Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Correlates of Mentalizing in Individuals With Clinical High Risk for Schizophrenia: ALE Meta-Analysis

Psychotic disorder refers to a spectrum of disorders that have multiple etiologies, due to the complex interaction of biological and genetic vulnerability with familial and cultural factors. A clinical high risk (CHR) for schizophrenia is defined as the presence of brief, attenuated, or intermittent psychotic symptoms in non-schizophrenic individuals. The transition to schizophrenia appears significantly more frequent in this at-risk population than in the general population. Moreover, the ability to attribute mental states to others, known as mentalizing or theory of mind, and its neural correlates found in individuals with CHR are similar to those described in patients with schizophrenia. We have therefore explored neurofunctional correlates of mentalizing in individuals with CHR vs. healthy controls, in order to identify the differences in brain activation. A neural coordinate-based activation likelihood estimation meta-analysis of existing neuroimaging data revealed that three regions displayed decreased activation in individuals with CHR, compared with healthy controls: the right temporoparietal junction, the right middle temporal gyrus, and the left precuneus. These results, combined with those in the literature, further support the hypothesis that abnormal activation of posterior brain regions involved in mentalizing correlates with psychotic symptoms in help-seeking individuals.
Understanding intimate partner violence in context: social and community correlates of special and general victimization

ABSTRACT In prior research, intimate partner violence (IPV) victimization has been predominantly studied as distinct from other forms of violent victimization. As a result, relatively little is known about IPV victimization in relation to other violent victimization and the extent to which same people tend to be both IPV and other violent victims. In this study, the combined data from five sweeps of the Finnish National Crime Victim Survey (N = 25,927) is used to examine violent poly-victimization among IPV victims and to compare social and community correlates of IPV victimization and other violent victimization. The results indicate that IPV victims are significantly more likely to be victims of other violent actions than those who have not been victimized by an intimate partner. Moreover, IPV victimization shares similar correlates with other violent victimization. However, more research is needed on the causal mechanisms behind the associations between IPV and general violence.
Molding Liquid Stone: A Computational and Experimental Mixed‐Method Study of 3D Print Formwork for Interlocking Concrete Modules

With bespoke fabrication on one end, and mass production on the other end of the fabrication spectrum, this study investigates custom repetitive manufacturing through molding concrete by using 3D printed formwork. The process demonstrates a proof‐of‐concept for 3D printing elastic resin as a formwork for repeated casting of interlocking concrete blocks. Among the challenges are the method of digitally generating the block geometry and designing the molds to accommodate complex curvatures on four sides of a block while operating within the material limitations of 3D printing with an elastic material. The overall process investigates the limitations of such a system in order to identify future potential for mass customized fabrication employing casting techniques.
Challenging post-communication: Beyond focus on a ‘few bad apples’ to multi-level public communication reform

ABSTRACT Since declaration of post-truth as Oxford Dictionaries’ word of the year in 2016, studies show that ‘fake news’, ‘alternative facts’, and disinformation have continued unabated – and even increased. Fingers have pointed at individuals such as Donald Trump and the activities of Russian ‘troll farms’. Also, global outrage has risen in relation to the deceptive and manipulative practices of organisations such as Cambridge Analytica and social media oligopolies, notably Facebook. However, transdisciplinary research challenges the ‘few bad apples’ argument and proposes that a wide range of culprits are responsible for what this study calls post-communication. Based on a review of reports related to public communication practices, and key informant interviews, this discussion proposes that reforms are required at three levels: top-down, such as updated regulation and legislation; bottom-up, including new approaches to media literacy; and increased attention to ethics and standards by professionals in advertising, marketing, public relations, government and political communication, and journalism.
Driving Under the Influence of Alcohol and Drugs: A Scoping Review

ABSTRACT Driving under the influence (DUI) of alcohol and drugs is a serious health and social issue and a leading cause of death and disability. We present the findings of a scoping review of the literature that examined behaviors and risk factors associated with DUI as well as currently available preventative interventions. To truly impact DUI behaviors, prevention and intervention strategies must educate on both social perceptions and associated risks, consider the root cause behind excessive or hazardous use leading to DUI, and address the general mental health of our society at large as they turn to substances as means of coping.
Tensions in care and control in the middle school classroom: Lessons for equitable social-emotional learning

Abstract Educators’ efforts at fostering social and emotional learning are grounded in, and closely related to caring: a belief that developing social and emotional skills will help students to flourish both personally and academically. Yet, research has shown that many middle grades students do not feel cared for in school and find educators’ efforts at caring alienating and inauthentic. This is particularly true for students from traditionally marginalized groups. One reason is educators struggle to balance caring with their positional and relational power. This study examines how students perceived the efforts of staff members in two diverse middle schools as they sought to balance caring and control in their efforts to practice social and emotional education. We suggest key intersections of care and control that underpin equitable social and emotional education.
‘Bad language’ in the Nordics: profanity and gender in a social media corpus

ABSTRACT This study looks at the relative frequency of ‘bad language’ according to gender in Nordic languages and in English in a 210-million-token corpus of messages by 18,686 Nordic Twitter users. For the Nordic languages, more than 19,000 ‘bad-language’ word forms were compiled on the basis of usage note annotations in major Nordic-language dictionaries. The most frequent terms overall are swear words, and while males use more of these items on average, the gender difference is less pronounced for English words. For potentially offensive words in the Nordic languages, males make more use of traditional profanities associated with the Devil, religion, and blasphemy. Both genders make more use of profanities when tweeting to people of their own gender. The study provides empirical evidence for a small gender-based discrepancy in the use of profanity in social media in the Nordic languages, mirroring results previously found in corpus-based studies of English-language data. The results are interpreted in light of previous findings as evidence for a gendered difference in sensitivity toward the use of language that could potentially be offensive.
Confidence-Aware Embedding for Knowledge Graph Entity Typing

Knowledge graphs (KGs) entity typing aims to predict the potential types to an entity, that is, (entity, entity type = ? ). Recently, several embedding models are proposed for KG entity types prediction according to the existing typing information of the (entity, entity type) tuples in KGs. However, most of them unreasonably assume that all existing entity typing instances in KGs are completely correct, which ignore the nonnegligible entity type noises and may lead to potential errors for the downstream tasks. To address this problem, we propose ConfE, a novel confidence-aware embedding approach for modeling the (entity, entity type) tuples, which takes tuple confidence into consideration for learning better embeddings. Specifically, we learn the embeddings of entities and entity types in separate entity space and entity type space since they are different objects in KGs. We utilize an asymmetric matrix to specify the interaction of their embeddings and incorporate the tuple confidence as well. To make the tuple confidence more universal, we consider only the internal structural information in existing KGs. We evaluate our model on two tasks, including entity type noise detection and entity type prediction. The extensive experimental results in two public benchmark datasets (i.e., FB15kET and YAGO43kET) demonstrate that our proposed model outperforms all baselines on all tasks, which verify the effectiveness of ConfE in learning better embeddings on noisy KGs. The source code and data of this work can be obtained from https://github.com/swufenlp/ConfE.
Prediction Approaches for Smart Cultivation: A Comparative Study

Crop cultivation is one of the oldest activities of civilization. For a long time, crop production was carried out based on knowledge passed from generation to generation. However, due to the rapid growth in the human population of the world, human knowledge-based cultivation is not enough to meet the demanding need. To address this issue, the usage of machine learning-based tools has been studied in this paper. An experiment has been carried out over 0.3 million data. This dataset identifies 46 prominent parameters for cultivation, which is collected from the Department of Agriculture Extension, Bangladesh. Comparison between neural networks and numbers of machine learning algorithms has been carried out in this research. It is observed that the neural network outperforms the other methods by maintaining an average prediction accuracy of 96.06% for six different crops. Other contemporary machine learning algorithms, namely, support vector machine, random forest, and logistic regression, have average prediction accuracy of around 68.9%, 91.2%, and 62.39%, respectively.
Le débourrement des bourgeons de la vigne : méthode de prévision et principes d'établissement d'une échelle de précocité de débourrement

La vitesse de debourrement des bourgeons de la Vigne (V) varie en fonction de la temperature (t) suivant une loi de nature logarithmique entre 5 et 25°C (V = K.tc). Les coefficients K et c sont relies entre eux par une relation lineaire, ce qui permet de caracteriser chaque variete par un seul coefficient varietal de precocite de debourrement (c). Dans les conditions naturelles, les temperatures journalieres exercent sur chaque variete une action specifique cumulative (aj = Vt = K.tc) qui fait progresser l'etat physiologique de bourgeons dormants d'une maniere differentielle durant la periode qui precede le debourrement. En faisant la somme (S) des actions journalieres (aj) il est possible de proposer une methode de prevision de l'epoque approximative du debourrement de la vigne dans les conditions naturelles. Elle est basee sur une evaluation precise de l'etat physiologique des bourgeons latents de varietes de reference en fonction de la temperature, durant la phase de pre-debourrement. Le developpement de cette methode a permis d'etablir une echelle de precocite de debourrement pour 22 cepages et de determiner les parametres varietaux qui caracterisent chacun d'entre eux (somme S des actions des temperatures journalieres, coefficient varietal de precocite de debourrement c, seuil de croissance apparente ou temperature de debourrement c, seuil de croissance apparente ou temperature de debourrement). Grâce a cette methodologie, il est possible de determiner ces parametres pour de nombreux cepages et d'etablir une echelle precise de precocite de debourrement. +++The variation of the rate of grapevine budburst (V) in relation to temperature (t) is a logarithme form (V = K.tc) between 5 and 25°C. The coefficients K and C are connected by a linear relation, so this makes possible to express earch variery by a single coefficient or varietal coefficient of budburst earliness (c). Under natural conditions, the daily temperatures exert on each variety a specific and cumulative action (aj = Vt = K.tc) which makes the pysiological status of dormant buds to progress in a differential way during the period of pre-bursting. Thanks to the sum (S) of the daily actions (aj), it is possible to propose a methode of forecasting the time of grapevine budburst under natural conditions. It is founded on a precise estimate of the physiological status of dormant buds as a function of temperatures for reference varieties, during the period of pre-bursting. So it is possible with this methode to set up a budburst scale for 22 varieties and to calculate the varietal parameters which characterize every one (sum S of the daily temprature actions, varietal coefficient of budburst earliness c, threshold of apparent growth or budburst temperature). Thanks to this methodology, it is possible to determine these parameters for any variety and to set up a precise scale of budburst.
Multi-Task Transfer Learning for Fine-Grained Named Entity Recognition

This paper describes Studio Ousia’s participation to the EDL track of TAC KBP 2019— (ultra) ﬁne-grained named entity recognition (NER). The proposed system ﬁrst trains a YAGO-based ultra ﬁne-grained NER model in a multi-label, multi-task fashion. The pre-trained model is then ﬁne-tuned to adopt it to the AIDA taxonomy by adding a lightweight conversion layer. The experiments have shown that this transfer learning approach outperforms a simpler direct method which directly trains the YAGO-based NER model.
Identification of Hub Genes Associated with Lung Adenocarcinoma Based on Bioinformatics Analysis

Lung adenocarcinoma (LUAD) is one of the malignant lung tumors. However, its pathology has not been fully understood. The purpose of this study is to identify the hub genes associated with LUAD by bioinformatics methods. Three gene expression datasets including GSE116959, GSE74706, and GSE85841 downloaded from the Gene Expression Omnibus (GEO) database were used in this study. The differentially expressed genes (DEGs) related to LUAD were screened by using the limma package. Gene Ontology (GO) and KEGG analysis of DEGs were carried out through the DAVID website. The protein-protein interaction (PPI) of differentially expressed genes was drawn by the STRING website, and the results were imported into Cytoscape for visualization. Then, the PPI network was analyzed by using MCODE, and the modules with a score greater than 5 were found by using cytoHubba. Finally, the GEPIA database and UALCAN database were used to verify and analyze the survival of hub genes. We identified 67 upregulated genes and 277 downregulated genes from three LUAD datasets. The results of GO analysis showed that the downregulated genes were significantly enriched in matrix adhesion and angiogenesis and upregulated differential genes were significantly enriched in cell adhesion and vascular development. KEGG pathway analysis showed that the differential genes of LUAD were significantly enriched in viral carcinogenesis and adhesion spots. The PPI network of differentially expressed genes consists of 269 nodes and 625 interactions. In addition, three modules with scores greater than 5 and seven hub genes, namely, MCM4, BIRC5, CDC20, CDC25C, FOXM1, GTSE1, and RFC4, playing an important role in the PPI network were screened out. In this study, we obtained the hub genes and pathways related to LUAD, revealing the molecular mechanism and pathogenesis of LUAD, which is helpful for the early detection of LUAD and provides a new idea for the treatment of LUAD.
Immersion and Invariance Adaptive Control for Spacecraft Pose Tracking via Dual Quaternions

This paper addresses the simultaneous attitude and position tracking of a target spacecraft in the presence of general unknown bounded disturbances in the framework of dual quaternions, which provides a concise and integrated description of the coupled rotational and translational motions. By virtue of the newly introduced dual direction cosine matrix, the dimension of the dual quaternion-based relative motion dynamics written in vector/matrix form can be lowered to six. Treating the disturbances as unknown parameters, a modular adaptive pose tracking control scheme composed of two separately designed parts is then derived. One part is the adaptive disturbance estimator designed based on the immersion and invariance theory. Driven by the disturbance estimation errors, it can realize exponential convergence of the estimations and has the nice “parameter lock” property, which can hardly be expected in the conventional certainty equivalent adaptive controllers. The other part is a proportional-derivative-like pose tracking controller where the estimated disturbances are directly used. The closed-loop stability of the relative motion system under different kinds of disturbances is proven by Lyapunov stability analysis. Simulations and comparisons with two previous dual quaternion-based controllers demonstrate the novel features and performance improvements of the proposed control scheme.
An Improved PageRank Algorithm Based on Text Similarity Approach for Critical Standards Identification in Complex Standard Citation Networks

A standard system, which is a powerful tool in maintaining the normal operations and development of a specific industry, is intrinsically a complex network composed of numerous standards which coordinate and interact with each other. In a networked standard system, the identification of critical standards is of great significance when drafting and revising standards. However, a majority of the existing literature has focused on the citation relationships between standards while ignoring the intrinsic interdependent relationships between the contents of standards. To overcome this limitation, we utilize the text similarity approach (TSA) to quantify the relationship intensity between each pair of standards, in order to generate a directed weighted network. The critical contribution of this study is that the similarity computed by the TSA is incorporated into the traditional PageRank algorithm for the identification of critical standards. The improved algorithm comprehensively considers the quantity and importance of neighboring standards and the citation intensity, as quantified by TSA. The algorithm is finally validated using the Chinese environmental health standards through comparison with the traditional PageRank algorithm and different classic measurements.
Optimization of Internet of Things E-Commerce Logistics Cloud Service Platform Based on Mobile Communication

E-commerce conceivable future trade, consumption, and service is a new digital employer mode. Therefore, in order to decorate the customary natural environment of operation, it is quintessential to get rid of the preferred desktop in the true field, create a social logistics and transportation administration computing device with commodity agents and distributors as the key features, and mix freight logistics, business enterprise approach waft and data waft advertising and marketing, and advertising and marketing organically. The notion of e-commerce logistics looks alongside the enchantment vogue of neighborhood technological knowledge and social demand. It is an integral part of ending e-commerce authentic economic value. All kinds of cell networks clear up the problem of data flow communication; in the technological know-how of telephone network, digital cost solves the bother of capital flow; at present, logistics is however a long-standing problem of e-commerce. Logistics has grown to be the last and entirely bottleneck of e-commerce. If logistics cannot adapt to the enchantment of e-commerce, it may also additionally lead to the slow enchantment of e-commerce. The purpose of this paper is to supply an Internet of things (IoT) e-commerce logistics cloud provider platform based totally absolutely on cell communication, which can be combined with e-commerce and beautify the transport tempo of e-commerce. This paper moreover optimizes the e-commerce logistics system, so that it can combine with e-commerce to increase out the distribution of goods, the distribution tempo of e-commerce is doubled, and e-commerce can in a similar fashion develop rapidly.
Spatial Spillover Effect of Government Public Health Spending on Regional Economic Growth during the COVID-19 Pandemic: An Evidence from China

The COVID-19 pandemic, which was first reported at the end of 2019, has had a massive impact on the Chinese economy and society. The pandemic has seriously tested the emergency management capabilities of the Chinese government regarding public health. Based on the panel data of 31 provinces in China for the period of 2006–2019, this paper examines the impacts of government public health spending on regional economic growth. Furthermore, the possibility of spatial spillover effects of government public health spending is investigated through spatial econometric analysis. Government public health spending and regional economic growth have significant positive spatial correlation and spatial agglomeration effects. The indicator of government public health spending significantly promotes regional economic growth. In addition, it significantly promotes the economic growth of neighboring areas through certain spatial spillovers.
Sample Identifiers and Metadata to Support Data Management and Reuse in Multidisciplinary Ecosystem Sciences

Joan E. Damerow1, Charuleka Varadharajan1, Kristin Boye2, Eoin L. Brodie1, Madison Burrus1, K. Dana Chadwick1,3, Robert Crystal-Ornelas1, Hesham Elbashandy4, Ricardo J. Eloy Alves1, Kim S. Ely5, Amy E. Goldman6, Ted Haberman7, Valerie Hendrix4, Zarine Kakalia1, Kenneth M. Kemner8, Annie B. Kersting9, Nancy Merino9, Fianna O’Brien4, Zach Perzan3, Emily Robles1, Patrick Sorensen1, James C. Stegen6, Ramona L. Walls10, Pamela Weisenhorn8, Mavrik Zavarin9, and Deborah Agarwal4
Two-Stage User Identification Based on User Topology Dynamic Community Clustering

In order to solve the problem of node information loss during user matching in the existing user identification method of fixed community across the social network based on user topological relationship, Two-Stage User Identification Based on User Topology Dynamic Community Clustering (UIUTDC) algorithm is proposed. Firstly, we perform community clustering on different social networks, calculate the similarity between different network communities, and screen out community pairs with greater similarity. Secondly, two-way marriage matching is carried out for users between pairs of communities with high similarity. Then, the dynamic community clustering was performed by resetting the different community clustering numbers. Finally, the iteration is repeated until no new matching user pairs are generated, or the set number of iterations is reached. Experiments conducted on real-world social networks Twitter-Foursquare datasets demonstrate that compared with the global user matching method and hidden label node method, the average accuracy of the proposed UIUTDC algorithm is improved by 33% and 26.8%, respectively. In the case of only user topology information, the proposed UIUTDC algorithm effectively improves the accuracy of identity recognition in practical applications.