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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Technical note: Error analysis of material-decomposition-based effective atomic number quantification method

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Medical Physics 2024年第1期51卷 419-427页

作者： Chen, Li Ji, Xu Wang, Zhe Chen, Yang Laboratory of Image Science and Technology the School of Computer Science and Engineering Southeast University Nanjing China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Nanjing China Beijing Engineering Research Center of Radiographic Techniques and Equipment Institute of High Energy Physics Chinese Academy of Sciences Beijing China Jinan Laboratory of Applied Nuclear Science Jinan China Ministry of Education China

Background: The effective atomic number (Zeff) is widely applied to the identification of unknown materials. One method to determine the Zeff is material-decomposition-based spectral X-ray imaging. The method relies on certain approximations of the X-ray interaction cross-sections such as empirical model coefficients. The impact of such approximations on the accuracy of Zeff quantification has not been fully investigated. Purpose: To perform an error analysis of the material-decomposition-based Zeff quantification method and propose a coefficient calibration-in-groups method to improve the modeling accuracy and reduce the Zeff quantification error. Methods: The model of the material-decomposition-based Zeff quantification method relies on the dependence of the interaction cross-sections (σPE) on the atomic number Z and corresponding coefficient, that is, (Formula presented.). In this work, all the data is from the National institute of Standards and Technology (NIST) website. First, the coefficient m is calibrated through a logarithmic fitting method to quickly determine the m values for any certain energy and Zeff ranges. Then materials including elements and common compounds with Zeff ranging from 6–20 are selected as the objects whose effective atomic numbers are to be quantified. Different combinations of basis materials are applied to decompose the object materials and their quantification errors are analyzed. With the help of error analysis, the object materials are divided into high-error and low-error groups based on the decomposition coefficient ratio (Formula presented.), which is found to have a strong correlation with error, and their coefficients are calibrated in groups. Finally, the average errors of three m selection strategies: (1) using an empirical m value of 3.94, which is also considered a standard method;(2) using a single m value, which is calibrated through the logarithmic fitting method;(3) using different m values calibrated in groups, are

关键词： Atoms

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OSAP‐Loss:Efficient optimization of average precision via involving samples after positive ones towards remote sensing image retrieval

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1191-1212页

作者： Xin Yuan Xin Xu Xiao Wang Kai Zhang Liang Liao Zheng Wang Chia‐Wen Lin School of Computer Science and Technology Wuhan University of Science and TechnologyWuhanChina Hubei Province Key Laboratory of Intelligent Information Processing and Real‐time Industrial System Wuhan University of Science and TechnologyWuhanChina Nanyang Technological University SingaporeSingapore School of Computer Science Wuhan UniversityWuhanChina Department of Electrical Engineering Industrial Technology Research InstituteNational Tsing Hua UniversityHsinchuChina

In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.

关键词： computer vision image retrieval metric learning

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Remarks on Gibbs Permutation Matrices for Ternary Bent Functions 53

Remarks on Gibbs Permutation Matrices for Ternary Bent Funct...

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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Stanković, Radomir S. Stanković, Milena Moraga, Claudio Astola, Jaakko T. Mathematical Institute of Sasa Belgrade Serbia Faculty of Electronic Engineering Department of Computer Science Niš Serbia Technical University of Dortmund Faculty of Computer Science Dortmund Germany Tampere University of Technology Department of Signal Processing Tampere Finland

ISBN: (纸本)9781665464161

As in the binary case, ternary bent functions are a very small portion of the set of all ternary functions for a given number of variables. For example, for n = 2, there are 486 ternary bent functions out of 19683 ternary functions, which is 2, 47%, and this number reduces exponentially with the increase of n. However, finding, or alternatively, constructing them is a challenging task. A possible approach is based upon the manipulation of known ternary bent functions to construct other ternary bent functions. In this paper, we define Gibbs permutation matrices derived from the Gibbs derivative with respect to the Vilenkin-Chrestenson transform and propose their usage in constructing bent functions. The method can be extended to p-valued bent functions, where p is a prime larger than 3. © 2023 IEEE.

关键词： Matrix algebra

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NPRportrait 1.0:A three-level benchmark for non-photorealistic rendering of portraits

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Computational Visual Media 2022年第3期8卷 445-465页

作者： Paul L.Rosin Yu-Kun Lai David Mould Ran Yi Itamar Berger Lars Doyle Seungyong Lee Chuan Li Yong-Jin Liu Amir Semmo Ariel Shamir Minjung Son Holger Winnemöller School of Computer Science and Informatics Cardiff UniversityCardiffUK School of Computer Science Carleton UniversityOttawaCanada Department of Computer Science and Technology Tsinghua UniversityBeijingChina Reichman University(the Interdisciplinary Center) HerzliyaIsrael Department of Computer Science and Engineering Pohang University of Science and TechnologyPohangRepublic of Korea Lambda Labs Inc.San FranciscoUSA Hasso Plattner Institute University of PotsdamPotsdamGermany Multimedia Processing Laboratory Samsung Advanced Institute of TechnologySuwonRepublic of Korea Adobe Systems Inc.San JoseUSA

Recently,there has been an upsurge of activity in image-based non-photorealistic rendering(NPR),and in particular portrait image stylisation,due to the advent of neural style transfer(NST).However,the state of performance evaluation in this field is poor,especially compared to the norms in the computer vision and machine learning ***,the task of evaluating image stylisation is thus far not well defined,since it involves subjective,perceptual,and aesthetic *** make progress towards a solution,this paper proposes a new structured,threelevel,benchmark dataset for the evaluation of stylised portrait *** criteria were used for its construction,and its consistency was validated by user ***,a new methodology has been developed for evaluating portrait stylisation algorithms,which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the *** perform evaluation for a wide variety of image stylisation methods(both portrait-specific and general purpose,and also both traditional NPR approaches and NST)using the new benchmark dataset.

关键词： non-photorealistic rendering(NPR) image stylization style transfer portrait evaluation benchmark

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Deep Learning Hybrid Models for Multilingual Cyberbullying Detection: Insights from Bangla and Chittagonian Languages 26

Deep Learning Hybrid Models for Multilingual Cyberbullying D...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Mahmud, Tanjim Ptaszynski, Michal Masui, Fumito Kitami Institute of Technology Text Information Processing Laboratory Hokkaido090-8507 Japan Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh

ISBN: (纸本)9798350359015

Cyberbullying is a serious problem that affects online communication, and stopping it is essential to building safer digital spaces. The goal of this study is to address the problem of identifying cyberbullying content within the different linguistic contexts of Bangla and Chittagonian languages by utilizing deep learning techniques, with a particular focus on hybrid multilingual models. We provide a thorough analysis of both traditional machine learning (ML) models, which in our study reached accuracy values between 0.63 and 0.711 (with SVM as the highest performer), and deep learning (DL) models, which produced accuracy values between 0.69 and 0.811 (with CNN as the top performer) when trained and tested on both languages together. We also propose a set of combined hybrid network-based models, which improve accuracy. Among the models implemented, including BiLSTM+GRU (achieving an accuracy of 0.799), CNN+LSTM (with an accuracy of 0.801), CNN+BiLSTM (yielding an accuracy of 0.78), and CNN+GRU (attaining an accuracy of 0.804), especially the most complex model ((CNN+LSTM)+BiLSTM) achieved the highest score of 0.82. In comparison, mBERT, a popular multilingual transformer model, reached 0.79 accuracy. This research emphasizes high-performing hybrid neural networks' significance in multilingual contexts, particularly for low-resource languages, contributing to effective cyberbullying detection in diverse populations. © 2023 IEEE.

关键词： Deep learning Hybrid models Low-resource languages Machine learning Multilingual models

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Multi-Scale Cascaded with Cross-Attention Network-Based Deformation Vector Field Estimation for Motion-Compensated 4D-CBCT Reconstruction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 717-731页

作者： Yuan, Peng Lyu, Fei Gao, Zhiqiang Yang, Chunfeng Hu, Dianlin Zhu, Jian Wu, Zhan Lyu, Tianling Zhao, Wei Dong, Jianmin Chen, Yang Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Ministry of Education Southeast University Nanjing210096 China Laboratory of Image Science and Technology Southeast University Nanjing210096 China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Academy for Advanced Interdisciplinary Science and Technology Zhejiang University of Technology Hangzhou China School of Physics Beihang University Beijing China Beihang Hangzhou Innovation Institute Zhejiang Hangzhou China School of Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan Shandong China School of Information Engineering Xizang Minzu University Xianyang712082 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology The School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Nanjing210096 China

Four-Dimensional Cone Beam Computed Tomography (4D-CBCT) imaging technology offers enhanced image quality and spatial resolution for intraoperative guidance, facilitating real-time tracking of tumor position changes during radiotherapy. However, this is still a task of great challenges due to insufficient projections at each respiratory phase after phase-sorting, and the image phases reconstructed directly from phase-sorted data are discrete and discontinuous. To generate high-quality 4D-CBCT deformation vector fields (DVFs), this paper leverages the preoperative static prior image to guide intraoperative dynamic sparse-view reconstruction images for reducing anatomical structure differences, ultimately achieving continuous and dynamic 4D-CBCT imaging. In this paper, we propose a Multi-scale Cascaded residual deformable vector field estimation framework based on Cross-attention in Motioncompensated 4D-CBCT reconstruction (MCCM), which combines Multi-Scale Cascaded residual registration network (MSCNet), Cross-Attention Enhanced feature Fusion (CAEF) module and Structure-Enhanced Motion-Compensated (SEMC) module: 1) the MCCM employs a multi-scale cascaded residual network strategy, merging multi-receptive fields and multi-resolution feature maps for large-scale internal changes. 2) the CAEF is embedded into MSC-Net to facilitate effective communication and learning between features and promote the flow of information. 3) the SEMC is developed to reduce artifacts after intraoperative CBCT sparse-view reconstruction, restore global lung motion changes and local details, and enhance structural information through image fusion. The proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Our approach exhibits significant improvements across various evaluation metrics. © 2024 IEEE.

关键词： image fusion

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Caustics of developable surfaces

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Frontiers of Information Technology & Electronic Engineering 2022年第3期23卷 479-487页

作者： Miklos HOFFMANN Imre JUHASZ Ede TROLL Institute of Mathematics and Computer Science Eszterhazy Karoly UniversityEger 3300Hungary Department of Computer Graphics and Image Processing University of DebrecenDebrecen 4010Hungary Department of Descriptive Geometry University of MiskolcMiskolc 3515Hungary

While considering a mirror and light rays coming either from a point source or from infinity,the reflected light rays may have an envelope,called a caustic *** this paper,we study developable surfaces as *** caustic surfaces,described in a closed form,are also developable surfaces of the same type as the original mirror *** provide efficient,algorithmic computation to find the caustic surface of each of the three types of developable surfaces(cone,cylinder,and tangent surface of a spatial curve).We also provide a potential application of the results in contemporary free-form architecture design.

关键词： Caustics Developable surface Reflected light rays Curve of regression

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Bidirectional Backpropagation Autoencoding Networks for image Compression and Denoising

Bidirectional Backpropagation Autoencoding Networks for Imag...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Olaoluwa Adigun Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute Los Angeles California

A bidirectional autoencoder learns or approximates an identity mapping as it trains a single network with a version of the new bidirectional backpropagation algorithm. Ordinary unidirectional autoencoders find many uses in image processing and in large language models. But they use separate networks for encoding and decoding. Bidirectional auto encoders use the same synaptic weights for encoding and decoding. The forward pass encodes while the backward pass decodes. Bidirectional auto encoders improved network performance and significantly reduced memory usage and used fewer parameters. Simulations compared unidirectional with bidirectional autoencoders for image compression and de noising. The models trained on the MNIST handwritten-digit and CIFAR-IO image datasets. The performance measures were the peak signal-to-noise ratio and the index of structural similarity. Bidirectional autoencoders outperformed unidirectional autoencoders and still reduced the number of trainable synaptic parameters by about 50%.

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TOWARDS LLM4QPE: UNSUPERVISED PRETRAINING OF QUANTUM PROPERTY ESTIMATION AND A BENCHMARK 12

TOWARDS LLM4QPE: UNSUPERVISED PRETRAINING OF QUANTUM PROPERT...

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12th International Conference on Learning Representations, ICLR 2024

作者： Tang, Yehui Xiong, Hao Yang, Nianzu Xiao, Tailong Yan, Junchi Department of Computer Science and Engineering Shanghai Jiao Tong University China Institute of Quantum Sensing and Information Processing Shanghai Jiao Tong University China Hefei National Laboratory Hefei China

Estimating the properties of quantum systems such as quantum phase has been critical in addressing the essential quantum many-body problems in physics and chemistry. Deep learning models have been recently introduced to property estimation, surpassing conventional statistical approaches. However, these methods are tailored to the specific task and quantum data at hand. It remains an open and attractive question for devising a more universal task-agnostic pretraining model for quantum property estimation. In this paper, we propose LLM4QPE, a large language model style quantum task-agnostic pretraining and finetuning paradigm that 1) performs unsupervised pretraining on diverse quantum systems with different physical conditions;2) uses the pretrained model for supervised finetuning and delivers high performance with limited training data, on downstream tasks. It mitigates the cost for quantum data collection and speeds up convergence. Extensive experiments show the promising efficacy of LLM4QPE in various tasks including classifying quantum phases of matter on Rydberg atom model and predicting two-body correlation function on anisotropic Heisenberg model. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep learning

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