Article
Multidisciplinary Sciences
Isabelle Buelthoff, Wonmo Jung, Regine G. M. Armann, Christian Wallraven
Summary: The study found that eyes and texture are major determinants of perceived biogeographic ancestry for both participant groups and for both face types. Contour, nose and mouth had decreasing and much weaker influence on race perception.
SCIENTIFIC REPORTS
(2021)
Article
Chemistry, Inorganic & Nuclear
Takashi Nakamura, Satoru Watanabe
Summary: A hexanuclear palladium complex can have two different conformations, Alternate and Twisted, depending on the guests. Linear ditopic α,ω-diamines can be captured in three distinct cross-linking modes, and the length of the diamines can regulate the macrocyclic conformations. Heteroleptic site-selective bridging of two different diamines has also been achieved.
INORGANIC CHEMISTRY
(2023)
Article
Public, Environmental & Occupational Health
David De Ridder, Jose Sandoval, Nicolas Vuilleumier, Andrew S. Azman, Silvia Stringhini, Laurent Kaiser, Stephane Joost, Idris Guessous
Summary: The study found that SARS-CoV-2 clusters persisted longer in socioeconomically disadvantaged neighborhoods, with the deprivation index being associated with an increased cluster persistence. These findings emphasize the need for interventions to reduce inequalities in the risk of SARS-CoV-2 infection and serious outcomes.
FRONTIERS IN PUBLIC HEALTH
(2021)
Article
Computer Science, Artificial Intelligence
Chaoyou Fu, Xiang Wu, Yibo Hu, Huaibo Huang, Ran He
Summary: In this paper, a novel method called DVG-Face is proposed to address the challenges in heterogeneous face recognition (HFR) through dual generation and contrastive learning mechanism. The method achieves superior performances on multiple HFR tasks, demonstrating its effectiveness.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Aythami Morales, Julian Fierrez, Ruben Vera-Rodriguez, Ruben Tolosana
Summary: This work proposes a novel privacy-preserving neural network feature representation to suppress sensitive information while maintaining the utility of data. The approach ensures privacy and equality of opportunity by enforcing privacy of selected attributes. Fairness improvement is a result of this privacy-preserving learning method rather than the direct objective.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Chemistry, Analytical
Zhenqing Dai, Junli Guo, Chenxi Zhao, Zhida Gao, Yan-Yan Song
Summary: In this study, a membrane integrating homochiral metal-organic frameworks (MOFs) with nanochannels was developed for the sensitive identification and quantification of chiral compounds. Through signal amplification strategy on homochiral nanochannels, the discrimination for chiral recognition is largely amplified, paving a new way for sensitive monitoring and chiral recognition.
ANALYTICAL CHEMISTRY
(2021)
Article
Multidisciplinary Sciences
Xiaoqian Yan, Angelique Volfart, Bruno Rossion
Summary: Adults are better at associating different views of a familiar face compared to an unfamiliar face. However, there is a lack of a consistent neural index for this behavioral face identity familiarity effect (FIFE) in non-human primate species. This study provides a neural FIFE index that is measured implicitly and with one fixation per face. The findings show that the neural response to familiar faces is 3.4 times larger than unfamiliar faces.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Fei Wu, Xiao-Yuan Jing, Yujian Feng, Yi-mu Ji, Ruchuan Wang
Summary: The paper introduces a novel face recognition approach named SDDL, which utilizes a discriminative multi-spectral network and considers the spectrum and class label information to project samples into a discriminant feature subspace, achieving superior performance over state-of-the-art methods.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Decheng Liu, Xinbo Gao, Chunlei Peng, Nannan Wang, Jie Li
Summary: The article explores learning interpretable representations for complex heterogeneous faces and proposes the HFIDR and M-HFIDR methods for cross-modality recognition and synthesis tasks, achieving efficiency in face recognition and synthesis.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Jawad Muhammad, Yunlong Wang, Caiyong Wang, Kunbo Zhang, Zhenan Sun
Summary: Face recognition is a widely studied field with racial bias being inherent in most advanced systems. The lack of large-scale African face image databases is a major restriction in studying this issue. The establishment of the CASIA-Face-Africa database provides a valuable benchmark for researching the facial biometrics of African subjects.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2021)
Article
Computer Science, Information Systems
Andre Sobiecki, Julius van Dijk, Hidde Folkertsma, Alexandru Telea
Summary: The study found that face verification methods perform well on standardized face images, but face challenges on low resolution, poor lighting, and non-standard face positions. Less than half of face restoration methods help with face verification, with some methods with lower quality evaluations actually being the most helpful. Experiments show that face verification works less effectively as resolution decreases.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Theory & Methods
Yuhao Zhu, Min Ren, Hui Jing, Linlin Dai, Zhenan Sun, Ping Li
Summary: With the increase in the use of face masks due to COVID-19, accurate recognition of masked faces has become extremely important. This paper explores the use of plain Vision Transformers (ViTs) for masked face recognition, which has been less researched compared to convolutional neural networks (CNNs). The paper proposes a model initialization method and two prompt-based strategies to integrate holistic and masked face recognition. Experimental results show that the proposed FaceT performs as well as or better than state-of-the-art CNNs on both holistic and masked face recognition benchmarks.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2023)
Article
Behavioral Sciences
Christopher M. Jernigan, Jay A. Stafstrom, Natalie C. Zaba, Caleb C. Vogt, Michael J. Sheehan
Summary: Visual individual recognition requires animals to distinguish among conspecifics based on appearance. Color plays an important role in the facial recognition system of northern paper wasp females, as grayscale versions of faces cannot be recognized as faces.
Article
Computer Science, Artificial Intelligence
Usman Cheema, Seungbin Moon
Summary: This paper introduces a multi-modal disguised face dataset to facilitate research in disguised face recognition and performs qualitative and quantitative analysis to evaluate the challenging nature of disguise add-ons. The dataset will be publicly available.
COMPUTER VISION AND IMAGE UNDERSTANDING
(2021)
Article
Computer Science, Artificial Intelligence
Qiangchang Wang, Guodong Guo
Summary: In this research, an Attention Augmented Network called AAN-Face is proposed to address the issue of imbalanced data distributions in face recognition. By utilizing attention erasing and attention center loss, the AAN-Face models outperform state-of-the-art methods, especially on test datasets involving masked faces, demonstrating the importance and effectiveness of the approach.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Editorial Material
Computer Science, Artificial Intelligence
Ke Lu, Fei Wang, Ling Shao, Weisheng Li
Review
Neurosciences
Chang Su, Jie Tong, Fei Wang
NPJ PARKINSONS DISEASE
(2020)
Article
Psychiatry
Chang Su, Robert Aseltine, Riddhi Doshi, Kun Chen, Steven C. Rogers, Fei Wang
TRANSLATIONAL PSYCHIATRY
(2020)
Article
Neurosciences
Matthew Brendel, Chang Su, Yu Hou, Claire Henchcliffe, Fei Wang
Summary: This study aimed to identify subtypes of moderate-to-advanced Parkinson's disease by comprehensively considering motor and non-motor manifestations. Three unique subtypes emerged from the clustering results, each characterized by different levels of symptom severity. These subtypes showed significant differences in motor and non-motor clinical features, providing important information for further research on the treatment and management of Parkinson's disease.
NPJ PARKINSONS DISEASE
(2021)
Article
Health Care Sciences & Services
Chang Su, Yongkang Zhang, James H. Flory, Mark G. Weiner, Rainu Kaushal, Edward J. Schenck, Fei Wang
Summary: The study identified 4 biologically distinct subphenotypes of COVID-19 using machine learning and clinical data, which were highly predictive of clinical outcomes. It found varying prevalence of subphenotypes across the peak of the outbreak in NYC. Furthermore, social determinants of health specifically influenced mortality outcomes in certain subphenotypes.
NPJ DIGITAL MEDICINE
(2021)
Article
Clinical Neurology
Ivan Guan, Maissa Trabilsy, Samantha Barkan, Ashwin Malhotra, Yu Hou, Fei Wang, Natalie Hellmers, Harini Sarva, Claire Henchcliffe
Summary: This study compared levodopa off/on testing with Parkinson's Kinetigraph motor scores in PD patients. The results showed that a robust off/on response does not necessarily indicate adequately controlled motor symptoms. The PKG may provide additional clinically relevant data on motor symptoms for prospective observational studies.
CLINICAL NEUROLOGY AND NEUROSURGERY
(2021)
Article
Computer Science, Interdisciplinary Applications
Zheng Yuan, Zhengyun Zhao, Haixia Sun, Jiao Li, Fei Wang, Sheng Yu
Summary: This paper introduces knowledge-aware embedding, CODER, a critical tool for medical term normalization. By utilizing contrastive learning and a medical knowledge graph, CODER can extract semantic similarity and relatedness of medical concepts, which can be used for medical term normalization or feature extraction for machine learning.
JOURNAL OF BIOMEDICAL INFORMATICS
(2022)
Article
Oncology
Zhaoyi Chen, Hansi Zhang, Thomas J. George, Yi Guo, Mattia Prosperi, Jingchuan Guo, Dejana Braithwaite, Fei Wang, Warren Kibbe, Lynne Wagner, Jiang Bian
Summary: In this study, we simulated colorectal cancer trials using real-world data and tested two simulation scenarios. The results showed that our simulations can generate effectiveness and safety outcomes comparable with the original trials.
JCO CLINICAL CANCER INFORMATICS
(2022)
Review
Medicine, General & Internal
Jie Xu, Yunyu Xiao, Wendy Hui Wang, Yue Ning, Elizabeth A. Shenkman, Jiang Bian, Fei Wang
Summary: Machine learning models are increasingly being used in clinical decision-making, but recent research has highlighted the potential biases that these techniques may introduce, particularly for vulnerable ethnic minorities. This paper provides a comprehensive review of algorithmic fairness in computational medicine, discussing different types of bias, metrics for quantifying fairness, and methods for mitigating bias. It also summarizes popular software libraries and tools for evaluating and mitigating bias, serving as a valuable resource for researchers and practitioners in computational medicine.
Article
Computer Science, Information Systems
Zijun Yao, Bin Liu, Fei Wang, Daby Sow, Ying Li
Summary: This article proposes a novel prescription recommendation framework called OntoPath, which predicts the next drug in chronic disease treatment pathways by integrating multiple medical evidence from domain knowledge guidance, medical history profiling, and side information utilization. Extensive experiments on a large-scale depression cohort demonstrate the effectiveness of OntoPath in prescription recommendation.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Review
Genetics & Heredity
Matthew Brendel, Chang Su, Zilong Bai, Hao Zhang, Olivier Elemento, Fei Wang
Summary: Single-cell RNA sequencing (scRNA-seq) is widely used to quantify the gene expression profile of thousands of single cells simultaneously. Deep learning techniques have emerged as a promising tool for scRNA-seq data analysis, allowing for the extraction of informative and compact features from noisy and high-dimensional data. This review surveys recent developments in deep learning for scRNA-seq data analysis, highlights key advancements made by deep learning in the analysis pipeline, and discusses the benefits and challenges of applying deep learning to scRNA-seq data.
GENOMICS PROTEOMICS & BIOINFORMATICS
(2022)
Review
Health Care Sciences & Services
Adrienne Kline, Hanyin Wang, Yikuan Li, Saya Dennis, Meghan Hutch, Zhenxing Xu, Fei Wang, Feixiong Cheng, Yuan Luo
Summary: This review summarizes current studies on multi-modal data fusion in the health sector, highlighting the common use of multi-modal methods in neurology and oncology and the improved predictive performance achieved through data fusion. However, the lack of clear clinical deployment strategies, FDA approval, and analysis of biases and healthcare disparities in diverse sub-populations was noted.
NPJ DIGITAL MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Feng-Lei Fan, Mengzhou Li, Fei Wang, Rongjie Lai, Ge Wang
Summary: Inspired by the diversity of biological neurons, this paper explores the application of quadratic artificial neurons in deep learning and highlights the differences in expressivity and training risk between traditional neurons and quadratic networks with or without quadratic activation. By applying spline theory and algebraic geometry, the superior model expressivity of quadratic networks over traditional networks is mathematically demonstrated, and an effective training strategy called ReLinear is proposed to stabilize the training process of quadratic networks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Proceedings Paper
Computer Science, Information Systems
Qian Yang, Yuexing Hao, Kexin Quan, Stephen Yang, Yiran Zhao, Volodymyr Kuleshov, Fei Wang
Summary: Clinical decision support tools (DSTs) powered by AI can improve diagnostic and treatment decision-making, but AI models are not always correct. To address this, researchers investigated how clinicians validate each other's suggestions and designed a new DST that incorporates these interactions. The design uses GPT-3 to provide literature evidence showcasing the robustness and applicability of AI suggestions. A prototype study with clinicians demonstrated the promise of this approach and revealed new opportunities for design and research.
PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2023)
(2023)
Article
Health Care Sciences & Services
Zhaoyi Chen, Hansi Zhang, Yi Guo, Thomas J. George, Mattia Prosperi, William R. Hogan, Zhe He, Elizabeth A. Shenkman, Fei Wang, Jiang Bian
Summary: This study explored the feasibility of using real-world data to simulate clinical trials for Alzheimer's disease, comparing different formulations of donepezil. Two main simulation scenarios were considered: one-arm simulation and two-arm simulation with propensity score matching. Higher SAE rates were observed in the simulated trials compared to the original trial, indicating potential limitations of the approach.
NPJ DIGITAL MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Guang-Hai Liu, Zuo-Yong Li, Jing-Yu Yang, David Zhang
Summary: This article introduces a novel image retrieval method that improves retrieval performance by using sublimated deep features. The method incorporates orientation-selective features and color perceptual features, effectively mimicking these mechanisms to provide a more discriminating representation.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Fengguang Peng, Zihan Ding, Ziming Chen, Gang Wang, Tianrui Hui, Si Liu, Hang Shi
Summary: RGB-Thermal (RGB-T) semantic segmentation is an emerging task that aims to improve the robustness of segmentation methods under extreme imaging conditions by using thermal infrared modality. The challenges of foreground-background distinguishment and complementary information mining are addressed by proposing a cross modulation process with two collaborative components. Experimental results show that the proposed method achieves state-of-the-art performances on current RGB-T segmentation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Baihong Han, Xiaoyan Jiang, Zhijun Fang, Hamido Fujita, Yongbin Gao
Summary: This paper proposes a novel automatic prompt generation method called F-SCP, which focuses on generating accurate prompts for low-accuracy classes and similar classes. Experimental results show that our approach outperforms state-of-the-art methods on six multi-domain datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Huikai Liu, Ao Zhang, Wenqian Zhu, Bin Fu, Bingjian Ding, Shengwu Xiong
Summary: Adverse weather conditions present challenges for computer vision tasks, and image de-weathering is an important component of image restoration. This paper proposes a multi-patch skip-forward structure and a Residual Deformable Convolutional module to improve feature extraction and pixel-wise reconstruction.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Oliver M. Crook, Mihai Cucuringu, Tim Hurst, Carola-Bibiane Schonlieb, Matthew Thorpe, Konstantinos C. Zygalakis
Summary: The transportation LP distance (TLP) is a generalization of the Wasserstein WP distance that can be applied directly to color or multi-channelled images, as well as multivariate time-series. TLP interprets signals as functions, while WP interprets signals as measures. Although both distances are powerful tools in modeling data with spatial or temporal perturbations, their computational cost can be prohibitively high for moderate pattern recognition tasks. The linear Wasserstein distance offers a method for projecting signals into a Euclidean space, and in this study, we propose linear versions of the TLP distance (LTLP) that show significant improvement over the linear WP distance in signal processing tasks while being several orders of magnitude faster to compute than the TLP distance.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Haitao Tian, Shiru Qu, Pierre Payeur
Summary: This paper proposes a method of target-dependent classifier, which optimizes the joint hypothesis of domain adaptation into a target-dependent hypothesis that better fits with the target domain clusters through an unsupervised fine-tuning strategy and the concept of meta-learning. Experimental results demonstrate that this method outperforms existing techniques in synthetic-to-real adaptation and cross-city adaptation benchmarks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Yifan Chen, Xuelong Li
Summary: Gait recognition is a popular technology for identification due to its ability to capture gait features over long distances without cooperation. However, current methods face challenges as they use a single network to extract both temporal and spatial features. To solve this problem, we propose a two-branch network that focuses on spatial and temporal feature extraction separately. By combining these features, we can effectively learn the spatio-temporal information of gait sequences.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Wei Shi, Wentao Zhang, Wei-shi Zheng, Ruixuan Wang
Summary: This article proposes a simple yet effective visualization framework called PAMI, which does not require detailed model structure and parameters to obtain visualization results. It can be applied to various prediction tasks with different model backbones and input formats.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Xiaobo Hu, Jianbo Su, Jun Zhang
Summary: This paper reviews the latest technologies in pattern recognition, highlighting their instabilities and failures in practical applications. From a control perspective, the significance of disturbance rejection in pattern recognition is discussed, and the existing problems are summarized. Finally, potential solutions related to the application of compensation on features are discussed to emphasize future research directions.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Andres Felipe Posada-Moreno, Nikita Surya, Sebastian Trimpe
Summary: Convolutional neural networks are widely used in critical systems, and explainable artificial intelligence has proposed methods for generating high-level explanations. However, these methods lack the ability to determine the location of concepts. To address this, we propose a novel method for automatic concept extraction and localization based on pixel-wise aggregations, and validate it using synthetic datasets.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Peng Bao, Jianian Li, Rong Yan, Zhongyi Liu
Summary: In this paper, a novel Dynamic Graph Contrastive Learning framework, DyGCL, is proposed to capture the temporal consistency in dynamic graphs and achieve good performance in node representation learning.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, Olaf Wolkenhauer
Summary: Research indicates that deep generative models perform poorly compared to linear interpolation-based methods for synthetic data generation on small, imbalanced tabular datasets. To address this, a new approach called ConvGeN, combining convex space learning with deep generative models, has been proposed. ConvGeN improves imbalanced classification on small datasets while remaining competitive with existing linear interpolation methods.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Khondaker Tasrif Noor, Antonio Robles-Kelly
Summary: In this paper, the authors propose H-CapsNet, a capsule network designed for hierarchical image classification. The network effectively captures hierarchical relationships using dedicated capsules for each class hierarchy. A modified hinge loss is utilized to enforce consistency among the involved hierarchies. Additionally, a strategy for dynamically adjusting training parameters is presented to achieve better balance between the class hierarchies. Experimental results demonstrate that H-CapsNet outperforms competing hierarchical classification networks.
PATTERN RECOGNITION
(2024)
Article
Computer Science, Artificial Intelligence
Lei Liu, Guorun Li, Yuefeng Du, Xiaoyu Li, Xiuheng Wu, Zhi Qiao, Tianyi Wang
Summary: This study proposes a new agricultural image segmentation model called CS-Net, which uses Simple-Attention Block and Simpleformer to improve accuracy and inference speed, and addresses the issue of performance collapse of Transformers in agricultural image processing.
PATTERN RECOGNITION
(2024)