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1. |
Record Nr. |
UNISA996365045003316 |
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Autore |
Armbrüster Christian |
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Titolo |
Gesellschaftsrecht und Verbraucherschutz - Zum Widerruf von Fondsbeteiligungen : Vortrag, gehalten vor der Juristischen Gesellschaft zu Berlin am 29. September 2004 / / Christian Armbrüster |
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Pubbl/distr/stampa |
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Berlin ; ; Boston : , : De Gruyter, , [2011] |
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©2005 |
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ISBN |
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Edizione |
[Reprint 2011] |
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Descrizione fisica |
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1 online resource (44 p.) |
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Collana |
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Schriftenreihe der Juristischen Gesellschaft zu Berlin ; ; 177 |
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Classificazione |
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Disciplina |
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Soggetti |
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Consumer protection - Law and legislation - Germany |
Corporation law - Germany |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Frontmatter -- Inhalt -- I. Einleitung -- II. Geltung der Vorschriften über verbraucherschützende Widerrufsrechte für den Fondsbeitritt -- III. Anwendbarkeit der Regeln über die fehlerhafte Gesellschaft -- IV. Weitere Tatbestandsvoraussetzungen der Regeln über die fehlerhafte Gesellschaft -- V. Folgerungen für konkurrierende Schadensersatzansprüche -- VI. Rechtspolitischer Ausblick zum Verbraucherschutz bei Fondsbeteiligungen -- VII. Fazit und Thesen |
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Sommario/riassunto |
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Angesichts eines oft unbefriedigenden wirtschaftlichen Verlaufs von Beteiligungen an geschlossenen Fonds erklären Anleger immer häufiger den Widerruf nach Haustürwiderrufs- oder Verbraucherkreditrecht. In der Rechtsprechung wird die Frage kontrovers beurteilt, inwieweit die Regeln über die fehlerhafte Gesellschaft einer Rückabwicklung nach Rücktrittsrecht vorgehen. Die umfassende Analyse und Erörterung des Autors mündet jeweils in konkrete Lösungsvorschläge. |
Written version of a lecture given to the Berlin Legal Society. The text deals with the relationship between Company law and Consumer Protection law in the field of capital investments. Several questions concerning this matter have recently been highly controversial before German courts. The main issue is whether an investor who exercises the right to revoke his accession to an investment company is thus able |
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to withdraw his initial share independent of the present value of his holding. |
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2. |
Record Nr. |
UNINA9910830064403321 |
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Autore |
Maître H (Henri) |
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Titolo |
Aesthetics in digital photography / / Henri Maître |
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Pubbl/distr/stampa |
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London, England : , : ISTE Ltd and John Wiley & Sons, Inc., , [2023] |
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©2023 |
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ISBN |
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1-394-22597-0 |
1-394-22595-4 |
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Descrizione fisica |
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1 online resource (324 pages) |
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Disciplina |
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Soggetti |
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Photography - Digital techniques |
Digital cameras |
Image processing - Digital techniques |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Cover -- Title Page -- Copyright Page -- Contents -- Introduction: Image and Gaze -- Chapter 1. The Legacy of Philosophers -- 1.1. The objectivist approach -- 1.1.1. The source: ancient Greece -- 1.1.2. After Greece -- 1.1.3. Kant and modern aesthetics -- 1.1.4. Objectivism after Kant: from pseudo-subjectivism to aesthetic realism -- 1.2. The subjectivist approach -- 1.2.1. From classicism to romanticism -- 1.2.2. The moderns -- 1.2.3. The influence of neurobiology -- 1.3. Subjectivism and objectivism: an ongoing debate -- Chapter 2. Neurobiology or the Arbitrator of Consciousness -- 2.1. fMRI protocols and neuroaesthetics -- 2.2. The fMRI quest for "beauty processes" in the brain -- 2.2.1. The role of the prefrontal cortex -- 2.2.2. The role of the insular cortex -- 2.2.3. The role of the visual areas -- 2.2.4. The role of memory and cognition -- 2.2.5. The role of embodiment -- 2.3. Responses from functional electric encephalography -- 2.4. A global cognitive scheme for aesthetic judgment? -- 2.4.1. J. Petitot's neurogeometric model -- 2.4.2. A. |
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Chatterjee's aesthetic emotion model -- 2.4.3. The model by Brown et al -- 2.4.4. Model proposed by H. Leder -- 2.4.5. The model by C. Redies -- 2.4.6. The emotions model developed by S. Koelsch et al. -- 2.4.7. L.H. Hsu's model of emotions based on A. Damásio -- 2.4.8. Other models -- 2.5. A critique of neuroaesthetic methods -- 2.5.1. Criticism of neuroaesthetic methods -- 2.5.2. Criticisms of the objectives of neuroaesthetics -- Chapter 3. What Are the Criteria For a Beautiful Photo? -- 3.1. Before we enter into the fray -- 3.1.1. What reference books do we have? -- 3.1.2. "Beauty of an image" or "quality of an image"? -- 3.1.3. A glossary of aesthetic appraisal -- 3.1.4. Measuring beauty -- 3.2. Composition -- 3.2.1. Complexity versus simplicity -- 3.2.2. Unity. |
3.2.3. A specific case in composition: landscapes -- 3.2.4. Using oculometry to analyze composition -- 3.2.5. Format or aspect ratio -- 3.2.6. The rule of thirds (RoT) -- 3.2.7. The center of the image -- 3.2.8. Other rules for composition -- 3.3. Histograms, spectral properties and textures -- 3.3.1. Histograms and gray levels -- 3.3.2. Focus, spectral density, fractals -- 3.3.3. Textures -- 3.4. Color -- 3.4.1. About the concept of color -- 3.4.2. Preferences related to isolated colors -- 3.4.3. Preferences related to color palettes -- 3.5. What behavioral psychosociology has to say -- 3.5.1. Images of nature -- 3.5.2. The aesthetics of faces -- 3.5.3. The role of the signature, title and context -- 3.5.4. Perception and memory: prototypicality -- Chapter 4. Algorithmic Approaches to "Calculate" Beauty -- 4.1. First steps: C. Henry -- 4.2. G.D. Birkhoff's mathematical approach -- 4.3. Those who followed G.D. Birkhoff -- 4.3.1. Beauty according to H.J. Eysenck -- 4.3.2. The Post-War years: the designers, A. Moles and M. Bense -- 4.3.3. A dynamic approach: P. Machado and A. Cardoso -- 4.3.4. Work carried out by J. Rigau, M. Feixas and M. Bert -- 4.4. Algorithmic approach with AI: J. Schmidhuber -- Chapter 5. The Holy Grail of the Digital World: Artificial Intelligence -- 5.1. Which artificial intelligence? -- 5.1.1. The principles -- 5.1.2. Learning algorithms -- 5.2. Why artificial intelligence in aesthetics? -- 5.3. Expert opinions -- 5.4. The database -- 5.4.1. Generalist databases, used for aesthetic judgments -- 5.4.2. Databases that are specialized for aesthetic photography -- 5.4.3. Databases dedicated to artistic judgment -- 5.4.4. Other image databases that are sometimes used -- 5.4.5. Increasing databases -- Chapter 6. Primitive-based Classification Methods -- 6.1. Judging aesthetics. |
6.1.1. Multimedia primitives: the ACQUINE system (Datta et al.) -- 6.1.2. Edges and chromatic distance: Ke et al. -- 6.1.3. Photography rules: Luo and Tang and Mavridaki and Mezaris -- 6.1.4. High-level primitives: Dhar et al. -- 6.1.5. Generic descriptors of vision: Marchesotti et al. -- 6.2. Help in composing beautiful photos -- 6.2.1. The library of aesthetic primitives developed by Su et al. -- 6.2.2. The OSCAR system by Yao et al. -- 6.2.3. Embedded systems: Lo et al. and Wang et al. -- 6.3. Some specific research related to the evaluation of aesthetics using primitives -- 6.3.1. Color harmony: Lu et al. -- 6.3.2. Group photography: Wang et al. -- 6.3.3. Social networks and crowdsourcing: Schifanella et al. -- 6.3.4. Looking at comments: San Pedro et al. -- Chapter 7. Deep Neural Network Systems -- 7.1. DNNs dedicated to aesthetic evaluation -- 7.1.1. High and low resolutions: the RAPID system, Lu et al. -- 7.1.2. The multi-path DMA-Net architecture: Lu et al. -- 7.1.3. Adapting to the size of the image: Mai et al. -- 7.1.4. Finding beauty on the Web: Redi et al. -- 7.1.5. Siamese and GAN networks: Kong et al. and Deng et al. -- 7.1.6. Paying attention to the image construction: A-Lamp -- 7.2. Variants around the basic DNN architecture -- 7.2.1. Comparing photos between |
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themselves: Schwarz et al. -- 7.2.2. Making use of knowledge of the subject: Kao et al. -- 7.2.3. BDN: halfway between classification and DNN -- 7.2.4. Using the distribution of the evaluations -- 7.2.5. Extracting a "dramatic" image from a panorama: the Creatism system -- 7.3. Written appraisals: analyzing them and formulating new ones -- 7.3.1. Photo critique captioning dataset (PCCD) -- 7.3.2. Neural aesthetic image retriever (NAIR) -- 7.3.3. Semantic processing by Ghosal et al. -- 7.3.4. Aesthetic multi attribute network (AMAN) -- 7.4. Measuring subjective beauty. |
7.4.1. Recommendation systems -- 7.4.2. Defining the user's psychological profile -- 7.4.3. Learning the user's tastes through tests -- 7.4.4. Multiplying concurrent expertise -- Chapter 8. A Critical Analysis of Machine Learning Techniques -- 8.1. The popularity of studies on aesthetics -- 8.2. A summary of learning methods -- 8.2.1. Which architecture? Which software? -- 8.2.2. What performances? -- 8.3. Questioning the hypotheses -- 8.4. Specific features of beautiful images detected by a computer -- 8.4.1. Some observations on the photos in the AVA database -- 8.4.2. The scores in the AVA database -- Conclusion -- Appendix 1. A Brief Review of Aesthetics -- Appendix 2. Aesthetics in China -- Appendix 3. The Aesthetic of Persian Miniatures -- Appendix 4. Aesthetics in Japan -- References -- Index -- EULA. |
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