LEADER 05048nam 22007455 450 001 996466185403316 005 20200704001909.0 010 $a3-030-18174-X 024 7 $a10.1007/978-3-030-18174-1 035 $a(CKB)4100000008217374 035 $a(DE-He213)978-3-030-18174-1 035 $a(MiAaPQ)EBC5923815 035 $a(PPN)236521942 035 $a(EXLCZ)994100000008217374 100 $a20190412d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAlgorithms for Computational Biology$b[electronic resource] $e6th International Conference, AlCoB 2019, Berkeley, CA, USA, May 28?30, 2019, Proceedings /$fedited by Ian Holmes, Carlos Martín-Vide, Miguel A. Vega-Rodríguez 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XVI, 225 p. 66 illus., 35 illus. in color.) 225 1 $aLecture Notes in Bioinformatics ;$v11488 311 $a3-030-18173-1 320 $aIncludes bibliographical references and index. 327 $aInvited Talk -- New Divide-and-Conquer Techniques for Large-Scale Phylogenetic Estimation -- Biological Networks and Graph Algorithms -- New Polynomial-Time Algorithm around the Scaffolding Problem -- Enumerating Dominant Pathways in Biological Networks by Information Flow Analysis -- Comparing Different Graphlet Measures for Evaluating Network Model Fits to BioGRID PPI Networks -- Graph-Theoretic Partitioning of RNAs and Classification of Pseudoknots -- PathRacer: Racing Profile HMM Paths on Assembly Graph -- Genome Rearrangement, Assembly and Classification -- A Uniform Theory of Adequate Subgraphs for the Genome Median, Halving, and Aliquoting Problems -- Lightweight Metagenomic Classification via eBWT -- MULKSG: MULtiple K Simultaneous Graph Assembly -- Counting Sorting Scenarios and Intermediate Genomes for the Rank Distance -- Generalizations of the Genomic Rank Distance to Indels -- Sequence Analysis, Phylogenetics and Other Biological Processes -- Using INC within Divide-and-Conquer Phylogeny Estimation -- Predicting Methylation from Sequence and Gene Expression Using Deep Learning with Attention -- A Mathematical Model for Enhancer Activation Kinetics During Cell Differentiation -- Transcript Abundance Estimation and the Laminar Packing Problem -- Efficient Algorithms for Finding Edit-Distance Based Motifs. 330 $aThis book constitutes the proceedings of the 6th InternationalConference on Algorithms for Computational Biology, AlCoB 2019, held in Berkeley, CA, USA, in May 2019. The 15 full papers presented together with 1 invited paper were carefully reviewed and selected from 30 submissions. They are organized in the following topical sections: Biological networks and graph algorithms; genome rearrangement, assembly and classification; sequence analysis, phylogenetics and other biological processes. 410 0$aLecture Notes in Bioinformatics ;$v11488 606 $aBioinformatics 606 $aArithmetic and logic units, Computer 606 $aAlgorithms 606 $aArtificial intelligence 606 $aNumerical analysis 606 $aComputer science?Mathematics 606 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 606 $aArithmetic and Logic Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I12026 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aNumeric Computing$3https://scigraph.springernature.com/ontologies/product-market-codes/I1701X 606 $aDiscrete Mathematics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17028 615 0$aBioinformatics. 615 0$aArithmetic and logic units, Computer. 615 0$aAlgorithms. 615 0$aArtificial intelligence. 615 0$aNumerical analysis. 615 0$aComputer science?Mathematics. 615 14$aComputational Biology/Bioinformatics. 615 24$aArithmetic and Logic Structures. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aArtificial Intelligence. 615 24$aNumeric Computing. 615 24$aDiscrete Mathematics in Computer Science. 676 $a570.285 702 $aHolmes$b Ian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMartín-Vide$b Carlos$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aVega-Rodríguez$b Miguel A$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466185403316 996 $aAlgorithms for Computational Biology$92077899 997 $aUNISA LEADER 02899nam 2200421 450 001 996279749003316 005 20231205193018.0 010 $a0-7381-0445-0 035 $a(CKB)1000000000035808 035 $a(NjHacI)991000000000035808 035 $a(EXLCZ)991000000000035808 100 $a20231205d1998 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aANSI/IEEE Std 802.12, 1998 IEEE Standard for Information technology-- telecommunications and information exchange between systems-- local and metropolitan area networks-- specific requirements-- : part 12 $edemand-priority access method, physical layer and repeater specifications /$fInstitute of Electrical and Electronics Engineers 205 $aFirst edition. 210 1$aNew York, N.Y. :$cIEEE,$d1998. 215 $a1 online resource (xix, 429 pages) $cillustrations 311 $a0-7381-1487-1 320 $aIncludes bibliographical references. 330 $aThe media access control characteristics for the demand-priority access method are specified. 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Introduction; II. Empirical Methodology; III. Brazil; A. Background on Economic Policy Changes; B. Data; C. Results; D. Evidence from Durable Goods Ownership and Anthropometrics; E. Food Consumption in the 1970's; IV. Mexico; A. Background on Economic Policy Changes; B. Data; C. Results; D. Evidence from Durable Goods Ownership and Anthropometrics; V. Discussion and Conclusion; Text Tables; 1. Descriptive Statistics for Brazil; 2. Regression Results for Brazil, Full Sample; 3. Annual Bias Estimates for Brazil Across Different Methods and Samples 327 $a4. Household Per Capita Expenditure and Net Income in Brazil 5. Anthropometric Measures for Children 0-60 Months Old in Brazil; 6. Brazil: Ownership of Durable Goods, and Sensitivity to Income; 7. Descriptive Statistics for Mexico; 8. Regression Results for Mexico, Pooled Sample; 9. Household Per Capita Expenditure and Net Income in Mexico: Headline And Corrected (in 2002 Pesos); 10. Anthropometric Measures for Children 0-60 Months Old in Mexico; 11. Mexico: Ownership for Durable Goods, and Sensitivity to Income; Text Figures; 1. Brazil: GDP Per Capita and Average Growth in Decade 327 $a2. Mexico: GDP Per Capita and Average Growth in Decade 3. Changes in Relative Prices in Brazil; 4. Non-Parametric Estimates of Relationship between Food Shares and Household Expenditure in Brazil; 5A. Estimated Bias in Brazil in 1987/88-1995/96 as a Function of CPI-Measured Real Expenditure in 1995/96; 5B. Estimated Bias in Brazil 1995/96-2002/03 as a Function of CPI-Measured Real Expenditure in 2002/03; 6A. Distribution of Expenditure in Brazil Deflated by the CPI: 87/88, 95/96 and 02/03 327 $a6B. Distribution of Expenditure in Brazil Deflated by the Estimated True Cost of Living Index: 97/88, 95/96 and 02/03 7A. Changes in Durable Goods Holdings in Brazil and Sensitivity to Income; 7B. Changes in Durable Goods Holdings in Brazil and Sensitivity to Income; 8. Evolution of the Food Budget Share in Brazil since in 1974/75-2002/03; 9. Changes in Relative Prices in Mexico; 10. Non-Parametric Estimates of Relationship Between Food Budget Shares And Household Expenditure in Mexico 327 $a11A. Estimated Bias in Mexico in 1984-1998 as a Function of CPI-Measured Real Expenditure in 1998 and Distribution of the Latter 11B. Estimated Bias in Mexico in 1998-2006 as a Function of CPI-Measured Real Expenditure in 2006 and Distribution of the Latter; 12. Estimated Cumulative Bias in Mexico Since 1984 Across Different Methods and Samples; 13A. Distribution of CPI-Measured Real Expenditure in Mexico; 13B. Distribution of Expenditure in Mexico Deflated by the Estimated True Cost of Living Index; 14A. Changes in Durable Goods Holdings in Mexico and Sensitivity to Income 327 $a14B. Changes in Durable Goods Holdings in Mexico and Sensitivity to Income 330 3 $aEconomic policies are often judged by a handful of statistics, some of which may be biased during periods of change. We estimate the income growth implied by the evolution of food demand and durable good ownership in post-reform Brazil and Mexico, and find that changes in consumption patterns are inconsistent with official estimates of near stagnant incomes. That is attributed to biases in the price deflator. The estimated unmeasured income gains are higher for poorer households, implying marked reductions in "real" inequality. 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