Miracle-dz/UniFAR-FTUs huggingface dataset:
THE RELATIONSHIP BETWEEN SELF-EFFICACY AND SELF-PERCEPTION WITH ANXIETY OF EXAMINATION IN HIGH SCHOOL STUDENTS

The aim of this study was to investigate the relation of self-efficacy and self-perception with anxiety of examination among students of Zahedan. The method used in this research was correlation. The statistic population of this study was all of the high school students of Zahedan. The number of the samples of this study was 320 students that were chosen by random cluster sampling method. Research instrument were the questionnaires of self-efficacy believes, Philips' scale of anxiety of examination and self-perception. The results showed that a negative significant correlation exists between the self-efficacy believes and the anxiety of examination. A significant positive correlation also exists among the anxiety of examination and the three factors of self-perception, passive, actorness and aggressive. The results of stepwise regression showed the passive self-perception, alone, can predict . 25 of variances of the anxiety of examination and with actorness self-perception they can predict . 29 of variances of anxiety of examination. In addition the results showed that the self-efficacy believes of male were more than female students. Also female students' anxiety of examination was more than male students.
Prevalence of Dentine Hypersensitivity among Adult Dental Patients in Dar-es-salaam, Tanzania

Objective: To assess the prevalence of dentine hypersensitivity (DH) among adult patient’s attending public dental clinics in Dar-es-salaam, Tanzania. Material and methods: This hospital based cross-sectional study assessed adults aged 18 years and above who attended public dental clinics in Dar-es-salaam, Tanzania. Prevalence of DH was clinically assessed in relation to age, sex, tooth type, presence of tooth wear (TW) and gingival recession (GR). Analysis of data used the SPSS program with level of statistical significance set at p<0.05. Result: The 323 participant’s age ranged between 18 and 72 years. Majority of participants were in age group 18 to 30 years (33.7%) and females (52.9%). The prevalence of clinically diagnosed DH was 46.4% (n=150) which had a similar prevalence to tooth wear (46.4%) and gingival recession (45.5%). DH statistically significantly increased with age, presence of tooth wear and presence of gingival recession (p<0.05). Teeth most diagnosed with DH were the first molars in the lower jaw (63.5%). Participants without tooth wear (OR=0.137; 95% CI 0.07 - 0.24) or gingival recession (OR=0.161; 95% CI 0.09 - 0.28) are less likely to have clinically diagnosed DH. Conclusion: This study demonstrates that prevalence of clinically diagnosed DH among subjects was common (46.4%) with first molars in lower jaw being most affected (63.5%). DH had a statistically significant increase with age, tooth wear and gingival recession however, logistic regression indicated that those without tooth wear or gingival recession were less likely to have DH.
Intersex or Diverse Sex Development: Critical Review of Psychosocial Health Care Research and Indications for Practice

Intersex or diverse sex development (dsd) can be conceptualized as an aspect of bodily diversity that has particular psychosocial implications. This is a review of psychosocial health care literature, published from 2007 to 2017, focusing on the well-being of people with a diagnosis relating to sex development. The analysis I offer here takes a critical psychological approach, questioning norms and taken-for-granted assumptions. This approach works from the understanding that how we know and how we talk about a phenomenon affects people in material and life-changing ways. This article offers recommendations concerning health care communication, the importance of taking time for emotion, and the process of building supportive relationships. The research reviewed provides clear evidence of psychosocial harm that is done through genital intervention and evidence that parents do not routinely give fully informed consent before their children undergo treatment. Finally, I highlight key points and recommendations for health professionals, indicating how psychosocial professionals can contribute to health care and well-being in the context of dsd when appropriately resourced and trained.
Volumes of quasifuchsian manifolds

Quasifuchsian hyperbolic manifolds, or more generally convex co-compact hyperbolic manifolds, have infinite volume, but they have a well-defined ``renormalized'' volume. We outline some relations between this renormalized volume and the volume, or more precisely the ``dual volume'', of the convex core. On one hand, there are striking similarities between them, for instance in their variational formulas. On the other, object related to them tend to be within bounded distance. Those analogies and proximities lead to several questions. Both the renormalized volume and the dual volume can be used for instance to bound the volume of the convex core in terms of the Weil-Petersson distance between the conformal metrics at infinity.
Topological reinforcement as a principle of modularity emergence in brain networks

Modularity is a ubiquitous topological feature of structural brain networks at various scales. While a variety of potential mechanisms have been proposed, the fundamental principles by which modularity emerges in neural networks remain elusive. We tackle this question with a plasticity model of neural networks derived from a purely topological perspective. The topological reinforcement (TR) model acts by enhancing the topological overlap (TO) between nodes. Specifically, the TR rule iteratively connects a randomly selected node to a non-neighbor with the highest TO, while pruning another network link at random, hence preserving graph density. The topological reinforcement reliably evolves synthetic random networks toward a modular architecture. Additionally, the final modular structure reflects features of the initial networks, particularly, local density variations (‘proto-modules’), thus allowing to predict the modules of the evolved graph. Subsequently, we show that this topological selection principle when implemented biologically is equivalent to the classic Hebbian rule. Concretely, we explore a simple model of excitable dynamics, the SER model, in which the activity of network nodes is described by discrete susceptible, excited and refractory states. In this case, the plasticity rule acts in a Hebbian-like fashion, i.e., based on the functional connectivity (FC) between nodes represented by co-activations. Results produced by the SER-based model are consistent with TR, showing a consistent final network configuration. Our findings suggest that the selective reinforcement of topological overlap may be a fundamental mechanism by which brain networks evolve toward modular structure.
Integrated analysis of human transcriptome data for Rett syndrome finds a network of involved genes

Rett syndrome (RTT) is a rare disorder causing severe intellectual and physical disability. The cause is a mutation in the gene coding for the methyl-CpG binding protein 2 (MECP2), a multifunctional regulator protein. Purpose of the study was integration and investigation of multiple gene expression profiles in human cells with impaired MECP2 gene to obtain a data-driven insight in downstream effects. Information about changed gene expression was extracted from five previously published studies. We identified a set of genes which are significantly changed not in all but several transcriptomics datasets and were not mentioned in the context of RTT before. Using overrepresentation analysis of molecular pathways and gene ontology we found that these genes are involved in several processes and molecular pathways known to be affected in RTT. Integrating transcription factors we identified a possible link how MECP2 regulates cytoskeleton organization via MEF2C and CAPG. Integrative analysis of omics data and prior knowledge databases is a powerful approach to identify links between mutation and phenotype especially in rare disease research where little data is available. Abbreviations Rett syndrome (RTT), embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), fold change (FC), Gene Ontology (GO), EIF (eukaryotic initiation of transcription factor) For genes the symbols according to the HGNC nomenclature were used.
Local Orthogonal Decomposition for Maximum Inner Product Search

Inverted file and asymmetric distance computation (IVFADC) have been successfully applied to approximate nearest neighbor search and subsequently maximum inner product search. In such a framework, vector quantization is used for coarse partitioning while product quantization is used for quantizing residuals. In the original IVFADC as well as all of its variants, after residuals are computed, the second production quantization step is completely independent of the first vector quantization step. In this work, we seek to exploit the connection between these two steps when we perform non-exhaustive search. More specifically, we decompose a residual vector locally into two orthogonal components and perform uniform quantization and multiscale quantization to each component respectively. The proposed method, called local orthogonal decomposition, combined with multiscale quantization consistently achieves higher recall than previous methods under the same bitrates. We conduct comprehensive experiments on large scale datasets as well as detailed ablation tests, demonstrating effectiveness of our method.
Perceptions of Humility: A Preliminary Study

Do people perceive humility as a strength or a weakness? The current study examined this question in a sample of 127 undergraduates. Contrary to common dictionary definitions of humility, which often emphasize its association with self-abasement, participants reported consistently positive views of humility. When recalling situations in which they felt humble, they typically reported success experiences associated with positive emotion. Participants clearly associated humility with good psychological adjustment, although they were less decisive about whether humility was associated with confidence or leadership. Although participants viewed humility as a strength across all social roles sampled, humility was viewed most favorably as a quality of religious seekers, less favorably as a quality of close others or subordinates, and least favorably as a quality of leaders or entertainers. Positive views of humility were associated with high self-esteem and religiosity. Less favorable views of humility were associated with narcissism—particularly its exploiting/entitled dimension.
An Enhanced Lyapunov-Function Based Control Scheme for Three-Phase Grid-Tied VSI With LCL Filter

In this paper, an enhanced Lyapunov-function based control scheme is proposed for three-phase grid-tied LCL-filtered voltage source inverters (VSIs). The grid-tied VSI is modeled in the synchronously rotating dq-frame. Motivated from the energy distribution in the system, an energy-like function (referred to as Lyapunov-function) is defined. Then, Lyapunov-function based control scheme is derived, which guarantees the global stability of the system. However, the derived Lyapunov-function based control scheme with single-loop cannot exhibit a satisfactory damping performance due to the poles located near imaginary axis. Therefore, an enhanced Lyapunov-function based control scheme is proposed by adding a second loop into the control variable. Unlike the existing control schemes where cross-coupling interaction exists between d- and q-axes actual currents, the cross coupling in the proposed control scheme is dependent on the inverter current references. The performance and validity of the proposed control scheme are verified by computer simulations and experimental results.
Fine-tune BERT for Extractive Summarization

BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state of the art on the CNN/Dailymail dataset, outperforming the previous best-performed system by 1.65 on ROUGE-L. The codes to reproduce our results are available at this https URL
A Wide-Band Tissue Numerical Model for Deeply Implantable Antennas for RF-Powered Leadless Pacemakers

Radio-frequency (RF)-based wireless power transfer method is highly desirable to power deep-body medical implants, such as cardiac pacemakers. The antenna is one of the essential components of such system; however, it poses significant design challenges for deep-body applications and must be modeled and characterized correctly to achieve the required performance. In this paper, design and validation of a novel wideband numerical model (WBNM) are proposed for deeply implantable antennas and to enable RF-powered leadless pacing. In particular, we acquired a wideband tissue simulating liquid (TSL) and fully characterized it using a dielectric probe. Based on the measured properties of the TSL, the design and numerical characterization of the WBNM were performed using a hybrid simulation method, i.e., by employing the finite-element method and method of moment. The proposed WBNM was validated experimentally as well as analytically using a reference microstrip antenna. Good agreement between the simulated, measured, and analytical results validated the proposed model. Furthermore, the application of this model and the TSL was demonstrated by the design, development, manufacture, and measurement of a novel metamaterial-based conformal antenna at 2.4 GHz. Moreover, good agreement was found between the simulated and measured results of the proposed conformal antenna. It is evident from the results that the proposed numerical model can be used to design deeply implantable antennas for any frequency, ranging from 800 to 5800 MHz. Finally, the proposed miniature conformal antenna and its successful integration with a leadless pacemaker model present a great potential for future RF-powered leadless pacemakers and other deep-body medical implants.
Deep Convolutional Neural Networks for WCE Abnormality Detection: CNN Architecture, Region Proposal and Transfer Learning

Wireless capsule endoscopy (WCE) plays an important role in the diagnosis of gastrointestinal diseases. However, it is very time-consuming and fatiguing for a physician to review a large number of WCE images. Some methods to address this problem have recently been presented. However, these methods generally employ classification algorithms to discriminate abnormal from normal images, which do not localize, recognize, or detect abnormal patterns in abnormal images. We sought to identify a better method for the WCE abnormal pattern detection. In this paper, convolutional neural networks (CNNs) are used to implement detection function, and several methods are also adopted to boost the performance of WCE abnormality detection from aspects of the CNN architecture, region proposal, and transfer learning. First, we present a deep cascade network, namely, CascadeProposal, trained end-to-end to generate a small number of region proposals with high-recall by a region proposal rejection module and to simultaneously detect abnormal patterns using a detection module. Second, we use a multiregional combination (MRC) method to obtain good coverage of the regions of interest and employ the salient region segmentation (SRS) method to capture accurate region locations. Third, we use the dense region fusion (DRF) method for object boundary refinement. Fourth, we introduce negative category (Neg) and transfer learning (TL) strategies into our CNNs to obtain a better model performance. The extensive experiments are performed on our WCE image dataset of more than 7k annotated images. A final mean average precision (mAP) of 70.3% and a better mAP of 72.3% can be achieved via CascadeProposal with ZF and Fast R-CNN with VGG-16 networks, respectively, using MRC+Neg+TL method in the training stage and MRC+DRF+SRS method in the testing stage. The comprehensive results demonstrate that our method is efficient and effective for WCE abnormality detection with high-localization accuracy.
The supernova-regulated ISM – VI. Magnetic effects on the structure of the interstellar medium

We explore the effect of magnetic fields on the vertical distribution and multiphase structure of the supernova-driven interstellar medium in simulations that admit dynamo action. As the magnetic field is amplified to become dynamically significant, gas becomes cooler and its distribution in the disc becomes more homogeneous. We attribute this to magnetic quenching of vertical velocity, which leads to a decrease in the cooling length of hot gas. A non-monotonic vertical distribution of the large-scale magnetic field strength, with the maximum at |z| ≈ 300 pc causes a downward pressure gradient below the maximum which acts against outflow driven by SN explosions, while it provides pressure support above the maximum.
Linear transient growth of particulate pipe flows

We tackle the question of whether the presence of particles in a pipe flow can influence the linear transient growth of infinitesimal perturbations, in view of better understanding the behaviour of particulate pipe flows in regimes of transition to turbulence. The problem is tackled numerically by means of a simple model where particles are modelled as a second fluid, that interacts with the fluid phase through a two-way Stokes drag only. The transient growth is found to be enhanced by the presence of particles, especially so if the particles are localised at a specific radial location of the pipe. At the same time, the mechanisms of transient growth themselves remain those of non-particulate flows. The effect is maximised for particles of intermediate size (somewhere between the ballistic limit of very light particles and the point where they are too heavy to be influenced by the flow). Most remarkably, the Segre-Silberberg radius (around 2/3 of the pipe radius), where particles naturally cluster in laminar flows, turns out to be close to the optimal location to enhance the transient growth.
Network Horizon Dynamics I: Qualitative Aspects

Mostly acyclic directed networks, treated mathematically as directed graphs, arise in machine learning, biology, social science, physics, and other applications. Newman [1] has noted the mathematical challenges of such networks. In this series of papers, we study their connectivity properties, focusing on three types of phase transitions that affect horizon sizes for typical nodes. The first two types involve the familiar emergence of giant components as average local connectivity increases, while the third type involves small-world horizon growth at variable distance from a typical node. In this first paper, we focus on qualitative behavior, simulations, and applications, leaving formal considerations for subsequent papers. We explain how such phase transitions distinguish deep neural networks from shallow machine learning architectures, and propose hybrid local/random network designs with surprising connectivity advantages. We also propose a small-world approach to the horizon problem in the cosmology of the early universe as a novel alternative to the inflationary hypothesis of Guth and Linde.
Depth Augmented Networks with Optimal Fine-tuning

Convolutional neural networks (CNN) have been shown to achieve state-of-the-art performance in a significant number of computer vision tasks. Although they require large labelled training datasets to learn the CNN models, they have striking attributes of transferring learned representations from large source sets to smaller target sets by normal fine-tuning approaches. Prior research has shown that these techniques boost the performance on smaller target sets. In this paper, we demonstrate that growing network depth capacity beyond classification layer along with careful normalization and scaling scheme boosts fine-tuning by creating harmony between the pre-trained and new layers to adjust more to the target task. This indicates pre-trained classification layer holds high-level (global) image information that can be propagated through the newly introduced layers in fine-tuning. We evaluate our depth augmented networks following our designed incremental fine-tuning scheme on several benchmark datatsets and show that they outperform contemporary transfer learning approaches. On average, for fine-grained datasets we achieve up to 6.7% (AlexNet), 5.4% (VGG16) and for coarse datasets 9.3% (AlexNet), 8.7% (VGG16) improvement than normal fine-tuning. In addition, our in-depth analysis manifests freezing highly generic layers encourage better learning of target tasks. Furthermore, we have found that the learning rate for newly introduced layers of depth augmented networks depend on target set and size of new layers.
Numerical Investigation on Feedback Insensitivity in Semiconductor Nanolasers

This paper presents numerical investigations into the effect of feedback phase on the stability of semiconductor nanolasers (SNLs) in the presence of the external optical feedback. For this purpose, numerical solutions are obtained from rate equations where the effects of Purcell cavity-enhanced spontaneous emission factor <italic>F</italic> and an enhanced spontaneous emission coupling factor <italic>β</italic> are included. In this way, a phase-insensitive stable SNL is identified when the feedback coupling fraction is below a critical threshold <italic>η</italic><sub>c</sub>. Furthermore, the relationship between <italic>η</italic><sub>c</sub> and two other important system parameters, namely the injection current (<italic>I</italic><sub>dc</sub>) and the initial external cavity length (<italic>L</italic><sub>0</sub>), is studied. The results show that <italic>η</italic><sub>c</sub> has a bi-exponential relationship with either <italic>I</italic><sub>dc</sub> or <italic>L</italic><sub>0</sub>. Moreover, the influence of <italic>F</italic> on the <italic>η</italic><sub>c</sub> is evaluated, and it is found that <italic>η</italic><sub>c</sub> increases with the increase of <italic>F</italic>. The results presented in this paper provide practical guidelines for the design of phase-insensitive stable SNLs, which are useful for densely integrated photonic circuits based applications, such as optical communications and sensing.
Service Quality, Administrative Process, and Citizens’ Evaluation of Local Government in the US

Abstract It is often assumed that citizens evaluate government based on service quality or outcomes (such as safe neighbourhoods or good schools), but aspects of administrative process (such as fairness and respect) are also important. Using data from two US surveys, this study examines how service quality and administrative process influence citizens’ evaluations of government. Results indicate that service quality matters most to ratings of the community; in contrast, administrative process is the dominant driver of trust; and both quality and process have large effects on judgements about government’s overall job performance. Implications for public management research and practice are discussed.
Technical Change and Superstar Effects: Evidence from the Rollout of Television

Technical change that improves economies of scale can generate fast income growth among top earners at the expense of everyone else. I test this classic “superstar model” in the labor market for entertainers where the historic roll-out of television led to a natural experiment in scale-related technological change. The launch of a local TV station multiplied audiences of top entertainers nearly fourfold and resulted in a 50% increase of the top percentile’s income share, a more right-skewed income distribution, and significant income losses for lowerranked entertainers. The results confirm the predictions of the “superstar model” and are at odds with canonical models of skill-biased technological change.