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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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Provable Discriminative Hyperspherical Embedding for Out-of-Distribution Detection 39

Provable Discriminative Hyperspherical Embedding for Out-of-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zou, Zhipeng Wan, Sheng Li, Guangyu Han, Bo Liu, Tongliang Zhao, Lin Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Hong Kong Baptist University China Sydney AI Centre The University of Sydney Sydney Australia Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Out-of-distribution (OOD) detection aims to identify the test examples that do not belong to the distribution of training data. The distance-based methods, which identify OOD examples based on their distances from the centroids of in-distribution (ID) examples, have demonstrated promising OOD detection performance. However, the objectives utilized in prior approaches are typically designed for classification and thus might not yield sufficient discriminative power to distinguish between ID and OOD examples. Therefore, this paper proposes a prototype-based contrastive learning framework for OOD detection, which is termed provable Discriminative Hyperspherical Embedding (DHE). The proposed framework provides a theoretical analysis of inter-class dispersion, which is proved to be fundamental in reducing the false positive rate (FPR) on OOD examples. Based on this, we devise an angular spread loss to achieve the maximal dispersion of the prototypes of different classes prior to training. Subsequently, a prototype-enhanced contrastive loss is introduced to align embeddings of ID examples closely with their corresponding prototypes. In our proposed DHE, the maximal prototype dispersion is theoretically proved, thereby avoiding the pitfalls of local optima commonly encountered by most existing methods. Experimental results demonstrate the effectiveness of our proposed DHE, which showcases a remarkable reduction in FPR95 (i.e., 5.37% on CIFAR-100) and more than doubling the computational efficiency when compared with the state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Optimized Spectral Purity of Heralded Single Photons at the Telecom O-Band

arXiv

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arXiv 2025年

作者： Cai, Wu-Hao Baek, Soyoung Jin, Rui-Bo Kaneda, Fumihiro Graduate School of Science Tohoku University 6-3 Aramaki Aza-Aoba Sendai980-8578 Japan Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Sendai980-8577 Japan Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China PRESTO Japan Science and Technology Agency Kawaguchi332-0012 Japan

We report on optimizing the spectral purity of heralded single photons in the telecom O-band, where single photons can be propagated with low loss and low dispersion in a standard telecom optical fiber. We numerically searched for various group-velocity-matching conditions and corresponding optimal poling structures of a potassium titanyl phosphate crystal for spontaneous parametric downconversion. Our poling optimization results using phase-matching coherence-length and sub-coherence-length modulation schemes show > 99.4% spectral purity with pump wavelengths ranging from 603.8 nm to 887.3 nm. Some optimized configurations are feasible with off-the-shelf lasers and single-photon detectors. Moreover, by investigating noise photon spectra for different poling optimization methods, we show that, in practice, appropriate, gentle spectral filtering helps achieve high purity. This study will pave the way for developing practical quantum sources for quantum information applications at the telecom O-band. © 2025, CC BY.

关键词： Optical fibers

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.

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Polarization Multiplexing of an Ultrafast Single-Photon Detector by Optical Kerr Gating

Polarization Multiplexing of an Ultrafast Single-Photon Dete...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Takahisa Kuwana Masahiro Yabuno Fumihiro China Shigehito Miki Hirotaka Terai Peter J. Mosley Rui-Bo Jin Ryosuke Shimizu The University of Electro-Communications Tokyo Japan Advanced ICT Research Institute National Institute of Information and Communications Technology Kobe Japan Department of Physics Centre for Photonics and Photonic Materials University of Bath Bath UK Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan China

We develop an ultrafast single-photon detector with optical Kerr gating using a photonic crystal fiber. Adopting the polarization-multiplexing technique, we achieved the subpicosecond resolution in the single-photon d...

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SVFAP: Self-supervised Video Facial Affect Perceiver

arXiv

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arXiv 2023年

作者： Sun, Licai Lian, Zheng Wang, Kexin He, Yu Xu, Mingyu Sun, Haiyang Liu, Bin Tao, Jianhua The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The Department of Automation Tsinghua University Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

Video-based facial affect analysis has recently attracted increasing attention owing to its critical role in human-computer interaction. Previous studies mainly focus on developing various deep learning architectures and training them in a fully supervised manner. Although significant progress has been achieved by these supervised methods, the longstanding lack of large-scale high-quality labeled data severely hinders their further improvements. Motivated by the recent success of self-supervised learning in computer vision, this paper introduces a self-supervised approach, termed Self-supervised Video Facial Affect Perceiver (SVFAP), to address the dilemma faced by supervised methods. Specifically, SVFAP leverages masked facial video autoencoding to perform self-supervised pre-training on massive unlabeled facial videos. Considering that large spatiotemporal redundancy exists in facial videos, we propose a novel temporal pyramid and spatial bottleneck Transformer as the encoder of SVFAP, which not only largely reduces computational costs but also achieves excellent performance. To verify the effectiveness of our method, we conduct experiments on nine datasets spanning three downstream tasks, including dynamic facial expression recognition, dimensional emotion recognition, and personality recognition. Comprehensive results demonstrate that SVFAP can learn powerful affect-related representations via large-scale self-supervised pre-training and it significantly outperforms previous state-of-the-art methods on all datasets. Code is available at https://***/sunlicai/SVFAP. Copyright © 2023, The Authors. All rights reserved.

关键词： Self-supervised learning

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Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models 29

Automatic Label Sequence Generation for Prompting Sequence-t...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Yu, Zichun Gao, Tianyu Zhang, Zhengyan Lin, Yankai Liu, Zhiyuan Sun, Maosong Zhou, Jie Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University China Princeton University United States Beijing National Research Center for Information Science and Technology China Gaoling School of Artificial Intelligence Renmin University of China China International Innovation Center of Tsinghua University Shanghai China Beijing Academy of Artificial Intelligence China Pattern Recognition Center WeChat AI Tencent Inc China

Prompting, which casts downstream applications as language modeling tasks, has shown to be sample efficient compared to standard fine-tuning with pre-trained models. However, one pitfall of prompting is the need of manually-designed patterns, whose outcome can be unintuitive and requires large validation sets to tune. To tackle the challenge, we propose AutoSeq, a fully automatic prompting method: (1) We adopt natural language prompts on sequence-to-sequence models, enabling free-form generation and larger label search space;(2) We propose label sequences – phrases with indefinite lengths to verbalize the labels – which eliminate the need of manual templates and are more expressive than single label words;(3) We use beam search to automatically generate a large amount of label sequence candidates and propose contrastive re-ranking to get the best combinations. AutoSeq significantly outperforms other no-manual-design methods, such as soft prompt tuning, adapter tuning, and automatic search on single label words;the generated label sequences are even better than curated manual ones on a variety of tasks. Our method reveals the potential of sequence-to-sequence models in few-shot learning and sheds light on a path to generic and automatic prompting. The source code of this paper can be obtained from https://***/thunlp/Seq2Seq-Prompt. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Modeling languages

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Mid-infrared Spectrally Pure Single-Photon States Generation from 22 Nonlinear Optical Crystals

Quantum Engineering

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Quantum Engineering 2023年第1期2023卷

作者： Cai, Wu-Hao Tian, Ying Jin, Rui-Bo Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China Research Institute of Electrical Communication Tohoku University 2-1-1 Katahira Sendai980-8577 Japan Graduate School of Engineering Tohoku University 6-6 Aramaki Aza Aoba Aoba-ku Sendai980-8579 Japan

We theoretically investigate the preparation of pure-state single-photon source from 14 birefringent crystals (CMTC, THI, LiIO3, AAS, HGS, CGA, TAS, AGS, AGSe, GaSe, LIS, LISe, LGS, and LGSe) and 8 periodic poling crystals (LT, LN, KTP, KN, BaTiO3, MgBaF4, PMN-0.38PT, and OP-ZnSe) in a wavelength range from 1224 nm to 11650 nm. The three kinds of group-velocity-matching (GVM) conditions, the phase-matching conditions, the spectral purity, and the Hong-Ou-Mandel interference are calculated for each crystal. This study may provide high-quality single-photon sources for quantum sensing, quantum imaging, and quantum communication applications at the mid-infrared wavelength range. © 2023 Wu-Hao Cai et al.

关键词： Selenium compounds

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Transfer Learning of Fuzzy Spatio-Temporal Rules in the NeuCube Brain-Inspired Spiking Neural Network: A Case Study on EEG Spatio-temporal Data

TechRxiv

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TechRxiv 2022年

作者： Kasabov, Nikola K. Tan, Yongyao Doborjeh, Maryam Tu, Enmei Yang, Jie School of Engineering Computer and Mathematical Science KEDRI Auckland University of Technology New Zealand Intelligent Systems Research Centre University of Ulster United Kingdom Bulgarian Academy of Sciences Bulgaria Institute of Image Processing and Pattern Recognition Shanghai Jiao Tiong University China

The paper demonstrates for the first time that a brain-inspired spiking neural network (SNN) architecture can be used not only to learn spatio-temporal data, but also to extract fuzzy spatio-temporal rules from such data and to update these rules incrementally in a transfer learning mode. We propose a method, where a SNN model learns incrementally new time-space data related to new classes/tasks/categories, always utilising some previously learned knowledge, and presents the evolved knowledge as fuzzy spatio-temporal rules. Similarly, to how the brain manifests transfer learning, these SNN models do not need to be restricted in number of layers, neurons in each layer, etc. as they adopt self-organising learning principles. The continuously evolved fuzzy rules from spatio-temporal data are interpretable for a better understanding of the processes that generate the data. The proposed method is based on a brain-inspired SNN architecture NeuCube and is illustrated on tasks of incremental and transfer learning and knowledge transfer using spatio-temporal EEG data measuring brain activity, when persons performing tasks in space and time. The method is a general one and opens the field to create new types of adaptable and explainable AI systems for applications across domain areas, e.g. brain-machine interfaces, robotics, speech and sound processing, cognitive studies and the creation of lifelong-learning cognitive machines. © 2022, CC BY.

关键词： Brain

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Extraction of n = 0 pick-up by locked mode detectors based on neural networks in J-TEXT

arXiv

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arXiv 2023年

作者： Shen, Chengshuo Li, Jianchao Ding, Yonghua Dong, Jiaolong Wang, Nengchao Han, Dongliang Mao, Feiyue Li, Da Chen, Zhipeng Yang, Zhoujun Chen, Zhongyong Pan, Yuan International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China

Measurement of locked mode (LM) is important for the physical research of Magnetohydrodynamic (MHD) instabilities and plasma disruption. The n = 0 pick-up need to be extracted and subtracted to calculate the amplitude and phase of the LM. A new method to extract this pick-up has been developed by predicting the n = 0 pick-up brn=0 by the LM detectors based on Neural Networks (NNs) in J-TEXT. An approach called Power Multiple Time Scale (PMTS) has been developed with outstanding regressing effect in multiple frequency ranges. Three models have been progressed based on PMTS NNs. PMTS could fit the brn=0 on the LM detectors with little errors both in time domain and frequency domain. The n>0 pick-up brn>0 generated by resonant magnetic perturbations (RMPs) can be obtained after subtracting the extracted brn=0. This new method uses only one LM instead of 4 LM detectors to extract brn=0. Therefore, the distribution of the LM detectors can also be optimized based on this new method. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

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