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[null,"1.","Record Nr.",null,"UNINA9910819797203321"]
[null,null,"Autore",null,"Schwabish Jonathan A."]
[null,null,"Titolo",null,"Better presentations : a guide for scholars, researchers, and wonks / / Jonathan Schwabish"]
[null,null,"Pubbl/distr/stampa",null,"New York, [New York] : , : Columbia University Press, , 2016"]
[{"$nulls":4},"©2016"]
[null,null,"ISBN",null,"0-231-54279-8"]
[null,null,"Descrizione fisica",null,"1 online resource (192 pages)"]
[null,null,"Classificazione",null,"MR 2000"]
[null,null,"Disciplina",null,"658.4/52"]
[null,null,"Soggetti",null,"Business presentations"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Previously issued in print: 2016."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Frontmatter -- Contents -- Acknowledgments -- Introduction -- Part One. Designing Your Presentation -- 1. Theory, Planning, and Design -- 2. Color -- 3. Type -- Part Two. Building Your Presentation -- 4. The Text Slide -- 5. The Data Visualization Slide -- 6. The Image Slide -- 7. The Scaffolding Slides -- Part Three. Giving Your Presentation -- 8. Presenting -- 9. The Technical Nitty-Gritty -- Conclusions -- Further Readings -- References"]
[null,null,null,"Sommario/riassunto","Whether you are a university professor, researcher at a think tank, graduate student, or analyst at a private firm, chances are that at some point you have presented your work in front of an audience. Most of us approach this task by converting a written document into slides, but the result is often a text-heavy presentation saddled with bullet points, stock images, and graphs too complex for an audience to decipher-much less understand. Presenting is fundamentally different from writing, and with only a little more time, a little more effort, and a little more planning, you can communicate your work with force and clarity. Designed for presenters of scholarly or data-intensive content, Better Presentations details essential strategies for developing clear, sophisticated, and visually captivating presentations. Following three core principles-visualize, unify, and focus-Better Presentations describes how to visualize data effectively, find and use images appropriately, choose sensible fonts and colors, edit text for powerful delivery, and restructure a written argument for maximum engagement and persuasion. With a range of clear examples for what to do (and what not to do), the practical package offered in Better Presentations "]
[{"$nulls":4},"shares the best techniques to display work and the best tactics for winning over audiences. It pushes presenters past the frustration and intimidation of the process to more effective, memorable, and persuasive presentations."]
[null,"2.","Record Nr.",null,"UNINA9911019173203321"]
[null,null,"Autore",null,"Murat Cecile"]
[null,null,"Titolo",null,"Probabilistic combinatorial optimization on graphs / / Cecile Murat and Vangelis Th. Paschos"]
[null,null,"Pubbl/distr/stampa",null,"London ; ; Newport Beach, CA, : ISTE, 2006"]
[null,null,"ISBN",null,"1-280-51061-7"]
[{"$nulls":4},"9786610510610"]
[{"$nulls":4},"1-84704-483-2"]
[{"$nulls":4},"0-470-39464-1"]
[{"$nulls":4},"0-470-61250-9"]
[{"$nulls":4},"1-84704-583-9"]
[null,null,"Descrizione fisica",null,"1 online resource (269 p.)"]
[null,null,"Collana",null,"ISTE ; ; v.105"]
[null,null,"Disciplina",null,"519.2"]
[null,null,"Soggetti",null,"Combinatorial probabilities"]
[{"$nulls":4},"Combinatorial optimization"]
[{"$nulls":4},"Random graphs"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. [255]-259) and index."]
[null,null,"Nota di contenuto",null,"Probabilistic Combinatorial Optimization on Graphs; Contents; Preface; Chapter 1. A Short Insight into Probabilistic Combinatorial Optimization; 1.1. Motivations and applications; 1.2. A formalism for probabilistic combinatorial optimization; 1.3. The main methodological issues dealing with probabilistic combinatorial optimization; 1.3.1. Complexity issues; 1.3.1.1. Membership in NPO is not always obvious; 1.3.1.2. Complexity of deterministic vs. complexity of probabilistic optimization problems; 1.3.2. Solution issues; 1.3.2.1. Characterization of optimal a priori solutions"]
[{"$nulls":4},"1.3.2.2. Polynomial subcases1.3.2.3. Exact solutions and polynomial approximation issues; 1.4. Miscellaneous and bibliographic notes; First Part. Probabilistic Graph-Problems; Chapter 2. The Probabilistic Maximum Independent Set; 2.1. The modification strategies and a preliminary result; 2.1.1. Strategy M1; 2.1.2. Strategies M2 and M3; 2.1.3. Strategy M4; 2.1.4. Strategy M5; 2.1.5. A general mathematical formulation for the five functionals; 2.2. PROBABILISTIC MAX INDEPENDENT SET1; 2.2.1. Computing optimal a priori solutions; 2.2.2. Approximating optimal solutions"]
[{"$nulls":4},"2.2.3. Dealing with bipartite graphs2.3. PROBABILISTIC MAX "]
[{"$nulls":4},"INDEPENDENT SET2 and 3; 2.3.1. Expressions for E(G, S, M2) and E(G, S, M3); 2.3.2. An upper bound for the complexity of E(G, S, M2); 2.3.3. Bounds for E(G, S, M2); 2.3.4. Approximating optimal solutions; 2.3.4.1. Using argmax {ΣviESpi} as an a priori solution; 2.3.4.2. Using approximations of MAX INDEPENDENT SET; 2.3.5. Dealing with bipartite graphs; 2.4. PROBABILISTIC MAX INDEPENDENT SET4; 2.4.1. An expression for E(G, S, M4); 2.4.2. Using S* or argmax{ΣviESpi} as an a priori solution; 2.4.3. Dealing with bipartite graphs"]
[{"$nulls":4},"2.5. PROBABILISTIC MAX INDEPENDENT SET52.5.1. In general graphs; 2.5.2. In bipartite graphs; 2.6. Summary of the results; 2.7. Methodological questions; 2.7.1. Maximizing a criterion associated with gain; 2.7.1.1. The minimum gain criterion; 2.7.1.2. The maximum gain criterion; 2.7.2. Minimizing a criterion associated with regret; 2.7.2.1. The maximum regret criterion; 2.7.3. Optimizing expectation; 2.8. Proofs of the results; 2.8.1. Proof of Proposition 2.1; 2.8.2. Proof of Theorem 2.6; 2.8.3. Proof of Proposition 2.3; 2.8.4. Proof of Theorem 2.13"]
[{"$nulls":4},"Chapter 3. The Probabilistic Minimum Vertex Cover3.1. The strategies M1, M2 and M3 and a general preliminary result; 3.1.1. Specification of M1, M2 and M3; 3.1.1.1. Strategy M1; 3.1.1.2. Strategy M2; 3.1.1.3. Strategy M3; 3.1.2. A first expression for the functionals; 3.2. PROBABILISTIC MIN VERTEX COVER1; 3.3. PROBABILISTIC MIN VERTEX COVER2; 3.4. PROBABILISTIC MIN VERTEX COVER3; 3.4.1. Building E(G, C, M3); 3.4.2. Bounds for E(G, C, M3); 3.5. Some methodological questions; 3.6. Proofs of the results; 3.6.1. Proof of Theorem 3.3; 3.6.2. On the the bounds obtained in Theorem 3.3"]
[{"$nulls":4},"Chapter 4. The Probabilistic Longest Path"]
[null,null,null,"Sommario/riassunto","This title provides a comprehensive survey over the subject of probabilistic combinatorial optimization, discussing probabilistic versions of some of the most paradigmatic combinatorial problems on graphs, such as the maximum independent set, the minimum vertex covering, the longest path and the minimum coloring. Those who possess a sound knowledge of the subject mater will find the title of great interest, but those who have only some mathematical familiarity and knowledge about complexity and approximation theory will also find it an accessible and informative read."]