The Causes of Chronic and Transient Poverty and Their Implications for Poverty Reduction Policy in Rural China

The study focuses on two components of total poverty: chronic and transient poverty, and investigates their relative importance in total observed poverty, as well as the determinants of each components. We found that transient poverty accounts for a large proportion of total poverty observed in the poor rural areas of China. By analyzing the determinants of the two types of poverty, we found that household demographic characteristics, such as age of the head of households, family sizes, labour participation ratio, and educational level of the head of the households, are very important to the poverty status of households. These factors matter more to chronic poverty than transient poverty, and have greater impacts on the poverty measured by consumption than that measured by income. Besides the demographic factors of households, other household factors like physical stocks, the composition of income, and the amount of cultivated lands also have significant effects on both chronic and transient poverty. It is also confirmed that change in cash holding and saving and borrowing grain are used by rural households to cope with income variation and smooth their consumption. Attributes of community where the households reside are also important to poverty. With very few exceptions, we did not find that poverty programs have significant impact on poverty reduction at the households' level. We interpreted this as the poverty programs benefiting the wealthy more than the poor in a given poor area. The main reason for this could be that the implementation design of these programs fails to target the poor.Income risk, chronic poverty, transient poverty, poverty program evaluation, China

Climate change impacts on food security in Sub-Saharan Africa: Insights from comprehensive climate change scenarios

Climate change impacts vary significantly, depending on the scenario and the Global Circulation Model (GCM) chosen. This is particularly true for Sub-Saharan Africa. This paper uses a comprehensive climate change scenario (CCC) based on ensembles of 17 GCMs selected based on their relative performance regarding past predictions of temperature and precipitation at the level of 2o x 2o grid cells, generated by a recently developed entropy-based downscaling model. Based on past performance, the effects of temperature and precipitation across the 17 GCMs are incorporated into a global hydrological model that is linked with IFPRI's IMPACT water and food projections model to assess the effects of climate change on food outcomes for the region. For Sub-Saharan Africa, the paper finds that the CCC scenario predicts consistently higher temperatures and mixed precipitation changes for the 2050 period. Compared to historic climate scenarios, climate change will lead to changes in yield and area growth, higher food prices and therefore lower affordability of food, reduced calorie availability, and growing childhood malnutrition in Sub-Saharan Africa.Climate change, hydrology, crop yield, food security,

Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms

Marginal-based methods achieve promising performance in the synthetic data competition hosted by the National Institute of Standards and Technology (NIST). To deal with high-dimensional data, the distribution of synthetic data is represented by a probabilistic graphical model (e.g., a Bayesian network), while the raw data distribution is approximated by a collection of low-dimensional marginals. Differential privacy (DP) is guaranteed by introducing random noise to each low-dimensional marginal distribution. Despite its promising performance in practice, the statistical properties of marginal-based methods are rarely studied in the literature. In this paper, we study DP data synthesis algorithms based on Bayesian networks (BN) from a statistical perspective. We establish a rigorous accuracy guarantee for BN-based algorithms, where the errors are measured by the total variation (TV) distance or the $L^2$ distance. Related to downstream machine learning tasks, an upper bound for the utility error of the DP synthetic data is also derived. To complete the picture, we establish a lower bound for TV accuracy that holds for every $\epsilon$-DP synthetic data generator

Plastic avalanches in metal-organic framework crystals

The compressive properties of metal-organic framework (MOF) crystals are not only crucial for their densification but also key in determining their performance in many applications. We herein investigated the mechanical responses of a classic crystalline MOF, HKUST-1 by using in situ compression tests. A serrated flow accompanied by the unique strain avalanches was found in individual and contacting crystals before their final flattening or fracture with splitting cracks. The plastic flow with serrations is ascribed to the dynamic phase mixing due to the progressive and irreversible local phase transition in HKUST-1 crystals, as revealed by molecular dynamics and finite element simulations. Such pressure-induced phase coexistence in HKUST-1 crystals also induces a significant loading-history dependence of their Young's modulus. The observation of plastic avalanches in HKUST-1 crystals here not only expands our current understanding of the plasticity of MOF crystals but also unveils a novel mechanism for the avalanches and plastic flow in crystal plasticity

A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis

Aspect-Based Sentiment Analysis is a fine-grained sentiment analysis task, which focuses on detecting the sentiment polarity towards the aspect in a sentence. However, it is always sensitive to the multi-aspect challenge, where features of multiple aspects in a sentence will affect each other. To mitigate this issue, we design a novel training framework, called Contrastive Cross-Channel Data Augmentation (C3DA). A source sentence will be fed a domain-specific generator to obtain some synthetic sentences and is concatenated with these generated sentences to conduct supervised training and proposed contrastive training. To be specific, considering the limited ABSA labeled data, we also introduce some parameter-efficient approaches to complete sentences generation. This novel generation method consists of an Aspect Augmentation Channel (AAC) to generate aspect-specific sentences and a Polarity Augmentation (PAC) to generate polarity-inverted sentences. According to our extensive experiments, our C3DA framework can outperform those baselines without any augmentations by about 1\% on accuracy and Macro-F1

DiffSketcher: Text Guided Vector Sketch Synthesis through Latent Diffusion Models

Even though trained mainly on images, we discover that pretrained diffusion models show impressive power in guiding sketch synthesis. In this paper, we present DiffSketcher, an innovative algorithm that creates vectorized free-hand sketches using natural language input. DiffSketcher is developed based on a pre-trained text-to-image diffusion model. It performs the task by directly optimizing a set of Bezier curves with an extended version of the score distillation sampling (SDS) loss, which allows us to use a raster-level diffusion model as a prior for optimizing a parametric vectorized sketch generator. Furthermore, we explore attention maps embedded in the diffusion model for effective stroke initialization to speed up the generation process. The generated sketches demonstrate multiple levels of abstraction while maintaining recognizability, underlying structure, and essential visual details of the subject drawn. Our experiments show that DiffSketcher achieves greater quality than prior work.Comment: 14 pages, 8 figures. update: improved experiment analysis, fixed typos, and fixed image error