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arXiv 2022年

作者： Shi, Minjia Lu, Haodong Zhou, Shuang Xu, Jiarui Zhu, Yuhang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China

In this paper, several conjectures proposed in [2] are studied, involving the equivalence and duality of polycyclic codes associated with trinomials. According to the results, we give methods to construct isodual and self-dual polycyclic codes, and study the self-orthogonal and dual-containing polycyclic codes over F2MSC Codes 94B05, 05E30 © 2022, CC0.

关键词： Algebra

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Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 913-925页

作者： Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Changping Laboratory Beijing 102206China Microsoft Research AI4Science Beijing 100080China Huawei Noah’s Ark Lab Wuhan 430206China Zhongke Big Data Academy Zhengzhou 450046China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer *** researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native *** research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic ***,we have also witnessed the great success of deep learning in protein structure *** this review,we present a survey of the efforts for protein structure *** compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep *** short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关键词： Protein folding Protein structure prediction Deep learning Transformer Language model

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Correction to: Singular value decomposition compressed ghost imaging

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Applied Physics B 2022年第4期128卷 1-2页

作者： Zhang, Cheng Tang, Jun Zhou, Jiaxuan Wei, Sui Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Key Laboratory of Modern Imaging and Displaying Technology Anhui University Hefei China

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Machine Learning Based FDTD Method for 3D Electromagnetic Simulation

Machine Learning Based FDTD Method for 3D Electromagnetic Si...

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IEEE International Conference on Computational Electromagnetics (ICCEM)

作者： Jinpeng Dai Guoda Xie Guilin Hou Liyuan Zhang Zhixiang Huang The Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Education of Anhui Province Luan Anhui China the Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Institute of Energym Hefei Comprehensive National Science Center Hefei China Institute of Plasma Physics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China Key Laboratory of Electromagnetic Environmental Sensing Anhui Province Anhui University Hefei China Anhui University Hefei China

ISBN: (数字)9798350383317

ISBN: (纸本)9798350383324

This article introduces an innovative and efficient deep learning-assisted Finite-Difference Time-Domain (DL-FDTD) method in the field of computational electromagnetics. This method ingeniously integrates the Gated Recurrent Unit (GRU) network and Particle Swarm Optimization (PSO) algorithm into the traditional FDTD framework, termed as the PSO-GRU-FDTD model. A key innovation of this model is the application of the particle swarm algorithm, which significantly simplifies the parameter tuning process, thereby accelerating model development. This advancement represents a notable breakthrough from the complex parameter adjustment process typical in traditional neural network models. Moreover, compared to the traditional Long Short-Term Memory (LSTM) networks, the GRU network excels in simplicity, convenience, and efficiency, while also ensuring accuracy. Ultimately, this method is applied to three-dimensional electromagnetic simulation and emulation. Numerical results demonstrate that this approach exhibits outstanding performance in both simulation accuracy and efficiency.

关键词： Accuracy Computational modeling Neural networks Computational electromagnetics Time-domain analysis Particle swarm optimization Electromagnetics

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Advanced Detection and signal Reception System Utilizing Rydberg Atomic Dynamics for Applications

Advanced Detection and Signal Reception System Utilizing Ryd...

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European Conference on Antennas and Propagation, EuCAP

作者： Wenjie Ding Guoda Xie Yingsong Li Zhixiang Huang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Ministry of Education Center for Big Data and Population Health of IHM Anhui University Hefei China The Jianghuai Advance Technology Center Hefei China

ISBN: (数字)9788831299107

ISBN: (纸本)9798350366327

In this paper, we introduce a novel Monte Carlo wave function method (MCWFM) for simulating the transient response of Rydberg atoms under rapidly varying electric fields, aimed at high-precision electric field measurements and wireless communication reception. This approach leverages the electromagnetically induced transparency (EIT) effect and Autler- Townes (AT) splitting in Rydberg atoms, offering enhanced efficiency compared to traditional density matrix methods by focusing solely on wave function evolution, thereby reducing computational complexity. Additionally, we account for Doppler effects in AT splitting through MCWFM, providing faster results than conventional Runge-Kutta methods. This work lays a solid foundation for advancing Rydberg atom technology in the development of high-capacity, high-fidelity wireless communication systems.

关键词： Wireless communication Atomic measurements Transient response Rydberg atoms Receivers Wave functions Optical fiber communication Doppler effect Electric fields Computational complexity

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Near-field Source DOA Estimation Algorithm Based on De-impact Function under Impulse Noise

Near-field Source DOA Estimation Algorithm Based on De-impac...

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

作者： Xingchao Xu Baohe Pang Yingsong Li Liping Li Dingcheng Zou Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In addressing the issue of localizing near-field source signals in the presence of impulse noise, this manuscript introduces a novel de-impact function designed for preprocessing received signals to alleviate the interference caused by impulse noise. Simulation experiments verify the effectiveness of the proposed de-impact function in effectively suppressing impulse noise. Furthermore, to augment estimation precision, a novel algorithm for near-field source localization is proposed. Building upon the framework of correlation entropy theory, a new correntropy covariance matrix is formulated to replace the original signal covariance matrix for signal processing. Through a succession of low-bias approximations between near-field and far-field scenarios, the algorithm converts the original 2-D search into 1-D operations. Additionally, the conventional spectral peak search is replaced by polynomial solving to further mitigate the computational complexity of the algorithm.

关键词： Location awareness Direction-of-arrival estimation Noise signal processing algorithms Estimation Interference Approximation algorithms

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A High-gain Triple Stopband Ultra-wideband MIMO Antenna

A High-gain Triple Stopband Ultra-wideband MIMO Antenna

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Jian Wu Yufa Sun Qing Wang Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China School of Electronic Engineering Anhui Xinhua University Hefei China

ISBN: (纸本)9781665482271

This paper designs a three-stopband ultra-wideband MIMO antenna with high-gain using FR4 as the dielectric substrate. The antenna consists of two horizontally placed semicircle patches with chamfer and slot. By corroding two C-shaped grooves on the semicircular radiation patch and etching two symmetrical C-shaped grooves on both sides of the feeder on the floor, the interference of WiMAX, WLAN and 7.5GHz-X band is blocked. In order to realize the isolation of the dual ports, a groove is made on the defective metal surface and a branch shaped like Chinese character mountain is added. The simulation results show that the working frequency range of the antenna is 2.5-11GHz, and three S11 stopbands higher than −10dB are formed at 3.3-3.8GHz, 5.15-5.825GHz, and 7.1-7.9GHz respectively. Therefore, it has good application prospects in the ultra-wideband field that blocks narrowband communications.

关键词： Microwave antennas Wireless LAN Slot antennas Simulation Dielectric substrates Interference WiMAX

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DragEntity:Trajectory Guided Video Generation using Entity and Positional Relationships

arXiv

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arXiv 2024年

作者： Wan, Zhang Tang, Sheng Wei, Jiawei Zhang, Ruize Cao, Juan Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Zhengzhou University Zhengzhou China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

In recent years, diffusion models have achieved tremendous success in the field of video generation, with controllable video generation receiving significant attention. However, existing control methods still face two limitations: Firstly, control conditions (such as depth maps, 3D Mesh) are difficult for ordinary users to obtain directly. Secondly, it’s challenging to drive multiple objects through complex motions with multiple trajectories simultaneously. In this paper, we introduce DragEntity, a video generation model that utilizes entity representation for controlling the motion of multiple objects. Compared to previous methods, DragEntity offers two main advantages: 1) Our method is more user-friendly for interaction because it allows users to drag entities within the image rather than individual pixels. 2) We use entity representation to represent any object in the image, and multiple objects can maintain relative spatial relationships. Therefore, we allow multiple trajectories to control multiple objects in the image with different levels of complexity simultaneously. Our experiments validate the effectiveness of DragEntity, demonstrating its excellent performance in fine-grained control in video generation. Copyright © 2024, The Authors. All rights reserved.

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EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame Interpolation

arXiv

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arXiv 2025年

作者： Zhang, Zihao Chen, Haoran Zhao, Haoyu Lu, Guansong Fu, Yanwei Xu, Hang Wu, Zuxuan Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Noah’s Ark Lab Huawei Canada

Handling complex or nonlinear motion patterns has long posed challenges for video frame interpolation. Although recent advances in diffusion-based methods offer improvements over traditional optical flow-based approaches, they still struggle to generate sharp, temporally consistent frames in scenarios with large motion. To address this limitation, we introduce EDEN, an Enhanced Diffusion for high-quality large-motion vidEo frame iNterpolation. Our approach first utilizes a transformer-based tokenizer to produce refined latent representations of the intermediate frames for diffusion models. We then enhance the diffusion transformer with temporal attention across the process and incorporate a start-end frame difference embedding to guide the generation of dynamic motion. Extensive experiments demonstrate that EDEN achieves state-of-the-art results across popular benchmarks, including nearly a 10% LPIPS reduction on DAVIS and SNU-FILM, and an 8% improvement on DAIN-HD. Copyright © 2025, The Authors. All rights reserved.

关键词： Optical flows

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Weighted ROC curve in cost space: extending AUC to cost-sensitive learning 23

Weighted ROC curve in cost space: extending AUC to cost-sens...

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Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Huiyang Shao Qianqian Xu Zhiyong Yang Peisong Wen Peifeng Gao Qingming Huang Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and BDKM University of Chinese Academy of Sciences

In this paper, we aim to tackle flexible cost requirements for long-tail datasets, where we need to construct a (1) cost-sensitive and (2) class-distribution robust learning framework. The misclassification cost and the area under the ROC curve (AUC) are popular metrics for (1) and (2), respectively. However, limited by their formulations, models trained with AUC are not well-suited for cost-sensitive decision problems, and models trained with fixed costs are sensitive to the class distribution shift. To address this issue, we present a new setting where costs are treated like a dataset to deal with arbitrarily unknown cost distributions. Moreover, we propose a novel weighted version of AUC where the cost distribution can be integrated into its calculation through decision thresholds. To formulate this setting, we propose a novel bilevel paradigm to bridge weighted AUC (WAUC) and cost. The inner-level problem approximates the optimal threshold from sampling costs, and the outer-level problem minimizes the WAUC loss over the optimal threshold distribution. To optimize this bilevel paradigm, we employ a stochastic optimization algorithm (SACCL) which enjoys the same convergence rate (O(ε-4)) with the SGD. Finally, experiment results show that our algorithm performs better than existing cost-sensitive learning methods and two-stage AUC decisions approach.

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