Miracle-dz/UniFAR-FTUs huggingface dataset:
Coronavirus Pandemic Anxiety impact during COVID-19: a systematic review

Introduction: The COVID-19 epidemic has been causing serious physical, but also psychological effects in society. This systematic review sought to identify studies that describe COVID-19 related anxiety, and to understand the impact of anxiety assessment in defining strategies to be implemented in future studies. Methodology: This systematic review included cross-sectional studies with no publication year limit. It was performed a systematic search through three databases, namely, PubMed, Science Direct and Web of Science using the descriptors “COVID-19” and “anxiety”. PRISMA criteria reporting of systematic reviews and meta-analyses were applied. Eligible articles were selected in accordance with inclusion and exclusion criteria. The inclusion criteria were: research articles related with anxiety measurement during the COVID-19 outbreak; interventions to reduce anxiety; and published in English. Results: From 44 references, just four scientific articles were accepted for inclusion within this review. These studies were analyzed regarding their sample, methodology, instruments used, and its results. Conclusions: This systematic review was based on published data at the onset of the pandemic, and it could serve as a basis for the development of implementations plans to improve anxiety disorders. The importance of this theme, the implications and potential directions for future investigations will be discussed.
Learning in-the-wild Temporal 3D Pose Estimation fromMoCap Data

Recovering 3D poses from 2D representations has been a challenging task due to its inherent ambiguity and complexities of the humanmotion. Lack of annotated data makes it even harder to train neural networks for 3Dpose estimation task. In this work, we propose a temporal two-stage architecture to estimate sequences of 3D poses from 2D joint detections, for which we use only 3Dmotion capture data without paired images for training. More specifically, we generate paired examples by projecting augmented 3D poses to 2D, on-the-fly. Wemodify our inputs during training through noise andmasking to obtainmodels robust to 2D detection errors. Our approaches utilize the relative simplicity of augmenting 2D and 3D poses rather than images that lie in higher dimensions. Resulting framework employs a simplemodel, trained on poses without paired images, that can achieve competitive performance on common evaluation scenarios. Wepresent ourwork as abaseline for the task at hand, discussing further directions for building upon the proposedmethodology.
Dog Breed Classification using Convolutional Neural Networks and Transfer Learning

: Convolution Neural Networks (CNN) with the help of transfer learning the system used for dog breed classification and detection. It also considers the combination of multiple basic CNN models for dog breed can be classified and give immense improved performances. This model supports the classification of 133 dog breeds whereas capital punishment the model. CNN may be a structure designed to method real-world user-defined pictures. Consider, a dog image is chosen the model can classify Associate in Nursing establish an analysis of the dog breeds, this model is used as a neighborhood of mobile or net app for the real-world and user-definition image. Once the image is given to the model for execution, it analyzes if a dog is a gift and returns the estimated breed. During this model, we tend to use a pair of datasets that are dog and human. Whereas we tend to execute our scratch model it’s impossible to induce high accuracy therefore we tend to come up with the Resnet50 model that has high accuracy share up to eighty-six and higher than roughly that processes information at intervals less time with the assistance of GPU (Graphical Process Unit). The result that occurred has shown Brobdingnagian improvement of accuracy as compared to the scratch model.
Coordinate Difference Matrices | SIAM Journal on Matrix Analysis and Applications | Vol. 41, No. 1 | Society for Industrial and Applied Mathematics

In many problems such as phase retrieval, molecular biology, source localization, and sensor array calibration, one can measure vector differences between pairs of points and attempt to recover the position of these points; this class of problems is called vector geometry problems (VGPs). A closely related field studies distance geometry problems (DGPs), where only the Euclidean distance between pairs of points is available. This has been extensively studied in the literature and is often associated with Euclidean distance matrices (EDMs). Although similar to DGPs, VGPs have received little attention in the literature; our goal is to fill in this gap and introduce a framework to solve VGPs. Inspired by EDM-related approaches, we arrange the differences in what we call a coordinate difference matrix (CDM) and introduce a methodology to reconstruct a set of points from CDM entries. We first propose a reconstruction scheme in 1D and then generalize it to higher dimensions. We show that our algorithm is optimal in the least-squares sense, even when we have only access to partial measurements. In addition, we provide necessary and sufficient conditions on the number and structure of measurements needed for a successful reconstruction, as well as a comparison with EDMs. In particular we show that compared to EDMs, CDMs are simpler objects, both from an algorithmic and a theoretical point of view. Therefore, CDMs should be favored over EDMs whenever vector differences are available. In the presence of noise, we provide a statistical analysis of the reconstruction error. Finally, we apply the established knowledge to five practical problems to demonstrate the versatility of this theory and showcase the wide range of applications covered by the CDM framework.
Winterface: a postprocessing tool for ab initio quantum transport simulations

In this work a framework for quantum transport simulation from first principles is introduced, focusing on the coherent case. The model is based on the non-equilibrium Green’s function (NEGF) formalism and maximally localized Wannier functions (MLWFs). Any device simulation, here based on two-dimensional (2-D) materials, starts by identifying a representative unit cell, computing its electronic structure with density functional theory (DFT), and converting the plane-wave results into a set of MLWFs. From this localized representation of the original unit cell, the device Hamiltonian can be constructed with the help of properly designed upscaling techniques. Here, a powerful tool called Winterface is presented to automatize the whole process and interface the initial MLWF representation with a quantum transport solver. Its concepts, algorithms, and general functionality are discussed on the basis of a molybdenum disulfide (2-D) monolayer structure, as well as its combination with tungsten disulfide. The developed approach can be considered as completely general, restricted only by the capability of the user to perform the required DFT calculations and to ”wannierize” its plane-wave results.
Webis at TREC 2020: Health Misinformation Track Extended Abstract

We give a brief overview of the Webis group’s participation in the TREC 2020 Health Misinformation track. e baseline retrieval results of our search engine ChatNoir (BM25F-based) are re-ranked in two di erent approaches: (1) axiomatically re-ranking the top-20 initial results for argumentative topics / queries, and (2) formulating keyqueries to retrieve relevant documents at the top ranks. Our axiomatic re-ranking uses three axioms that capture argumentative-ness, while for the keyqueries approach, we use low-e ort manual pilot judgments to identify several relevant documents per topic.
My Partner was a Good Partner: Investigating the Relationship between Dialogue Acts and Satisfaction among Middle School Computer Science Learners

: Collaborative dialogue provides a rich information source for understanding the effectiveness of student interactions. While many studies emphasize the importance of productive dialogue behaviors, the impact of those behaviors on learners’ perceptions of their partners is not yet understood. This paper examines a dialogue corpus of 18 pairs of middle school students as they engage in block-based coding activities. We tagged the corpus with a collaborative dialogue act taxonomy and identified sequences of one to two dialogue acts ( n - grams) that are significantly associated with partner satisfaction during collaborative learning. Six n-grams were found to be significant predictors: n -grams that were positively associated with satisfaction included some questions and clarifications. In contrast, n -grams that were negatively associated with satisfaction included off-task utterances, pairs of consecutive questions, and unexpectedly, positive feedback. These findings contribute to our understanding of how learners prefer to interact with their partners and how that interaction impacts collaborative experiences.
High spatial resolution microdosimetry with monolithic Delta E-E High spatial resolution microdosimetry with monolithic Delta E-E detector on C-12 beam: Monte Carlo simulations and experiment detector on C-12 beam: Monte Carlo simulations and experiment

Abstract Nuclear fragmentation produced in 12 C ion therapeutic beams contributes significantly to the Relative Biological Effectiveness (RBE)—weighted dose in the distal edge of the Spread out Bragg Peak (SOBP) and surrounding tissues in out-of-field. Complex mixed radiation field originated by the therapeutic 12 C ion beam in a phantom is difficult to measure. This study presents a new method to characterise the radiation field produced in a 12 C ion beam using a monolithic Delta E-E telescope which provides the capability to identify the particle components of the mixed radiation field as well as the microdosimetric spectra that allows derivation of the RBE based on a radiobiological model. resolution monolithic E-E detector on Abstract. Nuclear fragmentation produced in 12 C ion therapeutic beams contributes significantly to the Relative Biological Effectiveness (RBE) - weighted dose in the distal edge of the Spread out Bragg Peak (SOBP) and surrounding tissues in out-of-field. 23 Complex mixed radiation field originated by the therapeutic 12 C ion beam in a phantom is difficult to measure. This study presents a new method to characterise the radiation field produced in a 12 C ion beam using a monolithic ΔE-E telescope which provides the capability to identify the particle components of the mixed radiation field as well as the microdosimetric spectra that allows derivation of the RBE based on a radiobiological model. The response of the monolithic ΔE-E telescope to a 290 MeV/u 12 C ion beam at 29 defined positions along the pristine Bragg Peak was studied using the Geant4 Monte Carlo toolkit. The microdosimetric spectra derived from the ΔE stage and the two-dimensional 31 scatter plots of energy deposition in ΔE and E stages of the device in coincidence are 32 presented, as calculated in-field and out-of-field. Partial dose weighted contribution to the 33 microdosimetric spectra from nuclear fragments and recoils, such as 1 H, 4 He, 3 He, 7 Li, 9 Be 34 and 11 B, have been analyzed for each position. Comparison of simulation and experimental 35 results are presented and demonstrates that the microdosimetric spectra changes 36 dramatically within 0.5 mm depth increments close to and at the distal edge of the Bragg 37 Peak which is impossible to identify using conventional Tissue Equivalent Proportional 38 Counter (TEPC).
Camera-based spatter detection in laser welding with a deep learning approach

Continuous quality monitoring is essential for automated production systems and efﬁcient manufacturing. Laser welding processes are a key technology for many industrial applications and must fulﬁll high-quality requirements. Various inﬂuencing factors can lead to defects in the weld seam, which impair the functionality and quality of the end product. Therefore, a reliable quality assurance is a prerequisite for high product quality in welding processes. An indicator for an unstable situation in welding processes is the occurrence of spatter on the component. Thus, the detection of spatter can serve as a signiﬁcant signal for defective weld seams. This article proposes the detection of spatter based on a camera image taken with an industrial camera, which is usually already integrated in the laser system. Due to the large variance of weld seams in image-based analysis, algorithms with a high degree of generalization are required. Using convolutional neural networks (CNN) and semantic segmentation the camera image is analyzed and classiﬁed pixel by pixel. The CNN is trained in a multi-class approach in order to recognize the weld seam as well as the spatter as result classes. The segmentation map constitutes the classiﬁcation result. The results of the deep learning algorithms are evaluated by different meth-ods and conclusions about their prediction quality are made.
Resummation-based quantum Monte Carlo for quantum paramagnetic phases

For spin rotational symmetric models with a positive-definite high-temperature expansion of the partition function, a stochastic sampling of the series expansion upon partial resummation becomes logically equivalent to sampling an uncoloured closely-packed loop-gas model in one higher dimension. Based on this, we devise quantum Monte Carlo updates that importance-sample loop configurations for general $SU(N)$ in fundamental and higher-symmetric representations. The algorithmic performance systematically improves with increase in (continuous) $N$ allowing efficient simulation of quantum paramagnets. The underlying reason for the increased efficacy is the correspondence of quantum paramagnetic phases like valence bond solids to short-loop phases on the loop-gas side rather than the particular value of $N$. This also gives a connection between Sandvik's $JQ$ model class and classical loop-gas models in the deconfined universality class.
Optimization of amylase activity in the presence of various metal ions and surfactants in aqueous system

The effect of Fe +2 , Mg +2 and Ca +2 cations, and sodiumdodecylsulphate (SDS) and cetyltrimethlyammonium bromide (CTAB) surfactants was investigated on amylase activity in the aqueous media. Three commercially available enzymes (Luzyme HP, Aquazym SDL and Bactosol PHC) were used for this purpose under various setups. In the first setup, individual effect of metal ions and surfactants was studied, and then based on the results from first setup; in the second setup, trend of amylase activity was studied for a concentration range of selected metal and surfactant. The third experimental setup was established to investigate the combined effect of metal ions and surfactant on amylase activity. In the case of metal ions, Fe 2+ ions showed promoting effect on amylase activity. CTAB inhibited amylase activity whereas SDS promoted it up to a certain level. Moreover, the effect of Fe 2+ ions and SDS concentration on activity was found that Fe 2+ enhanced activity up to 5 mM while SDS concentration didn’t show considerable effect. Combination of both Fe 2+ and SDS showed increment in activity up to a certain level. All three commercially available amylases showed almost similar behavior towards metals and surfactants; however, the activity of Bactosol PHC was the highest than others.
Working paper series Tax Evasion at the Top of the Income Distribution : Theory and Evidence

This paper studies tax evasion at the top of the U.S. income distribution using IRS micro-data from (i) random audits, (ii) targeted enforcement activities, and (iii) operational audits. Drawing on this unique combination of data, we demonstrate empirically that random audits underestimate tax evasion at the top of the income distribution. Specifically, random audits do not capture most tax evasion through offshore accounts and pass-through businesses, both of which are quantitatively important at the top. We provide a theoretical explanation for this phenomenon, and we construct new estimates of the size and distribution of tax noncompliance in the United States. In our model, individuals can adopt a technology that would better conceal evasion at some fixed cost. Risk preferences and relatively high audit rates at the top drive the adoption of such sophisticated evasion technologies by high-income individuals. Consequently, random audits, which do not detect most sophisticated evasion, underestimate top tax evasion. After correcting for this bias, we find that unreported income as a fraction of true income rises from 7% in the bottom 50% to more than 20% in the top 1%, of which 6 percentage points correspond to undetected sophisticated evasion. Accounting for tax evasion increases the top 1% fiscal income share significantly. ∗Corresponding Author: Daniel Reck, d.h. reck@lse.ac. uk. We thank Gerald Auten, Brian Galle, Bhanu Gupta, Tom Hertz, Xavier Jaravel, Drew Johns, Barry Johnson, Camille Landais, Katie Lim, Emily Lin, Larry May, Alicia Miller, Annette Portz, Mary-Helen Risler, Peter Rose, Emmanuel Saez, Brenda Schafer, Clifford Scherwinski, Joel Slemrod, Matt Smith, Johannes Spinnewijn, David Splinter, Alex Turk, and Alex Yuskavage for helpful discussion, support, and comments on preliminary versions of this work. Jeanne Bomare and Baptiste Roux provided excellent research assistance. All remaining errors are our own. Financial support from the Washington Center for Equitable Growth, the Stone foundation, Arnold Ventures, and the Economic and Social Research Council is gratefully acknowledged. The views expressed here are those of the authors and do not necessarily reflect the official view of the Internal Revenue Service. This project was conducted through the Joint Statistical Research Program of the Statistics of Income Division of the IRS. All data work for this project involving confidential taxpayer information was done at IRS facilities, on IRS computers, by IRS employees, and at no time was confidential taxpayer data ever outside of the IRS computing environment. Reck and Risch are IRS employees under an agreement made possible by the Intragovernmental Personnel Act of 1970 (5 U.S.C. 3371-3376).
PROBLEMS WITH LONG-TERM FINANCIAL

12 This paper focuses on national support for export and examines whether, following the OECD arrangement, long-term financial sustainability is assured without sustained fiscal help. Rules on permitted state support in this area are stated by the OECD Consensus, which is meant to guarantee a level playing field for all exporters, to encourage competition among exporters based on the quality and prices of goods, and at the same time, not distort the free market. The main objective is that every export credit agency (ECA) should be self-sustainable with no need for state subsidies. However, this may not be achieved. This research proves inadequacy of minimum premium rates (MPR) due to insured interest. This issue arises mainly from the application of MPRs to the principal value of the insured loan with no correspondence to the insured interest amount. As many ECAs are publicly owned, all systematic losses must be covered by state budgets, which is against OECD agreements and is not allowed.
Preliminary results of assessments on the impacts of Payments for Forest Environmental Services (PFES) on forest networks and governance, household income and forest loss in Vietnam

Key messagesPFES and the large number of workshops have helped catalyze or strengthen cooperation between participants, which in turn can help organizations contribute more effectively to forest governance initiatives. However, when organizations work in the same provinces, they are less likely to collaborate. The impact of PFES on household income depends on the levels of payments, livelihood options available in the area, the area of forest managed by households, and PFES contract types (between individual households and the fund or between village administrations and PFES agencies). There were substantial differences between study sites. PFES appears to have been successful in lowering rates of deforestation for forest plots that were standing in 2000. The longer PFES programs have been active in a province, the greater the expected reductions in deforestation. Estimated PFES impacts can vary significantly depending on the assessment method used. While this infobrief discusses the impacts of PFES on forest governance, local livelihoods and forest loss separately, their interaction could offer a more complex picture of PFES impacts.
The Assessment of Visuospatial Skills and Verbal Fluency in the Diagnosis of Alzheimer’s Disease

Background In the diagnosis of Alzheimer’s disease (AD), examining memory is predominant. Our aim was to analyze the potential role of various cognitive domains in the cognitive evaluation of AD. Methods In total, 110 individuals with clinically defined AD and 45 healthy control participants underwent neuropsychological evaluation including Addenbrooke’s Cognitive Examination (ACE). Patients with AD were selected in three groups based on disease duration in years (Group 1: ≤2 years, n = 36; Group 2: 2–4 years, n = 44; Group 3: ≥4 years, n = 30). Covariance-weighted intergroup comparison was performed on the global cognitive score and subscores of cognitive domains. Spearman’s rho was applied to study the correlation between cognitive subscores and disease duration. The Wilcoxon signed-rank test was used for within-group analysis among ACE cognitive subscores. Results Significant difference was found between ACE total scores among groups (χ2 = 119.1; p < 0.001) with a high negative correlation (p < 0.001; r = −0.643). With a longer disease duration, all the subscores of ACE significantly decreased (p-values < 0.001). The visuospatial score showed the strongest negative correlation with disease duration with a linear trajectory in decline (r = −0.85). In the early phase of cognitive decline, verbal fluency was the most impaired cognitive subdomain (normalized value = 0.64), and it was significantly reduced compared to all other subdomains (p-values < 0.05). Conclusion We found that the impairment of verbal fluency is the most characteristic feature of early cognitive decline; therefore, it might have crucial importance in the early detection of AD. Based on our results, the visuospatial assessment might be an ideal marker to monitor the progression of cognitive decline in AD.
NBER WORKING PAPER SERIES JOB MARKET SIGNALING THROUGH OCCUPATIONAL LICENSING

In the presence of occupational licensing, we find evidence that firms rely less on observable characteristics such as race and gender in determining employee wages. As a result, licensed minorities and women experience smaller racial and gender wage gaps than their unlicensed peers. Black men benefit from licenses that are accessible to individuals without criminal records, whereas white women benefit from licenses with a human capital requirement. Certification, a less distortionary alternative to licensing, generates an equivalent wage premium for white men, but lower wage premiums than licensing for women and black men.