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arXiv 2024年

作者： Xia, Xin Zhang, Yajie Zeng, Xiangxiang Zhang, Xingyi Zheng, Chunhou Su, Yansen The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China College of Computer Science and Electronic Engineering Hunan University Changsha410012 China

Molecular optimization, which aims to discover improved molecules from a vast chemical search space, is a critical step in chemical development. Various artificial intelligence technologies have demonstrated high effectiveness and efficiency on molecular optimization tasks. However, few of these technologies focus on balancing property optimization with constraint satisfaction, making it difficult to obtain high-quality molecules that not only possess desirable properties but also meet various constraints. To address this issue, we propose a constrained multi-property molecular optimization framework (CMOMO), which is a flexible and efficient method to simultaneously optimize multiple molecular properties while satisfying several druglike constraints. CMOMO improves multiple properties of molecules with constraints based on dynamic cooperative optimization, which dynamically handles the constraints across various scenarios. Besides, CMOMO evaluates multiple properties within discrete chemical spaces cooperatively with the evolution of molecules within an implicit molecular space to guide the evolutionary search. Experimental results show the superior performance of the proposed CMOMO over five state-of-the-art molecular optimization methods on two benchmark tasks of simultaneously optimizing multiple non-biological activity properties while satisfying two structural constraints. Furthermore, the practical applicability of CMOMO is verified on two practical tasks, where it identified a collection of candidate ligands of β2-adrenoceptor GPCR and candidate inhibitors of glycogen synthase kinase-3β with high properties and under drug-like constraints. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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A Compact Double-Layer Wide Beam Circularly Polarized Beidou Antenna

A Compact Double-Layer Wide Beam Circularly Polarized Beidou...

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

作者： Lulu Meng Sipei Min Zhixiang Huang Yingsong Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

This paper proposes and designs a microstrip antenna that can work in the B1 band for Beidou with a double-layer substrate, and realizes the circularly polarized radiation characteristics. The antenna consists of a ground plane, two substrates, and two metal patches. The first layer of the patch is a rectangular structure as well as the second layer of the patch is a T-shaped structure with two vertical branches which are to achieve circular polarization. This structure adopts a simple coaxial feeding method, with the probe passing through the second layer of the substrate to connect the ground plane and the T-shaped strip. The simulation optimization results using the HFSS 2021 show that the axial ratio (AR) beam width in both planes exceeds 160°, and the AR at the design frequency is 0.18 dB. The impedance bandwidth is 70 MHz, which can meet the requirements of Beidou's operation.

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Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network

arXiv

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arXiv 2024年

作者： Wang, Kunpeng Chen, Keke Li, Chenglong Tu, Zhengzheng Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University China Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University China

Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from unaligned visible-thermal image pairs, without requiring manual alignment. However, the labor-intensive process of collecting and annotating image pairs limits the scale of existing benchmarks, hindering the advancement of alignment-free RGB-T SOD. In this paper, we construct a large-scale and high-diversity unaligned RGB-T SOD dataset named UVT20K, comprising 20,000 image pairs, 407 scenes, and 1256 object categories. All samples are collected from real-world scenarios with various challenges, such as low illumination, image clutter, complex salient objects, and so on. To support the exploration for further research, each sample in UVT20K is annotated with a comprehensive set of ground truths, including saliency masks, scribbles, boundaries, and challenge attributes. In addition, we propose a Progressive Correlation Network (PCNet), which models inter- and intra-modal correlations on the basis of explicit alignment to achieve accurate predictions in unaligned image pairs. Extensive experiments conducted on unaligned and aligned datasets demonstrate the effectiveness of our method. Copyright © 2024, The Authors. All rights reserved.

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A Band-pass FSS with Wide Angular Stability and High Polarization Stability

A Band-pass FSS with Wide Angular Stability and High Polariz...

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

作者： Yunshuai Gao Shuying Ding Zhixiang Huang Yingsong Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

A mimic multi-layer polarization-independent FSS with accessible angular-stability is reported using a three-layer structure, where the top layer and the bottom layer are realized based the metal rings that are same, and the middle is constructed by a slotted metal structure. The mentioned three layers are aimed to separate the used two dielectric substrates. For a well illustration, the constructed dimensions of the installed FSS are $\boldsymbol{5\times 5}\mathbf{mm}^{2}$ . As a case of the FSS cell design, here the dimension is limited as $\boldsymbol{0.15}\lambda\times \boldsymbol{0.15}\lambda$ . Since two different structure is used, the FSS has double resonace, making a wide operation. From the numerical evaluations, the FSS can reject the signal from C-and X-band.

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model 13

Structure-constrain Source Free Domain Adaptation with Regul...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Yang, Dan Peng, Xin Su, Cheng Tan, Dayu Chen, Chaoyang Zhong, Weimin Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Anhui Hefei230601 China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350361674

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness. © 2024 IEEE.

关键词： Domain Knowledge

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Extending generalized unsupervised manifold alignment

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Science China(Information Sciences) 2022年第7期65卷 139-156页

作者： Xiaoyi YIN Zhen CUI Hong CHANG Bingpeng MA Shiguang SHAN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Engineering Nanjing University of Science and Technology CAS Center for Excellence in Brain Science and Intelligence Technology

Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold alignment, the correspondences between data sets will assist us with comprehensive study of data processes and analyses. Despite the several progresses in semi-supervised and unsupervised scenarios, potent manifold alignment methods in generalized and realistic circumstances remain in absence. Besides, theretofore unsupervised algorithms seldom prove themselves mathematically. In this paper, we devise an efficient method to properly solve the unsupervised manifold alignment problem and denominate it as extending generalized unsupervised manifold alignment(EGUMA)method. More specifically, an explicit relaxed integer programming method is adopted to solve the unsupervised manifold alignment problem, which reconciles three factors covering the updated local structure matching, the the feature comparability and geometric preservation. An additional effort is retained on extending the Frank Wolfe algorithm to tacking our optimization problem. Besides our previous endeavors we adopt a new strategy for neighborhood discovery in the manifolds. The main advantages over previous methods accommodate(1) simultaneous alignment and discovery of manifolds;(2) complete unsupervised learning structure without any prerequisite correspondence;(3) more concise local geometry for the embedding space;(4) efficient alternative optimization;(5) strict mathematical analysis on the convergence and efficiency issues. Experiments on real-world applications verify the high accuracy and efficiency of our proposed method.

关键词： unsupervised manifold alignment relaxed integer programming Frank Wolfe algorithm

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A RIS-Supported DOA Estimation Using Combined Norm Constraint in Non-Gaussian Noise Environments

A RIS-Supported DOA Estimation Using Combined Norm Constrain...

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

作者： Lei He Liping Li Run Ying Canping Yu Qiang Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

Reconfigurable Intelligent Surface (RIS) is a revolutionary technology in modern wireless communication systems, since it can adjust the wireless communication environment with passive components. In this paper, we consider the system environment in the presence of Base Station (BS) interference and implement the estimation of the target Direction-of-Arrival (DOA). To improve the estimation performance, we propose a DOA estimation algorithm based on RIS. The proposed algorithm consists of an error term for the lorentzian bound function and a regular term for the atomic norm (termed as LBF-AN), which are implemented to eliminate system noise and signal interference, respectively. The simulation results show that the DOA estimation performance of the proposed algorithm is better than existing algorithms.

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An Improved Optimal Transport Kernel Embedding Method with Gating Mechanism for Singing Voice Separation and Speaker Identification 48

An Improved Optimal Transport Kernel Embedding Method with G...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Yuan, Weitao Bian, Yuren Wang, Shengbei Unoki, Masashi Wang, Wenwu Tiangong University Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems China Japan Advanced Institute of Science and Technology Japan University of Surrey Centre for Vision Speech and Signal Processing Guildford United Kingdom

ISBN: (纸本)9781728163277

Singing voice separation (SVS) and speaker identification (SI) are two classic problems in speech signal processing. Deep neural networks (DNNs) solve these two problems by extracting effective representations of the target signal from the input mixture. Since essential features of a signal can be well reflected on its latent geometric structure of the feature distribution, a natural way to address SVS/SI is to extract the geometry-aware and distribution-related features of the target signal. To do this, this work introduces the concept of optimal transport (OT) to SVS/SI and proposes an improved optimal transport kernel embedding (iOTKE) to extract the target-distribution-related features. The iOTKE learns an OT from the input signal to the target signal on the basis of a reference set learned from all training data. Thus it can maintain the feature diversity and preserve the latent geometric structure of the distribution for the target signal. To further improve the feature selection ability, we extend the proposed iOTKE to a gated version, i.e., gated iOTKE (G-iOTKE), by incorporating a lightweight gating mechanism. The gating mechanism controls effective information flow and enables the proposed method to select important features for a specific input signal. We evaluated the proposed G-iOTKE on SVS/SI. Experimental results showed that the proposed method provided better results than other models. © 2023 IEEE.

关键词： Embeddings

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Classification of Cervical Images Based On Deep Neural Network With Attention Mechanism

Classification of Cervical Images Based On Deep Neural Netwo...

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

作者： Yun Feng Lan Chen Xia Zhang Yingsong Li Liping Li Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

In this paper, we propose a DenseNet-CBAM model that utilizes DenseNet121 as the backbone network for feature extraction. The features are then weighted using the Convolutional Block Attention Module (CBAM), which incorporates both channel and spatial attention mechanisms. The features are further processed with ReLU activation and adaptive average pooling operations. Finally, a linear classifier is applied to output the final category predictions. Experiment results show that the proposed model performs the best in terms of the ability in detecting patients with high-grade squamous intraepithelial lesions (HSIL).

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A Negative Survey based Method for Preserving Topology Privacy in Social Networks 4

A Negative Survey based Method for Preserving Topology Priva...

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4th International Conference on Data intelligence and Security, ICDIS 2022

作者： Jiang, Hao Liao, Yuerong Yu, Qianwei Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China The Second Affiliated Hospital of Anhui Medical University Department of Gastroenterology Hefei230601 China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459686

Currently, the rapid popularity of social network platforms makes the real social relations in social networks face the potential risk of disclosure. Therefore, most users may refuse to provide their social relations for the purpose of privacy preservation, which is not conducive to the development of social networks. The negative survey is an emerging sensitive information collection method, which can collect sensitive information while preserving personal privacy. In this paper, based on negative survey, we propose an algorithm called NetNS to preserve the privacy of social relations, namely the topology privacy of social networks. The proposed NetNS is simple and efficient, which only randomly divides the nodes in social networks into different groups, and disturbs the topology of each group by a dedicated negative survey model. Experiments are carried out on three real networks of different sizes. The experimental results show that compared to three algorithms tailored for topology privacy of social networks, the proposed NetNS can against graph structure attack while realizing better utility of social networks. © 2022 IEEE.

关键词： Topology

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