Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of pesticide use on amphibian chytrid fungal infections varies with host life stage across broad spatial scales

Funding information National Institutes of Health, Grant/ Award Number: R01GM109499 and R01TW010286‐01; National Science Foundation, Grant/Award Number: EEID‐1518681 and EF‐1241889; U.S. Department of Agriculture, Grant/Award Number: NRI 2009‐35102‐0543. Abstract Aim: Widespread pesticides might influence pathogen distributions across landscapes via effects on host–pathogen interactions. Empirical research supports two hypotheses regarding effects of pesticides on amphibians and the aquatic fungal pathogen Batrachochytrium dendrobatidis (Bd): (a) pesticides can be toxic to Bd, reducing infection risk of aquatic larval amphibians, and (b) exposure to pesticides early in amphibian development can increase disease risk after metamorphosis. The aim of this research is to evaluate whether these patterns from laboratory experiments are consistent across host species and occur in the field at broad spatial scales. Location: Contiguous U.S.A. Time period: 1998–2009. Major taxa studied: Amphibian hosts and Bd. Methods: Our data included 3,946 individuals evaluated for Bd infection across 49 amphibian species, which resulted in 155 estimates of Bd prevalence in populations. We used multimodel inference to examine associations between Bd infection prevalence in amphibian populations and (a) total pesticide use, (b) pesticide use by type (herbicide, insecticide or fungicide), and (c) the most commonly used pesticide compounds across life stages, controlling for several factors documented to affect the distribution of Bd. Results: Consistent with laboratory findings, our results indicate that use of multiple herbicide compounds is most closely associated with low infection risk in the aquatic larval stage but high risk in the terrestrial post‐metamorphic stage when controlling for insecticide and fungicide use. We caution against assuming that insecticides and fungicides do not influence Bd distributions, because use of herbicides, insecticides and fungicides are all correlated positively. Main conclusions: The effects that pesticides can have on disease distributions are complex and should be considered strongly at broad scales and across host species, especially in environments in which use and exposure are widespread. Accurate predictions of disease distributions may lead to more effective management strategies to limit disease spread.
A FAVAR Modeling Approach to Credit Risk Stress Testing and Its Application to the Hong Kong Banking Industry

In October 2018, the Basel Committee on Banking Supervision (BCBS) published its stress-testing principles. One of these principles is about stress testing model validation, aided by business interpretation, benchmark comparison and backtesting. In this paper, a credit risk stress testing model based on the factor-augmented vector autoregressive (FAVAR) approach is proposed to project credit risk loss under stressed scenarios. Inherited from both factor analysis (FA) and the vector autoregressive (VAR) model, the FAVAR approach ensures that the proposed model has many appealing features. First, a large number of model input variables can be reduced to a handful of latent common factors to avoid the curse of dimensionality. Second, the dynamic interrelationship among macroeconomic variables and credit risk loss measures can be studied without exogeneity assumptions. Moreover, the application of the impulse response function facilitates the multiperiod projection of credit risk loss in response to macroeconomic shocks. All of these features make the proposed modeling framework a potentially handy solution to fulfilling the BCBS requirement of quantitative adequacy assessment of banks’ internal stress testing results with a benchmark model. The scope of its application can also extend to impairment modeling for International Financial Reporting Standard 9, which requires the projection of credit risk losses over consecutive periods under different macroeconomic scenarios.
Recovery of undamaged electron-density maps in the presence of damage-induced partial coherence in single-particle imaging

Single-particle imaging using X-ray free-electron lasers requires an intensive X-ray fluence which damages the sample as it is imaged. This paper explores the potential for including theoretical simulations of single-particle electrodynamics into a reconstruction of the electron density imaged using X-ray free-electron lasers. While partial coherence, induced by radiation damage, poses a challenge for phase retrieval, it is demonstrated that additional coherent modes enable suppression of the effect of radiation damage in the reconstructed sample electron density.
Scan to Knit - From Body Scan Directly to the Knitting Machine

In this work, we describe an integrated solution to manufacture individualized made-to-measure (MtM) garments based on 3D body scanning by means of automatic data transmission from scanner to the flat knitting machine. The underlying process automatization reduces the complexity of the production of individualized lot-size-one garments. The process consists of two main steps: from scan to garment order files and from garment order files to Jacquard patterns and subsequent knitting process. Initially, a person is scanned with a 3D body scanner and body measures are extracted by the scanning software. After uploading the obtained body measures, the best fitting size is automatically suggested from size charts and corresponding fit rules for the selected garment type. The MtM Plugin of the scanning software is responsible for evaluating individual alteration rules of selected measures for the best fit. The resulting order file thus contains references to the garment piece file in the size chart database, the basic size and the alteration values. The second step comprises the algorithmic interpretation of 2D garment pieces and subsequent generation of Jacquard patterns. In addition to the piece file, information such as the material parameters is provided to the algorithm. The procedure starts with sampling of the garment piece according to the stitch construction, followed by piece manipulation to insure knitability. The resulting Jacquard pattern can be produced immediately by an appropriately configured flat knitting machine.
Semisupervised Learning for Seismic Monitoring Applications

The impressive performance that deep neural networks demonstrate on a range of seismic monitoring tasks depends largely on the availability of event catalogs that have been manually curated over many years or decades. However, the quality, duration, and availability of seismic event catalogs vary significantly across the range of monitoring operations, regions, and objectives. Semisupervised learning (SSL) enables learning from both labeled and unlabeled data and provides a framework to leverage the abundance of unreviewed seismic data for training deep neural networks on a variety of target tasks. We apply two SSL algorithms (mean-teacher and virtual adversarial training) as well as a novel hybrid technique (exponential average adversarial training) to seismic event classification to examine how unlabeled data with SSL can enhance model performance. In general, we find that SSL can perform as well as supervised learning with fewer labels. We also observe in some scenarios that almost half of the benefits of SSL are the result of the meaningful regularization enforced through SSL techniques and may not be attributable to unlabeled data directly. Lastly, the benefits from unlabeled data scale with the difficulty of the predictive task when we evaluate the use of unlabeled data to characterize sources in new geographic regions. In geographic areas where supervised model performance is low, SSL significantly increases the accuracy of source-type classification using unlabeled data.
A review of the literature on corporal punishment in The Bahamas, with an emphasis on its use in schools

This review of the literature concerning corporal punishment arising from The Bahamas enables us to identify several strands: (1) corporal punishment is an historically accepted method of controlling children which only recently has been called into question; (2) school teachers have typically seen corporal punishment as a useful classroom management tool; (3) there has been unease about its use in schools which has resulted in its regulation; (4) more recently, there has been evidence of the awareness of the long-term negative effects of corporal punishment; and (5) the rise of social media has made corporal punishment and its potential abuses visible to a wider audience. These strands appear to have woven together to move the country in a direction of outlawing corporal punishment, at least in schools.
DISTRIBUTION OF INTEGERS WITH PRESCRIBED STRUCTURE AND APPLICATIONS

where n is [ y, z]-smooth (numbers whose prime factors lie in the interval [ y, z] ⊆ [1, x]) for infinitely many real numbers x. In addition, we also establish an asymptotic formula with an extra square-free condition on n. Moreover, if α is quadratic irrational, then the asymptotic formulas hold for all sufficiently large x. Our tools come from the Harman sieve [6], which we adapt suitably to sieve for [ y, z]-smooth numbers. The arithmetic information comes from estimates for exponential sums. The results in Chapter 1 have been published in [11]. In Chapter 2, we consider a variant of a conjecture (EOSC) due to Erdős et al. [4], which states that for all sufficiently large q, every reduced residue class modulo q can be written as a product of two primes, each no more than q. We establish estimates for the number of ways to represent any reduced residue class as a product of a prime and an integer free of small prime factors. That is, for (a, q) = 1 and a positive real number z, we count the number of pairs (p, u) such that
Learning to ‘tell China's story well’: the constructions of international students in Chinese higher education policy

ABSTRACT This article takes a policy-as-discourse approach to understanding how Chinese policy texts construct international students. This is important given the growth of China as a study destination, and the fact that the majority of the studies on Chinese internationalisation policy are descriptive, and poorly integrated with literature focused on other contexts. Drawing upon a thematic analysis of 19 policy texts, the article reveals that economic rationales are absent in the policy texts; it also reveals five somewhat contradictory ways in which international students are constructed in policies: As ‘para-diplomats’ and ‘future elites’, but also as sources of ‘mutual exchange’, of ‘insufficient quality’ and finally, as requiring ‘guidance’ to understand Chinese laws. The article also discusses these constructions and problems with the assumptions within them, and how such problems could be thought about differently, with reference to the ‘critical internationalisation studies’ literature developed largely with a focus on the Global North.
Quantification of the reduction factors of seismic capacity for damaged RC column members using the experiment database

Static‐cyclic loading is used to identify the reduction factors of seismic capacity of damaged reinforced concrete (RC) column members. The reduction factors of seismic capacity that are obtained from specimens damaged under static‐cyclic loading differ from those obtained in dynamic testing, and this difference is investigated in this work. The experimental results obtained in this work, previously obtained results, and the “Pacific Earthquake Engineering Research Center Structural Performance Database (PEER)” are used to obtain reduction factors of strength, stiffness, and energy dissipation. From the damage levels of column members after an earthquake, corresponding reduction factors can be determined and used in the seismic assessment of an RC building.
Measuring the Significance of Policy Outputs with Positive Unlabeled Learning

Identifying important policy outputs has long been of interest to political scientists. In this work, we propose a novel approach to the classification of policies. Instead of obtaining and aggregating expert evaluations of significance for a finite set of policy outputs, we use experts to identify a small set of significant outputs and then employ positive unlabeled (PU) learning to search for other similar examples in a large unlabeled set. We further propose to automate the first step by harvesting “seed” sets of significant outputs from web data. We offer an application of the new approach by classifying over 9,000 government regulations in the United Kingdom. The obtained estimates are successfully validated against human experts, by forecasting web citations, and with a construct validity test.
Wireless Localization Based on Deep Learning: State of Art and Challenges

The problem of position estimation has always been widely discussed in the field of wireless communication. In recent years, deep learning technology is rapidly developing and attracting numerous applications. The high-dimension modeling capability of deep learning makes it possible to solve the localization problems under many nonideal scenarios which are hard to handle by classical models. Consequently, wireless localization based on deep learning has attracted extensive research during the last decade. The research and applications on wireless localization technology based on deep learning are reviewed in this paper. Typical deep learning models are summarized with emphasis on their inputs, outputs, and localization methods. Technical details helpful for enhancing localization ability are also mentioned. Finally, some problems worth further research are discussed.
Host specificity determines the assemblage of root endophytic bacteria of plants growing in metal contaminated soil

The diversity of endophytic bacteria colonizing the roots of an invasive plant, a pioneer plant, and an endemic plant from varied plant communities developing in a lead-zinc mine tailing pond, southwestern China, was analyzed by the culture-independent method. A total of 1650 16S rDNA sequences were screened for the establishment of four clone libraries, from the pure stands of Arthraxon hispidus (LS-Ah) and Ageratina adenophora (LS-Aa), and a mixed stand of A. adenophora (LC-Aa) and Alnus nepalensis (LC-An) (co-dominant community), respectively. Phylogenetic analysis revealed that the sequences were clustered into at least 17 phylogroups, which consisted of alpha, beta, gamma, delta subclasses of the Proteobacteria, Tenericutes, Bacteroidetes, Chloroflexi, Actinobacteria, Spirochaetes, Chlamydiae, Firmicutes, Deinococcus-Thermus, Planctomycetes, Nitrospirae, Gemmatimonadetes and unclassified bacteria Candidatus Saccharibacteria. The dominant phylum was Proteobacteria (50.49% of the total clones), and the dominant genus was Candidatus Phytoplasma (19.94% of the total clones). The invasive plant (A. adenophora) accumulated more parasitic endophytic bacteria (Phytoplasma) than the other two native plants. Phylogenetic structures of the four 16S rDNA clone libraries were distinct with their similarity indices being less than 0.5. The results also revealed that the dominant phyla and dominant genera in the four clone libraries varied a lot, and the endemic grass harbored a higher diversity of endophytic bacteria than the pioneer and invasive plants, the host-specificity took a more important role in shaping the endophytic bacteria community than the habitats in the metal stressed environment.
Total Roman Domination Number of Rooted Product Graphs

Let G be a graph with no isolated vertex and f:V(G)→{0,1,2} a function. If f satisfies that every vertex in the set {v∈V(G):f(v)=0} is adjacent to at least one vertex in the set {v∈V(G):f(v)=2}, and if the subgraph induced by the set {v∈V(G):f(v)≥1} has no isolated vertex, then we say that f is a total Roman dominating function on G. The minimum weight ω(f)=∑v∈V(G)f(v) among all total Roman dominating functions f on G is the total Roman domination number of G. In this article we study this parameter for the rooted product graphs. Specifically, we obtain closed formulas and tight bounds for the total Roman domination number of rooted product graphs in terms of domination invariants of the factor graphs involved in this product.
Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer

Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by brachytherapy. Despite radiation therapy advances, the toxicity rate remains significant. In this study, we compared the prediction of toxicity events after radiotherapy for locally advanced cervical cancer (LACC), based on either dose-volume histogram (DVH) parameters or the use of a radiomics approach applied to dose maps at the voxel level. Toxicity scores using the Common Terminology Criteria for Adverse Events (CTCAE v4), spatial dose distributions, and usual clinical predictors for the toxicity of 102 patients treated with chemoradiotherapy followed by brachytherapy for LACC were used in this study. In addition to usual DVH parameters, 91 radiomic features were extracted from rectum, bladder and vaginal 3D dose distributions, after discretization into a fixed bin width of 1 Gy. They were evaluated for predictive modelling of rectal, genitourinary (GU) and vaginal toxicities (grade ≥ 2). Logistic Normal Tissue Complication Probability (NTCP) models were derived using clinical parameters only or combinations of clinical, DVH and radiomics. For rectal acute/late toxicities, the area under the curve (AUC) using clinical parameters was 0.53/0.65, which increased to 0.66/0.63, and 0.76/0.87, with the addition of DVH or radiomics parameters, respectively. For GU acute/late toxicities, the AUC increased from 0.55/0.56 (clinical only) to 0.84/0.90 (+DVH) and 0.83/0.96 (clinical + DVH + radiomics). For vaginal acute/late toxicities, the AUC increased from 0.51/0.57 (clinical only) to 0.58/0.72 (+DVH) and 0.82/0.89 (clinical + DVH + radiomics). The predictive performance of NTCP models based on radiomics features was higher than the commonly used clinical and DVH parameters. Dosimetric radiomics analysis is a promising tool for NTCP modelling in radiotherapy.
Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records

Reliable soil moisture retrievals from passive microwave satellite sensors are limited during certain conditions, e.g., snow coverage, radio-frequency interference, and dense vegetation. In these cases, the retrievals can be masked using flagging algorithms. Currently available single- and multi-sensor soil moisture products utilize different flagging approaches. However, a clear overview and comparison of these approaches and their impact on soil moisture data are still lacking. For long-term climate records such as the soil moisture products of the European Space Agency (ESA) Climate Change Initiative (CCI), the effect of any flagging inconsistency resulting from combining multiple sensor datasets is not yet understood. Therefore, the first objective of this study is to review the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP). The SMOS and SMAP soil moisture flagging systems differ substantially in number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems were compared for the SMOS and SMAP soil moisture datasets. Major differences in data availability were observed globally, especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlight the importance of a consistent and well-performing approach that is applicable to all individual products used in long-term soil moisture data records. Consequently, the second objective of the present study is to design a consistent and model-independent flagging strategy to improve soil moisture climate records such as the ESA CCI products. As snow cover, ice, and frozen conditions were demonstrated to have the biggest impact on data availability, a uniform satellite driven flagging strategy was designed for these conditions and evaluated against two ground observation networks. The new flagging strategy demonstrated to be a robust flagging alternative when compared to the individual flagging strategies adopted by the SMOS and SMAP soil moisture datasets with a similar performance, but with the applicability to the entire ESA CCI time record without the use of modelled approximations.
A Low Cost Aqueous Zn–S Battery Realizing Ultrahigh Energy Density

Rechargeable aqueous zinc ion batteries are enabled by the (de)intercalation chemistry, but bottlenecked by the limited energy density due to the low capacity of cathodes. In this work, carbon nanotubes supported 50 wt% sulfur (denoted as S@CNTs‐50), as a conversional cathode, is employed and a high energy density aqueous zinc–sulfur (Zn–S) battery is constructed . In the electrolyte of 1 m Zn(CH3COO)2 (pH = 6.5) with 0.05 wt% I2 additive where I2 can serve as medium of Zn2+ ions to reduce the voltage hysteresis of S@CNTs‐50 and stabilize Zn stripping/plating, S@CNTs‐50 delivers a high capacity of 1105 mAh g−1 with a flat discharge voltage of 0.5 V, realizing an energy density of 502 Wh kg−1 based on sulfur, which is one of the highest values reported in aqueous Zn‐based batteries that use mild electrolyte. Moreover, the chemical materials cost of this aqueous Zn–S battery can be lowered to be $45 kWh−1 due to the cheap raw materials, reaching to the level of pumped energy storage. Ex situ X‐ray diffraction, Raman spectra, X‐ray photoelectron spectrum, and transmission electron microscopy measurements reveal that sulfur cathode undergoes a conversion reaction between S and ZnS.
Does SEO Matter for Startups? Identifying Insights from UGC Twitter Communities

In the present study, we analyzed User Generated Content (UGC) to measure the importance of Search Engine Optimization (SEO) for startups. For this purpose, we used several clustering algorithms to identify user communities on Twitter. The dataset contained a total of 67,126 tweets. A three-step UGC analysis process was applied to the data. First, a Latent Dirichlet allocation (LDA) was developed to divide the UGC-sample into topics. Next, a sentiment analysis (SA) with machine-learning was applied to divide the sample of topics into negative, positive, and neutral feelings. Finally, a textual analysis (TA) process with data mining techniques was used to extract indicators related to the SEO technique optimization in startups. The results helped us identify UGC communities in Twitter about SEO for startups and the main optimization indicators according to the feelings expressed in tweets. Our results also demonstrated that Black Hack SEO is not the most relevant strategy of positioning of digital marketing for startups and that, although this strategy is used by the startups, it is predominantly negatively perceived by SEO UGC communities.