Miracle-dz/UniFAR-FTUs huggingface dataset:
Earth Observation and Machine Learning to Meet Sustainable Development Goal 8.7: Mapping Sites Associated with Slavery from Space

A large proportion of the workforce in the brick kilns of the Brick Belt of Asia are modern-day slaves. Work to liberate slaves and contribute to UN Sustainable Development Goal 8.7 would benefit from maps showing the location of brick kilns. Previous work has shown that brick kilns can be accurately identified and located visually from fine spatial resolution remote-sensing images. Furthermore, via crowdsourcing, it would be possible to map very large areas. However, concerns over the ability to maintain a motivated crowd to allow accurate mapping over time together with the development of advanced machine learning methods suggest considerable potential for rapid, accurate and repeatable automated mapping of brick kilns. This potential is explored here using fine spatial resolution images of a region of Rajasthan, India. A contemporary deep-learning classifier founded on region-based convolution neural networks (R-CNN), the Faster R-CNN, was trained to classify brick kilns. This approach mapped all of the brick kilns within the study area correctly, with a producer’s accuracy of 100%, but at the cost of substantial over-estimation of kiln numbers. Applying a second classifier to the outputs substantially reduced the over-estimation. This second classifier could be visual classification, which, as it focused on a relatively small number of sites, should be feasible to acquire, or an additional automated classifier. The result of applying a CNN classifier to the outputs of the original classification was a map with an overall accuracy of 94.94% with both low omission and commission error that should help direct anti-slavery activity on the ground. These results indicate that contemporary Earth observation resources and machine learning methods may be successfully applied to help address slavery from space.
The influence of complexity: a bibliometric analysis of complexity science in healthcare

Objectives To analyse trends in the academic literature applying complexity science to healthcare, focusing specifically on bibliometric characteristics and indicators of influence. Design This study reports a bibliometric analysis via a systematic search of the academic literature applying complexity science to healthcare. Method A search of four academic databases was performed on 19 April 2018. Article details were downloaded and screened against inclusion criteria (peer-reviewed journal articles applying complexity science to healthcare). Publication and content data were then collected from included articles, with analysis focusing on trends over time in the types and topics of articles, and where they are published. We also analysed the influence of this body of work through citation and network analyses. Results Articles on complexity science in healthcare were published in 268 journals, though a much smaller subset was responsible for a substantial proportion of this literature. USA contributed the largest number of articles, followed by the UK, Canada and Australia. Over time, the number of empirical and review articles increased, relative to non-empirical contributions. However, in general, non-empirical literature was more influential, with a series of introductory conceptual papers being the most influential based on both overall citations and their use as index references within a citation network. The most common topics of focus were health systems and organisations generally, and education, with recent uptake in research, policy, and change and improvement. Conclusions This study identified changes in the types of articles on complexity science in healthcare published over time, and their content. There was evidence to suggest a shift from conceptual work to the application of concrete improvement strategies and increasingly in-depth examination of complex healthcare systems. We also identified variation in the influence of this literature at article level, and to a lesser extent by topic of focus.
Making Sense of the Scottish Community Energy Sector – An Organising Typology

ABSTRACT The community energy sector is widely understood to be a pluralistic sector, encompassing a broad range of aims, motivations, and practices. This heterogeneity is, however, still not very well understood. Dominant analytical frameworks in the study of energy geographies offer little in the way of exploring the diversity of motivations, visions, and social relations that are present within the community energy sector, nor their causes or consequences. Drawing on the notion of ‘critical pluralism’, this paper aims to highlight the diverse range of sociotechnical configurations that make up this sector in Scotland, and begins to discuss some potential causes and consequences. To do so, it presents a novel community energy typology, based on an analysis of technical and social dimensions of 367 projects in Scotland. This shows that whilst the Scottish community energy sector contains a broad range of motivations, technologies, and social practices, the sector has become dominated by groups for whom energy generation is a means to achieve local socio-economic development. The paper discusses the community-level and policy drivers that may have contributed to this. It also discusses how we can begin to understand a diverse sector’s ability to affect the wider structures in which it operates.
Longitudinal cross-lagged relationships between posttraumatic stress disorder and depression in adolescents following the Yancheng tornado in China.

OBJECTIVE The aim of this study was to examine the comorbidity and longitudinal cross-lagged relationships between posttraumatic stress disorder (PTSD) and depression among Chinese adolescents after their experience of the Yancheng tornado. METHOD A total of 154 adolescents from 2 local middle schools in Yancheng city participated in this longitudinal study. Participants completed the Child PTSD Symptom Scale and the Center Epidemiological Studies of Depression at 6 months (T1), 9 months (T2), and 12 months (T3) after the tornado, respectively. RESULTS Results include the following: (a) the prevalence of PTSD was 55.84% at T1, 50.00% at T2, and 47.40% at T3; the prevalence of depression was 56.49% at T1, 65.58% at T2, and 66.01% at T3; (b) the comorbidity between PTSD and depression was high, 46.75% at T1, 42.86% at T2, 43.51% at T3; (c) PTSD at T1 significantly predicted depression at T2 (β = 0.42, p < .001), and PTSD at T2 significantly predicted depression at T3 (β = 0.64, p < .001); however, depression cannot significantly predict PTSD at different cross-time points. CONCLUSIONS The results suggest that there is comorbidity between PTSD and depression and that PTSD positively influence the development of depression in adolescents during the early period of the tornado, whereas depression did not predict PTSD. The implications of the results for the psychological services provided to children and youth are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Modified semantic feature analysis for anomia: a single case study

ABSTRACT Semantic feature analysis (SFA) is a treatment approach designed for patients with lexical retrieval difficulty caused by semantic deficits. During training, a structured framework (e.g. requiring the patient to name the category, function, and colour of the target items) is usually provided to facilitate the patient in thinking of the semantic features of the target items. Nevertheless, the use of a structured framework potentially limits the variety of semantic features activated for lexical retrieval. This study investigated the effectiveness of modified SFA training to address this potential limitation. An odd-man-out task was carried out in the modified SFA training with an anomic patient with impaired access to the phonological output lexicon. The task aimed to encourage a detailed comparison of semantic features among items in the same category. It is hypothesized that activations of more distinctive semantic features belonging to the target items will result in greater extent of generalizations in the lexical processing. The patient’s abilities in naming semantic features at the beginning and at the end of each treatment session were compared. Besides, accuracy and error patterns in oral and written naming in the initial and post-treatment assessment were also compared. The results showed a significant increase in the number of semantic features retrieved within treatment sessions. Moreover, a significant improvement in oral picture naming was also observed subsequent to the modified SFA training. Finally, the results of the error analyses further supported that the modified SFA was effective in promoting overall lexical processing. The findings indicated that both quantity and distinctiveness of concepts activated in the semantic system are essential to effective lexical retrieval. Theoretical and clinical implications were also discussed.
Maxwell eigenmode approach to the Casimir-Lifshitz torque

More than forty years ago, Barash published a calculation of the full retarded Casimir-Lifshitz torque for planar birefringent media with arbitrary degrees of anisotropy. An independent theoretical confirmation has been lacking since. We report a systematic and transparent derivation of the torque between two media with both electric and magnetic birefringence. Our approach, based on an eigenmode decomposition of Maxwell's equations, generalizes Barash's result for electrically birefringent materials, and can be generalized to a wide range of anisotropic materials and finite thickness effects.
Frustrated Quantum Rare-Earth Pyrochlores

In this review, we provide an introduction to the physics of a series of frustrated quantum rare-earth pyrochlores. We first give a background on the microscopic single- and two-ion physics of these materials, discussing the origins and properties of their exchange interactions and their minimal low-energy effective models before outlining what is known about their classical and quantum phases. We then make use of this understanding to discuss four important material examples, Er2Ti2O7, Yb2Ti2O7, Tb2Ti2O7, and Pr2Zr2O7, covering in some detail what is known experimentally and theoretically for each and then summarizing some key questions that remain open. Finally, we offer an outlook on some alternative material platforms for realizing similar physics and discuss what we see as prospects for future investigations on these quantum rare-earth pyrochlores.
Connections Between Spectral Properties of Asymptotic Mappings and Solutions to Wireless Network Problems

In this study, we establish connections between asymptotic functions and properties of solutions to important problems in wireless networks. We start by introducing a class of self-mappings (called asymptotic mappings) constructed with asymptotic functions, and we show that spectral properties of these mappings explain the behavior of solutions to some max–min utility optimization problems. For example, in a common family of max–min utility power control problems, we prove that the optimal utility as a function of the power available to transmitters is approximately linear in the low power regime. However, as we move away from this regime, there exists a transition point, easily computed from the spectral radius of an asymptotic mapping, from which gains in utility become increasingly marginal. From these results we derive analogous properties of the transmit energy efficiency. In this study, we also generalize and unify existing approaches for feasibility analysis in wireless networks. Feasibility problems often reduce to determining the existence of the fixed point of a standard interference mapping, and we show that the spectral radius of an asymptotic mapping provides a necessary and sufficient condition for the existence of such a fixed point. We further present a result that determines whether the fixed point satisfies a constraint given in terms of a monotone norm.
Decomposed Attention: Self-Attention with Linear Complexities

Recent works have been applying self-attention to various ﬁelds in computer vision and natural language processing. However, the memory and computational demands of existing self-attention operations grow quadratically with the spatiotemporal size of the input. This prohibits the application of self-attention on large inputs, e.g., long sequences, high-deﬁnition images, or large videos. To remedy this drawback, this paper proposes a novel decomposed attention (DA) module with substantially less memory and computational consumption. The resource-efﬁciency allows more widespread and ﬂexible application. Empirical evaluations on object recognition demonstrated the effectiveness of these advantages. DA-augmented models achieved state-of-the-art performance for object recognition on MS-COCO 2017 and signiﬁcant improvement for image classiﬁcation on ImageNet. Further, the resource-efﬁciency of DA democ-ratizes self-attention to ﬁelds where the prohibitively high costs have been preventing its application. The state-of-the-art result for stereo depth estimation on the Scene Flow dataset exempliﬁed this.
Learning a Multi-Modal Policy via Imitating Demonstrations with Mixed Behaviors

We propose a novel approach to train a multi-modal policy from mixed demonstrations without their behavior labels. We develop a method to discover the latent factors of variation in the demonstrations. Specifically, our method is based on the variational autoencoder with a categorical latent variable. The encoder infers discrete latent factors corresponding to different behaviors from demonstrations. The decoder, as a policy, performs the behaviors accordingly. Once learned, the policy is able to reproduce a specific behavior by simply conditioning on a categorical vector. We evaluate our method on three different tasks, including a challenging task with high-dimensional visual inputs. Experimental results show that our approach is better than various baseline methods and competitive with a multi-modal policy trained by ground truth behavior labels.
CD44v3 and CD44v6 isoforms on T cells are able to discriminate different disease activity degrees and phenotypes in systemic lupus erythematosus patients

Background Adhesion molecule CD44 contributes to T cell migration into target organs. A higher expression of CD44v3 and v6 isoforms has been identified on T cells from systemic lupus erythematosus (SLE) patients. The aim of this study was to investigate the expression of CD44v3/v6 on T cells of SLE patients in order to evaluate their correlation with clinical features. Methods Sixteen healthy subjects (HSs) and 33 SLE female patients were enrolled. Fifteen patients were in remission (Systemic Lupus Erythematosus Disease Activity Index-2000 (SLEDAI-2K) = 0) and 18 patients had an active disease (SLEDAI-2K ≥ 4). Experiments were conducted by flow cytometry. Results Expression of CD44v3 on CD4+ and CD8+ T cells was higher in active patients compared to HSs (p = 0.0097 and p = 0.0096). CD44v3 on CD8+ T cells was also higher in active patients compared to patients in remission (p = 0.038). CD44v6 was higher on CD4+ and CD8+ T cells from active patients compared to HSs (p = 0.003 and p = 0.0036) and to patients in remission (p = 0.01 and p = 0.02). In active patients the ratio CD44v3/v6 was unbalanced towards isoform v6 on both T cell populations. In a receiver operating characteristic curve analysis, CD44v6 on CD4+ T cells was the most sensitive and specific one (specificity of 81.8%, sensitivity of 75%). Expression of CD44v6 on CD4+ and CD8+ T cells correlated with the SLEDAI-2K (p = 0.03, r = 0.38 and p = 0.02, r = 0.39). CD44v6 and CD44v3 on CD8+ T cells associated with nephritis and arthritis (p = 0.047 and p = 0.023). Conclusions CD44v3/v6 can be used as biomarkers of disease activity and phenotypes; isoform v6 on CD4+ T cells can be useful as a diagnostic biomarker.
Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR); machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mg·ha−1 and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms.
Desorption Kinetics and Binding Energies of Small Hydrocarbons

Small hydrocarbons are an important organic reservoir in protostellar and protoplanetary environments. Constraints on desorption temperatures and binding energies of such hydrocarbons are needed for accurate predictions of where these molecules exist in the ice versus gas phase during the different stages of star and planet formation. Through a series of temperature programmed desorption experiments, we constrain the binding energies of 2- and 3-carbon hydrocarbons (C2H2—acetylene, C2H4—ethylene, C2H6—ethane, C3H4—propyne, C3H6—propene, and C3H8—propane) to 2200–4200 K in the case of pure amorphous ices, to 2400–4400 K on compact amorphous H2O, and to 2800–4700 K on porous amorphous H2O. The 3-carbon hydrocarbon binding energies are always larger than the 2-carbon hydrocarbon binding energies. Within the 2- and 3-carbon hydrocarbon families, the alkynes (i.e., least-saturated) hydrocarbons exhibit the largest binding energies, while the alkane and alkene binding energies are comparable. Binding energies are ∼5%–20% higher on water ice substrates compared to pure ices, which is a small increase compared to what has been measured for other volatile molecules such as CO and N2. Thus in the case of hydrocarbons, H2O has a less pronounced effect on sublimation front locations (i.e., snowlines) in protoplanetary disks.
Identification and Estimation of a Partially Linear Regression Model Using Network Data

I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify and fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ijth entry of which contains the number of other agents linked to both agents i and j. The intuition behind this approach is that for a large class of network formation models the columns of the squared adjacency matrix characterize all of the identifiable information about individual linking behavior. In this paper, I describe the model, formalize this intuition, and provide consistent estimators for the parameters of the regression model.
Are students intentionally using self-reflection to improve how they learn? Conceptualising self-induced self-reflective thinking

Difficulties in identifying the quality of students’ self-reflective practice can come forth from students who know about self-reflection because they have been informed about it but who do not intentionally use it to improve how they learn. The aim of this study was to explore and describe the construct of self-induced self-reflective thinking. Forty-one 11th-grade high-school students filled in open-ended questions regarding self-reflecting thinking. Qualitative analyses indicated that the construct was characterised as including an understanding that goes beyond a learned reaction in terms of providing a description of the process of self-reflective thinking. Consequences for reflective practice are discussed.
Role of Debt and Loan Forgiveness/Repayment Programs in Osteopathic Medical Graduates’ Plans to Enter Primary Care

Abstract Context Osteopathic medicine emphasizes partnering with patients to help them attain or maintain health. This philosophy encourages physicians to practice primary care and a mission of improving community health. However, there is currently a shortage of primary care physicians in many areas of the United States. Objective To determine whether intended practice patterns of recent graduates of colleges of osteopathic medicine favor primary care and whether practice patterns correlate with medical education debt. Methods Responses were analyzed from the American Association of Colleges of Osteopathic Medicine survey of pending medical school graduates from 2007 through 2016 regarding indebtedness and specialty selection. Results The percentage of graduating osteopathic medical students who chose a primary care specialty increased from 28.1% (676 students) in 2007 to 33.2% (1377 students) in 2016. Among graduates, those above the 75th percentile of debt had a general move toward more non–primary care positions, with a value of 74.4% in 2007 and 79.9% in 2016. Graduates below the 25th percentile had a gradual increase in primary care representation, moving from 24.6% in 2007 to 29.4% in 2016. In 2007, graduates with a loan forgiveness/repayment program were more likely to choose primary care over graduates without such a program (OR, 0.681 [95% CI, 0.505-0.920]; P=.02). Analysis of subsequent years showed a declining OR with increasing significance. Conclusions Results of this analysis indicated that increased educational debt loan directly influenced physician practice choice. Graduates with high debt burden were more likely to enter primary care fields and use loan forgiveness/repayment programs. Graduates with high debt burden who did not use loan forgiveness/repayment programs were more likely to enter non–primary care specialty fields, with this trend increasing as mean medical school debt increased. This association has implications for policies that could affect choice of primary care. However, further research is needed to fully understand the primary care choice by graduates of colleges of osteopathic medicine.
de Sitter-covariant Hamiltonian formalism of Einstein--Cartan gravity

The Hamiltonian formalism of Einstein--Cartan (EC) gravity is a starting point for canonical quantum gravity. The existing formalisms are at most Lorentz covariant, or diffeomorphism covariant. Here we analyze the Hamiltonian EC gravity in a 5d covariant way, with the gauge group being the de Sitter (dS) group, which unifies the Lorentz transformations and translation in an elegant manner, and also coincides with the acceleration of the universe. We reformulate the EC equations into a dS-covariant form, then find out the dS-covariant constraints of the phase space, and make all the constraint functions constitute a closed algebra by constructing a dS-invariant Dirac bracket, for the purpose of quantization.