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arXiv 2019年

作者： Hopkins, Max Kane, Daniel Lovett, Shachar Department of Computer Science and Engineering UCSD CaliforniaCA92092 United States Department of Computer Science and Engineering Department of Mathematics UCSD CaliforniaCA92092 United States

In the world of big data, large but costly to label datasets dominate many fields. Active learning, an unsupervised alternative to the standard PAC-learning model, was introduced to explore whether adap- tive labeling could learn concepts with exponentially fewer labeled samples. While previous results show that active learning performs no better than its supervised alternative for important concept classes such as linear separators, we show that by adding weak distributional assumptions and allowing comparison queries, active learning requires exponentially fewer samples. Further, we show that these results hold as well for a stronger model of learning called Reliable and Probably Useful (RPU) learning. In this model, our learner is not allowed to make mistakes, but may instead answer "I don't know." While previous negative results showed this model to have intractably large sample complexity for label queries, we show that comparison queries make RPU-learning at worst logarithmically more expensive in the passive case, and quadratically more expensive in the active *** Codes 68Q32 Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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Author Correction: A multilineage screen identifies actionable synthetic lethal interactions in human cancers

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Nature genetics 2025年第2期57卷 480页

作者： Samson H Fong Brent M Kuenzi Nicole M Mattson John Lee Kyle Sanchez Ana Bojorquez-Gomez Kyle Ford Brenton P Munson Katherine Licon Sarah Bergendahl John Paul Shen Jason F Kreisberg Prashant Mali Jeffrey H Hager Michael A White Trey Ideker Division of Human Genomics and Precision Medicine Department of Medicine University of California San Diego La Jolla CA USA. Department of Bioengineering University of California San Diego La Jolla CA USA. IDEAYA Biosciences San Francisco CA USA. Division of Human Genomics and Precision Medicine Department of Medicine University of California San Diego La Jolla CA USA. tideker@ucsd.edu. Department of Bioengineering University of California San Diego La Jolla CA USA. tideker@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. tideker@ucsd.edu.

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Applying surface normal information in drivable area and road anomaly detection for ground mobile robots

arXiv

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arXiv 2020年

作者： Wang, Hengli Fan, Rui Sun, Yuxiang Liu, Ming Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Jacobs School of Engineering as well as the UCSD Health University of California San Diego San DiegoCA92093 United States

The joint detection of drivable areas and road anomalies is a crucial task for ground mobile robots. In recent years, many impressive semantic segmentation networks, which can be used for pixel-level drivable area and road anomaly detection, have been developed. However, the detection accuracy still needs improvement. Therefore, we develop a novel module named the Normal Inference Module (NIM), which can generate surface normal information from dense depth images with high accuracy and efficiency. Our NIM can be deployed in existing convolutional neural networks (CNNs) to refine the segmentation performance. To evaluate the effectiveness and robustness of our NIM, we embed it in twelve state-of-the-art CNNs. The experimental results illustrate that our NIM can greatly improve the performance of the CNNs for drivable area and road anomaly detection. Furthermore, our proposed NIM-RTFNet ranks 8th on the KITTI road benchmark and exhibits a real-time inference speed. Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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Low Noise, High Gain-Bandwidth Photodetectors Using Cycling Exciting Process (CEP) as Amplification Mechanism

Low Noise, High Gain-Bandwidth Photodetectors Using Cycling ...

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2018 IEEE International Conference on Electron Devices and Solid State Circuits, EDSSC 2018

作者： Yan, Lujiang Yu, Yugang Xu, Zihan Niaz, Iftikhar Miah, Mohammad Abu Raihan Zhang, Alex Ce Zhou, Jiayun Lo, Yuhwa Electrical and Computer Engineering Department UCSD United States Materials Science and Engineering UCSD San Diego92093 United States

ISBN: (纸本)9781538662342

We report a new physical mechanism, cycling excitation process (CEP), that produces intrinsic gain for photocurrent. The CEP process has shown much greater efficiency and lower noise than the impact ionization process, the mechanism for avalanche photodetectors. Using the CEP process, we have obtained detectors of low excess noise and a record high gain-bandwidth product of over 2 THz. Above all, such performance was obtained from a thin (30nm) layer of amorphous silicon as the gain medium. © 2018 IEEE.

关键词： Photons

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Publisher Correction: An Artificial Intelligence-guided signature reveals the shared host immune response in MIS-C and Kawasaki disease

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Nature communications 2022年第1期13卷 4729页

作者： Pradipta Ghosh Gajanan D Katkar Chisato Shimizu Jihoon Kim Soni Khandelwal Adriana H Tremoulet John T Kanegaye Joseph Bocchini Soumita Das Jane C Burns Debashis Sahoo Department of Cellular and Molecular Medicine University of California San Diego San Diego USA. prghosh@ucsd.edu. Department of Medicine University of California San Diego San Diego USA. prghosh@ucsd.edu. Department of Cellular and Molecular Medicine University of California San Diego San Diego USA. Department of Pediatrics University of California San Diego San Diego USA. Rady Children's Hospital-San Diego San Diego CA USA. Department of Biomedical informatics University of California San Diego San Diego USA. Department of Computer Science and Engineering Jacob's School of Engineering University of California San Diego San Diego USA. Willis-Knighton Health System Shreveport LA USA. Department of Pathology University of California San Diego USA. Department of Pediatrics University of California San Diego San Diego USA. jcburns@health.ucsd.edu. Rady Children's Hospital-San Diego San Diego CA USA. jcburns@health.ucsd.edu. Department of Pediatrics University of California San Diego San Diego USA. dsahoo@ucsd.edu. Department of Computer Science and Engineering Jacob's School of Engineering University of California San Diego San Diego USA. dsahoo@ucsd.edu.

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Author Correction: Greengenes2 unifies microbial data in a single reference tree

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Nature biotechnology 2024年第5期42卷 813页

作者： Daniel McDonald Yueyu Jiang Metin Balaban Kalen Cantrell Qiyun Zhu Antonio Gonzalez James T Morton Giorgia Nicolaou Donovan H Parks Søren M Karst Mads Albertsen Philip Hugenholtz Todd DeSantis Se Jin Song Andrew Bartko Aki S Havulinna Pekka Jousilahti Susan Cheng Michael Inouye Teemu Niiranen Mohit Jain Veikko Salomaa Leo Lahti Siavash Mirarab Rob Knight Department of Pediatrics University of California San Diego School of Medicine La Jolla CA USA. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA. Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA USA. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. School of Life Sciences Arizona State University Tempe AZ USA. Biodesign Center for Fundamental and Applied Microbiomics Arizona State University Tempe AZ USA. Biostatistics & Bioinformatics Branch Eunice Kennedy Shriver National Institute of Child Health and Human Development National Institutes of Health Bethesda MD USA. Halicioglu Data Science Institute University of California San Diego La Jolla CA USA. Australian Centre for Ecogenomics School of Chemistry and Molecular Biosciences The University of Queensland St Lucia Queensland Australia. Department of Obstetrics and Gynecology Columbia University New York NY USA. Department of Chemistry and Bioscience Aalborg University Aalborg Denmark. Department of Informatics Second Genome Brisbane CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. Finnish Institute for Health and Welfare Helsinki Finland. Institute for Molecular Medicine Finland FIMM-HiLIFE Helsinki Finland. Division of Cardiology Brigham and Women's Hospital Boston MA USA. Cedars-Sinai Medical Center Los Angeles CA USA. Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute Melbourne Victoria Australia. Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge UK. Division of Medicine Turku University Hospital and University of Turku Turku Finland. Sapient Bioanalytics LLC San Diego CA USA. Department of Computing University of Turku Turku Finland. Department of Pediatrics Uni

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Correction to: Compositional and genetic alterations in Graves' disease gut microbiome reveal specific diagnostic biomarkers

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The ISME journal 2022年第4期16卷 1201页

作者： Qiyun Zhu Qiangchuan Hou Shi Huang Qianying Ou Dongxue Huo Yoshiki Vázquez-Baeza Chaoping Cen Victor Cantu Mehrbod Estaki Haibo Chang Pedro Belda-Ferre Ho-Cheol Kim Kaining Chen Rob Knight Jiachao Zhang Department of Pediatrics University of California San Diego La Jolla CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. School of Food Science and Engineering Hainan University Haikou China. Department of Endocrinology Hainan General Hospital Hainan Affiliated Hospital of Hainan Medical University Haikou China. Hubei Provincial Engineering and Technology Research Center for Food Ingredients Hubei University of Arts and Science Xiangyang Hubei province China. Key Laboratory of Food Nutrition and Functional Food of Hainan Province Haikou China. Department of Bioengineering University of California San Diego La Jolla CA USA. Scalable Knowledge Intelligence IBM Research-Almaden San Jose CA USA. School of Food Science and Engineering Hainan University Haikou China. kainch@***. Department of Endocrinology Hainan General Hospital Hainan Affiliated Hospital of Hainan Medical University Haikou China. kainch@***. Department of Pediatrics University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Bioengineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Pediatrics University of California San Diego La Jolla CA USA. zhjch321123@***. School of Food Science and Engineering Hainan University Haikou China. zhjch321123@***. Key Laboratory of Food Nutrition and Functional Food of Hainan Province Haikou China. zhjch321123@***.

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Low Noise, High Gain-Bandwidth Photodetectors Using Cycling Exciting Process (CEP) as Amplification Mechanism

Low Noise, High Gain-Bandwidth Photodetectors Using Cycling ...

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IEEE Conference on Electron Devices and Solid-State Circuits

作者： Lujiang Yan Yugang Yu Zihan Xu Iftikhar Niaz Mohammad Abu Raihan Miah Alex Ce Zhang Jiayun Zhou Yuhwa Lo Electrical and Computer Engineering Department UCSD Materials Science and Engineering UCSD CA 9500 Gilman Drive USA

ISBN: (纸本)9781538662359

关键词： Photoconductivity Impact ionization Detectors Avalanche photodiodes Amorphous silicon Noise measurement

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Microbiome analyses of blood and tissues suggest cancer diagnostic approach

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Nature 2020年第7800期579卷 567-574页

作者： Gregory D Poore Evguenia Kopylova Qiyun Zhu Carolina Carpenter Serena Fraraccio Stephen Wandro Tomasz Kosciolek Stefan Janssen Jessica Metcalf Se Jin Song Jad Kanbar Sandrine Miller-Montgomery Robert Heaton Rana Mckay Sandip Pravin Patel Austin D Swafford Rob Knight Department of Bioengineering University of California San Diego La Jolla CA USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Clarity Genomics Beerse Belgium. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. Malopolska Centre of Biotechnology Jagiellonian University in Krakow Krakow Poland. Algorithmic Bioinformatics Department of Biology and Chemistry Justus Liebig University Gießen Gießen Germany. Department of Animal Sciences Colorado State University Fort Collins CO USA. Department of Medicine University of California San Diego La Jolla CA USA. Department of Psychiatry University of California San Diego La Jolla CA USA. Moores Cancer Center University of California San Diego Health La Jolla CA USA. Department of Bioengineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Pediatrics University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu.

Systematic characterization of the cancer microbiome provides the opportunity to develop techniques that exploit non-human, microorganism-derived molecules in the diagnosis of a major human disease. Following recent demonstrations that some types of cancer show substantial microbial contributions, we re-examined whole-genome and whole-transcriptome sequencing studies in The Cancer Genome Atlas (TCGA) of 33 types of cancer from treatment-naive patients (a total of 18,116 samples) for microbial reads, and found unique microbial signatures in tissue and blood within and between most major types of cancer. These TCGA blood signatures remained predictive when applied to patients with stage Ia-IIc cancer and cancers lacking any genomic alterations currently measured on two commercial-grade cell-free tumour DNA platforms, despite the use of very stringent decontamination analyses that discarded up to 92.3% of total sequence data. In addition, we could discriminate among samples from healthy, cancer-free individuals (n = 69) and those from patients with multiple types of cancer (prostate, lung, and melanoma; 100 samples in total) solely using plasma-derived, cell-free microbial nucleic acids. This potential microbiome-based oncology diagnostic tool warrants further exploration.

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Correction: Robustness of cancer microbiome signals over a broad range of methodological variation

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Oncogene 2024年第20期43卷 1579页

作者： Gregory D Sepich-Poore Daniel McDonald Evguenia Kopylova Caitlin Guccione Qiyun Zhu George Austin Carolina Carpenter Serena Fraraccio Stephen Wandro Tomasz Kosciolek Stefan Janssen Jessica L Metcalf Se Jin Song Jad Kanbar Sandrine Miller-Montgomery Robert Heaton Rana Mckay Sandip Pravin Patel Austin D Swafford Tal Korem Rob Knight Department of Bioengineering University of California San Diego La Jolla CA USA. Micronoma San Diego CA USA. Feinberg School of Medicine Northwestern University Chicago IL USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Clarity Genomics Antwerp Belgium. School of Life Sciences Arizona State University Tempe AZ USA. Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Program for Mathematical Genomics Department of Systems Biology Columbia University Irving Medical Center New York NY USA. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. Malopolska Centre of Biotechnology Jagiellonian University in Kraków Kraków Poland. Algorithmic Bioinformatics Department of Biology and Chemistry Justus Liebig University Gießen Gießen Germany. Department of Animal Sciences Colorado State University Fort Collins CO USA. Department of Medicine University of California San Diego La Jolla CA USA. Department of Psychiatry University of California San Diego La Jolla CA USA. Moores Cancer Center University of California San Diego Health La Jolla CA USA. Department of Obstetrics and Gynecology Columbia University Irving Medical Center New York NY USA. Department of Bioengineering University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Department of Pediatrics University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Center for Microbiome Innovation University of California San Diego La Jolla CA USA. rknight@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. rknight@ucsd.edu.

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