Bildungsbürgertum und völkische Ideologie : Konstitution und gesellschaftliche Tiefenwirkung eines Netzwerks völkischer Autoren (1919-1959) / / Thomas Vordermayer

• Record Nr.: UNISA996312650203316
• Autore: Vordermayer Thomas
• Pubbl/distr/stampa: Berlin, Germany ; ; Boston, [Massachusetts] : , : De Gruyter Oldenbourg, , 2016; ©2016
• ISBN: 3-11-041553-4; 3-11-041569-0
• Descrizione fisica: 1 online resource (480 p.)
• Collana: Quellen und Darstellungen zur Zeitgeschichte, , 0481-3545 ; ; Band 109
• Classificazione: MS 4710
• Disciplina: 830.900914
• Soggetti: German literature - 20th century - History and criticism; Literature and society - Germany - History - 20th century; National socialism and literature; Right and left (Political science) in literature
• Lingua di pubblicazione: Tedesco
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Frontmatter -- Inhalt -- 1. Einleitung -- 2. Die Hauptfiguren: Hans Grimm, Erwin Guido Kolbenheyer und Wilhelm Stapel -- 3. Ebenen gesellschaftlicher Tiefenwirkung -- 4. Völkisches Denken in Publikationen ideologisch wahlverwandter Professoren. Drei Fallbeispiele -- 5. Große Erwartungen und bittere Enttäuschung: Grimm, Kolbenheyer und Stapel in ihrem Verhältnis zur NSDAP -- 6. Restöffentlichkeit und gesellschaftliche Ausgrenzung: Grimm, Kolbenheyer und Stapel nach 1945 -- Zusammenfassung -- Dank -- Abkürzungsverzeichnis -- Ungedruckte Quellen -- Gedruckte Quellen und Literatur -- Personenregister
• Sommario/riassunto: Die gemäßigt agierenden völkischen Ideologen Hans Grimm, Erwin Guido Kolbenheyer und Wilhelm Stapel beeinflussten die bildungsbürgerlichen Eliten ihrer Zeit in einer Weise, die weniger distinguiert auftretenden völkischen Agitatoren verschlossen blieb. Thomas Vordermayer zeichnet die Karrieren der drei Erfolgsautoren ; zwischen 1919 und 1959 nach. Er zeigt, wie sie unter den politisch-ideologischen "Multiplikatoren" der deutschen Gesellschaft - vor allem den Professoren, Journalisten und Redakteuren - Deutungsmacht erlangten und wie sie sich bemühten, sich gegenseitig privat und öffentlich zu stärken und zu unterstützen. Durch die Auswertung bislang kaum genutzter, vielfach völlig unbekannter Nachlassmaterialien und unter Rückgriff auf netzwerkanalytische Instrumentarien eröffnen sich dem Leser ganz neue Perspektiven auf die ideologische Verführbarkeit des Weimarer Bildungsbürgertums sowie auf das Denken und Handeln völkischer Schriftsteller und Publizisten. Wie sie sich untereinander abstimmten und bestätigten, wie sie sich im "Dritten Reich" positionierten und wie sie ihren jähen Bedeutungsverlust nach 1945 mental verarbeiteten, ist noch nie so nuanciert und tiefgründig beschrieben worden, wie in dieser preisgekrönten Studie.

Geometric structures of statistical physics, information geometry, and learning : SPIGL'20, Les Houches, France, July 27-31 / / Frédéric Barbaresco and Frank Nielsen (editors)

• Record Nr.: UNISA996466400503316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-77957-2
• Descrizione fisica: 1 online resource (466 pages)
• Collana: Springer Proceedings in Mathematics and Statistics ; ; v.361
• Disciplina: 530.13
• Soggetti: Física estadística; Intel·ligència artificial; Statistical physics; Artificial intelligence; Congressos; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Preface -- Contents -- Part I: Tribute to Jean-Marie Souriau Seminal Works -- Structure des Systèmes Dynamiques Jean-Marie Souriau's Book 50th Birthday -- 1 A Few Introductory Words -- 2 Introduction -- 3 Chapter I: Differential Geometry -- 4 Chapter II: Symplectic Geometry -- 5 Chapter III: Mechanics -- 6 Chapter IV: Statistical Mechanics -- 7 Chapter V: A Method of Quantization -- 8 Conclusions -- References -- Jean-Marie Souriau's Symplectic Model of Statistical Physics: Seminal Papers on Lie Groups Thermodynamics - Quod Erat Demonstrandum -- 1 Preamble -- 2 Jean-Marie Souriau Biography -- 3 1st Souriau Paper: "Statistical Mechanics, Lie Group and Cosmology - 1st Part: Symplectic Model of Statistical Mechanics" -- 3.1 Distribution Functions -- 3.2 Statistical States -- 3.3 Image of Measures -- 3.4 Tensorial Products of Measure -- 3.5 Entropy -- 3.6 Canonical Gibbs Ensemble -- 3.7 Gibbs Ensemble of a Dynamic Group -- 3.8 Broken Symmetries -- 3.9 Thermodynamic Applications -- 3.10 Relativistic Thermodynamics -- 3.11 What is a Thermodynamic Equilibrium? -- 3.12 Proof of the Theorem (12) -- 4 2nd Souriau Paper: "Symplectic Geometry and Mathematical Physics" -- 4.1 1 - Since 1788. The Mechanics are Symplectic -- 4.2 2 - Emmy Noether and Measurable Quantities -- 4.3 3 - Mass and Cosmology -- 4.4 7 - Thermodynamics and Lie Groups -- 4.5 8 - Why the Earth Turns -- 5 3rd Souriau Paper: "Classical Mechanics and Symplectic Geometry" -- 5.1 Statistical Mechanics (Chapter 3.2) -- 5.2 Galilean Relativity (Chapter 2.7 in Souriau Paper) -- References -- Part II: Lie Group Geometry and Diffeological Model of Statistical Physics and Information Geometry -- Souriau-Casimir Lie Groups Thermodynamics and Machine Learning -- 1 Preamble -- 2 Souriau Lie Groups Thermodynamics and Covariant Gibbs Density -- 2.1 Geometric Structure of Information.; 2.2 Lie Groups Thermodynamics and Souriau-Koszul-Fisher Metric -- 2.3 Souriau Entropy and Souriau-Fisher-Koszul Metric Invariance and Covariant Souriau Gibbs Density -- 3 New Entropy Characterization as Generalized Casimir Invariant Function in Coadjoint Representation -- 3.1 Souriau Entropy as Generalized Casimir Invariant in Coadjoint Representation -- 3.2 Souriau Entropy Invariance in Coadjoint Representation -- 3.3 Algebraic Method for Construction of Casimir Invariant Functions in Coadjoint Representation -- 4 Souriau Gibbs Density for Classical Lie Groups -- 4.1 Gibbs Density for SU(1,1) Lie Groups and Poincaré Disk in Case of Null Cohomology -- 4.2 Gibbs Density for SE(2) Lie Groups in Case of Non-null Cohomology -- 5 Conclusion -- References -- An Exponential Family on the Upper Half Plane and Its Conjugate Prior -- 1 Introduction -- 1.1 G/H-Method -- 1.2 Poincaré Distribution -- 1.3 Conjugate Prior of Exponential Family -- 2 Main Theorem -- 2.1 Main Theorem -- 2.2 Proof of Proposition 4 -- References -- Wrapped Statistical Models on Manifolds: Motivations, The Case SE(n), and Generalization to Symmetric Spaces -- 1 Introduction -- 2 Some Classical Probability Densities on Manifolds -- 3 Some Important Characteristics of Statistical Models on Manifolds -- 3.1 Expression of the Density Functions -- 3.2 Moments -- 3.3 Invariances and Estimation -- 4 Probability Densities on SE(n) -- 4.1 Wrapped Models on SE(n) -- 4.2 Density Estimation on SE(n) -- 5 Towards a Generalization to Symmetric Spaces -- 6 Conclusion -- References -- Galilean Thermodynamics of Continua -- 1 Some Words of Introduction -- 2 Space-Time and Galileo's Group -- 3 Geometric Structure of Thermodynamics -- 4 Temperature Vector and Friction Tensor -- 5 Momentum Tensors and First Principle -- 6 Reversible Processes and Thermodynamical Potentials.; 7 Dissipative Continuum and Second Principle -- References -- ; Nonparametric Estimations and the Diffeological Fisher Metric -- 1 Introduction -- 2 Diffeological Fisher Metric, Diffeological Fisher Distance and Probabilistic Morphisms -- 3 Diffeological Cramér-Rao Inequality -- 4 Diffeological Hausdorff-Jeffrey Measure -- 5 Conclusion and Outlook -- References -- Part III: Advanced Geometrical Models of Statistical Manifolds in Information Geometry -- Information Geometry and Integrable Hamiltonian Systems -- 1 Introduction -- 1.1 The Toda Lattice and the Flaschka Transform -- 1.2 The Peakons System -- 1.3 Information Geometry, Toda System and Peakon System -- 2 Jacobi Flows and String Equation -- 2.1 Stieltjes Theorem -- 2.2 Hamburger Theorems and Stieltjes Integral -- 2.3 Discrete String -- 3 Finite Information Geometry -- 4 Conclusions and Perspectives -- References -- Relevant Differential Topology in Statistical Manifolds -- 1 Prologue -- 2 Intoduction -- 3 Basic Definitions -- 3.1 The Canonical Koszul Class of a Symmetric Gauge Structure -- 3.2 The Canonical Koszul Class of a Gauge Structure -- 3.3 Gauge Extensions of Gauge Dynamics -- 3.4 Transverse Statistical Structures -- 4 Reductions of Homogeneous Statistical Models -- 4.1 Canonical Projective Systems of Affinely Foliated H-Homogeneous Manifolds -- 4.2 Projective Sequence of Homogeneous Affinely Foliated Manifolds -- 4.3 Relative Invariant Subordinate Foliations -- 4.4 Subordinate Foliations and Topology of &lt -- H, M&gt -- -- 4.5 Metric Rigidity of FE() -- 5 The Case of Fisher Information -- 5.1 -equivalence -- 6 Relevant Foliations in Statistical Manifolds -- 6.1 Statistical Manifolds -- 6.2 Gauge Differential Operators -- 6.3 Relevant Constructions in Gauge Structures -- 6.4 Relevant Foliations in Positive Statistical Manifols -- 6.5 Symplectic Statistical Foliations.; 6.6 Almost Hermitian Foliations in Statistical Manifolds -- 6.7 Riemannian Statistical Foliations -- 6.8 -Family of 4-Webs in Statistical Models of Measurable Sets -- References -- A Lecture About the Use of Orlicz Spaces in Information Geometry -- 1 Introduction -- 2 Orlicz Spaces -- 3 Calculus of the Gaussian Space -- 4 Exponential Statistical Bundle -- 5 Gaussian Orlicz-Sobolev Spaces -- 6 Selected Bibliography -- References -- Quasiconvex Jensen Divergences and Quasiconvex Bregman Divergences -- 1 Introduction, Motivation, and Contributions -- 2 Divergences Based on Inequality Gaps of Quasiconvex or Quasiconcave Generators -- 2.1 Quasiconvex and Quasiconcave Difference Dissimilarities -- 2.2 Relationship of Quasiconvex Difference Distances with Jensen Difference Distances -- 2.3 Quasiconvex Difference Distances from the Viewpoint of Comparative Convexity -- 3 Bregman Divergences for Quasiconvex Generators -- 3.1 Quasiconvex Bregman Divergences as Limit Cases of Quasiconvex Jensen Divergences -- 3.2 The -averaged Quasiconvex Bregman Divergence -- 3.3 Multivariate Quasiconvex Generators Q -- 3.4 Quasiconvex Bregman Divergences as Limit Cases of Power Mean Bregman Divergences -- 3.5 Some Illustrating Examples of Quasiconvex Bregman Divergences -- 4 Statistical Divergences, Parametric Families of Distributions and Equivalent Parameter Divergences -- 5 Conclusion and Perspectives -- 6 Calculations Using a Computer Algebra System -- References -- Part IV: Geometric Structures of Mechanics, Thermodynamics and Inference for Learning -- Dirac Structures and Variational Formulation of Thermodynamics for Open Systems -- 1 Fundamentals of Open Systems -- 1.1 Stueckelberg's Formulation of Nonequilibrium Thermodynamics -- 1.2 An Illustrative Example of Open Systems -- 2 A Variational Formulation for Open Systems.; 2.1 Fundamental Setting for Open Nonequilibrium Thermodynamics -- 2.2 A Lagrangian Variational Formulation for Open Systems -- 3 Dirac Formulation for Time-Dependent Nonholonomic Systems of ; Thermodynamic Type -- 3.1 Time-Dependent Constraints of Thermodynamic Type -- 3.2 Dirac Structures on Covariant Pontryagin Bundles -- 3.3 Dirac Dynamical Systems on the Covariant Pontryagin Bundle -- 3.4 The Lagrange-d'Alembert-Pontryagin Principle on the Covariant Pontryagin Bundle -- 4 Dirac Formulation for Open Thermodynamic Systems -- 4.1 Application to the Piston-Cylinder System with External Ports -- 4.2 Dirac Dynamical Systems on the Covariant Pontryagin Bundle -- References -- The Geometry of Some Thermodynamic Systems -- 1 Introduction -- 2 Contact Geometry -- 2.1 The Jacobi Structure of a Contact Manifold -- 2.2 Hamiltonian and Evolution Vector Fields -- 3 The Lagrangian Formalism -- 3.1 The Geometric Setting -- 3.2 Generalized Chetaev Principle -- 4 The Evolution Vector Field and Simple Mechanical Systems with Friction -- 4.1 About the First and Second Laws of Thermodynamics -- 4.2 Examples -- 5 Composed Thermodynamic Systems Without Friction -- 6 Geometric Integration of Thermodynamic Systems -- 6.1 Simple Thermodynamic Systems with Friction -- 6.2 Composed Thermodynamic Systems -- 6.3 ``Variational Integration'' of the Evolution Vector Field -- 7 Conclusions and Future Work -- References -- Learning Physics from Data: A Thermodynamic Interpretation -- 1 Introduction -- 2 Pattern Recognition in Statistical Physics and Thermodynamics -- 2.1 Reduction and Pattern Recognition -- 2.2 Reducing Dynamics, Thermodynamics -- 2.3 Reduced Dynamics -- 3 Pattern Recognition in Machine Learning -- 3.1 General Scheme -- 3.2 Reduced Manifold Recognition by POD -- 3.3 Reduced Vector Field -- 4 Illustration on Learning from Particle Dynamics.; 4.1 Smoothed Particle Hydrodynamics.