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32,578 results on '"Gao Wei"'

351. On the Optimization of Margin Distribution

Author

Qian, Meng-Zhang, Ai, Zheng, Zhang, Teng, and Gao, Wei

Subjects
Computer Science - Machine Learning
Abstract

Margin has played an important role on the design and analysis of learning algorithms during the past years, mostly working with the maximization of the minimum margin. Recent years have witnessed the increasing empirical studies on the optimization of margin distribution according to different statistics such as medium margin, average margin, margin variance, etc., whereas there is a relative paucity of theoretical understanding. In this work, we take one step on this direction by providing a new generalization error bound, which is heavily relevant to margin distribution by incorporating ingredients such as average margin and semi-variance, a new margin statistics for the characterization of margin distribution. Inspired by the theoretical findings, we propose the MSVMAv, an efficient approach to achieve better performance by optimizing margin distribution in terms of its empirical average margin and semi-variance. We finally conduct extensive experiments to show the superiority of the proposed MSVMAv approach.

Published
2022

352. Deep Geometry Post-Processing for Decompressed Point Clouds

Author

Fan, Xiaoqing, Li, Ge, Li, Dingquan, Ren, Yurui, Gao, Wei, and Li, Thomas H.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Point cloud compression plays a crucial role in reducing the huge cost of data storage and transmission. However, distortions can be introduced into the decompressed point clouds due to quantization. In this paper, we propose a novel learning-based post-processing method to enhance the decompressed point clouds. Specifically, a voxelized point cloud is first divided into small cubes. Then, a 3D convolutional network is proposed to predict the occupancy probability for each location of a cube. We leverage both local and global contexts by generating multi-scale probabilities. These probabilities are progressively summed to predict the results in a coarse-to-fine manner. Finally, we obtain the geometry-refined point clouds based on the predicted probabilities. Different from previous methods, we deal with decompressed point clouds with huge variety of distortions using a single model. Experimental results show that the proposed method can significantly improve the quality of the decompressed point clouds, achieving 9.30dB BDPSNR gain on three representative datasets on average.

Published
2022

353. Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation

Author

Zhao, Wenbo, Liu, Xianming, Zhong, Zhiwei, Jiang, Junjun, Gao, Wei, Li, Ge, and Ji, Xiangyang

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Point clouds upsampling is a challenging issue to generate dense and uniform point clouds from the given sparse input. Most existing methods either take the end-to-end supervised learning based manner, where large amounts of pairs of sparse input and dense ground-truth are exploited as supervision information; or treat up-scaling of different scale factors as independent tasks, and have to build multiple networks to handle upsampling with varying factors. In this paper, we propose a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously. We formulate point clouds upsampling as the task of seeking nearest projection points on the implicit surface for seed points. To this end, we define two implicit neural functions to estimate projection direction and distance respectively, which can be trained by two pretext learning tasks. Experimental results demonstrate that our self-supervised learning based scheme achieves competitive or even better performance than supervised learning based state-of-the-art methods. The source code is publicly available at https://github.com/xnowbzhao/sapcu.

Published
2022

354. A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning

Author

Yang, Ruichao, Ma, Jing, Lin, Hongzhan, and Gao, Wei

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

The diffusion of rumors on microblogs generally follows a propagation tree structure, that provides valuable clues on how an original message is transmitted and responded by users over time. Recent studies reveal that rumor detection and stance detection are two different but relevant tasks which can jointly enhance each other, e.g., rumors can be debunked by cross-checking the stances conveyed by their relevant microblog posts, and stances are also conditioned on the nature of the rumor. However, most stance detection methods require enormous post-level stance labels for training, which are labor-intensive given a large number of posts. Enlightened by Multiple Instance Learning (MIL) scheme, we first represent the diffusion of claims with bottom-up and top-down trees, then propose two tree-structured weakly supervised frameworks to jointly classify rumors and stances, where only the bag-level labels concerning claim's veracity are needed. Specifically, we convert the multi-class problem into a multiple MIL-based binary classification problem where each binary model focuses on differentiating a target stance or rumor type and other types. Finally, we propose a hierarchical attention mechanism to aggregate the binary predictions, including (1) a bottom-up or top-down tree attention layer to aggregate binary stances into binary veracity; and (2) a discriminative attention layer to aggregate the binary class into finer-grained classes. Extensive experiments conducted on three Twitter-based datasets demonstrate promising performance of our model on both claim-level rumor detection and post-level stance classification compared with state-of-the-art methods., Comment: Accepted by SIGIR 2022

Published
2022

359. Wearable chemical sensors for biomarker discovery in the omics era

Author

Sempionatto, Juliane R, Lasalde-Ramírez, José A, Mahato, Kuldeep, Wang, Joseph, and Gao, Wei

Subjects
Analytical Chemistry, Chemical Sciences, Clinical Research, Precision Medicine, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Good Health and Well Being, Wearable Electronic Devices, Biosensing Techniques, Body Fluids, Sweat, Biomarkers, Bioanalytical chemistry, Engineering, Sensors, Techniques and instrumentation, Chemical sciences
Abstract

Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine.

Published
2022

373. Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol

Author

Dean, Douglas C., III, Tisdall, M Dylan, Wisnowski, Jessica L., Feczko, Eric, Gagoski, Borjan, Alexander, Andrew L., Edden, Richard A.E., Gao, Wei, Hendrickson, Timothy J., Howell, Brittany R., Huang, Hao, Humphreys, Kathryn L., Riggins, Tracy, Sylvester, Chad M., Weldon, Kimberly B., Yacoub, Essa, Ahtam, Banu, Beck, Natacha, Banerjee, Suchandrima, Boroday, Sergiy, Caprihan, Arvind, Caron, Bryan, Carpenter, Samuel, Chang, Yulin, Chung, Ai Wern, Cieslak, Matthew, Clarke, William T., Dale, Anders, Das, Samir, Davies-Jenkins, Christopher W., Dufford, Alexander J., Evans, Alan C., Fesselier, Laetitia, Ganji, Sandeep K., Gilbert, Guillaume, Graham, Alice M., Gudmundson, Aaron T., Macgregor-Hannah, Maren, Harms, Michael P., Hilbert, Tom, Hui, Steve C.N., Irfanoglu, M. Okan, Kecskemeti, Steven, Kober, Tobias, Kuperman, Joshua M., Lamichhane, Bidhan, Landman, Bennett A., Lecour-Bourcher, Xavier, Lee, Erik G., Li, Xu, MacIntyre, Leigh, Madjar, Cecile, Manhard, Mary Kate, Mayer, Andrew R., Mehta, Kahini, Moore, Lucille A., Murali-Manohar, Saipavitra, Navarro, Cristian, Nebel, Mary Beth, Newman, Sharlene D., Newton, Allen T., Noeske, Ralph, Norton, Elizabeth S., Oeltzschner, Georg, Ongaro-Carcy, Regis, Ou, Xiawei, Ouyang, Minhui, Parrish, Todd B., Pekar, James J., Pengo, Thomas, Pierpaoli, Carlo, Poldrack, Russell A., Rajagopalan, Vidya, Rettmann, Dan W., Rioux, Pierre, Rosenberg, Jens T., Salo, Taylor, Satterthwaite, Theodore D., Scott, Lisa S., Shin, Eunkyung, Simegn, Gizeaddis, Simmons, W. Kyle, Song, Yulu, Tikalsky, Barry J., Tkach, Jean, van Zijl, Peter C.M., Vannest, Jennifer, Versluis, Maarten, Zhao, Yansong, Zöllner, Helge J., Fair, Damien A., Smyser, Christopher D., and Elison, Jed T.

Published
2024
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393. Long-term antiretroviral therapy mitigates mortality and morbidity independent of HIV tropism: 18 years follow-up in a women's cohort

Author

Weiser, Barbara, Shi, Binshan, Kemal, Kimdar, Burger, Harold, Minkoff, Howard, Shi, Qiuhu, Gao, Wei, Robison, Esther, Holman, Susan, Schroeder, Tamara, Gormley, Alissa, Anastos, Kathryn, and Ramirez, Christina

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Health Sciences, HIV/AIDS, Infectious Diseases, Pediatric, Infection, Good Health and Well Being, Female, Humans, Male, HIV Infections, Acquired Immunodeficiency Syndrome, Follow-Up Studies, HIV-1, Viral Tropism, Tropism, Morbidity, cART, CCR5, combination antiretroviral therapy, CXCR4, HAART, HIV infection in women, HIV-1 coreceptor usage, HIV-1 tropism, immunologic nonresponders, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Virology, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveCXCR4 (X4)-tropic HIV-1 was found previously to herald CD4 + cell depletion and disease progression in individuals who were antiretroviral-naive or took combination antiretroviral therapy (cART) for less than 5 years. We updated this finding by investigating whether the deleterious effect of X4-tropic strains is mitigated by long-term cART.DesignWe examined morbidity and mortality in relation to HIV-1 tropism and cART in 529 participants followed up to 18 years in the Women's Interagency HIV Study; 91% were women of color.MethodsPlasma-derived HIV-1 tropism was determined genotypically.ResultsWe categorized participants according to the number of visits reported on cART after initiation. Group 1: three or less visits, 74% of these participants reporting no cART; group 2: at least four visits and less than 70% of visits on cART; group 3: at least 70% of visits on cART. AIDS mortality rates for participants in each group with X4 virus compared with those with R5 virus exclusively were, respectively: 62 vs. 40% ( P  = 0.0088); 23% vs. 22% [nonsignificant (NS)]; 7% vs. 14% (NS). Kaplan-Meier curves showed accelerated progression to AIDS death or AIDS-defining illness in participants with three or less cART visits and X4 viruses ( P  = 0.0028) but no difference in progression rates stratified by tropism in other groups. Logistic regression found that HIV-1 suppression for at least 10 semiannual visits (≥5 years total) mitigated X4 tropism's deleterious effect on mortality, controlling for maximal viral load, and CD4 + nadir.ConclusionLong-term cART markedly mitigated the deleterious effect of X4 viruses on AIDS morbidity and mortality. Mitigation was correlated with duration of viral suppression, supporting HIV-1 suppression as a crucial goal.

Published
2022

394. A wearable electrochemical biosensor for the monitoring of metabolites and nutrients

Author

Wang, Minqiang, Yang, Yiran, Min, Jihong, Song, Yu, Tu, Jiaobing, Mukasa, Daniel, Ye, Cui, Xu, Changhao, Heflin, Nicole, McCune, Jeannine S, Hsiai, Tzung K, Li, Zhaoping, and Gao, Wei

Subjects
Bioengineering, Nutrition, Networking and Information Technology R&D (NITRD), Good Health and Well Being, Humans, Wearable Electronic Devices, Monitoring, Physiologic, Sweat, Biosensing Techniques, Nutrients
Abstract

Wearable non-invasive biosensors for the continuous monitoring of metabolites in sweat can detect a few analytes at sufficiently high concentrations, typically during vigorous exercise so as to generate sufficient quantity of the biofluid. Here we report the design and performance of a wearable electrochemical biosensor for the continuous analysis, in sweat during physical exercise and at rest, of trace levels of multiple metabolites and nutrients, including all essential amino acids and vitamins. The biosensor consists of graphene electrodes that can be repeatedly regenerated in situ, functionalized with metabolite-specific antibody-like molecularly imprinted polymers and redox-active reporter nanoparticles, and integrated with modules for iontophoresis-based sweat induction, microfluidic sweat sampling, signal processing and calibration, and wireless communication. In volunteers, the biosensor enabled the real-time monitoring of the intake of amino acids and their levels during physical exercise, as well as the assessment of the risk of metabolic syndrome (by correlating amino acid levels in serum and sweat). The monitoring of metabolites for the early identification of abnormal health conditions could facilitate applications in precision nutrition.

Published
2022

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