Miracle-dz/UniFAR-FTUs huggingface dataset:
SARS-CoV-2 Seroepidemiology in Children and Adolescents

ObjectivesPediatric SARS-CoV-2 data remain limited and seropositivity rates in children were reported as <1% early in the pandemic. Seroepidemiologic evaluation of SARS-CoV-2 in children in a major metropolitan region of the United States was performed. MethodsChildren and adolescents [≤]19 years were enrolled in a cross-sectional, observational study of SARS-CoV-2 seroprevalence from July-October 2020 in Northern Virginia, United States. Demographic, health, and COVID-19 exposure information was collected, and blood was analyzed for SARS-CoV-2 spike protein total antibody. Risk factors associated with SARS-CoV-2 seropositivity were analyzed. Orthogonal antibody testing was performed, and samples were evaluated for responses to different antigens. ResultsIn 1038 children, the anti-SARS-CoV-2 total antibody positivity rate was 8.5%. After multivariate logistic regression, significant risk factors included Hispanic ethnicity, public or absent insurance, a history of COVID-19 symptoms, exposure to person with COVID-19, a household member positive for SARS-CoV-2 and multi-family or apartment dwelling without a private entrance. 66% of seropositive children had no symptoms of COVID-19. Orthogonal antibody testing with a receptor binding domain specific antigen revealed a high concordance of 80.5%. Children also demonstrated a robust immune response to the nucleocapsid antigen. ConclusionsA much higher burden of SARS-CoV-2 infection, as determined by seropositivity, was found in children than previously reported; this was also higher compared to adults in the same region at a similar time. Contrary to prior reports, we determined children shoulder a significant burden of COVID-19 infection. The role of childrens disease transmission must be considered in COVID-19 mitigation strategies including vaccination. Article Summary8.5% of children had SARS-CoV-2 antibodies in Fall 2020, double the adult rate. The role of pediatric infection is important to consider in mitigation strategies. Whats Known on This SubjectSARS-CoV-2 pediatric seroepidemiologic data is limited. Reported viral rates underestimate the burden of infection in children due to mild or asymptomatic disease. Limited cohorts of children suggest low seropositivity rates compared to adults. What This Study AddsUS children in the largest SARS-CoV-2 seroepidemiology study to date had double the rate of antibodies compared to adults. Most children were asymptomatic. Risk factors include age, ethnicity and living conditions. Most children made antibodies to different antigens of SARS-CoV-2.
Extraction behavior of short-lived isotopes of Mo/W using α-benzoinoxime: A model system for the study of seaborgium

Abstract The liquid-liquid extraction behavior of short-lived molybdenum and tungsten isotopes from HCl and HNO3 as well as HF/HNO3 acid media was studied using the α-benzoinoxime/chloroform system. The goal of the present experiments was to find an extraction system with a high selectivity for the group 6 elements in the periodic table which is suitable for the study the solution chemistry of seaborgium (Z=106). It was found that Mo and W are both effectively extracted using α-benzoinoxime as an extractant and the maximum extraction yield is 100% and 94% for Mo and W, respectively. Ions of MO22+ (M=Mo, W) exist in HCl between the concentration range of 0.1−3 M and can be extracted quantitatively into the organic phase. The decrease of extraction yields at lower or higher acid concentration is due to the formation of anionic species MO42- or MO2Cl3-. The extraction behavior of Mo and W in HNO3 solution is similar to that in HCl solution. In HNO3/HF mixed acid solutions, due to the formation of MO2F3- a decrease of extraction yield at higher HF concentration, similar to the case of HCl is observed. The extraction equilibrium is reached within 10−30 s, which means the extraction kinetics of α-benzoinoxime/chloroform extraction system is fast enough for the study of the isotope 265Sg. The extraction behavior of molybdenum and tungsten shows a distinct difference in the range of 0.05 to 0.2 M HF with 1 M HNO3. This shows that differences in the complexation with fluoride ion exist in Mo and W. This study focuses on the investigation of the tendency of group 6 element for complexation with fluoride ion.
Chemical stability and decomposition rate of iron cyanide complexes in soil solutions

In order to improve the assessment of the bioavailability and behavior of cyanide in the environment, the speciation of dissolved cyanide was studied under pH and redox conditions relevant to soil and groundwater environments. The partition of cyanide over free cyanide [HCN(aq)+ CN - ] and iron cyanide complexes [or hexacyanoferrates, e.g., Fe(CN) 6 3- and Fe(CN) 6 4- ] at thermodynamic equilibrium was calculated as a function of pH and redox potential. These calculations show that the free cyanide form will predominate at chemical equilibrium in the soil
Students' Understandings of Acid Strength: How Meaningful Is Reliability When Measuring Alternative Conceptions.

Most organic chemistry reactions occur by a mechanism that includes acid–base chemistry, so it is important that students develop and learn to use correct conceptions of acids and acid strength. Recent studies have described undergraduate organic chemistry students’ cognitive resources related to the Bronsted–Lowry acid model and the Lewis acid model, providing both qualitative and quantitative analyses of these understandings. To drive changes in pedagogy and curriculum, however, faculty need to be able to quickly assess students’ conceptions of acids and acid strength. We recently reported on the development and assessment of a nine-item, multiple-tier, multiple-choice concept inventory about acid strength, named ACID I. Coefficient α for ACID I was calculated to be below 0.70. In this manuscript, we demonstrate that despite this low coefficient α, the data generated by ACID I are indeed reliable. Thus, the purpose of this paper is to (i) report two significant alternative conceptions about acid strengt.
Dry deposition of reduced nitrogen

The paper reviews the results of measurements of ammonia (NH 3 ) exchange and considers the application of this information in approaches to modelling atmospheric NH 3 inputs to ecosystems. Ammonia dry deposition is seen to be highly dependent on land management and the magnitude of agricultural nitrogen inputs. Efficient dry deposition is observed to many semi-natural and forest ecosystems, with typical deposition velocities ( V d ) in the range 5–50 mm s -1 . By contrast, intensively grazed pastures and fertilized arable croplands frequently show net NH 3 emission, with fluxes dependent on the form of N input, environmental conditions (temperature, wetness) and plant growth stage. Other factors affecting the exchange of NH 3 with ecosystems include the existence of an NH 3 “compensation point” within plant tissues and interactions with acid gases such as SO 2 . Available information on NH 3 exchange over the sea and aquatic water bodies suggests that both emission and deposition can occur depending on NH 3 air concentrations and water compensation points. Approaches to mapping NH 3 dry deposition include inferring deposition from monitored air concentrations and values of V d (“inferential technique”), and atmospheric transport models. The former may be more site specific, but is limited by lack of NH 3 concentration monitoring, while the latter provides good regional coverage, but poor estimates of actual inputs to specific ecosystems (grids often > 50 km × 50 km). A possible advance is to apply the air concentration field outputs of large scale transport models, with locally derived V d , though this fails to treat the significant local variability in NH 3 emissions and air concentrations. An improvement would be the further development of local scale atmospheric transport models (e.g., 5 km × 5 km grids) incorporating land use information and ecosystem specific V d on a fine spatial scale. DOI: 10.1034/j.1600-0889.1994. t01-2-00002.x
Europium oxidation state and local structure in silicate glasses

Abstract Europium LIII-edge XAS spectra were recorded for silicate glasses of different compositions, quenched from melts equilibrated at different oxygen fugacity (fO₂). The Eu XANES spectra vary systematically with glass composition and with fO₂ (-log fO₂ ~0 to ~11.9) indicating changes in the Eu oxidation state. The intensity of the main peaks on the absorption edges were quantified and used to determine the Eu2+/(Eu2++Eu3+) ratio. All the Eu-bearing glasses synthesized in air show the prevalent presence of Eu3+ but also, unexpectedly, the presence of a small amount of Eu2+ in the basaltic glasses and up to 20% of Eu2+ in the haplogranitic sample. Moreover, XANES analyses of the samples synthesized at reducing conditions (from FMQ to IW-2) show that europium in haplogranitic glasses is always more reduced than in basaltic glasses. No relationship has been found between Eu valence and alkali content in the studied glasses. The structural environment of Eu in the glasses was determined by EXAFS analyses, demonstrating the different Eu behavior as function of the fO₂. In fact, in air, Eu3+ both for basaltic and haplogranitic compositions, is bonded to six O atoms in a regular octahedron (CN = [6 ± 0.5]) with similar distances of about 2.30 ± 0.02 Å. On the other hand, the almost purely divalent samples have Eu2+ in a higher coordination (CN = [9 ± 1]) and longer distances (2.68 ± 0.02 Å). This work clearly demonstrates that, in addition to oxygen fugacity, melt composition also plays a strong role in affecting Eu oxidation state. Moreover, for the first time, experimentally derived structural data of Eu2+ in silicate glasses of geological interest are presented.
Racial and Ethnic Disparities in Years of Potential Life Lost Attributable to COVID-19 in the United States: An Analysis of 45 States and the District of Columbia

The coronavirus disease 2019 (COVID-19) epidemic in the United States has disproportionately impacted communities of color across the country. Focusing on COVID-19-attributable mortality, we expand upon a national comparative analysis of years of potential life lost (YPLL) attributable to COVID-19 by race/ethnicity (Bassett et al., 2020), estimating percentages of total YPLL for non-Hispanic Whites, non-Hispanic Blacks, Hispanics, non-Hispanic Asians, and non-Hispanic American Indian or Alaska Natives, contrasting them with their respective percent population shares, as well as age-adjusted YPLL rate ratios - anchoring comparisons to non-Hispanic Whites - in each of 45 states and the District of Columbia using data from the National Center for Health Statistics as of December 30, 2020. Using a novel Monte Carlo simulation procedure to quantify estimation uncertainty, our results reveal substantial racial/ethnic disparities in COVID-19-attributable YPLL across states, with a prevailing pattern of non-Hispanic Blacks and Hispanics experiencing disproportionately high and non-Hispanic Whites experiencing disproportionately low COVID-19-attributable YPLL. Furthermore, observed disparities are generally more pronounced when measuring mortality in terms of YPLL compared to death counts, reflecting the greater intensity of the disparities at younger ages. We also find substantial state-to-state variability in the magnitudes of the estimated racial/ethnic disparities, suggesting that they are driven in large part by social determinants of health whose degree of association with race/ethnicity varies by state.
Leveraging domain labels for object detection from UAVs

Object detection from Unmanned Aerial Vehicles (UAVs) is of great importance in many aerial vision-based applications. Despite the great success of generic object detection methods, a large performance drop is observed when applied to images captured by UAVs. This is due to large variations in imaging conditions, such as varying altitudes, dynamically changing viewing angles, and different capture times. We demonstrate that domain knowledge is a valuable source of information and thus propose domain-aware object detectors by us-ing freely accessible sensor data. By splitting the model into cross-domain and domain-speciﬁc parts, substantial performance improvements are achieved on multiple datasets across multiple models and metrics. In particular, we achieve a new state-of-the-art performance on UAVDT for real-time detectors. Furthermore, we create a new airborne image dataset by annotating 13 713 objects in 2 900 images featuring precise altitude and viewing angle annotations 1 .
Applying Bayesian Analysis Guidelines to Empirical Software Engineering Data: The Case of Programming Languages and Code Quality

Statistical analysis is the tool of choice to turn data into information and then information into empirical knowledge. However, the process that goes from data to knowledge is long, uncertain, and riddled with pitfalls. To be valid, it should be supported by detailed, rigorous guidelines that help ferret out issues with the data or model and lead to qualified results that strike a reasonable balance between generality and practical relevance. Such guidelines are being developed by statisticians to support the latest techniques for Bayesian data analysis. In this article, we frame these guidelines in a way that is apt to empirical research in software engineering. To demonstrate the guidelines in practice, we apply them to reanalyze a GitHub dataset about code quality in different programming languages. The dataset’s original analysis [Ray et al. 55] and a critical reanalysis [Berger et al. 6] have attracted considerable attention—in no small part because they target a topic (the impact of different programming languages) on which strong opinions abound. The goals of our reanalysis are largely orthogonal to this previous work, as we are concerned with demonstrating, on data in an interesting domain, how to build a principled Bayesian data analysis and to showcase its benefits. In the process, we will also shed light on some critical aspects of the analyzed data and of the relationship between programming languages and code quality—such as the impact of project-specific characteristics other than the used programming language. The high-level conclusions of our exercise will be that Bayesian statistical techniques can be applied to analyze software engineering data in a way that is principled, flexible, and leads to convincing results that inform the state-of-the-art while highlighting the boundaries of its validity. The guidelines can support building solid statistical analyses and connecting their results. Thus, they can help buttress continued progress in empirical software engineering research.
FedChain: Secure Proof-of-Stake-Based Framework for Federated-Blockchain Systems

In this article, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain's security and performance.
Machine Translation, Sentiment Analysis, Text Similarity, Topic Modelling, and Tweets: Understanding Social Media Usage Among Police and Gendarmerie Organizations

It is well known that social media has revolutionized communication. Nowadays, citizens, companies, and public institutions actively use social media in order to express themselves better to the population they address. This active use is also carried out by the gendarmerie and police organizations to communicate with the public with the purpose of improving social relations. However, it has been seen that the posts by the gendarmerie and police organizations did not attract much attention from their target audience from time to time, and it has been discovered that there was not enough research in the literature on this issue. In this study, it was aimed to investigate the use of social media by the gendarmerie and police organizations operating in Turkey (Jandarma Polis), Italy (Carabinieri Polizia), France (Gendarmerie Police) and Spain (Guardia Civil Policía), and the extent to which they can be effective on the followers, by comparatively examining their activity on twitter. According to the obtained results, it was found that Jandarma (Turkey) has the highest power of influence in the twitter sample, and the findings were comparatively presented in the study. Keywords— policing, sentiment analysis, topic modelling, text similarity, twitter
A statistical model of light scattering in biological continuous random media based on the Born approximation

A comprehensive three-parameter statistical model is presented for the refractive index fluctuations in continuous homogeneous random media, and the light-scattering properties of these media are investigated in the Born (or single-scattering) approximation. Because biological media are usually weakly scattering, the results are applicable to many biomedical light-scattering problems. A rigorous error analysis is presented for the scattering coefficient under the Born approximation in a biologically-relevant, albeit more simplified geometry. The finitedifference- time-domain (FDTD) computational electromagnetic analysis is used to obtain the exact solutions for this error analysis. The ranges for the correlation length and the refractive index fluctuation strength under which Born approximation is valid are clearly identified.
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation

3D pose estimation is a challenging but important task in computer vision. In this work, we show that standard deep learning approaches to 3D pose estimation are not robust when objects are partially occluded or viewed from a previously unseen pose. Inspired by the robustness of generative vision models to partial occlusion, we propose to integrate deep neural networks with 3D generative representations of objects into a unified neural architecture that we term NeMo. In particular, NeMo learns a generative model of neural feature activations at each vertex on a dense 3D mesh. Using differentiable rendering we estimate the 3D object pose by minimizing the reconstruction error between NeMo and the feature representation of the target image. To avoid local optima in the reconstruction loss, we train the feature extractor to maximize the distance between the individual feature representations on the mesh using contrastive learning. Our extensive experiments on PASCAL3D+, occluded-PASCAL3D+ and ObjectNet3D show that NeMo is much more robust to partial occlusion and unseen pose compared to standard deep networks, while retaining competitive performance on regular data. Interestingly, our experiments also show that NeMo performs reasonably well even when the mesh representation only crudely approximates the true object geometry with a cuboid, hence revealing that the detailed 3D geometry is not needed for accurate 3D pose estimation. The code is publicly available at https://github.com/Angtian/NeMo.
An Automated Theorem Proving Framework for Information-Theoretic Results

We present a versatile automated theorem proving framework capable of automated proofs of outer bounds in network information theory, automated discovery of inner bounds in network information theory (in conjunction with the method by Lee and Chung), simplification of capacity regions involving auxiliary random variables, automated deduction of properties of information-theoretic quantities (e.g. Wyner and Gács-Körner common information), and automated discovery of non-Shannon-type inequalities, under a unified framework. Our method is based on the linear programming approach for proving Shannon-type information inequalities by Yeung and Zhang, together with a novel pruning method for searching auxiliary random variables. We introduce the concept of existential information inequalities, which provides an axiomatic framework for a wide range of problems in information theory. A full version of this paper is accessible at: https://arxiv.org/pdf/2101.12370. pdf
CDˆ2CR: Co-reference resolution across documents and domains

Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both scientific articles and news can help scientists understand how their work is represented in mainstream media. We propose a new task and English language dataset for cross-document cross-domain co-reference resolution (CDˆ2CR). The task aims to identify links between entities across heterogeneous document types. We show that in this cross-domain, cross-document setting, existing CDCR models do not perform well and we provide a baseline model that outperforms current state-of-the-art CDCR models on CDˆ2CR. Our data set, annotation tool and guidelines as well as our model for cross-document cross-domain co-reference are all supplied as open access open source resources.