The Test of torrential rains—— Analysis of factors influencing the credibility of government microblogs in major natural disasters

Social media has become an important platform for the government to release information, publicize policies, and communicate with the public due to its instantaneous, synchronized, interactive advantages. And it plays an irreplaceable role in the rescue and relief process of major natural disasters. It is because of the special attributes and unique effectiveness of government social media that it has also become a visible window to reflect and evaluate the credibility of the Government. This research focuses on the rare and extremely heavy rainstorm that oc-curred in Zhengzhou City, Henan Province, China in July 2021, and uses it as a scenario. By collecting and analyzing the information release data and public interaction information of governments’ microblog accounts in Zhengzhou City, the research is carried out from three dimensions: government information supply, public information demand, and the deviations between the them. After that we will examine the credibility and effectiveness of government social media during the "720" Zhengzhou heavy rainstorm, and try to create a model of the factors which influence the credibility of government social media in major natural disasters, and then propose strategies to improve it

Implementation of a satellite-based prognostic daily surface albedo depending on soil wetness : impact study in SURFEX modelling platform over France

The main objective of the thesis is to develop a prognostic surface albedo of the visible spectrum and near infrared and assess its impact on the energy balance and hydrology in the modelling platform of SURFEX. First, a statistical approach has generated a global climate albedo product at 0.05 ° for bare soil and vegetation using multiple years 8 -day MODIS onboard TERRA and AQUA satellites heliosynchronous data. Then, an original method has been developed to reduce temporal resolution of MODIS 500m albedo to daily. The result is validated against in situ measurements as well as daily albedo from geostationary satellite MSG / SEVIRI Land SAF project after projection of MODIS. Then a method of separating albedo of bare soil and vegetation is applied to the datasets of the two satellite systems. Using a threshold of vegetation cover, a calibration of the albedo bare soil with measured soil moisture is derived from 2007 to 2010 for 12 SMOSMANIA stations over southwestern France. We derived a parameterization of the albedo of bare soil with moisture to make the climate changing albedo. The albedo and simulated happens to be very well correlated with observations from space, which helps to explain the albedo variations at very short notice. To change seasonally albedo, a simple parameterization of canopy albedo derived from detailed radiative transfer code PROSAIL is used. The variables are the albedo of the sheet, canopy geometry and chlorophyll content. In order to be sensitive to chlorophyll, the study is based on an albedo at 560 nm. The theoretical approach is validated with MODIS satellite data for the site Majadas (Spain). The next step is to conduct an impact study of this new predictive albedo on the energy balance and hydrology within SURFEX over France and highlighting effects on temperature. More preliminary restricted to a SMOSMANIA station, an assimilation scheme is developed for surface albedo together with the leaf area index LAI and surface moisture. This effects an improvement in the prescribed LAI at the beginning of crop growth

Implementation of a satellite-based prognostic daily surface albedo depending on soil wetness : impact study in SURFEX modelling platform over France

L'objectif de la thèse est de développer un albédo de surface journalier pronostique dans les modèles météorologiques et d’évaluer son impact pour le bilan d'énergie et l'hydrologie dans la plate-forme de modélisation SURFEX sur le domaine France. En premier lieu, un albédo climatologique est à ce jour considéré dans SURFEX. Il est analysé dans cette étude par rapport aux albédos quotidiens de SEVIRI et MODIS dont ce dernier est obtenu à partir d'une méthode originale que l'on valide. Ensuite, une méthode est développée pour obtenir des albédos du sol et de la végétation de façon séparée à la fois statiquement, donc sur une base climatologique, puis dynamiquement en s'appuyant sur plusieurs années de données du satellite MODIS. Une fois réglé l'albédo du sol journalier, il est recherché une calibration avec l'humidité du sol nu à l'aide des données du réseau de stations sol SMOSMANIA du sud-ouest de la France. Il est montré que l'on peut prédire l'évolution de l'albédo de surface, par comparaison avec les observations spatiales avec l'humidité seule dans la limite d'une végétation faiblement couvrante. Cet albédo simulé est complété par celui de la végétation seule à partir d'une paramétrisation simplifiée du code de transfert radiatif PROSAIL. L'approche théorique est validée avec les données du site de Majadas pour lequel on montre que l'on sait simuler le cycle d'évolution de l'albédo total avec prise en compte de la chlorophylle au niveau de la feuille. En dernier lieu, il a été réalisé une étude d'impact du nouveau albédo évolutif sur le bilan d'énergie et l'hydrologie dans SURFEX sur la France. Il est aussi mis en place une assimilation de l'albédo conjointement avec l'indice foliaire et l'humidité superficielle, ce qui a des effets positifs pour le cas des végétations qui ne sont pas trop denses. ABSTRACT : The main objective of the thesis is to develop a prognostic surface albedo of the visible spectrum and near infrared and assess its impact on the energy balance and hydrology in the modelling platform of SURFEX. First, a statistical approach has generated a global climate albedo product at 0.05 ° for bare soil and vegetation using multiple years 8 -day MODIS onboard TERRA and AQUA satellites heliosynchronous data. Then, an original method has been developed to reduce temporal resolution of MODIS 500m albedo to daily. The result is validated against in situ measurements as well as daily albedo from geostationary satellite MSG / SEVIRI Land SAF project after projection of MODIS. Then a method of separating albedo of bare soil and vegetation is applied to the datasets of the two satellite systems. Using a threshold of vegetation cover, a calibration of the albedo bare soil with measured soil moisture is derived from 2007 to 2010 for 12 SMOSMANIA stations over southwestern France. We derived a parameterization of the albedo of bare soil with moisture to make the climate changing albedo. The albedo and simulated happens to be very well correlated with observations from space, which helps to explain the albedo variations at very short notice. To change seasonally albedo, a simple parameterization of canopy albedo derived from detailed radiative transfer code PROSAIL is used. The variables are the albedo of the sheet, canopy geometry and chlorophyll content. In order to be sensitive to chlorophyll, the study is based on an albedo at 560 nm. The theoretical approach is validated with MODIS satellite data for the site Majadas (Spain). The next step is to conduct an impact study of this new predictive albedo on the energy balance and hydrology within SURFEX over France and highlighting effects on temperature. More preliminary restricted to a SMOSMANIA station, an assimilation scheme is developed for surface albedo together with the leaf area index LAI and surface moisture. This effects an improvement in the prescribed LAI at the beginning of crop growth

Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.Comment: AAAI 201

An improved stochastic EM algorithm for large-scale full-information item factor analysis

In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt, 1985) for large-scale full-information item factor analysis. Innovations have been made on its implementation, including (1) an adaptive-rejection-based Gibbs sampler for the stochastic E step, (2) a proximal gradient descent algorithm for the optimization in the M step, and (3) diagnostic procedures for determining the burn-in size and the stopping of the algorithm. These developments are based on the theoretical results of Nielsen (2000), as well as advanced sampling and optimization techniques. The proposed algorithm is computationally efficient and virtually tuning-free, making it scalable to large-scale data with many latent traits (e.g. more than five latent traits) and easy to use for practitioners. Standard errors of parameter estimation are also obtained based on the missing information identity (Louis, 1982). The performance of the algorithm is evaluated through simulation studies and an application to the analysis of the IPIP-NEO personality inventory. Extensions of the proposed algorithm to other latent variable models are discussed

Identificación de las especies: Ommastrephes bartramii, Dosidicus gigas, Sthenoteuthis oualaniensis e Illex argentinus (Ommastrephidae) a través de medidas morfológicas de sus picos

Four oceanic squid species, Ommastrephes bartramii, Dosidicus gigas, Sthenoteuthis oualaniensis and Illex argentinus, not only support important commercial fisheries, but also play a vital role in their marine ecosystems. It is therefore important to identify them in the analyses of their predators’ stomach contents as this can yield critical information on the trophic dynamics of ecosystems. Hard beaks of the four species frequently found in their predators’ stomachs can be used to identify them. In this study, to remove the effect of size differences among individuals, measurements of upper and lower beaks were standardized with an allometric model. A discriminant analysis was carried out to compare morphological differences among the four species and between the sexes for each species. The upper rostral width and upper rostral length showed the greatest interspecific variation in the beak morphological variables of the four Ommastrephidae. The linear discriminant functions of beak morphological variables were developed for the four Ommastraphidae, which resulted in a rate of correct species classification of over 97%. Sexual dimorphism was also found in the beak morphology of O. bartramii and I. argentinus. This study suggests that morphological variables can be used to reliably classify Ommastrephidae at genus level, which can help identify the specie in the stomachs of cephalopod predators. This helps to improve the understanding of the role cephalopods play in their marine ecosystems.Las cuatro especies de calamares: Ommastrephes bartramii, Dosidicus gigas, Sthenoteuthis oualaniensis e Illex argentinus, sometidas a una importante presión pesquera, juegan un papel significativo dentro de los ecosistemas marinos a los que pertenecen. Al ser los picos de estas especies resistentes, las medidas de diversos aspectos de su morfología pueden servir para identificarlas en análisis de contenidos estomacales de sus depredadores. Ello permite obtener una información crucial sobre la dinámica trófica de los ecosistemas. En el presente estudio, las medidas realizadas en los picos superior e inferior de los Ommastrephidae se han normalizado mediante un modelo de crecimiento alométrico, para evitar la influencia del efecto tamaño de los individuos. A continuación, mediante un análisis discriminante, se han estudiado las diferencias morfológicas entre las cuatro especies, así como entre machos y hembras. Las medidas que presentaban mayores variaciones eran la anchura y longitud del rostro superior. Mediante funciones discriminantes lineales de las medidas morfológicas normalizadas de sus picos, se han conseguido clasificar las cuatro especies de Ommastraphidae, con una fiabilidad superior al 97%. Asimismo, a través de sus medidas morfológicas, se ha encontrado un claro dimorfismo sexual en los picos de O. bartramii e I. argentinus. El presente estudio sugiere que las medidas morfológicas pueden ser útiles para clasificar correctamente los Ommastrephidae a nivel de género, y puede permitir identificar la especie en contenidos estomacales de depredadores de cefalópodos, lo cual mejorará el conocimiento del papel de los cefalópodos en los ecosistemas marinos en los que se integran

Meta-augmented Prompt Tuning for Better Few-shot Learning

Prompt tuning is a parameter-efficient method, which freezes all PLM parameters and only prepends some additional tunable tokens called soft prompts to the input text. However, soft prompts heavily rely on a better initialization and may easily result in overfitting under few-shot settings, which causes prompt-tuning performing much worse than fine-tuning. To address the above issues, this paper proposes a novel Self-sUpervised Meta-prompt learning framework with MEtagradient Regularization for few shot generalization (SUMMER). We leverage self-supervised meta-learning to better initialize soft prompts and curriculum-based task augmentation is further proposed to enrich the meta-task distribution. Besides, a novel meta-gradient regularization method is integrated into the meta-prompt learning framework, which meta-learns to transform the raw gradient during few-shot learning into a domain-generalizable direction, thus alleviating the problem of overfitting. Extensive experiments show that SUMMER achieves better performance for different few-shot downstream tasks, and also exhibits a stronger domain generalization ability

Insight into the effect of hospital-based prehabilitation on postoperative outcomes in patients with total knee arthroplasty: A retrospective comparative study

Background: Osteoarthritis (OA) has become one of the most prevalent joint diseases worldwide, leading to a growing burden of pain and disability as populations age. Although there is consistent evidence to support postoperative rehabilitation and high-intensity prehabilitation for total knee arthroplasty (TKA), the clinical outcomes of hospital-based prehabilitation remain unclear. We aimed to evaluate the effect of a hospital-based prehabilitation program on knee score (KS), function score (FS), and length of stay (LOS) among patients with knee OA after TKA. Methods: A retrospective comparative study was conducted at Renmin Hospital of Wuhan University among patients with primary knee OA. Seventy-two postopearative patients who did not undergo the prehabilitation program were included as the control group, while 68 postoperative patients who underwent the prehabilitation program were assigned to the intervention group. All patients went through the same care after TKA. The KS, FS, and pain levels were measured 5 days before surgery, immediately preceding surgery, immediately after the surgery, and at 1 week and 1 month postoperatively. LOS for each patient was recorded. Results: The new prehabilitation training program significantly improved the KS over time in the intervention group. However, no significant between-group difference was identified in the change of FS. The prehabilitation program also provided shorter LOS. Conclusions: The hospital-based prehabilitation program leads to improved recovery, as indicated by higher KS postoperatively, which may result in improved clinical outcomes of TKA