LEADER 02189nam a22003135a 4500 001 991001862409707536 008 121024s2012 nyu 000 0 eng d 020 $a9789400722743 035 $ab14082664-39ule_inst 040 $aDip.to Fisica$beng 082 04$a551.51/1$223 084 $aLC QC879.6 084 $a52.9.3 100 1 $aWarneck, Peter$023125 245 14$aThe atmospheric chemist's companion :$bnumerical data for use in the atmospheric sciences /$cPeter Warneck, Jonathan Williams 260 $aNew York :$bSpringer,$c2012 300 $ax, 437 p. :$bill. ;$c24 cm 504 $aIncludes bibliographical references and index 505 00$tFundamental Quantities and Units --$tData Regarding the Earth --$tStructure of the Atmosphere --$tTrace Gases --$tThe Atmospheric Aerosol --$tGas-Phase Photochemistry --$tRate Coefficients for Gas-Phase Reactions --$tAqueous Phase Chemistry --$tThe Upper Atmosphere --$tMeasurement Techniques for Atmospheric Trace Species 520 $a"This companion provides a collection of frequently needed numerical data as a convenient desk-top or pocket reference for atmospheric scientists as well as a concise source of information for others interested in this matter. The material contained in this book was extracted from the recent and the past scientific literature; it covers essentially all aspects of atmospheric chemistry. The data are presented primarily in the form of annotated tables while any explanatory text is kept to a minimum. 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I A 6/2 (Fondo Ferretti)$g1$i2002000177988$lle002$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i12710052$z08-10-03 996 $aImmagini di sogni$9162034 997 $aUNISALENTO 998 $ale002$b08-10-03$cm$da $e-$fita$git $h3$i1 LEADER 00761nam0-2200265-i-450 001 9910839698403321 005 20240319111635.0 035 $aFED01000983357 035 $a(Aleph)000983357FED01 035 $a000983357 100 $a20140306d1998----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay---a---001yy 200 1 $aBibliografia degli scritti di Fulvio Tessitore, 1961-1997 210 $aNapoli$cEditoriale scientifica$d1998 215 $a102 p.$d23 cm 676 $a016.19$v23$zita 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aLG 912 $a9910839698403321 952 $aDFT OPUSC. 20 (23)$b2024/1982$fFLFBC 959 $aBFS 996 $aBibliografia degli scritti di Fulvio Tessitore, 1961-1997$94133728 997 $aUNINA LEADER 04808nam 22005535 450 001 9910881096003321 005 20251225195113.0 010 $a3-031-67977-6 024 7 $a10.1007/978-3-031-67977-3 035 $a(MiAaPQ)EBC31608185 035 $a(Au-PeEL)EBL31608185 035 $a(CKB)34119666800041 035 $a(DE-He213)978-3-031-67977-3 035 $a(EXLCZ)9934119666800041 100 $a20240819d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBelief Functions: Theory and Applications $e8th International Conference, BELIEF 2024, Belfast, UK, September 2?4, 2024, Proceedings /$fedited by Yaxin Bi, Anne-Laure Jousselme, Thierry Denoeux 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (0 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14909 311 08$a3-031-67976-8 320 $aIncludes bibliographical references and index. 327 $a -- Machine learning. -- Deep evidential clustering of images. -- Incremental Belief-peaks Evidential Clustering. -- Imprecise Deep Networks for Uncertain Image Classification. -- Dempster-Shafer Credal Probabilistic Circuits. -- Uncertainty quantification in regression neural networks using likelihood-based belief functions. -- An evidential time-to-event prediction model based on Gaussian random fuzzy numbers. -- Object Hallucination Detection in Large Vision Language Models via Evidential Conflict. -- Multi-oversampling with evidence fusion for imbalanced data classification. -- An Evidence-based Framework For Heterogeneous Electronic Health Records: A Case Study In Mortality Prediction. -- Conflict Management in a Distance to Prototype-Based Evidential Deep Learning. -- A Novel Privacy Preserving Framework for Training Dempster-Shafer Theory-based Evidential Deep Neural Network. -- Statistical inference. -- Large-sample theory for inferential models: A possibilistic Bernstein?von Mises theorem. -- Variational approximations of possibilistic inferential models. -- Decision theory via model-free generalized fiducial inference. -- Which statistical hypotheses are afflicted with false confidence?. -- Algebraic expression for the relative likelihood-based evidential prediction of an ordinal variable. -- Information fusion and optimization. -- Why Combining Belief Functions on Quantum Circuits?. -- SHADED: Shapley Value-based Deceptive Evidence Detection in Belief Functions. -- A Novel Optimization-Based Combination Rule for Dempster-Shafer Theory. -- Fusing independent inferential models in a black-box manner. -- Optimization under Severe Uncertainty: a Generalized Minimax Regret Approach for Problems with Linear Objectives. -- Measures of uncertainty, conflict and distances. -- A mean distance between elements of same class for rich labels. -- Threshold Functions and Operations in the Theory of Evidence. -- Mutual Information and Kullback-Leibler Divergence in the Dempster-Shafer Theory. -- An OWA-based Distance Measure for Ordered Frames of Discernment. -- Automated Hierarchical Conflict Reduction for Crowdsourced Annotation Tasks using Belief Functions. -- Continuous belief functions, logics, computation. -- Gamma Belief Functions. -- Combination of Dependent Gaussian Random Fuzzy Numbers. -- A 3-valued Logical Foundation for Evidential Reasoning. -- Accelerated Dempster Shafer using Tensor Train Representation. 330 $aThis book constitutes the refereed proceedings of the 8th International Conference on Belief Functions, BELIEF 2024, held in Belfast, UK, in September 2?4, 2024. The 30 full papers presented in this book were carefully selected and reviewed from 36 submissions. The papers cover a wide range on theoretical aspects on Machine learning; Statistical inference; Information fusion and optimization; Measures of uncertainty, conflict and distances; Continuous belief functions, logics, computation. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v14909 606 $aArtificial intelligence 606 $aProbabilities 606 $aArtificial Intelligence 606 $aProbability Theory 615 0$aArtificial intelligence. 615 0$aProbabilities. 615 14$aArtificial Intelligence. 615 24$aProbability Theory. 676 $a658.403 702 $aBi$b Yaxin 702 $aJousselme$b Anne-Laure 702 $aDenoeux$b Thierry 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910881096003321 996 $aBelief Functions: Theory and Applications$92154579 997 $aUNINA