Assessing the Generalizability of a Performance Predictive Model

A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model. The predictive model uses the feature representation of a set of problem instances as input data and predicts the algorithm performance achieved on them. Common machine learning models struggle to make predictions for instances with feature representations not covered by the training data, resulting in poor generalization to unseen problems. In this study, we propose a workflow to estimate the generalizability of a predictive model for algorithm performance, trained on one benchmark suite to another. The workflow has been tested by training predictive models across benchmark suites and the results show that generalizability patterns in the landscape feature space are reflected in the performance space.Comment: To appear at GECCO 202

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field

Multiple Scenario Generation of Subsurface Models:Consistent Integration of Information from Geophysical and Geological Data throuh Combination of Probabilistic Inverse Problem Theory and Geostatistics

Neutrinos with energies above 1017 eV are detectable with the Surface Detector Array of the Pierre Auger Observatory. The identification is efficiently performed for neutrinos of all flavors interacting in the atmosphere at large zenith angles, as well as for Earth-skimming \u3c4 neutrinos with nearly tangential trajectories relative to the Earth. No neutrino candidates were found in 3c 14.7 years of data taken up to 31 August 2018. This leads to restrictive upper bounds on their flux. The 90% C.L. single-flavor limit to the diffuse flux of ultra-high-energy neutrinos with an E\u3bd-2 spectrum in the energy range 1.0 7 1017 eV -2.5 7 1019 eV is E2 dN\u3bd/dE\u3bd < 4.4 7 10-9 GeV cm-2 s-1 sr-1, placing strong constraints on several models of neutrino production at EeV energies and on the properties of the sources of ultra-high-energy cosmic rays

An Exploratory Landscape Analysis-Based Benchmark Suite

The choice of which objective functions, or benchmark problems, should be used to test an optimization algorithm is a crucial part of the algorithm selection framework. Benchmark suites that are often used in the literature have been shown to exhibit poor coverage of the problem space. Exploratory landscape analysis can be used to quantify characteristics of objective functions. However, exploratory landscape analysis measures are based on samples of the objective function, and there is a lack of work on the appropriate choice of sample size needed to produce reliable measures. This study presents an approach to determine the minimum sample size needed to obtain robust exploratory landscape analysis measures. Based on reliable exploratory landscape analysis measures, a self-organizing feature map is used to cluster a comprehensive set of benchmark functions. From this, a benchmark suite that has better coverage of the single-objective, boundary-constrained problem space is proposed

An Exploratory Landscape Analysis-Based Benchmark Suite

The choice of which objective functions, or benchmark problems, should be used to test an optimization algorithm is a crucial part of the algorithm selection framework. Benchmark suites that are often used in the literature have been shown to exhibit poor coverage of the problem space. Exploratory landscape analysis can be used to quantify characteristics of objective functions. However, exploratory landscape analysis measures are based on samples of the objective function, and there is a lack of work on the appropriate choice of sample size needed to produce reliable measures. This study presents an approach to determine the minimum sample size needed to obtain robust exploratory landscape analysis measures. Based on reliable exploratory landscape analysis measures, a self-organizing feature map is used to cluster a comprehensive set of benchmark functions. From this, a benchmark suite that has better coverage of the single-objective, boundary-constrained problem space is proposed

Measurement of the Fluctuations in the Number of Muons in Extensive Air Showers with the Pierre Auger Observatory

The successful installation, commissioning, and operation of the Pierre Auger Observatory would not have been possible without the strong commitment and effort from the technical and administrative staff in Malargue. We are very grateful to the following agencies and organizations for financial support: Argentina-Comision Nacional de Energia Atomica, Agencia Nacional de Promocion Cientifica y Tecnologica (ANPCyT), Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Gobierno de la Provincia de Mendoza, Municipalidad de Malargue, NDM Holdings and Valle Las Lenas; in gratitude for their continuing cooperation over land access; Australia-the Australian Research Council; BrazilConselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Financiadora de Estudos e Projetos (FINEP), Fundacao de Amparo a Pesquisa do Estado de Rio de Janeiro (FAPERJ), Sao Paulo Research Foundation (FAPESP) Grants No. 2019/10151-2, No. 2010/07359-6, and No. 1999/05404-3, Ministerio da Ciencia, Tecnologia, Inovacoes e Comunicacoes (MCTIC); Ministry of Education, Youth and Sports of the Czech RepublicGrants No. MSMT CR LTT18004, No. LM2015038, No. LM2018102, No. CZ.02.1.01/0.0/0.0/16_013/0001402, No. CZ.02.1.01/0.0/0.0/18_046/0016010, and No. CZ.02.1.01/0.0/0.0/17_049/0008422; France-Centre de Calcul IN2P3/CNRS, Centre National de la Recherche Scientifique (CNRS), Conseil Regional Ile-de-France, Departement Physique Nucl ' eaire et Corpusculaire (PNC-IN2P3/CNRS), Departement Sciences de l'Univers (SDU-INSU/CNRS), Institut Lagrange de Paris (ILP) Grant No. LABEX ANR-10-LABX-63 within the Investissements d'Avenir Programme Grant No. ANR11-IDEX-0004-02; Germany-Bundesministerium fur Bildung und Forschung (BMBF), Deutsche Forschungsgemeinschaft (DFG), Finanzministerium Baden-Wurttemberg, Helmholtz Alliance for Astroparticle Physics (HAP), Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF), Ministerium fur Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen, Ministerium fur Wissenschaft, Forschung und Kunst des Landes Baden-Wurttemberg; Italy-Istituto Nazionale di Fisica Nucleare (INFN), Istituto Nazionale di Astrofisica (INAF), Ministero dell'Istruzione, dell'Universita e della Ricerca (MIUR), CETEMPS Center of Excellence, Ministero degli Affari Esteri (MAE); Mexico-Consejo Nacional de Ciencia y Tecnologia (CONACYT) Grant No. 167733, Universidad Nacional Autonoma de Mexico (UNAM), PAPIIT DGAPA-UNAM; The Netherlands-Ministry of Education, Culture and Science, Netherlands Organisation for Scientific Research (NWO), Dutch national e-infrastructure with the support of SURF Cooperative; Poland-Ministry of Science and Higher Education, Grant No. DIR/WK/2018/11, National Science Centre, Grants No. 2013/08/M/ST9/00322, No. 2016/23/B/ST9/01635, and No. HARMONIA 5-2013/10/M/ST9/00062, UMO-2016/22/M/ST9/00198; Portugal -Portuguese national funds and FEDER funds within Programa Operacional Factores de Competitividade through Fundacao para a Ciencia e a Tecnologia (COMPETE); Romania-Romanian Ministry of Education and Research, the Program Nucleu within MCI (PN19150201/16N/2019 and PN19060102), and project PN-III-P1-1.2-PCCDI-2017-0839/19PCCDI/2018 within PNCDI III; Slovenia-Slovenian Research Agency, Grants No. P1-0031, No. P1-0385, No. I00033, No. N1-0111; Spain-Ministerio de Economia, Industria y Competitividad (FPA2017-85114-P and FPA2017-85197-P), Xunta de Galicia (ED431C 2017/07), Junta de Andalucia (SOMM17/6104/UGR), Feder Funds, RENATA Red Nacional Tematica de Astroparticulas (FPA2015-68783-REDT), and Maria de Maeztu Unit of Excellence (MDM-2016-0692); U.S.Department of Energy, Awards No. DE-AC0207CH11359, No. DE-FR02-04ER41300, No. DE-FG0299ER41107, and No. DE-SC0011689, National Science Foundation, Grant No. 0450696, The Grainger Foundation, Marie Curie-IRSES/EPLANET, European Particle Physics Latin American Network, and UNESCO.We present the first measurement of the fluctuations in the number of muons in extensive air showers produced by ultrahigh energy cosmic rays. We find that the measured fluctuations are in good agreement with predictions from air shower simulations. This observation provides new insights into the origin of the previously reported deficit of muons in air shower simulations and constrains models of hadronic interactions at ultrahigh energies. Our measurement is compatible with the muon deficit originating from small deviations in the predictions from hadronic interaction models of particle production that accumulate as the showers develop.Argentina-Comision Nacional de Energia AtomicaANPCyTConsejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)Gobierno de la Provincia de MendozaMunicipalidad de MalargueNDM HoldingsValle Las LenasAustralian Research CouncilConselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)Fundacao de Apoio a Pesquisa do Distrito Federal (FAPDF)Financiadora de Inovacao e Pesquisa (Finep)Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) 2019/10151-2 2010/07359-6 1999/05404-3Ministerio da Ciencia, Tecnologia, Inovacoes e Comunicacoes (MCTIC)Ministry of Education, Youth & Sports - Czech Republic MSMT CR LTT18004 LM2015038 LM2018102 CZ.02.1.01/0.0/0.0/16_013/0001402 CZ.02.1.01/0.0/0.0/18_046/0016010 CZ.02.1.01/0.0/0.0/17_049/0008422France-Centre de Calcul IN2P3/CNRSCentre National de la Recherche Scientifique (CNRS)Region Ile-de-FranceCentre National de la Recherche Scientifique (CNRS)Departement Sciences de l'Univers (SDU-INSU/CNRS)French National Research Agency (ANR) LABEX ANR-10-LABX-63 ANR11-IDEX-0004-02Federal Ministry of Education & Research (BMBF)German Research Foundation (DFG)Finanzministerium Baden-WurttembergHelmholtz Alliance for Astroparticle Physics (HAP)Helmholtz AssociationMinisterium fur Innovation, Wissenschaft und Forschung des Landes Nordrhein-WestfalenMinisterium fur Wissenschaft, Forschung und Kunst des Landes Baden-WurttembergItaly-Istituto Nazionale di Fisica Nucleare (INFN)Istituto Nazionale Astrofisica (INAF)Ministry of Education, Universities and Research (MIUR)CETEMPS Center of ExcellenceMinistry of Foreign Affairs and International Cooperation (Italy)Consejo Nacional de Ciencia y Tecnologia (CONACyT) 167733Universidad Nacional Autonoma de Mexico (UNAM), PAPIIT DGAPA-UNAMNetherlands-Ministry of Education, Culture and ScienceNetherlands Organization for Scientific Research (NWO)Dutch national e-infrastructureSURF CooperativePoland-Ministry of Science and Higher Education DIR/WK/2018/11National Science Centre, Poland 2013/08/M/ST9/00322 2016/23/B/ST9/01635 HARMONIA 5-2013/10/M/ST9/00062 UMO-2016/22/M/ST9/00198Portugal -Portuguese national fundsFEDER funds within Programa Operacional Factores de Competitividade through Fundacao para a Ciencia e a Tecnologia (COMPETE)Romania-Romanian Ministry of Education and Research, the Program Nucleu within MCI PN19150201/16N/2019 PN19060102Romania-Romanian Ministry of Educatio n and Research, the Program Nucleu within PNCDI III PN-III-P1-1.2-PCCDI-2017-0839/19PCCDI/2018Slovenian Research Agency - Slovenia P1-0031 P1-0385 I00033 N1-0111Spain-Ministerio de Economia, Industria y Competitividad FPA2017-85114-P FPA2017-85197-PXunta de Galicia European Commission ED431C 2017/07Junta de Andalucia SOMM17/6104/UGREuropean CommissionRENATA Red Nacional Tematica de Astroparticulas FPA2015-68783-REDTMaria de Maeztu Unit of Excellence MDM-2016-0692United States Department of Energy (DOE) DE-AC0207CH11359 DE-FR02-04ER41300 DE-FG0299ER41107 DE-SC0011689National Science Foundation (NSF) 0450696Grainger FoundationMarie Curie-IRSES/EPLANETEuropean Particle Physics Latin American NetworkUNESC