03373 am 22005173u 450 99620165800331620210315130643.0(CKB)3450000000002986(SSID)ssj0000985884(PQKBManifestationID)11628924(PQKBTitleCode)TC0000985884(PQKBWorkID)10933389(PQKB)10416065(OCoLC)994340827(WaSeSS)Ind00074422(EXLCZ)99345000000000298620160829h20112011 uy 0gerurmn#nnn|||||txtrdacontentcrdamediacrrdacarrierExekutive vetorechte im deutschen Verfassungssystem eine systematische darstellung und kritische Würdigung unter besonderer berücksichtigung der rechtshistorischen Herausbildung sowie der institutionellen einpassung in die parlamentarischen demokratiestruk und Europas /Stefan MalornyGöttingen :Universitätsverlag Göttingen,2011.©20111 online resource (viii, 513 pages)Open Access e-BooksKnowledge UnlatchedGöttinger Schriften zum Öffentlichen Recht ;Band 2Inlcudes bibliographical references.Hiergegen lege ich mein Veto ein. Diese Aussage lässt sich oft in Zusammenhängen finden, in denen es darum geht, das Handeln eines Anderen besonders nachhaltig und beeindruckend zu unterminieren. Genau in jener Destruktionsenergie liegt die Magie der Vetorechte begründet. Es verwundert daher nicht, dass von diesem Terminus seit jeher reger Gebrauch gemacht wird und selbst das Blockadepotential der ständigen Mitglieder im UN-Sicherheitsrat aber auch das exekutive Durchgriffsrecht des amerikanischen Präsidenten als Vetorecht firmiert. Solch reger Einsatz dieser Begrifflichkeit ist in juristischen wie in nichtjuristischen Bereichen zu verzeichnen. Auch für das deutsche Verfassungssystem lässt sich der Gebrauch der Vetoformulierung eruieren. Die vorliegende Dissertation kapriziert sich auf die Begutachtung damit einhergehender Fragestellungen. Rechtshistorische Herleitungen und eine definitorische Exegese des Vetobegriffs tragen dazu bei, das Bewusstsein für die mit dem Vetoeinsatz einhergehende Machtausübung zu schärfen. Es werden dabei Antworten gegeben auf Fragen wie: Woher kommt diese Macht? Welche Formen kann sie annehmen? Welche Möglichkeiten wohnen ihr inne, wo liegen ihre Grenzen? Darüber hinaus dient diese Studie dazu, für den demokratischen Rechtsstaat Bundesrepublik Deutschland nach der Einpassung von Vetorechten in die grundgesetzlichen Parameter zu fragen. All diesen Facetten der Vetorechte, erweitert um den Spannungsbogen der politischen Dimension, geht diese Dissertation nach.Göttinger Schriften zum Öffentlichen Recht ;Band 2.Constitutional lawGermanyAdministrative lawGermanyElectronic books.Constitutional lawAdministrative law342.43057Malorny Stefan801020PQKBAuAdUSAUkMaJRUBOOK996201658003316Exekutive vetorechte im deutschen Verfassungssystem1985826UNISA11269nam 2200565 450 991056129730332120231110233358.03-031-03789-8(MiAaPQ)EBC6953666(Au-PeEL)EBL6953666(CKB)21513296400041(PPN)262167565(EXLCZ)992151329640004120221118d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial intelligence in music, sound, art and design 11th international conference, EvoMUSART 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, proceedings /edited by Tiago Martins, Nereida Rodríguez-Fernández, and Sérgio M. RebeloCham, Switzerland :Springer,[2022]©20221 online resource (427 pages)Lecture Notes in Computer Science ;v.13221Print version: Martins, Tiago Artificial Intelligence in Music, Sound, Art and Design Cham : Springer International Publishing AG,c2022 9783031037887 Includes bibliographical references and index.Intro -- Preface -- Organization -- Contents -- Long Talks -- SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms -- 1 Introduction -- 2 Related Work -- 3 Methodology and System Overview -- 4 Experimental Study -- 4.1 Experimental Setup -- 4.2 Experimental Analysis -- 5 Conclusion and Future Work -- References -- A Systematic Evaluation of GPT-2-Based Music Generation -- 1 Introduction -- 2 Background -- 2.1 GPT-2 Models -- 2.2 Representing Music Data for GPT-2 Input -- 2.3 Statistical Analysis of Generative Music Models -- 3 Musical Metrics -- 4 Dataset Curation -- 5 Evaluating Generative Model Output -- 5.1 Varying the Training Level -- 5.2 Varying the Training Corpus -- 6 Web Application -- 7 Conclusions and Future Work -- References -- Expressive Aliens - Laban Effort Factors for Non-anthropomorphic Morphologies -- 1 Introduction -- 2 Background -- 2.1 Movement Qualities -- 2.2 Motion Synthesis -- 2.3 Anthropomorphic versus Non-anthropomorphic Characters -- 2.4 Physical Validity versus Expressivity -- 3 Implementation -- 3.1 Morphologies -- 3.2 SAC Algorithm -- 3.3 Observation Vector -- 3.4 Rewards -- 4 Training -- 5 Results -- 6 Discussion -- 7 Conclusion and Outlook -- References -- Painting with Evolutionary Algorithms -- 1 Introduction -- 2 Rearranging Brush Strokes -- 3 Algorithms -- 4 Experiment and Results -- 5 Extrapolation -- 6 Some Final Remarks -- References -- Evolutionary Construction of Stories that Combine Several Plot Lines -- 1 Introduction -- 2 Related Work -- 2.1 Plot Line Combination -- 2.2 Computational Metrics for Stories -- 2.3 Evolutionary Construction of Narratives -- 3 An Evolutionary Multiplot Story Composer -- 3.1 The Knowledge Resources -- 3.2 Character Fusion and Discourse Planning -- 3.3 Representing Multiplot Stories for Evolutionary Construction -- 3.4 Constructing an Initial Population.3.5 Evolutionary Operators -- 3.6 Fitness Functions -- 4 Discussion -- 4.1 Results -- 4.2 Relation with Previous Work -- 5 Conclusions -- References -- Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space -- 1 Introduction -- 2 Related Work -- 2.1 GANs -- 2.2 Fashion Styles -- 2.3 Evolutionary Search of GANs' Latent Space -- 3 Dataset -- 4 Model -- 4.1 Generative Model -- 4.2 Style Model -- 4.3 Evolutionary Search -- 5 Results -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Classification of Guitar Effects and Extraction of Their Parameter Settings from Instrument Mixes Using Convolutional Neural Networks -- 1 Introduction -- 2 Method and Materials -- 2.1 Dataset for Guitar Effect Parameter Extraction -- 2.2 Dataset for Guitar Effect Classification -- 2.3 Time-Frequency Representations -- 2.4 Convolutional Neural Networks -- 2.5 Training and Evaluation -- 2.6 Baseline -- 2.7 Robustness Analysis -- 3 Results -- 3.1 Effect Classification -- 3.2 Effect Parameter Extraction -- 3.3 Robustness to Noise and Pitch Shifts -- 4 Discussion -- 4.1 Guitar Effect Classification -- 4.2 Guitar Effect Parameter Extraction -- 4.3 Robustness -- 4.4 Limitations -- 5 Conclusion -- References -- Aesthetic Evaluation of Experimental Stimuli Using Spatial Complexity and Kolmogorov Complexity -- 1 Introduction -- 2 Conceptual Model -- 3 Spatial Complexity Measure -- 4 Kolmogorov Complexity of 2D Patterns -- 5 Experiment and Results -- 5.1 Method -- 5.2 Material -- 5.3 Procedure -- 5.4 Results -- 5.5 Procedure for the Extended Study -- 5.6 Results and Analysis -- 6 Discussions -- References -- Towards the Generation of Musical Explanations with GPT-3 -- 1 Introduction -- 2 Background -- 2.1 Communication in Human-Machine Music Interactions -- 2.2 Transformer-Based Approaches in Music.2.3 Transformer-Based Approaches in Explainable AI -- 3 Musical Capability of GPT-3 -- 3.1 Extracting the Key from a Sequence of Notes -- 3.2 Providing Explanations for a Fictional Song -- 3.3 Extracting Musical Information Using MusicABC Notation -- 4 Explaining Musical Decisions -- 4.1 Methodology -- 4.2 Results -- 5 Discussion -- 6 Conclusion -- References -- Lamuse: Leveraging Artificial Intelligence for Sparking Inspiration -- 1 Introduction -- 2 The Creative Process -- 2.1 Context -- 2.2 General Assumptions and Process -- 2.3 Data Requirements -- 3 The Artificial Intelligence -- 3.1 Artwork Decomposition -- 3.2 Recomposing with a Visual Universe and Projection -- 3.3 Style Transfer -- 4 Results Analysis and Discussion -- 4.1 E. Potier -- 4.2 Rarès-Victor -- 4.3 N. Varoqui -- 4.4 O. Masmonteil -- 5 Conclusion -- References -- EvoDesigner: Towards Aiding Creativity in Graphic Design -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Evolutionary Engine -- 4 Experimental Setup and Results -- 5 Conclusion -- References -- Conditional Drums Generation Using Compound Word Representations -- 1 Introduction -- 2 Related Work -- 3 Data Encoding Representation -- 3.1 Encoder Representation - Conditional Information -- 3.2 Decoder Representation - Generated Drum Sequences -- 4 Proposed Architecture -- 4.1 Encoder - Decoder -- 4.2 Implementation Details -- 5 Experimental Setup -- 5.1 Dataset and Preprocessing -- 5.2 Evaluation Metrics -- 6 Results -- 6.1 Objective Evaluation -- 6.2 Subjective Evaluation -- 7 Conclusions -- References -- Music Style Transfer Using Constant-Q Transform Spectrograms -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 4 Experiments and Results -- 4.1 CycleGAN Hyperparameter Experiment -- 4.2 CQTGAN Sample Rates Experiment -- 4.3 Unseen Audio Examples Experiment -- 5 Survey Evaluation -- 6 Discussion.7 Conclusions and Future Work -- References -- SpeechTyper: From Speech to Typographic Composition -- 1 Introduction -- 2 Related Work -- 3 SpeechTyper -- 3.1 Extracting Speech Data -- 3.2 Designing Glyphs -- 3.3 Creating Typographic Compositions Based on Speech -- 4 Experimentation -- 4.1 Setup -- 4.2 Results/Discussion -- 5 Conclusion and Future Work -- References -- A Creative Tool for the Musician Combining LSTM and Markov Chains in Max/MSP -- 1 Introduction -- 2 Data Representation -- 2.1 Pitch -- 2.2 Time -- 2.3 MIDI Analyzer -- 3 LSTM Data Encoding -- 3.1 Pitch -- 3.2 Rhythm -- 3.3 Machine Learning Datasets -- 4 Scramble -- 5 User Interface -- 6 Experimental Results -- 7 Conclusions and Future Work -- References -- Translating Emotions from EEG to Visual Arts -- 1 Introduction -- 2 Background and Related Works -- 3 Preparation of Datasets -- 4 Pipeline -- 4.1 Extra Losses -- 5 Experiments and Results -- 5.1 Example Experiment -- 6 Results Assessment: Online Survey -- 7 Discussion -- 8 Conclusions -- References -- Emotion-Driven Interactive Storytelling: Let Me Tell You How to Feel -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Emotion Recognition -- 3.2 Interface Design -- 3.3 System's Assessment -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts -- 1 Introduction -- 2 Background -- 3 Modern Evolution Strategies for Creativity -- 4 Fitting Concrete Target Image -- 5 Fitting Abstract Concept with CLIP -- 6 Discussion and Conclusion -- References -- Co-creative Product Design with Interactive Evolutionary Algorithms: A Practice-Based Reflection -- 1 Introduction -- 2 Background -- 3 Related Work -- 4 Design Study -- 4.1 Design Task -- 4.2 Study Method -- 4.3 Participant: The Designer -- 4.4 Software Tools and Algorithm.4.5 Findings: Introspective Design Reflections -- 5 Discussion -- 5.1 Early Constraining of the Design Space -- 5.2 Support in Problem-Solution Co-evolution -- 5.3 Escaping and Falling in a Fixation Trap -- 5.4 The IGA as Creative Partner -- 5.5 Study Limitations -- 6 Conclusion and Future Work -- References -- Sound Model Factory: An Integrated System Architecture for Generative Audio Modelling -- 1 Background and Motivation -- 1.1 Previous Work -- 2 Architecture -- 2.1 System Overview -- 2.2 System Components -- 3 Connecting the GAN and the RNN -- 3.1 Parameter Linearization -- 4 Evaluation -- 4.1 Human Evaluation Adaptively Smoothed Latent Space -- 4.2 Parameter Response Time -- 4.3 Sound Quality Evaluation Based on Audio Classification -- 4.4 Continuous Interpolation of Pitch and Timbre -- 5 Conclusion -- References -- Short Talks -- An Application of Neural Embedding Models for Representing Artistic Periods -- 1 Introduction -- 2 Data -- 3 Methodology -- 3.1 word2vec -- 3.2 t-SNE -- 4 Results -- 4.1 Salvador Dalí -- 4.2 Vincent van Gogh -- 4.3 Pablo Picasso -- 5 Discussion -- 6 Conclusion -- References -- MusIAC: An Extensible Generative Framework for Music Infilling Applications with Multi-level Control -- 1 Introduction -- 2 Related Work -- 3 Proposed Model and Representation -- 3.1 Adding Control Features -- 3.2 Data Representation -- 3.3 Model Architecture -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Model Configuration and Training -- 4.3 Inference Strategy -- 5 Evaluation -- 5.1 Objective Evaluation Using Selected Metrics -- 5.2 The Interactive Interface and Controllability -- 6 Conclusion -- References -- A Study on Noise, Complexity, and Audio Aesthetics -- 1 Introduction -- 2 Conceptual over Perceptual -- 2.1 Japanoise -- 3 Complexity -- 3.1 Complexity and Computational Aesthetics -- 4 Proposed Aesthetic Metrics -- 5 Experiment.6 Discussion.Lecture Notes in Computer Science Computer graphicsComputer musicArtificial intelligenceComputer graphics.Computer music.Artificial intelligence.700.28563Rodríguez-Fernández NereidaMartins TiagoRebelo Sérgio M.MiAaPQMiAaPQMiAaPQBOOK9910561297303321Artificial Intelligence in Music, Sound, Art and Design2257587UNINA