Search Results - (((((((ant OR alter) OR find) OR kantor) OR cantor) OR anne) OR salted) OR wanting) algorithms.

Layout techniques for integrated circuit designers by Sahrling, Mikael, 1964-

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Kaplan's clinical hypertension by Kaplan, Norman M., 1931-2020, Victor, Ronald G., Flynn, Joseph T.

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Cognitive radar : the knowledge-aided fully adaptive approach by Guerci, J. R.

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Resource allocation problems in supply chains by Ganesh, K., Malairajan, R. A., Mohapatra, Sanjay, Punniymoorthy, M.

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Code : Collaborative Ownership and the Digital Economy. by Ghosh, Rishab Aiyer

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Structural-functional studies in English grammar : in honor of Lachlan Mackenzie

Table of Contents: “…Theoretical relevance of findings -- References -- 'It was you that told me that, wasn't it?' …”
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Clinical simulation : operations, engineering and management

Table of Contents: “…; 3.5 The Systems Approach to Training; 3.6 Defining the Performance Requirement; 3.7 Cost Versus Value Added; 3.8 Operations Cost; 3.9 Standardization: What is it, and who Wants it?; 3.10 Patients as Training Conditions; 3.11 Equipment as Training Conditions; 3.12 Increase in Training System Cost; 3.13 You as the Leader-Manager; 3.14 Conclusion; Endnotes; Topic II What's In It For Me.…”
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Digitalization of Society and Socio-Political Issues. 1, Digital, Communication, and Culture

Table of Contents: “…The Digitalization of Cultural Policies in France 149; Anne BELLON 14.1.…”
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Computational methods for physics by Franklin, Joel, 1975-

Table of Contents: “…2.5 Stability of numerical methods2.6 Multi-step methods; Further reading; Problems; Lab problems; 3 Root-finding; 3.1 Physical motivation; 3.2 Finding roots; Further reading; Problems; Lab problems; 4 Partial differential equations; 4.1 Physical motivation; 4.2 Finite difference in one dimension; 4.3 Finite difference in two dimensions; 4.4 Examples; Further reading; Problems; Lab problems; 5 Time-dependent problems; 5.1 Physical motivation; 5.2 Exactly solvable cases; 5.3 Discretization and methods; 5.4 Crank-Nicolson for the Schrodinger equation; Further reading; Problems; Lab problems.…”
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On Different Concepts for the Linearization of Matrix Polynomials and Canonical Decompositions of Structured Matrices with Respect to Indefinite Sesquilinear Forms by Saltenberger, Philip

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Complex Analysis and Dynamical Systems VII. by Agranovsky, Mark L.

Table of Contents: “…The Schwarz function method of finding exact solutions for the Muskat problem…”
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Blockchain Developer's Guide : Develop Smart Applications with Blockchain Technologies - Ethereum, JavaScript, Hyperledger Fabric, and Corda. by Hill, Brenn

Table of Contents: “…Asymmetric (public-key) cryptographySignatures; Hashing; The avalanche effect; Collisions; Hashing a block; Hashing outside PoW; Summary; Chapter 5: Bitcoin; The history of Bitcoin; Why Bitcoin is volatile; Keys and addresses; Currency units; Vanity addresses; Base58 check encoding; Transactions; Types; Pay-to-Public-Key Hash; Pay-to-Script Hash; Blocks; Genesis block; Bitcoin network; Types of nodes; Full node; Lightweight nodes; Other nodes; Network discovery; Finding peers; DNS seeds; Static IPs; Wallets; Types; Deterministic wallet; Deterministic wallets; HD wallets…”
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Decision Intelligence for Dummies by Baker, Pamela

Table of Contents: “…Preventing Wrong Influences from Affecting Decisions -- Bad influences in AI and analytics -- The blame game -- Ugly politics and happy influencers -- Risk Factors in Decision Intelligence -- DI and Hyperautomation -- Part 5 The Part of Tens -- Chapter 17 Ten Steps to Setting Up a Smart Decision -- Check Your Data Source -- Track Your Data Lineage -- Know Your Tools -- Use Automated Visualizations -- Impact = Decision -- Do Reality Checks -- Limit Your Assumptions -- Think Like a Science Teacher -- Solve for Missing Data -- Partial versus incomplete data -- Clues and missing answers -- Take Two Perspectives and Call Me in the Morning -- Chapter 18 Bias In, Bias Out (and Other Pitfalls) -- A Pitfalls Overview -- Relying on Racist Algorithms -- Following a Flawed Model for Repeat Offenders -- Using A Sexist Hiring Algorithm -- Redlining Loans -- Leaning on Irrelevant Information -- Falling Victim to Framing Foibles -- Being Overconfident -- Lulled by Percentages -- Dismissing with Prejudice -- Index -- EULA.…”
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The Oil Spill Recovery Institute : past, present, and future directions

Table of Contents: “…STATUS OF NOWCAST/FORECAST MODEL COMPONENTS -- Hydrodynamics Model -- Atmospheric Model -- Oil Spill Trajectory and Fates Model -- Biological Effects Modeling -- SCIENTIFIC SIGNIFICANCE AND IMPORTANCE -- FUTURE DIRECTIONS -- Hydrodynamics and Transport in Oil Spill Model -- Chemistry and Oil-Weathering Algorithms -- Spreading -- Evaporation from (Thicker) Emulsified Oil -- Emulsification -- Dispersed Oil Droplet-SPM Interactions -- Biological Effects -- SUMMARY -- 8 Education and Outreach Activities -- FUTURE DIRECTIONS -- 9 Findings and Recommendations -- STRATEGIC PLANNING -- PROGRAM BALANCE AND RESPONSIVENESS TO MISSION -- MODELING AND REAL-TIME OIL SPILL RESPONSE -- GEOGRAPHIC FOCUS -- PROGRAM OVERSIGHT -- FAIRNESS ISSUES -- ACTIONS NEEDED -- ORGANIZATION AND ADMINISTRATION -- GRANT AWARD POLICIES AND PROCEDURES -- PROGRAM PLANNING -- PREDICTIVE ECOLOGY -- APPLIED TECHNOLOGY -- PROGRAM BALANCE -- MODELING -- EDUCATION AND OUTREACH -- References -- Appendixes -- A Committee Biosketches -- B OSRI Bylaws -- Table of Contents -- Article I. …”
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Cytopathology of the head and neck : ultrasound guided FNAC by Kocjan, Gabrijela

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Open science by design : realizing a vision for 21st century research

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Qualitative Comparative Analysis : An Introduction To Research Design And Application. by Mello, Patrick A.

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Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

Table of Contents: “…DISCOVERING KNOWLEDGE IN DATA -- Contents -- Preface -- 1 An Introduction to Data Mining -- 1.1 What is Data Mining? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Practice for Data Mining -- 1.4.1 Crisp-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish? …”
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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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Database technology for life sciences and medicine

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