Miracle-dz/UniFAR-FTUs huggingface dataset:
Racial Disparities in Breast Cancer Outcomes in the Metropolitan Atlanta Area: New Insights and Approaches for Health Equity

Abstract Background Racial disparities in breast cancer (BC) outcomes persist where non-Hispanic black (NHB) women are more likely to die from BC than non-Hispanic white (NHW) women, and the extent of this disparity varies geographically. We evaluated tumor, treatment, and patient characteristics that contribute to racial differences in BC mortality in Atlanta, Georgia, where the disparity was previously characterized as especially large. Methods We identified 4943 NHW and 3580 NHB women in the Georgia Cancer Registry with stage I–IV BC diagnoses in Atlanta (2010–2014). We used Cox proportional hazard regression to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) comparing NHB vs NHW BC mortality by tumor, treatment, and patient characteristics on the additive and multiplicative scales. We additionally estimated the mediating effects of these characteristics on the association between race and BC mortality. Results At diagnosis, NHB women were younger—with higher stage, node-positive, and triple-negative tumors relative to NHW women. In age-adjusted models, NHB women with luminal A disease had a 2.43 times higher rate of BC mortality compared to their NHW counterparts (95% CI = 1.99 to 2.97). High socioeconomic status (SES) NHB women had more than twice the mortality rates than their white counterparts (HR = 2.67, 95% CI = 1.65 to 4.33). Racial disparities among women without insurance, in the lowest SES index, or diagnosed with triple-negative BC were less pronounced. Conclusions In Atlanta, the largest racial disparities are observed in luminal tumors and most pronounced among women of high SES. More research is needed to understand drivers of disparities within these treatable features.
A comparative study of vector-control strategies for maximum wind-power extraction of a grid-connected wind-driven brushless doubly-fed reluctance generator

Abstract This paper proposes a vector-control technique of a grid-connected wind-driven Brushless Doubly Fed Reluctance Generator (BDFRG) for maximum wind-power extraction under three strategies, summarised as unity power-factor operation, minimum converter current and minimum copper losses. The adopted generator has two stator windings namely; power winding, directly connected to the grid, and control winding, connected to the grid through a bi-directional converter. In addition, a soft starting method is suggested to avoid the employed converter over-current. The first control strategy can be achieved by adjusting the command power-winding reactive power at zero for a unity power-factor operation. However, the second strategy depends on setting the command d-axis control-winding current at zero to maximise the ratio of the generator electromagnetic-torque per the converter current. This enables the system to get a certain command torque under minimum converter current. On the other hand, the third strategy can be realised by determining the control-winding current angle at which the BDFRG can operate with minimum copper losses. A sample of the obtained simulation results is presented to check the effectiveness of the proposed control strategies.
Face Detection Using Gabor Wavelet Transform with Histogram of Local Binary Pattern with Dominant Color Descriptor

— Machine learning (ML) assignments are normally characterized into three general classifications, rely upon the life of the learning "flag" or "input" accessible to a learning framework. In ML, Gabor wavelet transform are supervised learning models with related learning calculations that examine information utilized for categorization and regression investigation. This research investigates the use of ML for biometric and object recognition. Examples are shown using this type of system in biometric application and in detecting face object. With the known dataset of images with known classifications, a system can predict the classification of new images. This study recommends face detection using Gabor Wavelet Transform technique with Histogram of local binary pattern (HOLBP) with Dominant Color Descriptor (DCD). The face detection (FD) approach is the way of skin color region segmentation has been considered in different perspectives: complex background pictures because of the area whose color is similar to the skin color cause high false positive rates. In this research, FD system is produced with the training of a Gabor wavelet transform classifier based on face properties related with DCD and a spatially enhanced local binary pattern. In Gabor wavelet transform approach, cross-validation process related with the Bao Database of user face properties which is used for solving classification precision problems. The implementation outcomes proved that the classification time reduced with an increase in the detection rate. The data collection of both the low-resolution and the high-resolution face image samples, the precision of FD system was over 89% with k-fold cross-validation scheme for an image data size of 450 and 400, respectively. Overall, the proposed algorithm provided a higher precision of FD compared with the precision for low-resolution face image (approximately 85%) of the existing schemes. This work presented a method to control the window size and detection area of a cascade classifier by detecting and using the skin color region with the pressing time reduced.
Group Reminiscence Therapy: An Effective Intervention to Improve Depression, Life Satisfaction, and Well-being in Older Adults?

The older adult population is projected to double by 2050 (PR Newswire, 2018; World Health Organization [WHO], 2017). Although research identifying influential factors on older adults’ well-being and life satisfaction has emerged, more rigorous research is needed in regards to harmful factors, such as levels of depression. Depression is this population’s most common mental health disorder globally, impairing functioning and increasing hospitalization rates, health care costs, and premature mortality (Johnson-Highsmith, Willamitis, Hanisch, & Webb, 2017; WHO, 2017). Often underdiagnosed and undertreated due to its comorbidity with other chronic health conditions that are associated with fatigue and pain, such as cancer, diabetes, pulmonary and cardiac conditions (WHO, 2017), its detection, prevention, and intervention are imperative (Zuiderveen, Ivey, Dordan, & Leiras, 2016). Older adults rarely seek help for depression, often refuse treatment, and may demonstrate inconsistent compliance with pharmaceutical interventions because of concerns regarding risks and side effects International Journal of Group Psychotherapy, 69: 460–469, 2019 © 2019 The American Group Psychotherapy Association, Inc. ISSN: 0020-7284 print/1943-2836 online DOI: https://doi.org/10.1080/00207284.2019. 1640610
A Robust Face Recognition System via Accurate Face Alignment and Sparse Representation

Due to its potential applications, face recognition has been receiving more and more research attention recently. In this paper, we present a robust real-time facial recognition system. The system comprises three functional components, which are face detection, eye alignment and face recognition, respectively. Within the context of computer vision, there are lots of candidate algorithms to accomplish the above tasks. Having compared the performance of a few state-of-the-art candidates, robust and efficient algorithms are implemented. As for face detection, we have proposed a new approach termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA) that produces better performances than most reported face detectors. Since face misalignment significantly deteriorates the recognition accuracy, we advocate a new cascade framework including two different methods for eye detection and face alignment. We have adopted a recent algorithm termed Sparse Representation-based Classification (SRC) for the face recognition component. Experiments demonstrate that the whole system is highly qualified for efficiency as well as accuracy.
Scraping EDGAR with Python

ABSTRACT This article presents Python codes that can be used to extract data from Securities and Exchange Commission (SEC) filings. The Python program web crawls to obtain URL paths for company filings of required reports, such as Form 10-K. The program then performs a textual analysis and counts the number of occurrences of words in the filing that reflect, for example, uncertainty (or any other quality specified by the researcher). The program can be easily modified to conduct other searches by changing the word list, company names, or SEC filings. The Python program could be used in an introductory graduate data analytics course in finance that has a web crawling or textual analysis component.
Web Semantics: Science, Services and Agents on the World Wide Web

The Internet of Musical Things (IoMusT) is an emerging research area consisting of the extension of the Internet of Things paradigm to the music domain. Interoperability represents a central issue within this domain, where heterogeneous objects dedicated to the production and/or reception of musical content (Musical Things) are envisioned to communicate between each other. This paper proposes an ontology for the representation of the knowledge related to IoMusT ecosystems to facilitate interoperability between Musical Things. There was no previous comprehensive data model for the IoMusT domain, however the new ontology relates to existing ontologies, including the SOSA Ontology for the representation of sensors and actuators and the Music Ontology focusing on the production and consumption of music. This paper documents the design of the ontology and its evaluation with respect to specific requirements gathered from an extensive literature review, which was based on scenarios involving IoMusT stakeholders, such as performers and audience members. The IoMusT Ontology can be accessed at: https://w3id.org/iomust#. © 2020ElsevierB.V. Allrightsreserved.
A Graph Based Conceptual Mining Model for Abstractive Text Summarization

Objectives: Themain objective of automatic text summarization is to compress the document into a smaller version by preserving the important concepts. Methods/Statistical Analysis:This work proposes a hybrid approach of Singular Value Decomposition and Named Entity Recognition to extract important sentences present in a document. The extracted sentences are used to create a probabilistic graphical model calledaBeliefcNetwork. This graph model represents documentsummary in concept level. We have used a modified Page Rank algorithm to find the most ranked noun phrase. From this noun phrase we extracted the most relevant sentences. Findings: Our abstractive graph based model for a document generates novel sentences as it uses the concept of triplets (Subject, Verb, and Object). It identifies whether a sentence is created by structural rearrangement of another sentence.Using SVD(Singualr Value Decomposition) and NER(Named Entity Recognition) we extracted relevant information present in a document so that entire document is crushed in to a graph model. We can use this model for documents similarity as well as for plagiarism detection. Application/Improvements.Experimental results of our proposed system show that use of named entities and SVD increases the accuracy of summarizer.
Human activity recognition

Human activity recognition is gaining importance, not only in the view of security and surveillance but also due to psychological interests in understanding the behavioral patterns of humans. This report is a study on various existing techniques that have been brought together to form a working pipeline to study human activity in social gatherings. Humans are first detected with Deformable part models and tracked as a feature point in 2.5D co-ordinate system using Lucas-Kanade algorithm. Linear cyclic pursuit model is then employed to predict short-term trajectory and understand behavior.
Request framing moderates the influence of affective images on charitable giving

AbstractFundraising organizations face difficult decisions regarding how to construct solicitations for donations. While these aid requests often include multiple salient features, their interactive effect on donation behavior and the psychological mechanisms that underlie their combined influence remain unclear. In six studies utilizing online and laboratory samples, as well as hypothetical and real incentives, we examine whether and how request framing moderates the impact of positive and negative images on charitable giving. Across all studies and in a single-paper meta-analysis, the influence of affective images on giving was moderated by the valence of request framing, such that affectively matched features most effectively elicited donations. Further, donors’ experienced positive affect could account for this matching effect – even in cases of matched negative features. These findings suggest that organizations can increase the effectiveness of aid requests by focusing on the affective match of request features. This work integrates previously discrepant findings on the impact of affect on donations and holds both conceptual implications for how affect can influence giving and practical implications for organizations seeking to design optimally effective requests for aid.
Nonlinear dynamics of laser systems with elements of a chaos: Advanced computational code

A general, uniform chaos-geometric computational approach to analysis, modelling and prediction of the non-linear dynamics of quantum and laser systems (laser and quantum generators system etc) with elements of the deterministic chaos is briefly presented. The approach is based on using the advanced generalized techniques such as the wavelet analysis, multi-fractal formalism, mutual information approach, correlation integral analysis, false nearest neighbour algorithm, the Lyapunov’s exponents analysis, and surrogate data method, prediction models etc There are firstly presented the numerical data on the topological and dynamical invariants (in particular, the correlation, embedding, Kaplan-York dimensions, the Lyapunov’s exponents, Kolmogorov’s entropy and other parameters) for laser system (the semiconductor GaAs/GaAlAs laser with a retarded feedback) dynamics in a chaotic and hyperchaotic regimes.
A modified Scrum story points estimation method based on fuzzy logic approach

Several known methods allow to estimate the overall effort(s) to be used up for the software development. The approach based on story points is preferable and quite common in the context of Sсrum agile development methodology. However, it might be rather challenging for people, who are new to this methodology or to a specific Scrum team to estimate the amount of work with story points. The proposed approach involves estimation of features on the basis of linguistic terms that are both habitual and clear for everyone. The presented fuzzy inference system (Mamdani’s model) makes it possible to calculate story points using people’s opinions expressed as sentences in natural language - the study shows empirically that beginners to Scrum methodology consider the proposed approach to be more convenient and easier in use than the ‘plain’ story points estimation. Also, four groups of people with different levels of qualification in Scrum were asked to estimate several features of a certain project using the developed approach and common story points approach to prove the relevance of the approach - it was shown that the results of basic story points estimation for Scrum experts differ slightly from the results revealed by proposed approach, while for Scrum beginners such difference is significant. To the opinion of authors, the proposed approach may allow to adapt to Scrum more smoothly, with better understanding of what is implied by story points, grasping the general idea and learning faster their use in practice. The experimental study conducted as a part of the research has shown results approaching the estimations provided by Scrum experts who have been working in real projects and making use of story points for several years. Continuation of the present work can be associated with intensive studies of more complicated methods of aggregation of the experts’ opinions, analysis of alternative representation forms of confidence degrees in estimates provided as well as the development of plugin for JIRA tracking system.
Effect of Pump Dithering at Each Stage of Cascaded Fiber Optical Parametric Amplifier

Cascaded fiber optical parametric amplifier (FOPA) can enhance gain and bandwidth. The gain and bandwidth can be further enhanced by dithering the FOPA pump. However, to our knowledge, the effects of a pump dithering at every stage of cascaded FOPA have not been discussed. The study of performance at every stage of cascaded FOPA is quite interesting and beneficial in designing the system. Here, we analyzed, using OptiSystem software, each stage of a cascaded FOPA, when there was a pump dithering and not. The results showed that the pump dithering enhanced the gain and broaden the bandwidth at every stage. The gain and bandwidth obtained with the pump dithering were 27 dB and 20 nm, respectively. On the other hand, when there was no pump dithering, the gain and bandwidth were 9 dB and 12 nm, respectively.
Bayesian Flexible Modeling of Interval-censored Failure Time Data

Interval-censored data are a special type of survival data, in which the survival time is not accurately observed but known to fall within a specific time interval. Intervalcensored data commonly arise in real-life epidemiological and medical studies that involve periodic examinations. In this dissertation, several semiparametric regression models are investigated to provide flexible modeling and robust inference for intervalcensored data from Bayesian perspectives. Chapter 1 provides a detailed description about interval-censored data and gives several examples. Existing models and methods for analyzing such interval-censored data are reviewed as well. Chapter 2 develops a unified Bayesian estimation approach under the framework of semiparametric linear transformation models for regression analysis of current status data, which is a special type of interval-censored data. This work provides an alternative estimation approach to the existing methods for the proportional hazards, proportional odds, and probit models. As a unified Bayesian estimation approach, the proposed method allows direct comparison of three different semiparametric regression models in the same framework of the Gibbs Sampler. Chapter 3 proposes a Bayesian estimation approach for analyzing general intervalcensored data under the generalized odds-rate hazards (GORH) models. The GORH models are a general class of semiparametric regression models including the proportional hazards and proportional odds models as special cases. Submodels of GORH models can be specified by indexing a non-negative value ν, where the "sub" prefix refers to the fact that for each ν, a semiparametric regression model is well-specified for regression analysis of general interval-censored data. It is found that treating ν as
Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention

Massive open online courses (MOOCs) show great potential to transform traditional education through the Internet. However, the high attrition rates in MOOCs have often been cited as a scale-efficacy tradeoff. Traditional educational approaches are usually unable to identify such large-scale number of at-risk students in danger of dropping out in time to support effective intervention design. While building dropout prediction models using learning analytics are promising in informing intervention design for these at-risk students, results of the current prediction model construction methods do not enable personalized intervention for these students. In this study, we take an initial step to optimize the dropout prediction model performance toward intervention personalization for at-risk students in MOOCs. Specifically, based on a temporal prediction mechanism, this study proposes to use the deep learning algorithm to construct the dropout prediction model and further produce the predicted individual student dropout probability. By taking advantage of the power of deep learning, this approach not only constructs more accurate dropout prediction models compared with baseline algorithms but also comes up with an approach to personalize and prioritize intervention for at-risk students in MOOCs through using individual drop out probabilities. The findings from this study and implications are then discussed.
High-throughput Quantification and Inference of Bio- Imaging Data

Author(s): Qin, Qin | Abstract: Molecular interactions and functions in single cells are largely dependent on their subcellular location and local environment. The molecular activities in live cells within a population are usually heterogeneous and dynamic. Therefore, it is crucial to develop high- throughput live- cell imaging and quantification methods to interpret the comprehensive landscape of molecular events in space and time. Here we present an efficient and user-friendly software package, Fluocell, to quantitatively analyze the dynamic sequences of FRET imaging data with subcellular resolutions. In addition to FRET image visualization and high throughput subcellular analysis, Fluocell also provides group statistics and kinetic analysis, as well as 3D re-construction functions of live-cell FRET images. Therefore, Fluocell provides an integrative environment for live-cell imaging analysis. The goal is to provide a standardized analysis pipeline, toward the ultimate goals of constructing comprehensive molecular maps with subcellular and dynamic resolutions in all cell types
The research circle - a tool for preschool teachers’ professional learning and preschool development

ABSTRACT The article explores a professional learning programme, a research circle, in which preschool teachers and researchers collaborate on content relating to sustainable development, science and technology. It investigates how collaborations between preschool teachers and researchers can contribute to professional learning and preschool development. The research focuses on experiences of participation in research circles and makes use of Participatory Action Research (PAR). The data consists of twelve preschool teachers’ written documentation as preparation for seminars in the research circle and semi-structured interviews with eight preschool teachers. The analysis explores three bodies of social and educational change: individuals, teams and organisations. The overall conclusion is that participation in a research circle support preschool teachers to become more aware of their own practices, address issues and challenges and make improvements in a collaborative and reflective way, it is a useful tool for preschool teachers’ professional learning and preschool development.