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On The Chain Rule Optimal Transport Distance

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arXiv 2018年

作者： Nielsen, Frank Sun, Ke Sony Computer Science Laboratories Inc. Tokyo Japan CSIRO Data61 Sydney Australia

We define a novel class of distances between statistical multivariate distributions by solving an optimal transportation problem on their marginal densities with respect to a ground distance defined on their conditional densities. By using the chain rule factorization of probabilities, we show how to perform optimal transport on a ground space being an information-geometric manifold of conditional probabilities. We prove that this new distance is a metric whenever the chosen ground distance is a metric. Our distance generalizes both the Wasserstein distances between point sets and a recently introduced metric distance between statistical mixtures. As a first application of this Chain Rule Optimal Transport (CROT) distance, we show that the ground distance between statistical mixtures is upper bounded by this optimal transport distance and its fast relaxed Sinkhorn distance, whenever the ground distance is joint convex. We report on our experiments which quantify the tightness of the CROT distance for the total variation distance, the square root generalization of the Jensen-Shannon divergence, the Wasserstein Wp metric and the Rényi divergence between mixtures. Copyright © 2018, The Authors. All rights reserved.

关键词： Mixtures

ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS

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ON THE GEOMETRY OF MIXTURES OF PRESCRIBED DISTRIBUTIONS

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Frank Nielsen Richard Nock école Polytechnique France Sony Computer Science Laboratories Japan Data 61 Australia The Australian National & Sydney Universities

ISBN: (纸本)9781538646595

We consider the space of w-mixtures that are finite statistical mixtures sharing the same prescribed component distributions, like Gaussian mixture models sharing the same components. The information geometry induced by the Kullback-Leibler (KL) divergence yields a dually flat space where the KL divergence between two w-mixtures amounts to a Bregman divergence for the negative Shannon entropy generator, called the Shannon information. Furthermore, we prove that the skew Jensen-Shannon statistical divergence between w-mixtures amount to skew Jensen divergences on their parameters and state several divergence inequalities between wmixtures and their closures.

关键词： GEOMETRY MIXTURES PRESCRIBED DISTRIBUTIONS geometry mixtures Divergence divergence Divergence divergence Shannon information Information geometry surgical closure Skew

data-Driven Modeling of Telluric Features and Stellar Variability with ***

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arXiv 2024年

作者： Gilbertson, Christian Ford, Eric B. Halverson, Samuel Fitzmaurice, Evan Blake, Cullen H. Stefánsson, Guðmundur Mahadevan, Suvrath Wright, Jason T. Luhn, Jacob K. Ninan, Joe P. Robertson, Paul Roy, Arpita Schwab, Christian Terrien, Ryan C. Center for Computing Research Sandia National Laboratories 1450 Innovation Pkwy SE AlbuquerqueNM87123 United States Department of Astronomy & Astrophysics Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Center for Exoplanets and Habitable Worlds Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Penn State Astrobiology Research Center University ParkPA16802 United States Center for Astrostatistics Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Institute for Computational and Data Sciences The Pennsylvania State University University ParkPA16802 United States Jet Propulsion Laboratory 4800 Oak Grove Drive PasadenaCA91109 United States Institute for Computational and Data Sciences Penn State University 224B Computer Building University ParkPA16802 United States Department of Physics & Astronomy University of Pennsylvania 209 South 33rd Street PhiladelphiaPA19104 United States NASA Sagan United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States ETH Zurich Institute for Particle Physics & Astrophysics Zurich Switzerland Penn State Extraterrestrial Intelligence Center Penn State University 525 Davey Laboratory 251 Pollock Road University ParkPA16802 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Space Telescope Science Institute 3700 San Martin Drive BaltimoreMD21218 United States Department of Physics and Astronomy Johns Hopkins University 3400 N Charles St BaltimoreMD21218 United States School of Mathematical and Physical Sciences Macquarie University Balaclava Road North RydeNSW2109 Australia Carleton College One North College St. Nor

A significant barrier to achieving the radial velocity (RV) measurement accuracy and precision required to characterize terrestrial mass exoplanets is the existence of time-variable features in the measured spectra, from both telluric absorption and stellar variability, which affect measured line shapes and can cause apparent RV shifts. Reaching the desired accuracy using traditional techniques often requires avoiding lines contaminated by stellar variability and/or changing tellurics, and thus discarding a large fraction of the spectrum, lowering precision. New data-driven methods can help achieve extremely precise and accurate RVs by enabling the use of a larger fraction of the available data. While there exist methods for modeling telluric features or the stellar variability individually, there is a need for additional tools that are capable of modeling them simultaneously at the spectral level. Here we present *** (SSOF), a Julia package for measuring Doppler shifts and creating data-driven models (with fast, physically-motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum, while accounting for the wavelength-dependent instrumental line-spread function. We demonstrate SSOF's state-of-the-art performance on data from the NEID RV spectrograph on the WIYN 3.5m Telescope for multiple stars. We show SSOF's,ability to accurately identify and characterize spectral variability and provide ∼2-6x smaller photon-limited errors over the NEID CCF-based pipeline and match the performance of SERVAL, a leading template-based pipeline, using only observed EPRV spectra. © 2024, CC BY.

关键词： Stars

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

关键词：

Qulacs: A fast and versatile quantum circuit simulator for research purpose

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arXiv 2020年

作者： Suzuki, Yasunari Kawase, Yoshiaki Masumura, Yuya Hiraga, Yuria Nakadai, Masahiro Chen, Jiabao Nakanishi, Ken M. Mitarai, Kosuke Imai, Ryosuke Tamiya, Shiro Yamamoto, Takahiro Yan, Tennin Kawakubo, Toru Nakagawa, Yuya O. Ibe, Yohei Zhang, Youyuan Yamashita, Hirotsugu Yoshimura, Hikaru Hayashi, Akihiro Fujii, Keisuke NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan JST PRESTO Saitama Kawaguchi332-0012 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Osaka Toyonaka560-8531 Japan Graduate School of Information Science and Technology Osaka University 1-1 Yamadaoka Osaka Suita565-0871 Japan Graduate School of Information and Science Nara Institute of Science and Technology Ikoma Nara Takayama630-0192 Japan Graduate School of Science Kyoto University Yoshida-Ushinomiya Sakyo Kyoto606-8302 Japan QunaSys Inc. Aqua Hakusan Building 9F 1-13-7 Hakusan Bunkyo Tokyo113-0001 Japan Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Center for Quantum Information and Quantum Biology Institute for Open and Transdisciplinary Research Initiatives Osaka University Japan Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan School of Computer Science Georgia Institute of Technology AtlantaGA30332 United States Center for Emergent Matter Science RIKEN Saitama Wako351-0198 Japan

To explore the possibilities of a near-term intermediate-scale quantum algorithm and long-term fault-tolerant quantum computing, a fast and versatile quantum circuit simulator is needed. Here, we introduce Qulacs, a fast simulator for quantum circuits intended for research purpose. We show the main concepts of Qulacs, explain how to use its features via examples, describe numerical techniques to speed-up simulation, and demonstrate its performance with numerical benchmarks. © 2020, CC BY.

关键词： Timing circuits

Perspectives for self-driving labs in synthetic biology

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arXiv 2022年

作者： Martin, Hector Garcia Radivojevic, Tijana Zucker, Jeremy Bouchard, Kristofer Sustarich, Jess Peisert, Sean Arnold, Dan Hillson, Nathan Babnigg, Gyorgy Marti, Jose Manuel Mungall, Christopher J. Beckham, Gregg T. Waldburger, Lucas Carothers, James Sundaram, ShivShankar Agarwal, Deb Simmons, Blake A. Backman, Tyler Banerjee, Deepanwita Tanjore, Deepti Ramakrishnan, Lavanya Singh, Anup Lawrence Berkeley National Laboratory Biological Systems and Engineering Division BerkeleyCA United States Department of Energy Agile BioFoundry EmeryvilleCA United States Joint BioEnergy Institute EmeryvilleCA United States BCAM Basque Center for Applied Mathematics Bilbao Spain Lawrence Berkeley National Laboratory Scientific Data Division BerkeleyCA United States Helen Wills Neuroscience Institute Redwood Center for Theoretical Neuroscience BerkeleyCA United States Lawrence Berkeley National Laboratory Energy Storage and Distributed Resources Division BerkeleyCA United States University of California Davis Department of Computer Science DavisCA United States Global Security Computing Applications Division Lawrence Livermore National Laboratory LivermoreCA United States Engineering Directorate Lawrence Livermore National Laboratory LivermoreCA United States Center for Bioengineering Lawrence Livermore National Laboratory LivermoreCA United States Department of Chemical Engineering Molecular Engineering & Sciences Institute Center for Synthetic Biology University of Washington SeattleWA United States Biosciences Division Argonne National Laboratory ArgonneIL United States Advanced Biofuels and Bioproducts Process Development Unit Lawrence Berkeley National Laboratory BerkeleyCA United States Biomaterials and Biomanufacturing Division Sandia National Laboratories LivermoreCA United States Earth and Biological Sciences Division Pacific Northwest National Laboratories RichlandWA United States Resources and Enabling Sciences Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Bioengineering University of California BerkeleyCA United States

Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology. © 2022, CC BY.

关键词： Synthetic biology

U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations

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arXiv 2021年

作者： Zhang, Yichi Kers, Jesper Cassol, Clarissa A. Roelofs, Joris J. Idrees, Najia Farber, Alik Haroon, Samir Daly, Kevin P. Ganguli, Suvranu Chitalia, Vipul C. Kolachalama, Vijaya B. Section of Computational Biomedicine Department of Medicine Boston University School of Medicine BostonMA02118 United States Department of Pathology Academic Medical Center Meibergdreef 9 Amsterdam1105 AZ Netherlands Department of Pathology Leiden University Medical Center Albinusdreef 2 Leiden2333 ZA Netherlands Arkana Laboratories Little RockAR72211 United States Amsterdam Cardiovascular Sciences University of Amsterdam Netherlands Renal Section Department of Medicine Boston University School of Medicine BostonMA02118 United States Department of Surgery Boston University School of Medicine BostonMA02118 United States Department of Radiology Boston University School of Medicine BostonMA02118 United States Veterans Affairs Boston Healthcare System BostonMA02118 United States Department of Computer Science College of Arts and Sciences Boston University MA02215 United States University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Faculty of Computing and Data Sciences Boston University BostonMA02215 United States

Development of deep learning systems for biomedical segmentation often requires access to expert-driven, manually annotated datasets. If more than a single expert is involved in the annotation of the same images, then the inter-expert agreement is not necessarily perfect, and no single expert's annotation can precisely capture the so-called ground truth of the regions of interest on all images. Also, it is not trivial to generate a reference estimate using annotations from multiple experts. Here we present a deep neural network, defined as U-Net-and-a-half, which can simultaneously learn from annotations performed by multiple experts on the same set of images. U-Net-and-a-half contains a convolutional encoder to generate features from the input images, multiple decoders that allow simultaneous learning from image masks obtained from annotations that were independently generated by multiple experts, and a shared lowdimensional feature space. To demonstrate the applicability of our framework, we used two distinct datasets from digital pathology and radiology, respectively. Specifically, we trained two separate models using pathologist-driven annotations of glomeruli on whole slide images of human kidney biopsies (n=10 patients), and radiologist-driven annotations of lumen crosssections of human arteriovenous fistulae obtained from intravascular ultrasound images (n=10 patients), respectively. The models based on U-Net-and-a-half exceeded the performance of the traditional U-Net models trained on single expert annotations alone, thus expanding the scope of multitask learning in the context of biomedical image *** Codes 68U10 © 2021, CC BY.

关键词： Deep neural networks

Distinct virologic trajectories in chronic hepatitis B identify heterogeneity in response to nucleos(t)ide analogue therapy

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JHEP Reports 2025年第1期7卷 101229-101229页

作者： Wang, Tingyan Campbell, Cori Stockdale, Alexander J. Todd, Stacy McIntyre, Karl Frankland, Andrew Jaworski, Jakub Glampson, Ben Papadimitriou, Dimitri Mercuri, Luca Mayer, Erik Jones, Christopher R. Salih, Hizni Roadknight, Gail Little, Stephanie Noble, Theresa Várnai, Kinga A. Davis, Cai Heinson, Ashley I. George, Michael Borca, Florina English, Louise Romão, Luis Ramlakhan, David Barnes, Eleanor Matthews, Philippa C. Gelson, William Cooke, Graham S. Khakoo, Salim I. Nastouli, Eleni Davies, Jim Woods, Kerrie Ryder, Stephen Elsharkawy, Ahmed Easom, Nicholas Bernal, William Ahmad, Shazaad Macdonald, Douglas Stanworth, Simon Maynard, Suzanne MacNaughton, Heidi Kim, Yun Meneses, Josune Olza Roberts, Timothy Rogers, Heather Roberts, Lara Higgins, Finola Vilar, Javier Norris, Ruth Tilston, George Serafimova, Ilina Montague, Sarah Verheyden, Juliette Juurlink, Irene Jack, Kathryn Waldren-Glenn, Alex Poole, Lizzie Day, Victoria Reglar, Berit NIHR Oxford Biomedical Research Centre Oxford United Kingdom Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Clinical Infection Microbiology and Immunology Institute of Infection Veterinary and Ecological Sciences University of Liverpool Liverpool United Kingdom Tropical Infectious Diseases Unit Royal Liverpool Hospital Liverpool University Hospitals NHS Trust Liverpool United Kingdom Liverpool Clinical Laboratories Liverpool University Hospitals NHS Trust Liverpool United Kingdom Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom iCARE Secure Data Environment Digital Collaboration Space Imperial College Healthcare NHS Trust London United Kingdom Department of Surgery and Cancer Imperial College London London United Kingdom Department of Infectious Disease Imperial College London London United Kingdom NIHR Health Informatics Collaborative Oxford University Hospitals NHS Foundation Trust Oxford United Kingdom Southampton Emerging Therapies and Technologies Centre University Hospital Southampton NHS Foundation Trust Southampton United Kingdom Clinical Informatics Research Unit Faculty of Medicine University of Southampton Southampton United Kingdom NIHR University College London Hospitals Biomedical Research Centre London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom Department of Infection Immunity and Inflammation UCL Great Ormond Street Institute of Child Health London United Kingdom Department of Virology UCLH London United Kingdom School of Clinical and Experimental Sciences Faculty of Medicine University of Southampton Southampton United Kingdom Cambridge Liver Unit Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom Faculty of Medicine Department of Infectious Disease Imperial College London United Kingdom The Francis Crick Institute London United Kingdom Division of Infection and Immunity University College Lon

Background & Aims: The dynamics of HBV viral load (VL) in patients with chronic hepatitis B (CHB) on nucleos(t)ide analogue (NA) treatment and its relationship with liver disease are poorly understood. We aimed to study longitudinal VL patterns and their associations with CHB clinical outcomes. Methods: Utilising large scale, routinely collected electronic health records from six centres in England, collated by the National Institute for Health and Care Research Health Informatics Collaborative (NIHR HIC), we applied latent class mixed models to investigate VL trajectory patterns in adults receiving NA treatment. We assessed associations of VL trajectory with alanine transaminase, and with liver fibrosis/cirrhosis. Results: We retrieved data from 1,885 adults on NA treatment (median follow-up 6.2 years, IQR 3.7–9.3 years), with 21,691 VL measurements (median 10 per patient, IQR 5–17). Five VL classes were identified from the derivation cohort (n = 1,367, discrimination: 0.93, entropy: 0.90): class 1 ‘long term suppression’ (n = 827, 60.5%), class 2 ‘timely virological suppression’ (n = 254, 18.6%), class 3 ‘persistent moderate viraemia’ (n = 140, 10.2%), class 4 ‘persistent high-level viraemia’ (n = 44, 3.2%), and class 5 ‘slow virological suppression’ (n = 102, 7.5%). The model demonstrated a discrimination of 0.93 and entropy of 0.88 for the validation cohort (n = 518). Alanine transaminase decreased variably over time in VL-suppressed groups (classes 1, 2, 5;all p <0.001), but did not significantly improve in those with persistent viraemia (classes 3, 4). Patients in class 5 had twofold increased hazards of fibrosis/cirrhosis compared with class 1 (adjusted hazard ratio, 2.00;95% CI, 1.33–3.02). Conclusions: Heterogeneity exists in virological response to NA therapy in CHB patients, with over 20% showing potentially suboptimal responses. Slow virological suppression is associated with liver disease progression. Impact and implications: Treatment recommendations f

关键词： Antiviral treatment Cirrhosis HBV Health Informatics Collaborative (HIC) Latent class mixed models Liver fibrosis Longitudinal Nucleotide analogue Viral load

O-41 The role of warping window size in case of EEG classification

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Clinical Neurophysiology 2019年第7期130卷 e34-e34页

作者： Krisztian Buza Tomas Horvath Telekom Innovation Laboratories Department of Data Science and Engineering Faculty of Informatics ELTE - Eötvös Loránd University Budapest Hungary Institute of Computer Science Faculty of Science Pavol Jozef Safarik University Kosice Slovakia

Background Classification of EEG signals is the common theoretical background of various EEG-related recognition tasks, such as the recognition of symptoms of diseases. We consider these tasks as time series classification tasks for which models based on dynamic time warping (DTW) are popular and effective ( Dau et al., 2018 , Buza et al., 2015 ). According to a recent study ( Dau et al., 2018 ), setting the appropriate warping window size (WWS) is crucial for the accuracy in various applications. Materials and methods Using a publicly available EEG dataset ( https://***/ml/datasets/eeg+database ), we examined whether the WWS is crucial in case of EEG classification. We considered two DTW-based methods, in particular, the wide-spread nearest neighbor and a more advanced approach, PROCESS ( Buza et al., 2015 ). We performed disease recognition experiments according to patient-based 10-fold cross-validation and measured the area under receiver operator characteristic curve (AUC) for both models with various WWS. Results A reasonably high accuracy is achieved in a relatively wide rage of WWS. Most importantly, WWS should not be set to zero. The AUC of the best examined model was around 0.875 which we achieved with relatively low WWS corresponding to approximately 170 ms. Conclusions Our observations show that in case of EEG classification, the examined classifiers are much less sensitive to the WWS than suggested by Dau et al. (2018) .

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