Agricultural producer support estimates for developing countries: Measurement issues and evidence from India, Indonesia, China, and Vietnam

"This study analyzes the evolution of agricultural policies from 1985 to 2002 in India, Indonesia, China, and Vietnam and provides empirical estimates of the degree of protection or disprotection to agriculture in these four countries, both by key commodities and in aggregate... Taken together the reported measures of support and disprotection of specific crops and agriculture in total provide a reasonable basis for assessing the stance of agricultural policies of India, Indonesia, China, and Vietnam. Attention to measurement issues provides a sensitivity analysis. The results reported are indicative of the range of outcomes likely to be found more broadly among developing countries. From regimes of heavy intervention in agricultural markets, each of the four countries in the study has undergone a substantial reform process." from textAgricultural support, Agricultural policies, Reform, Pro-poor policies,

Infrared Divergence and Twist-3 Distribution Amplitudes in QCD Factorization For B→PPB \to PP

Since b quark mass is not asymptotically large, chirally enhanced corrections which arise from twist-3 wave functions may be important in B decays. We thus evaluate the hadronic matrix elements with the final light pseudoscalar mesons described by leading twist and twist-3 distribution amplitudes. We find that chirally enhanced corrections can be included consistently in the framework of QCD factorization only if the twist-3 distribution amplitudes are symmetric. We then give explicit expressions of $a_i^p$ for $B \to \pi\pi$ at the next-to-leading order of $\alpha_s$ including chirally enhanced corrections. We also briefly discuss the divergence appeared in the hard spectator contributions.Comment: 12 pages, 3 figures, A revised version to appear in Phys. Lett.

DyExplainer: Explainable Dynamic Graph Neural Networks

Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box nature of GNNs presents a significant challenge in terms of comprehending and trusting these models, thereby limiting their practical applications in mission-critical scenarios. Although there has been substantial progress in the field of explaining GNNs in recent years, the majority of these studies are centered on static graphs, leaving the explanation of dynamic GNNs largely unexplored. Dynamic GNNs, with their ever-evolving graph structures, pose a unique challenge and require additional efforts to effectively capture temporal dependencies and structural relationships. To address this challenge, we present DyExplainer, a novel approach to explaining dynamic GNNs on the fly. DyExplainer trains a dynamic GNN backbone to extract representations of the graph at each snapshot, while simultaneously exploring structural relationships and temporal dependencies through a sparse attention technique. To preserve the desired properties of the explanation, such as structural consistency and temporal continuity, we augment our approach with contrastive learning techniques to provide priori-guided regularization. To model longer-term temporal dependencies, we develop a buffer-based live-updating scheme for training. The results of our extensive experiments on various datasets demonstrate the superiority of DyExplainer, not only providing faithful explainability of the model predictions but also significantly improving the model prediction accuracy, as evidenced in the link prediction task.Comment: 9 page

Label Propagation for Graph Label Noise

Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph models encompass both node features and graph topology as input, and become more susceptible to label noise through message-passing mechanisms. Recently, only a few works have been proposed to tackle the label noise on graphs. One major limitation is that they assume the graph is homophilous and the labels are smoothly distributed. Nevertheless, real-world graphs may contain varying degrees of heterophily or even be heterophily-dominated, leading to the inadequacy of current methods. In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes. We begin by conducting two empirical analyses to explore the impact of graph homophily on graph label noise. Following observations, we propose a simple yet efficient algorithm, denoted as LP4GLN. Specifically, LP4GLN is an iterative algorithm with three steps: (1) reconstruct the graph to recover the homophily property, (2) utilize label propagation to rectify the noisy labels, (3) select high-confidence labels to retain for the next iteration. By iterating these steps, we obtain a set of correct labels, ultimately achieving high accuracy in the node classification task. The theoretical analysis is also provided to demonstrate its remarkable denoising "effect". Finally, we conduct experiments on 10 benchmark datasets under varying graph heterophily levels and noise types, comparing the performance of LP4GLN with 7 typical baselines. Our results illustrate the superior performance of the proposed LP4GLN

W-exchange and W-annihilation processes of B mesons

Using the PQCD method we calculate the W-exchange and the W-annihilation processes of B mesons, which in general involve a charm quark or anti-quark in the final state. The nonvanishing amplitudes of these processes are found to be suppressed by a factor of $m_c/m_b$ compared to the tree or the time-like penguin processes, but some of them are within the reach of observation at the future B-factories, and $\bar B_d^0 \to D^+_s K^-$ whose branching ratio is found to be $6.6 \times 10^{-6}$ can be found even before the B-factory era. Comparisons with the results based on the BSW model are also given.Comment: 11 Pages including figures, accepted in Phys. Lett.

Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification

In recent years, person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks. In this paper, we focus on the backdoor attack on deep ReID models. Existing backdoor attack methods follow an all-to-one/all attack scenario, where all the target classes in the test set have already been seen in the training set. However, ReID is a much more complex fine-grained open-set recognition problem, where the identities in the test set are not contained in the training set. Thus, previous backdoor attack methods for classification are not applicable for ReID. To ameliorate this issue, we propose a novel backdoor attack on deep ReID under a new all-to-unknown scenario, called Dynamic Triggers Invisible Backdoor Attack (DT-IBA). Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities. Specifically, an identity hashing network is proposed to first extract target identity information from a reference image, which is then injected into the benign images by image steganography. We extensively validate the effectiveness and stealthiness of the proposed attack on benchmark datasets, and evaluate the effectiveness of several defense methods against our attack

Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes---specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.Comment: Accepted to AAAI 202

A Novel Equivalent Continuous Metering Control With a Uniform Switching Strategy for Digital Valve System

Pulse number modulation (PNM) combined with pulse width modulation (PWM) control is an effective solution to improve the resolution of digital valve systems. However, the numerous discrete variables that use parallel on / off valves cause difficult control coordination and uneven switching. To address this issue, this article defines the equivalent spool displacement of the digital flow control unit by the number of PNM-controlled valves and the duty cycle of PWM-controlled valves to replace multiple discrete variables and develops the equivalent continuous metering control method. Furthermore, a uniform switching control strategy is proposed for the PWM-controlled valve using a uniformly distributed permutation for each on / off valve. The proposed control methods are verified by simulation on the built mathematical model of the equal-coded digital valve system. Experimental results for the displacement control of a hydraulic cylinder at 1 rad/s show that the average error of the equivalent continuous metering control is about 0.236 mm and the dispersion index reaches 20%, while the uniform switching control strategy achieves 80% with an average error of 0.215 mm. Simulated and experimental results demonstrate that the equivalent continuous metering control with a uniform switching strategy can almost evenly distribute switching numbers without compromising the accuracy of the displacement control.Peer reviewe