Attraction Domain Analysis for Steady States of Markovian Open Quantum Systems

This article concerns the attraction domain analysis for steady states in Markovian open quantum systems. The central question is proposed as: given a steady state, which part of the state space of density operators does it attract and which part does it not attract? We answer this question by presenting necessary and sufficient conditions that determine, for any steady state and initial state, whether the latter belongs to the attraction domain of the former. Moreover, we show that steady states without uniqueness in the set of density operators have attraction domains with measure zero under some translation invariant and locally finite measures. Finally, an example regarding an open Heisenberg XXZ spin chain is presented

DocPrompt: Large-scale continue pretrain for zero-shot and few-shot document question answering

In this paper, we propose Docprompt for document question answering tasks with powerful zero-shot and few-shot performance. We proposed a novel weakly supervised data generation method, a novel multl-stage training method and a novel understanding model & generation model ensemble method. Experiment results show that the Docprompt model after continue pretrain significantly outperforms the existing strong baseline models on document question answering tasks. This method greatly improves the delivery efficiency and model performance of document question answering customer projects, reducing annotation costs and labor costs. Our demo can be found at https://huggingface.co/spaces/PaddlePaddle/ERNIE-Layout

Maternal pre-pregnancy infection with hepatitis B virus and the risk of preterm birth: a population-based cohort study

Background Preterm birth is the leading cause of child death in children younger than 5 years. Large cohort studies in developed countries have shown that maternal hepatitis B virus infection is associated with preterm birth, but there is little reliable evidence from China and other developing countries, where hepatitis B virus prevalence is intermediate or high. Hence, we designed this study to investigate the association between pre-pregnancy hepatitis B virus infection and risk of preterm and early preterm birth. Methods Between Jan 1, 2010, and Dec 31, 2012, we did a population-based cohort study using data from 489 965 rural women aged 21–49 years who had singleton livebirths from 220 counties of China who participated in the National Free Preconception Health Examination Project. Participants were divided into three groups according to their prepregnancy status of hepatitis B virus infection: women uninfected with hepatitis B virus (control group), women who were HBsAg positive and HBeAg negative (exposure group 1), and women who were both HBsAg and HBeAg positive (exposure group 2). The primary outcome was preterm birth (gestation at less than 37 weeks). We used log-binomial regression to estimate adjusted risk ratios (aRR) of preterm birth for women with pre-pregnancy hepatitis B virus infection, and risk of early preterm birth (gestation less than 34 weeks). Findings 489 965 women met inclusion criteria and were included in this study; of these, 20 827 (4·3%) were infected with hepatitis B virus. Compared with women who were not infected with hepatitis B virus, women who were HBsAg positive and HBeAg negative had a 26% higher risk of preterm birth (aRR 1·26, 95% CI 1·18–1·34) and women who were both HBsAg and HBeAg positive had a 20% higher risk of preterm birth (aRR 1·20, 1·08–1·32). Compared with women who were not infected with hepatitis B virus, women who were HBsAg positive and HBeAg negative manifested an 18% higher risk of early preterm birth (gestation less than 34 weeks; aRR 1·18, 1·04–1·34) and women who were both HBsAg and HBeAg positive had a 34% higher risk of early preterm birth (aRR 1·34, 1·10–1·61). Maternal pre-pregnancy hepatitis B virus infection was independently associated with higher risk of preterm birth and early preterm birth. These associations were similar in subgroups of participants as defined by baseline characteristics. Interpretation Besides mother-to-child transmission, the risk of preterm birth in women infected with hepatitis B virus should not be neglected. Comprehensive programmes that focus on early detection of hepatitis B virus infection before pregnancy and provide appropriate medical intervention for women infected with hepatitis B virus before and during pregnancy would be helpful in improving maternal and neonatal outcomes and reducing child mortality

Nonlinear interaction of head−-on solitary waves in integrable and nonintegrable systems

This study numerically investigates the nonlinear interaction of head-on solitary waves in a granular chain (a nonintegrable system) and compares the simulation results with the theoretical results in fluid (an integrable system). Three stages (i.e., pre-in-phase traveling stage, central-collision stage, and post-in-phase traveling stage) are identified to describe the nonlinear interaction processes in the granular chain. The nonlinear scattering effect occurs in the central-collision stage, which decreases the amplitude of incident solitary waves. Compared with the leading-time phase in the incident and separation collision processes, the lagging-time phase in the separation collision process is smaller. This asymmetrical nonlinear collision results in an occurrence of leading phase shifts of time and space in the post-in-phase traveling stage. We next find that solitary wave amplitude does not influence the immediate space-phase shift in the granular chain. The space$-$phase shift of the post-in-phase traveling stage is only determined by measurement position rather than wave amplitude. The results are reversed in the fluid. An increase in solitary wave amplitude leads to decreased attachment, detachment and residence times for granular chain and fluid. For the immediate time-phase shift, leading and lagging phenomena appear in the granular chain and the fluid, respectively. These results offer new knowledge for designing mechanical metamaterials and energy-mitigating systems

Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm

Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels, making noisy labels a more practical alternative. Motivated by noisy multi-class learning, the introduction of transition matrices can help model multi-label noise and enable the development of statistically consistent algorithms for noisy multi-label learning. However, estimating multi-label noise transition matrices remains a challenging task, as most existing estimators in noisy multi-class learning rely on anchor points and accurate fitting of noisy class posteriors, which is hard to satisfy in noisy multi-label learning. In this paper, we address this problem by first investigating the identifiability of class-dependent transition matrices in noisy multi-label learning. Building upon the identifiability results, we propose a novel estimator that leverages label correlations without the need for anchor points or precise fitting of noisy class posteriors. Specifically, we first estimate the occurrence probability of two noisy labels to capture noisy label correlations. Subsequently, we employ sample selection techniques to extract information implying clean label correlations, which are then used to estimate the occurrence probability of one noisy label when a certain clean label appears. By exploiting the mismatches in label correlations implied by these occurrence probabilities, we demonstrate that the transition matrix becomes identifiable and can be acquired by solving a bilinear decomposition problem. Theoretically, we establish an estimation error bound for our multi-label transition matrix estimator and derive a generalization error bound for our statistically consistent algorithm. Empirically, we validate the effectiveness of our estimator in estimating multi-label noise transition matrices, leading to excellent classification performance

Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation

Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI between anchors and their positives, while we theoretically prove that MI involving negatives also matters when noises commonly exist. Guided by a more general lower bound form for optimization, we propose a contrastive learning strategy regulated by progressively refined cross-modal similarity, to more accurately optimize MI between an image/text anchor and its negative texts/images instead of improperly minimizing it. Our method performs competitively on four downstream cross-modal tasks and systematically balances the beneficial and harmful effects of (partial) false negative samples under theoretical guidance.Comment: Accepted by ACL202

Seroprevalence of Cytomegalovirus and Associated Factors Among Preconception Women: A Cross-Sectional Nationwide Study in China

Background: Cytomegalovirus seroconversion during pregnancy is common and has a substantial risk of congenital infection with longterm sequale. Screening during pregnancy or vaccination have not been shown to be effective for eliminating congenital infections. Preconception screening policy has not been evaluated adequately in a large scale. This nationwide study aimed to investigate epidemiological features of cytomegalovirus seropositivity and its geographic variation among Chinese women planning a pregnancy to gather epidemiological evidence as an essential for developing novel prevention strategies. Method: This cross-sectional sero-epidemiological survey enrolled women intending to become pregnant within 6 months in mainland China during 2010–2012. The primary outcomes in this study were cytomegalovirus Immunoglobulin G and M seropositivity. Secondary outcomes were the associations between Immunoglobulin G and Immunoglobulin M, with socio-demographic characteristics, including age, occupation, education level, place of residence, and ethnicity. The overall seropositivity and regional disparity was analyzed on the individual and regional level, respectively. Results: This study included data from 1,564,649 women from 31 provinces in mainland China. Among participants, 38.6% (n = 603,511) were cytomegalovirus immunoglobulin G+, 0.4% (n = 6,747) were immunoglobulin M+, and 0.2% (n = 2,879) were immunoglobulin M+ and immunoglobulin G+. On individual level, participant's age, ethnicity, and residing region were significantly associated with IgG+, IgM+, and IgM+IgG+ (P 0.05). On regional level, cytomegalovirus immunoglobulin G and immunoglobulin M seropositivity was highest in the eastern region (49.5 and 0.5%, respectively), and lowest in the western region (26.9 and 0.4%, respectively). This geographic variation was also noted at the provincial level, characterized by higher provincial immunoglobulin M+ and immunoglobulin G+ rates associated with higher immunoglobulin G seropositivity. In the subgroup analysis of immunoglobulin G seropositivity, areas of higher immunoglobulin G positivity had a higher rate of immunoglobulin M+, indicating an expected increased risk of reinfection and primary infection. Conclusions: A substantial proportion of women (>60%) were susceptible to cytomegalovirus in preconception period in China, and immunoglobulin G seropositivity was seen at a low-medium level with substantial geographic variation. Integration of cytomegalovirus antibody testing in preconception screening program based on regional immunoglobulin G seropositivity, should be considered to promote strategies directed toward preventing sero-conversion during pregnancy to reduce the risk of this congenital infection