Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of hot stellar systems with confidence

Hot stellar systems (HSS) are a collection of stars bound together by gravitational attraction. These systems hold clues to many mysteries of outer space so understanding their origin, evolution and physical properties is important but remains a huge challenge. We used multivariate-t-mixtures model-based clustering to analyze 13456 hot stellar systems from Misgeld & Hilker (2011) that included 12763 candidate globular clusters and found eight homogeneous groups using the Bayesian Information Criterion (BIC). A nonparametric bootstrap procedure was used to estimate the confidence of each of our clustering assignments. The eight obtained groups can be characterized in terms of the correlation, mass, effective radius and surface density. Using conventional correlation-mass-effective radius-surface density notation, the largest group, Group 1, can be described as having positive-low-low-moderate characteristics. The other groups, numbered in decreasing sizes are similarly characterised, with Group 2 having positive-low-low-high characteristics, Group 3 displaying positivelow-low-moderate characteristics, Group 4 having positive-low-low-high characteristic, Group 5 displaying positive-low-moderate-moderate characteristic and Group 6 showing positive-moderate-low-high characteristic. The smallest group (Group 8) shows negative-low-moderate-moderate characteristic. Group 7 has no candidate clusters and so cannot be similarly labeled but the mass, effective radius correlation for these noncandidates indicates that they zare larger than typical globular clusters. Assertions drawn for each group are ambiguous for a few HSS having low confidence in classification. Our analysis identifies distinct kinds of HSS with varying confidence and provides novel insight into their physical and evolutionary properties.
On Birthing Dancing Stars: The Need for Bounded Chaos in Information Interaction

While computers causing chaos is a common social trope, nearly the entirety of the history of computing is dedicated to generating order. Typical interactive information retrieval tasks ask computers to support the traversal and exploration of large, complex information spaces. The implicit assumption is that they are to support users in simplifying the complexity (i.e. in creating order from chaos). But for some types of task, particularly those that involve the creative application or synthesis of knowledge or the creation of new knowledge, this assumption may be incorrect. It is increasingly evident that perfect order-and the systems we create with it-support highly-structured information tasks well, but provide poor support for less-structured tasks. We need digital information environments that help create a little more chaos from order to spark creative thinking and knowledge creation. This paper argues for the need for information systems that offer what we term 'bounded chaos', and offers research directions that may support the creation of such interfaces
Factors that Drive Volunteerism in Nonprofit Organizations: A Theoretical Framework

This chapter aims at examining and reviewing the factors that drive volunteering in nonprofit organizations. The chapter follows a multidisciplinary approach in defining and examining the factors that drives individuals to volunteer in nonprofit organizations. The chapter provides a theoretical framework on how different factors are associated with volunteerism in nonprofit organizations. This chapter provides analysis of the volunteerism concept by looking at factors that drive volunteerism from diverse standpoints.
Update on Fundamental Mechanisms of Thyroid Cancer

The incidence of thyroid cancer (TC) has increased worldwide over the past four decades. TC is divided into three main histological types: differentiated (papillary and follicular TC), undifferentiated (poorly differentiated and anaplastic TC), and medullary TC, arising from TC cells. This review discusses the molecular mechanisms associated to the pathogenesis of different types of TC and their clinical relevance. In the last years, progresses in the genetic characterization of TC have provided molecular markers for diagnosis, risk stratification, and treatment targets. Recently, papillary TC, the most frequent form of TC, has been reclassified into two molecular subtypes, named BRAF-like and RAS-like, associated to a different range of cancer risks. Similarly, the genetic characterization of follicular TC has been proposed to complement the new histopathological classification in order to estimate the prognosis. New analyses characterized a comprehensive molecular profile of medullary TC, raising the role of RET mutations. More recent evidences suggested that immune microenvironment associated to TC may play a critical role in tumor invasion, with potential immunotherapeutic implications in advanced and metastatic TC. Several types of ancillary approaches have been developed to improve the diagnostic value of fine needle aspiration biopsies in indeterminate thyroid nodules. Finally, liquid biopsy, as a non-invasive diagnostic tool for body fluid genotyping, brings a new prospective of disease and therapy monitoring. Despite all these novelties, much work remains to be done to fully understand the pathogenesis and biological behaviors of the different types of TC and to transfer this knowledge in clinical practice.
Data-Driven Deep Learning to Design Pilot and Channel Estimator for Massive MIMO

In this paper, we propose a data-driven deep learning (DL) approach to jointly design the pilot signals and channel estimator for wideband massive multiple-input multiple-output (MIMO) systems. By exploiting the angular-domain compressibility of massive MIMO channels, the conceived DL framework can reliably reconstruct the high-dimensional channels from the under-determined measurements. Specifically, we design an end-to-end deep neural network (DNN) architecture composed of dimensionality reduction network and reconstruction network to respectively mimic the pilot signals and channel estimator, which can be acquired by data-driven deep learning. For the dimensionality reduction network, we design a fully-connected layer by compressing the high-dimensional massive MIMO channel vector as input to low-dimensional received measurements, where the weights are regarded as the pilot signals. For the reconstruction network, we design a fully-connected layer followed by multiple cascaded convolutional layers, which will reconstruct the high-dimensional channel as the output. By defining the mean square error between input and output as loss function, we leverage Adam algorithm to train the end-to-end DNN aforementioned with extensive channel samples. In this way, both the pilot signals and channel estimator can be simultaneously obtained. The simulation results demonstrate that the superiority of the proposed solution over state-of-the-art compressive sensing approaches.
Occupational health status of construction workers: A review

In the development of a country, construction industry has a significant role. About 350 million people of the world are directly involved in this sector, and the number is growing at a fast rate in the cities. Due to several occupational health risks, this sector is placed top of the list according to workplace accidents and injuries. Workers of this sector expose several types of fatal occupational health hazards daily. Moreover, socioeconomically poor people, particularly from rural areas, are engaged in this sector. Research on occupational health is highly needed to give safety and awareness to the mason workers. Present work is a review of researches from the available published articles to get a broad spectrum of occupational health problems of construction workers. Standard databases are used to collect articles on occupation, health, and working environment. Musculoskeletal disorders are very common among the construction workers. Workplace injuries are mainly due to improper use of personal protective equipments.
Trait-similarity and trait-hierarchy jointly determine co-occurrences of resident and invasive ant species

Interspecific competition, a dominant process structuring ecological communities, acts on species’ phenotypic differences. Species with similar traits should compete intensely (trait-similarity), while those with traits that confer competitive ability should outcompete others (trait-hierarchy). Either or both of these mechanisms may drive competitive exclusion within a community, but their relative importance and interacting effects are rarely studied. We show empirically that spatial associations (pairwise co-occurrences) between an invasive ant Solenopsis invicta and 28 other ant species across a relatively homogenous landscape are explained largely by an interaction of trait-similarity and trait-hierarchy in one morphological trait. We find that increasing trait-hierarchy leads to more negative associations; however these effects are counteracted when species are sufficiently dissimilar (by 37-95%) in their trait ranges. We also show that a model of species co-occurrences integrating trait-similarity and trait-hierarchy consolidates predictions of different theoretical assembly rules. This highlights the explanatory potential of the trait-based co-occurrence approach.
Unresolved stellar companions with Gaia DR2 astrometry

For stars with unresolved companions, motions of the centre of light and that of mass decouple, causing a single-source astrometric model to perform poorly. We show that such stars can be easily detected with the reduced χ2 statistic, or renormalized unit weight error (RUWE), provided as part of Gaia DR2. We convert RUWE into the amplitude of the image centroid wobble, which, if scaled by the source distance, is proportional to the physical separation between companions (for periods up to several years). We test this idea on a sample of known spectroscopic binaries and demonstrate that the amplitude of the centroid perturbation scales with the binary period and the mass ratio as expected. We apply this technique to the Gaia DR2 data and show how the binary fraction evolves across the Hertzsprung–Russell diagram. The observed incidence of unresolved companions is high for massive young stars and drops steadily with stellar mass, reaching its lowest levels for white dwarfs. We highlight the elevated binary fraction for the nearby blue stragglers and blue horizontal branch stars. We also illustrate how unresolved hierarchical triples inflate the relative velocity signal in wide binaries. Finally, we point out a hint of evidence for the existence of additional companions to the hosts of extrasolar hot Jupiters.
Simulation of Alfvén Wave Propagation in the Magnetic Chromosphere with Radiative Loss: Effects of Nonlinear Mode Coupling on Chromospheric Heating

We perform magnetohydrodynamic simulations to investigate the propagation of Alfvén waves in the magnetic chromosphere. We use the 1.5D expanding flux tube geometry setting and transverse perturbation at the bottom to generate the Alfvén wave. Compared with previous studies, our expansion is that we include the radiative loss term introduced by Carlsson & Leenaarts. We find that when an observation-based transverse wave generator is applied, the spatial distribution of the time-averaged radiative loss profile in our simulation is consistent with that in the classic atmospheric model. In addition, the energy flux in the corona is larger than the required value for coronal heating in the quiet region. Our study shows that the Alfvén wave-driven model has the potential to simultaneously explain chromospheric heating and how energy is transported to the corona.
Rural Finance, Capital Constrained Small Farms, and Financial Performance: Findings from a Primary Survey

Abstract Capital and credit constraints limit the small farm’s ability to adequately use resources for optimum performance. Farmers’ access to capital is constrained in multiple ways, including price factors, risk factors, and transaction factors, as well as access to and ease of rural agricultural financing. Using a primary survey data of small farms in Tennessee, we analyzed factors influencing credit constraint and its impact on farm performance. Farm operators’ gender, off-farm work, land acreage holdings, farm specialization, and the use of smart phone with Internet significantly influenced credit constraint. We found that the financial performance of credit constrained small farmers was significantly lower than that of unconstrained small farmers—an adverse impact of constrained capacity to credit could result in up to $51,000 lower in gross farm sales. Additionally, our reason-specific results within credit constraint suggested that around $32,000 to $39,000 lower performance in gross sales can be attributable to the constrained borrowing with deficit to obtain agricultural loans at required or desired level.
Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.
Classification of Social Anxiety Disorder With Support Vector Machine Analysis Using Neural Correlates of Social Signals of Threat

Threatening faces are potent cues in social anxiety disorder (SAD); therefore, neural response to threatening faces, particularly regions in the “fear” circuit such as amygdala, may classify individuals with SAD. Previous studies of indirect/implicit processing of threatening faces have shown that support vector machine (SVM) pattern recognition significantly differentiates individuals with SAD from healthy participants, though evidence for the role of the fear circuit in classification has been inconsistent. We extend this literature by using SVM during direct face processing. Individuals with SAD (n=47) and healthy controls (n=46) completed a validated emotional face matching task during functional MRI, which included a matching shapes control condition. SVM was based on brain response to threat (vs. happy) faces, threat faces (vs. shapes), and threat/happy faces (vs. shapes) in 90 regions encompassing frontal, limbic, parietal, temporal, and occipital systems. Recursive feature elimination (RFE) was used for feature selection and to rank the contribution of regions in predicting SAD diagnosis. SVM results for threat (vs. happy) faces revealed satisfactory accuracy (e.g., area under the curve=0.72); results with shapes as “baseline” yielded less optimal classification. RFE for threat (vs. happy) indicated that all 90 brain regions were necessary for classification. RFE-based ranking suggested diffuse neurofunctional activation to threat (vs. happy) faces in classification. When using an RFE cut-point, regions implicated in sensory and goal-directed processes contributed relatively more in differentiating SAD from controls than other regions. Results suggest that neural activity across large-scale systems, as opposed to fear circuitry alone, may aid in the diagnosis of SAD.
Diversity and biogeography of picoplankton communities from the Straits of Malacca to the South China Sea

Abstract Marine picoplankton, including prokaryotic and eukaryotic picoplankton, drive many biogeochemical processes, such as carbon, nitrogen and sulfur cycles, making them crucial to the marine ecosystem. Despite the fact that picoplankton is prevalent, its diversity and spatial distribution from the Straits of Malacca (SM) to the South China Sea (SCS) remain poorly investigated. This work explores the phylogenetic diversity and community structure of picoplankton in relation to environmental factors from the SM to the SCS. To this end, the Illumina MiSeq sequencing technique was applied to 16S and 18S rRNA genes. The results showed significant differences in the dynamics of picoplankton between the open sea and the strait region. Proteobacteria and Cyanobacteria constituted a larger part of the prokaryotic group. Within Cyanobacteria, the abundance of Prochlorococcus in the open sea was significantly higher than that of Synechococcus, while the opposite trend was observed in the strait. Dinoflagellata, Cnidaria, Retaria, Tunicata, and Arthropoda dominated among the eukaryotic taxa. High-throughput sequencing data indicated that salinity, temperature and NO2-N were the key factors determining the prokaryotic community structure, while temperature and dissolved oxygen determined the eukaryotic community structure in the studied region. The network analysis demonstrated that the cooperation and competition were also important factors affecting the picoplankton community.
The Red Blood Cell—Inflammation Vicious Circle in Sickle Cell Disease

Sickle cell disease (SCD) is a genetic disease caused by a single mutation in the β-globin gene, leading to the production of an abnormal hemoglobin called hemoglobin S (HbS), which polymerizes under deoxygenation, and induces the sickling of red blood cells (RBCs). Sickled RBCs are very fragile and rigid, and patients consequently become anemic and develop frequent and recurrent vaso-occlusive crises. However, it is now evident that SCD is not only a RBC rheological disease. Accumulating evidence shows that SCD is also characterized by the presence of chronic inflammation and oxidative stress, participating in the development of chronic vasculopathy and several chronic complications. The accumulation of hemoglobin and heme in the plasma, as a consequence of enhanced intravascular hemolysis, decreases nitric oxide bioavailability and enhances the production of reactive oxygen species (ROS). Heme and hemoglobin also represent erythrocytic danger-associated molecular pattern molecules (eDAMPs), which may activate endothelial inflammation through TLR-4 signaling and promote the development of complications, such as acute chest syndrome. It is also suspected that heme may activate the innate immune complement system and stimulate neutrophils to release neutrophil extracellular traps. A large amount of microparticles (MPs) from various cellular origins (platelets, RBCs, white blood cells, endothelial cells) is also released into the plasma of SCD patients and participate in the inflammation and oxidative stress in SCD. In turn, this pro-inflammatory and oxidative stress environment further alters the RBC properties. Increased pro-inflammatory cytokine concentrations promote the activation of RBC NADPH oxidase and, thus, raise the production of intra-erythrocyte ROS. Such enhanced oxidative stress causes deleterious damage to the RBC membrane and further alters the deformability of the cells, modifying their aggregation properties. These RBC rheological alterations have been shown to be associated to specific SCD complications, such as leg ulcers, priapism, and glomerulopathy. Moreover, RBCs positive for the Duffy antigen receptor for chemokines may be very sensitive to various inflammatory molecules that promote RBC dehydration and increase RBC adhesiveness to the vascular wall. In summary, SCD is characterized by a vicious circle between abnormal RBC rheology and inflammation, which modulates the clinical severity of patients.
Uncoupling Sensation and Perception in Human Time Processing

Abstract Timing emerges from a hierarchy of computations ranging from early encoding of physical duration (time sensation) to abstract time representations (time perception) suitable for storage and decisional processes. However, the neural basis of the perceptual experience of time remains elusive. To address this, we dissociate brain activity uniquely related to lower-level sensory and higher-order perceptual timing operations, using event-related fMRI. Participants compared subsecond (500 msec) sinusoidal gratings drifting with constant velocity (standard) against two probe stimuli: (1) control gratings drifting at constant velocity or (2) accelerating gratings, which induced illusory shortening of time. We tested two probe intervals: a 500-msec duration (Short) and a longer duration required for an accelerating probe to be perceived as long as the standard (Long—individually determined). On each trial, participants classified the probe as shorter or longer than the standard. This allowed for comparison of trials with an “Objective” (physical) or “Subjective” (perceived) difference in duration, based on participant classifications. Objective duration revealed responses in bilateral early extrastriate areas, extending to higher visual areas in the fusiform gyrus (at more lenient thresholds). By contrast, Subjective duration was reflected by distributed responses in a cortical/subcortical areas. This comprised the left superior frontal gyrus and the left cerebellum, and a wider set of common timing areas including the BG, parietal cortex, and posterior cingulate cortex. These results suggest two functionally independent timing stages: early extraction of duration information in sensory cortices and Subjective experience of duration in a higher-order cortical–subcortical timing areas.
Reflecting upon Perceptual Speed Tests in Information Retrieval: Limitations, Challenges, and Recommendations

Perceptual Speed (PS) is a cognitive ability defined by an individual's accuracy and speed to scan information while completing visual search tasks. Prior studies using PS tests have demonstrated that PS affects multiple factors in Information Retrieval (IR), such as a user's search performance, interaction with the system, time spent completing tasks, and subjective impression of their workload. With greater knowledge of PS, systems could be designed that accommodate users with low PS to improve their overall search experience. However, in this perspectives paper, we analyse how PS tests have been used in IR, and identify multiple uncertainties regarding PS content, administration, analysis, and reporting of findings. Consequently, we aim to stir discussion between IR researchers by drawing awareness to these issues. As a result, we further discuss challenges involved in advancing how future PS tests are used in IR. Finally, we propose recommendations that have the potential for enhancing the reliability and validity of current PS tests.