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Hidden Attractors in a Delayed Memristive Differential System with Fractional Order and Chaos Synchronization

Journal of Harbin Institute of Technology(New Series) 2020年第1期27卷 67-75页

作者： Dawei Ding Yecui Weng Nian Wang School of Electronics and Information Engineering Anhui UniversityHefei 230601China Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityHefei 230601China

As an important research branch,memristor has attracted a range of scholars to study the property of memristive chaotic ***,time⁃delayed systems are considered a significant and newly⁃developing field in modern *** combining memristor and time⁃delay,a delayed memristive differential system with fractional order is proposed in this paper,which can generate hidden ***,we discussed the dynamics of the proposed system where the parameter was set as the bifurcation parameter,and showed that with the increase of the parameter,the system generated rich chaotic phenomena such as bifurcation,chaos,and *** we derived adequate and appropriate stability criteria to guarantee the system to achieve ***,examples were provided to analyze and confirm the influence of parameter a,fractional order q,and time delayτon chaos *** simulation results confirm that the chaotic synchronization is affected by a,q andτ.

关键词： fractional order memristive time⁃delay hidden attractors chaos synchronization

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Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling

arXiv

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arXiv 2022年

作者： Wang, Rui Wu, Zuxuan Chen, Dongdong Chen, Yinpeng Dai, Xiyang Liu, Mengchen Zhou, Luowei Yuan, Lu Jiang, Yu-Gang Shanghai Key Lab of Intelligent Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Microsoft Cloud&AI China

Transformer-based models have achieved top performance on major video recognition benchmarks. Benefiting from the self-attention mechanism, these models show stronger ability of modeling long-range dependencies compared to CNN-based models. However, significant computation overheads, resulted from the quadratic complexity of self-attention on top of a tremendous number of tokens, limit the use of existing video transformers in applications with limited resources like mobile devices. In this paper, we extend Mobile-Former to Video Mobile-Former, which decouples the video architecture into a lightweight 3D-CNNs for local context modeling and a Transformer modules for global interaction modeling in a parallel fashion. To avoid significant computational cost incurred by computing self-attention between the large number of local patches in videos, we propose to use very few global tokens (e.g., 6) for a whole video in Transformers to exchange information with 3D-CNNs with a cross-attention mechanism. Through efficient global spatial-temporal modeling, Video Mobile-Former significantly improves the video recognition performance of alternative lightweight baselines, and outperforms other efficient CNN-based models at the low FLOP regime from 500M to 6G total FLOPs on various video recognition tasks. It is worth noting that Video Mobile-Former is the first Transformer-based video model which constrains the computational budget within 1G FLOPs. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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Surface Simulation and Design of C-band Energy Selective Surface

Surface Simulation and Design of C-band Energy Selective Sur...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Shangyu Lu Guohui Yang Kuang Zhang Yingsong Li School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Based on the equivalent circuit model of PIN diode and the full wave simulation method of CST software, the system performance of finite large energy selective surface (ESS) is simulated and analyzed when the number of cells is small. The simulation results show that the working performance of ESS will be degraded by the slight surface deformation caused by external force but not fail completely. Therefore, in the practical application of ESS, attention should be paid to avoid deformation caused by external force extrusion, which will reduce the shielding performance of ESS. The simulation results provide reference value for the practical design and application of energy selective surface.

关键词： Analytical models Deformation Surface waves PIN photodiodes Simulation System performance Force

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Self-decoupling 5G MIMO Antenna via Grounding for Mobile Phones

Self-decoupling 5G MIMO Antenna via Grounding for Mobile Pho...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Wei Zhou Junwei Qi Yingsong Li College of Information and Communication Engineering Harbin Engineering University (HEU) China College of Computer Science and Technology HEU China Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University Hefei China

This paper presents a self-decoupling high isolation terminal antenna for mobile phones which can applied to 5G multiple-input-multiple-output (MIMO) communication systems. The significant decoupling effect is achieved by introducing only one ground point in each asymmetric antenna pair with an interval of very close (2mm). Through the simulations and optimizations, it can be obtained that the bandwidth of the two antennas is different owing-to the asymmetric structures of the constructed MIMO antenna. The -10 dB resonant frequency ranges of the two antennas are 4.65 - 5.17 GHz and 4.45 - 5.58 GHz, respectively, but the coincidence bandwidth of the two antennas covers the 5G band of 4.8 - 5 GHz, and the coupling of the proposed MIMO antenna in the same bandwidth is lower than -18dB as well.

关键词： Couplings Wireless communication 5G mobile communication Grounding Resonant frequency Bandwidth Mobile antennas

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Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition

Efficient Robust Principal Component Analysis via Block Kryl...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shun Fang Zhengqin Xu Shiqian Wu Shoulie Xie School of Information Science and Engineering Wuhan University of Science and Technology China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Signal Processing RF & Optical Dept. Institute for Infocomm Research A*STAR Singapore

Robust principal component analysis (RPCA) is widely studied in computer vision. Recently an adaptive rank estimate based RPCA has achieved top performance in low-level vision tasks without the prior rank, but both the rank estimate and RPCA optimization algorithm involve singular value decomposition, which requires extremely huge computational resource for large-scale matrices. To address these issues, an efficient RPCA (eRPCA) algorithm is proposed based on block Krylov iteration and CUR decomposition in this paper. Specifically, the Krylov iteration method is employed to approximate the eigenvalue decomposition in the rank estimation, which requires $O(ndrq+n(rq)^{2})$ for an $(n\times d)$ input matrix, in which $q$ is a parameter with a small value, $r$ is the target rank. Based on the estimated rank, CUR decomposition is adopted to replace SVD in updating low-rank matrix component, whose complexity reduces from $O(rnd)$ to $O(r^{2}n)$ per iteration. Experimental results verify the efficiency and effectiveness of the proposed eRPCA over the state-of-the-art methods in various low-level vision applications.

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Lifting the Answer: Reranking Candidates on Data Augmented Text-to-SQL

Lifting the Answer: Reranking Candidates on Data Augmented T...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Guang-Yu Cheng Shuo Yu Wen-Bin Jiang Shuai Ma Xiang Ao Qing He Henan Institutes of Advanced Technology Zhengzhou University Zhengzhou P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Baidu inc Beijing China

The text-to-SQL task is a realistic and challenging job, which aims to translate natural language questions into corresponding SQL queries. When attempting to generate SQL queries in the text-to-SQL task, prevailing semantic parsing models employ beam search to generate several candidates. Based on our pilot study, we observe that the gold-standard SQL answer may exist in the n-best candidate list produced by the decoder, rather than the first. Hence, we aim to lift the correct answer given several candidates generated by semantic parsing models for text-to-SQL. To this end, we propose a reranking module that reorders the n-best list of candidate SQL queries by pair-wise hinge loss. Meanwhile, a data augmentation module is leveraged to enrich the inadequate training instances for providing candidates with better qualities. These two modules can be easily grafted onto text-to-SQL backbone networks, and extensive experiments on the cross-domain text-to-SQL benchmark Spider demonstrate that our method achieves 73.8% in accuracy on the Spider dataset, surpassing the base model by up to 3.5%.

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Implicit Temporal Modeling with Learnable Alignment for Video Recognition

arXiv

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arXiv 2023年

作者： Tu, Shuyuan Dai, Qi Wu, Zuxuan Cheng, Zhi-Qi Hu, Han Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Microsoft Research Asia China Carnegie Mellon University United States

Contrastive language-image pretraining (CLIP) has demonstrated remarkable success in various image tasks. However, how to extend CLIP with effective temporal modeling is still an open and crucial problem. Existing factorized or joint spatial-temporal modeling trades off between the efficiency and performance. While modeling temporal information within straight through tube is widely adopted in literature, we find that simple frame alignment already provides enough essence without temporal attention. To this end, in this paper, we proposed a novel Implicit Learnable Alignment (ILA) method, which minimizes the temporal modeling effort while achieving incredibly high performance. Specifically, for a frame pair, an interactive point is predicted in each frame, serving as a mutual information rich region. By enhancing the features around the interactive point, two frames are implicitly aligned. The aligned features are then pooled into a single token, which is leveraged in the subsequent spatial self-attention. Our method allows eliminating the costly or insufficient temporal self-attention in video. Extensive experiments on benchmarks demonstrate the superiority and generality of our module. Particularly, the proposed ILA achieves a top-1 accuracy of 88.7% on Kinetics-400 with much fewer FLOPs compared with Swin-L and ViViT-H. Code is released at https://***/Francis-Rings/ILA. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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Spatiotemporal Activity Modeling via Hierarchical Cross-Modal Embedding : Extended Abstract

Spatiotemporal Activity Modeling via Hierarchical Cross-Moda...

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International Conference on Data Engineering

作者： Yang Liu Xiang Ao Linfeng Dong Chao Zhang Jin Wang Qing He Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Zhejiang University Hangzhou China College of Computing Georgia Tech United States Megagon Labs Mountain View United States

With the ever-increasing urbanization process, modeling people’s spatiotemporal activities from their online traces has become a crucial task. State-of-the-art methods for this task rely on cross-modal embedding, which maps items from different modalities (e.g., location, time, text) into the same latent space. Despite their inspiring results, existing cross-modal embedding methods merely capture co-occurrences between items without modeling their high-order interactions. In this paper, we first construct the user interaction graph and the activity graph from raw data records and propose a hierarchical cross-modal embedding method that takes the high-order relationships into consideration. We introduce both inter-record and intra-record meta-graph structures, which enable learning distributed representations that preserve high-order proximities across graphs from different layers. Our empirical experiments on three real-world datasets demonstrate that our method not only outperforms state-of-the-art methods for spatiotemporal activity prediction but also captures cross-modal proximity at a finer granularity.

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DOA Estimation for RIS Assisted Multi-User Wireless Communication System with Squared-Sine Error Criterion and Lawson Norm Constraint

DOA Estimation for RIS Assisted Multi-User Wireless Communic...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Canping Yu Li Li Yingsong Li Dazhi Sun Liping Li Zhixiang Huang Tian Hong Loh Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology (Center of Aviation Safety Technology CAST) Beijing China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Teddington Middlesex UK

Reconfigurable intelligent surface (RIS) can control the phase of reflected signals, and has become a hotspot in the field of wireless communication. In this paper, we propose a direction-of-arrival (DOA) estimation algorithm for RIS assisted multi-user wireless communication systems with squared-sine error criterion and Lawson norm constraint. This algorithm utilizes the sparsity of the signal and can achieve fast and accurate DOA estimation under the interference of impulsive noises. To suppress the line-of-sight (LOS) interference and thus to maximize the received signal-to-interference-plus-noise ratio (SINR), we also propose a random optimal phase selection (ROPS) technique to design the control matrix of RIS elements. The simulation results show that the proposed algorithm performs better than the existing methods.

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Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning

arXiv

引用

arXiv 2022年

作者： Huang, Bingchen Chen, Zhineng Zhou, Peng Chen, Jiayin Wu, Zuxuan Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China University of Maryland College ParkMD United States

The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting. However, task confusion is not well assessed within this framework, e.g., the discrepancy between classes of different tasks is not well learned (i.e., inter-task confusion, ITC), and certain priority is still given to the latest class batch (i.e., old-new confusion, ONC). We empirically validate the side effects of the two types of confusion. Meanwhile, a novel solution called Task Correlated Incremental Learning (TCIL) is proposed to encourage discriminative and fair feature utilization across tasks. TCIL performs a multi-level knowledge distillation to propagate knowledge learned from old tasks to the new one. It establishes information flow paths at both feature and logit levels, enabling the learning to be aware of old classes. Besides, attention mechanism and classifier re-scoring are applied to generate more fair classification scores. We conduct extensive experiments on CIFAR100 and ImageNet100 datasets. The results demonstrate that TCIL consistently achieves state-of-the-art accuracy. It mitigates both ITC and ONC, while showing advantages in battle with catastrophic forgetting even no rehearsal memory is reserved. 1 Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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