02323nam 2200601Ia 450 991070194980332120121101143807.0(CKB)5470000002422620(OCoLC)815650686(EXLCZ)99547000000242262020121101d2012 ua 0engurcn|||||||||txtrdacontentcrdamediacrrdacarrierHeat pipes and heat rejection component testing at NASA Glenn Research Center[electronic resource] /James L. Sanzi, Donald A. JaworskeCleveland, Ohio :National Aeronautics and Space Administration, Glenn Research Center,[2012]1 online resource (7 pages) color illustrationsNASA TM ;2012-217205Title from title screen (viewed on Nov. 1, 2012)."February 2012.""Prepared for the Nuclear and Emerging Technologies for Space (NETS-2011) cosponsored by the ANS Aerospace Nuclear Science and Technology Division, the ANS Trinity Section, and the American Institute of Aeronautics and Space Administration, Albuquerque, New Mexico, February 7-10, 2011.""NETS-2011-3497."Includes bibliographical references (page 7).Heat pipesnasatRadiant heatingnasatTitaniumnasatWaternasatFissionnasatSpacecraft power suppliesnasatCooling systemsnasatLiquid coolingnasatPerformance testsnasatHeat pipes.Radiant heating.Titanium.Water.Fission.Spacecraft power supplies.Cooling systems.Liquid cooling.Performance tests.Sanzi James L1395670Jaworske Donald A1394112NASA Glenn Research Center.Nuclear and Emerging Technologies for Space (Conference)(2011 :Albuquerque, N.M.)GPOGPOBOOK9910701949803321Heat pipes and heat rejection component testing at NASA Glenn Research Center3454599UNINA03918nam 2200937z- 450 991061946340332120231214132951.03-0365-5308-8(CKB)5670000000391640(oapen)https://directory.doabooks.org/handle/20.500.12854/93254(EXLCZ)99567000000039164020202210d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierInformation Theory and Machine LearningMDPI - Multidisciplinary Digital Publishing Institute20221 electronic resource (254 p.)3-0365-5307-X The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems.Technology: general issuesbicsscHistory of engineering & technologybicsscsupervised classificationindependent and non-identically distributed featuresanalytical error probabilityempirical riskgeneralization errorK-means clusteringmodel compressionpopulation riskrate distortion theoryvector quantizationoverfittinginformation criteriaentropymodel-based clusteringmerging mixture componentscomponent overlapinterpretabilitytime series predictionfinite state machineshidden Markov modelsrecurrent neural networksreservoir computerslong short-term memorydeep neural networkinformation theorylocal information geometryfeature extractionspiking neural networkmeta-learninginformation theoretic learningminimum error entropyartificial general intelligenceclosed-loop transcriptionlinear discriminative representationrate reductionminimax gamefairnessHGR maximal correlationindependence criterionseparation criterionpattern dictionaryatypicalityLempel–Ziv algorithmlossless compressionanomaly detectioninformation-theoretic boundsdistribution and federated learningTechnology: general issuesHistory of engineering & technologyZheng Lizhongedt1319283Tian ChaoedtZheng LizhongothTian ChaoothBOOK9910619463403321Information Theory and Machine Learning3033697UNINA