LEADER 04090nam 2200745Ia 450 001 9910956624003321 005 20200520144314.0 010 $a9786611223373 010 $a9781281223371 010 $a1281223379 010 $a9780226241760 010 $a0226241769 024 7 $a10.7208/9780226241760 035 $a(CKB)1000000000401918 035 $a(EBL)408506 035 $a(OCoLC)212414658 035 $a(SSID)ssj0000131216 035 $a(PQKBManifestationID)11148797 035 $a(PQKBTitleCode)TC0000131216 035 $a(PQKBWorkID)10008739 035 $a(PQKB)11307970 035 $a(DE-B1597)535853 035 $a(OCoLC)824143745 035 $a(DE-B1597)9780226241760 035 $a(Au-PeEL)EBL408506 035 $a(CaPaEBR)ebr10216977 035 $a(CaONFJC)MIL122337 035 $a(MiAaPQ)EBC408506 035 $a(Perlego)1852893 035 $a(EXLCZ)991000000000401918 100 $a19981029d1999 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 04$aThe costs and benefits of price stability /$fedited by Martin Feldstein 205 $a1st ed. 210 $aChicago $cUniversity of Chicago Press$d1999 215 $a1 online resource (374 p.) 225 1 $aA National Bureau of Economic Research conference report 300 $aPapers presented at an NBER conference held at the Federal Reserve Bank of New York on Feb. 20-21, 1997. 311 08$a9780226240992 311 08$a0226240991 320 $aIncludes bibliographical references and indexes. 327 $tFront matter --$tNational Bureau of Economic Research --$tContents --$tPreface --$tIntroduction --$t1. Capital Income Taxes and the Benefit of Price Stability --$t2. Price Stability versus Low Inflation in Germany: An Analysis of Costs and Benefits --$t3. A Cost-Benefit Analysis of Going from Low Inflation to Price Stability in Spain --$t4. Some Costs and Benefits of Price Stability in the United Kingdom --$t5. Inflation and the User Cost of Capital: Does Inflation Still Matter? --$t6. Excess Capital Flows and the Burden of Inflation in Open Economies --$t7. Identifying Inflation's Grease and Sand Effects in the Labor Market --$t8. Does Inflation Harm Economic Growth? Evidence from the OECD --$tContributors --$tAuthor Index --$tSubject Index 330 $aIn recent years, the Federal Reserve and central banks worldwide have enjoyed remarkable success in their battle against inflation. The challenge now confronting the Fed and its counterparts is how to proceed in this newly benign economic environment: Should monetary policy seek to maintain a rate of low-level inflation or eliminate inflation altogether in an effort to attain full price stability? In a seminal article published in 1997, Martin Feldstein developed a framework for calculating the gains in economic welfare that might result from a move from a low level of inflation to full price stability. The present volume extends that analysis, focusing on the likely costs and benefits of achieving price stability not only in the United States, but in Germany, Spain, and the United Kingdom as well. The results show that even small changes in already low inflation rates can have a substantial impact on the economic performance of different countries, and that variations in national tax rules can affect the level of gain from disinflation. 410 0$aConference report (National Bureau of Economic Research) 606 $aInflation (Finance)$vCongresses 606 $aMonetary policy$vCongresses 606 $aPrice regulation$vCongresses 606 $aPrices$xGovernment policy$vCongresses 615 0$aInflation (Finance) 615 0$aMonetary policy 615 0$aPrice regulation 615 0$aPrices$xGovernment policy 676 $a338.5/26 701 $aFeldstein$b Martin S$088785 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910956624003321 996 $aCosts and benefits of price stability$9506870 997 $aUNINA LEADER 12991nam 22009135 450 001 9910483404003321 005 20251225205441.0 010 $a3-319-70772-8 024 7 $a10.1007/978-3-319-70772-3 035 $a(CKB)4340000000223557 035 $a(DE-He213)978-3-319-70772-3 035 $a(MiAaPQ)EBC6296475 035 $a(MiAaPQ)EBC5596001 035 $a(Au-PeEL)EBL5596001 035 $a(OCoLC)1011035894 035 $a(PPN)221251677 035 $a(EXLCZ)994340000000223557 100 $a20171103d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBrain Informatics $eInternational Conference, BI 2017, Beijing, China, November 16-18, 2017, Proceedings /$fedited by Yi Zeng, Yong He, Jeanette Hellgren Kotaleski, Maryann Martone, Bo Xu, Hanchuan Peng, Qingming Luo 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XVI, 336 p. 100 illus.) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v10654 300 $aIncludes index. 311 08$a3-319-70771-X 327 $aIntro -- Preface -- Organization -- Contents -- Cognitive and Computational Foundations of Brain Science -- Speech Emotion Recognition Using Local and Global Features -- 1 Introduction -- 2 Materials and Methods -- 2.1 Database -- 2.2 Features for Speech Emotion Recognition -- 3 Results/Discussion -- 3.1 Classification Results for EMODB -- 3.2 Classification Results for RAVDESS -- 3.3 SFFS -- 4 Conclusions -- References -- Advertisement and Expectation in Lifestyle Changes: A Computational Model -- 1 Introduction -- 2 Temporal-Causal Modeling -- 3 The Computational Model -- 3.1 Graphical Representation of the Model -- 3.2 Numerical Representations and Parameters -- 4 Simulation Experiments -- 4.1 Hypotheses -- 4.2 Scenarios and Results -- 4.3 Explanation -- 5 Conclusion -- References -- A Computational Cognitive Model of Self-monitoring and Decision Making for Desire Regulation -- Abstract -- 1 Introduction -- 2 Background -- 3 Conceptual Representation of the Model -- 3.1 Desire Generation and Choosing Actions -- 3.2 Self-monitoring and Regulation Strategies -- 3.3 Numerical Representation of the Model -- 4 Simulation Results -- 5 Conclusion -- References -- Video Category Classification Using Wireless EEG -- Abstract -- 1 Introduction -- 2 Experimental Setup and Data Acquisition Techniques -- 2.1 Demographics of Subjects -- 2.2 EEG Recordings -- 2.3 Experimental Setup -- 3 Experimental Study and Findings -- 3.1 Algorithms and Methods -- 3.2 Experimental Results -- 4 Discussion -- 5 Conclusion -- References -- Learning Music Emotions via Quantum Convolutional Neural Network -- 1 Introduction -- 2 Related Work on Quantum Information -- 3 Quantum Convolutional Neural Network for Music Emotion Analysis -- 3.1 Rationale -- 3.2 Quantum Convolutional Neural Network -- 4 Experiments -- 5 Conclusions -- References. 327 $aSupervised EEG Source Imaging with Graph Regularization in Transformed Domain -- 1 Introduction -- 2 Inverse Problem -- 3 Graph Regularized EEG Source Imaging in Transformed Domain -- 3.1 EEG Source Imaging in Transformed Domain -- 3.2 Discriminative Source Reconstruction with Graph Regularization -- 4 Optimization with ADMM Algorithm -- 5 Numerical Experiment -- 6 Conclusion -- References -- Insula Functional Parcellation from FMRI Data via Improved Artificial Bee-Colony Clustering -- 1 Introduction -- 2 Related Content -- 2.1 Insula Functional Parcellation Based on FMRI Data -- 2.2 Artificial Bee Colony (ABC) Algorithm -- 3 DABCC Algorithm -- 3.1 Food Source Representation -- 3.2 Initialization -- 3.3 Self-adaptive Multidimensional Search Mechanism Based on Difference Bias for Employed Bee Search -- 3.4 Algorithm Description -- 4 Experimental Results and Analysis -- 4.1 Data Description and Preprocessing -- 4.2 Evaluation Metrics -- 4.3 Search Capability -- 4.4 Parcellation Results -- 4.5 Functional Consistency -- 5 Conclusion -- References -- EEG-Based Emotion Recognition via Fast and Robust Feature Smoothing -- 1 Introduction -- 2 Related Work -- 3 Moving Average Smoothing on Statistical Feature Set -- 3.1 Feature Extraction -- 3.2 Moving Average Smoothing on Extracted Features -- 3.3 Classification Algorithm -- 4 Emotion Recognition on DEAP Dataset -- 4.1 Experimental Setup -- 4.2 Results and Discussions -- 5 Conclusion -- References -- Human Information Processing Systems -- Stronger Activation in Widely Distributed Regions May not Compensate for an Ineffectively Connected Neural Network When Reading a Second Language -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Participants -- 2.2 Materials -- 2.3 Experimental Procedure -- 2.4 Data Acquisition -- 2.5 Data Processing -- 3 Results -- 4 Discussion. 327 $a4.1 Assimilated and Accommodated Neural Network for L2 -- 4.2 Stronger Activation but an Ineffectively Connected Neural Network -- Acknowledgments -- References -- Objects Categorization on fMRI Data: Evidences for Feature-Map Representation of Objects in Human Brain -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Subjects and fMRI Data Acquisition -- 2.2 Stimuli and Experimental Procedure -- 2.3 Data Preprocessing -- 2.4 Voxel Selection -- 2.5 SVM Method -- 3 Results -- 3.1 Classification Results for One vs. One Classifiers -- 3.2 Classification Results for One vs. Two Classifiers -- 3.3 Classification Results for Two vs. Two Classifiers -- 3.4 Classification Results for Regions Maximally Responsive to One Category of Objects -- 4 Discussion and Conclusions -- Acknowledgments -- References -- Gender Role Differences of Female College Students in Facial Expression Recognition: Evidence from N170 and VPP -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Participants -- 2.2 Stimuli -- 2.3 Experimental Procedure -- 2.4 Behavioral Data Analysis -- 2.5 EEG Recordings and Analysis -- 3 Results -- 3.1 Behavioral Results -- 3.2 ERP Results -- 4 Discussion -- 4.1 Gender Role Differences on Facial Expression Recognition: Evidence on Early ERP Components -- 4.2 Emotional Negativity Bias: Evidence on VPP -- 4.3 Emotion Congruency: Evidence on Behavior -- 5 Conclusion -- Acknowledgments -- References -- Brain Big Data Analytics, Curation and Management -- Overview of Acquisition Protocol in EEG Based Recognition System -- Abstract -- 1 Introduction -- 2 Signal Acquisition -- 2.1 The Noninvasive Electroencephalography Method -- 3 EEG Signal Based Recognition System -- 3.1 Relaxation -- 3.2 Motor/Non-motor Imaginary -- 3.3 Exposed to Stimuli (Evoked Potentials) -- 4 Analysis and Discussions -- 5 Conclusion -- Acknowledgments -- References. 327 $aA Study on Automatic Sleep Stage Classification Based on Clustering Algorithm -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Automatic Sleep Staging Classification Algorithm Based on K-Means Clustering -- 3.1 Denoising -- 3.2 Feature Extraction and Feature Selection -- 3.3 Automatic Sleep Stage Classification Based on Improving K-Means Algorithm -- 4 Experimental Results and Analysis -- 4.1 Sleep Data Set -- 4.2 Evaluation Metrics -- 4.3 Experimental Results and Discussion -- 5 Conclusion -- References -- Speaker Verification Method Based on Two-Layer GMM-UBM Model in the Complex Environment -- 1 Introduction -- 2 Methods -- 2.1 Voice Data Acquisition and Preprocessing -- 2.2 Feature Extraction -- 2.3 Speaker Verification Architecture Based on Two-Layer GMM-UBM Model -- 3 Results -- 3.1 Evaluation Criterion -- 3.2 GMM-UBM Speaker Verification Based on Segmented Voice Data -- 3.3 GMM-UBM Speaker Verification Based on Continuous Long-Term Voice Data -- 4 Discussion -- References -- Emotion Recognition from EEG Using Rhythm Synchronization Patterns with Joint Time-Frequency-Space Correlation -- 1 Introduction -- 2 Architecture of Emotional Recognition Model Based on Rhythm Synchronization Patterns (RSP-ERM) -- 2.1 Functions of Each Layer -- 2.2 Defining Emotional States - Class Label -- 3 Experimental Design -- 3.1 Data Description -- 3.2 Learning and Testing Process -- 3.3 Contrast Methods -- 4 Experimental Results and Discussion -- 5 Conclusions -- References -- Informatics Paradigms for Brain and Mental Health -- Patients with Major Depressive Disorder Alters Dorsal Medial Prefrontal Cortex Response to Anticipation with Different Saliences -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Subjects -- 2.2 Task Design -- 2.3 Data Acquisition and Analysis -- 3 Results -- 3.1 Anticipation Period Findings. 327 $a3.2 The Findings of Anticipation Effect on Picture Viewing -- 4 Discussion -- Acknowledgment -- References -- Abnormal Brain Activity in ADHD: A Study of Resting-State fMRI -- Abstract -- 1 Introduction -- 2 Method -- 2.1 Dataset -- 2.2 Image Processing -- 3 Statistics Analysis -- 4 Result -- 4.1 The Comparison of ALFF, fALFF and ReHo Between Two Groups -- 4.2 The Comparison of ALFF, fALFF and ReHo of Two Age Groups -- 5 Discussion -- Acknowledgements -- References -- Wearable EEG-Based Real-Time System for Depression Monitoring -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Making Sense of the Raw Data -- 4.1 Hardware -- 4.2 Resting EEG -- 4.3 Stimulus -- 4.4 Real-Time Signal Preprocessing -- 4.5 Feature Extraction -- 4.6 Classification -- 4.7 Visualization -- 5 Experiment -- 5.1 Participants -- 5.2 Results -- 6 Conclusions and Future Work -- References -- Group Guided Sparse Group Lasso Multi-task Learning for Cognitive Performance Prediction of Alzheimer's Disease -- 1 Introduction -- 2 Proposed Method -- 2.1 Group Guided Sparse Group Lasso Multi-task Learning -- 2.2 Optimization -- 3 Experimental Results -- 3.1 Data and Experimental Setting -- 3.2 The Results of Comparing with the Comparable Methods -- 3.3 Identification of MRI Biomarkers -- 4 Conclusions -- References -- A Novel Deep Learning Based Multi-class Classification Method for Alzheimer's Disease Detection Using Brain MRI Data -- 1 Introduction -- 2 Related Work -- 3 Proposed Network Architecture -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Results -- 5 Conclusion -- References -- A Quantitative Analysis Method for Objectively Assessing the Depression Mood Status Based on Portable EEG and Self-rating Scale -- 1 Introduction -- 2 Material and Method -- 2.1 Experimental Design -- 2.2 Data Analysis -- 3 Results. 327 $a3.1 Depressive Mood Status Assessment Based on POMS-BCN Data. 330 $aThis book constitutes the refereed proceedings of the International Conference on Brain Informatics, BI 2017, held in Beijing, China, in November 2017. The 31 revised full papers were carefully reviewed and selected from 64 submissions. BI addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics, as well as topics related to mental health and well-being. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v10654 606 $aArtificial intelligence 606 $aPattern recognition systems 606 $aComputer vision 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aData mining 606 $aOperating systems (Computers) 606 $aArtificial Intelligence 606 $aAutomated Pattern Recognition 606 $aComputer Vision 606 $aUser Interfaces and Human Computer Interaction 606 $aData Mining and Knowledge Discovery 606 $aOperating Systems 615 0$aArtificial intelligence. 615 0$aPattern recognition systems. 615 0$aComputer vision. 615 0$aUser interfaces (Computer systems). 615 0$aHuman-computer interaction. 615 0$aData mining. 615 0$aOperating systems (Computers). 615 14$aArtificial Intelligence. 615 24$aAutomated Pattern Recognition. 615 24$aComputer Vision. 615 24$aUser Interfaces and Human Computer Interaction. 615 24$aData Mining and Knowledge Discovery. 615 24$aOperating Systems. 676 $a006.32 702 $aZeng$b Yi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHe$b Yong$f1975-$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKotaleski$b Jeanette Hellgren$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMartone$b Maryann$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aXu$b Bo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPeng$b Hanchuan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLuo$b Qingming$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483404003321 996 $aBrain Informatics$9773781 997 $aUNINA