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Correction to: Analysis of the dynamics of anthrax epidemic model with delay

Discover applied sciences 2024年第11期6卷 1-1页

作者： Raza, Ali Abdella, Kenzu Department of Physical Sciences The University of Chenab Gujrat Pakistan Mathematics Research Center Department of Mathematics Near East University Mersin 10 Türkiye Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mathematics Applied Modelling Graduate Program Trent University Peterborough Canada

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3D Tortuosity: a Morphological Characterization of the Central Sulcus to Differentiate Patients with Alzheimer's Disease and Controls

3D Tortuosity: a Morphological Characterization of the Centr...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Maria Julieta Mateos Jorge Márquez Ernesto Bribiesca Universidad Nacional Autónoma de México Graduate Program in Computer Science and Engineering Ciudad Universitaria Coyoacán CDMX México Instituto de Ciencias Aplicadas y Tecnología Universidad Nacional Autónoma de México Circuito Exterior S/N Ciudad Universitaria Mexico City Universidad Nacional Autónoma de México Institute of Research in Applied Mathematics and Systems (IIMAS) Circuito Escolar 3000 Ciudad Universitaria Coyoacán CDMX

Magnetic Resonance Images of the brain provide detailed anatomical information that allows morphological analysis of the different brain structures. The analysis of the cortical folding patterns variation is inspiring great interest, as this relates to cognitive function. The sulci are a depression in the cerebral cortex and represent ≈ 70% of the cortical surface. The sulcal width, depth and cortical thickness are the most frequent morphological descriptors applied to the sulci. In this work, the 3D tortuosity is proposed as new descriptor to capture information about the twist and turns of the sulci. 1 The 3D tortuosity of the central sulcus of both hemispheres was measured using the Minimal Interval Resonance Imaging in the Alzheimer's Disease (MIRIAD) database, for a set of high-resolution MRI of 66 subjects: 43 patients with Alzheimer Disease (AD) and 23 control subjects. As it is known AD causes significant gray matter loss which cause morphological changes in the cortical structure. It is expected for the tortuosity to capture these changes and serve as a biomarker to differentiate between populations. The result of the Wilcoxon tests show that the tortuosity values of the central sulci are significantly larger for the AD patients (p < 0.05) for the left hemisphere, which is consistent with the hypothesis. The evaluation of the proposed tortuosity measurement of the Central Sulcus as a potential biomarker was performed and the results indicate its effectiveness to extract additional anatomical information and discriminate between AD patients and Controls.

关键词： Brain Three-dimensional displays Shape Shape measurement Sociology Surface morphology Skeleton

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Multi-level computational methods for interdisciplinary research in the hathi trust digital library

arXiv

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arXiv 2017年

作者： Murdock, Jaimie Allen, Colin Borner, Katy Light, Robert McAlister, Simon Ravenscroft, Andrew Rose1, Robert Rose, Doori Otsuka, Jun Bourget, David Lawrence, John Reed, Chris Program in Cognitive Science Indiana University BloomingtonIN United States School of Informatics and Computing Indiana University BloomingtonIN United States Department of History and Philosophy of Science and Medicine Indiana University BloomingtonIN United States Indiana University Network Science Institute IUNI BloomingtonIN United States User-Centered Social Media Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Duisburg Germany International Centre for Public Pedagogy ICPuP Cass School of Education and Communities University of East London London Department of Mathematics Indiana University BloomingtonIN United States Department of Philosophy Kyoto University Kyoto Japan Department of Philosophy University of Western Ontario LondonON Canada Centre for G Technology University of Dundee Dundee United Kingdom

We show how faceted search using a combination of traditional classification sys-tems and mixed-membership topic models can go beyond keyword search to inform re-source discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ul-timately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identi-fying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology. We show how a combination of classifi-cation systems and mixed-membership models trained over large digital libraries can inform resource discovery in this domain. Through a novel approach of \drill-down" topic modeling|simultaneously reducing both the size of the corpus and the unit of analysis|we are able to reduce a large collection of fulltext volumes to a much smaller set of pages within six focal volumes containing arguments of interest to historiansand philosophers of comparative psychology. The volumes identified in this way did not appear among the first ten results of the keyword search in the HathiTrust digital library and the pages bear the kind of \close reading" needed to generate original in-terpretations that is the heart of scholarly work in the humanities. Zooming back out, we provide a way to place the books onto a map of science originally constructed from very different data and for different purposes. The multilevel approach advances un-derstanding of the intellectual and societal contexts in which writings are interpreted. Copyright © 2017, The Authors. All rights reserved.

关键词： Digital libraries

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Derivative-free superiorization: Principle and algorithm

arXiv

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arXiv 2019年

作者： Censor, Yair Garduño, Edgar Helou, Elias S. Herman, Gabor T. Department of Mathematics University of Haifa Mt. Carmel Haifa3498838 Israel Departamento de Ciencias de la Computación Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Universidad Nacional Autónoma de México Cd. Universitaria Mexico CityC.P. 04510 Mexico Department of Applied Mathematics and Statistics University of São Paulo São Carlos São Paulo13566-590 Brazil Computer Science Ph.D. Program Graduate Center City University of New York New YorkNY10016 United States

The superiorization methodology is intended to work with input data of constrained minimization problems, that is, a target function and a set of constraints. However, it is based on an antipodal way of thinking to what leads to constrained minimization methods. Instead of adapting unconstrained minimization algorithms to handling constraints, it adapts feasibility-seeking algorithms to reduce (not necessarily minimize) target function values. This is done by inserting target-function-reducing perturbations into a feasibility-seeking algorithm while retaining its feasibility-seeking ability and without paying a high computational price. A superiorized algorithm that employs component-wise target function reduction steps is presented. This enables derivative-free superiorization (DFS), meaning that superiorization can be applied to target functions that have no calculable partial derivatives or subgradients. The numerical behavior of our derivative-free superiorization algorithm is illustrated on a data set generated by simulating a problem of image reconstruction from projections. The plots of proximity-target curves of our experiments demonstrate the advantage of the proposed derivative-free superiorization algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Constrained optimization

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

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Imaging Atomic-Scale Chemistry from Fused Multi-Modal Electron Microscopy

arXiv

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arXiv 2022年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Fielitz, Alyssa J. Ha, Don-Hyung Perera, Sanjaya D. Baggari, Ismail El Robinson, Richard D. Fessler, Jeffrey A. Ophus, Colin Rozeveld, Steve Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Dow Chemical Co. MidlandMI United States Department of Material Science and Engineering Cornell University IthacaNY United States School of Integrative Engineering Chung-Ang University Seoul Korea Republic of Department of Physics Cornell University IthacaNY United States The Rowland Institute at Harvard CambridgeMA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR) recovery of material chemistry at nano- and atomic- resolution by coupling correlated information encoded within both elastic scattering (high-angle annular dark field (HAADF)) and inelastic spectroscopic signals (electron energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these simultaneously acquired signals, or modalities, the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware. In many cases, the dose requirements can be reduced by over one order of magnitude. This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials. © 2022, CC BY.

关键词： Electron microscopy

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Unsupervised extraction of phenotypes from cancer clinical notes for association studies

arXiv

引用

arXiv 2019年

作者： Stark, Stefan G. Hyland, Stephanie L. Pradier, Melanie F. Lehmann, Kjong-Van Wicki, Andreas Perez-Cruz, Fernando Vogt, Julia E. Rätsch, Gunnar Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional Ph.D. Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Department of Computer Science ETH Zürich Zürich Switzerland Medical Informatics Group University Hospital Zürich Zürich Switzerland Swiss Institute for Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland Department of Signal Processing and Information Theory University Carlos III in Madrid Leganés Spain School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Biomedicine University of Basel Basel Switzerland Tumorzentrum University Hospital Basel Basel Switzerland Swiss Data Science Center ETH Zürich and EPFL Lausanne Switzerland Department of Mathematics and Computer Science University of Basel Basel Switzerland

The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features

关键词： Diseases

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Topological data analysis of black hole images

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Physical Review D 2022年第2期106卷 023017-023017页

作者： Pierre Christian Chi-kwan Chan Anthony Hsu Feryal Özel Dimitrios Psaltis Iniyan Natarajan Physics Department Fairfield University 1073 N Benson Road Fairfield Connecticut 06824 USA Department of Astronomy University of Arizona 933 North Cherry Avenue Tucson Arizona 85721 USA Data Science Institute University of Arizona 1230 N. Cherry Avenue Tucson Arizona 85721 USA Program in Applied Mathematics University of Arizona 617 N. Santa Rita Tucson Arizona 85721 USA Department of Computer Science University of Arizona 1040 4th Street Tucson Arizona 85721 USA Wits Centre for Astrophysics University of the Witwatersrand 1 Jan Smuts Avenue Braamfontein Johannesburg 2050 South Africa South African Radio Astronomy Observatory Observatory 7925 Cape Town South Africa

Features such as photon rings, jets, or hot spots can leave particular topological signatures in a black hole image. As such, topological data analysis can be used to characterize images resulting from high-resolution observations (synthetic or real) of black holes in the electromagnetic sector. We demonstrate that persistent homology allows for this characterization to be made automatically by counting the number of connected components and one-dimensional holes. Further, persistent homology also allows for the distance between connected components or the diameter of holes to be extracted from the image. In order to apply persistent homology on synthetic black hole images, we also introduce metronization, a new algorithm to prepare black hole images in a form that is suitable for topological analysis.

关键词： Astronomical black holes

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Automating the Practice of science – Opportunities, Challenges, and Implications

arXiv

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

arXiv

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arXiv 2023年

作者： Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Manassa, Jason Pietryga, Jacob Qian, Yiwen Cho, Min Gee Rowell, Jonathan L. Zheng, Huihuo Robinson, Richard D. Gu, Junsi Kirilin, Alexey Rozeveld, Steve Ercius, Peter Fessler, Jeffrey A. Xu, Ting Scott, Mary Hovden, Robert Department of Materials Science and Engineering University of Michigan Ann ArborMI United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Advanced Photon Source Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Northwestern University EvanstonIL United States Department of Materials Science and Engineering University of California at Berkeley BerkeleyCA United States Department of Chemistry and Chemical Biology Cornell University IthacaNY United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States Department of Material Science and Engineering Cornell University IthacaNY United States Kavli Institute at Cornell for Nanoscale Science Cornell University IthacaNY United States Dow Chemical Co. MidlandMI United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA United States Applied Physics Program University of Michigan Ann ArborMI United States

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe3O4 metamaterial, Co3O4 - Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials. © 2023, CC BY.

关键词： Zinc sulfide

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