途上国経済における家計内所得移転と所得ショックに関する研究：公共政策設計への含意

政策分析プログラム / Policy Analysis Program政策研究大学院大学 / National Graduate Institute for Policy Studies論文審査委員: Minchung Hsu（主査）, 藤本 淳一, Ponpoje Porapakkarm, Boo Teik Khoo, 田中 隆一（東京大学

The question of quality

Phuong-Thao T. Trinh, Thu-Hien T. Le, Thu-Trang Vuong, Phuong-Hanh Hoang (2019). Chapter 6. The question of quality. In Quan-Hoang Vuong, Trung Tran (Eds.), The Vietnamese Social Sciences at a Fork in the Road (pp. 121–142). Warsaw, Poland: De Gruyter. DOI:10.2478/9783110686081-011. Online ISBN: 9783110686081 © 2019 Sciendo / De Gruyte

SCHATTEN MATRIX NORM BASED POLARIMETRIC SAR DATA REGULARIZATION. APPLICATION OVER CHAMONIX MONT-BLANC

International audienceThe paper addresses the filtering of Polarimetry Synthetic Aperture Radar (PolSAR) images. The filtering strategy is based on a regularizing cost function associated with matrix norms called the Schatten p-norms. These norms apply on matrix singular values. The proposed approach is illustrated upon scattering and coherency matrices on RADARSAT-2 PolSAR images over the Chamonix Mont-Blanc site. Several p values of Schatten p-norms are surveyed and their capabilities on filtering PolSAR images is provided in comparison with conventional strategies for filtering PolSAR data

AMEE: A Robust Framework for Explanation Evaluation in Time Series Classification

This paper aims to provide a framework to quantitatively evaluate and rank explanation methods for the time series classification task, which deals with a prevalent data type in critical domains such as healthcare and finance. The recent surge of research interest in explanation methods for time series classification has provided a great variety of explanation techniques. Nevertheless, when these explanation techniques disagree on a specific problem, it remains unclear which of them to use. Comparing the explanations to find the right answer is non-trivial. Two key challenges remain: how to quantitatively and robustly evaluate the informativeness (i.e., relevance for the classification task) of a given explanation method, and how to compare explanation methods side-by-side. We propose AMEE, a Model-Agnostic Explanation Evaluation framework for quantifying and comparing multiple saliency-based explanations for time series classification. Perturbation is added to the input time series guided by the saliency maps (i.e., importance weights for each point in the time series). The impact of perturbation on classification accuracy is measured and used for explanation evaluation. The results show that perturbing discriminative parts of the time series leads to significant changes in classification accuracy. To be robust to different types of perturbations and different types of classifiers, we aggregate the accuracy loss across perturbations and classifiers. This allows us to objectively quantify and rank different explanation methods. We provide a quantitative and qualitative analysis for synthetic datasets, a variety of UCR benchmark datasets, as well as a real-world dataset with known expert ground truth.Comment: Pre-prin

Wavelet Operators and Multiplicative Observation Models - Application to Change-Enhanced Regularization of SAR Image Time Series

This paper first provides statistical properties of wavelet operators when the observation model can be seen as the product of a deterministic piecewise regular function (signal) and a stationary random field (noise). This multiplicative observation model is analyzed in two standard frameworks by considering either (1) a direct wavelet transform of the model or (2) a log-transform of the model prior to wavelet decomposition. The paper shows that, in Framework (1), wavelet coefficients of the time series are affected by intricate correlation structures which affect the signal singularities. Framework (2) is shown to be associated with a multiplicative (or geometric) wavelet transform and the multiplicative interactions between wavelets and the model highlight both sparsity of signal changes near singularities (dominant coefficients) and decorrelation of speckle wavelet coefficients. The paper then derives that, for time series of synthetic aperture radar data, geometric wavelets represent a more intuitive and relevant framework for the analysis of smooth earth fields observed in the presence of speckle. From this analysis, the paper proposes a fast-and-concise geometric wavelet based method for joint change detection and regularization of synthetic aperture radar image time series. In this method, geometric wavelet details are first computed with respect to the temporal axis in order to derive generalized-ratio change-images from the time series. The changes are then enhanced and speckle is attenuated by using spatial bloc sigmoid shrinkage. Finally, a regularized time series is reconstructed from the sigmoid shrunken change-images. An application of this method highlights the relevancy of the method for change detection and regularization of SENTINEL-1A dual-polarimetric image time series over Chamonix-Mont-Blanc test site

TEMPORAL ADAPTIVE FILTERING OF SAR IMAGE TIME SERIES BASED ON THE DETECTION OF STABLE AND CHANGE AREAS

International audienceThis paper presents a novel multitemporal filtering approach for Synthetic Aperture Radar (SAR) images. This is a temporal adaptive filter for a time series of SAR images based on coefficient of variation test to detect stable areas and change areas. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from 11/06/2009 to 09/25/2011 over Chamonix-MontBlanc test-site which includes different kind of changes: parking occupation, glacier surface evolution, etc

TEMPORAL ADAPTIVE FILTERING OF SAR IMAGE TIME SERIES BASED ON THE DETECTION OF STABLE AND CHANGE AREAS

International audienceThis paper presents a novel multitemporal filtering approach for Synthetic Aperture Radar (SAR) images. This is a temporal adaptive filter for a time series of SAR images based on coefficient of variation test to detect stable areas and change areas. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from 11/06/2009 to 09/25/2011 over Chamonix-MontBlanc test-site which includes different kind of changes: parking occupation, glacier surface evolution, etc

Adaptive Multitemporal SAR Image Filtering Based on the Change Detection Matrix

5 pagesInternational audienceThis letter presents an adaptive filtering approach of synthetic aperture radar (SAR) image times series based on the analysis of the temporal evolution. First, change detection matrices (CDMs) containing information on changed and unchanged pixels are constructed for each spatial position over the time series by implementing coefficient of variation (CV) cross tests. Afterwards, the CDM provides for each pixel in each image, an adaptive spatiotemporal neighborhood which is used to derive the filtered value. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from November 6, 2009 to September 25, 2011 over the Chamonix-MontBlanc test-site which includes different kinds of change such as parking occupation, glacier surface evolution, etc

Interest rate pass-through estimates from error correction models ECM

This paper examines the degree of pass-through and adjustment speed of retail interest rates in response to changes in monetary policy rates in commercial banks of Viet Nam during the period 07/2004 to 06/2014. The results show that the degree of pass-through of retail interest rates is incomplete but high (0.7-0.93). The adjustment speed of money market rates & retail interest rates is relatively slow. It takes from 3 to 6 months for money market rates & retail interest rates to be adjusted to long-term equilibrium, except 1 month VNIBOR. 1 month VNIBOR is sensitive to changes of discount rate & refinancing rate in short-term, contrary to 3 month VNIBOR . The degree of pass-through from market rates to retail interest rates is fairly high in the long-term but low in the short-term. The degree of pass-through is different between various retail interest rates. Specifically, the degree of pass-through of deposit rates is higher than that of lending rates both in the short-term & long-term