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Additive complementary dual codes over F4

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arXiv 2022年

作者： Shi, Minjia Liu, Na Kim, Jon-Lark Solé, Patrick Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei230601 China School of Mathematical Sciences Anhui University Anhui Hefei230601 China Department of Mathematics Sogang University Seoul Korea Republic of Aix Marseille Univ CNRS Centrale Marseille I2M Marseille France

A linear code is linear complementary dual (LCD) if it meets its dual trivially. LCD codes have been a hot topic recently due to Boolean masking application in the security of embarked electronics (Carlet and Guilley, 2014). Additive codes over F4 are F4-codes that are stable by codeword addition but not necessarily by scalar multiplication. An additive code over F4 is additive complementary dual (ACD) if it meets its dual trivially. The aim of this research is to study such codes which meet their dual trivially. All the techniques and problems used to study LCD codes are potentially relevant to ACD codes. Interesting constructions of ACD codes from binary codes are given with respect to the trace Hermitian and trace Euclidean inner product. The former product is relevant to quantum *** Codes 94B05 © 2022, CC0.

关键词： Additives

Ekblom Promoting Adaptive Algorithm for System Identification

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TechRxiv

TechRxiv 2022年

作者： Huang, Xinqi Li, Yingsong Han, Xiao Tu, Huawei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China College of Computer Science and Technology Harbin Engineering University Harbin150001 China Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia

We propose an Ekblom promoting adaptive algorithm (EPAA) which uses Ekblom norm to construct a data reusing scheme to achieve better performance under impulsive noise (IN) environments. By exerting Ekblom-norm constraint on the a posterior error vector, a sliding window cost function is created to realize the EPAA that is derived to reduce IN effects. The derivation and theoretical convergence analysis of the EPAA are presented in detail. The simulation examples are setup to illustrate the superior performance of the EPAA compared to popular data reusing algorithms for system identification under IN environments. © 2022, CC BY.

关键词： Adaptive filtering

Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation

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arXiv 2024年

作者： Wang, Shanshan Zhou, Hao Yang, Xun He, Zhenwei Wang, Mengzhu Zhang, Xingyi Wang, Meng The Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Hefei230601 China The Department of Electronic Engineering and Information Science School of Information Science and Technology University of Science and Technology of China Hefei230026 China The College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The School of Artificial Intelligence Hebei University of Technology Tianjin China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education The School of Computer Science and Technology Anhui University Hefei230601 China

Unsupervised domain adaptation (UDA) is a critical problem for transfer learning, which aims to transfer the semantic information from labeled source domain to unlabeled target domain. Recent advancements in UDA models have demonstrated significant generalization capabilities on the target domain. However, the generalization boundary of UDA models remains unclear. When the domain discrepancy is too large, the model can not preserve the distribution structure, leading to distribution collapse during the alignment. To address this challenge, we propose an efficient UDA framework named Gradually Vanishing Gap in Prototypical Network (GVG-PN), which achieves transfer learning from both global and local perspectives. From the global alignment standpoint, our model generates a domain-biased intermediate domain that helps preserve the distribution structures. By entangling cross-domain features, our model progressively reduces the risk of distribution collapse. However, only relying on global alignment is insufficient to preserve the distribution structure. To further enhance the inner relationships of features, we introduce the local perspective. We utilize the graph convolutional network (GCN) as an intuitive method to explore the internal relationships between features, ensuring the preservation of manifold structures and generating domain-biased prototypes. Additionally, we consider the discriminability of the inner relationships between features. We propose a pro-contrastive loss to enhance the discriminability at the prototype level by separating hard negative pairs. By incorporating both GCN and the pro-contrastive loss, our model fully explores fine-grained semantic relationships. Experiments on several UDA benchmarks validated that the proposed GVG-PN can clearly outperform the SOTA models. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

Lecture Notes in Control and Information Sciences

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Preface

Lecture Notes in Control and Information Sciences 2023年 492卷 v-vi页

作者： Luan, Xiaoli He, Shuping Liu, Fei Key Laboratory of Advanced Process Control for Light Industry [Ministry of Education Institute of Automation Jiangnan University Jiangsu Wuxi China Key Laboratory of Intelligent Computing and Signal Processing [Ministry of Education School of Electrical Engineering and Automation Anhui University Anhui Hefei China

A Wideband Millimeter Wave E-shape Antenna in Package with Embedded Wafer Level Ball Grid Array Technology

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A Wideband Millimeter Wave E-shape Antenna in Package with E...

International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Bin Lu Haoran Zhu Zongming Duan Yuefei Dai Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhu University Hefei China Key Lab of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Engineer Laboratory for Antennas and Microwave The 38th Research Institute of China Electronic Technology Group Corporationy Hefei China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

The feasibility of an antenna-in-package (AiP) concept is demonstrated for automotive radar sensors at the 77 GHz-band. Infineon's embedded wafer level ball grid array (eWLB) is employed as packaging technology. We developed a E-shape patch antenna in 6×6 mm 2 eWLB package that include 2×2 mm 2 dummy Si chips. From the simulated results of the proposed E-plane patch antenna, provides a wide bandwidth of 19.2% (|S 11 |

关键词： Patch antennas Radar antennas Sensors Broadband antennas Wideband Antenna arrays Electronics packaging

An enumeration of 1-perfect ternary codes

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arXiv 2021年

作者： Shi, Minjia Krotov, Denis S. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China Sobolev Institute of Mathematics Novosibirsk630090 Russia

MSC Codes 94B25We study codes with parameters of the ternary Hamming (n=(3m−1)/2, 3n−m, 3) code, i.e., ternary 1-perfect codes. The rank of the code is defined to be the dimension of its affine span. We characterize ternary 1-perfect codes of rank n − m + 1, count their number, and prove that all such codes can be obtained from each other by a sequence of two-coordinate switchings. We enumerate ternary 1-perfect codes of length 13 obtained by concatenation from codes of lengths 9 and 4;we find that there are 93241327 equivalence classes of such codes. Copyright © 2021, The Authors. All rights reserved.

关键词： Equivalence classes

Co-training based prediction of multi-label protein–protein interactions

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Computers in Biology and Medicine 2024年 177卷 108623-108623页

作者： Tang, Tao Zhang, Xiaocai Li, Weizhuo Wang, Qing Liu, Yuansheng Cao, Xiaofeng School of Modern Posts Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China 1 Fusionopolis Way Singapore138632 Singapore School of Management Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China College of Computer Science and Electronic Engineering Hunan University 2 Lushan Rd Hunan Changsha410086 China Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education Anhui University 111 Jiulong Road Anhui Hefei230601 China School of Artificial Intelligence Jilin University 2699 Qianjin St Changchun Jilin130012 China

Prediction of protein–protein interaction (PPI) types enhances the comprehension of the underlying structural characteristics and functions of proteins, which gives rise to a multi-label classification problem. The nominal features describe the physicochemical characteristics of proteins directly, establishing a more robust correlation with the interaction types between proteins than ordered features. Motivated by this, we propose a multi-label PPI prediction model referred to as CoMPPI (Co-training based Multi-label prediction of Protein–Protein Interaction). This approach aims to maximize the utility of both ordered and nominal features extracted from protein sequences. Specifically, CoMPPI incorporates graph convolutional network (GCN) and 1D convolution operation to process the complementary subsets of features individually, leveraging both local and contextualized information in a more efficient way. In addition, two multi-type PPI datasets were constructed to eliminate the duplication in previous datasets. We compare the performance of CoMPPI with three state-of-the-art methods on three datasets partitioned using distinct schemes (Breadth-first search, Depth-first search, and Random), CoMPPI consistently outperforms the other methods across all cases, demonstrating improvements ranging from 3.81% to 32.40% in Micro-F1. The subsequent ablation experiment confirms the efficacy of employing the co-training framework for multi-label PPI prediction, indicating promising avenues for future advancements in this domain. © 2024 Elsevier Ltd

关键词： Proteins

Static Gesture Recognition and Distance Estimation Using a Capacitive Proximity Sensor Array

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SSRN

SSRN 2023年

作者： Ye, Yong Liu, Yuting Du, Chunhui Deng, Jiahao Qiu, Chenguang School of Electronics and Information Engineering The Information Materials and Intelligent Sensing Laboratory of Anhui Province The Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education The Key Laboratory of Electromagnetic Environmental Sensing of Anhui Higher Education Institutes Anhui University No. 111 Jiulong Road Anhui Province Hefei230601 China Institute of high energy physics Chinese academy of sciences The School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China Tai Yuan institute Co. Ltd. of China coal technology and engineering group Shanxi Province Taiyuan030006 China East China Institute of Photo-Electronic IC Jiangsu Province Suzhou215163 China

In this paper, we present a capacitive proximity sensor (CPS) array for static gesture recognition and distance estimation. A 4×4 electrode matrix is employed as the sensing element that is configured to 14 electrode operation patterns to obtain 14 capacitive sensors to percept objects within their sensing region. These 14 capacitances are fused into one feature vector which is used as the input of machine learning (ML) classifier for gesture recognition and neural network or regression model for distance estimation. In our experiments, we selected 10 static gestures to verify the proposed method. We compared the performance of 8 ML classifiers and their variations with training data size. The results show that the Bagging achieves the highest average accuracy of 93.2% and the KNN shows a good performance even with a small training data size. The results in distance estimation experiment demonstrate that the RBF network performs a high accuracy. © 2023, The Authors. All rights reserved.

关键词： Machine learning

VANER: Leveraging Large Language Model for Versatile and Adaptive Biomedical Named Entity Recognition

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arXiv 2024年

作者： Bian, Junyi Zhai, Weiqi Huang, Xiaodi Zheng, Jiaxuan Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China School of Computing and Mathematics Charles Sturt University AlburyNSW2640 Australia

Prevalent solution for BioNER involves using representation learning techniques coupled with sequence labeling. However, such methods are inherently task-specific, demonstrate poor generalizability, and often require dedicated model for each dataset. To leverage the versatile capabilities of recently remarkable large language models (LLMs), several endeavors have explored generative approaches to entity extraction. Yet, these approaches often fall short of the effectiveness of previouly sequence labeling approaches. In this paper, we utilize the open-sourced LLM LLaMA2 as the backbone model, and design specific instructions to distinguish between different types of entities and datasets. By combining the LLM’s understanding of instructions with sequence labeling techniques, we use mix of datasets to train a model capable of extracting various types of entities. Given that the backbone LLMs lacks specialized medical knowledge, we also integrate external entity knowledge bases and employ instruction tuning to compel the model to densely recognize carefully curated entities. Our model VANER, trained with a small partition of parameters, significantly outperforms previous LLMs-based models and, for the first time, as a model based on LLM, surpasses the majority of conventional state-of-the-art BioNER systems, achieving the highest F1 scores across three datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics