Analyzing the Impact of Food Safety Information on Food Demand in China

This study analyzed the impact of food safety information on food demand in urban China. The LA/AIDS model was estimated by using national province level food consumption data and quantities of articles about food safety event on public media from 2000 to 2008. The results of the study show that urban Chinese consumer food demand was influenced by food safety information from daily newspapers and GM labeling policy. This paper also indicates food price elasticities, expenditure elasticities by categories and the effect of food safety information.food safety, food demand, Linear Approximated Almost Ideal Demand System( LA/AIDS), Genetically modified( GM), food consumption, price elasticity, expenditure elasticity, Consumer/Household Economics, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, D12, Q11,

“Kulak” and Food Crisis during the Civil War in Soviet Russia

Food crisis was a very serious issue during the Civil War in Soviet Russia, which was mainly due to the revolt or resistance of the kulak in the countryside of Soviet Russia. To some extent, “Kulak” allowed the hunger situation in some regions of the Soviet Russia to go unchecked, which was believed to be a very severe threat for the new Soviet regime. Therefore, to eradicate the menace, the Bolsheviks led by Lenin adopted the policy of the resolute repression and deprivation of the kulak. As a matter of fact, in a word, according to the logic of the Bolsheviks, kulak was the culprit that engendered the famine, so kulak must be responsible for it, and kulak must pay the bill

How to Base Security on the Perfect/Statistical Binding Property of Quantum Bit Commitment?

The concept of quantum bit commitment was introduced in the early 1980s for the purpose of basing bit commitments solely on principles of quantum theory. Unfortunately, such unconditional quantum bit commitments still turn out to be impossible. As a compromise like in classical cryptography, Dumais et al. [Paul Dumais et al., 2000] introduce the conditional quantum bit commitments that additionally rely on complexity assumptions. However, in contrast to classical bit commitments which are widely used in classical cryptography, up until now there is relatively little work towards studying the application of quantum bit commitments in quantum cryptography. This may be partly due to the well-known weakness of the general quantum binding that comes from the possible superposition attack of the sender of quantum commitments, making it unclear whether quantum commitments could be useful in quantum cryptography. In this work, following Yan et al. [Jun Yan et al., 2015] we continue studying using (canonical non-interactive) perfectly/statistically-binding quantum bit commitments as the drop-in replacement of classical bit commitments in some well-known constructions. Specifically, we show that the (quantum) security can still be established for zero-knowledge proof, oblivious transfer, and proof-of-knowledge. In spite of this, we stress that the corresponding security analyses are by no means trivial extensions of their classical analyses; new techniques are needed to handle possible superposition attacks by the cheating sender of quantum bit commitments. Since (canonical non-interactive) statistically-binding quantum bit commitments can be constructed from quantum-secure one-way functions, we hope using them (as opposed to classical commitments) in cryptographic constructions can reduce the round complexity and weaken the complexity assumption simultaneously

PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data

It is highly desirable to borrow information from external data to augment a control arm in a randomized clinical trial, especially in settings where the sample size for the control arm is limited. However, a main challenge in borrowing information from external data is to accommodate potential heterogeneous subpopulations across the external and trial data. We apply a Bayesian nonparametric model called Plaid Atoms Model (PAM) to identify overlapping and unique subpopulations across datasets, with which we restrict the information borrowing to the common subpopulations. This forms a hybrid control (HC) that leads to more precise estimation of treatment effects Simulation studies demonstrate the robustness of the new method, and an application to an Atopic Dermatitis dataset shows improved treatment effect estimation

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural architecture search (NAS), as an emerging field, has demonstrated its capabilities in discovering powerful neural network architectures, which motivates us to explore its potential for CTR predictions. Due to 1) diverse unstructured feature interactions, 2) heterogeneous feature space, and 3) high data volume and intrinsic data randomness, it is challenging to construct, search, and compare different architectures effectively for recommendation models. To address these challenges, we propose an automated interaction architecture discovering framework for CTR prediction named AutoCTR. Via modularizing simple yet representative interactions as virtual building blocks and wiring them into a space of direct acyclic graphs, AutoCTR performs evolutionary architecture exploration with learning-to-rank guidance at the architecture level and achieves acceleration using low-fidelity model. Empirical analysis demonstrates the effectiveness of AutoCTR on different datasets comparing to human-crafted architectures. The discovered architecture also enjoys generalizability and transferability among different datasets