“…The program committee reviewed all papers and accepted 13 technical papers for presentation, which represents an acceptance ratio of roughly 25%. We thank all the actors of this Q2SWinet edition: firstly, authors for providing the content of the program; secondly, members of the program committee for their valuable efforts in reviewing papers and providing feedback for authors…”

“…The program committee accepted 7 papers based on their overall quality and novelty (acceptance ratio: 35%). We hope these proceedings will serve as a valuable reference for researchers and practitioners in the field of public-key cryptography and its applications…”

“…The book wraps up with Dan discussing some of the lesser-known composition tools at your disposal (golden ratio, fibonacci spiral, golden triangle) to help you create your best composition when you're both shooting and editing your images…”

“…In the coming decades, people aged 65 and over will make up an increasingly large percentage of the population: The ratio of people aged 65+ to people aged 20-64 will rise by 80%. …”

“…He has published papers in several journals such as The Review of Metaphysics, Ratio, The Thomist, International Philosophical Quarterly, The Modern Schoolman, The New Scholasticism and Grazer Philosophische Studien. …”

Tabla de Contenidos: “…-- PREFACE xv -- PART I INTRODUCTION TO COGNITIVE RADIOS 1 -- 1 Introduction 3 -- 1.1 Introduction, 3 -- 1.2 Signal Processing and Cognitive Radios, 4 -- 1.3 Software-Defined Radios, 6 -- 1.3.1 Software-Defined Radio Platforms, 14 -- 1.3.2 Software-Defined Radio Systems, 15 -- 1.4 From Software-Defined Radios to Cognitive Radios, 19 -- 1.4.1 The Spectrum Scarcity Problem, 19 -- 1.4.2 Emergence of CRs, 21 -- 1.5 What this Book is About, 22 -- 1.6 Summary, 26 -- 2 The Cognitive Radio 27 -- 2.1 Introduction, 27 -- 2.2 A Functional Model of a Cognitive Radio, 30 -- 2.2.1 Spectrum Knowledge Acquisition (Spectrum Awareness), 30 -- 2.2.2 Communications Decision-Making, 33 -- 2.2.3 Learning in Cognitive Radios, 33 -- 2.3 The Cognitive Radio Architecture, 35 -- 2.3.1 Spectrum Sensing Region of a Cognitive Engine, 36 -- 2.3.2 Radio Reconfiguration Region of a Cognitive Engine, 36 -- 2.3.3 Learning Region of a Cognitive Engine, 37 -- 2.3.4 Memory Region of a Cognitive Engine, 37 -- 2.4 The Ideal Cognitive Radio, 38 -- 2.5 Signal Processing Challenges in Cognitive Radios, 39 -- 2.6 Summary, 40 -- 3 Cognitive Radios and Dynamic Spectrum Sharing 42 -- 3.1 Introduction, 42 -- 3.2 Interference and Spectrum Opportunities, 46 -- 3.3 Dynamic Spectrum Access, 50 -- 3.4 Dynamic Spectrum Leasing, 54 -- 3.5 Challenges in DSS Cognitive Radios, 55 -- 3.6 Cognitive Radios and Future of Wireless Communications, 60 -- 3.7 Summary, 61 -- PART II THEORETICAL FOUNDATIONS 65 -- 4 Introduction to Detection Theory 67 -- 4.1 Introduction, 67 -- 4.2 Optimality Criteria: Bayesian versus Non-Bayesian, 71 -- 4.2.1 The Bayesian Approach, 72 -- 4.2.2 A Non-Bayesian Approach: Neyman / Pearson Optimality Criterion, 73 -- 4.3 Parametric Signal Detection Theory, 75 -- 4.3.1 Bayesian Optimal Detection, 76 -- 4.3.2 Neyman / Pearson Optimal Detection, 82 -- 4.3.3 Another Non-Bayesian Alternative: The Generalized Likelihood Ratio Test, 99 -- 4.3.4 Parametric Signal Detection in Additive Noise, 103 -- 4.4 Nonparametric Signal Detection Theory, 122.…”

Tabla de Contenidos: “…About the Author xiii Preface xv 1 Forecasting a Monthly Time Series 1 1.1 Introduction 1 1.2 Forecasting Using LV(p) Models 1 1.2.1 Basic or Regular LV(p) Models 1 1.2.2 Special LV(p) Models 6 1.3 Forecasting Using the LVARMA(p,q,r) Model 8 1.3.1 Special Notes on the ARMA Model 9 1.3.2 Application of Special LVAR Models 10 1.4 Forecasting Using TGARCH(a,b,c) Models 12 1.4.1 Application of ARCH(a), GARCH(b), and TARCH(c) Models 14 1.4.2 Application of TGARCH(a,b,0) Models 14 1.4.3 Application of TGARCH(a,b,c) Models 20 1.4.4 Other Alternative Models 20 1.5 Instrumental Variables Models 20 1.5.1 Application of the GMM Estimation Method 21 1.5.2 Application of the TSLS Estimation Method 36 1.6 Special Notes and Comments on Residual Analysis 42 1.6.1 Specific Residual Analysis 43 1.6.2 Additional Special Notes and Comments 61 1.6.3 Serial Correlation Tests 65 1.7 Statistical Results Using Alternative Options 67 1.7.1 Application of an Alternative Coefficient Covariance Matrix 67 1.7.2 Application of Selected Combinations of Options 70 1.7.3 Final Notes and Conclusions 71 2 Forecasting with Time Predictors 73 2.1 Introduction 73 2.2 Application of LV(p) Models of HS on MONTH by YEAR 73 2.2.1 Special LV(12) Models of HS on MONTH by YEAR 73 2.2.2 Application of the Omitted Variables Test - Likelihood Ratio 75 2.2.3 Heterogeneous Model of HS on HS(−12) and Month by YEAR 79 2.3 Forecast Models of HS on MONTH by YEAR 79 2.3.1 Application of LV(1) Models of HS on MONTH by YEAR 79 2.3.2 Application of Basic LV(p) Models of HS on MONTH by YEAR 82 2.3.3 Application of AR(q) Models of HS on MONTH by YEAR 86 2.3.4 Application of ARMA(q,r) Models of HS on MONTH by YEAR 89 2.3.5 Application of LVAR(p,q) Models of HS on MONTH by YEAR 89 2.3.6 Application of LVAR(p,q) Models of HS on YEAR by MONTH 92 2.4 Heterogeneous Classical Growth Models 95 2.4.1 Forecasting Based on LV(p) Het_CGMs of HS 95 2.4.2 Forecasting Based on AR(q) Het_CGMs 99 2.4.3 Forecasting Based on LVAR(p,q) Het_CGMs 101 2.5 Forecast Models of G in Currency.wf1 103 2.5.1 LVAR(p,q) Additive Models of G by @Month with @Trend 104 2.5.2 LV(1) Heterogeneous Models of G by @Month 111 2.6 Forecast Models of G on G(−1) and Polynomial Time Variables 116 2.6.1 Heterogeneous Model of G on G(−1) and Polynomial T by @Month 116 2.6.2 Forecast Model of G on G(−1) with Heterogeneous Polynomial Trend 138 2.7 Forecast Models of CURR in Currency.wf1 140 2.7.1 Developing Scatter Graphs with Regressions 141 2.7.2 Additive Forecast Models of CURR with a Time Predictor 143 2.7.3 Interaction Forecast Models of CURR 159 2.7.4 Forecast Models Based on Subsamples 169 3 Continuous Forecast Models 185 3.1 Introduction 185 3.2 Forecasting of FSPCOM 185 3.2.1 Simple Continuous Models of FSPCOM 185 3.2.2 LVAR(P,Q) Models of Y = FSPCOM with Polynomial Trend 190 3.2.3 Translog Models with Time Predictor 195 3.3 Forecasting Based on Subsamples 207 3.3.1 Lag Variable Models With Lower and Upper Bounds 209 3.4 Special LV(12) Models of HS with Upper and Lower Bounds 222 3.4.1 Special LVARMA(12,q,r) Model of LNYul Without Time Predictor 223 3.4.2 Special LVARMA(12,q,r) of LNYul With Time Predictor 223 4 Forecasting Based on (Xt,Yt) 229 4.1 Introduction 229 4.2 Forecast Models Based on (Xt,Yt) 229 4.3 Data Analysis Based on a Monthly Time Series 230 4.4 Forecast Models without a Time Predictor 230 4.4.1 Two-Way Interaction Models 230 4.4.2 Cobb-Douglass Model and Alternatives 235 4.5 Translog Quadratic Model 236 4.5.1 Forecasting Using a Subsample 240 4.5.2 Forecast Model with Trend 243 4.6 Forecasting of FSXDP 247 4.6.1 Forecasting of Y2 Based on a Subsample 247 4.6.2 Extension of the Model (4.25) with Time Variables 252 4.7 Translog Linear Models 256 4.7.1 Basic Translog Linear Model 256 4.7.2 Tanslog Linear Model with Trend 256 4.7.3 Heterogeneous Tanslog Linear Model 260 4.8 Application of VAR Models 262 4.8.1 Unstructured VAR Models Based on (X1t,Y1t) 262 4.8.2 The Simplest VAR Models with Alternative Trends 264 4.8.3 Complete Heterogeneous VAR Models by @Month 270 4.8.4 Bayesian VAR Models 271 4.8.5 VEC Models 271 4.9 Forecast Models Based on (Y1t,Y2t) 275 4.9.1 Forecast Models Based on Figures 4.42a and b 275 4.9.2 Reciprocal Causal Effects Models 279 4.9.3 Models with the Time Independent Variables 280 4.10 Special Notes and Comments 287 5 Forecasting Based On (X1t,X2t,Yt) 289 5.1 Introduction 289 5.2 Translog Linear Models Based on (X1,X2,Y1) 289 5.2.1 Basic Translog Linear Model 289 5.2.2 Tanslog Linear Model with Trend 292 5.2.3 Tanslog Linear Model with Heterogeneous Trends 292 5.3 Translog Linear Models Based on (X1,X2,Y2) 293 5.3.1 Translog Linear Models Using the Subsample {@Year>1990} 296 5.3.2 Translog Linear Models Using the Subsample {@Year>1975} 298 5.3.3 Translog Linear Models Using the Whole Sample 298 5.4 Forecast Models Using Original (X1,X2,Y) 300 5.4.1 Model Based on Figure 5.6a 300 5.4.2 Model Based on Figure 5.6b 301 5.4.3 Model Based on Figure 5.6c 307 5.5 Alternative Forecast Models Using Original (X1,X2,Y) 310 5.5.1 Three-Way Interaction Based on Figure 5.14a 311 5.5.2 Three-Way Interaction Based on Figure 5.14b and c 311 5.6 Forecasting Models with Trends Using Original (X1,X2,Y) 311 5.7 Application of VAR Models Based on (X1t,X2t,Y1t) 316 5.7.1 Unrestricted VAR Models 316 5.7.2 The Simplest Two-Way Interaction VAR Model 317 5.7.3 The Simplest Three-Way Interaction VAR Model 318 5.8 Applications of the Object "System" 320 5.8.1 The MLV(1,1,1) Models of (Y1,Y2,Y3) on (Y1(−1),Y2(−1),Y3(−1)) 320 5.8.2 Circular Effects MLV(1,1,1) Models 328 5.9 Models Presenting Causal Relationships Y1,Y2, and Y3 335 5.9.1 Triangular Effects Models 335 5.9.2 Circular Effects Models 340 5.9.3 Reciprocal Effects Models 341 5.10 Extended Models 344 5.10.1 Extension to the Models with Additional Exogenous Variables 344 5.10.2 Extension to the Models with Alternative Trends 347 5.10.3 Extension to LVARMA(p,q,r) 352 5.10.4 Extension to Heterogeneous Regressions by Months 356 5.11 Special Notes and Comments 369 6 Forecasting Quarterly Time Series 371 6.1 Introduction 371 6.2 Alternative LVARMA(p,q,r) Of a Single Time Series 371 6.2.1 LV(P) Forecast Model of GCDANt 371 6.2.2 LVARMA(p,q.r) Forecast Models of GCDN 372 6.2.3 Forecast Models of GCDAN with Time Variables 374 6.2.4 Special Notes on Uncommon Models 381 6.3 Complete Heterogeneous LV(2) Models of GCDAN By @Quarter 383 6.3.1 Using the Simplest Equation Specification 383 6.3.2 Using a Complete Equation Specification 387 6.4 LV(2) Models of GCDAN with Exogenous Variables 387 6.4.1 LV(2) Models with an Exogenous Variable 387 6.4.2 LV(2) Models with Two Exogenous Variables 390 6.5 Alternative Forecast Models Based on (Y1,Y2) 393 6.5.1 LV(2) Basic Interaction Models 393 6.5.2 LV(2) Models of (Y1,Y2) with an Exogenous Variable and @Trend 394 6.5.3 LV(2) Models of (Y1,Y2) with two Exogenous Variables and Trend 400 6.5.4 LV(2) Models of (Y1,Y2) with Three Exogenous Variables and Trend 409 6.6 Triangular Effects Models Based on (X1,X2,Y1) 413 6.6.1 Partial Two-Way Interaction LV(p) TE_Models 413 6.6.2 A Complete Two-Way Interaction LV(p) TE_Models 414 6.6.3 Three-Way Interaction LV(p) TE_ Models 415 6.7 Bivariate Triangular Effects Models Based on (X1,X2,Y1,Y2) 417 6.7.1 Partial Two-Way Interaction Models 417 6.7.2 Three-Way Interaction TE_Models 418 6.8 Models with Exogenous Variables and Alternative Trends 422 6.8.1 Models Based on (X1,X2,Y1) 422 6.8.2 Models Based on (X1,X2,Y1,Y2) with Trend 424 6.9 Special LV(4) Models with Exogenous Variables 427 6.10 Models with Exogenous Variables by @Quarter 433 6.10.1 Alternative Models Based on the Whole Sample 433 6.10.2 Forecasting Based on each Quarter's Level 435 7 Forecasting Based on Time Series by States 447 7.1 Introduction 447 7.2 Models Based on a Bivariate (Y1_1,Y1_2) 447 7.2.1 Alternative LV(p) Models Based on Figure 7.1a 448 7.2.2 Alternative LV(p) Models Based on Figure 7.1b 451 7.2.3 Alternative LV(p) Models Based on Figure 7.1c 454 7.3 Advanced LP(p) Models of (Y1_1,Y1_2) 455 7.3.1 Two-Way Interaction LV(p) Models 455 7.3.2 Three-Way Interaction LV(p) Models 456 7.3.3 Alternative Additive Models 456 7.4 Advanced LP(p) Models of (Y1_1,Y1_2,Y1_3) 457 7.4.1 Triangular Effects Model of (Y1_1,Y1_2,Y1_3) 457 7.4.2 Full-Lag Variables Triangular Effects Model 462 7.4.3 Translog-Linear Triangular Effects Model 466 7.5 Full-Lag Variables Circular Effects Model 466 7.5.1 Two-Way Interaction…”

“…L’arte retorica, o «della persuasione», occupa la scena nell’ultimo capitolo, dedicato a una delle manifestazioni qualificanti dell’oratoria forense: l’arringa. Gli esempi proposti, tratti da raccolte scritte, vengono ad aggiungersi al ricco corpus di testi preso in esame nei capitoli precedenti --…”

“…El cuerpo principal del estudio está compuesto por el análisis de las distintas orationes de Cicerón, ordenadas cronológicamente para que pueda apreciarse la evolución del estilo retórico del orador y del tratamiento procesal que sufre el crimen repetundarum en función de ley aplicable en cada momento histórico. -- En este orden de cosas, se ha intentado poner el foco de atención en la determinación de los ilícitos que formaban o podían formar parte de la accusatio formal en un juicio de repetundis, para diferenciarlos de los meros reproches jurídicos o éticos, que componían las denominadas acusaciones de contorno y que tenían como objetivo influenciar en el jurado a la hora de emitir su voto de absolución o condena --…”

“…Aprovechando la coyuntura, el colegio de enfermería y las facultades recuperan una vieja propuesta de cambio de sistema y de aumento de ratios. La Unión Europea recomienda ocho enfermeras por cada 1.000 habitantes. …”
Acceso desde SUMMA. Acceso restringido UPSA

“…Fruits, vegetables, grains, and derived products can be excellent sources of minerals, vitamins, and fiber and usually have a favorable nutrient-to-energy ratio. Furthermore, plant foods are also a rich source of phytochemicals such as polyphenols, carotenoids, and betalains, with potential health benefits for humans. …”

“…Conservative regulation of the banking sector helped this segment avoid a financial meltdown, and low loan-to-value ratios in mortgage lending are undoubtedly helping limit the pace of house-price increases. …”

“…The differential is also likely to rise in the future because the number of countries which have debt-to-GDP ratios above a threshold at which there appears to be an effect on sovereign risk premia has risen sharply. …”

“…House prices may become an exception with higher levels mortgage lending and with high owner occupancy ratios. While the credit channel could be a powerful channel of monetary transmission - as new funds raised on capital markets are close to zero in CEE - it is actually not, as both commercial banks and non-financial corporations can escape domestic monetary conditions by borrowing from their foreign mother companies. …”

“…Doubling the sub-central tax or spending share (e.g. moving from a decentralisation ratio of 15 to 30%) is associated with an increase of GDP per capita by 3% on average. …”

“…The paper provides a (preliminary) analysis of the tax-to-GDP ratio and the tax mix in China as well as the average and marginal tax wedge on labour income, by applying the OECD’s Revenue Statistics and Taxing Wages methodology. …”

“…The plan should target a primary budget surplus large enough to stabilise the public debt ratio by 2020. The fiscal policy framework should be improved to help reinforce confidence in Japan's fiscal position and prevent a run-up in interest rates. …”

“…Comportement de l'épargne et de l'investissement et mesures s'y rapportant, notamment dans le secteur du logement -Principales caractéristiques du ratio épargne-investissement dans l'ensemble de l'économie et dans les différents secteurs -Politiques d'épargne et d'investissement -Les inefficiences de la politique économique: quelques exemples IV. …”

“…For each field test site (at least two independent) the processing factor (Pf) is calculated as the ratio between the residue level in the processed commodity and in the RAC or the commodity to be processed. …”

“…While most of the inequality originates from the labour market, wealth – and thus capital income – is also highly concentrated and the tax and transfer system has little redistributive impact. The tax-to-GDP ratio remains low. Consumption taxes, which tend to be regressive, account for the bulk. …”