Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Study of Co-Renamed Identifiers

Background: The renaming of program identifiers is the most common refactoring operation. Because some identifiers are related to each other, developers may need to rename related identifiers together. Aims: To understand how developers rename multiple identifiers simultaneously, it is necessary to consider the relationships between identifiers in the program and the brief matching for non-identical but semantically similar identifiers. Method: We investigate the relationships between co-renamed identifiers and identify the types of their relationships that contribute to improving the recommendation using more than 1M of renaming instances collected from the histories of opensource software projects. We also evaluate and compare the impact of co-renaming and the relationships between identifiers when inflections occur in the words in identifiers are taken into account. Results: We revealed several relationships of identifiers that are frequently found in the co-renamed identifiers, such as the identifiers of methods in the same class or an identifier defining a variable and another used for initializing the variable, depending on the type of the renamed identifiers. Additionally, the consideration of inflections did not affect the tendency of the relationships. Conclusion: These results suggest an approach that prioritizes the identifiers to be recommended depending on their types and the type of the renamed identifier.
Wrapping Cycles in Delaunay Complexes: Bridging Persistent Homology and Discrete Morse Theory

We study the connection between discrete Morse theory and persistent homology in the context of shape reconstruction methods. Specifically, we consider the construction of Wrap complexes, introduced by Edelsbrunner as a subcomplex of the Delaunay complex, and the construction of lexicographic optimal homologous cycles, also considered by Cohen-Steiner, Lieutier, and Vuillamy in a similar setting. We show that for any cycle in a Delaunay complex for a given radius parameter, the lexicographically optimal homologous cycle is supported on the Wrap complex for the same parameter, thereby establishing a close connection between the two methods. We obtain this result by establishing a fundamental connection between reduction of cycles in the computation of persistent homology and gradient flows in the algebraic generalization of discrete Morse theory.
IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection

Due to the lack of depth information of images and poor detection accuracy in monocular 3D object detection, we proposed the instance depth for multi-scale monocular 3D object detection method. Firstly, to enhance the model's processing ability for different scale targets, a multi-scale perception module based on dilated convolution is designed, and the depth features containing multi-scale information are re-refined from both spatial and channel directions considering the inconsistency between feature maps of different scales. Firstly, we designed a multi-scale perception module based on dilated convolution to enhance the model's processing ability for different scale targets. The depth features containing multi-scale information are re-refined from spatial and channel directions considering the inconsistency between feature maps of different scales. Secondly, so as to make the model obtain better 3D perception, this paper proposed to use the instance depth information as an auxiliary learning task to enhance the spatial depth feature of the 3D target and use the sparse instance depth to supervise the auxiliary task. Finally, by verifying the proposed algorithm on the KITTI test set and evaluation set, the experimental results show that compared with the baseline method, the proposed method improves by 5.27\% in AP40 in the car category, effectively improving the detection performance of the monocular 3D object detection algorithm.
A low-rank algorithm for solving Lyapunov operator $\varphi$-functions within the matrix-valued exponential integrators

In this work we develop a low-rank algorithm for the computation of low-rank approximations to the large-scale Lyapunov operator ϕ -functions. Such computations constitute the important ingredient to the implementation of matrix-valued exponential integrators for large-scale sti ﬀ matrix di ﬀ erential equations where the (approximate) solutions are of low rank. We evaluate the approximate solutions of LDL T -type based on a scaling and recursive procedure. The key parameters of the method are determined using a quasi-backward error bound combined with the computational cost. Numerical results demonstrate that our method can be used as a basis of matrix-valued exponential integrators for solving large-scale di ﬀ erential Lyapunov equations and di ﬀ erential Riccati equations.
Investigations on the Influence of Combined Inter-Aural Cue Distortions on Overall Audio Quality

There is a considerable interest in developing algorithms that can predict audio quality of perceptually coded signals to avoid the cost of extensive listening tests during development time. While many established algorithms for predicting the perceived quality of signals with monaural (timbral) distortions are available (PEAQ, POLQA), predicting the quality degradation of stereo and multi-channel spatial signals is still considered a challenge. Audio quality degradation arising from spatial distortions is usually measured in terms of well known inter-aural cue distortion measures such as Inter-aural Level Difference Distortions (ILDD), Inter-aural Time Difference Distortions (ITDD) and Inter-aural Cross Correlation Distortions (IACCD). However, the extent to which their interaction influences the overall audio quality degradation in complex signals as expressed - for example - in a multiple stimuli test is not yet thoroughly studied. We propose a systematic approach that introduces controlled combinations of spatial distortions on a representative set of signals and evaluates their influence on overall perceived quality degradation by analyzing listening test scores over said signals. From this study we derive guidelines for designing meaningful distortion measures that consider inter-aural cue distortion interactions.
QCD mesonic screening masses and restoration of chiral symmetry at high T

We present a strategy to study QCD non-perturbatively on the lattice at very high temperatures. This strategy exploits a non-perturbative, finite-volume, definition of the strong coupling constant to renormalize the theory. As a first application we compute the flavor non-singlet mesonic screening masses in a wide range of temperature, from $T\sim 1 $ GeV up to $\sim 160 $ GeV with three flavors in the chiral limit of QCD. Our results show very interesting features of the screening spectrum at very high temperatures. On one hand the mass splitting between the vector and the pseudoscalar screening masses is clearly visible up to the electroweak scale and cannot be explained by the known 1-loop perturbative result. On the other hand the restoration of chiral symmetry manifests itself through the degeneracy of the pseudoscalar and the scalar channels and of the vector and the axial-vector ones. This degeneracy pattern is the one expected by Ward identities associated to the presence of chiral symmetry.
Parametric modal regression with error in covariates.

An inference procedure is proposed to provide consistent estimators of parameters in a modal regression model with a covariate prone to measurement error. A score-based diagnostic tool exploiting parametric bootstrap is developed to assess adequacy of parametric assumptions imposed on the regression model. The proposed estimation method and diagnostic tool are applied to synthetic data generated from simulation experiments and data from real-world applications to demonstrate their implementation and performance. These empirical examples illustrate the importance of adequately accounting for measurement error in the error-prone covariate when inferring the association between a response and covariates based on a modal regression model that is especially suitable for skewed and heavy-tailed response data.
Isotropic Mie resonance-based metamaterial perfect absorber

An isotropic Mie resonance-based metamaterial perfect absorber with near unity absorbance is experimentally and numerically demonstrated. The metamaterial is constructed with an array of dielectric cubes and a metallic ground plane. The absorption peak of the metamaterial perfect absorber is dependent on the permittivity and the side length of the dielectric cubes. A good agreement between experimental and simulated result at X band absorption is achieved.
Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems

—Massive multiple-input multiple-output (MIMO) communications using low-resolution analog-to-digital converters (ADCs) is a promising technology for providing high spectral and energy efﬁciency with affordable hardware cost and power consumption. However, the use of low-resolution ADCs requires special signal processing methods for channel estimation and data detection since the resulting system is severely non-linear. This paper proposes joint channel estimation and data detection methods for massive MIMO systems with low-resolution ADCs based on the variational Bayes (VB) inference framework. We ﬁrst derive matched-ﬁlter quantized VB (MF-QVB) and linear minimum mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the channel state information (CSI) is available. Then we extend these methods to the joint channel estimation and data detection (JED) problem and propose two methods we refer to as MF-QVB-JED and LMMSE-QVB-JED. Unlike conventional VB-based detection methods that assume knowledge of the second-order statistics of the additive noise, we propose to ﬂoat the noise variance/covariance matrix as an unknown random variable that is used to account for both the noise and the residual inter-user interference. We also present practical aspects of the QVB framework to improve its implementation stability. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also signiﬁcantly outperform existing methods.
Space-Time- and Frequency- Spreading for Interference Minimization in Dense IoT

In this article, we propose a space spreading-assisted framework that leverages either {time or frequency diversity or both to reduce interference and signal loss} owing to channel impairments and facilitate the efficient operation of large-scale dense Internet-of-Things (IoT). Our approach employs dispersion of data-streams transmitted from individual IoT devices over indexed space-time (ST), space-frequency (SF) or space-time-frequency (STF) blocks. As a result, no two devices transmit on the same block; only one is activated while the rest of the devices in the network is silent, thereby minimizing possibility of interference on the transmit side. On the receive side, multiple-antenna array ameliorates performance in presence of channel impairments while exploiting array-processing gain. As interference due to superposition of multiple data-streams is killed at its root, no extra energy is wasted in fighting interference and other impairments, thereby enabling energy-efficient transmission from multiple devices over multiple access channel (MAC). To validate the proposed concept, we simulate the performance of the framework against dense IoT networks deployed in generalized indoor and outdoor scenarios in terms of probability of signal outage. Results demonstrate that our conceptualized framework benefits from interference-free transmission as well as enhancement in overall system performance.
Distribution Fitting for Combating Mode Collapse in GANs

—Mode collapse is still a major unsolved problem in generative adversarial networks. In this work, we analyze the causes of mode collapse from a new perspective. Due to the nonuniform sampling in the training process, some sub-distributions can be missed while sampling data. Therefore, the GAN objective can reach the minimum when the generated distribution is not the same as the real one. To alleviate the problem, we propose a global distribution ﬁtting (GDF) method by a penalty term to constrain generated data distribution. On the basis of not changing the global minimum of the GAN objective, GDF will make it harder to reach the minimum value when the generated distribution is not the same as the real one. Furthermore, we also propose a local distribution ﬁtting (LDF) method to cope with the situation that the real distribution is unknown. Experiments on several benchmarks demonstrate the effectiveness and competitive performance of GDF and LDF.
On the Equivalence of Automatic and Symbolic Differentiation

We show that reverse mode automatic differentiation and symbolic differentiation are equivalent in the sense that they both perform the same operations when computing derivatives. This is in stark contrast to the common claim that they are substantially different. The difference is often illustrated by claiming that symbolic differentiation suffers from"expression swell"whereas automatic differentiation does not. Here, we show that this statement is not true. "Expression swell"refers to the phenomenon of a much larger representation of the derivative as opposed to the representation of the original function.
Direct measurement of basilar membrane stiffness in the guinea pig

Measurements of basilar membrane stiffness were made in the 0.8–2.3‐mm region of the guinea pig cochlea, within 1‐h post mortem. A tungsten wire probe glued to a piezoelectric crystal was used to statically displace the scala tympani side of the basilar membrane. The extirpated temporal bone was fixed to a barium titanate crystal and vibrated at 40 Hz with a displacement of 10 nm (rms). The filtered signal from the piezoelectric crystal was analyzed with a lock‐in amplifier and the stiffness calculated as a function of static displacement. The resulting curve was flat for a displacement of 1–3 μm, whereupon it increased, exhibiting two distinct sections, until piercing occurred at 50–60 μm. For measurements made at the membrane center the plateau value of the stiffness decreased exponentially with distance from the stapes with a space constant of 1.7±0.8 mm, in agreement with theory. The radial flexural rigidity and the effective membrane thickness were estimated from the first 10 μm of these curves; the .
A Network Theory Investigation into the Altered Resting State Functional Connectivity in Attention-Deficit Hyperactivity Disorder

In the last two decades, functional magnetic resonance imaging (fMRI) has emerged as one of the most effective technologies in clinical research of the human brain. fMRI allows researchers to study healthy and pathological brains while they perform various neuropsychological functions. Beyond task-related activations, the human brain has some intrinsic activity at a task-negative (resting) state that surprisingly consumes a lot of energy to support communication among neurons. Recent neuroimaging research has also seen an increase in modelling and analysing brain activity in terms of a graph or network. Since graph models facilitate a systems-theoretic explanation of the brain, they have become increasingly relevant with advances in network science and the popularization of complex systems theory. The purpose of this study is to look into the abnormalities in resting brain functions in adults with Attention Deficit Hyperactivity Disorder (ADHD). The primary goal is to investigate resting-state functional connectivity (FC), which can be construed as a significant temporal coincidence in blood-oxygen-level dependent (BOLD) signals between functionally related brain regions in the absence of any stimulus or task. When compared to healthy controls, ADHD patients have lower average connectivity in the Supramarginal Gyrus and Superior Parietal Lobule, but higher connectivity in the Lateral Occipital Cortex and Inferior Temporal Gyrus. We also hypothesise that the network organisation of several default mode and dorsal attention regions is abnormal in ADHD patients.
Explore of exfoliable multifunctional high-k two-dimensional oxides

As the continuing down-scaling of field-effect transistors (FETs) in more-than-Moore integrated circuits, finding new functional two-dimensional (2D) materials with a higher dielectric constant (high- k ) serve as gate dielectrics is critical. Here, we identify dozens of binary 2D oxides by screening potentially exfoliable bulk metal oxides despite of their non-layered structures followed by simulation of the exfoliation process. For dynamically stable materials, we fully characterize their static dielectric constants and electronic structures, among which GeO 2 (011)/(101)/(1-11) 2D oxides exhibit unusually high k values (85-99), being much higher than the k of the currently highly regarded 2D dielectrics CaF 2 (k ~6) and β-Bi 2 SeO 5 (k ~22), together with band gap of 3.3 eV. We further design 2D high- k oxides/2D semiconductors (such as MoS 2 ) heterostructures, and determine by DFT calculations whether they can form Van der Waals interfaces to evaluate their compatibility as gate dielectrics in 2D FETs. In addition to dielectric properties, we also explore magnetic and mechanical properties of potentially exfoliable 2D oxides, revealing a number of functional materials that can be studied experimentally, notably including ferromagnetic half semiconductors, non-magnetic spintronic materials, flexible high- k 2D oxides, and auxetic monolayers.
Stability of Cnoidal Waves for the Damped Nonlinear Schr\"odinger Equation

We consider the cubic nonlinear Schr\"odinger (NLS) equation with a linear damping on the one dimensional torus and we investigate the stability of some solitary wave profiles within the dissipative dynamics. The undamped cubic NLS equation is well known to admit a family of periodic waves given by Jacobi elliptic functions of cnoidal type. We show that the family of cnoidal waves is orbitally stable. More precisely, by considering a sufficiently small perturbation of a given cnoidal wave at initial time, the evolution will always remain close (up to symmetries of the equation) to the cnoidal wave whose mass is modulated according to the dissipative dynamics. This result extends the concept of orbital stability to this non-Hamiltonian evolution. Since cnoidal waves are not exact solutions to the damped NLS, the perturbation is forced away from the family of solitary wave profiles. In order to control this secular growth of the error, we find a first order approximation of the solitary wave that takes into account the dissipative term. Then we use a suitable, exponentially decreasing Lyapunov functional that controls the $H^1$-norm of the perturbation around the approximated solitons.
Triglobal resolvent analysis of swept-wing wakes

Abstract Through triglobal resolvent analysis, we reveal the effects of wing tip and sweep angle on laminar separated wakes over swept wings. For the present study, we consider wings with semi-aspect ratios from $1$ to $4$, sweep angles from $0^\circ$ to $45^\circ$ and angles of attack of $20^\circ$ and $30^\circ$ at a chord-based Reynolds number of $400$ and a Mach number of $0.1$. Using direct numerical simulations, we observe that unswept wings develop vortex shedding near the wing root with a quasi-steady tip vortex. For swept wings, vortex shedding is seen near the wing tip for low sweep angles, while the wakes are steady for wings with high sweep angles. To gain further insights into the mechanisms of flow unsteadiness, triglobal resolvent analysis is used to identify the optimal spatial input–output mode pairs and the associated gains over a range of frequencies. The three-dimensional forcing and response modes reveal that harmonic fluctuations are directed towards the root for unswept wings and towards the wing tip for swept wings. The overlapping region of the forcing–response mode pairs uncovers triglobal resolvent wavemakers associated with self-sustained unsteady wakes of swept wings. Furthermore, we show that for low-aspect-ratio wings optimal perturbations develop globally over the entire wingspan. The present study uncovers physical insights on the effects of tip and sweep on the growth of optimal harmonic perturbations and the wake dynamics of separated flows over swept wings.
Open RAN Security: Challenges and Opportunities

Open RAN (ORAN, O-RAN) represents a novel industry-level standard for RAN (Radio Access Network), which defines interfaces that support inter-operation between vendors' equipment and offer network flexibility at a lower cost. Open RAN integrates the benefits and advancements of network softwarization and Artificial Intelligence to enhance the operation of RAN devices and operations. Open RAN offers new possibilities so that different stakeholders can develop the RAN solution in this open ecosystem. However, the benefits of Open RAN bring new security and privacy challenges. As Open RAN offers an entirely different RAN configuration than what exists today, it could lead to severe security and privacy issues if mismanaged, and stakeholders are understandably taking a cautious approach towards the security of Open RAN deployment. In particular, this paper provides a deep analysis of the security and privacy risks and challenges associated with Open RAN architecture. Then, it discusses possible security and privacy solutions to secure Open RAN architecture and presents relevant security standardization efforts relevant to Open RAN security. Finally, we discuss how Open RAN can be used to deploy more advanced security and privacy solutions in 5G and beyond RAN.
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics

Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks–a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies.
On the fractal dimensions for deterministic and random substitution graph systems

In this paper, we construct and study the fractal-like graphs by the method of substitution, called deterministic and random substitution graph systems. Though the idea of substitution is common in terms of fractal geometry and dynamical systems, the analysis of fractals regarding graph theory remains an immature ﬁeld. This paper mainly includes two focuses, the ﬁrst of which, for deterministic substitution graph systems, the box-counting dimension and Hausdorﬀ dimension are analytically coincidental by explicit formulae. Another focus is, for random substitution graph systems, that we prove almost surely every graph limit has the same box-counting and Hausdorﬀ dimensions numerically by their Lyapunov exponents.