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Record Nr. |
UNINA9910151822303321 |
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Titolo |
Bayerisches Jahrbuch : Auskunfts- und Adressenwerk über Behörden, Ministerien, Verbände und Gemeinden. . 96. Jahrgang, 2017 |
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Pubbl/distr/stampa |
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Berlin ; ; Boston : , : De Gruyter Saur, , [2016] |
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©2017 |
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ISBN |
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9783110452303 |
3110452308 |
9783110454666 |
3110454661 |
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Descrizione fisica |
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1 online resource (586 pages) |
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Collana |
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Bayerisches Jahrbuch ; ; 96. Jahrgang |
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Disciplina |
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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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Nota di contenuto |
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Frontmatter -- Vorwort -- Inhaltsverzeichnis -- Der Bayerische Landtag -- Die Bayerischen Staatsbehörden -- Bayerisches Staatsministerium des Innern, für Bau und Verkehr -- Bayerisches Staatsministerium der Justiz -- Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst -- Bayerisches Staatsministerium der Finanzen, für Landesentwicklung und Heimat -- Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie -- Bayerisches Staatsministerium für Umwelt und Verbraucherschutz -- Bayerisches Staatsministerium für Ernährung, Landwirtschaft und Forsten -- Bayerisches Staatsministerium für Arbeit und Soziales, Familie und Integration -- Bayerisches Staatsministerium für Gesundheit und Pflege -- Bundesrepublik Deutschland -- Behörden und Dienststellen der Bundesrepublik Deutschland in Bayern -- Kommunale Verwaltung Bayerns -- Religionsgemeinschaften -- Vereinigungen, Verbände, Organisationen -- Öffentlich-rechtliche Kreditinstitute -- Statistischer Überblick -- Abkürzungsverzeichnis -- Personenregister -- Sach- und Institutionenregister |
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Sommario/riassunto |
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Das Bayerische Jahrbuch verzeichnet Behörden, Ministerien, Körperschaften und mit ihnen verbundene Einrichtungen des |
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öffentlichen Lebens im Freistaat Bayern: Behörden und Dienststellen der staatlichen und kommunalen Verwaltung die Gerichtsbarkeit Interessenverbände und andere Organisationen aus Politik, Wirtschaft, Wissenschaft, Kunst usw. Notare, Kirchenbehörden, Hochschulen, Museen, Bibliotheken, Kreditinstitute über 12.000 Bürgermeister, Landräte, Vorsitzende, Geschäftsführer, Präsidenten, Direktoren und andere Personen in leitender Funktion |
Up-to-date information on some 7,000 institutions and approx. 12,000 persons in public life in Bavaria: authorities and departments of local, state and federal administration jurisdiction syndicates and other organizations from politics, business, academic life, the arts etc. notaries, church offices, universities, museums, libraries, banks mayors, District Administrators, chairpersons, managing directors and other executives |
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2. |
Record Nr. |
UNINA9911114396403321 |
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Autore |
Zhang Yingqian |
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Titolo |
Learning and Intelligent Optimization : 19th International Conference, LION 19, Prague, Czech Republic, June 15–19, 2025, Proceedings, Part I / / edited by Yingqian Zhang, Milan Hladik, Hossein Moosaei |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026 |
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ISBN |
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3-032-09156-X |
9783032091567 |
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Edizione |
[1st ed. 2026.] |
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Descrizione fisica |
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1 online resource (574 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 15744 |
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Altri autori (Persone) |
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HladikMilan |
MoosaeiHossein |
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Disciplina |
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Soggetti |
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Numerical analysis |
Computer science - Mathematics |
Mathematical statistics |
Computer science |
Artificial intelligence |
Social sciences - Data processing |
Computers |
Numerical Analysis |
Probability and Statistics in Computer Science |
Theory of Computation |
Artificial Intelligence |
Computer Application in Social and Behavioral Sciences |
Computing Milieux |
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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 contenuto |
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-- Multi-Agent Soft Actor-Critic with Coordinated Loss for Autonomous Mobility-on-Demand Fleet Control. -- Do LLMs Understand Constraint Programming? Zero-Shot Constraint Programming Model Generation Using LLMs. -- Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding. -- Automated Refutation with Monte Carlo Search of Graph Theory Conjectures on the Maximum Laplacian Eigenvalue. -- Demand Selection for VRP with Emission Quota. -- Multi-target tree regression approach for surrogate-based optimisation. -- Information Preserving Line Search via Bayesian Optimization. -- Bayesian Optimisation Against Climate Change: Applications and Benchmarks. -- Empirical Analysis of Upper Bounds of Robustness Distributions using Adversarial Attacks. -- e$^2$HPO: energy efficient Hyperparameter Optimization via energy-aware multiple information source Bayesian optimization. -- Reinforcement Learning for Dynamic Pricing with resource constraints in a competitive context. -- Decision Maker Preferences in Surrogate-based Multi-Objective Optimization: A Survey. -- Deep Reinforcement Learning Based Genetic Framework for Flexible Job-Shop Scheduling under Practical Constraints. -- Solving influence diagrams: efficient mixed-integer programming formulation and heuristic. -- Mixed-Integer Linear Optimization via Learning-Based Two-Layer Large Neighborhood Search. -- Geometrically Invariant and Equivariant Graph Neural Networks for TSP Algorithm Selection and Hardness Prediction. -- Time-Varying Multi-Objective Optimization: Tradeoff Regret Bounds. -- Vector Bin Packing with Bin Clusters, Variable Bin Sizes and Costs - A Model and Heuristics for Cloud Capacity Planning. -- SchedulExpert: Graph Attention Meets Mixture-of-Experts for JSSP. -- Reinforcement Learning for AMR Charging Decisions: The Impact of Reward and Action Space Design. -- The Post-Enrollment Course Timetabling Problem with Flexible Teacher Assignments. |
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Sommario/riassunto |
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The two-volume set LNCS 15744 + 15745 constitutes the proceedings of the 19th International Conference on Learning and Intelligent Optimization, LION 2025, which was held in Prague, Czech Republic, during June 15–19, 2025. The 40 full papers included in the proceedings were carefully reviewed and selected from 70 submissions. They focus on exploring the intersections of Artificial Intelligence, Machine Learning, and Operations Research. |
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