Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating quantum matter through lattice field theories

Quantum many-body systems are comprised of complex networks of microscopic interactions that work together to produce novel collective phases and phenomena. For stronglyinteracting systems, the connection between the underlying atomic-scale behaviour and the resulting emergent observable phenomena is exponentially (and, in practice, infinitely) complicated such that it becomes impossible to fully understand the connection between the physics at the microscopic and macroscopic levels. The most promising theoretical approaches to addressing this infinite complexity utilize microscopic coarse-graining along with powerful computational simulations and strategies. In this thesis, we apply numerical methods to effective lattice models and field theories with the goal of shedding light onto universal critical behaviour and exotic low-temperature phases in condensed matter physics. We start by exploring universal features of critical non-interacting systems, for which several analytical strategies are readily available for calculating certain observables. We study the behaviour of the system’s entanglement for various entangling geometries, and utilize numerical techniques in order to isolate new universal features of Gaussian fixed points, which provide insight into the underlying critical theory’s renormalization group flow. We then proceed to examine computational strategies and, in particular, Monte Carlo simulations for studying more general interacting models. We focus on the application of such strategies to the field of high-temperature superconductivity, for which we develop a coarse-grained model, simulate macroscopic observables and compare our results with those of recent experiments. We conclude that lattice field theories together with innovative computational methods offer new perspectives on both universality and emergent phenomena in quantum matter.
Critical success factors for implementation of decision support systems in South Africa

The authors compare published findings of identified critical success factors (CSFs) for the implementation of decision support systems (DSS) in developed countries with survey findings from a selection of organisations in South Africa. As no previously published literature exists, the authors seek to establish whether these same CSFs exist in South Africa. The authors identified from their survey several differences between the accepted DSS implementation norms prevalent in developed countries and those in South Africa. These include: CSFs may be absent and yet an organisation's DSS may be successful; and an additional CSF was identified.
Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms.

BACKGROUND Despite trials of mammography and widespread use, optimal screening policy is controversial. OBJECTIVE To evaluate U.S. breast cancer screening strategies. DESIGN 6 models using common data elements. DATA SOURCES National data on age-specific incidence, competing mortality, mammography characteristics, and treatment effects. TARGET POPULATION A contemporary population cohort. TIME HORIZON Lifetime. PERSPECTIVE Societal. INTERVENTIONS 20 screening strategies with varying initiation and cessation ages applied annually or biennially. OUTCOME MEASURES Number of mammograms, reduction in deaths from breast cancer or life-years gained (vs. no screening), false-positive results, unnecessary biopsies, and overdiagnosis. RESULTS OF BASE-CASE ANALYSIS The 6 models produced consistent rankings of screening strategies. Screening biennially maintained an average of 81% (range across strategies and models, 67% to 99%) of the benefit of annual screening with almost half the number of false-positive results. Screening biennially from ages 50 to 69 years achieved a median 16.5% (range, 15% to 23%) reduction in breast cancer deaths versus no screening. Initiating biennial screening at age 40 years (vs. 50 years) reduced mortality by an additional 3% (range, 1% to 6%), consumed more resources, and yielded more false-positive results. Biennial screening after age 69 years yielded some additional mortality reduction in all models, but overdiagnosis increased most substantially at older ages. RESULTS OF SENSITIVITY ANALYSIS Varying test sensitivity or treatment patterns did not change conclusions. LIMITATION Results do not include morbidity from false-positive results, patient knowledge of earlier diagnosis, or unnecessary treatment. CONCLUSION Biennial screening achieves most of the benefit of annual screening with less harm. Decisions about the best strategy depend on program and individual objectives and the weight placed on benefits, harms, and resource considerations. PRIMARY FUNDING SOURCE National Cancer Institute.
Volatility Spillover Effects in European Equity Markets

Abstract This paper investigates to what extent globalization and regional integration lead to increasing equity market interdependence. I focus on Western Europe, as this region has gone through a unique period of economic, financial, and monetary integration. More specifically, I quantify the magnitude and time-varying nature of volatility spillovers from the aggregate European (EU) and U.S. market to 13 local European equity markets. To account for time-varying integration, I use a regime-switching model to allow the shock sensitivities to change over time. I find regime switches to be both statistically and economically important. Both the EU and U.S. shock spillover intensity increased substantially over the 1980s and 1990s, though the rise is more pronounced for EU spillovers. Shock spillover intensities increased most strongly in the second half of the 1980s and the first half of the 1990s. I show that increased trade integration, equity market development, and low inflation contribute to the increase in EU shock spillover intensity. I also find evidence for contagion from the U.S. market to a number of local European equity markets during periods of high world market volatility.
The Environmental Kuznets Curve: Exploring a Fresh Specification

Abstract The objective of this paper is primarily methodological. Using a new specification, we reanalyze the data on worldwide environmental quality investigated by Gene Grossman and Alan Krueger in their well-known paper on the environmental Kuznets curve (which postulates an inverse U-shaped relationship between income level and pollution). This new specification avoids using nonlinear transformations of potentially nonstationary regressors in panel estimation, which is a major unresolved econometric problem plaguing much of the existing literature. We furthermore draw conclusions from fixed effects estimation, which had eluded Grossman and Krueger. Our estimation results indicate the presence of an EKC for only six of the fourteen pollutants, whereas Grossman and Krueger find support for all but one pollutant.
Heterogeneity in disease risk induces falling vaccine protection with rising disease incidence

ABSTRACT This paper is concerned with the analysis of phase 3 vaccine trials. In a randomized controlled trial, a representative sample of a population is given a vaccine and a matched sample is given a placebo. These individuals are followed for a stipulated length of time, while infection (or disease) occurrences are registered. Vaccine efficacy is then calculated to measure the reduction in disease rate (or risk) attributed to the vaccine. Seemingly very reasonable, this procedure often results in the most disparate estimates when conducted in different parts of the world. Here, we argue that this is due to cohort selection acting on the trial participants as follows. The more susceptible individuals are infected first, leaving behind a pool whose mean susceptibility decreases over time. As a result, infection rates decrease, and this effect is stronger in the control group, provided that the vaccine reduces susceptibility. Therefore, any direct measure of vaccine efficacy is expected to decrease as the trial progresses, and this happens faster in settings where the intensity of pathogen exposure is higher. We propose an analytical scheme that takes this phenomenon into account while estimating efficacy more consistently across settings. We provide analytical results concerning the dependence of vaccine efficacy on the intensity of pathogen exposure as well as on the mean and variance of the distribution of disease risk.
How and why children change in aggression and delinquency from childhood to adolescence: moderation of overreactive parenting by child personality.

BACKGROUND This study examines how and why children change in aggression and delinquency from age 6 to 15 years. Besides assessing the shape of the developmental trajectories of aggression and delinquency, we investigated whether child personality characteristics, parenting, and interactions between these two predict the development of aggression/delinquency. METHODS Employing a cohort-sequential design, data from the Flemish Study on Parenting, Personality, and Development were used. The sample consisted of 586 children aged 6-9 years at the first assessment, and their parents. Mothers, fathers, and teachers rated child personality at Time 1 using the Hierarchical Personality Inventory for Children, and mothers and fathers rated externalizing behaviors at the first, second, and third assessment using the Child Behavior Checklist. Parents rated their own overreactive parenting at Time 1 with the Parenting Scale. RESULTS Aggression and delinquency showed differential, though interrelated development. Less extraverted, benevolent and conscientious children, and more imaginative children were more susceptible to overreactivity. The model replicated across fathers' overreactivity. CONCLUSIONS Parents of children who are less extraverted, benevolent and conscientious, or more imaginative, are in particular need of being supported in developing and maintaining effective disciplinary techniques.
Optimizing Academic Achievement through Comprehensive Integration of Formative Assessment into Teaching

Learning activities are conducted to help students achieve optimal academic achievement. This research aims to optimize student academic achievement through a learning process that integrates comprehensive formative assessments, including formative tests, self-assessment, peer assessment, and the initiator of creating summaries or concept maps that are given to students in a structured manner at the end of every lesson. The research method used was a quasi-experimental method with a 2x2 factorial design. Students enrolled in the biology education program of the basic physics course for the 1st semester of the 2019 academic year participated in this study. The participants were 66 undergraduate students divided into two classes. Thirty-four students in the experimental group were in class A, while 32 students in the control group were in class B. Data were collected using a learning outcome test instrument to measure academic achievement, which was tested at the end of the semester. Data were analyzed using a two-way ANOVA. This study concluded that a learning process that includes comprehensive formative assessment significantly affects students' academic achievement. These findings support the theory that formative assessment provides feedback, correction, and improvement in student learning.
Perturbed Input Ensemble Modeling With the Space Weather Modeling Framework

To assess the effect of uncertainties in solar wind driving on the predictions from the operational configuration of the Space Weather Modeling Framework, we have developed a nonparametric method for generating multiple possible realizations of the solar wind just upstream of the bow shock, based on observations near the first Lagrangian point. We have applied this method to the solar wind inputs at the upstream boundary of Space Weather Modeling Framework and have simulated the geomagnetic storm of 5 April 2010. We ran a 40‐member ensemble for this event and have used this ensemble to quantify the uncertainty in the predicted Sym‐H index and ground magnetic disturbances due to the uncertainty in the upstream boundary conditions. Both the ensemble mean and the unperturbed simulation tend to underpredict the magnitude of Sym‐H in the quiet interval before the storm and overpredict in the storm itself, consistent with previous work. The ensemble mean is a more accurate predictor of Sym‐H, improving the mean absolute error by nearly 2 nT for this interval and displaying a smaller bias. We also examine the uncertainty in predicted maxima in ground magnetic disturbances. The confidence intervals are typically narrow during periods where the predicted dBH/dt is low. The confidence intervals are often much wider where the median prediction is for enhanced dBH/dt. The ensemble also allows us to identify intervals of activity that cannot be explained by uncertainty in the solar wind driver, driving further model improvements. This work demonstrates the feasibility and importance of ensemble modeling for space weather applications.
Bioconvective electromagnetic nanofluid transport from a wedge geometry: Simulation of smart electro-conductive bio-nanopolymer processing

A mathematical model is presented for steady, two-dimensional, stagnation-point flow, heat, mass, and micro-organism transfer in a viscous, incompressible, bioconvective, electromagnetic nanofluid along a wedge with Stefan blowing effects, hydrodynamic slip, and multiple convective boundary conditions. Gyrotactic micro-organisms are present in the nanofluid and bioconvection arises, characterized by micro-organisms swimming under a competing torque. Similarity transformations are used to render the system of governing partial differential equations into a system of coupled similarity equations. The transformed equations are solved numerically with the BVP5C method. The impact of emerging parameters on dimensionless velocity, temperature, magnetic induction function, nanoparticle volume fraction, and density of motile micro-organisms is studied graphically. Furthermore, the responses of the local skin friction, local Nusselt number, local Sherwood number, and the wall gradient of density of motile micro-organism number to variation in these parameters are elaborated. Validation of solutions with previous studies based on special cases of the general model is included. The simulations are relevant to the processing of biological, electro-conductive nanomaterials and industrial hygienic coating systems exploiting combined electromagnetics, nano-systems, and microscopic, bio-propulsion mechanisms.
Disparities in Autism Diagnoses: The Role of Physicians, Schools, and Families

Early intervention is crucial for improved outcomes for individuals with autism spectrum disorder (ASD). Clear disparities exist, however, within rates and patterns of diagnosis such that underserved communities do not receive the same support as those with better access to healthcare. These disparities fall significantly along racial and socioeconomic lines, and diagnostic disparities lead directly into disparate treatment and outcomes for individuals with ASD. This project investigates the role of healthcare providers, schools, early childhood educators, and families in the identification and diagnosis of ASD. This research includes conversations with pediatricians, psychiatrists, early childhood educators, and experts in the field of autism in order to gather background information and determine potential directions of research, explored through a review of the literature. Effective strategies for partnerships will be explored and described, along with typical challenges to meaningful collaboration. Generally, the purpose of this capstone is to identify ways to create productive partnerships and systems to reduce disparities in early ASD diagnoses. Suggested Citation: Ryan, S. (2022). “Disparities in Autism Diagnoses: The Role of Physicians, Schools, and Families” (Unpublished Education Studies capstone). Yale University, New Haven, CT. This capstone is a work of Yale student research. The arguments and research in the project are those of the individual student. They are not endorsed by Yale, nor are they official university positions or statements.
Robust sound

Traditional sound event recognition methods based on informative front end features such as MFCC, with back end sequencing methods such as HMM, tend to perform poorly in the presence of interfering acoustic noise. Since noise corruption may be unavoidable in practical situations, it is important to develop more robust features and classiﬁers. Recent advances in this ﬁeld use powerful machine learning techniques with high dimensional input features such as spectrograms or auditory image. These improve robustness largely thanks to the discriminative capabilities of the back end classiﬁers. We extend this further by proposing novel features derived from spectrogram energy triggering, allied with the powerful classiﬁcation capabilities of a convolutional neural network (CNN). The proposed method demonstrates excellent performance under noise-corrupted conditions when compared against state-of-the-art approaches on standard evaluation tasks. To the author’s knowledge this in the ﬁrst application of CNN in this ﬁeld.
Measuring and Improving the Use of Graph Information in Graph Neural Networks

Graph neural networks (GNNs) have been widely used for representation learning on graph data. However, there is limited understanding on how much performance GNNs actually gain from graph data. This paper introduces a context-surrounding GNN framework and proposes two smoothness metrics to measure the quantity and quality of information obtained from graph data. A new GNN model, called CS-GNN, is then designed to improve the use of graph information based on the smoothness values of a graph. CS-GNN is shown to achieve better performance than existing methods in different types of real graphs.
Assessment of the Compliance of Problem-Based Learning Case Assessment of the Compliance of Problem-Based Learning Case Scenarios with The Social Accountability Values in Undergraduate Scenarios with The Social Accountability Values in Undergraduate Medical Education Curriculum Medical Education Cur

Purpose : To assess the integration of Social Accountability (SA) values in newly developed problem-based learning (PBL) case scenarios. Method : A validated " SA inventory for PBL ” was used to assess the integration of SA values; relevance; quality; equity; cost-effectiveness in PBL case scenarios in a problem-based learning (PBL) medical curriculum. Results : Of the 63 PBL cases scenarios, in the relevancy domain, there were 52 cases (82.5%) that were relevant to social health concerns, 30 cases (47.6%) which addressed more than one social determinant of health, and 21 cases (33.3%) which addressed the principles of health promotion. In the equity domain, 61 (98.4%) and 62 (96.8%) of cases addressed the patient ' s gender and age group respectively, 28 cases (44.4%) which addressed the society ' s underserved, disad-vantaged, and vulnerable populations, and only 3 cases (4.8%) addressing the patient ' s ethnicity. In the cost-effectiveness domain, 40 out of 63 case scenarios (63.5%) needed modi ﬁ cations to address treatment costs and providing alternatives. In the quality domain, 65.1% of cases (41 out of 63) needed modi ﬁ cations to address the concept of person-centered healthcare. Discussion : Adapting and utilizing a validated inventory to evaluate values related to SA is a useful educational practice for informing medical educators ’ leaders to develop a socially accountable PBL medical curriculum. These ﬁ ndings could be useful to adjust curricula content to prepare the next generation of doctors who can meet societal needs.
Central Banking with Shadow Banks

This paper investigates how the presence of shadow banks aﬀects the ability of central banks to oﬀset a liquidity crisis. We propose an asset pricing model with heterogeneous banks subject to funding risk. While traditional banks have direct access to central bank operations, shadow banks rely on the intermediation of liquidity from traditional banks. In a crisis, this intermediation stops due to lack of collateral and shadow banks are left without lender-of-last-resort. Traditional instruments are not suﬃcient to fully mitigate the crisis. Opening liquidity facilities to shadow banks and purchasing illiquid assets is then necessary to further boost asset prices and tackle the crisis.
Second-Order Asymptotics of Sequential Hypothesis Testing

We consider the classical sequential binary hypothesis testing problem in which there are two hypotheses governed respectively by distributions <inline-formula> <tex-math notation="LaTeX">$P_{0}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$P_{1}$ </tex-math></inline-formula> and we would like to decide which hypothesis is true using a sequential test. It is known from the work of Wald and Wolfowitz that as the expectation of the length of the test grows, the optimal type-I and type-II error exponents approach the relative entropies <inline-formula> <tex-math notation="LaTeX">$D(P_{1}\|P_{0})$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$D(P_{0}\|P_{1})$ </tex-math></inline-formula>. We refine this result by considering the optimal backoff—or second-order asymptotics—from the corner point of the achievable exponent region <inline-formula> <tex-math notation="LaTeX">$(D(P_{1}\|P_{0}),D(P_{0}\|P_{1}))$ </tex-math></inline-formula> under two different constraints on the length of the test (or the sample size). First, we consider a probabilistic constraint in which the probability that the length of test exceeds a prescribed integer <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is less than a certain threshold <inline-formula> <tex-math notation="LaTeX">$0 < \varepsilon < 1$ </tex-math></inline-formula>. Second, the expectation of the sample size is bounded by <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>. In both cases, and under mild conditions, the second-order asymptotics is characterized exactly. Numerical examples are provided to illustrate our results.
Flexible semiparametric generalized Pareto modeling of the entire range of rainfall amount

Precipitation amounts at daily or hourly scales are skewed to the right, and heavy rainfall is poorly modeled by a simple gamma distribution. An important yet challenging topic in hydrometeorology is to find a probability distribution that is able to model well low, moderate, and heavy rainfall. To address this issue, we present a semiparametric distribution suitable for modeling the entire range of rainfall amount. This model is based on a recent parametric statistical model called the class of extended generalized Pareto distributions (EGPDs). The EGPD family is in compliance with extreme value theory for both small and large values, while it keeps a smooth transition between these tails and bypasses the hurdle of selecting thresholds to define extremes. In particular, return levels beyond the largest observation can be inferred. To add flexibility to this EGPD class, we propose to model the transition function in a nonparametric fashion. A fast and efficient nonparametric scheme based on Bernstein polynomial approximations is investigated. We perform simulation studies to assess the performance of our approach. It is compared to two parametric models: a parametric EGPD and the classical generalized Pareto distribution (GPD), the latter being only fitted to excesses above a high threshold. We also apply our semiparametric version of EGPD to a large network of 180 precipitation time series over France.