM Flicker Nois...

International Electron Devices Meeting (IEDM)

作者： Tiancheng Gong Qiao Hu Danian Dong Haijun Jiang Jianguo Yang Xiaoxin Xu Xiaoming Chen Qing Luo Qi Liu Steve S. Chung Hangbing Lyu Ming Liu Key Laboratory of Microelectronics Devices and Integrated Technology Institute of Microelectronics of Chinese Academy of Sciences Beijing China Zhejiang Lab HangzhouChina Zhejiang Lab Hangzhou China Institute of Computing Technology of Chinese Academy of Sciences Beijing China Frontier Institute of Chip and System Fudan University China National Yang Ming Chiao Tung University Taiwan

ISBN: (纸本)9781665425735

In this work, a 128kb RRAM flicker noise based Stochastic computing (SC) chip is demonstrated on 28-nm HKMG CMOS platform for the first time. Flicker noise in RRAM is selected as the entropy source owing to the higher noise density compared with the conventional logic devices, even the advanced 14-nm FinFET. The reliability of the flicker noise in RRAM is further improved by an optimized weak forming scheme at the array level towards accurate and stable SC applications. Moreover, a probability modulated true random number generator (PM-TRNG) circuit is proposed to generate stochastic bit-streams (SBS) with the desired probability and eliminated correlation. The autocorrelation function (ACF) and NIST testing results show that the correlation of output SBS is nearly zero. Finally, this RRAM-flicker noise based SC chip is successfully implemented for edge detection in image processing system and extremely low error rate (3.13%@128 BSL) can be achieved.

关键词： Error analysis Image processing Image edge detection NIST FinFETs Generators Autocorrelation

STAR-RIS Enabled ISAC systems: Joint Rate Splitting and Beamforming Optimization

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Liu, Yuan Zhang, Ruichen Jiang, Ruihong Zhu, Yongdong Hu, Huimin Ni, Qiang Fei, Zesong Niyato, Dusit The Research Center for Space Computing System Zhejiang Laboratory Hangzhou311100 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Communications and Information Engineering The School of Artificial Intelligence Xi’an University of Posts and Telecommunications Xi’an710121 China The School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom The School of Information and Electronics Beijing Institute of Technology Beijing100081 China

This paper delves into an integrated sensing and communication (ISAC) system bolstered by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Within this system, a base station (BS) is equipped with communication and radar capabilities, enabling it to communicate with ground terminals (GTs) and concurrently probe for echo signals from a target of interest. Moreover, to manage interference and improve communication quality, the rate splitting multiple access (RSMA) scheme is incorporated into the system. The signal-to-interference-plus-noise ratio (SINR) of the received sensing echo signals is a measure of sensing performance. We formulate a joint optimization problem of common rates, transmit beamforming at the BS, and passive beamforming vectors of the STAR-RIS. The objective is to maximize sensing SINR while guaranteeing the communication rate requirements for each GT. We present an iterative algorithm to address the non-convex problem by invoking Dinkelbach’s transform, semidefinite relaxation (SDR), majorization-minimization, and sequential rank-one constraint relaxation (SROCR) theories. Simulation results manifest that the performance of the studied ISAC network enhanced by the STAR-RIS and RSMA surpasses other benchmarks considerably. The results evidently indicate the superior performance improvement of the ISAC system with the proposed RSMA-based transmission strategy design and the dynamic optimization of both transmission and reflection beamforming at STAR-RIS. © 2024, CC BY.

关键词： Signal to noise ratio

Abuse-Resistant Deniable Encryption

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Cao, Yanmei Wei, Jianghong Xiang, Yang Susilo, Willy Chen, Xiaofeng State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University Zhengzhou450001 China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia Institute of Cybersecurity and Cryptology University of Wollongong WollongongNSW2522 Australia

Deniable encryption (DE) allows private communication over an insecure channel even under the coercion. That is, after an adversary forces communication participants to reveal their secret keys and randomness used during the communication, the message confidentiality can still be preserved. Since its introduction, a large body of studies have been made to improve the DE system in terms of efficiency, security definition and functionality, which is the focus of this paper. However, as far as we know, none of existing DE systems considers the abuse of deniability caused by malicious users, which is a crucial feature from the view of practical applications of DE. For instance, a malicious user (e.g., an employee who holds some sensitive documents and intends to sell these confidential contents for obtaining financial gain) can utilize a DE system to transmit the confidential content without the risk of being caught, which is extremely dangerous to the interests and security of his/her organization. In this paper, to mitigate this threat, we formally define the syntax and security notions of abuse-resistant deniable encryption, which restricts what users can deny. Then, we put forward a concrete construction of abuse-resistant DE scheme, and prove its security under the assumptions of indistinguishability obfuscation and oneway function. Compared with other related work, the proposed construction has advantages in terms of functionality and ciphertext rate simultaneously. © 2023, The Authors. All rights reserved.

关键词： Data obfuscation

Learning converged propagations with deep prior ensemble for image enhancement

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Liu, Risheng Ma, Long Wang, Yiyang Zhang, Lei DUT-RU International School of Information Science & Engineering Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology Dalian China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Institute of Atmospheric Sciences Fudan University Shanghai Institute of Meteorological Science Shanghai China Department of Computing Hong Kong Polytechnic University Hong Kong

Enhancing visual qualities of images plays very important roles in various vision and learning applications. In the past few years, both knowledge-driven maximum a posterior (MAP) with prior modelings and fully data-dependent convolutional neural network (CNN) techniques have been investigated to address specific enhancement tasks. In this paper, by exploiting the advantages of these two types of mechanisms within a complementary propagation perspective, we propose a unified framework, named deep prior ensemble (DPE), for solving various image enhancement tasks. Specifically, we first establish the basic propagation scheme based on the fundamental image modeling cues and then introduce residual CNNs to help predicting the propagation direction at each stage. By designing prior projections to perform feedback control, we theoretically prove that even with experience-inspired CNNs, DPE is definitely converged and the output will always satisfy our fundamental task constraints. The main advantage against conventional optimization-based MAP approaches is that our descent directions are learned from collected training data, thus are much more robust to unwanted local minimums. While, compared with existing CNN type networks, which are often designed in heuristic manners without theoretical guarantees, DPE is able to gain advantages from rich task cues investigated on the bases of domain knowledges. Therefore, DPE actually provides a generic ensemble methodology to integrate both knowledge and data-based cues for different image enhancement tasks. More importantly, our theoretical investigations verify that the feed-forward propagations of DPE are properly controlled toward our desired solution. Experimental results demonstrate that the proposed DPE outperforms state-of-the-arts on a variety of image enhancement tasks in terms of both quantitative measure and visual perception quality. Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

RASTE: A Relation-Guided Joint Method for Aspect Sentiment Triplet Extraction

学校读者我要写书评

暂无评论

RASTE: A Relation-Guided Joint Method for Aspect Sentiment T...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Linyang Zhong Jiaxing Shang Linjiang Zheng Fei Hao Weiwei Cao Hong Sun Key Laboratory of Dependable Service Computing in Cyber Physical Society College of Computer Science Chongqing University Chongqing China School of Computer Science Shaanxi Normal University Xian China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Guanghan China Flight Technology and Flight Safety Research Base Civil Aviation Flight University of China Guanghan China

Aspect Sentiment Triplet Extraction (ASTE) is an important subtask of aspect-based sentiment analysis. It aims to extract aspect terms, corresponding sentiment polarities and related opinion terms from a sentence. The most advanced research efforts solve this problem in an end-to-end manner. However, they usually use one classifier to handle both relation detection and sentiment polarity classification tasks for aspectopinion pairs, which affects the prediction performance of the classifier. Moreover, the previous works treat subtasks equally, ignoring the differences between subtasks. In this paper, we propose a relation-guided method that models the relation detection and sentiment polarity classification subtasks differently to extract aspect sentiment triplets more effectively. Further, we capture the corresponding contextual features for each subtask separately to improve the overall performance of the model. We conducted experiments on two group benchmark datasets. Our model turns out to be one of the state-of-the-art models with effectiveness and superiority according to the experimental results.

关键词：

Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Pei, Jiangbo Jiang, Zhuqing Men, Aidong Wang, Haiying Luo, Haiyong Wen, Shiping The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing100876 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

Single-camera-training person re-identification (SCT re-ID) aims to train a re-ID model using SCT datasets where each person appears in only one camera. The main challenge of SCT re-ID is to learn camera-invariant feature representations without cross-camera same-person (CCSP) data as supervision. Previous methods address it by assuming that the most similar person should be found in another camera. However, this assumption is not guaranteed to be correct. In this paper, we propose a Camera-Invariant Meta-Learning Network (CIMN) for SCT re-ID. CIMN assumes that the camera-invariant feature representations should be robust to camera changes. To this end, we split the training data into meta-train set and meta-test set based on camera IDs and perform a cross-camera simulation via meta-learning strategy, aiming to enforce the representations learned from the meta-train set to be robust to the meta-test set. With the cross-camera simulation, CIMN can learn camera-invariant and identity-discriminative representations even there are no CCSP data. However, this simulation also causes the separation of the meta-train set and the meta-test set, which ignores some beneficial relations between them. Thus, we introduce three losses: meta triplet loss, meta classification loss, and meta camera alignment loss, to leverage the ignored relations. The experiment results demonstrate that our method achieves comparable performance with and without CCSP data, and outperforms the state-of-the-art methods on SCT re-ID benchmarks. In addition, it is also effective in improving the domain generalization ability of the model. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras