Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Aspect Transfer Learning for Detecting Low Resource Mental Disorders on Social Media

Mental disorders are a serious and increasingly relevant public health issue. NLP methods have the potential to assist with automatic mental health disorder detection, but building annotated datasets for this task can be challenging; moreover, annotated data is very scarce for disorders other than depression. Understanding the commonalities between certain disorders is also important for clinicians who face the problem of shifting standards of diagnosis. We propose that transfer learning with linguistic features can be useful for approaching both the technical problem of improving mental disorder detection in the context of data scarcity, and the clinical problem of understanding the overlapping symptoms between certain disorders. In this paper, we target four disorders: depression, PTSD, anorexia and self-harm. We explore multi-aspect transfer learning for detecting mental disorders from social media texts, using deep learning models with multi-aspect representations of language (including multiple types of interpretable linguistic features). We explore different transfer learning strategies for cross-disorder and cross-platform transfer, and show that transfer learning can be effective for improving prediction performance for disorders where little annotated data is available. We offer insights into which linguistic features are the most useful vehicles for transferring knowledge, through ablation experiments, as well as error analysis.
How’s Business Going Worldwide ? A Multilingual Annotated Corpus for Business Relation Extraction

The business world has changed due to the 21st century economy, where borders have melted and trades became free. Nowadays,competition is no longer only at the local market level but also at the global level. In this context, the World Wide Web has become a major source of information for companies and professionals to keep track of their complex, rapidly changing, and competitive business environment. A lot of effort is nonetheless needed to collect and analyze this information due to information overload problem and the huge number of web pages to process and analyze. In this paper, we propose the BizRel resource, the first multilingual (French,English, Spanish, and Chinese) dataset for automatic extraction of binary business relations involving organizations from the web.This dataset is used to train several monolingual and cross-lingual deep learning models to detect these relations in texts. Our results are encouraging, demonstrating the effectiveness of such a resource for both research and business communities. In particular, we believe multilingual business relation extraction systems are crucial tools for decision makers to identify links between specific market stakeholders and build business networks which enable to anticipate changes and discover new threats or opportunities. Our work is therefore an important direction toward such tools.
An Improved K-Epsilon Model for Near-Wall Turbulence and Comparison with Direct Numerical Simulation

An improved k-epsilon model for low Reynolds number turbulence near a wall is presented. The near-wall asymptotic behavior of the eddy viscosity and the pressure transport term in the turbulent kinetic energy equation is analyzed. Based on this analysis, a modified eddy viscosity model, having correct near-wall behavior, is suggested, and a model for the pressure transport term in the k-equation is proposed. In addition, a modeled dissipation rate equation is reformulated. Fully developed channel flows were used for model testing. The calculations using various k-epsilon models are compared with direct numerical simulations. The results show that the present k-epsilon model performs well in predicting the behavior of near-wall turbulence. Significant improvement over previous k-epsilon models is obtained.
-96-A New Circuit Model of Bipolar Junction Transistor

: Bipolar junction transistor or BJT is supposed to cost much more power and areas than MOSFET, but its frequency domain response seems better than MOSFETs. In this paper, a new circuit model for BJT is proposed, and the three-terminal circuit includes two MOSFETs and one Schottky diode, which can then be denoted by one MOSFET and a reversely-biased diode. If the new circuits are applied in technology processes for manufacturing, then MOS technology may be illuminated by early BJT to fabricate new devices.
Dynamic-Deep Compression: Tuning Cloud Costs and ECG Task Performance

Monitoring medical data, e.g., Electrocardiogram (ECG) signals, is a common application of Internet of Things (IoT) devices. Compression methods are often applied on the massive amounts of sensor data generated prior to sending it to the Cloud to reduce the storage and delivery costs. A lossy compression provides high compression gain (CG), but may reduce the performance of an ECG application (downstream task) due to information loss. Previous works on ECG monitoring focus either on optimizing the signal reconstruction or the task’s performance. Instead, we advocate a lossy compression solution that allows configuring a desired performance level on the downstream tasks while maintaining an optimized CG that reduces Cloud costs. We propose Dynamic-Deep, a task-aware compression geared for Cloud systems. Our compressor is trained to optimize the CG while maintaining the performance requirement of the downstream tasks chosen out of a wide range. In deployment, the IoT edge device adapts the compression and sends an optimized representation for each data segment, accounting for the downstream task’s desired performance without relying on feedback from the Cloud. We conduct an extensive evaluation of our approach on common ECG datasets using two popular ECG applications, which includes heart rate (HR) arrhythmia classification. We demonstrate that Dynamic-Deep can be configured to improve HR classification F1-score by a factor of 3 and increases CG by up to 83% compared to the previous state-of-the-art (autoencoder-based) compressor. Analyzing Dynamic-Deep on the Google Cloud Platform, we observe a 97% reduction in cloud costs compared to a no compression solution. To the best of our knowledge, Dynamic-Deep is the first proposal to focus on balancing the need for high performance of cloud-based downstream tasks and the desire to achieve optimized compression in IoT ECG monitoring settings.
Argument Similarity Assessment in German for Intelligent Tutoring: Crowdsourced Dataset and First Experiments

NLP technologies such as text similarity assessment, question answering and text classification are increasingly being used to develop intelligent educational applications. The long-term goal of our work is an intelligent tutoring system for German secondary schools, which will support students in a school exercise that requires them to identify arguments in an argumentative source text. The present paper presents our work on a central subtask, viz. the automatic assessment of similarity between a pair of argumentative text snippets in German. In the designated use case, students write out key arguments from a given source text; the tutoring system then evaluates them against a target reference, assessing the similarity level between student work and the reference. We collect a dataset for our similarity assessment task through crowdsourcing as authentic German student data are scarce; we label the collected text pairs with similarity scores on a 5-point scale and run first experiments on the task. We see that a model based on BERT shows promising results, while we also discuss some challenges that we observe.
The Synergy of Ultraviolet QSO Absorption Spectroscopy and 21 cm Emission Studies

The H I 21 cm transition provides a valuable and unique probe of the roles that gas plays in galaxy evolution and cosmology, and our understanding of these roles is far from complete. This talk discussed the synergy obtained by combining 21 cm observations with ultraviolet measurements of QSO absorption lines. In some respects, QSO absorption spectroscopy provides a much more sensitive tool for the study of intergalactic and interstellar gases—QSO absorption is six orders of magnitude more sensitive to NHI than 21 cm emission, and absorption spectroscopy reveals a rich array of metals including low‐, intermediate‐, and high‐ionization stages and even various molecules—but the UV absorption data only probes the pencil beam to the background QSO. This brief contribution provides an example of how the combination of UV absorption spectroscopy and 21 cm emission provides a more complete picture of the nature of the gas.
-43-Growth and Characterization of InGaAs Nanowires

: In this paper, high crystal quality InAs, GaAs and InGaAs nanowires were grown by chemical vapor deposition in a double temperature zone tubular furnace by accurately controlling the source material and substrate temperature. Scanning electron microscopy (SEM) showed that the nanowires had uniform diameter, smooth surface and length up to tens of microns. XRD results showed that the nanowires were evenly distributed in composition and have a single crystal wurtzite structure. Raman spectral images further verified the crystal structure and composition of the nanowires. They showed that high-quality InGaAs nanowires were grown. The photodetector has good spectral response characteristics and sensitive sensing ability to rapidly changing optical signals.
Tackling Irony Detection using Ensemble Classifiers

Automatic approaches to irony detection have been of interest to the NLP community for a long time, yet, state-of-the-art approaches still fall way short of what one would consider a desirable performance. In part this is due to the inherent difficulty of the problem. However, in recent years ensembles of transformer-based approaches have emerged as a promising direction to push the state of the art forward in a wide range of NLP applications. A different, more recent, development is the automatic augmentation of training data. In this paper we will explore both these directions for the task of irony detection in social media. Using the common SemEval 2018 Task 3 benchmark collection we demonstrate that transformer models are well suited in ensemble classifiers for the task at hand. In the multi-class classification task we observe statistically significant improvements over strong baselines. For binary classification we achieve performance that is on par with state-of-the-art alternatives. The examined data augmentation strategies showed an effect, but are not decisive for good results.
ProDial – An Annotated Proactive Dialogue Act Corpus for Conversational Assistants using Crowdsourcing

Robots will eventually enter our daily lives and assist with a variety of tasks. Especially in the household domain, robots may become indispensable helpers by overtaking tedious tasks, e.g. keeping the place tidy. Their effectiveness and efficiency, however, depend on their ability to adapt to our needs, routines, and personal characteristics. Otherwise, they may not be accepted and trusted in our private domain. For enabling adaptation, the interaction between a human and a robot needs to be personalized. Therefore, the robot needs to collect personal information from the user. However, it is unclear how such sensitive data can be collected in an understandable way without losing a user’s trust in the system. In this paper, we present a conversational approach for explicitly collecting personal user information using natural dialogue. For creating a sound interactive personalization, we have developed an empathy-augmented dialogue strategy. In an online study, the empathy-augmented strategy was compared to a baseline dialogue strategy for interactive personalization. We have found the empathy-augmented strategy to perform notably friendlier. Overall, using dialogue for interactive personalization has generally shown positive user reception.
Analysis of the Relationship between Financial Technology Development and Income Distribution

: China's economy has achieved rapid development, and its per capita income has officially entered the ranks of upper-middle-income countries. However, it should be noted that while my country's economy is growing rapidly, the people do not enjoy the fruits of development equally, and the gap between the rich and the poor in my country still remains. In a higher position. Whether the gap between the rich and the poor can be maintained within a reasonable range is related to whether we can adhere to the concept of shared development and lead the people to gradually achieve common prosperity. At present, the development of financial technology is booming and changing with each passing day. Relying on artificial intelligence, blockchain, cloud computing and big data technology has greatly improved the benefits of financial services, that is, reducing costs and improving efficiency. The development of financial technology does take into account the marginal long-tail distribution. So, can the development of financial technology improve my country's income inequality and narrow the gap between the rich and the poor? This paper selects China's panel data from 2008 to 2020, and empirically tests the linear relationship between fintech and income inequality within the sample interval. With the development of fintech, income inequality will worsen. This conclusion is consistent with the current development of fintech in my country. It is basically consistent with the reality of income disparity, and is closely related to the fact that my country's fintech is in its infancy and that with the vigorous development of the financial industry, financial assets have exacerbated income inequality.
DeepREF: A Framework for Optimized Deep Learning-based Relation Classification

The Relation Extraction (RE) is an important basic Natural Language Processing (NLP) for many applications, such as search engines, recommender systems, question-answering systems and others. There are many studies in this subarea of NLP that continue to be explored, such as SemEval campaigns (2010 to 2018), or DDI Extraction (2013). For more than ten years, different RE systems using mainly statistical models have been proposed as well as the frameworks to develop them. This paper focuses on frameworks allowing to develop such RE systems using deep learning models. Such frameworks should make it possible to reproduce experiments of various deep learning models and pre-processing techniques proposed in various publications. Currently, there are very few frameworks of this type, and we propose a new open and optimizable framework, called DeepREF, which is inspired by the OpenNRE and REflex existing frameworks. DeepREF allows the employment of various deep learning models, to optimize their use, to identify the best inputs and to get better results with each data set for RE and compare with other experiments, making ablation studies possible. The DeepREF Framework is evaluated on several reference corpora from various application domains.
Strategy-level Entrainment of Dialogue System Users in a Creative Visual Reference Resolution Task

In this work, we study entrainment of users playing a creative reference resolution game with an autonomous dialogue system. The language understanding module in our dialogue system leverages annotated human-wizard conversational data, openly available knowledge graphs, and crowd-augmented data. Unlike previous entrainment work, our dialogue system does not attempt to make the human conversation partner adopt lexical items in their dialogue, but rather to adapt their descriptive strategy to one that is simpler to parse for our natural language understanding unit. By deploying this dialogue system through a crowd-sourced study, we show that users indeed entrain on a “strategy-level” without the change of strategy impinging on their creativity. Our work thus presents a promising future research direction for developing dialogue management systems that can strategically influence people’s descriptive strategy to ease the system’s language understanding in creative tasks.
Problem-solving Recognition in Scientific Text

As far back as Aristotle, problems and solutions have been recognised as a core pattern of thought, and in particular of the scientific method. In this work, we present the novel task of problem-solving recognition in scientific text. Previous work on problem-solving either is not computational, is not adapted to scientific text, or has been narrow in scope. This work provides a new annotation scheme of problem-solving tailored to the scientific domain. We validate the scheme with an annotation study, and model the task using state-of-the-art baselines such as a Neural Relational Topic Model. The agreement study indicates that our annotation is reliable, and results from modelling show that problem-solving expressions in text can be recognised to a high degree of accuracy.
The strategic dynamism of digital platforms: An analysis of the online training market

: Platforms play a key role in the architecture of various markets. The market of online training is typical. Platforms have sprung up following a new business model based on strategies for using digital technology to capture and create value. Business models in the training market have developed around two-sided platforms, which enable them to make offers that simultaneously attract categories of users on each side of the market. Nonetheless, the online platforms in education, their numbers swelling, have a plurality of strategies for gaining leverage so as to process collected data and use algorithms. The characteristics of these strategies are analyzed by identifying their technostrategic attributes.
PHMT_A_334744 309..323

Migraine is a neurological disorder that affects millions of children and adolescents worldwide. Chronic migraine is a subtype of migraine in which patients experience headaches for more days than not each month, with accompanying symptoms of phonophobia, photophobia, nausea or vomiting for most of these headaches. The burden and impact of chronic migraine in the daily lives of children and adolescents is substantial, requiring a holistic, multidisciplinary, and biopsychosocial approach to conceptualization and treatment. The purpose of this review is to provide a comprehensive “2022” overview of acute and preventive treatments for the management of chronic migraine in youth. We first describe diagnostic criteria for chronic migraine and highlight the state of evidence for acute and preventive treatment in children and adolescents. We then discuss emerging treatments currently receiving rigorous clinical research effort, special considerations for the treatment of chronic migraine in children and adolescents, and avenues for improving existing treatments and expanding access to evidence-based care.
Tides and Tidal Capture in post-Main Sequence Binaries: A Period Gap for Planets Around White Dwarfs

The presence of a close, low-mass companion is thought to play a substantial and perhaps necessary role in shaping post-Asymptotic Giant Branch and Planetary Nebula outflows. During post-main-sequence evolution, radial expansion of the primary star, accompanied by intense winds, can significantly alter the binary orbit via tidal dissipation and mass loss. To investigate this, we couple stellar evolution models (from the zero-age main-sequence through the end of the post-main sequence) to a tidal evolution code. The binary's fate is determined by the initial masses of the primary and the companion, the initial orbit (taken to be circular), and the Reimer's mass-loss parameter. For a range of these parameters, we determine whether the orbit expands due to mass loss or decays due to tidal torques. Where a common envelope phase (CEP) ensues, we estimate the final orbital separation based on the energy required to unbind the envelope. These calculations predict a period gap for planetary companions to white dwarfs. The upper end of the gap is the shortest period at which a CEP is avoided. The lower end is the longest period at which companions survive their CEP. We predict a paucity of planetary companions with periods between $\sim130$ days ($\sim0.35$ AU) and $\sim250$ days ($\sim0.6$ AU) around white dwarfs. This is consistent with the tentative detection of an $\sim2$ $M_{\rm J}$ planet in an $\gtrsim 2.7$ AU orbit around GD66.
05 02 18 3 v 2 2 9 Ju l 2 00 5 Ground-state van der Waals forces in planar multilayer magnetodielectrics

Within the frame of lowest-order perturbation theory, the van der Waals potential of a groundstate atom placed within an arbitrary dispersing and absorbing magnetodielectric multilayer system is given. Examples of an atom situated in front of a magnetodielectric plate or between two such plates are studied in detail. Special emphasis is placed on the competing attractive and repulsive force components associated with the electric and magnetic matter properties, respectively, and conditions for the formation of repulsive potential walls are given. Both numerical and analytical results are presented.
Diagnostic test accuracy of waist-to-height ratio as a screening tool for cardiovascular risk in children and adolescents: a meta-analysis

Abstract Context Waist-to-height ratio (WHtR) is a controversial evaluation index of cardiovascular risk factors (CVRFs) in children and adolescents. Objective To assess the accuracy of WHtR as a measure to screen for clusters of at least one CVRF (CVRF1), two CVRFs (CVRF2), and three CVRFs (CVRF3) in different ages, sexes, regions and cut-offs. Methods The PubMed, Web of Science, EBSCOhost, Springer, Taylor & Francis Online, Wiley Online Library, Wanfang, and CNKI databases were searched for eligible publications up to June 2021. The QUADAS-2 checklist was used to assess the methodology of the included studies. Results Twenty-two studies that evaluated 85281 children and adolescents aged 5–19 years were included in the meta-analysis. The AUSROC values were 0.56 (95% CI: 0.54–0.57), 0.82 (95% CI: 0.81–0.83), and 0.89 (95% CI: 0.89–0.90) for CVRF1, CVRF2, and CVRF3, respectively. Higher AUSROC values were found for adolescents (12–19 years), that is, 0.91 (95% CI: 0.88–0.93), 0.90 (95% CI: 0.87–0.92) for males, and 0.91 (95% CI: 0.90–0.91) for a cut-off of ≥ 0.51 in the identification of CVRF3. Conclusion WHtR can be used as an accurate screening tool for CVRF3 and CVRF2 in children and adolescents, and it is recommended to select different cut-offs according to different ages, sexes, and regions.
Expectations Come True: The Placebo Effect of Exercise on Affective Responses

ABSTRACT Objective: This study investigated the placebo and nocebo effects of habitual and induced expectations. Methods: In Study 1, 95 participants (M age = 19.65 ± 2.21 years, M BMI = 21.94 ± 2.55) were grouped according to habitual expectations (positive, neutral, or negative) and completed a 30-min moderate-intensity aerobic exercise, and their affective response, rating of perceived exertion (RPE), and heart rate variability (HRV) were measured before, during, and after the exercise. In Study 2, the participants from Study 1 were grouped according to induced expectations (control, positive, or negative) and completed a 30-min moderate-intensity aerobic exercise. Affective response, RPE, and HRV were measured before, during, and after exercise. Results: In Study 1, positive habitual expectations increased affective response during and after exercise [F(2, 92) = 2.959, p = . 057, ηp 2 = . 060], and negative habitual expectations increased RPE during exercise [F(2, 92) = 5.174, p = . 007, ηp 2 = . 101]. In Study 2, positive induced expectation increased affective response during [F(2, 86) = 5.492, p = . 006, ηp 2 = . 113] and after exercise [F(2, 86) = 6.096, p = . 003, ηp 2 = . 124] and was not affected by habitual expectation. Conclusion: The affective response during and after exercise is influenced by habitual and induced expectations. Positive expectations enhance affective benefits, while negative expectations reduce affective benefits.