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[null,"1.","Record Nr.",null,"UNISALENTO991001862409707536"]
[null,null,"Autore",null,"Warneck, Peter"]
[null,null,"Titolo",null,"The atmospheric chemist's companion : numerical data for use in the atmospheric sciences / Peter Warneck, Jonathan Williams"]
[null,null,"Pubbl/distr/stampa",null,"New York : Springer, 2012"]
[null,null,"ISBN",null,"9789400722743"]
[null,null,"Descrizione fisica",null,"x, 437 p. : ill. ; 24 cm"]
[null,null,"Classificazione",null,"LC QC879.6"]
[{"$nulls":4},"52.9.3"]
[null,null,"Disciplina",null,"551.51/1"]
[null,null,"Soggetti",null,"Atmospheric chemistry"]
[{"$nulls":4},"Atmospheric chemistry - Mathematical models"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index"]
[null,null,"Nota di contenuto",null,"Fundamental Quantities and Units -- Data Regarding the Earth -- Structure of the Atmosphere -- Trace Gases -- The Atmospheric Aerosol -- Gas-Phase Photochemistry -- Rate Coefficients for Gas-Phase Reactions -- Aqueous Phase Chemistry -- The Upper Atmosphere -- Measurement Techniques for Atmospheric Trace Species"]
[null,null,null,"Sommario/riassunto","\"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. In this condensed form of presentation, the volume may serve also as a supplement to many textbooks used in teaching the subject at various universities\"--Provided by publisher"]
[null,"2.","Record Nr.",null,"UNISALENTO991002608969707536"]
[null,null,"Autore",null,"Calvi, Bartolomeo"]
[null,null,"Titolo",null,"Le immagini di sogni / Bartolomeo Calvi"]
[null,null,"Pubbl/distr/stampa",null,"Torino : R. Accademia delle scienze, 1941"]
[null,null,"Descrizione fisica",null,"48 p. ; 25 cm"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Estr. da: Atti della Reale Accademia delle scienze di Torino, 76"]
[null,"3.","Record Nr.",null,"UNINA9910881096003321"]
[null,null,"Titolo",null,"Belief Functions: Theory and Applications : 8th International Conference, BELIEF 2024, Belfast, UK, September 2–4, 2024, Proceedings / / edited by Yaxin Bi, Anne-Laure Jousselme, Thierry Denoeux"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-67977-6"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (0 pages)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 14909"]
[null,null,"Disciplina",null,"658.403"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Probabilities"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Probability Theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"-- Machine learning.  -- Deep evidential clustering of images.  -- Incremental Belief-peaks Evidential Clustering.  -- Imprecise Deep Networks for Uncertain Image Classification.  -- Dempster-Shafer Credal Probabilistic Circuits.  -- Uncertainty quantification in regression neural networks using likelihood-based belief functions.  -- An evidential time-to-event prediction model based on Gaussian random fuzzy numbers.  -- Object Hallucination Detection in Large Vision Language Models via Evidential Conflict.  -- Multi-oversampling with evidence fusion for imbalanced data classification.  -- An Evidence-based Framework For Heterogeneous Electronic Health Records: A Case Study In Mortality Prediction.  -- Conflict Management in a Distance to Prototype-Based Evidential Deep Learning.  -- A Novel Privacy Preserving Framework for Training Dempster-Shafer Theory-based Evidential Deep Neural Network.  -- Statistical inference.  -- Large-sample theory for inferential models: A possibilistic Bernstein–von Mises theorem.  -- Variational approximations of possibilistic inferential models.  -- Decision theory via model-free generalized fiducial inference.  -- Which statistical hypotheses are afflicted with false confidence?.  -- Algebraic expression for the relative likelihood-based evidential prediction of an ordinal variable.  -- Information fusion and optimization.  -- Why Combining Belief Functions on "]
[{"$nulls":4},"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."]
[null,null,null,"Sommario/riassunto","This 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."]