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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Ren, Fuxin Zhang, Zhongbao Zhang, Jiawei Su, Sen Sun, Li Zhu, Guozhen Guo, Congying State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China IFM Lab Department of Computer Science Florida State University FL United States

ISBN: (纸本)9780999241165

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users' behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover's distance based alignment component to fuse users' behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network component to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Alignment

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Building Bridge Across the Time: Disruption and Restoration of Murals In the Wild

Building Bridge Across the Time: Disruption and Restoration ...

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International Conference on computer Vision (ICCV)

作者： Huiyang Shao Qianqian Xu Peisong Wen Peifeng Gao Zhiyong Yang Qingming Huang Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences State Key Lab of Info. Security Inst. of Info. Engineering CAS School of Cyber Security University of Chinese Academy of Sciences BDKM University of Chinese Academy of Sciences Peng Cheng Laboratory

In this paper, we focus on the mural-restoration task, which aims to detect damaged regions in the mural and repaint them automatically. Different from traditional image restoration tasks like in/out/blind-painting and image renovation, the corrupted mural suffers from more complicated degradation. However, existing mural-restoration methods and datasets still focus on simple degradation like masking. Such a significant gap prevents mural-restoration from being applied to real scenarios. To fill this gap, in this work, we propose a systematic framework to simulate the physical process for damaged murals and provide a new benchmark dataset for mural-restoration. Limited by the simplification of the data synthesis process, the previous mural-restoration methods suffer from poor performance in our proposed dataset. To handle this problem, we propose the Attention Diffusion Framework (ADF) for this challenging task. Within the framework, a damage attention map module is proposed to estimate the damage extent. Facing the diversity of defects, we propose a series of loss functions to choose repair strategies adaptively. Finally, experimental results support the effectiveness of the proposed framework in terms of both mural synthesis and restoration.

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Near Optimal Hybrid Digital-Analog Beamforming for mmWave Point-to-Point MIMO Transmissions Using OTFS Waveforms

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IEEE Transactions on Communications 2025年

作者： Liu, Mengmeng Li, Shuangyang Wei, Zhiqiang Bai, Baoming Caire, Giuseppe Ng, Derrick Wing Kwan Xidian University State Key Lab. of ISN Xi'an710071 China Technische Universität Berlin Electrical Engineering and Computer Science Department Berlin10587 Germany Xi'an Jiaotong University School of Mathematics and Statistics Xi'an710049 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China University of New South Wales School of Electrical Engineering and Telecommunications SydneyNSW2052 Australia

In this paper, a point-to-point (P2P) orthogonal time frequency space (OTFS)-based multiple-input multiple-output (MIMO-OTFS) transmission scheme is devised for millimeter wave (mmWave) channels. The proposed transmission scheme relies on a low-complexity hybrid digital-analog beamforming (HBF) scheme that exploits the delay-Doppler (DD) domain channel properties, where detailed design criteria for different channel conditions are presented, including the case where paths are indistinguishable by angles. Thanks to the proposed HBF scheme, approximate path-wise interference-free transmission of multiple data streams is achieved, and consequently, only little pre-equalization is required for combating the residual channel impairments. The achievable rate of the proposed scheme is studied and compared with the orthogonal frequency-division multiplexing (OFDM) counterpart. In particular, we unveil that the condition number of the effective angular domain matrix for OTFS is smaller than that for OFDM, due to the enhanced path separability in the DD domain. As a result, the proposed MIMO-OTFS transmission scheme demonstrates superior performance over the MIMO-OFDM transmission scheme. Our numerical results corroborate our theoretical analysis and show a near-optimal rate performance with significantly reduced complexity compared to the optimal singular value decomposition (SVD) precoding method. © 1972-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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Approximate Equivalence Checking of Noisy Quantum Circuits 21

Approximate Equivalence Checking of Noisy Quantum Circuits

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Proceedings of the 58th Annual ACM/IEEE Design Automation Conference

作者： Xin Hong Mingsheng Ying Yuan Feng Xiangzhen Zhou Sanjiang Li Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Software and Information University of Technology Sydney Australia and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China and Department of Computer Science and Technology Tsinghua University China Centre for Quantum Software and Information University of Technology Sydney Australia and State Key Lab of Millimeter Waves Southeast University China

ISBN: (纸本)9781665432740

We study the fundamental design automation problem of equivalence checking in the NISQ (Noisy Intermediate-Scale Quantum) computing realm where quantum noise is present inevitably. The notion of approximate equivalence of (possibly noisy) quantum circuits is defined based on the Jamiolkowski fidelity which measures the average distance between output states of two super-operators when the input is chosen at random. By employing tensor network contraction, we present two algorithms, aiming at different situations where the number of noises varies, for computing the fidelity between an ideal quantum circuit and its noisy implementation. The effectiveness of our algorithms is demonstrated by experimenting on benchmarks of real NISQ circuits. When compared with the state-of-the-art implementation incorporated in Qiskit, experimental results show that the proposed algorithms outperform in both efficiency and scalability.

关键词： quantum computing

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The minority matters: a diversity-promoting collaborative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

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Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models

arXiv

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arXiv 2023年

作者： Sun, Wei Wen, Wen Min, Xiongkuo Lan, Long Zhai, Guangtao Ma, Kede The Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Institute of Image Communication and Information Processing The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science The Shenzhen Research Institute City University of Hong Kong Kowloon Hong Kong

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users’ viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models. By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations. By comparing the quality prediction performance of different model variants on eight VQA datasets with realistic distortions, we find that nearly all datasets suffer from the easy dataset problem of varying severity, some of which even admit blind image quality assessment (BIQA) solutions. We additionally justify our claims by comparing our model generalization capabilities on these VQA datasets, and by ablating a dizzying set of BVQA design choices related to the basic building blocks. Our results cast doubt on the current progress in BVQA, and meanwhile shed light on good practices of constructing next-generation VQA datasets and models. Code is available at https://***/sunwei925/***. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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An enhanced MinHash encryption scheme for encrypted deduplication

An enhanced MinHash encryption scheme for encrypted deduplic...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Qiaowen Jia Guanxiong Ha Hanwei Wang Haoyu Ma Hang Chen State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Hangzhou China The School of Cyber Engineering Xidian University Xi’an China

The encrypted deduplication can provide both storage savings and data confidentiality for cloud storage systems. Convergent encryption (CE) is a well-known solution for encrypted deduplication, but it brings huge computation and storage overheads for key management. MinHash encryption is an effective solution to this issue. It reduces the number of keys by grouping multiple consecutive chunks into segments and generating one key for each segment. However, MinHash encryption does not take full advantage of the segment similarity, which can be used to further reduce the overhead for key management. To this end, we augment MinHash encryption with Bloom filter and Locality Sensitivity Hash (LSH) to design an enhanced MinHash encryption scheme. Firstly, our scheme generates a sketch for each segment based on the Bloom filter, and projects sketches to the points in a hash table through LSH functions. Secondly, we detect the segment similarity by the distance between points, and similar segments are grouped into super-segments. Finally, we combine MinHash encryption and server-aided message-locked encryption to encrypt the super-segments for achieving encrypted deduplication. We conduct trace-driven experiments using a realworld dataset. Compared with MinHash encryption, our scheme has higher storage efficiency and better encryption performance.

关键词： Privacy Maximum likelihood estimation Data privacy Cloud computing Sensitivity Encryption

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Clinical efficacy and safety of vortioxetine as an adjuvant drug for patients with bipolar depression

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Journal of Zhejiang University-science B(Biomedicine & Biotechnology) 2025年第1期26卷 26-38页

作者： Chunxiao DAI Yaoyang FU Xuanwei LI Meihua LIN Yinbo LI Xiao LI Keke HUANG Chengcheng ZHOU Jian XIE Qingwei ZHAO Shaohua HU Department of Psychiatry the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Department of Clinical Psychology Hangzhou First People’s HospitalHangzhou 310006China Department of Clinical Pharmacy the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Fuyang Third People’s Hospital Hangzhou 311402China Nanhu Brain-Computer Interface Institute Hangzhou 311100China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou 310003China School of Brain Science and Brain Medicine and MOE Frontier Science Center for Brain Science and Brain-Machine IntegrationZhejiang University School of MedicineHangzhou 310012China Brain Research Institute of Zhejiang University Hangzhou 310003China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou 310003China Zhejiang Engineering Center for Mathematical Mental Health Hangzhou 310003China

Objective:Whether vortioxetine has a utility as an adjuvant drug in the treatment of bipolar depression remains *** study aimed to validate the efficacy and safety of vortioxetine in bipolar ***:Patients with bipolarⅡdepression were enrolled in this prospective,two-center,randomized,12-week pilot *** main indicator for assessing treatment effectiveness was a Montgomery-Asberg Depression Rating Scale(MADRS)of≥50%.All eligible patients initially received four weeks of lurasidone *** who responded well continued to receive this kind of ***,no-response patients were randomly assigned to either valproate or vortioxetine treatment for eight *** comprehensively comparing the results of MADRS over a period of 4-12 weeks,a systematic analysis was conducted to determine whether vortioxetine could be used as an adjuvant drug for treating bipolar ***:Thirty-seven patients responded to lurasidone monotherapy,and 60 patients were randomly assigned to the valproate or vortioxetine group for eight *** two weeks of combined valproate or vortioxetine treatment,the MADRS score in the vortioxetine group was significantly lower than that in the valproate *** was no difference in the MADRS scores between the two groups at 8 and 12 *** incidence of side effects did not significantly differ between the valproate and vortioxetine ***,three patients in the vortioxetine group appeared to switch to mania or ***:This study suggested that lurasidone combination with vortioxetine might have potential benefits to bipolar II depression in the early stage,while disease progression should be monitored closely for the risk of switching to mania.

关键词： BipolarⅡdepression Lurasidone Vortioxetine Combination

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Learning deep multimodal feature representation with asymmetric multi-layer fusion

arXiv

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arXiv 2021年

作者： Wang, Yikai Sun, Fuchun Lu, Ming Yao, Anbang State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Cognitive Computing Laboratory Intel Labs China

We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. The framework consists of two innovative fusion schemes. Firstly, unlike existing multimodal methods that necessitate individual encoders for different modalities, we verify that multimodal features can be learnt within a shared single network by merely maintaining modality-specific batch normalization layers in the encoder, which also enables implicit fusion via joint feature representation learning. Secondly, we propose a bidirectional multi-layer fusion scheme, where multimodal features can be exploited progressively. To take advantage of such scheme, we introduce two asymmetric fusion operations including channel shuffle and pixel shift, which learn different fused features with respect to different fusion directions. These two operations are parameter-free and strengthen the multimodal feature interactions across channels as well as enhance the spatial feature discrimination within channels. We conduct extensive experiments on semantic segmentation and image translation tasks, based on three publicly available datasets covering diverse modalities. Results indicate that our proposed framework is general, compact and is superior to state-of-the-art fusion frameworks. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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Hard-instance learning for quantum adiabatic prime factorization

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Physical Review A 2022年第6期105卷 062455-062455页

作者： Jian Lin Zhengfeng Zhang Junping Zhang Xiaopeng Li State Key Laboratory of Surface Physics Institute of Nanoelectronics and Quantum Computing and Department of Physics Fudan University Shanghai 200433 China Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai 200433 China Shanghai Qi Zhi Institute Xuhui District Shanghai 200032 China Shanghai Research Center for Quantum Sciences Shanghai 201315 China

Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman cryptography. With programable quantum devices, adiabatic quantum computing has been proposed as a plausible approach to solve prime factorization, having promising advantage over classical computing. Here, we find there are certain hard instances that are consistently intractable for both classical simulated annealing and unconfigured adiabatic quantum computing (AQC). Aiming at an automated architecture for optimal configuration of quantum adiabatic factorization, we apply a deep reinforcement learning (RL) method to configure the AQC algorithm. By setting the success probability of the worst-case problem instances as the reward to RL, we show the AQC performance on the hard instances is dramatically improved by RL configuration. The success probability also becomes more evenly distributed over different problem instances, meaning the configured AQC is more stable as compared to the unconfigured case. Through a technique of transfer learning, we find prominent evidence that the framework of AQC configuration is scalable—the configured AQC as trained on five qubits remains working efficiently on nine qubits with a minimal amount of additional training cost.

关键词： Machine learning Quantum algorithms Quantum computation Quantum simulation

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