Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault localization to detect co-change fixing locations

Fault Localization (FL) is a precursor step to most Automated Program Repair (APR) approaches, which fix the faulty statements identified by the FL tools. We present FixLocator, a Deep Learning (DL)-based fault localization approach supporting the detection of faulty statements in one or multiple methods that need to be modified accordingly in the same fix. Let us call them co-change (CC) fixing locations for a fault. We treat this FL problem as dual-task learning with two models. The method-level FL model, MethFL, learns the methods to be fixed together. The statement-level FL model, StmtFL, learns the statements to be co-fixed. Correct learning in one model can benefit the other and vice versa. Thus, we simultaneously train them with soft-sharing the models' parameters via cross-stitch units to enable the propagation of the impact of MethFL and StmtFL onto each other. Moreover, we explore a novel feature for FL: the co-changed statements. We also use Graph-based Convolution Network to integrate different types of program dependencies. Our empirical results show that FixLocator relatively improves over the state-of-the-art statement-level FL baselines by locating 26.5%–155.6% more CC fixing statements. To evaluate its usefulness in APR, we used FixLocator in combination with the state-of-the-art APR tools. The results show that FixLocator+DEAR (the original FL in DEAR replaced by FixLocator) and FixLocator+CURE improve relatively over the original DEAR and Ochiai+CURE by 10.5% and 42.9% in terms of the number of fixed bugs.
Electroresistance effects in ferroelectric tunnel barriers

Electron transport through fully depleted ferroelectric tunnel barriers sandwiched between two metal electrodes and its dependence on ferroelectric polarization direction are investigated. The model assumes a polarization direction dependent ferroelectric barrier. The transport mechanisms, including direct tunneling, Fowler-Nordheim tunneling and thermionic injection, are considered in the calculation of the electroresistance as a function of ferroelectric barrier properties, given by the properties of the ferroelectric, the barrier thickness, and the metal properties, and in turn of the polarization direction. Large electroresistance is favored in thicker films for all three transport mechanisms but on the expense of current density. However, switching between two transport mechanisms, i.e., direct tunneling and Fowler-Nordheim tunneling, by polarization switching yields a large electroresistance. Furthermore, the most versatile playground in optimizing the device performance was found to be the electrode properties, especially screening length and band offset with the ferroelectric.
Minkowski vacua can be metastable

We investigate the recent suggestion that a Minkowski vacuum is either absolutely stable, or it has a divergent decay rate and thus fails to have a locally Minkowski description. The divergence comes from boost integration over momenta of the vacuum bubbles. We point out that a prototypical example of false-vacuum decay is pair production in a uniform electric field, so if the argument leading to the divergence is correct, it should apply to this case as well. We provide evidence that no catastrophic vacuum instability occurs in a constant electric field, indicating that the argument cannot be right. Instead, we argue that the boost integration that leads to the divergence is unnecessary: when all possible fluctuations of the vacuum bubble are included, the quantum state of the bubble is invariant under Lorentz boosts.
ATOMIC CHEMISTRY IN TURBULENT ASTROPHYSICAL MEDIA. I. EFFECT OF ATOMIC COOLING

We carry out direct numerical simulations of turbulent astrophysical media that explicitly track ionizations, recombinations, and species-by-species radiative cooling. The simulations assume solar composition and follows the evolution of hydrogen, helium, carbon, oxygen, sodium, and magnesium, but they do not include the presence of an ionizing background. In this case, the medium reaches a global steady state that is purely a function of the one-dimensional turbulent velocity dispersion, σ1D, and the product of the mean density and the driving scale of turbulence, nL. Our simulations span a grid of models with &sgr; 1 D ? > ranging from 6 to 58 km s−1 and nL ranging from 1016 to 1020 cm−2, which correspond to turbulent Mach numbers from M = 0.2 to 10.6. The species abundances are well described by single-temperature estimates whenever M is small, but local equilibrium models can not accurately predict the global equilibrium abundances when M ≳ 1 . > To allow future studies to account for nonequilibrium effects in turbulent media, we gather our results into a series of tables, which we will extend in the future to encompass a wider range of elements, compositions, and ionizing processes.
Demystifying “removed reviews” in iOS app store

The app markets enable users to submit feedback for downloaded apps in the form of star ratings and text reviews, which are meant to be helpful and trustworthy for decision making to both developers and other users. App markets have released strict guidelines/policies for user review submissions. However, there has been growing evidence showing the untrustworthy and poor-quality of app reviews, making the app store review environment a shambles. Therefore, review removal is a common practice, and market maintainers have to remove undesired reviews from the market periodically in a reactive manner. Although some reports and news outlets have mentioned removed reviews, our research community still lacks the comprehensive understanding of the landscape of this kind of reviews. To fill the void, in this paper, we present a large-scale and longitudinal study of removed reviews in iOS App Store. We first collaborate with our industry partner to collect over 30 million removed reviews for 33,665 popular apps over the course of a full year in 2020. This comprehensive dataset enables us to characterize the overall landscape of removed reviews. We next investigate the practical reasons leading to the removal of policy-violating reviews, and summarize several interesting reasons, including fake reviews, offensive reviews, etc. More importantly, most of these mis-behaviors can be reflected on reviews’ basic information including the posters, narrative content, and posting time. It motivates us to design an automated approach to flag the policy-violation reviews, and our experiment result on the labelled benchmark can achieve a good performance (F1=97%). We further make an attempt to apply our approach to the large-scale industry setting, and the result suggests the promising industry usage scenario of our approach. Our approach can act as a gatekeeper to pinpoint policy-violation reviews beforehand, which will be quite effective in improving the maintenance process of app reviews in the industrial setting.
Porous dust grains in debris disks

Aims. When modeling the density and grain size distribution in debris disks, the minimum particle size is often significantly larger than the corresponding blowout size. While the dust particles are usually modeled as compact, homogenous spheres, we instead investigate the impact of porosity. The optical properties of porous particles are determined, and the influences of porosity on the blowout size and dust temperatures investigated. Methods. Using the method of discrete dipole approximation, we calculate the scattering and absorption cross sections of porous particles and derive the blowout size and the behavior of the dust temperature. Results. We determine the e ciency factors of absorption and the radiation pressure for porous particles, and investigate the influence on the -ratio. Blowout sizes are calculated for various stellar luminosities and porosities, and an approximation equation is derived to estimate the blowout size as a function of these parameters. Furthermore, we investigate the influence of the porosity on the dust equilibrium temperature. Conclusions. The blowout size increases with the particle porosity and stellar luminosity. The dust temperature of porous particles is lower than the one of the compact spheres; in particular, the temperature of blowout grains decreases for porous particles.
Towards developer-centered automatic program repair: findings from Bloomberg

This paper reports on qualitative research into automatic program repair (APR) at Bloomberg. Six focus groups were conducted with a total of seventeen participants (including both developers of the APR tool and developers using the tool) to consider: the development at Bloomberg of a prototype APR tool (Fixie); developers’ early experiences using the tool; and developers’ perspectives on how they would like to interact with the tool in future. APR is developing rapidly and it is important to understand in greater detail developers' experiences using this emerging technology. In this paper, we provide in-depth, qualitative data from an industrial setting. We found that the development of APR at Bloomberg had become increasingly user-centered, emphasising how fixes were presented to developers, as well as particular features, such as customisability. From the focus groups with developers who had used Fixie, we found particular concern with the pragmatic aspects of APR, such as how and when fixes were presented to them. Based on our findings, we make a series of recommendations to inform future APR development, highlighting how APR tools should 'start small', be customisable, and fit with developers' workflows. We also suggest that APR tools should capitalise on the promise of repair bots and draw on advances in explainable AI.
DeLashNet: A Deep Network for Eyelash Artifact Removal in Ultra-Wide-Field Fundus Images

The interference of eyelash artifacts in ultra-wide-field fundus (UWF) images has always been a serious problem in preventing precise clinical observations of pathology. Currently, the automatic removal of eyelash artifacts in UWF images remains unsolved and thus will eventually affect the diagnosis accuracy. In this paper, we propose a deep learning architecture called DeLashNet to eliminate eyelash artifacts from UWF images. Our DeLashNet consists of two stages: the first stage is the eyelash artifact removal stage based on a conditional generative adversarial network, and the second stage is the background refinement stage using an encoder-decoder structure. To solve the issue of lacking training samples with eyelashes, we design a novel eyelash growing model to generate synthetic eyelashes with labels and finally established a paired synthetic eyelashes (PSE) dataset. Experiments are conducted to verify the effectiveness of our proposed DeLashNet on eyelash artifact removal. The comparative and ablation studies demonstrate that the proposed DeLashNet achieved satisfactory removal performance on eyelash artifacts of UWF images.
Scalar fields in the Lense-Thirring background with a cosmic string and Hawking radiation

We analyze the influence of the gravitational field produced by a slowly rotating black hole with a cosmic string along the axis of symmetry on a massive scalar field. Exact solutions of both angular and radial parts of the Klein-Gordon equation in this spacetime are obtained, and are given in terms of the confluent Heun functions. We emphasize the role of the presence of the cosmic string in these solutions. We also investigate the solutions in regions near and far from the event horizon. From the radial solution, we obtain the exact wave solutions near the exterior horizon of the black hole, and discuss the Hawking radiation of massive scalar particles.
A Survey of Binary Code Fingerprinting Approaches: Taxonomy, Methodologies, and Features

Binary code fingerprinting is crucial in many security applications. Examples include malware detection, software infringement, vulnerability analysis, and digital forensics. It is also useful for security researchers and reverse engineers since it enables high fidelity reasoning about the binary code such as revealing the functionality, authorship, libraries used, and vulnerabilities. Numerous studies have investigated binary code with the goal of extracting fingerprints that can illuminate the semantics of a target application. However, extracting fingerprints is a challenging task since a substantial amount of significant information will be lost during compilation, notably, variable and function naming, the original data and control flow structures, comments, semantic information, and the code layout. This article provides the first systematic review of existing binary code fingerprinting approaches and the contexts in which they are used. In addition, it discusses the applications that rely on binary code fingerprints, the information that can be captured during the fingerprinting process, and the approaches used and their implementations. It also addresses limitations and open questions related to the fingerprinting process and proposes future directions.
JSIMutate: understanding performance results through mutations

Understanding the performance characteristics of software systems is particular relevant when looking at design alternatives. However, it is a very challenging problem, due to the complexity of interpreting the role and incidence of the different system elements on performance metrics of interest, such as system response time or resources utilisation. This work introduces JSIMutate, a tool that makes use of queueing network performance models and enables the analysis of mutations of a model reflecting possible design changes to support designers in identifying the model elements that contribute to improving or worsening the system's performance.
Geodesic motion in the (rotating) black string spacetime

In this article we study the geodesic motion of test particles and light in the five-dimensional (rotating) black string spacetime. If a compact dimension is added to the four-dimensional Schwarzschild or Kerr spacetime, the new five-dimensional metric describes a (rotating) black string. The geodesics in the Schwarzschild and Kerr spacetime have been studied in great detail, however, when a compact dimension is added new behaviour occurs. We present the analytical solutions of the geodesic equations and discuss the possible orbits. The motion in the ordinary four-dimensional Schwarzschild and Kerr spacetime is compared to the motion in the (rotating) black string spacetime.
Primary beam effects of radio astronomy antennas – I. Modelling the Karl G. Jansky Very Large Array (VLA) L-band beam using holography

Modern interferometric imaging relies on advanced calibration that incorporates direction-dependent effects. Their increasing number of antennas (e.g. in LOFAR, VLA, MeerKAT/SKA) and sensitivity are often tempered with the accuracy of their calibration. Beam accuracy drives particularly the capability for high dynamic range imaging (HDR - contrast > 1:$10^6$). The Radio Interferometric Measurement Equation (RIME) proposes a refined calibration framework for wide field of views (i.e. beyond the primary lobe and first null) using beam models. We have used holography data taken on 12 antennas of the Very Large Array (VLA) with two different approaches: a `data-driven' representation derived from Principal Component Analysis (PCA) and a projection on the Zernike polynomials. We determined sparse representations of the beam to encode its spatial and spectral variations. For each approach, we compressed the spatial and spectral distribution of coefficients using low-rank approximations. The spectral behaviour was encoded with a Discrete Cosine Transform (DCT). We compared our modelling to that of the Cassbeam software which provides a parametric model of the antenna and its radiated field. We present comparisons of the beam reconstruction fidelity vs. `compressibility'. We found that the PCA method provides the most accurate model. In the case of VLA antennas, we discuss the frequency ripple over L-band which is associated with a standing wave between antenna reflectors. The results are a series of coefficients that can easily be used `on-the-fly' in calibration pipelines to generate accurate beams at low computing costs.
Loschmidt Echo for quantum metrology

We propose a versatile Loschmidt echo protocol to detect and quantify multiparticle entanglement. It allows us to extract the quantum Fisher information for arbitrary pure states, and finds direct application in quantum metrology. In particular, the protocol applies to states that are generally difficult to characterize, as non-Gaussian states, and states that are not symmetric under particle exchange. We focus on atomic systems, including trapped ions, polar molecules, and Rydberg atoms, where entanglement is generated dynamically via long-range interaction, and show that the protocol is stable against experimental detection errors.
Practitioner’s guide to social network analysis: Examining physics anxiety in an active-learning setting

The application of social network analysis (SNA) has recently grown prevalent in science, technology, engineering, and mathematics education research. Research on classroom networks has led to greater understandings of student persistence in physics majors, changes in their career-related beliefs (e.g., physics interest), and their academic success. In this paper, we aim to provide a practitioner's guide to carrying out research using SNA, including how to develop data collection instruments, set up protocols for gathering data, as well as identify network methodologies relevant to a wide range of research questions beyond what one might find in a typical primer. We illustrate these techniques using student anxiety data from active-learning physics classrooms. We explore the relationship between students' physics anxiety and the social networks they participate in throughout the course of a semester. We find that students' with greater numbers of outgoing interactions are more likely to experience negative anxiety shifts even while we control for {\it pre} anxiety, gender, and final course grade. We also explore the evolution of student networks and find that the second half of the semester is a critical period for participating in interactions associated with decreased physics anxiety. Our study further supports the benefits of dynamic group formation strategies that give students an opportunity to interact with as many peers as possible throughout a semester. To complement our guide to SNA in education research, we also provide a set of tools for letting other researchers use this approach in their work -- the {\it SNA toolbox} -- that can be accessed on GitHub.
L2‐gain robust trajectory tracking control for quadrotor UAV with unknown disturbance

In this paper, the trajectory tracking problem of the position and yaw angle of an underactuated quadrotor UAV is studied. Considering that the quadrotor UAV often encounters unknown disturbance resulted by model parameter perturbations and environmental changes, a new L2‐gain robust control method is presented based on dissipation theory. First, the dynamic model of the quadrotor UAV with disturbances is transformed by introducing three virtual control variables. In this way, the trajectory tracking control can be implemented by the dual loop control, that is, the position control loop and the attitude control loop. Then, based on dissipation inequality, the robust position controller and attitude controller are developed to guarantee the L2‐gain of the system to the unknown disturbance. It is proved that all the tracking errors are uniformly ultimate boundedness. In addition, the controller is further reconstructed by introducing a new class K functions to improve the transient and steady‐state performances of the quadrotor UAV system. In the end, two simulation examples are provided to check the validity of the control method proposed and the convergence of the quadrotor UAV trajectory tracking error.
system associated with the

Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we describe the first quantitative model that extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Liminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work outlines the roadmap to integrate single-cell data for survival prediction at population scale.
The transcriptional repressor Opi1 modulates the DNA Damage Response by downregulation of inositol pyrophosphates in Saccharomyces cerevisiae

Inositol is a six-carbon sugar that functions as a precursor for signaling molecules such 4 as phosphoinositides and inositol polyphosphates, which are involved in the regulation of 5 important biological processes such as energy metabolism, environmental stress 6 response, phosphate signaling, among others. Given its role in a myriad of signaling 7 pathways, regulation of inositol synthesis is essential for cellular homeostasis. In budding 8 yeast, transcription of genes involved in inositol metabolism is regulated by the 9 transcriptional repressor Opi1, which repress transcription of genes containing cis-acting 10 inositol-sensitive upstream activation sequences (UAS INO ). Upon genotoxic stress, cells 11 activate the DNA Damage Response (DDR) to coordinate DNA repair and cell cycle 12 progression. It has been proposed that inositol containing molecules might act as 13 modulators of the DDR, but evidences are still scarce. Herein, we report that opi1  cells 14 fail to downregulate the inositol phosphate pathway leading to sensitivity to genotoxins 15 and replication defects. Moreover, cells lacking Opi1 show decreased gamma-H2A levels 16 which might indicate that Opi1 contributes to the activation of the DDR kinases Mec1/Tel1 17 (ATR/ATM in mammals). Importantly, we show that deletion of the inositol pyrophosphate 18 kinase Kcs1 (IP6K1/2/3 in mammals), which leads to inhibition of inositol pyrophosphate 19 synthesis, rescues the MMS sensitivity and replication defects of opi1  cells. Further, 20 overexpression of Kcs1 recapitulates the MMS sensitivity of cells lacking Opi1. Therefore, 21 we propose that cells must downregulate inositol pyrophosphate synthesis during 22 replication stress in order to trigger an effective DNA Damage Response.