Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear angular momentum multiplexing—conceptualization and experimental evaluation with antenna arrays

Linear angular momentum multiplexing is a new method for providing highly spectrally efficient short-range communication between a transmitter and receiver, where one may move at speed transverse to the propagation. Such applications include rail, vehicle and hyperloop transport systems communicating with fixed infrastructure on the ground. This paper describes how the scientific concept of linear angular momentum multiplexing evolves from orbital angular momentum multiplexing. The essential parameters for implementing this concept are a long array at least at one of the ends of the link; antenna element radiation characteristics and the array element spacing relative to the propagation distance. These parameters are also backed by short-range measurements carried out at 2.4 GHz used to model the Rice fading channel and determine resilience to multipath fading.
PRODUCTION OF VITAMIN B12 BY RHIZOBIUM SPECIES

A study was made of the capacity for synthesis of vitamin B12 by 70 strains representing six species of Rhizobium, namely, R. meliloti, R. trifolii, R. leguminosarum, R. japonicum, R. lupini, and R. phaseoli. Rhizobium meliloti was sharply distinguished from the other species by its ability to produce significantly higher quantities of the vitamin, one strain producing, under the experimental conditions, over 1000 mμgm. per ml. of culture liquid. The remaining species did not show significant differences in B12 synthesizing capacity. No correlation was noted between vitamin-producing capacity and effectiveness of the individual strains with respect to nitrogen fixation.
Differentiations in Visibility-Male Advantages and Female Disadvantages in Gender-Segregated Programmes

This article stresses the importance of understanding that women and men in gender-segregated programmes experience their gender minority positions very differently. It stems from an interest in the kind of interventions that academia should address in order to reduce gender segregation and provide women and men with the same educational opportunities and personal development. In relation to the obvious and continuing gender differences along a horizontal dimension, previous research seems to have had a limited impact in breaking gender stereotypes and promoting women and men to more atypical fields. The empirical data consists of 25 semi-structured, individual interviews from underrepresented students' gender-related experiences/thoughts about their programmes. By using the concepts of “visibility,” “sense of belonging,” and “negotiating otherness” to analyze how negotiation and belonging are part of students' everyday university lives this study's most important contributions are its findings regarding the differentiations in visibility. A continuum of visibility experiences is explored, from men who receive positive attention to women who are being considered as less knowledgeable. Our visibility scale indicates, as does previous research, that there are differences between how female and male students become visible, but the differences can also appear within both groups of students. This knowledge is crucial when designing interventions so as to provide positive study environments for both women and men. Also—in a broader perspective—it is important in order to recruit and ensure that gender minority students remain in the programs.
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks

Many tasks in natural language processing involve predicting structured outputs, e.g., sequence labeling, semantic role labeling, parsing, and machine translation. Researchers are increasingly applying deep representation learning to these problems, but the structured component of these approaches is usually quite simplistic. In this work, we propose several high-order energy terms to capture complex dependencies among labels in sequence labeling, including several that consider the entire label sequence. We use neural parameterizations for these energy terms, drawing from convolutional, recurrent, and self-attention networks. We use the framework of learning energy-based inference networks (Tu and Gimpel, 2018) for dealing with the difficulties of training and inference with such models. We empirically demonstrate that this approach achieves substantial improvement using a variety of high-order energy terms on four sequence labeling tasks, while having the same decoding speed as simple, local classifiers. We also find high-order energies to help in noisy data conditions.
Mixup-Transfomer: Dynamic Data Augmentation for NLP Tasks

Mixup is the latest data augmentation technique that linearly interpolates input examples and the corresponding labels. It has shown strong effectiveness in image classification by interpolating images at the pixel level. Inspired by this line of research, in this paper, we explore i) how to apply mixup to natural language processing tasks since text data can hardly be mixed in the raw format; ii) if mixup is still effective in transformer-based learning models, e.g., BERT. To achieve the goal, we incorporate mixup to transformer-based pre-trained architecture, named "mixup-transformer", for a wide range of NLP tasks while keeping the whole end-to-end training system. We evaluate the proposed framework by running extensive experiments on the GLUE benchmark. Furthermore, we also examine the performance of mixup-transformer in low-resource scenarios by reducing the training data with a certain ratio. Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach

Human-annotated labels are often prone to noise, and the presence of such noise will degrade the performance of the resulting deep neural network (DNN) models. Much of the literature (with several recent exceptions) of learning with noisy labels focuses on the case when the label noise is independent from features. Practically, annotations errors tend to be instance-dependent and often depend on the difficulty levels of recognizing a certain task. Applying existing results from instance-independent settings would require a significant amount of estimation of noise rates. Therefore, learning with instance-dependent label noise remains a challenge. In this paper, we propose CORES^2 (COnfidence REgularized Sample Sieve), which progressively sieves out corrupted samples. The implementation of CORES^2 does not require specifying noise rates and yet we are able to provide theoretical guarantees of CORES^2 in filtering out the corrupted examples. This high-quality sample sieve allows us to treat clean examples and the corrupted ones separately in training a DNN solution, and such a separation is shown to be advantageous in the instance-dependent noise setting. We demonstrate the performance of CORES^2 on CIFAR10 and CIFAR100 datasets with synthetic instance-dependent label noise and Clothing1M with real-world human noise. As of independent interests, our sample sieve provides a generic machinery for anatomizing noisy datasets and provides a flexible interface for various robust training techniques to further improve the performance.
A Comparison of Above-Ground Dry-Biomass Estimators for Trees in the Northeastern United States

In the northeastern United States, both component and total aboveground tree dry-biomass estimates are available from several sources. In this study, comparisons were made among four methods to promote understanding of the similarities and differences in live-tree biomass estimators. The methods use various equations developed from biomass data collected in the United States and Canada. For hardwood species, estimates for biomass components tended to differ among the methods; however, the estimates for total aboveground biomass were more compatible. For softwood species, the biomass estimates among methods were more consistent for components and total aboveground biomass. Considerable variation in biomass component estimates exists among the four methods, suggesting that further study of biomass is needed in the northeastern United States. Ideally, reliable biomass estimators would be established via a regionwide study having consistent and precise definitions and measurement protocols.
Forced Imbibition in Stratified Porous Media

Imbibition plays a central role in diverse energy, environmental, and industrial processes. In many cases, the medium has multiple parallel strata of different permeabilities; however, how this stratification impacts imbibition is poorly understood. We address this gap in knowledge by directly visualizing forced imbibition in three-dimensional (3D) porous media with two parallel strata. We find that imbibition is spatially heterogeneous: for small capillary number Ca, the wetting fluid preferentially invades the fine stratum, while for Ca above a threshold value, the fluid instead preferentially invades the coarse stratum. This threshold value depends on the medium geometry, the fluid properties, and the presence of residual wetting films in the pore space. These findings are well described by a linear stability analysis that incorporates crossflow between the strata. Thus, our work provides quantitative guidelines for predicting and controlling flow in stratified porous media.
Short-period Variability in the Globular Cluster NGC 419 and the SMC Field

Delta Scuti (δ Sct) stars have been extensively studied in our Galaxy but far less in extragalactic systems. Here we study the population of δ Sct variables in NGC 419, an intermediate-age globular cluster of the Small Magellanic Cloud (SMC), using g, r, and i Gemini-S/Gemini Multi-Object Spectrograph (GMOS) time-series observations. Our goal is to study the role of such variables in the cluster extended main-sequence turnoff (MSTO). We report the discovery of 54 δ Sct stars and three eclipsing binaries in the NGC 419 field. We find only a handful of the δ Sct stars at the MSTO of NGC 419, while the majority are fainter, indicating that the cluster is younger (≲1.2 Gyr) than previously thought. Considering their radial distribution, we identify only six δ Sct stars as probable members of NGC 419, while the 48 remaining are likely δ Sct stars of the SMC field. Cluster δ Sct stars appear close to the red edge of the MSTO, supporting the idea that the extended MSTO has its origin in an age spread. The 48 field δ Sct stars represent the largest detection of δ Sct stars made in the SMC. The period distribution of these newly detected δ Sct stars (0.04 day ≲ P ≲ 0.15 day) is similar to that detected in other systems. The amplitude distribution (0.05 mag ≲ Δr ≲ 0.60 mag) is likely biased because of the lack of low-amplitude stars. We finally use the δ Sct stars to calculate distances using different period–luminosity relations. The average distance moduli obtained are 18.76 ± 0.14 mag for NGC 419 and 18.86 ± 0.11 mag for the SMC field, which agree with previous measurements.
An Immobilization Technique for Long-Term Time-Lapse Imaging of Explanted Drosophila Tissues

Time-lapse imaging is an essential tool to study dynamic biological processes that cannot be discerned from fixed samples alone. However, imaging cell- and tissue-level processes in intact animals poses numerous challenges if the organism is opaque and/or motile. Explant cultures of intact tissues circumvent some of these challenges, but sample drift remains a considerable obstacle. We employed a simple yet effective technique to immobilize tissues in medium-bathed agarose. We applied this technique to study multiple Drosophila tissues from first-instar larvae to adult stages in various orientations and with no evidence of anisotropic pressure or stress damage. Using this method, we were able to image fine features for up to 18 hours and make novel observations. Specifically, we report that fibers characteristic of quiescent neuroblasts are inherited by their basal daughters during reactivation; that the lamina in the developing visual system is assembled roughly 2-3 columns at a time; that lamina glia positions are dynamic during development; and that the nuclear envelopes of adult testis cyst stem cells do not break down completely during mitosis. In all, we demonstrate that our protocol is well-suited for tissue immobilization and long-term live imaging, enabling new insights into tissue and cell dynamics in Drosophila.
Euthyroid Sick Syndrome in Patients With COVID-19

Background Coronavirus disease 2019 (COVID-19) has been shown to affect almost every organ throughout the body. However, it is not clear whether the thyroid gland is impaired in COVID-19 patients. Euthyroid sick syndrome (ESS) is usually associated with the disease severity and deterioration prognosis in critical illness. In this study, the thyroid function of COVID-19 patients was assessed and factors associated with outcomes were analyzed to determine the potential predictive value of ESS. Methods Clinical and laboratory data of COVID-19 patients with or without ESS in Changsha, China, were collected and analyzed on admission. Kaplan-Meier curve and cox regression model were utilized to determine the correlation between ESS and the endpoints. Subsequently, a receiver operating characteristic (ROC) curve was plotted to evaluate the predictive performances of FT3 and C-reactive protein (CRP) in the disease severity. Results Forty-one (27.52%) cases of COVID-19 patients diagnosed with ESS. ESS patients had higher proportions of fever, shortness of breath, hypertension, diabetes, and severe events than those of non-ESS patients. The levels of erythrocyte sedimentation rate and C-reactive protein, and the positive rate of procalcitonin were significantly higher, whereas the lymphocyte count was apparently lower in ESS patients than in non-ESS patients. The regression analysis showed that ESS was significantly associated with the disease severity of COVID-19 (HR = 2.515, 95% CI: 1.050–6.026, P = 0.039). The areas under the curve (AUCs) for predicting the severe disease were [0.809 (95% CI 0.727–0.892), P < 0.001] and [0.792 (95% CI 0.689–0.895), P < 0.001] for FT3 and CRP, respectively. Conclusion ESS was significantly associated with the disease severity and inflammatory parameters in COVID-19 patients.
Common CNN-based Face Embedding Spaces are (Almost) Equivalent

CNNs are the dominant method for creating face embeddings for recognition. It might be assumed that, since these networks are distinct, complex, nonlinear functions, that their embeddings are network specific, and thus have some degree of anonymity. However, recent research has shown that distinct networks' features can be directly mapped with little performance penalty (median 1.9% reduction across 90 distinct mappings) in the context of the 1,000 object ImageNet recognition task. This finding has revealed that embeddings coming from different systems can be meaningfully compared, provided the mapping. However, prior work only considered networks trained and tested on a closed set classification task. Here, we present evidence that a linear mapping between feature spaces can be easily discovered in the context of open set face recognition. Specifically, we demonstrate that the feature spaces of four face recognition models, of varying architecture and training datasets, can be mapped between with no more than a 1.0% penalty in recognition accuracy on LFW . This finding, which we also replicate on YouTube Faces, demonstrates that embeddings from different systems can be readily compared once the linear mapping is determined. In further analysis, fewer than 500 pairs of corresponding embeddings from two systems are required to calculate the full mapping between embedding spaces, and reducing the dimensionality of the mapping from 512 to 64 produces negligible performance penalty.
Principal Component Analysis of the Well-Being at Work and Respect for Human Rights Questionnaire (WWRRR) in the Mediterranean Region

Background: The Well-Being at Work and Respect for human Rights Questionnaire (WWRR) was conceived based on the hypothesis that the perception of respect for users' rights is an essential element of well-being in the workplace in healthcare. The objective of the study is to examine the principal components of the WWRR. Methods: A random sample representative of a set of professionals working in three different healthcare networks in Tunisia, North-Macedonia, and Italy was enrolled (n=426). Each professional completed a questionnaire on sociodemographic data and the WWRR. The WWRR consists of six items on beliefs about: satisfaction at work, users’ satisfaction, organization at work, respect of users’ and staff human rights, adequacy of resources. A seventh item assesses the perceived needs of personnel. Correlation between the items was evaluated by analysing the principal components with Varimax rotation and Kaiser normalization (which included all components with an Eigen value> 1). Results: A single factor covered over 50% of the variance, all the items of the questionnaire were closely related and compose a single factor. Tunisia presented some differences regarding the item about the human rights of staff. Conclusion: Satisfaction with the respect for the rights of users is strongly correlated with the other factors that are part of the concept of the organizational well-being of health care providers. The WWRR provides a means of measuring this important and often neglected dimension.
Effectiveness of four antiretroviral regimens for treating people living with HIV

The aim of this study was to compare 4 different ARV regimens in a clinical cohort in Brazil, with regard to the virologic and immunologic responses, clinical failure and reasons for changing. To compare the virologic response and clinical failure between groups we used the Cox and Kaplan Meier proportional hazard models. To analyze the immunologic outcome, we used multilevel GLLAMM and mixed effect linear regression models. To compare regimen change outcomes we used the Pearson's chi-square test. We included 840 participants distributed across the groups according to the initial ART regimen. The mean follow-up period was 27.8 months. Almost half the sample initiated ART with AIDS-related signs/symptoms. Virologic response was effective in 79.6% of participants within 12 months. The tenofovir/lamivudine/efavirenz group presented a higher proportion of virologic response (VL<50 at 6 months) when compared to the zidovudine/lamivudine/efavirenz group. There was no difference between the regimens regarding the immunologic response. A total of 17.3% of individuals changed regimen because of failure and 46.5% due to adverse events. Changes due to adverse events were more frequent in the group using zidovudine/lamivudine/efavirenz. The proportion of hospitalizations at 1 year was higher in the zidovudine/lamivudine/efavirenz group when compared to the tenofovir/lamivudine/efavirenz group. The effectiveness outcomes between the regimens were similar. Some differences may be due to the individual characteristics of patients, toxicity and acceptability of drugs. Studies are needed that compare similarly effective regimens and their respective treatment costs and financial impacts on SUS (Integrated Healthcare System).
Hardy inequalities for fractional $(k,a)$-generalized harmonic oscillator.

In this paper, we prove a Hardy inequality for fractional powers of the $a$-deformed Dunkl harmonic operator $\triangle_{k,a}:=\left|x\right|^{2-a}\triangle_k-\left|x\right|^a$. This also implies a concise proof of the Hardy inequality for fractional powers of the Dunkl-Hermite operator in \cite{CR}(O. Ciaurri, L. Roncal, S. Thangavelu, \newblock {\em Hardy-type inequalities for fractional powers of the Dunkl--Hermite operator}[J], Proceedings of the Edinburgh Mathematical Society(2018), 1-32), the case for $a=2$ of the Hardy inequality in this paper. Specifically, the proof of the relation between Dunkl Hermite semigroup and Laguerre heat semigroup(see \cite[Theorem 4.5]{CR}) is significantly simplified in this paper by interpreting $\triangle_{k,a}$ in the framework of $sl_2$-representation and using the basis given in \cite[Corallary 2.2]{BSK2}(S. Ben Said, T. Kobayashi and B. Orsted, \newblock {\em Laguerre semigroup and Dunkl operators}, Compos. Math., {\bf 148(4)}(2012), 1265-1336) in terms of Laguerre polynomials, instead of Dunkl-Hermite expansions as in \cite{CR}.
The Influence of Apparent Temperature on Mortality in the Kintampo Health and Demographic Surveillance Area in the Middle Belt of Ghana: A Retrospective Time-Series Analysis

Globally, studies have shown that diurnal changes in weather conditions and extreme weather events have a profound effect on mortality. Here, we assessed the effect of apparent temperature on all-cause mortality and the modifying effect of sex on the apparent temperature-mortality relationship using mortality and weather data archived over an eleven-year period. An overdispersed Poisson regression and distributed lag nonlinear models were used for this analysis. With these models, we analysed the relative risk of mortality at different temperature values over a 10-day lag period. By and large, we observed a nonlinear association between mean daily apparent temperature and all-cause mortality. An assessment of different temperature values over a 10-day lag period showed an increased risk of death at the lowest apparent temperature (18°C) from lag 2 to 4 with the highest relative risk of mortality (RR = 1.61, 95% CI: 1.2, 2.15, p value = 0.001) occurring three days after exposure. The relative risk of death also varied between males (RR = 0.31, 95% CI: 0.10, 0.94) and females (RR = 4.88, 95% CI: 1.40, 16.99) by apparent temperature and lag. On the whole, males are sensitive to both temperature extremes whilst females are more vulnerable to low temperature-related mortality. Accordingly, our findings could inform efforts at reducing temperature-related mortality in this context and other settings with similar environmental and demographic characteristics.