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arXiv 2022年

作者： Schatz, Hendrik Becerril Reyes, A.D. Best, A. Brown, E.F. Chatziioannou, K. Chipps, K.A. Deibel, C.M. Ezzeddine, R. Galloway, D.K. Hansen, C.J. Herwig, F. Ji, A.P. Lugaro, M. Meisel, Z. Norman, D. Read, J.S. Roberts, L.F. Spyrou, A. Tews, I. Timmes, F.X. Travaglio, C. Vassh, N. Abia, C. Adsley, P. Agarwal, S. Aliotta, M. Aoki, W. Arcones, A. Aryan, A. Bandyopadhyay, A. Banu, A. Bardayan, D.W. Barnes, J. Bauswein, A. Beers, T.C. Bishop, J. Boztepe, T. Côté, B. Caplan, M.E. Champagne, A.E. Clark, J.A. Couder, M. Couture, A. de Mink, S.E. Debnath, S. deBoer, R.J. den Hartogh, J. Denissenkov, P. Dexheimer, V. Dillmann, I. Escher, J.E. Famiano, M.A. Farmer, R. Fisher, R. Fröhlich, C. Frebel, A. Fryer, C. Fuller, G. Ganguly, A.K. Ghosh, S. Gibson, B.K. Gorda, T. Gourgouliatos, K.N. Graber, V. Gupta, M. Haxton, W. Heger, A. Hix, W.R. Ho, W.C.G. Holmbeck, E.M. Hood, A.A. Huth, S. Imbriani, G. Izzard, R.G. Jain, R. Jayatissa, H. Johnston, Z. Kajino, T. Kankainen, A. Kiss, G.G. Kwiatkowski, A. La Cognata, M. Laird, A.M. Lamia, L. Landry, P. Laplace, E. Launey, K.D. Leahy, D. Leckenby, G. Lennarz, A. Longfellow, B. Lovell, A.E. Lynch, W.G. Lyons, S.M. Maeda, K. Masha, E. Matei, C. Merc, J. Messer, B. Montes, F. Mukherjee, A. Mumpower, M. Neto, D. Nevins, B. Newton, W.G. Nguyen, L.Q. Nishikawa, K. Nishimura, N. Nunes, F.M. O’Connor, E. O’Shea, B.W. Ong, W.-J. Pain, S.D. Pajkos, M.A. Pignatari, M. Pizzone, R.G. Placco, V.M. Plewa, T. Pritychenko, B. Psaltis, A. Puentes, D. Qian, Y.-Z. Radice, D. Rapagnani, D. Rebeiro, B.M. Reifarth, R. Richard, A.L. Rijal, N. Roederer, I.U. Rojo, J.S. Sk, J. Saito, Y. Schwenk, A. Sergi, M.L. Sidhu, R.S. Simon, A. Sivarani, T. Skúladóttir, Á. Smith, M.S. Spiridon, A. Sprouse, T.M. Starrfield, S. Steiner, A.W. Strieder, F. Sultana, I. Surman, R. Szücs, T. Tawfik, A. Thielemann, F. Trache, L. Trappitsch, R. Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Facility for Rare Isotope Beams Michigan State University East LansingMI48824 United States United States Department of Physics University of Naples"Federico II" Napoli80126 Italy INFN - Sezione di Napoli Napoli80126 Italy Department of Computational Mathematics Science and Engineering MichiganState University East LansingMI48824 United States Department of Physics California Institute of Technology PasadenaCA91125 United States LIGO Laboratory California Institute of Technology PasadenaCA91125 United States Physics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Physics and Astronomy University of Tennessee Knoxville KnoxvilleTN37996 United States Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States Department of Astronomy University of Florida GainesvilleFL32601 United States School of Physics and Astronomy Monash University VIC3800 Australia OzGRav-Monash School of Physics & Astronomy Monash University VIC3800 Australia Goethe University Frankfurt Institute for Applied Physics Max-von-Laue-Str. 12 Frankfurt am Main60438 Germany Technical University of Darmstadt Institute for Nuclear Physics Germany Max Planck Institute for Astronomy Heidelberg Germany Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Department of Astronomy & Astrophysics University of Chicago 5640 S Ellis Avenue ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Excellence Centre of the Hungarian Academy of Sciences Konkoly-Thege Miklós út 15-17 BudapestH-1121 Hungary ELTE Eötvös Loránd University Institute of Physics Pázmány Péter sétány 1/A Budapest1117 Hungary Institute of Nuclear & Particle Physics Department of Physics & Astronomy Ohio University AthensOH45701

Nuclear Astrophysics is a field at the intersection of nuclear physics and astrophysics, which seeks to understand the nuclear engines of astronomical objects and the origin of the chemical elements. This white paper summarizes progress and status of the field, the new open questions that have emerged, and the tremendous scientific opportunities that have opened up with major advances in capabilities across an ever growing number of disciplines and subfields that need to be integrated. We take a holistic view of the field discussing the unique challenges and opportunities in nuclear astrophysics in regards to science, diversity, education, and the interdisciplinarity and breadth of the field. Clearly nuclear astrophysics is a dynamic field with a bright future that is entering a new era of discovery opportunities. © 2022, CC BY-NC-SA.

关键词： Astrophysics

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Reducing the size of the optimization problems in fuzzy ontology reasoning 11

Reducing the size of the optimization problems in fuzzy onto...

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11th International Workshop on Uncertainty Reasoning for the Semantic Web, URSW 2015 - collocated with the 14th International Semantic Web Conference, ISWC 2015

作者： Bobillo, Fernando Straccia, Umberto Dpt. of Computer Science and Systems Engineering University of Zaragoza Spain Pisa Italy

Fuzzy ontologies allow the representation of imprecise structured knowledge, typical in many real-world application domains. A key factor in the practical success of fuzzy ontologies is the availability of highly optimized reasoners. This short paper discusses a novel optimization technique: a reduction of the size of the optimization problems obtained during the inference by the fuzzy ontology reasoner fuzzyDL.

关键词： Ontology

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BAYESIAN LOGISTIC REGRESSION WITH SPARSE GENERAL REPRESENTATION PRIOR FOR MULTISPECTRAL IMAGE CLASSIFICATION

BAYESIAN LOGISTIC REGRESSION WITH SPARSE GENERAL REPRESENTAT...

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IEEE International Conference on Image Processing

作者： Juan G. Serra Pablo Ruiz Rafael Molina Aggelos K. Katsaggelos Dpto. de Ciencia de la Computacion e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science Northwestern University

ISBN: (纸本)9781467399623

In this work we address the multispectral image classification problem from a Bayesian perspective. We develop an algorithm which utilizes the logistic regression function as the observation model in a probabilistic framework, Super-Gaussian (SG) priors which promote sparsity on the adaptive coefficients, and Variational inference to obtain estimates of all the model unknowns. The proposed algorithm is validated on both synthetic and real experiments and compared with other state-of-the-art methods, such as Support Vector Machine and Gaussian Processes, demonstrating its improved performance.

关键词： Image Classification Bayes Methods Inference Algorithms Image classification Bayes methods Inference algorithms Variational Inference MULTISPECTRAL Taxonomy Gaussian processes regression function images Support Vector Network

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Forbidden time travel: Characterization of time-consistent tree reconciliation maps

arXiv

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arXiv 2017年

作者： Nøjgaard, Nikolai Geiß, Manuela Stadler, Peter F. Merkle, Daniel Wieseke, Nicolas Hellmuth, Marc Dpt. of Mathematics and Computer Science University of Greifswald Walther- Rathenau-Strasse 47 GreifswaldD-17487 Saarland University Center for Bioinformatics Building e 2.1 P.O. Box 151150 SaarbrückenD-66041 Bioinformatics Group Department of Computer Science Interdisciplinary Center of Bioinformatics University of Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Department of Mathematics and Computer Science University of Southern Denmark Max-Planck-Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Inst. F. Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Santa Fe Institute 1399 Hyde Park Rd. Santa Fe United States Parallel Computing and Complex Systems Group Department of Computer Science Leipzig University Augustusplatz 10 Leipzig04109 Germany

Motivation: In the absence of horizontal gene transfer it is possible to reconstruct the history of gene families from empirically determined orthology relations, which are equivalent to eventlabeled gene trees. Knowledge of the event labels considerably simplifies the problem of reconciling a gene tree T with a species trees S, relative to the reconciliation problem without prior knowledge of the event types. It is well-known that optimal reconciliations in the unlabeled case may violate time-consistency and thus are not biologically feasible. Here we investigate the mathematical structure of the event labeled reconciliation problem with horizontal transfer. Results: We investigate the issue of time-consistency for the event-labeled version of the reconciliation problem, provide a convenient axiomatic framework, and derive a complete characterization of time-consistent reconciliations. This characterization depends on certain weak conditions on the event-labeled gene trees that reflect conditions under which evolutionary events are observable at least in principle. We give an O(|V(T)|log(|V (S)|))-time algorithm to decide whether a time-consistent reconciliation map exists. It does not require the construction of explicit timing maps, but relies entirely on the comparably easy task of checking whether a small auxiliary graph is acyclic. Significance: The combinatorial characterization of time consistency and thus biologically feasible reconciliation is an important step towards the inference of gene family histories with horizontal transfer from orthology data, i.e., without presupposed gene and species trees. The fast algorithm to decide time consistency is useful in a broader context because it constitutes an attractive component for all tools that address tree reconciliation problems. Copyright © 2017, The Authors. All rights reserved.

关键词： Genes

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Maximin Fairness-profit Tradeoff in Project Budget Allocation

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Procedia computer science 2016年 100卷 313-320页

作者： Maurizio Naldi Gaia Nicosia Andrea Pacifici Ulrich Pferschy Dpt. of Computer Science and Civil Engineering University of Rome Tor Vergata Rome Italy Dipartimento di Ingegneria Universita’ Roma Tre Rome Italy Department of Statistics and Operations Research University of Graz Graz Austria

Companies typically select those projects that maximize their profit as the primary criterion, within the limited budget at their disposal. This criterion may lead to some company departments getting an exceedingly large share of the overall budget and induce a negative perception of unfairness among the less favourite ones. We investigate how profit optimization can be sought after while achieving the desired level of fairness at the same time. Adopting a maximin approach to fairness and using an Integer Linear Programming solver, we show that a linear trade-off is possible, since fairness and profit exhibit a nearly perfect linear anticorrelation. Fairness can be improved by even a relatively small reduction of profit, especially in large companies (i.e., managing a large number of projects).

关键词： Budget allocation Multi-project management Project Portfolio Fairness

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Model for the organization, storage and processing of large data banks of physiological variables

Model for the organization, storage and processing of large ...

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International Conference on computers Communications and Control (ICCCC)

作者： Fredi E. Palominos Hernán Diaz Felisa M. Córdova A. Lucio Canete Claudia A. Durán Dpt. Mathematics and Computer Science University of Santiago de Chile Santiago Chile Dpt. Chemistry and Biology University of Santiago de Chile Santiago Chile School of Industrial Engineering University Finis Terrae Santiago Chile Universidad de Santiago de Chile Santiago de Chile CL Dpt. of Industrial Engineering University of Santiago de Chile Santiago Chile

ISBN: (纸本)9781509017362

The proliferation and popularization of new instruments for measuring different types of electrophysiological variables, has generated the need to store huge volumes of information, corresponding to the records obtained by applying this instruments on experimental subjects. Together with this must be added the data derived from the analysis and purification processes. Moreover, several stages involved in the processing of data, is associated with one or more specific methods related to the area of research and to the treatment at which the base information (RAW) is subjected. As a result of this and with the passage of time, various problems occur, which are the most obvious consequence of that data and metadata derived from the treatment processes and analysis, and can end up accumulating and requiring more storage space than the base data. In addition, the enormous amount of information, as it increases over time, can lead to the loss of the link between the processed data, the methods of treatment used, and the analysis performed, so that eventually all becomes simply a huge repository of biometric data, devoid of meaning and sense. Current approaches around the concept of big data, while take over the storage and other aspects such as information search mechanisms, are far from incorporating metadata about the neurophysiological and emotional records. This type of information requires the construction of chronologies of events, including the methods of processing and analysis. In addition, it is required to maintain an adequate link between those responsible for the data (those who recorded and analyzed) and subjects that are under investigation, without breaking confidentiality to which they are entitled. This paper presents an approach founded in a data model that can adequately handle different types of chronologies of physiological and emotional information, ensuring confidentiality of information according to the experimental protocols and relevant ethical

关键词： Electroencephalography Big data Instruments Metadata computers Cleaning

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MedCare: Leveraging Medication Similarity for Disease Prediction

MedCare: Leveraging Medication Similarity for Disease Predic...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Dipanwita Dasgupta Nitesh V. Chawla Dpt of Computer Science and Engineering and iCeNSA University of Notre Dame Notre Dame Indiana

ISBN: (纸本)9781509052073

The emergence of electronic health records (EHRs) has made medical history including past and current diseases, and prescribed medications easily available. This has facilitated development of personalized and population health care management systems. Contemporary disease prediction systems leverage data such as disease diagnoses codes to compute patients' similarity and predict the possible future disease risks of an individual. However, we posit that not all diseases (such as pre-existing conditions) may be represented in an EHR as a disease diagnosis code. It is likely that a patient is already taking a medication but does not have a corresponding disease in the EHR. To that end, we posit that the medication history can serve as a proxy for disease diagnoses, and ask the question whether medication and disease diagnoses combined together can improve the predictability of such systems. Building on our prior work in predicting disease risks (CARE), we develop two disease prediction systems: one using medication-based similarity (medCARE) and the other using both disease and medication-based similarity (combinedCARE). We show that combinedCARE provided a greater coverage and a higher average rank.

关键词： Diseases Medical diagnostic imaging Hypertension Diabetes Predictive models History

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Variational Gaussian process for missing label crowdsourcing classification problems

Variational Gaussian process for missing label crowdsourcing...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Pablo Ruiz Emre Besler Rafael Molina Aggelos K. Katsaggelos Dpto. de Ciencias de la Computación e I.A. Universidad de Granada Dpt. of Electrical Engineering and Computer Science Northwestern University

ISBN: (纸本)9781509007479

In this paper we address the crowdsourcing problem, where a classifier must be trained without knowing the real labels. For each sample, labels (which may not be the same) are provided by different annotators (usually with different degrees of expertise). The problem is formulated using Bayesian modeling, and considers scenarios where each annotator may label a subset of the training set samples only. Although Bayesian approaches have been previously proposed in the literature, we introduce Variational Bayes inference to develop an iterative algorithm where all latent variables are automatically estimated. In the experimental section the proposed model is evaluated and compared with other state-of-the-art methods on two real datasets.

关键词： Crowdsourcing Bayes methods Reliability Probabilistic logic Training Gaussian processes Noise measurement

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Correlating elastic and plastic deformation with magnetic permeability values

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Journal of Physics: Conference Series 2017年第1期939卷

作者： S Papadopoulou Dpt. of Electrical and Computer Engineering National Technical University of Athens Zografou Campus Athens 15780 Greece Phone number : +30 210 772 2183

This paper investigates the utilization of magnetic permeability method in determining elastic and plastic deformation state of ferromagnetic steels. The results have shown a strong degradation of the magnetic values ...

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BACH: grand challenge on breast cancer histology images

arXiv

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arXiv 2018年

作者： Aresta, Guilherme Araújo, Teresa Kwok, Scotty Chennamsetty, Sai Saketh Safwan, Mohammed Alex, Varghese Marami, Bahram Prastawa, Marcel Chan, Monica Donovan, Michael Fernandez, Gerardo Zeineh, Jack Kohl, Matthias Walz, Christoph Ludwig, Florian Braunewell, Stefan Baust, Maximilian Vu, Quoc Dang Nhat To, Minh Nguyen Kim, Eal Kwak, Jin Tae Galal, Sameh Sanchez-Freire, Veronica Brancati, Nadia Frucci, Maria Riccio, Daniel Wang, Yaqi Sun, Lingling Ma, Kaiqiang Fang, Jiannan Kone, Ismael Boulmane, Lahsen Campilho, Aurélio Eloy, Catarina Polónia, António Aguiar, Paulo INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal Faculty of Engineering of University of Porto Porto4200-465 Portugal Seek AI Limited Hong Kong Bangalore India Gurgaon India Chennai India Center for Computational and Systems Pathology Dpt. of Pathology Icahn School of Medicine at Mount Sinai Mount Sinai Hospital New York United States Konica Minolta Laboratory Europe Munich Germany Institute of Pathology Faculty of Medicine LMU Munich Munich Germany Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of Chicago IL United States Naples Italy University of Naples "Federico II" Naples Italy Key Laboratory of RF Circuits and Systems Ministry of Education Hangzhou Dianzi University Hangzhou310018 China Zhejiang Provincial Laboratory of Integrated Circuits Design Hangzhou Dianzi University Hangzhou310018 China p2MIA Research Group LEM2A Lab Faculté des Sciences Université Moulay Ismail Meknes Morocco qLaboratório de Anatomia Patológica Ipatimup Diagnósticos Rua Júilio Amaral de Carvalho 45 Porto4200-135 Portugal Faculdade de Medicina Universidade do Porto Alameda Prof Hernâni Monteiro Porto4200-319 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

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