Marylou Gabrié, Grant M. Rotskoff, E. Vanden-Eijnden
Significance Monte Carlo methods, tools for sampling data from probability distributions, are widely used in the physical sciences, applied mathematics, and Bayesian statistics. Nevertheless, there are many situations in which it is computationally prohibitive to use Monte Carlo due to slow “mixing” between modes of a distribution unless hand-tuned algorithms are used to accelerate the scheme. Machine learning techniques based on generative models offer a compelling alternative to the challenge of designing efficient schemes for a specific system. Here, we formalize Monte Carlo augmented with normalizing flows and show that, with limited prior data and a physically inspired algorithm, we can substantially accelerate sampling with generative models.
Norazah Umar, Nurhafizah Ahmad, Jamal Othman
et al.
The transition to hybrid learning in Malaysian higher education created a need for centralized platforms that enhance academic communication and class management. UnityHub was introduced as a web-based system to simplify group registration and strengthen student–lecturer interaction. The purpose of this study was to assess students’ acceptance of UnityHub using the Technology Acceptance Model (TAM) and to examine both the overall level of acceptance and the relative strengths and weaknesses of its dimensions. A total of 90 students from Universiti Teknologi MARA, Penang Branch participated in the survey, which used a TAM-based questionnaire comprising 24 items. Data were analyzed using descriptive statistics, including mean scores and standard deviations. The findings show that satisfaction scored highest (mean = 4.16), followed by attitude (3.80) and ease of use (3.78). Perceived usefulness (3.71) and intention to use (3.72) were moderately high, while self-efficacy was lowest (3.42). Overall, UnityHub achieved strong acceptance for usability and efficiency, though improvements in academic integration and user confidence are needed. The study is limited to a single department sample; future research should involve multiple faculties and compare UnityHub with other platforms to enhance generalizability.
Rachit Dhiman, Sofia Miteff, Yuancheng Wang
et al.
In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persist about its potential negative impacts, including substantial energy consumption and ethical challenges. This paper critically reviews the evolving landscape of AI sustainability, addressing economic, social, and environmental dimensions. The literature is systematically categorized into “Sustainability of AI” and “AI for Sustainability”, revealing a balanced perspective between the two. The study also identifies a notable trend towards holistic approaches, with a surge in publications and empirical studies since 2019, signaling the field’s maturity. Future research directions emphasize delving into the relatively under-explored economic dimension, aligning with the United Nations’ Sustainable Development Goals (SDGs), and addressing stakeholders’ influence.
Wiji Umiati, Evy Sulistianingsih, Shantika Martha
et al.
Stocks, as investment products, tend to carry risks due to fluctuations. The tendency of stock prices to rise over time leads investors to opt for call options, which are one of the derivative investment products. However, call options are influenced by several factors that can pose risks and have nonlinear dependence on market risk factors. Therefore, methods are needed to measure the risk of call options, such as Delta Normal Value at Risk and Delta Gamma Normal Value at Risk. Delta and Gamma are part of Option Greeks, parameters that measure the sensitivity of options to various factors used in determining option prices with the Black-Scholes model. This study uses an approach with the addition of Theta, which can measure the sensitivity of options to time. This study aims to analyze Value at Risk with the Delta Gamma Theta Normal approach for call options on Google (GOOGL) and Amazon (AMZN) stocks. The analysis uses closing stock price data from September 7, 2022, to September 7, 2023, and three in-the-money and out-of-the-money call option prices. The study begins by collecting closing stock prices and call option contract components, testing the normality of stock returns, calculating volatility, , Delta, Gamma, and Theta, then calculating the Value at Risk. Based on the analysis, it is found that GOOGL and AMZN call options have a Value at Risk of $0.89588 and $0.92760, respectively, at a 99% confidence level with a strike price of $120. Furthermore, based on the comparison of Value at Risk between in-the-money and out-of-the-money call options, it can be concluded that out-of-the-money call options tend to have larger estimated losses.
In this work, we introduce a new concept of the stream function and derive the equation for the stream function in the three-dimensional case. To construct a basis in the space of solutions of the NavierStokes system, we solve an auxiliary spectral problem for the bi-Laplacian with Dirichlet conditions on the boundary. Then, using the formulas employed for introducing the stream function, we find a system of functions forming a basis in the space of solutions of the Navier-Stokes system. It is worth noting that this basis can be utilized for the approximate solution of direct and inverse problems for the Navier-Stokes system, both in its linearized and nonlinear forms. The main idea of this work can be summarized as follows: instead of changing the boundary conditions (which remain unchanged), we change the differential equations for the stream function with a spectral parameter. As a result, we obtain a spectral problem for the bi-Laplacian in the domain represented by a three-dimensional unit sphere, with Dirichlet conditions on the boundary of the domain. By solving this problem, we find a system of eigenfunctions forming a basis in the space of solutions to the Navier-Stokes equations. Importantly, the boundary conditions are preserved, and the continuity equation for the fluid is satisfied. It is also noteworthy that, for the three-dimensional case of the Navier-Stokes system, an analogue of the stream function was previously unknown.
AbstractSelf-efficacy is associated with a range of educational outcomes, including science and math degree attainment. Project-based statistics courses have the potential to increase students’ math self-efficacy because projects may represent a mastery experience, but students enter courses with preexisting math self-efficacy. This study explored associations between pre-course math confidence and coding confidence with post-course statistical intentions and perceived achievement among students in a project-based statistics course at 28 private and public colleges and universities between fall 2018 and winter 2020 (n = 801) using multilevel mixed-effects multivariate linear regression within multiply imputed data with a cross-validation approach (testing n = 508 at 20 colleges/universities). We found that pre-course coding confidence was associated with, respectively, 9 points greater post-course statistical intentions and 10 points greater perceived achievement on a scale 0–100 (0.09, 95% confidence interval (0.02, 0.17), p = 0.02; 0.10, 95% CI (0.01, 0.19), p = 0.04), and that minoritized students have greater post-course statistical intentions than nonminoritized students. These results concur with past research showing the potential effectiveness of the project-based approach for increasing the interest of minoritized students in statistics. Pre-course interventions to increase coding confidence such as pre-college coding experiences may improve students’ post-course motivations and perceived achievement in a project-based course. Supplementary materials for this article are available online.
Probabilities. Mathematical statistics, Special aspects of education
<p>Recent heatwaves such as the 2021 Pacific Northwest heatwave have shattered temperature records across the globe. The likelihood of experiencing extreme temperature events today is already strongly increased by anthropogenic climate change, but it remains challenging to determine to what degree prevalent atmospheric and land surface conditions aggravated the intensity of a specific heatwave event. Quantifying the respective contributions is therefore paramount for process understanding but also for attribution and future projection statements conditional on the state of atmospheric circulation or land surface conditions. We here propose and evaluate a statistical framework based on extreme value theory, which enables us to learn the respective statistical relationship between extreme temperature and process variables in initial-condition large ensemble climate model simulations. Elements of statistical learning theory are implemented in order to integrate the effect of the governing regional circulation pattern. The learned statistical models can be applied to reanalysis data to quantify the relevance of physical process variables in observed heatwave events. The method also allows us to make conditional attribution statements and answer “what if” questions. For instance, how much would a heatwave intensify given the same dynamic conditions but at a different warming level? How much additional warming is needed for the same heatwave intensity to occur under average circulation conditions? Changes in the exceedance probability under varying large- and regional-scale conditions can also be assessed. We show that each additional degree of global warming increases the 7 d maximum temperature for the Pacific Northwest area by almost <span class="inline-formula">2</span> <span class="inline-formula"><sup>∘</sup></span>C, and likewise, we quantify the direct effect of anti-cyclonic conditions on heatwave intensity. Based on this, we find that the combined global warming and circulation effect of at least <span class="inline-formula">2.9</span> <span class="inline-formula"><sup>∘</sup></span>C accounts for 60 %–80 % of the 2021 excess event intensity relative to average pre-industrial heatwave conditions.</p>
Correspondence analysis has been widely applied in various fields as a graphical method to depict the association structure between two categorical random variables on a low-dimensional plot. This study built a simple algorithm to determine the principal coordinates and construct the circular confidence regions on the correspondence plot. In this algorithm, the determination of the standard residual matrix and the principal coordinates is built directly from the contingency table (without calculating a correspondence matrix). The algorithm was developed using R and applied to data on Covid-19 cases in West Java.
Rheza Vahlepy, Winarno Winarno, Fahriza Nurul Azizah
et al.
To achieve client satisfaction, every company must be able to do its best. Companies must be able to provide services that meet or surpass their consumers' expectations to achieve customer satisfaction. As a result, the goal of this research is to look at client satisfaction with the Sales Engineer services that have been delivered. Reliability, responsiveness, assurance, empathy, and tangible customer satisfaction at PT XYZ are the characteristics used in this study. 150 consumers who were served by Sales Engineers provided the data for this study. To perform data processing, this research used SERVQUAL and Factor Analysis for determining customer satisfaction. Based on the findings of the data processing with SERVQUAL, it has been determined that two variables, Assurance, and Empathy, are capable of bringing consumer satisfaction. Based on the overall analysis using Factor Analysis, it can be concluded that the majority of the services provided by the Sales Engineer are able to meet the expectations of customers, particularly in terms of the most important factor in the emergence of customer satisfaction, to encourage these customers to be loyal to the company. Customers, as well as being responsible for and the ultimate action taken by sales in response to consumer complaints.
This paper deals with a proposal for joint modeling and process optimization for split-plot designs analyzed through mixed response surface models. It addresses the following main issues: i) the building of a joint mixed response
surface model for a multiple response situation, by defining only one response through which specific coefficients are
included for studying the association among the responses; ii) the considering of fixed as well as random effects within a joint modeling and optimization context; iii) the achievement of an optimal solution by involving specific as well as common coefficients for the responses. We illustrate our contribution through a case-study related to a split-plot design on electronic components of printed circuit boards (PCBs); we obtain satisfactory results by confirming the validity of this
contribution, where the qualitative factor PCB is also studied and optimized.
Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high‐dimensional probability measures. However, these methods struggle to capture the temporal dependence of joint probability distributions induced by time‐series data. Furthermore, long time‐series data streams hugely increase the dimension of the target space, which may render generative modeling infeasible. To overcome these challenges, motivated by the autoregressive models in econometric, we are interested in the conditional distribution of future time series given the past information. We propose the generic conditional Sig‐WGAN framework by integrating Wasserstein‐GANs (WGANs) with mathematically principled and efficient path feature extraction called the signature of a path. The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterizes the law of the time‐series model. In particular, we develop the conditional Sig‐ W1$W_1$ metric that captures the conditional joint law of time series models and use it as a discriminator. The signature feature space enables the explicit representation of the proposed discriminators, which alleviates the need for expensive training. We validate our method on both synthetic and empirical dataset and observe that our method consistently and significantly outperforms state‐of‐the‐art benchmarks with respect to measures of similarity and predictive ability.
Euler-Cauchy equation is the typical example of a linear ordinary differential equation with variable coefficients. In this paper, we apply the alternative method to determine the particular solution of Euler-Cauchy nonhomogenous with polynomial and natural logarithm form. An explicit formula of the particular solution is derived from the use of an upper triangular Toeplitz matrix. The study showed that this method could be finding the particular solution for the Euler-Cauchy equation
Kebijakan pengentasan kemiskinan pada pemerintahan presiden Ir. H. Joko Widodo dilakukan melalui empat strategi kunci yang salah satunya adalah pemberdayaan kelompok masyarakat miskin. Ketersediaan informasi mengenai kemiskinan sangatlah minim padahal untuk menerapkan strategi kebijakan tersebut seharusnya dimulai pada kelompok masyarakat terkecil yakni masyarakat desa. Guna memperoleh informasi kemiskinan pada tingkat desa, penelitian ini menerapkan metode pendugaan area kecil sebagai akibat kurang efektifnya pendugaan langsung pada area kecil. Metode pendugaan area kecil yang umum digunakan yakni metode empirical best linear unbiased prediction (EBLUP), empirical Bayes (EB), dan metode hierarchical Bayes (HB). Hasil yang diperoleh pada pendugaan area kecil pada tingkat desa di Provinsi Bali menujukkan bahwa dugaan proporsi rumah tangga miskin di tingkat desa di Provinsi Bali berada di antara 0,00423 dan 0,03910 serta nilai mean square error yang berada di antara 0,0013 dan 0,1291 diperoleh melalui metode hierarchical Bayes, kemudian untuk metode empirical Bayes diperoleh dugaan proporsi rumah tangga miskin di antara 0,00423 dan 0,03909 serta nilai mean square error di antara 0,0011 dan 0,1288 dan metode empirical best linear unbiased prediction diperoleh dugaan proporsi rumah tangga miskin berada di antara 0,00425 dan 0,03910 serta nilai mean square error di antara 0,00010 dan 0,1291. Secara umum nilai mean square error berada di kisaran yang sama. Sehingga ketiga metode pendugaan tidak dapat disimpulkan yang lebih baik satu dengan yang lainnya.
Letη= (η1, …,ηN) be a multinomial random vector with parametersn=η1+ ⋯ +ηNandpm> 0,m= 1, …,N,p1+ ⋯ +pN= 1. We assume thatN→∞and maxpm→ 0 asn→∞. The probabilities of large deviations for statistics of the formh1(η1) + ⋯ +hN(ηN) are studied, wherehm(x) is a real-valued function of a non-negative integer-valued argument. The new large deviation results for the power-divergence statistics and its most popular special variants, as well as for several count statistics are derived as consequences of the general theorems.
The coefficient of determination, the R2, is often used to measure the variance explained by an affine combination of multiple explanatory covariates. An attribution of this explanatory contribution to each of the individual covariates is often sought in order to draw inference regarding the importance of each covariate with respect to the response phenomenon. A recent method for ascertaining such an attribution is via the game theoretic Shapley value decomposition of the coefficient of determination. Such a decomposition has the desirable efficiency, monotonicity, and equal treatment properties. Under a weak assumption that the joint distribution is pseudo-elliptical, we obtain the asymptotic normality of the Shapley values. We then utilize this result in order to construct confidence intervals and hypothesis tests for Shapley values. Monte Carlo studies regarding our results are provided. We found that our asymptotic confidence intervals required less computational time to competing bootstrap methods and are able to exhibit improved coverage, especially on small samples. In an expository application to Australian real estate price modeling, we employ Shapley value confidence intervals to identify significant differences between the explanatory contributions of covariates, between models, which otherwise share approximately the same R2 value. These different models are based on real estate data from the same periods in 2019 and 2020, the latter covering the early stages of the arrival of the novel coronavirus, COVID-19.