Miracle-dz/UniFAR-FTUs huggingface dataset:
Exact results in the gauge/gravity duality

In this thesis we study the gauge/gravity duality and exact results in supersymmetric quantum field theories obtained using localization. We construct the gravity duals to a broad class ofN = 2 supersymmetric gauge theories defined on a general class of three-manifolds. The gravity backgrounds are supersymmetric solutions of gauged four-dimensional supergravity and encompass all known examples of such solutions. We find that the holographically renormalized on-shell action agrees with the free energy of the field theory, which has previously been computed via localization of the partition function. Next, we study the Casimir energy of four-dimensional N = 1 supersymmetric field theories in the context of the rigid limit of new minimal supergravity. We revisit the computation of the localized partition function on S × S, and consider the same theories in the Hamiltonian formalism on R × S. We compute the vacuum expectation value of the canonical Hamiltonian using zeta function regularization, and show that this interpolates between the supersymmetric Casimir energy and the ordinary Casimir energy of supersymmetric field theories. In general, the Casimir energy depends on the regularization scheme and is therefore ambiguous. However, we show that for N = 1 supersymmetric field theories on the cylinder R × S, the supersymmetric Casimir energy is well-defined and schemeindependent, provided the regularization scheme preserves supersymmetry. Finally, we investigate the gravity duals of such N = 1 theories on R × S. Specifically, we study supersymmetric solutions of five-dimensional minimal gauged supergravity using a known classification. We propose an ansatz based on a four-dimensional local orthotoric Kähler metric and reduce the problem to a single sixth-order equation for two functions, each of one variable. We find an analytic, asymptotically locally AdS solution comprising five parameters. For a conformally flat boundary, this reduces to a previously known solution with three parameters, representing the most general solution of this type known in minimal gauged supergravity. We discuss the relevance for this solution to account for the supersymmetric Casimir energy, finding the answer to be in the negative.
Postprocessing of GEFS Precipitation Ensemble Reforecasts over the U.S. Mid-Atlantic Region

AbstractThe potential of Bayesian model averaging (BMA) and heteroscedastic censored logistic regression (HCLR) to postprocess precipitation ensembles is investigated. For this, outputs from the National Oceanic and Atmospheric Administration’s (NOAA’s) National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast, version 2 (GEFSRv2), dataset are used. As part of the experimental setting, 24-h precipitation accumulations and forecast lead times of 24 to 120 h are used, over the mid-Atlantic region (MAR) of the United States. In contrast with previous postprocessing studies, a wider range of forecasting conditions is considered here when evaluating BMA and HCLR. Additionally, BMA and HCLR have not yet been compared against each other under a common and consistent experimental setting. To compare and verify the postprocessors, different metrics are used (e.g., skills scores and reliability diagrams) conditioned upon the forecast lead time, precipitation threshold.
Applications of Heegaard Floer Homology to Knot Concordance

We consider several applications of Heegaard Floer homology to the study of knot concordance. Using the techniques of bordered Heegaard Floer homology, we compute the concordance invariant τ for a family of satellite knots that generalizes Whitehead doubles. We also construct an integer lift ̃ of the concordance invariant . We introduce an interpretation of ̃ in terms of a filtration on ĈF(S NK) induced by a family of knots μn ⊂ S NK. Finally, we use truncated Heegaard Floer homology to construct a sequence of concordance invariants νn that generalizes previously known concordance invariants ν, ν′, and ν.
The Case for Learned Provenance Graph Storage Systems

Cyberattacks are becoming more frequent and sophisticated, and investigating them becomes more challenging. Prove-nance graphs are the primary data source to support forensics analysis. Because of system complexity and long attack duration, provenance graphs can be huge, and efﬁciently storing them remains a challenging problem. Existing works typically use relational or graph databases to store provenance graphs. These solutions suffer from high storage overhead and low query efﬁciency. Recently, researchers leveraged Deep Neural Networks (DNNs) in storage system design and achieved promising results. We observe that DNNs can embed given inputs as context-aware numerical vector representations, which are compact and support parallel query operations. In this paper, we propose to learn a DNN as the storage system for provenance graphs to achieve storage and query efﬁciency. We also present novel designs that leverage domain knowledge to reduce provenance data redundancy and build fast-query processing with indexes. We built a prototype L EONARD and evaluated it on 12 datasets. Compared with the relational database Quickstep and the graph database Neo4j, L EONARD reduced the space overhead by up to 25.90x and boosted up to 99.6% query executions.
Gender differences in STEM enrolment and graduation: What are the roles of academic performance and preparation?

Despite women outnumbering men in postsecondary institutions, men continue to earn more than women in the labour market. While many factors may explain the gender wage gap, the gap in educational attainment favouring women may offer the potential for future reductions in the wage gap. In this regard, field of study choice is key as earnings tend to vary substantially along this dimension. For example, graduates of science, technology, engineering and mathematics (STEM) programs typically earn more than non-STEM graduates, but women in universities and colleges are considerably less likely than their male counterparts to select STEM fields. The goal of this study is to estimate gender differences in the probability of enrolling in and graduating from a STEM-related postsecondary program (particularly at the bachelor’s degree level). Results are generated for all high school graduates and the subsample who enrolled in a bachelor’s degree program. Results are also generated based on a traditional definition of STEM and for alternative broad and narrow definitions of STEM. The study relies on enrolment and graduation data from universities and colleges (the Postsecondary Student Information System), and academic performance and preparation data before high school graduation for British Columbia students. The results suggest that among high school graduates, women are 29.8% less likely than men to enrol in a postsecondary STEM program shortly after graduation. The gender gap in STEM enrolment falls to 19.9% when focusing only on bachelor’s degree STEM programs. However, when the subsample of high school graduates who enrolled in a bachelor’s degree program is considered, the gender gap in STEM enrolment is almost twice as large (36.4%). For both the high school graduates and bachelor’s degree student samples, the gender gap in graduation from a bachelor’s degree STEM program is about as large as the gender gap in STEM enrolment. Among bachelor’s degree students, by far the largest gap in STEM enrolment is within engineering programs, while much smaller gaps exist in other math-intensive STEM
Existence and Asymptotic Behavior of Solutions for Hénon Type Systems

Abstract This paper is concerned with the following H’enon type problem where B1(0) is the unit ball in ℝN centered at the origin, 1 < p, q and if N ≥ 3. We show that there exists α∗ > 0 such that the ground state solution of the problem is non-radial if α > α∗. We also consider the limiting behavior of the ground state solution (u, v) as p + q → 2∗. We prove that the maximum points of two components u and v concentrate at the same point on the boundary ∂B1(0).
Dexamethasone, dexamethasone + remdesivir in treating moderate to severe COVID-19: retrospective observational cohort study.

INTRODUCTION The aim of this study was to demonstrate the purpose of adding antiviral (remdesivir) to the existing steroidal (dexamethasone) therapy in treating coronavirus disease 2019 (COVID-19). METHODOLOGY A retrospective observational case cohort study was carried out to compare the effect of dexamethasone alone and in combination with remdesivir in treating moderate and severe COVID-19 disease. The patients were divided into 2 groups: Group 1 included patients treated with dexamethasone alone, and Group 2 included patients treated with dexamethasone and remdesivir. Levels of inflammatory markers (C-reactive protein, D- dimer and lactate dehydrogenase), World Health Organization (WHO) ordinal scale scoring, symptomatic improvement in terms of fever, cough, shortness of breath, 6-minutes' walk test and SpO2 levels on day of admission (D0), 3 days and 5 days after admission (D3 and D5), and 10 days overall outcome (determined as death, or discharge with or without Long Term Oxygenation Therapy) were collected and analyzed. RESULTS Addition of remdesivir to dexamethasone in treating COVID 19 did not have any additional benefits. No additional role of remdesivir is seen in combating the disease except in case of 10 days outcome. However, the better 10-day outcome associated with the use of remdesivir was thought to be due to the patients who were on mechanical ventilation in the dexamethasone treated group at the time of inclusion. CONCLUSIONS Since a similar trend was seen in both groups, our study concluded no additional role of remdesivir in combating COVID-19.
Rheology of cellulose nanofibers suspensions: Boundary driven flow

Cellulose nanofibers (CNFs) are an exciting new renewable material produced from wood fibers. Even at low solids content, CNF-water suspensions have a complex rheology that includes extreme shear-thinning as well as viscoelastic properties and a yield stress similar to other suspensions of nanoscale particles. When characterizing the rheology of CNF suspensions, the measurement method may influence the results due to a water layer expected at the boundary, but it is unclear how the behavior near walls influences the measurement method. Parallel-plate, Couette, and vane geometries were used to compare yielding and flow of CNF suspensions obtained by steady-state shear and oscillatory rheological measurements. Five different techniques were compared as methods to obtain a yield stress. Cone and plate geometries were found to lead to sample ejection at low shear rates: Floc-floc interactions can explain this ejection. The suspensions violated the Cox-Merz rule by a significant amount; this behavior has been explained in the past as weak gel structures that break down in shear, but for this material it seems that the acting mechanism involves the formation of a water-rich layer near the solid boundaries in steady shear, while for oscillatory tests, these layers do not form. For suspensions lower than 3% solids, the yield stress measured by different procedures was within 20% of each other, but for high solids suspensions, differences between the methods could be as large as 100%; the water-rich layer formation likely is the cause of these results. Oscillatory methods are suggested as a method to obtain yield stress values for this type of material. The Couette geometry data were below the power-law lines fitted to the parallel-plate geometry data from steady-shear measurements perhaps again attributable to different water-rich layers that form in these different geometries.
Dynamical Insights into Extreme Short-Term Precipitation Associated with Supercells and Mesovortices

In some prominent extreme precipitation and flash flood events, radar and rain gauge observations have suggested that the heaviest short-term rainfall accumulations (up to 177 mm h−1) were associated with supercells or mesovortices embedded within larger convective systems. In this research, we aim to identify the influence that rotation has on the storm-scale processes associated with heavy precipitation. Numerical model simulations conducted herein were inspired by a rainfall event that occurred in central Texas in October 2015 where the most extreme rainfall accumulations were collocated with meso-β-scale vortices. Five total simulations were performed to test the sensitivity of precipitation processes to rotation. A control simulation, based on a wind profile from the aforementioned event, was compared with two experiments with successively weaker low-level shear. With greater environmental low-level shear, more precipitation fell, in both a point-maximum and an area-averaged sense. Intense, rotationally induced low-level vertical accelerations associated with the dynamic nonlinear perturbation vertical pressure gradient force were found to enhance the low- to midlevel updraft strength and total vertical mass flux and allowed access to otherwise inhibited sources of moisture and CAPE in the higher-shear simulations. The dynamical accelerations, which increased with the intensity of the low-level shear, dominated over buoyant accelerations in the low levels and were responsible for inducing more intense low-level updrafts that were sustained despite a stable boundary layer.
How persistent are the misconceptions about force and motion held by college students?

The purpose of this work is to study the effectiveness of our introductory mechanics instructions in changing students’ misconceptions about Newtonian concepts. We gave the force concept inventory (FCI) test to 341 of our students twice, before and after instructions. The data were analyzed using a method first reported by Martín-Blas et al (2010 Eur. J. Eng. Educ. 35 597–606). We examined all of the known misconceptions found in the original paper by Hestenes et al (1992 Phys. Teach. 30 141–51) and determined the dominant ones from students’ incorrect answers for each of the 30 questions in the inventory. By comparing the dominant misconceptions held by our students in the pre- and post-tests, we were able to determine which ones were unchangeable by instructions and students couldn’t overcome. Results indicate some overall improvement in students’ performance but also the existence of very resistive to change misconceptions that were not affected by instructions.
Inferences for Heteroscedastic Location-Scale Time Series Models with Application to VaR and ES Estimation

This thesis first considers nonlinear expectile regression models to estimate conditional expected shortfall (ES) and value-at-risk (VaR). In the literature, the asymmetric least squares (ALS) regression method has been widely used to estimate expectile regression models. However, no literatures rigorously investigated the asymptotic properties of the ALS estimates in nonlinear models with heteroscadasticity. Motivated by this aspect, this thesis studies the consistency and asymptotic normality of the ALS estimates and conditional VaR and ES in those models. To illustrate, a simulation study and real data analysis are conducted. Secondly, this thesis studies the asymptotic properties of a class of conditionally heteroscedastic location-scale time series models with innovations following a generalized asymmetric Student-t distribution (ASTD) or asymmetric exponential power distribution (AEPD). We show the consistency and asymptotic normality of the conditional maximum likelihood estimator (MLE) of model parameters under certain regularity conditions, and then, based on the MLE, we estimate the conditional VaR and ES using their closed forms induced from the model. Their performance is compared with that of conditional autoregressive value-at-risk (CAViaR) and expectile (CARE) methods. Meanwhile, one should be convinced of the model adequacy in advance of the VaR and ES calculation. For this task,
L EARNING D IFFUSION B RIDGES ON C ONSTRAINED D OMAINS

Diffusion models have achieved promising results on generative learning recently. However, because diffusion processes are most naturally applied on the unconstrained Euclidean space R d , key challenges arise for developing diffusion based models for learning data on constrained and structured domains. We present a simple and unified framework to achieve this that can be easily adopted to various types of domains, including product spaces of any type (be it bounded/unbounded, continuous/discrete, categorical/ordinal
Assessment of Students’ Attitudes in Higher Education: The Impact of Education and the Family Environment

Entrepreneurship education is not limited to theory, but there must be an attitude formation and a mindset to be ready to become an entrepreneur. The research aims to determine the correlation between entrepreneurship education and family environment to entrepreneurial attitudes. The study design used in this research is a descriptive study. The population in this study were 174 students of the Department of Engineering and Planning Education class of 2021. The sample used in this study is Purposive sampling. The total of samples used in the study was 85 respondents. The research results processed in this study are answers to a list of statements whose alternative explanations use a Likert scale. The analytical model used in this study is a multiple regression analysis model. The study results and discussion that have been described show that the survey results indicate that entrepreneurial knowledge and family environment signiﬁcantly affect entrepreneurial attitudes. Entrepreneurial knowledge fosters enterprising principles, particularly among students, and is intended to foster an entrepreneurial spirit.
Causativization in Asuriní of Xingu

: This article aims to discuss causativization in the Asuriní of Xingu language, a Tupí-Guaraní language spoken by the Asuriní people who live in the municipality of Altamira in the Brazilian state of Pará. Based on theoretical assumptions from typological-functional linguistics, the article discusses the formation of causative predicates, the types of causativization found in the language, and the effects they cause in the sentence. Morphological causativization is the predominant type in this language, with the morphemes { mu-}, { eru-} and { -ukat } being responsible for it. The analysis shows that the morpheme { eru-} plays a causative role in this language, as it does in the related Kamayurá and Emerillon languages. At the same time, Asuriní of Xingu is distinguished from other languages of the Tupí-Guaraní family, such as Guajá and Tenetehára, where which this morpheme is described as an applicative. Research results show that the morphemes { mu-} and { eru-} are affixed to intransitive verb stems, while the morpheme { -ukat } is affixed to transitive stems. After affixing the causative morpheme to the verb, the sentence undergoes structural and functional changes.
Does Relational Polarization Entail Ideological Polarization? The Case of the 2017 Norwegian Election Campaign on Twitter

This article investigates different polarizing mechanisms—relational homophily and ideological partisanship—characterizing political communications using Twitter data collected during the 2017 Norwegian election. By combining two computational approches—partition-specific network analysis and quantitative analysis of language polarization—we can examine the linkages between the structure of interactions and political polarization. The results show that the Norwegian political Twittersphere is not made of isolated echo chambers but is structured around crosscutting communities of interaction. There are no signs that communities with higher degrees of polarization are the ones that display higher degrees of homophily. Yet, the degree of ideological polarization differs across communities and topics. Some topics, such as political hate and far right and economy and taxes, are more polarized than others.
Measuring the Psychological Impact of Mindfulness Meditation on Health Among Patients With Cancer: A Literature Review

varied, suggesting different yet promising relationships. For some instruments, data were insufﬁcient to conclude sufﬁciently whether any were strong or appropriate to use in future intervention studies. Conclusions: To enhance knowledge of MBSR, more intervention research studies of MBSR in patients with cancer and reexamination of speciﬁc instruments are needed. Implications for Nursing: Based on the review, instruments can measure MBSR effects and found MBSR to be a potentially beneﬁcial oncology nursing intervention.
International Journal of Renewable Energy Development

Plant-Microbial Fuel Cell (PMFC) is an emerging technology that converts plant waste into electrical energy through rhizodeposition, offering a renewable and sustainable source of energy. Deviating from the traditional PMFC configurations, additive manufacturing was utilized to create intricate and efficient designs using polymer-carbon composites. Concerning the agricultural sector, the effect of 3D-printed PMFCs on the growth and biomass distribution of Phaseolus lunatus and Ipomoea aquatica was determined. The experiment showed that electrostimulation promoted the average daily leaf number and plant height of both polarized plants, which were statistically proven to be greater than the control (α = 0.05), by energizing the flow of ions in the soil, boosting nutrient uptake and metabolism. It also stimulated the growth of roots, increasing the root dry mass of polarized plants by 155.44% and 66.30% for I. aquatica and P. Lunatus against their non-polarized counterpart. Due to the biofilm formation on the anode surface, the number of root nodules of the polarized P. lunatus was 51.30% higher than the control, while the protein content in the PMFC setup was 42.22% and 8.26% higher than the control for I. aquatica and P. lunatus, respectively. The voltage readings resemble the plants' average growth rate, and the polarization studies showed that the optimum external resistances in the I. aquaticaand P. lunatus-powered PMFC were 4.7 kΩ and 10 kΩ, respectively. Due to other prevailing pathways of organic carbon consumption, such as methanogenesis, the effect of polarization on the organic carbon content in soil is currently inconclusive and requires further study.
Cognitive Feedback Theories and Artificial Intelligence: A Case for A Grammarly of UI/UX Design

This thesis is concerned with utilizing artificial intelligence and machine learning (AI/ML) techniques and cognitive theories of feedback to enhance learning outcomes in the field of user interface and user experience (UI/UX) design. The capabilities of AI/ML have expanded immensely over the past several years, and it is now being effectively used in software programs like Grammarly, a tool that provides intelligent feedback on writing skills including grammar, tone, and clarity. Grammarly has been uniquely successful as a feedback tool because it relies on lessons from cognitive science regarding student feedback and learning outcomes. Currently, there is no comparable software available for UI/UX, making it a uniquely untapped area for effective learning tools. The question that this thesis attempts to answer, therefore, is: How can the successes of Grammarly and established cognitive feedback principles inform the design of an AI/ML-based feedback tool for UI/UX design? To answer that question, this thesis explores previous work on AI/ML techniques, cognitive feedback theories, structural similarities between grammar and design, and design heuristics in order to ultimately define the theoretical groundwork for a “Grammarly for UI/UX design. ”