Miracle-dz/UniFAR-FTUs huggingface dataset:
SourceWarp: A scalable, SCM-driven testing and benchmarking approach to support data-driven and agile decision making for CI/CD tools and DevOps platforms

The rising popularity and adoption of source-code management systems in combination with Continuous Integration and Continuous Delivery (CI/CD) processes have contributed to the adoption of agile software development with short release and feedback cycles between software producers and their customers. DevOps platforms streamline and enhance automation around source-code management systems by providing a uniform interface for managing all the aspects of the software development lifecycle starting from software development until software deployment and by integrating and orchestrating various tools that provide automation around software development processes such as automated bug detection, security testing, dependency scanning, etc.Applying changes to the DevOps platform or to one of the integrated tools without providing data regarding its real world impact increases the risk of having to remove/revert the change. This could lead to service disruption or loss of confidence in the platform if it does not perform as expected. In addition, integrating alpha or beta features, which may not meet the robustness of a finalised feature, may pose security or stability risks to the entire platform. Hence, short release cycles require testing and benchmarking approaches that make it possible to prototype, test, and benchmark ideas quickly and at scale to support Data-Driven Decision Making, with respect to the features that are about to be integrated into the platform.In this paper, we propose a scalable testing and benchmarking approach called SourceWarp that is targeted towards DevOps platforms and supports both testing and benchmarking in a cost effective and reproducible manner. We have implemented the proposed approach in the publicly available SourceWarp tool which we have evaluated in the context of a real-world industrial case-study. We successfully applied SourceWarp to test and benchmark a newly developed feature at GitLab which has been successfully integrated into the product. In the case study we demonstrate that SourceWarp is scalable and highly effective in supporting agile Data-Driven Decision Making by providing automation for testing and benchmarking proof-of-concept ideas for CI/CD tools, chained CI/CD tools (also referred to as pipeline), for the DevOps platform or a combination of them without having to deploy features to the staging or production environments.
The p‐filter: multilayer false discovery rate control for grouped hypotheses

In many practical applications of multiple testing, there are natural ways to partition the hypotheses into groups by using the structural, spatial or temporal relatedness of the hypotheses, and this prior knowledge is not used in the classical Benjamini–Hochberg procedure for controlling the false discovery rate (FDR). When one can define (possibly several) such partitions, it may be desirable to control the group FDR simultaneously for all partitions (as special cases, the ‘finest’ partition divides the n hypotheses into n groups of one hypothesis each, and this corresponds to controlling the usual notion of FDR, whereas the ‘coarsest’ partition puts all n hypotheses into a single group, and this corresponds to testing the global null hypothesis). We introduce the p‐filter, which takes as input a list of n p‐values and M⩾1 partitions of hypotheses, and produces as output a list of n or fewer discoveries such that the group FDR is provably simultaneously controlled for all partitions. Importantly, since the partitions are arbitrary, our procedure can also handle multiple partitions which are non‐hierarchical. The p‐filter generalizes two classical procedures—when M=1, choosing the finest partition into n singletons, we exactly recover the Benjamini–Hochberg procedure, whereas, choosing instead the coarsest partition with a single group of size n, we exactly recover the Simes test for the global null hypothesis. We verify our findings with simulations that show how this technique can not only lead to the aforementioned multilayer FDR control but also lead to improved precision of rejected hypotheses. We present some illustrative results from an application to a neuroscience problem with functional magnetic resonance imaging data, where hypotheses are explicitly grouped according to predefined regions of interest in the brain, thus allowing the scientist to employ field‐specific prior knowledge explicitly and flexibly.
PLS Analysis: How Family Support Affect Students’ Self-Conﬁdence in Completing Thesis

A student’s success can not be separated from a ﬁnal project called a thesis as a determinant of student graduation in college. However, in the process, students often face various obstacles and challenges. To overcome this, students need to have high self-conﬁdence. In increasing self-conﬁdence, the role of the family is required as the main factor that encourages students to succeed. The purpose of this paper is to determine the inﬂuence of forms of family support in the form of informational support, instrumental support, appraisal and reward support, and emotional support on the self-conﬁdence of 58 students of the Islamic Guidance and Counseling Study Program class of 2018 UIN Sunan Kalijaga Yogyakarta who are working on a thesis for students. The data analysis method used is Partial Least Square (PLS). Partial Least Square (PLS) is a component or variant-based Structural Equation Model (SEM) model. Partial Least Square (PLS) is a variant-based Structural Equation Model (SEM) model.The results of this study state that 46.7% of students’ self-conﬁdence variations in completing a thesis are inﬂuenced by family support while the rest is inﬂuenced by other factors. In addition, emotional support has a signiﬁcant effect on student self-conﬁdence in doing a thesis with a p-value of 0.000. Thus, it can be concluded that emotional support becoming the form of family support that played the most important role in increasing student self-conﬁdence. The results of this study will later be used as suggestion for studentss family and also for counselors in modifying behavior to increase studentss self-conﬁdence
APPLICATIONS AND ASPECTS OF SUPERCAPACITOR IN IOT DEVICES AS A SUSTAINABLE ENERGY SOURCE

Today, the Internet of Things (IoT) is an emerging field of recent technologies. Every field of engineering, technology, and real-time management has IoT applications such as transportation, agriculture, healthcare, manufacturing, wearable, smart grid and energy saving, smart home, smart management systems, etc. Also, AI has captured wide-area applications in the automobile industry, surveillance, security, education, entertainment, gaming, e-commerce, portable devices, robotics, biomedical instruments, etc. Electrical and electronic devices are used in IoT-based applications. They are required efficient energy to operate for a long time effectively. These types of devices required offline and online energy storage devices that charge on time or require less recharge. The supercapacitor has a high capacitor, rapid charging/discharging cyclic rate, cost-effective and eco-friendly. Our objectives are to study supercapacitors for providing energy and power supply backup to IoT devices. Electronic devices mainly operate on dc signals and electrical instruments work on signals. The supercapacitor plays an important role to supply energy which stores an extremely large amount of electrical charge. It is the future of batteries and replaces the old bulk batteries with tiny high-performance flexible sustainable supercapacitors.
Optimal control of the 2D constrained Navier-Stokes equations

We study the 2D Navier-Stokes equations within the framework of a constraint that ensures energy conservation throughout the solution. By employing the Galerkin approximation method, we demonstrate the existence and uniqueness of a global solution for the constrained Navier-Stokes equation on the torus $\mathbb{T}^2$. Moreover, we investigate the linearized system associated with the 2D-constrained Navier-Stokes equations, exploring its existence and uniqueness. Subsequently, we establish the Lipschitz continuity and Fr$\'{e}$chet differentiability properties of the solution mapping. Finally, employing the formal Lagrange method, we prove the first-order necessary optimality conditions.
Function-Space Regularization for Deep Bayesian Classification

Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior. However, weight-space priors are model-specific, can be difficult to interpret and are hard to specify. Instead, we apply a Dirichlet prior in predictive space and perform approximate function-space variational inference. To this end, we interpret conventional categorical predictions from stochastic neural network classifiers as samples from an implicit Dirichlet distribution. By adapting the inference, the same function-space prior can be combined with different models without affecting model architecture or size. We illustrate the flexibility and efficacy of such a prior with toy experiments and demonstrate scalability, improved uncertainty quantification and adversarial robustness with large-scale image classification experiments.
Nonlinear Interaction of a Shock Wave with an Anisotropic Entropy Perturbation Field

The problem of the interaction of a shock wave with an anisotropic entropy perturbation field has been solved including second-order corrections to hydrodynamic quantities. It has been shown that nonlinear interactions between acoustic waves result in the localization of acoustic perturbations behind the shock front. This effect is observed when sound attenuation is absent in the linear approximation. The problem of the propagation of the shock wave in an incident sample, where the spatially anisotropic density perturbation field initially exists, has been numerically solved in application to the collision of two plates. Numerical calculations confirm the results of the theoretical analysis.
Characterization of interfacial morphology of low temperature, low pressure Au–Au thermocompression bonding

Au–Au thermocompression bonding is a versatile technique of high interest for a variety of applications. We have investigated Au–Au bonding using sputter deposited Au films under conditions of low temperature (150–250 °C) and low bonding pressure (∼3 MPa) for short times (15 min). The combination of low temperature and short times is important for applications involving both hermetic sealing and packaging scaling. The initial surface roughness of the Au film was in the 3–5 nm range with peak-to-valley heights of 20–30 nm and a lateral correlation length of ∼400 nm. For samples bonded at 150 °C, the void morphology at the bonded interface was related to the initial surface roughness. The void morphology was different when bonding at the higher temperatures: the void length (along the bonded interface) decreased significantly but the void height (perpendicular to the interface) increased. These results can be understood in terms of a combination of increased surface Au diffusivity and decreased yield stress and elastic modulus with increased bonding temperature.
The dynamics of higher-order novelties

Understanding how humans explore the world in search of novelties is key to foster innovation. Previous studies analyzed novelties in various exploration processes, defining them as the first appearance of an element. However, innovation can also be generated by novel association of what is already known. We hence define higher-order novelties as the first appearances of combinations of two or more elements, and we introduce higher-order Heaps' exponents as a way to characterize their pace of discovery. Through extensive analysis of real-world data, we find that processes with the same pace of discovery, as measured by the standard Heaps' exponent, can instead differ at higher orders. We then propose to model the dynamics of an exploration process as a random walk on an evolving network of the possible connections between elements. The model reproduces the empirical properties of higher-order novelties, revealing how the space of possibilities expands over time along with the exploration process.
Global sharp interface limit of the Hele–Shaw–Cahn–Hilliard system

In this paper, we focus on a diffuse interface model named by Hele–Shaw–Cahn–Hilliard system, which describes a two‐phase Hele–Shaw flow with matched densities and arbitrary viscosity contrast in a bounded domain. The diffuse interface thickness is measured by ϵ, and the mobility coefficient (the diffusional Peclet number) is ϵα. We will prove rigorously that the global weak solutions of the Hele–Shaw–Cahn–Hilliard system converge to a varifold solution of the sharp interface model as ϵ→0 in the case of 0≤α < 1. Copyright © 2016 John Wiley & Sons, Ltd.
Wave scattering in 1D: D'Alembert-type representations and a reconstruction method

We derive the extension of the classical d'Alembert formula for the wave equation, which provides the analytical solution for the direct scattering problem for a medium with constant refractive index; this is achieved by employing results obtained via the Fokas method. This methodology is further extended to a medium with piecewise constant refractive index, providing the apparatus for the solution of the associated inverse scattering problem. Hence, we provide an exact reconstruction method which is valid for both full and phaseless data.
Fast and stable rational approximation of generalized hypergeometric functions

Rational approximations of generalized hypergeometric functions ${}_pF_q$ of type $(n+k,k)$ are constructed by the Drummond and factorial Levin-type sequence transformations. We derive recurrence relations for these rational approximations that require $\mathcal{O}[\max\{p,q\}(n+k)]$ flops. These recurrence relations come in two forms: for the successive numerators and denominators; and, for an auxiliary rational sequence and the rational approximations themselves. Numerical evidence suggests that these recurrence relations are much more stable than the original formul\ae~for the Drummond and factorial Levin-type sequence transformations. Theoretical results on the placement of the poles of both transformations confirm the superiority of factorial Levin-type transformation over the Drummond transformation.
Newell's theory based feature transformations for spatio-temporal traffic prediction

Deep learning (DL) models for spatio-temporal traffic flow forecasting employ convolutional or graph-convolutional filters along with recurrent neural networks to capture spatial and temporal dependencies in traffic data. These models, such as CNN-LSTM, utilize traffic flows from neighboring detector stations to predict flows at a specific location of interest. However, these models are limited in their ability to capture the broader dynamics of the traffic system, as they primarily learn features specific to the detector configuration and traffic characteristics at the target location. Hence, the transferability of these models to different locations becomes challenging, particularly when data is unavailable at the new location for model training. To address this limitation, we propose a traffic flow physics-based feature transformation for spatio-temporal DL models. This transformation incorporates Newell's uncongested and congested-state estimators of traffic flows at the target locations, enabling the models to learn broader dynamics of the system. Our methodology is empirically validated using traffic data from two different locations. The results demonstrate that the proposed feature transformation improves the models' performance in predicting traffic flows over different prediction horizons, as indicated by better goodness-of-fit statistics. An important advantage of our framework is its ability to be transferred to new locations where data is unavailable. This is achieved by appropriately accounting for spatial dependencies based on station distances and various traffic parameters. In contrast, regular DL models are not easily transferable as their inputs remain fixed. It should be noted that due to data limitations, we were unable to perform spatial sensitivity analysis, which calls for further research using simulated data.
A Causal Ordering Prior for Unsupervised Representation Learning

Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables. Causal representation learning (CRL), however, argues that factors of variation in a dataset are, in fact, causally related. Allowing latent variables to be correlated, as a consequence of causal relationships, is more realistic and generalisable. So far, provably identifiable methods rely on: auxiliary information, weak labels, and interventional or even counterfactual data. Inspired by causal discovery with functional causal models, we propose a fully unsupervised representation learning method that considers a data generation process with a latent additive noise model (ANM). We encourage the latent space to follow a causal ordering via loss function based on the Hessian of the latent distribution.
Pulse-modulated intermittent control for consensus of multi-agent systems with switching topology

An intermittent control scheme is presented for consensus of the multi-agent system, in term of the pulse function, which is introduce to describe the constraints of the actuator. The proposed scheme is a general framework covering the impulsive control, and (zero-order hold) sampled control by choosing special functions as the pulse function. By using graph theory and the property of stochastic matrices, some sufficient conditions are obtained to ensure the consensus of the controlled network. It is shown that under certain assumptions upon the control gains, sampling interval and pulse function, consensus can be achieved if the communication graph has a spanning tree jointly. Finally, two simulation examples are given to verify and illustrate the theoretical results.
3D Medical Image Segmentation based on multi-scale MPU-Net

The high cure rate of cancer is inextricably linked to physicians' accuracy in diagnosis and treatment, therefore a model that can accomplish high-precision tumor segmentation has become a necessity in many applications of the medical industry. It can effectively lower the rate of misdiagnosis while considerably lessening the burden on clinicians. However, fully automated target organ segmentation is problematic due to the irregular stereo structure of 3D volume organs. As a basic model for this class of real applications, U-Net excels. It can learn certain global and local features, but still lacks the capacity to grasp spatial long-range relationships and contextual information at multiple scales. This paper proposes a tumor segmentation model MPU-Net for patient volume CT images, which is inspired by Transformer with a global attention mechanism. By combining image serialization with the Position Attention Module, the model attempts to comprehend deeper contextual dependencies and accomplish precise positioning. Each layer of the decoder is also equipped with a multi-scale module and a cross-attention mechanism. The capability of feature extraction and integration at different levels has been enhanced, and the hybrid loss function developed in this study can better exploit high-resolution characteristic information. Moreover, the suggested architecture is tested and evaluated on the Liver Tumor Segmentation Challenge 2017 (LiTS 2017) dataset. Compared with the benchmark model U-Net, MPU-Net shows excellent segmentation results. The dice, accuracy, precision, specificity, IOU, and MCC metrics for the best model segmentation results are 92.17%, 99.08%, 91.91%, 99.52%, 85.91%, and 91.74%, respectively. Outstanding indicators in various aspects illustrate the exceptional performance of this framework in automatic medical image segmentation.
Auxiliary physics-informed neural networks for forward, inverse, and coupled radiative transfer problems

In this paper, we develop and employ auxiliary physics-informed neural networks (APINNs) to solve forward, inverse, and coupled integrodifferential problems of radiative transfer theory. Specifically, by focusing on the relevant slab geometry and scattering media described by different types of phase functions, we show how the proposed APINN framework enables the efficient solution of Boltzmann-type transport equations through multi-output neural networks with multiple auxiliary variables associated with the Legendre expansion terms of the considered phase functions. Furthermore, we demonstrate the application of APINN to the coupled radiation-conduction problem of a participating medium and find distinctive temperature profiles beyond the Fourier thermal conduction limit. Finally, we solve the inverse problem for the Schwarzschild–Milne integral equation and retrieve the single scattering albedo based solely on the knowledge of boundary data, similar to what is often available in experimental settings. The present work significantly expands the current capabilities of physics-informed neural networks for radiative transfer problems that are relevant to the design and understanding of complex scattering media and photonic structures with applications to metamaterials, biomedical imaging, thermal transport, and semiconductor device modeling.
SpreadNUTS - Moderate Dynamic Extension of Paths for No-U-Turn Sampling & Partitioning Visited Regions

Markov chain Monte Carlo (MCMC) methods have existed for a long time and the field is well-explored. The purpose of MCMC methods is to approximate a distribution through repeated sampling; most MCMC algorithms exhibit asymptotically optimal behavior in that they converge to the true distribution at the limit. However, what differentiates these algorithms are their practical convergence guarantees and efficiency. While a sampler may eventually approximate a distribution well, because it is used in the real world it is necessary that the point at which the sampler yields a good estimate of the distribution is reachable in a reasonable amount of time. Similarly, if it is computationally difficult or intractable to produce good samples from a distribution for use in estimation, then there is no real-world utility afforded by the sampler. Thus, most MCMC methods these days focus on improving efficiency and speeding up convergence. However, many MCMC algorithms suffer from random walk behavior and often only mitigate such behavior as outright erasing random walks is difficult. Hamiltonian Monte Carlo (HMC) is a class of MCMC methods that theoretically exhibit no random walk behavior because of properties related to Hamiltonian dynamics. This paper introduces modifications to a specific HMC algorithm known as the no-U-turn sampler (NUTS) that aims to explore the sample space faster than NUTS, yielding a sampler that has faster convergence to the true distribution than NUTS.