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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2014年 8749卷 52-61页

作者： Dibeklioğlu, Hamdi Gevers, Theo Lucassen, Marcel Salah, Albert Ali Intelligent Systems Lab Amsterdam University of Amsterdam Netherlands Pattern Recognition and Bioinformatics Group Delft University of Technology Netherlands Department of Computer Engineering Boğaziçi University Istanbul Turkey

Computer based methods for facial age estimation can be improved by incorporating experimental findings from human psychophysics. Moreover, the latter can be used in creating systems that are not necessarily more accurate in age estimation, but strongly resemble human age estimations. In this paper we investigate the perceptual hypothesis that contrast is a useful cue for estimating age from facial appearance. Using an extensive evaluation paradigm, we establish that using a perceptual color space improves computer’s age estimation, and more importantly, using contrast-enabled features results in estimations that are more correlated to human estimations. © Springer International Publishing Switzerland 2014.

关键词： Estimation

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ROSE: Robust Selective Fine-tuning for Pre-trained Language Models

ROSE: Robust Selective Fine-tuning for Pre-trained Language ...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Jiang, Lan Zhou, Hao Lin, Yankai Li, Peng Zhou, Jie Jiang, Rui MOE Key Laboratory of Bioinformatics Center for Synthetic and Systems Biology Department of Automation BNRist Tsinghua University China Pattern Recognition Center WeChat AI Tencent Inc. China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tsinghua University China

Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks. A large body of defense methods has been proposed. However, they are still limited due to redundant attack search spaces and the inability to defend against various types of attacks. In this work, we present a novel fine-tuning approach called RObust SEletive fine-tuning (ROSE) to address this issue. ROSE conducts selective updates when adapting pre-trained models to downstream tasks, filtering out invaluable and unrobust updates of parameters. Specifically, we propose two strategies: the first-order and second-order ROSE for selecting target robust parameters. The experimental results show that ROSE achieves significant improvements in adversarial robustness on various downstream NLP tasks, and the ensemble method even surpasses both variants above. Furthermore, ROSE can be easily incorporated into existing fine-tuning methods to improve their adversarial robustness further. The empirical analysis confirms that ROSE eliminates unrobust spurious updates during fine-tuning, leading to solutions corresponding to flatter and wider optima than the conventional method. Code is available at https://***/jiangllan/ROSE. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Probabilistic Inference for Camera Calibration in Light Microscopy Under Circular Motion

Probabilistic Inference for Camera Calibration in Light Micr...

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IEEE International Symposium on Biomedical Imaging

作者： Yuanhao Guo Fons J. Verbeek Ge Yang Computational Biology and Machine Intelligence National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences LIACS Bioinformatics and Imaging Leiden University School of Artificial Intelligence University of Chinese Academy of Sciences

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Robust and accurate camera calibration is essential for 3D reconstruction in light microscopy under circular motion. Conventional methods require either accurate key point matching or precise segmentation of the axial-view images. Both remain challenging because specimens often exhibit transparency/translucency in a light microscope. To address these issues, we propose a probabilistic inference based method for the camera calibration that does not require sophisticated image pre-processing. Based on 3D projective geometry, our method assigns a probability on each of a range of voxels that cover the whole object. The probability indicates the likelihood of a voxel belonging to the object to be reconstructed. Our method maximizes a joint probability that distinguishes the object from the background. Experimental results show that the proposed method can accurately recover camera configurations in both light microscopy and natural scene imaging. Furthermore, the method can be used to produce high-fidelity 3D reconstructions and accurate 3D measurements.

关键词： Cameras Three-dimensional displays Calibration Probabilistic logic Microscopy Solid modeling

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Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development 10

Latent Variable Models for Predicting File Dependencies in L...

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Annual Conference on Neural Information Processing Systems

作者： Diane J. Hu Laurens van der Maaten Youngmin Cho Lawrence K. Saul Sorin Lerner Dept. of Computer Science & Engineering University of California San Diego Dept. of Computer Science & Engineering University of California San DiegoPattern Recognition & Bioinformatics Lab Delft University of Technology

ISBN: (纸本)9781617823800

When software developers modify one or more files in a large code base, they must also identify and update other related files. Many file dependencies can be detected by mining the development history of the code base: in essence, groups of related files are revealed by the logs of previous workflows. From data of this form, we show how to detect dependent files by solving a problem in binary matrix completion. We explore different latent variable models (LVMs) for this problem, including Bernoulli mixture models, exponential family PCA, restricted Boltz-mann machines, and fully Bayesian approaches. We evaluate these models on the development histories of three large, open-source software systems: Mozilla Fire-fox, Eclipse Subversive, and Gimp. In all of these applications, we find that LVMs improve the performance of related file prediction over current leading methods.

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Key-point detection with multi-layer center-surround inhibition

Key-point detection with multi-layer center-surround inhibit...

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International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Foti Coleca Sabrina Zîrnovean Thomas Käster Thomas Martinetz Erhardt Barth Institute for Neuro- and Bioinformatics University of Lübeck Lübeck Germany University “POLITEHNICA” of Bucureşti Bucureşti Romania Pattern Recognition Company GmbH Innovations Campus Lübeck Lübeck Germany

ISBN: (纸本)9781479976867

We present a biologically inspired algorithm for key-point detection based on multi-layer and nonlinear center-surround inhibition. A Bag-of-Visual-Words framework is used to evaluate the performance of the detector on the Oxford III-T Pet Dataset for pet recognition. The results demonstrate an increased performance of our algorithm compared to the SIFT key-point detector. We further improve the recognition rate by separately training codebooks for the ON- and OFF-type key points. The results show that our key-point detection algorithms outperform the SIFT detector by having a lower recognition-error rate over a whole range of different key-point densities. Randomly selected key-points are also outperformed.

关键词： Detectors Feature extraction Positron emission tomography Training Image edge detection Biological information theory Ions

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Predicting functional impact of single amino acid polymorphisms by integrating sequence and structural features

Predicting functional impact of single amino acid polymorphi...

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5th IEEE International Conference on Systems Biology, ISB 2011

作者： Wang, Mingjun Shen, Hong-Bin Akutsu, Tatsuya Song, Jiangning State Engineering Laboratory for Industrial Enzymes Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences Tianjin 300308 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto 611-0011 Japan Department of Biochemistry and Molecular Biology Faculty of Medicine Monash University Melbourne VIC 3800 Australia

ISBN: (纸本)9781457716669

Single amino acid polymorphisms (SAPs) are the most abundant form of known genetic variations associated with human diseases. It is of great interest to study the sequence-structure-function relationship underlying SAPs. In this work, we collected the human variant data from three databases and divided them into three categories, i.e. cancer somatic mutations (CSM), Mendelian disease-related variant (SVD) and neutral polymorphisms (SVP). We built support vector machine (SVM) classifiers to predict these three classes of SAPs, using the optimal features selected by a random forest algorithm. Consequently, 280 sequence-derived and structural features were initially extracted from the curated datasets from which 18 optimal candidate features were further selected by random forest. Furthermore, we performed a stepwise feature selection to select characteristic sequence and structural features that are important for predicting each SAPs class. As a result, our predictors achieved a prediction accuracy (ACC) of 84.97, 96.93, 86.98 and 88.24%, for the three classes, CSM, SVD and SVP, respectively. Performance comparison with other previously developed tools such as SIFT, SNAP and Polyphen2 indicates that our method provides a favorable performance with higher Sensitivity scores and Matthew's correlation coefficients (MCC). These results indicate that the prediction performance of SAPs classifiers can be effectively improved by feature selection. Moreover, division of SAPs into three respective categories and construction of accurate SVM-based classifiers for each class provides a practically useful way for investigating the difference between Mendelian disease-related variants and cancer somatic mutations. © 2011 IEEE.

关键词： Amino acids

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Toward a Realistic Model for Gene Network Evolution

Toward a Realistic Model for Gene Network Evolution

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Wenhai Chen Tianzi Jiang Tian Xia Xun Gu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Center for Bioinformatics and Computational Biology Iowa State University Ames IA USA Center for Bioinformatics and Biological Statistics Department of Genetics Development and Cell Biology Iowa State University Ames IA USA

We investigated how to establish a realistic model for the evolutionary emergence of gene network that is scale-free. After theoretical analysis and compute simulation, we have demonstrated the evolutionary principles... 详细信息

关键词： Proteins Biological system modeling bioinformatics Laboratories pattern recognition Automation Genomics Network topology Large-scale systems Evolution (biology)

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Heart disease detection using an acceleration-deceleration curve-based neural network with consumer-grade smartwatch data

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Heliyon 2024年第21期10卷 e39927页

作者： Naseri, Arman Tax, David M.J. Reinders, Marcel van der Bilt, Ivo Department of Cardiology Haga Teaching Hospital The Hague Netherlands Pattern Recognition and Bioinformatics Delft University of Technology Delft Netherlands Department of Cardiology University Medical Center Utrecht Utrecht Netherlands

Cardiovascular disease (CVD) is the most important cause of morbidity and mortality worldwide. Early detection, prevention or even prediction is of pivotal importance to reduce the burden of cardiovascular disease and its associated costs. Low cost, consumer-grade smartwatches have the potential to revolutionize cardiovascular medicine by enabling continuous monitoring of heart rate and activity. When combined with machine learning(ML), the resulting large amounts of time series data hold the potential of detection, or exclusion of CVD. However, analyzing such large datasets is challenging due to the sparse presence of informative segments. Efficient selection of these segments is essential for developing predictive models for clinical deployment. The objective of this paper was to investigate the potential of an acceleration-deceleration curvebased ML model as a novel clinical indicator for the detection of cardiovascular diseases. We used data from the ME-TIME study;42 participants from which 21 have a cardiovascular disease and 21 are health controls. Data from each subject was normalized to decrease inter-subject variability. A neural network model aggregated predictions per week. We showed that per-subject normalization by the peak value of curves during inactivity, aggregation of model predictions over a week, and using a contrastive loss, resulted in a predictive model with 99 % ± 3 % specificity and 40 % ± 49 % sensitivity on the development set, and 100 % specificity with 67 % ± 47 % sensitivity on the test set. Acceleration-deceleration curves are effective patterns for ruling out the presence of cardiovascular disease, but caution must be taken to properly pre-process the curves and carefully choosing a model that reduces the variability in the extracted curves. © 2024 The Authors

关键词： Atrial fibrillation Ehealth Heart failure Machine learning Mhealth Smartwatch Wearables

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： Image reconstruction

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Kernel ELM and CNN Based Facial Age Estimation

Kernel ELM and CNN Based Facial Age Estimation

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IEEE Computer Society Conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Furkan Gürpinar Heysem Kaya Hamdi Dibeklioglu Albert Ali Salah Bogazici Universitesi Istanbul TR Department of Computer Engineering Namik Kemal University Tekirdag Turkey Pattern Recognition & Bioinformatics Group Delft University of Technology Delft The Netherlands Department of Computer Engineering Bogaziçi University Istanbul Turkey

We propose a two-level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, whose outputs are then fused for the final estimate. We use a deformable parts model based face detector, and features from a pretrained deep convolutional network. Kernel extreme learning machines are used for classification. We evaluate our system on the ChaLearn Looking at People 2016 - Apparent Age Estimation challenge dataset, and report 0.3740 normal score on the sequestered test set.

关键词： Face Estimation Kernel Feature extraction Detectors Pipelines Aging

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