Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Nature of Issues in Five Open Source Microservices Systems: An Empirical Study

Due to its enormous benefits, the research and industry communities have shown an increasing interest in the Microservices Architecture (MSA) style over the last few years. Despite this, there is a limited evidence-based and thorough understanding of the types of issues (e.g., faults, errors, failures, mistakes) faced by microservices system developers and causes that trigger the issues. Such evidence-based understanding of issues and causes is vital for long-term, impactful, and quality research and practice in the MSA style. To that end, we conducted an empirical study on 1,345 issue discussions extracted from five open source microservices systems hosted on GitHub. Our analysis led to the first of its kind taxonomy of the types of issues in open source microservices systems, informing that the problems originating from Technical debt (321, 23.86%), Build (145, 10.78%), Security (137, 10.18%), and Service execution and communication (119, 8.84%) are prominent. We identified that “General programming errors”, “Poor security management”, “Invalid configuration and communication”, and “Legacy versions, compatibility and dependency” are the predominant causes for the leading four issue categories. Study results streamline a taxonomy of issues, their mapping with underlying causes, and present empirical findings that could facilitate research and development on emerging and next-generation microservices systems.
What Makes a Message Persuasive? Identifying Adaptations Towards Persuasiveness in Nine Exploratory Case Studies

The ability to persuade others is critical to professional and personal success. However, crafting persuasive messages is demanding and poses various challenges. We conducted nine exploratory case studies to identify adaptations that professional and non-professional writers make in written scenarios to increase their subjective persuasiveness. Furthermore, we identified challenges that those writers faced and identified strategies to resolve them with persuasive natural language generation, i.e., artificial intelligence. Our findings show that humans can achieve high degrees of persuasiveness (more so for professional-level writers), and artificial intelligence can complement them to achieve increased celerity and alignment in the process.
Interference, Bias, and Variance in Two-Sided Marketplace Experimentation: Guidance for Platforms

Two-sided marketplace platforms often run experiments (or A/B tests) to test the effect of an intervention before launching it platform-wide. A typical approach is to randomize users into a treatment group, which receives the intervention, and a control group, which does not. The platform then compares the performance in the two groups to estimate the effect if the intervention were launched to everyone. We focus on two common experiment types, where the platform randomizes users either on the supply side or on the demand side. For these experiments, it is known that the resulting estimates of the treatment effect are typically biased: individuals in the market compete with each other, which creates interference and leads to a biased estimate. Here, we observe that economic interactions (competition among demand and supply) lead to statistical phenomenon (biased estimates). We develop a simple, tractable market model to study bias and variance in these experiments with interference. We focus on two choices available to the platform: (1) Which side of the platform should it randomize on (supply or demand)? (2) What proportion of individuals should be allocated to treatment? We find that both choices affect the bias and variance of the resulting estimators, but in different ways. The bias-optimal choice of experiment type depends on the relative amounts of supply and demand in the market, and we discuss how a platform can use market data to select the experiment type. Importantly, we find that in many circumstances choosing the bias-optimal experiment type has little effect on variance, and in some cases coincide with the variance-optimal type. On the other hand, we find that the choice of treatment proportion can induce a bias-variance tradeoff, where the bias-minimizing proportion increases variance. We discuss how a platform can navigate this tradeoff and best choose the proportion, using a combination of modeling as well as contextual knowledge about the market, the risk of the intervention, and reasonable effect sizes of the intervention.
Suboptimal coverings for continuous spaces of control tasks

We propose the {\alpha}-suboptimal covering number to characterize multi-task control problems where the set of dynamical systems and/or cost functions is infinite, analogous to the cardinality of finite task sets. This notion may help quantify the function class expressiveness needed to represent a good multi-task policy, which is important for learning-based control methods that use parameterized function approximation. We study suboptimal covering numbers for linear dynamical systems with quadratic cost (LQR problems) and construct a class of multi-task LQR problems amenable to analysis. For the scalar case, we show logarithmic dependence on the"breadth"of the space. For the matrix case, we present experiments 1) measuring the efficiency of a particular constructive cover, and 2) visualizing the behavior of two candidate systems for the lower bound.
On Measure Quantifiers in First-Order Arithmetic (Long Version)

We study the logic obtained by endowing the language of first-order arithmetic with second-order measure quantifiers. This new kind of quantification allows us to express that the argument formula is true in a certain portion of all possible interpretations of the quantified variable. We show that first-order arithmetic with measure quantifiers is capable of formalizing simple results from probability theory and, most importantly, of representing every recursive random function. Moreover, we introduce a realizability interpretation of this logic in which programs have access to an oracle from the Cantor space.
The SIR model of an epidemic

The SIR model is a three-compartment model of the time development of an epidemic. After normalizing the dependent variables, the model is a system of two non-linear differential equations for the susceptible proportion $S$ and the infected proportion $I$. After normalizing the time variable there is only one remaining parameter. This largely expository article is mainly about aspects of this model that can be understood with calculus. It also discusses an alternative exactly solvable model that appeared in early work of Kermack and McKendrick. This model may be obtained by replacing $SI$ factors by $\sqrt{2S-1}I$ factors. For a mild epidemic, where $S$ is decreasing from 1 but remains fairly close to 1, this is a reasonable approximation.
Math Operation Embeddings for Open-ended Solution Analysis and Feedback

Feedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education. Such feedback can help students correct their errors and ultimately lead to improved learning outcomes. Most existing approaches for automated student solution analysis and feedback require manually constructing cognitive models and anticipating student errors for each question. This process requires significant human effort and does not scale to most questions used in homework and practices that do not come with this information. In this paper, we analyze students' step-by-step solution processes to equation solving questions in an attempt to scale up error diagnostics and feedback mechanisms developed for a small number of questions to a much larger number of questions. Leveraging a recent math expression encoding method, we represent each math operation applied in solution steps as a transition in the math embedding vector space. We use a dataset that contains student solution steps in the Cognitive Tutor system to learn implicit and explicit representations of math operations. We explore whether these representations can i) identify math operations a student intends to perform in each solution step, regardless of whether they did it correctly or not, and ii) select the appropriate feedback type for incorrect steps. Experimental results show that our learned math operation representations generalize well across different data distributions.
baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents

In many multi-agent spatiotemporal systems, agents operate under the influence of shared, unobserved variables (e.g., the play a team is executing in a game of basketball). As a result, the trajectories of the agents are often statistically dependent at any given time step; however, almost universally, multi-agent models implicitly assume the agents' trajectories are statistically independent at each time step. In this paper, we introduce baller2vec++, a multi-entity Transformer that can effectively model coordinated agents. Specifically, baller2vec++ applies a specially designed self-attention mask to a mixture of location and"look-ahead"trajectory sequences to learn the distributions of statistically dependent agent trajectories. We show that, unlike baller2vec (baller2vec++'s predecessor), baller2vec++ can learn to emulate the behavior of perfectly coordinated agents in a simulated toy dataset. Additionally, when modeling the trajectories of professional basketball players, baller2vec++ outperforms baller2vec by a wide margin.
Tidally perturbed oblique pulsations in the hierarchical triple system V1031 Orionis

We present TESS photometry of the triple star system V1031 Ori (AB)C, which exhibits shortperiod oscillations superimposed on an eclipsing light curve. The high-quality data were analyzed in detail and combined with the published spectroscopic measurements to obtain the fundamental stellar parameters of the program target. The masses and radii of the eclipsing pair (AB) were determined to about 0.5 % and 0.7 % precision, respectively. We found 23 significant frequencies in two ranges of < 2 day and 10.9−12.7 day from the eclipsesubtracted residuals. Among them, sixteen in the gravity (g)-mode region may be aliases and artefacts due to imperfect removal of the systematic trends and the binary effects from the TESS data. Seven frequencies in the pressure (p)-mode region were separated by the orbital frequency and split by the average offset of 0.042 ± 0.003 day from their adjacent harmonics. Further, the pulsation amplitudes are clearly modulated with the binary star orbit. The results indicate that the p-mode frequencies are tidally perturbed pulsations from the secondary component and the pulsation axis could be aligned with the tidal axis.
A Novel Unified Stereo Stimuli based Binocular Eye-Tracking System for Accurate 3D Gaze Estimation

—In addition to the high cost and complex setup, the main reason for the limitation of the three-dimensional (3D) display is the problem of accurately estimating the user’s current point-of-gaze (PoG) in a 3D space. In this paper, we present a novel noncontact technique for the PoG estimation in a stereoscopic environment, which integrates a 3D stereoscopic display system and an eye-tracking system. The 3D stereoscopic display system can provide users with a friendly and immersive high-deﬁnition viewing experience without wearing any equipment. To accurately locate the user’s 3D PoG in the ﬁeld of view, we build a regression-based 3D eye-tracking model with the eye movement data and stereo stimulus videos as input. Besides, to train an optimal regression model, we also design and annotate a dataset that contains 30 users’ eye-tracking data corresponding to two designed stereo test scenes. Innovatively, this dataset introduces feature vectors between eye region landmarks for the gaze vector estimation and a combined feature set for the gaze depth estimation. Moreover, ﬁve traditional regression models are trained and evaluated based on this dataset. Experimental results show that the average errors of the 3D PoG are about 0.90 cm on the X-axis, 0.83 cm on the Y-axis, and 1.48 cm / 0.12 m along the Z-axis with the scene-depth range in 75 cm / 8 m, respectively.
3D/2D regularized CNN feature hierarchy for Hyperspectral image classification

Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels. Several regularization techniques have been used to overcome the aforesaid issues. However, sometimes models learn to predict the samples extremely confidently which is not good from a generalization point of view. Therefore, this paper proposed an idea to enhance the generalization performance of a hybrid CNN for HSIC using soft labels that are a weighted average of the hard labels and uniform distribution over ground labels. The proposed method helps to prevent CNN from becoming over-confident. We empirically show that in improving generalization performance, label smoothing also improves model calibration which significantly improves beam-search. Several publicly available Hyperspectral datasets are used to validate the experimental evaluation which reveals improved generalization performance, statistical significance, and computational complexity as compared to the state-of-the-art models. The code will be made available at https://github.com/mahmad00.
SDSS-IV MaNGA: enhanced star formation in galactic-scale outflows

Using the integral field unit (IFU) data from Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, we collect a sample of 36 star-forming galaxies that host galactic-scale outflows in ionized gas phase. The control sample is matched in the three-dimensional parameter space of stellar mass, star formation rate, and inclination angle. Concerning the global properties, the outflows host galaxies tend to have smaller size, more asymmetric gas disc, more active star formation in the centre, and older stellar population than the control galaxies. Comparing the stellar population properties along axes, we conclude that the star formation in the outflows host galaxies can be divided into two branches. One branch evolves following the inside-out formation scenario. The other located in the galactic centre is triggered by gas accretion or galaxy interaction, and further drives the galactic-scale outflows. Besides, the enhanced star formation and metallicity along minor axis of outflows host galaxies uncover the positive feedback and metal entrainment in the galactic-scale outflows. Observational data in different phases with higher spatial resolution are needed to reveal the influence of galactic-scale outflows on the star formation progress in detail.
Syft 0.5: A Platform for Universally Deployable Structured Transparency

We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems. This framework is demonstrated through the design and implementation of a novel privacy-preserving inference information flow where we pass homomorphically encrypted activation signals through a split neural network for inference. We show that splitting the model further up the computation chain significantly reduces the computation time of inference and the payload size of activation signals at the cost of model secrecy. We evaluate our proposed flow with respect to its provision of the core structural transparency principles.
Blockchains and Self-Sovereign Identities Applied to Healthcare Solutions: A Systematic Review

Self-Sovereign Identity (SSI), a Blockchain-based technology for digital identity management, is a promising concept for handling health data. It could represent a step forward in empowering users, granting them control over their data. This work conducts a systematic literature review to investigate state-of-the-art measures based on SSI and Blockchain technologies for dealing with electronic health records (EHRs), identifying gaps, and determining the key questions for future research. As a result, this review shows a growing interest in Blockchain methods to handle EHRs, but few works consider using the self-sovereign identity approaches. The results obtained in this work also suggest that: Blockchain technologies provide a viable alternative to deliver EHR solutions such as patient monitoring, healthcare data trading, and prescription control; consolidated Blockchain technologies are the preferred core components of most effective strategies; keeping raw health data off-chain helps to create scalable solutions; health data standards make searching medical records in Blockchain structures feasible; Smart Contracts are essential components of Blockchain-based EHR solutions; the concepts of data ownership and Self-Sovereign Identity have been neither adequately defined nor employed in the health context.
Molecular Dynamics Study of Droplets on Flat Crystalline Surface during Cooling and Ice formation

Condensation and frost formation degrade the heat transfer performance of air-conditioners and refrigerators. Yet, the frost formation mechanism has not been fully understood. In the present study, we numerically investigated H2O droplets during cooling and ice formation by means of classical molecular dynamics simulations. The mW potential was employed for the H2O molecules. A nanoscale H2O droplet was placed on a flat solid wall consisting of Pt atoms. The fcc (110) crystal plane faced the droplet. We examined where ice nucleation was formed and how the ice formation proceeded inside the droplet, and then evaluated temporal change in interfacial thermal resistance between the H2O molecules and the solid wall. We found that the cooling and ice formation changed the interfacial thermal resistance; however, its tendency differed depending on the solid-wall wettability. It was influenced by the nearest neighbor adsorption layer consisting of H2O molecules transformed during the cooling and ice formation.
Efficient training of physics‐informed neural networks via importance sampling

Physics‐informed neural networks (PINNs) are a class of deep neural networks that are trained, using automatic differentiation, to compute the response of systems governed by partial differential equations (PDEs). The training of PINNs is simulation free, and does not require any training data set to be obtained from numerical PDE solvers. Instead, it only requires the physical problem description, including the governing laws of physics, domain geometry, initial/boundary conditions, and the material properties. This training usually involves solving a nonconvex optimization problem using variants of the stochastic gradient descent method, with the gradient of the loss function approximated on a batch of collocation points, selected randomly in each iteration according to a uniform distribution. Despite the success of PINNs in accurately solving a wide variety of PDEs, the method still requires improvements in terms of computational efficiency. To this end, in this paper, we study the performance of an importance sampling approach for efficient training of PINNs. Using numerical examples together with theoretical evidences, we show that in each training iteration, sampling the collocation points according to a distribution proportional to the loss function will improve the convergence behavior of the PINNs training. Additionally, we show that providing a piecewise constant approximation to the loss function for faster importance sampling can further improve the training efficiency. This importance sampling approach is straightforward and easy to implement in the existing PINN codes, and also does not introduce any new hyperparameter to calibrate. The numerical examples include elasticity, diffusion, and plane stress problems, through which we numerically verify the accuracy and efficiency of the importance sampling approach compared to the predominant uniform sampling approach.
Algebraic framework for determining laminar pattern bifurcations by lateral-inhibition in 2D and 3D bilayer geometries

Fine-grain patterns produced by juxtacrine signalling, have been studied using static monolayers as cellular domains. Unfortunately, analytical results are restricted to a few cells due to the algebraic complexity of nonlinear dynamical systems. Motivated by concentric patterning of Notch expression observed in the mammary gland, we combine concepts from graph and control theory to represent cellular connectivity. The resulting theoretical framework allows us to exploit the symmetry of multicellular bilayer structures in 2D and 3D, thereby deriving analytical conditions that drive the dynamical system to form laminar patterns consistent with the formulation of cell polarity. Critically, the conditions are independent of the precise dynamical details, thus the framework allows for the utmost generality in understanding the inﬂuence of cellular geometry on patterning in lateral-inhibition systems. Applying the analytic conditions to mammary organoids suggests that intense cell signalling polarity is required for the maintenance of stratiﬁed cell-types within a static bilayer using a lateral-inhibition mechanism. Furthermore, by employing 2D and 3D cell-based models, we highlight that the cellular polarity conditions derived from static domains have the capacity to generate laminar patterning in dynamic environments. However, they are insuﬃcient for the maintenance of patterning when subjected to substantial morphological perturbations. In agreement with the mathematical implications of strict signalling polarity induced on the cells, we propose an adhesion dependent Notch-Delta biological process which has the potential to initiate bilayer stratiﬁcation in a developing mammary organoid.
Dynamic Degradation for Image Restoration and Fusion

The deep-learning-based image restoration and fusion methods have achieved remarkable results. However, the existing restoration and fusion methods paid little research attention to the robustness problem caused by dynamic degradation. In this paper, we propose a novel dynamic image restoration and fusion neural network, termed as DDRF-Net, which is capable of solving two problems, i.e., static restoration and fusion, dynamic degradation. In order to solve the static fusion problem of existing methods, dynamic convolution is introduced to learn dynamic restoration and fusion weights. In addition, a dynamic degradation kernel is proposed to improve the robustness of image restoration and fusion. Our network framework can e ﬀ ectively combine image degradation with image fusion tasks, provide more detailed information for image fusion tasks through image restoration loss, and optimize image restoration tasks through image fusion loss. Therefore, the stumbling blocks of deep learning in image fusion, e.g., static fusion weight and speciﬁcally designed network architecture, are greatly mitigated. Extensive experiments show that our method is more superior compared with the state-of-the-art methods.