Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Defective Lines Using a Model-Agnostic Technique

Defect prediction models are proposed to help a team prioritize the areas of source code files that need Software Quality Assurance (SQA) based on the likelihood of having defects. However, developers may waste their unnecessary effort on the whole file while only a small fraction of its source code lines are defective. Indeed, we find that as little as 1-3 percent of lines of a file are defective. Hence, in this work, we propose a novel framework (called Line-DP) to identify defective lines using a model-agnostic technique, i.e., an Explainable AI technique that provides information why the model makes such a prediction. Broadly speaking, our Line-DP first builds a file-level defect model using code token features. Then, our Line-DP uses a state-of-the-art model-agnostic technique (i.e., LIME) to identify risky tokens, i.e., code tokens that lead the file-level defect model to predict that the file will be defective. Then, the lines that contain risky tokens are predicted as defective lines. Through a case study of 32 releases of nine Java open source systems, our evaluation results show that our Line-DP achieves an average recall of 0.61, a false alarm rate of 0.47, a top 20%LOC recall of 0.27, and an initial false alarm of 16, which are statistically better than six baseline approaches. Our evaluation shows that our Line-DP requires an average computation time of 10 seconds including model construction and defective line identification time. In addition, we find that 63 percent of defective lines that can be identified by our Line-DP are related to common defects (e.g., argument change, condition change). These results suggest that our Line-DP can effectively identify defective lines that contain common defects while requiring a smaller amount of inspection effort and a manageable computation cost. The contribution of this paper builds an important step towards line-level defect prediction by leveraging a model-agnostic technique.
The VIMOS Ultra Deep Survey: The reversal of the star-formation rate - density relation at 2 < z < 5

Utilizing spectroscopic observations taken for the VIMOS Ultra-Deep Survey (VUDS), new observations from Keck/DEIMOS, and publicly available observations of large samples of star-forming galaxies, we report here on the relationship between the star formation rate (SFR) and the local environment ($\delta_{gal}$) of galaxies in the early universe ($2<z<5$). Unlike what is observed at lower redshifts ($z<2$), we observe a definite, nearly monotonic increase in the average SFR with increasing galaxy overdensity over more than an order of magnitude in $\delta_{gal}$. The robustness of this trend is quantified by accounting for both uncertainties in our measurements and galaxy populations that are either underrepresented or not present in our sample finding that the trend remains significant under all circumstances. This trend appears to be primarily driven by the fractional increase of galaxies in high density environments that are more massive in their stellar content and are forming stars at a higher rate than their less massive counterparts. We find that, even after stellar mass effects are accounted for, there remains a weak but significant SFR-$\delta_{gal}$ trend in our sample implying that additional environmentally-related processes are helping to drive this trend. We also find clear evidence that the average SFR of galaxies in the densest environments increases with increasing redshift. These results lend themselves to a picture in which massive gas-rich galaxies coalesce into proto-cluster environments at $z\sim3$, interact with other galaxies or with a forming large-scale medium, subsequently using or losing most of their gas in the process, and begin to seed the nascent red sequence that is present in clusters at slightly lower redshifts.
Region Comparison Network for Interpretable Few-shot Image Classification

While deep learning has been successfully applied to many real-world computer vision tasks, training robust classifiers usually requires a large amount of well-labeled data. However, the annotation is often expensive and time-consuming. Few-shot image classification has thus been proposed to effectively use only a limited number of labeled examples to train models for new classes. Recent works based on transferable metric learning methods have achieved promising classification performance through learning the similarity between the features of samples from the query and support sets. However, rare of them explicitly considers the model interpretability, which can actually be revealed during the training phase. For that, in this work, we propose a metric learning based method named Region Comparison Network (RCN), which is able to reveal how few-shot learning works as in a neural network as well as to find out specific regions that are related to each other in images coming from the query and support sets. Moreover, we also present a visualization strategy named Region Activation Mapping (RAM) to intuitively explain what our method has learned by visualizing intermediate variables in our network. We also present a new way to generalize the interpretability from the level of tasks to categories, which can also be viewed as a method to find the prototypical parts for supporting the final decision of our RCN. Extensive experiments on four benchmark datasets clearly show the effectiveness of our method over existing baselines.
Machine Learning Towards Enabling Spectrum-as-a-Service Dynamic Sharing

The growth in wireless broadband users, devices, and novel applications has led to a significant increase in the demand for new radio frequency spectrum. This is expected to grow even further given the projection that the global traffic per year will reach 4.8 zettabytes by 2022. Moreover, it is projected that the number of Internet users will reach 4.8 billion and the number of connected devices will be close 28.5 billion devices. However, due to the spectrum being mostly allocated and divided, providing more spectrum to expand existing services or offer new ones has become more challenging. To address this, spectrum sharing has been proposed as a potential solution to improve spectrum utilization efficiency. Adopting effective and efficient spectrum sharing mechanisms is in itself a challenging task given the multitude of levels and techniques that can be integrated to enable it. To that end, this paper provides an overview of the different spectrum sharing levels and techniques that have been proposed in the literature. Moreover, it discusses the potential of adopting dynamic sharing mechanisms by offering Spectrum-as-a-Service architecture. Furthermore, it describes the potential role of machine learning models in facilitating the automated and efficient dynamic sharing of the spectrum and offering Spectrum-as-a-Service.
CP
 asymmetries in the rare top decays 
t→cγ
 and 
t→cg

The rare radiative flavour changing top decays $t\to c\gamma$ and $t\to cg$ (and the even rarer $t\to u\gamma$ and $t\to ug$) have been processes of interest for decades as they offer a key probe for studying top quark properties. However an explicit analytical study of the branching ratios and $CP$ asymmetries resulting from the non-unitarity effect of the Cabibbo-Kobayashi-Maskawa (CKM) matrix from these loop level processes has thus far evaded attention. In this work, we provide the formulation for the $CP$ asymmetry resulting from the total kinetic contribution of the loop integrals and their imaginary parts, as well as an updated numerical computation of the predicted Standard Model (SM) branching fractions. These rare processes are suppressed in the SM by the Glashow-Iliopoulos-Maiani (GIM) mechanism. However, the results presented here can easily be exported for use in minimal extensions of the SM including vector-like quarks or in Two-Higgs-Doublet models where radiative fermionic decay processes can be enhanced relative to the SM by several orders of magnitude. Such processes provide an experimentally clean signature for new fundamental physics and can potentially be tested by current collider experiments. These topical beyond the SM theories are an elegant means to provide improved global fits to the latest results emerging from flavour physics, CKM and precision electroweak measurements.
Statistical Study of Gamma-Ray Bursts with Jet Break Features in Multiwavelength Afterglow Emissions

It is generally supposed that a transition from the normal decay phase (decay slope ∼−1) to a steeper phase (decay slope ∼−2) could be suggested as a jet break. The jet opening angle θjet is then calculated from the jet break time of the afterglow light curve. This allows the derivation of the collimation-corrected energy Ejet of those gamma-ray bursts (GRBs). We extensively searched for the GRBs with jet break features from multiwavelength afterglow light curves, and 138 GRBs with significant breaks were collected. The jet break times of those GRBs mainly range from 1000 to 106 s, and the distribution of the collimation-corrected energy Ejet peaks at ∼1050 erg. We also confirmed the Eγ,iso−Ep,i, Ejet−Ep,i and Eγ,iso−θjet relations, and found Eγ,iso−Tj,z−Ep,i relation remains tight with more multiwavelength data. This tight Eγ,iso−Tj,z−Ep,i relation is also conformed by different groups of our selected GRBs in the paper. In addition, another two new and tighter correlations among Ejet−Tj,z−Ep,i are well confirmed for different circumburst mediums in this paper. We suggest that those tight three-parameter correlations are more physical, and could be widely applied to constrain the cosmological parameters.
GPU Parallel Computation of Morse-Smale Complexes

The Morse-Smale complex is a well studied topological structure that represents the gradient flow behavior of a scalar function. It supports multi-scale topological analysis and visualization of large scientific data. Its computation poses significant algorithmic challenges when considering large scale data and increased feature complexity. Several parallel algorithms have been proposed towards the fast computation of the 3D Morse-Smale complex. The non-trivial structure of the saddle-saddle connections are not amenable to parallel computation. This paper describes a fine grained parallel method for computing the Morse-Smale complex that is implemented on a GPU. The saddle-saddle reachability is first determined via a transformation into a sequence of vector operations followed by the path traversal, which is achieved via a sequence of matrix operations. Computational experiments show that the method achieves up to 7 × speedup over current shared memory implementations.
Quantum receiver for quadrature phase-shift keying at the single photon level

Quantum enhanced receivers are endowed with resources to achieve higher sensitivities than conventional technologies. For application in optical communications, they provide improved discriminatory capabilities for multiple non-orthogonal quantum states. In this work, we propose and experimentally demonstrate a new decoding scheme for quadrature phase-shift encoded signals. Our receiver surpasses the standard quantum limit and outperforms all previously known non-adaptive detectors at low input powers. Unlike existing approaches, our receiver only exploits auxiliary coherent-state fields, linear optics, and on-off photo-detection. This circumvents the requirement for challenging feed-forward operations that limit communication transmission rates and can be readily implemented with current technology.
Discovery of thermonuclear (Type I) X-ray bursts in the X-ray binary Swift J1858.6–0814 observed with NICER and NuSTAR

Swift J1858.6–0814 is a recently discovered X-ray binary notable for extremely strong variability (by factors of >100 in soft X-rays) in its discovery state. We present the detection of five thermonuclear (Type I) X-ray bursts from Swift J1858.6–0814, implying that the compact object in the system is a neutron star (NS). Some of the bursts show photospheric radius expansion, so their peak flux can be used to estimate the distance to the system. The peak luminosity, and hence distance, can depend on several system parameters; for the most likely values, a high inclination and a helium atmosphere, $D=12. 8_{-0.6}^{+0.8}$ kpc, although systematic effects allow a conservative range of 9–18 kpc. Before one burst, we detect a QPO at 9.6 ± 0.5 mHz with a fractional rms amplitude of 2.2 ± 0.2 per cent (0.5–10 keV), likely due to marginally stable burning of helium; similar oscillations may be present before the other bursts but the light curves are not long enough to allow their detection. We also search for burst oscillations but do not detect any, with an upper limit in the best case of 15 per cent fractional amplitude (over 1–8 keV). Finally, we discuss the implications of the NS accretor and this distance on other inferences which have been made about the system. In particular, we find that Swift J1858.6–0814 was observed at super-Eddington luminosities at least during bright flares during the variable stage of its outburst.
Spectrotemporal Analysis of a Sample of Bursts from FRB 121102

FRB 121102 was the first Fast Radio Burst (FRB) that was shown to repeat. Since its discovery in 2012, more than two hundred bursts have been detected from the source. These bursts exhibit a diverse range of spectral and temporal characteristics and many questions about their origin and form remain unanswered. Here, we present a sample of radio bursts from FRB 121102 detected using the Lovell telescope at Jodrell Bank Observatory. We show four examples of bursts that show peculiar spectro-temporal characteristics and compare them with properties of bursts of FRB 121102 detected at other observatories. We report on a precursor burst that is separated by just 17 ms from the main burst, the shortest reported separation between two individual bursts to date. We also provide access to data for all the detections of FRB 121102 in this campaign.
Traces of some weighted function spaces and related non‐standard real interpolation of Besov spaces

We study traces of weighted Triebel–Lizorkin spaces Fp,qs(Rn,w)$F^s_{p,q}(\mathbb {R}^n,w)$ on hyperplanes Rn−k$\mathbb {R}^{n-k}$ , where the weight is of Muckenhoupt type. We concentrate on the example weight wα(x)=|xn|α$w_\alpha (x) = {\big\vert x_n\big\vert }^\alpha$ when |xn|≤1$\big\vert x_n\big\vert \le 1$ , x∈Rn$x\in \mathbb {R}^n$ , and wα(x)=1$w_\alpha (x)=1$ otherwise, where α>−1$\alpha >-1$ . Here we use some refined atomic decomposition argument as well as an appropriate wavelet representation in corresponding (unweighted) Besov spaces. The second main outcome is the description of the real interpolation space (Bp1,p1s1(Rn−k),Bp2,p2s2(Rn−k))θ,r$\big (B^{s_1}_{p_1,p_1}\big (\mathbb {R}^{n-k}\big ), B^{s_2}_{p_2,p_2}{\big (\mathbb {R}^{n-k}\big )\big )}_{\theta ,r}$ , 00$s>0$ sufficiently large, 0
Dynamic Max-Consensus and Size Estimation of Anonymous Multi-Agent Networks

In this paper we propose a novel consensus protocol for discrete-time multi-agent systems (MAS), which solves the dynamic consensus problem on the max value, i.e., the dynamic max-consensus problem. In the dynamic max-consensus problem to each agent is fed a an exogenous reference signal, the objective of each agent is to estimate the instantaneous and time-varying value of the maximum among the signals fed to the network, by exploiting only local and anonymous interactions among the agents. The absolute and relative tracking error of the proposed distributed control protocol is theoretically characterized and is shown to be bounded and by tuning its parameters it is possible to trade-off convergence time for steady-state error. The dynamic Max-consensus algorithm is then applied to solve the distributed size estimation problem in a dynamic setting where the size of the network is time-varying during the execution of the estimation algorithm. Numerical simulations are provided to corroborate the theoretical analysis.
TanhSoft - a family of activation functions combining Tanh and Softplus

Deep learning at its core, contains functions that are composition of a linear transformation with a non-linear function known as activation function. In past few years, there is an increasing interest in construction of novel activation functions resulting in better learning. In this work, we propose a family of novel activation functions, namely TanhSoft, with four undetermined hyper-parameters of the form tanh({\alpha}x+{\beta}e^{{\gamma}x})ln({\delta}+e^x) and tune these hyper-parameters to obtain activation functions which are shown to outperform several well known activation functions. For instance, replacing ReLU with xtanh(0.6e^x)improves top-1 classification accuracy on CIFAR-10 by 0.46% for DenseNet-169 and 0.7% for Inception-v3 while with tanh(0.87x)ln(1 +e^x) top-1 classification accuracy on CIFAR-100 improves by 1.24% for DenseNet-169 and 2.57% for SimpleNet model.
An inverse potential problem for subdiffusion: stability and reconstruction

In this work, we study the inverse problem of recovering a potential coefficient in the subdiffusion model, which involves a Djrbashian–Caputo derivative of order α ∈ (0, 1) in time, from the terminal data. We prove that the inverse problem is locally Lipschitz for small terminal time, under certain conditions on the initial data. This result extends the result in [] for the standard parabolic case to the fractional case. The analysis relies on refined properties of two-parameter Mittag–Leffler functions, e.g., complete monotonicity and asymptotics. Further, we develop an efficient and easy-to-implement algorithm for numerically recovering the coefficient based on (preconditioned) fixed point iteration and Anderson acceleration. The efficiency and accuracy of the algorithm is illustrated with several numerical examples.
Symmetry-adapted tight-binding electronic structure analysis of carbon nanotubes with defects, kinks, twist, and stretch

In this paper, a symmetry-adapted method is applied to examine the influence of deformation and defects on the electronic structure and band structure in carbon nanotubes. First, the symmetry-adapted approach is used to develop the analog of Bloch waves. Building on this, the technique of perfectly matched layers is applied to develop a method to truncate the computational domain of electronic structure calculations without spurious size effects. This provides an efficient and accurate numerical approach to compute the electronic structure and electromechanics of defects in nanotubes. The computational method is applied to study the effect of twist, stretch, and bending, with and without various types of defects, on the band structure of nanotubes. Specifically, the effect of stretch and twist on band structure in defect-free conducting and semiconducting nanotubes is examined, and the interaction with vacancy defects is elucidated. Next, the effect of localized bending or kinking on the electronic structure is studied. Finally, the paper examines the effect of 5–8–5 Stone–Wales defects. In all of these settings, the perfectly matched layer method enables the calculation of localized non-propagating defect modes with energies in the bandgap of the defect-free nanotube.
Highly Accurate CNN Inference Using Approximate Activation Functions over Homomorphic Encryption

In the big data era, cloud-based machine learning as a service (MLaaS) has attracted considerable attention. However, when handling sensitive data, such as financial and medical data, a privacy issue emerges, because the cloud server can access clients’ raw data. A common method of handling sensitive data in the cloud uses homomorphic encryption, which allows computation over encrypted data without decryption. Previous research adopted a low-degree polynomial mapping function, such as the square function, for data classification. However, this technique results in low classification accuracy. This study seeks to improve the classification accuracy for inference processing in a convolutional neural network (CNN) while using homomorphic encryption. We apply various orders of the polynomial approximations of Google’s Swish and ReLU activation functions. We also adopt batch normalization to normalize the inputs for the approximated activation functions to fit the input range to minimize the error. We implemented CNN inference labeling over homomorphic encryption using the Microsoft’s Simple Encrypted Arithmetic Library (SEAL) for the Cheon–Kim–Kim–Song (CKKS) scheme. The experimental evaluations confirmed classification accuracies of 99.29% and 81.06% for MNIST and CIFAR-10, respectively, which entails 0.11% and 4.69% improvements, respectively, over previous methods.
The COMBS Survey - II. Distinguishing the metal-poor bulge from the halo interlopers

Abstract The metal-poor stars in the bulge are important relics of the Milky Way’s formation history, as simulations predict that they are some of the oldest stars in the Galaxy. In order to determine if they are truly ancient stars, we must understand their origins. Currently, it is unclear if the metal-poor stars in the bulge ([Fe/H] l -1 dex) are merely halo interlopers, a unique accreted population, part of the boxy/peanut-shaped bulge or a classical bulge population. In this work, we use spectra from the VLT/FLAMES spectrograph to obtain metallicity estimates using the Ca-II triplet of 473 bulge stars (187 of which have [Fe/H]l-1 dex), targeted using SkyMapper photometry. We also use Gaia DR2 data to infer the Galactic positions and velocities along with orbital properties for 523 stars. We employ a probabilistic orbit analysis and find that about half of our sample has a g 50% probability of being bound to the bulge, and half are halo interlopers. We also see that the occurrence rate of halo interlopers increases steadily with decreasing metallicity across the full range of our sample (-3 l [Fe/H] l 0.5). Our examination of the kinematics of the confined compared to the unbound stars indicates the metal-poor bulge comprises at least two populations; those confined to the boxy/peanut bulge and halo stars passing through the inner galaxy. We conclude that an orbital analysis approach, as we have employed, is important to understand the composite nature of the metal-poor stars in the inner region.
A sea of tides: star formation and the central-satellite dichotomy in a continuum of tidal environments

The environment-dependent bimodality of the distribution of stellar mass ($M_\ast$) and specific star formation rate (sSFR) of galaxies, and its explanation in terms of the central-satellite dichotomy, form a cornerstone of our current understanding of galaxy evolution in the hierarchical structure formation paradigm. We revisit this framework in the IllustrisTNG simulation in the context of the most extreme local tidal anisotropy $\alpha_{\rm peak}$ experienced by each galaxy over cosmic time, which is an excellent proxy for environmental influence. We show that, while sharing a common monotonic $M_\ast$-$v_{\rm peak}$ relation, central, satellite and `splashback' galaxies define a hierarchy of increasing $\alpha_{\rm peak}$. We also find that the sSFR of objects in small haloes unaffected by feedback from an active nucleus typically decreases with increasing $\alpha_{\rm peak}$. Our results support an alternate viewpoint in which a galaxy can be identified by the value of $\alpha_{\rm peak}$; i.e., rather than being placed on the central-satellite dichotomy, a galaxy is better classified by its location in a continuum of tidal environments. This conceptual shift can potentially yield a more robust understanding of galaxy evolution and the galaxy-dark matter connection, e.g., in accurately modelling subtle effects such as sSFR-induced secondary clustering.