Miracle-dz/UniFAR-FTUs huggingface dataset:
Preceptor as Frontline Leader Utilizing Emotional Intelligence

Problem This paper describes a DNP project implemented in an acute care hospital in 2020. The hospital implemented a nurse residency program, with an evidence-based preceptor role development (PRD) component, which proved to be beneficial in growing the nursing workforce and building infrastructure; however, nurse turnover increased. Context – Preceptors expressed feeling unsupported and not valued. The nurse executive team recommended the recognition committee expand its efforts to develop strategies to develop frontline leaders by building preceptor resiliency and increase retention. Preceptor development could positively impact the nursing workforce. Intervention The DNP student, was the executive sponsor of the recognition team, and had oversight of the nurse residency program, integrated leadership and emotional intelligence training into the current PRD program by developing a training module to teach preceptors strategies to gain or improve the core skills of emotional intelligence (EI). Measures In addition to measuring retention, a valid and reliable EI assessment tool was utilized per preceptor. Results The project outcome was the reduction in nurse turnover, including preceptors, from 15.5% to 14.7%. Conclusion As a result of the project implementation, the preceptors that received the training utilized the new skills during a crisis and remained employed with the hospital ninety days after completing the training. EI training is now incorporated into the PRD curriculum. There is consideration to provide EI training to all nurses in the future as a tactic to increase engagement and improve retention.
minBERT for Multi-Task Learning

Modern NLP models place a large focus on building robust word embeddings to gain a strong foundation upon which we can build strong performing models. Better embeddings and understanding of the relationships between words would allow us to use the same embeddings across different Natural Language tasks without needing to relearn the relationship between words and phrases. While we can use word embeddings across models, using the same model for multiple tasks can be difficult. In this project, we use Multi-task learning to simultaneously fine-tune a minBert model to perform classification across sentiment analysis, paraphrase detection, and classification of semantic textual similarity tasks. To reuse as much knowledge as possible gained from training of other tasks, we perform backpropagation across each task to support learning across all tasks
Supplementary information for “Modular representation and control of ﬂoppy networks”

In this section, we provide the review and development of algorithms for solving the sparse null space decomposition (SND) [1]. We first introduce the ABS class of algorithms proposed by Abaffy, Broyden and Spedicato [2, 3]. The ABS class of algorithms are iterative algorithms for solving the linear system Ax = b where A ∈ Rm×n has full row rank and b ∈ R. Let A = [a1, a2, . , an], where each ai is an m × 1 column vector. We start with an arbitrary initial vector x1 ∈ R and an arbitrary nonsingular matrix H1 ∈ Rn×n, e.g. the identity matrix. At the i-th step, suppose we already have a solution of the first i − 1 equations of Ax = b (denoted as xi) and an an n × n matrix with rows generating the null space of the first i− 1 rows of A (denoted as Hi, also called the Abaffian matrix). We get xi+1 and Hi+1 from xi and Hi using the formulas below:
Dynamic Fed Attention

The Federal Reserve (“the Fed”) is the principal monetary policy body of the United States, and its policies have ramifications throughout the economy. As a result, significant attention is paid to the statements and speeches of its principal officers and committees. Past literature has sought to apply static topic models to these texts in an attempt to gauge the attention of the Federal Reserve (Jegadeesh and Wu, 2017). This project seeks to extend this literature to the use of dynamic embedded topic models (D-ETM), formulated by Dieng et al. (2019a), comparing the results to dynamic latent Dirichlet allocation (D-LDA) established in Blei and Lafferty (2006). I show that D-ETM can significantly outperform D-LDA in terms of quantitative measures such as topic coherence and diversity and investigate the variable effect of using different classes of embedding models for D-ETM.
C U P C A K E : A C OMPRESSION O PTIMIZER FOR S CALABLE C OMMUNICATION -E FFICIENT D ISTRIBUTED T RAINING

Data-parallel distributed training (DDT) is the de facto way to accelerate deep learning on multiple GPUs. In DDT, communication for gradient synchronization is the major efficiency bottleneck. Many gradient compression (GC) algorithms have been proposed to address this communication bottleneck by reducing the amount of communicated data. Unfortunately, it has been observed that GC only achieves moderate performance improvement in DDT, or even harms the performance. In this paper, we argue that the current way of deploying GC in a layer-wise fashion reduces communication time but at the cost of non-negligible compression overheads. To address this problem, we propose Cupcake, a compression optimizer to fully unleash GC algorithms’ advantages in accelerating DDT. It applies GC algorithms in a fusion fashion and determines the provably optimal fusion strategy to maximize the training throughput of compression-enabled DDT jobs. Experimental evaluations show that GC algorithms with Cupcake can achieve up to 2.03× speedup in training throughput over training without GC, and up to 1.79× speedup over the state-of-the-art approaches of applying GC to DDT in a layer-wise fashion.
Disney Curves

We describe a system for generating smooth organic curves like those used as outlines by Disney animators. Our system is based on an analysis of a large collection of such curves, the recognition of a small family of curves that seems to contain all of these, and an approach to ﬁtting a member of this small family to a user-drawn curve, so that ordinary users can have their curves beautiﬁed. We verify the method by showing that when actual curves drawn by Disney animators are used as input, the output is essentially the same as the input.
Default Final Project: minBERT and Downstream Tasks (Multi-task Learning)

Our goal for this project is to see if a pre-trained minBERT model can create embeddings that generalize well to more than one task, namely: (1) Sentiment Classification, (2) Paraphrase Detection, and (3) Semantic Textual Similarity. In an attempt to get better performance on all three tasks, we implemented multiple methods: Elastic Net regularization, Gradient Surgery for fine-tuning, Cosine Embedding Loss, and some data restructuring. Our findings show that Elastic Net regularization improved Sentiment Classification and Cosine Embedding Loss improved Paraphrase Detection. In our experiments, we also note that the method of inputting paired data into BERT strongly affects the results, with concatenation of tokens pre-BERT significantly outperforming concatenation of embeddings post-BERT. Our results also show that the model is able to generalize well to the three tasks, sometimes even outperforming the single-task model.
Doc2Vec Approach for Extracting Knowledge Representation in Research Publications

Effective scientific team assembly and collaboration play more and more important roles in innovation for science and technology. As knowledge becomes ever more specialized, analyze team expertise diversity becomes crucial. Thanks to the giant amount of documents and publications that have exploded in the Internet era, we can get the scholar collaboration relation and scientific text easily. However, how to measure team composition and diversity from publication’s meta data and the text contained in scientific publications is kind of complex and still an ongoing area to study. In this report, we propose the definitions of the team’s expertise and diversity and illustrates the pipeline for collecting and identifying scientists’ prior collaboration networks, conducting text preprocessing, decoding scientists’ areas of expertise using text analytics tools such as Doc2Vec approach.
Improving MinBERT: Gradient Surgery and Mixed-Precision Training

This aim of this project is to implement parts of the transformer model BERT (in particular, a lite-version called "minBERT") and train it to perform a number of downstream language tasks in tandem (multitask classification) including sentiment analysis of movie reviews. To extend my model performance, I performed a randomized hyperparameter sweep, incorporated "gradient surgery" techniques to address conflicting task gradients, performed mixed-precision training for model speedup, and model ensemble methods to combine per-task finetuned models for an "ultimate" model. In the end, I was surprised to find that gradient surgery contributed little to my model’s overall performance, and saw that mixed-precision training led to both faster training and better accuracy, at the cost of numerical instability. Ultimately, the ensemble method combining multiple finetuned models led to the best outcome that I was able to achieve.
A note on p-value multiple-testing adjustment for two-step genome-wide gene-environment interactions scans

Two-step testing is the state-of-the art approach for performing genome-wide interaction scans (GWIS). It is computationally efficient and yields higher power than standard single-step-based GWIS for virtually all biologically plausible scenarios. However, while two-step tests control the genome-wide type I error rate at the desired level, the lack of associated valid p-values can make it difficult for users to compare with single step-results. We show how multiple-testing adjusted p-values can be defined for two-step test based on standard multiple-testing theory, and how they can be in turn scaled to make valid comparisons with single-step tests possible.
Association of Domains of Sedentary Behavior with Cardiovascular Disease Events in The Multi-Ethnic Study of Atherosclerosis

Background: Sedentary behavior is associated with an increased risk for adverse health outcomes, including cardiovascular disease (CVD), independent of physical activity status. Little is known about this relationship in an ethnically diverse population. The objective of our study is to assess the effects of leisure time and occupational sedentary behavior on multiple cardiovascular outcomes in a multi-ethnic cohort. Methods: The Multi-Ethnic Study of Atherosclerosis (MESA) includes 2619 Caucasian, 1495 Hispanic, 1891 Black, and 804 Chinese-American adults aged 45-84 years and free of clinical CVD at enrollment, Sedentary behavior was self-reported at baseline. Participants were followed for an average of 13.6 years, and 14 types of cardiovascular outcomes were ascertained. Hazards of each cardiovascular outcome were modeled with adjustment for potential confounders, including physical activity. Results: Every one hour per day increase in leisure time sedentary behavior predicts a 6% increase in the adjusted hazards for CVD death (P < 0.05). Every one hour increase in occupational sedentary time predicts a 21% and 20% decrease in the hazard for PVD and other revascularization, respectively (P < 0.05). Conclusions: Leisure time sedentary behavior was associated with increased hazards for CVD death, but occupational sedentary time appears to be protective of peripheral vascular disease and other revascularization.
Harmonizing Healthy Cohorts to Support Multicenter Studies on Migraine Classification using Brain MRI Data

Multicenter and multi scanner imaging studies might be needed to provide sample sizes large enough for developing accurate predictive models. However, multicenter studies, which likely include confounding factors due to subtle differences in research participant characteristics, MRI scanners, and imaging acquisition protocols, might not yield generalizable machine learning models, that is, models developed using one dataset may not be applicable to a different dataset. The generalizability of classification models is key for multi-scanner and multicenter studies, and for providing reproducible results. This study developed a data harmonization strategy to identify healthy controls with similar (homogenous) characteristics from multicenter studies to validate the generalization of machine-learning techniques for classifying individual migraine patients and healthy controls using brain MRI data. The Maximum Mean Discrepancy (MMD) was used to compare the two datasets represented in Geodesic Flow Kernel (GFK) space, capturing the data variabilities for identifying a "healthy core". A set of homogeneous healthy controls can assist in overcoming some of the unwanted heterogeneity and allow for the development of classification models that have high accuracy when applied to new datasets. Extensive experimental results show that the utilization of a healthy core. One dataset consists of 120 individuals (66 with migraine and 54 healthy controls) and another dataset consists of 76 (34 with migraine and 42 healthy controls) individuals. A homogeneous dataset derived from a cohort of healthy controls improves the performance of classification models by about 25% accuracy improvements for both episodic and chronic migraineurs.
Early life seizures and epileptic spasms in STXBP1-related disorders

Background and Objectives: Individuals with disease-causing variants in STXBP1 frequently have epilepsy onset in the first year of life with a variety of seizure types, including epileptic spasms. However, the impact of early-onset seizures and anti-seizure medication (ASM) on the risk of developing epileptic spasms and impact on their trajectory is poorly understood, limiting informed and anticipatory treatment, as well as trial design. Methods: We retrospectively reconstructed seizure and medication histories in weekly intervals for individuals with STXBP1-related disorders with epilepsy onset in the first year of life and quantitatively analyzed longitudinal seizure histories and medication response. Results: We included 61 individuals with early onset seizures, 29 of whom had epileptic spasms. Individuals with neonatal seizures were likely to have continued seizures after the neonatal period (25/26). The risk of developing epileptic spasms was not increased in individuals with neonatal seizures or early infantile seizures (21/41 vs. 8/16; OR 1, 95% CI 0.3-3.9, p = 1). We did not find any ASM associated with the development of epileptic spasms following prior seizures. Individuals with prior seizures (n = 16/21, 76%) had a higher risk to develop refractory epileptic spasms (n = 5/8, 63%, OR =1.9, 95% CI 0.2-14.6, p = 0.6). Individuals with refractory epileptic spasms had a later onset of epileptic spasms (n = 20, median 20 weeks) compared to individuals with non-refractory epileptic spasms (n = 8, median 13 weeks; p = 0.08). When assessing treatment response, we found that clonazepam (n = 3, OR 12.6, 95% CI 2.2-509.4; p < 0.01), clobazam (n=7, OR 3, 95% CI 1.6-6.2; p < 0.01), topiramate (n=9, OR 2.3, 95% CI 1.4-3.9; p < 0.01), and levetiracetam (n=16, OR 1.7, 95% CI 1.2-2.4; p < 0.01) were more likely to reduce seizure frequency and/or to maintain seizure freedom with regards to epileptic spasms than other medications. Discussion: We provide a comprehensive assessment of early-onset seizures in STXBP1-related disorders and show that the risk of epileptic spasms is not increased following a prior history of early-life seizures, nor by certain ASM. Our study provides baseline information for targeted treatment and prognostication in early-life seizures in STXBP1-related disorders.
Research on power customer's satisfaction degree based on fuzzy entropy

Considering the evaluation issue of power customer's satisfaction degree, the triangle fuzzy number is used to process the qualitative indexes. The entropy evaluation method is adopted to reflect the quality of information into the combined weighting. The combined weight is determined by the combination of AHP. The traditional AHP method used to determine the weight is improved. A new scheme is suggested for the qualitative and hierarchical
Linear Machine: A Novel Approach to Point Location Problem

Abstract In recent literature, explicit model predictive control (e-MPC) has been proposed to facilitate implementation of the popular model predictive control (MPC) approach to fast dynamical systems. e-MPC is based on multi-parametric programming. The key idea in e-MPC is to replace the online optimization problem in MPC by a point location problem. After locating the current point, the control law is simply computed as an appropriate linear function of the states. A variety of approaches have been proposed in literature for the point location problem. In this work, we present a novel approach based on linear machines for solving this problem. Linear machines are widely used in multi-category pattern classification literature for developing linear classifiers given representative data from various classes. The idea in linear machines is to associate a linear discriminant function with each class. A given point is then assigned to the class with the largest discriminant function value. In this work, we develop an approach for identifying such discriminant functions from the hyperplanes characterizing the given regions as in multi-parametric programming. Apart from being an elegant solution to the point location problem as required in e-MPC, the proposed approach also links two apparently diverse fields namely e-MPC and multi-category pattern classification. To illustrate the utility of the approach, it is implemented on a hypothetical example as well as on a quadruple tank benchmark system taken from literature.
Food system emissions: a review of trends, drivers, and policy approaches, 1990–2018

The food system, spanning from pre-production processes to post-production stages, is responsible for about one third of global greenhouse gas emissions and requires significant mitigation efforts to prevent dangerous levels of global warming. This article summarises trends and drivers of global food system emissions from 1990 to 2018. We highlight regional diversity in patterns of food system emissions and identify the highest global emitters. While food system emissions have stabilised in some regions and countries, global emissions are increasing, with growth in certain sectors and countries outweighing the handful of cases where sustained emissions reductions have been realised. Emissions from livestock rearing account for a large portion of global emissions, and the contribution of post-production emissions is steadily increasing in all regions. We also provide an overview of food system policies at the national level, mapping them to each emissions segment. This highlights the significant shortfall in policy activity required to address the challenge of climate change mitigation in general, and the impacts of livestock and post-production emissions in particular. Our work lays the groundwork for addressing specific country-level questions on optimal policy pathways to achieve emission reductions.
Aerosol indirect effects on glaciated clouds. Part 2: Sensitivity tests using solute aerosols

Sensitivity tests were performed on a midlatitude continental case using a state‐of‐the‐art aerosol–cloud model to determine the salient mechanisms of aerosol indirect effects (AIE) from solute aerosols. The simulations showed that increased solute aerosols doubled cloud‐droplet number concentrations and hence reduced cloud particle sizes by about 20% and consequently inhibited warm rain processes, thus enhancing the chances of homogeneous freezing of cloud droplets and aerosols. Cloud fractions and their optical thicknesses increased quite substantially with increasing solute aerosols. Although liquid mixing ratios were boosted, there was, however, a substantial reduction of ice mixing ratios in the upper troposphere, owing to the increase in snow production aloft. The predicted total aerosol indirect effect was equal to −9.46 ± 1.4 W m−2. The AIEs of glaciated clouds (−6.33 ± 0.95 W m−2) were greater than those of water‐only clouds (−3.13 ± 0.47 W m−2) by a factor of two in this continental case. The higher radiative importance of glaciated clouds compared with water‐only clouds emerged from their larger collective spatial extent and their existence above water‐only clouds. In addition to the traditional AIEs (glaciation, riming and thermodynamic), the new AIEs sedimentation, aggregation and coalescence were identified.
Using GEDI as training data for an ongoing mapping of landscape-scale dynamics of the plant area index

Leaf or plant area index (LAI, PAI) information is frequently used to describe vegetation structure in environmental science. While field measurements are time-consuming and do not scale to landscapes, model-based air- or space-borne remote-sensing methods have been used for many years for area-wide monitoring. As of 2019, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission delivers a point-based LAI product with 25 m footprints and periodical repetition. This opens up new possibilities in integrating GEDI as frequently generated training samples with high resolution (spectral) sensors. However, the foreseeable duration of the system installed on the ISS is limited. In this study we want to test the potential of GEDI for regional comprehensive LAI estimations throughout the year with a focus on its usability beyond the lifespan of the GEDI mission. We study the landscape of Hesse, Germany, with its pronounced seasonal changes. Assuming a relationship between GEDI’s PAI and Sentinel-1 and -2 data, we used a Random Forest approach together with spatial variable selection to make predictions for new Sentinel scenes. The model was trained with two years of GEDI PAI data and validated against a third year to provide a robust and temporally independent model validation. This ensures the applicability of the validation for years outside the training period, reaching a total RMSE of 1.12. Predictions for the test year showed the expected seasonal and spatial patterns indicated by RMSE values ranging between 0.75 and 1.44, depending on the land cover class. The overall prediction performance shows good agreement with the test data set of the independent year which supports our assumption that the usage of GEDI’s PAI beyond the mission lifespan is feasible for regional studies.
An Enhancement Technology for X-Ray Images Based on Wavelet Transform

Common enhancement methods cannot improve the contrast as well as preserve edges well for X-ray images with bad contrast,narrow grayscale range and fuzzy image.An enhancing technology based on wavelet transform is proposed.Experiments were carried out with some actual X-ray images and the experimental results show that the new method has better enhancing effect than the traditional approaches.
Outlier Detection Based on Ridge Estimation

In order to solve the problem of both ill-conditioning in the normal matrix and outlier existing simultaneously in Gauss-Markov model,a new method called outlier detection based on ridge estimation is proposed by combining the advantages of the ordinary ridge estimation and outlier detection.At first,the method regards the ordinary ridge estimation as least square(LS) estimation with pseudo-observations.Then the outlier and the corresponding statistic in the meaning of the ordinary ridge are discussed in detail by using the LS outlier detection technique.Numerical examples illustrate that the new method not only can overcome the ill-conditioning effectively,but also detect the outlier successfully.