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This volume argues that adults can learn English as a second language if their typical errors are corrected systematically and in line with their preferred style of learning. The remedy designed for this purpose relies on artificial intelligence. The book describes original research which demonstrates the success of this approach.
Computergestuurd onderwijs --- Taal en talen --- Tweede-taalverwerving --- Vreemdetalenonderwijs --- didactiek --- gebruik van technologie --- gebruik van computers --- foutenalalyse --- 800.515 --- 800.515 Computerondersteunend (taal)onderwijs. Computer Assisted Language Learning --- Computerondersteunend (taal)onderwijs. Computer Assisted Language Learning --- Computergestuurd onderwijs. --- gebruik van technologie. --- didactiek. --- gebruik van computers. --- foutenalalyse. --- Didactiek --- Gebruik van technologie. --- Didactiek. --- Gebruik van computers. --- Foutenalalyse. --- English language --- Language and languages --- Second language acquisition --- Second language learning --- Language acquisition --- Foreign languages --- Languages --- Anthropology --- Communication --- Ethnology --- Information theory --- Meaning (Psychology) --- Philology --- Linguistics --- Computer-assisted instruction for foreign speakers --- Computer-assisted instruction --- Study and teaching&delete& --- Error analysis --- Second language acquisition. --- Computer-assisted instruction. --- Study and teaching --- Error analysis. --- Computer-assisted instruction for foreign speakers. --- Error analysis in language teaching --- Errors --- Germanic languages --- CALL and SLA. --- Computer Assisted Language Learning. --- artificial intelligence and SLA. --- artificial intelligence and language learning. --- correcting errors and learners of English. --- error correction and CALL. --- error correction and SLA.
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Today, a single laboratory can generate a vast amount of biological data. There is a wealth of data already available in public databases, which makes the modern life sciences almost dependent on bioinformatics. This book brings together an international team of experts to discuss the state-of-the-art from several fields of bioinformatics, from the automatic identification and classification of viruses to the analysis of the transcriptome of single cells and plants, including artificial intelligence algorithms to discover biomarkers and text mining approaches to help in the interpretation of the findings. Machine learning, pattern discovery and analysis, error correction, Bayesian inference and novel computational techniques to discover chromosomal rearrangements continue to play crucial roles in biological discovery, and all of them are explored in chapters of this book. In sum, this book contains high-quality chapters that provide excellent views into key topics of current bioinformatics research, topics that should remain important for the next several years.
Bioinformatics. --- Text Mining Gene Selection; Biological Big Data; Single-Cell RNA Sequencing; Large-Scale Structural Rearrangements in Chromosomes; Machine Learning Approaches; Biomarker Discovery; Gene Expression Data; Bayesian Inference of Gene Expression; Error-Correction Methodologies; Genome Sequencing Data; Plant Transcriptome Assembly; Aligned Pattern Clustering System; Pattern Analysis; Hidden Markov Models; Viral Classification and Discovery; Pattern Discovery and Disentanglement; Aligned Pattern Cluster Analysis; Protein Binding Complexes Detection --- Text Mining Gene Selection; Biological Big Data; Single-Cell RNA Sequencing; Large-Scale Structural Rearrangements in Chromosomes; Machine Learning Approaches; Biomarker Discovery; Gene Expression Data; Bayesian Inference of Gene Expression; Error-Correction Methodologies; Genome Sequencing Data; Plant Transcriptome Assembly; Aligned Pattern Clustering System; Pattern Analysis; Hidden Markov Models; Viral Classification and Discovery; Pattern Discovery and Disentanglement; Aligned Pattern Cluster Analysis; Protein Binding Complexes Detection
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A puzzle in international macroeconomics is that observed real exchange rates are highly volatile. Standard international real business cycle (IRBC) models cannot reproduce this fact. We show that TFP processes for the U.S. and the "rest of the world," is characterized by a vector error correction (VECM) and that adding cointegrated technology shocks to the standard IRBC model helps explaining the observed high real exchange rate volatility. Also we show that the observed increase of the real exchange rate volatility with respect to output in the last 20 year can be explained by changes in the parameter of the VECM.
Finance --- Business & Economics --- International Finance --- Business cycles --- Foreign exchange rates --- Econometric models. --- Econometrics --- Foreign Exchange --- Macroeconomics --- Production and Operations Management --- Production --- Cost --- Capital and Total Factor Productivity --- Capacity --- Multiple or Simultaneous Equation Models --- Multiple Variables: General --- Macroeconomics: Consumption --- Saving --- Wealth --- Environment and Growth --- Currency --- Foreign exchange --- Econometrics & economic statistics --- Economic growth --- Real exchange rates --- Total factor productivity --- Vector error correction models --- Consumption --- Sustainable growth --- Industrial productivity --- Econometric models --- Economics --- Economic development --- United States
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This paper attempts to explain short- and long-term dynamics of-and forecast-inflation in Tajikistan using the Vector Error Correction Model (VECM) and Autoregressive Moving Average Model (ARMA). By analyzing different transmission channels through the VECM, we were able to evaluate their relative dominance, magnitude, and speed of transition to the equilibrium price level, with the view of identifying those policy tools that will enhance the effectiveness of monetary policy. We found that excess supply of broad money is inflationary in both the short and long term. The dynamic analysis also demonstrates that the exchange rate and international inflation have a strong impact on local prices. Available monetary instruments, such as the refinancing rate, have proven to be ineffective. Therefore, the Tajik monetary authority could greatly benefit from enhancing its monetary instruments toolkit, including by developing the interest rate channel, to improve its monetary policy execution and to achieve stable inflationary conditions.
Inflation (Finance) --- Monetary policy --- Monetary management --- Economic policy --- Currency boards --- Money supply --- Finance --- Natural rate of unemployment --- Econometrics --- Foreign Exchange --- Inflation --- Money and Monetary Policy --- Forecasting --- Price Level --- Deflation --- Monetary Policy, Central Banking, and the Supply of Money and Credit: General --- Forecasting and Other Model Applications --- Multiple or Simultaneous Equation Models --- Multiple Variables: General --- Macroeconomics --- Monetary economics --- Currency --- Foreign exchange --- Economic Forecasting --- Econometrics & economic statistics --- Monetary base --- Exchange rates --- Economic forecasting --- Vector error correction models --- Prices --- Econometric models --- Tajikistan, Republic of
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This paper examines the transmission of changes in the U.S. monetary policy to localcurrency sovereign bond yields of Brazil and Mexico. Using vector error-correction models, we find that the U.S. 10-year bond yield was a key driver of long-term yields in these countries, and that Brazilian yields were more sensitive to U.S. shocks than Mexican yields during 2010–13. Remarkably, the propagation of shocks from U.S. long-term yields was amplified by changes in the policy rate in Brazil, but not in Mexico. Our counterfactual analysis suggests that yields in both countries temporarily overshot the values predicted by the model in the aftermath of the Fed’s “tapering” announcement in May 2013. This study suggests that emerging markets will need to contend with potential spillovers from shifts in monetary policy expectations in the U.S., which often lead to higher government bond interest rates and bouts of volatility.
Monetary policy --- Banks and Banking --- Econometrics --- Finance: General --- Investments: Bonds --- Interest Rates: Determination, Term Structure, and Effects --- General Financial Markets: General (includes Measurement and Data) --- Multiple or Simultaneous Equation Models --- Multiple Variables: General --- Finance --- Investment & securities --- Banking --- Econometrics & economic statistics --- Yield curve --- Sovereign bonds --- Central bank policy rate --- Securities markets --- Vector error correction models --- Bond yields --- Financial institutions --- Financial services --- Financial markets --- Interest rates --- Bonds --- Capital market --- Econometric models --- United States
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There has been growing interest in the use of financial spreads as advance indicators of real activity and inflation. Empirical evidence is marshalled on a range of spreads when these are used in vector autoregressive models of the UK and German economies. It is found that they do have significant information, even after allowing for the effects of other influences upon macro-economic activity.
Banks and Banking --- Bonds --- Currency --- Deflation --- Econometric analysis --- Econometric models --- Econometrics & economic statistics --- Econometrics --- Finance --- Financial institutions --- Financial services --- Foreign Exchange --- Foreign exchange --- General Financial Markets: General (includes Measurement and Data) --- Inflation --- Interest rates --- Interest Rates: Determination, Term Structure, and Effects --- Investment & securities --- Investments: Bonds --- Macroeconomics --- Model Construction and Estimation --- Multiple or Simultaneous Equation Models --- Multiple Variables: General --- Price Level --- Prices --- Real exchange rates --- Sovereign bonds --- Vector error correction models --- Yield curve --- Germany
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Today, a single laboratory can generate a vast amount of biological data. There is a wealth of data already available in public databases, which makes the modern life sciences almost dependent on bioinformatics. This book brings together an international team of experts to discuss the state-of-the-art from several fields of bioinformatics, from the automatic identification and classification of viruses to the analysis of the transcriptome of single cells and plants, including artificial intelligence algorithms to discover biomarkers and text mining approaches to help in the interpretation of the findings. Machine learning, pattern discovery and analysis, error correction, Bayesian inference and novel computational techniques to discover chromosomal rearrangements continue to play crucial roles in biological discovery, and all of them are explored in chapters of this book. In sum, this book contains high-quality chapters that provide excellent views into key topics of current bioinformatics research, topics that should remain important for the next several years.
Bioinformatics. --- Text Mining Gene Selection; Biological Big Data; Single-Cell RNA Sequencing; Large-Scale Structural Rearrangements in Chromosomes; Machine Learning Approaches; Biomarker Discovery; Gene Expression Data; Bayesian Inference of Gene Expression; Error-Correction Methodologies; Genome Sequencing Data; Plant Transcriptome Assembly; Aligned Pattern Clustering System; Pattern Analysis; Hidden Markov Models; Viral Classification and Discovery; Pattern Discovery and Disentanglement; Aligned Pattern Cluster Analysis; Protein Binding Complexes Detection
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Today, a single laboratory can generate a vast amount of biological data. There is a wealth of data already available in public databases, which makes the modern life sciences almost dependent on bioinformatics. This book brings together an international team of experts to discuss the state-of-the-art from several fields of bioinformatics, from the automatic identification and classification of viruses to the analysis of the transcriptome of single cells and plants, including artificial intelligence algorithms to discover biomarkers and text mining approaches to help in the interpretation of the findings. Machine learning, pattern discovery and analysis, error correction, Bayesian inference and novel computational techniques to discover chromosomal rearrangements continue to play crucial roles in biological discovery, and all of them are explored in chapters of this book. In sum, this book contains high-quality chapters that provide excellent views into key topics of current bioinformatics research, topics that should remain important for the next several years.
Bioinformatics. --- Text Mining Gene Selection; Biological Big Data; Single-Cell RNA Sequencing; Large-Scale Structural Rearrangements in Chromosomes; Machine Learning Approaches; Biomarker Discovery; Gene Expression Data; Bayesian Inference of Gene Expression; Error-Correction Methodologies; Genome Sequencing Data; Plant Transcriptome Assembly; Aligned Pattern Clustering System; Pattern Analysis; Hidden Markov Models; Viral Classification and Discovery; Pattern Discovery and Disentanglement; Aligned Pattern Cluster Analysis; Protein Binding Complexes Detection
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This paper empirically examines the demand for commercial bank deposits in Lebanon, a regional financial center. With Lebanon's high fiscal deficits financed largely by domestic commercial banks that rely on deposit funding, deposit growth is a key variable to assess government financing conditions. At the macro level, we find that domestic factors such as economic activity, prices, and the interest differential between the Lebanese pound and the U.S. dollar are significant in explaining deposit demand, as are external factors such as advanced economy economic and financial conditions and variables proxying the availability of funds from the Gulf. Impulse response functions and variance decomposition analyses underscore the relative importance of the external variables. At the micro level, we find that in addition, bank-specific variables, such as the perceived riskiness of individual banks, their liquidity buffers, loan exposure, and interest margins, bear a significant influence on the demand for deposits.
Banks and banking -- Lebanon. --- Finance -- Lebanon. --- Lebanon -- Economic conditions. --- Money -- Lebanon. --- Banks and Banking --- Macroeconomics --- Econometrics --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- Diffusion Processes --- State Space Models --- Demand for Money --- Financial Markets and the Macroeconomy --- Monetary Policy --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data) --- Energy: Demand and Supply --- Prices --- Multiple or Simultaneous Equation Models --- Multiple Variables: General --- Banking --- Economic growth --- Econometrics & economic statistics --- Bank deposits --- Cyclical indicators --- Commercial banks --- Oil prices --- Financial services --- Financial institutions --- Vector error correction models --- Econometric analysis --- Banks and banking --- Business cycles --- Econometric models --- Lebanon
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The driving force of U.S. economic growth is expected to rotate from the fiscal stimulus and inventory rebuilding in 2009 to private demand in 2010, with consumption and particularly investment expected to be important contributors to growth. The strength of U.S. investment will hence be a crucial issue for the U.S. and global recovery. On the basis of several traditional models of investment, we forecast that the U.S. investment in equipment and software will grow by about 10 percent on average over the 2010-12 period. The contribution of investment to real GDP growth will be 0.8 percentage points on average over the same period.
Investment analysis --- Business forecasting --- Business --- Business forecasts --- Forecasting, Business --- Economic forecasting --- Forecasting --- Econometrics --- Investments: General --- Production and Operations Management --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- Diffusion Processes --- Employment --- Unemployment --- Wages --- Intergenerational Income Distribution --- Aggregate Human Capital --- Aggregate Labor Productivity --- Multiple or Simultaneous Equation Models --- Multiple Variables: General --- Investment --- Capital --- Intangible Capital --- Capacity --- Forecasting and Other Model Applications --- Econometrics & economic statistics --- Macroeconomics --- Economic Forecasting --- Vector autoregression --- Capital productivity --- Vector error correction models --- Private investment --- Econometric models --- Saving and investment --- United States