Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-based Repair for Asynchronous Wait Flaky Tests in Web Testing

Asynchronous waits are one of the most prevalent root causes of flaky tests and a major time-influential factor of web application testing. To investigate the characteristics of asynchronous wait flaky tests and their fixes in web testing, we build a dataset of 49 reproducible flaky tests, from 26 open-source projects, caused by asynchronous waits, along with their corresponding developer-written fixes. Our study of these flaky tests reveals that in approximately 63% of them (31 out of 49), developers addressed Asynchronous Wait flaky tests by adapting the wait time, even for cases where the root causes lie elsewhere. Based on this finding, we propose TRaf, an automated time-based repair method for asynchronous wait flaky tests in web applications. TRaf tackles the flakiness issues by suggesting a proper waiting time for each asynchronous call in a web application, using code similarity and past change history. The core insight is that as developers often make similar mistakes more than once, hints for the efficient wait time exist in the current or past codebase. Our analysis shows that TRaf can suggest a shorter wait time to resolve the test flakiness compared to developer-written fixes, reducing the test execution time by 11.1%. With additional dynamic tuning of the new wait time, TRaf further reduces the execution time by 20.2%.
A crash course in Pesin theory

This is more or less the content of a 5-hour mini-course that I have given at KTH in the program ``Master class in low-dimensional dynamics'' during May 1-5, 2023. It was not intended to be exhaustive but to give the students a grasp of basic ideas in Pesin Theory in a very short period. It is to be taken as a first approximation and as an invitation to study it in more detail. As such, there was a lot of hand-waving, probably there were mistakes and inaccuracies too. If you have any remark, correction, comment, you can contact me at {\tt janarhertz@gmail.com}.
Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning

Gesture recognition using low-resolution instantaneous HD-sEMG images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the data variability between inter-session and inter-subject scenarios presents a great challenge. The existing approaches employed very large and complex deep ConvNet or 2SRNN-based domain adaptation methods to approximate the distribution shift caused by these inter-session and inter-subject data variability. Hence, these methods also require learning over millions of training parameters and a large pre-trained and target domain dataset in both the pre-training and adaptation stages. As a result, it makes high-end resource-bounded and computationally very expensive for deployment in real-time applications. To overcome this problem, we propose a lightweight All-ConvNet+TL model that leverages lightweight All-ConvNet and transfer learning (TL) for the enhancement of inter-session and inter-subject gesture recognition performance. The All-ConvNet+TL model consists solely of convolutional layers, a simple yet efficient framework for learning invariant and discriminative representations to address the distribution shifts caused by inter-session and inter-subject data variability. Experiments on four datasets demonstrate that our proposed methods outperform the most complex existing approaches by a large margin and achieve state-of-the-art results on inter-session and inter-subject scenarios and perform on par or competitively on intra-session gesture recognition. These performance gaps increase even more when a tiny amount (e.g., a single trial) of data is available on the target domain for adaptation. These outstanding experimental results provide evidence that the current state-of-the-art models may be overparameterized for sEMG-based inter-session and inter-subject gesture recognition tasks.
" Valency of Certain Complex-Valued Functions"

The valence of a function f at a point z0 is the number of distinct,finite solutions to f(z) = z0 . In this paper we bound the valence of complex-valued harmonic polynomials in the plane for some special harmonic polynomials of the form f(z) =p(z)q(z), where p(z) is an analytic polynomial of degree n and q(z) is an analytic polynomial of degree m, and q(z) = αp(z) for some constant α. Using techinques of complex dynamics used in the work Sheil-Small and Wilmshurst on the valence of harmonic polynomial, we prove that the harmonic polynomial f(z) = p(z)q(z) has the valency of m + n.
Research on Cooperative Search Technology of Heterogeneous UAVs in Complex Environments

This paper studies heterogeneous UAVs cooperative search technology suitable for complex environments. In the application, a fixed-wing UAV drops rotor UAVs to deploy the cluster rapidly. Meanwhile, the fixed-wing UAV works as a communication relay node to improve the cooperative search performance of the cluster further. Aiming at the cooperative search requirements of heterogeneous UAVs, a jumping grid decision method is proposed to satisfy the maneuverability constraints of UAVs, a parameter dynamic selection method is developed to make search decision more responsive to task requirements, and a search information transmission method with low bandwidth is designed, which significantly improves the communication efficiency of UAVs. The simulation results show that heterogeneous UAVs have the capabilities of adaptive decision-making, threat avoidance, and collision avoidance. The cooperative search performance of the heterogeneous cluster under communication constraints is significantly improved compared to the homogeneous cluster.
A Fast and Robust Camera-IMU Online Calibration Method For Localization System

Autonomous driving has spurred the development of sensor fusion techniques, which combine data from multiple sensors to improve system performance. In particular, localization system based on sensor fusion , such as Visual Simultaneous Localization and Mapping (VSLAM), is an important component in environment perception, and is the basis of decision-making and motion control for intelligent vehicles. The accuracy of extrinsic calibration parameters between camera and IMU has significant effect on the positioning precision when performing VSLAM system. Currently, existing methods are time-consuming using complex optimization methods and sensitive to noise and outliers due to off-calibration, which can negatively impact system performance. To address these problems, this paper presents a fast and robust camera-IMU online calibration method based space coordinate transformation constraints and SVD (singular Value Decomposition) tricks. First, constraint equations are constructed based on equality of rotation and transformation matrices between camera frames and IMU coordinates at different moments. Secondly, the external parameters of the camera-IMU are solved using quaternion transformation and SVD techniques. Finally, the proposed method is validated using ROS platform, where images from the camera and velocity, acceleration, and angular velocity data from the IMU are recorded in a ROS bag file. The results showed that the proposed method can achieve robust and reliable camera-IMU online calibration parameters results with less tune consuming and less uncertainty.
Sustaining Quasi De-Sitter Inflation with Bulk Viscosity

The de-Sitter spacetime is a maximally symmetric Lorentzian manifold with constant positive scalar curvature that plays a fundamental role in modern cosmology. Here, we investigate bulk-viscosity-assisted quasi de-Sitter inflation, that is the period of accelerated expansion in the early universe during which −H˙≪H2, with H(t) being the Hubble expansion rate. We do so in the framework of a causal theory of relativistic hydrodynamics, which takes into account non-equilibrium effects associated with bulk viscosity, which may have been present as the early universe underwent an accelerated expansion. In this framework, the existence of a quasi de-Sitter universe emerges as a natural consequence of the presence of bulk viscosity, without requiring introducing additional scalar fields. As a result, the equation of state, determined by numerically solving the generalized momentum-conservation equation involving bulk viscosity pressure turns out to be time dependent. The transition timescale characterising its departure from an exact de-Sitter phase is intricately related to the magnitude of the bulk viscosity. We examine the properties of the new equation of state, as well as the transition timescale in the presence of bulk viscosity pressure. In addition, we construct a fluid description of inflation and demonstrate that, in the context of the causal formalism, it is equivalent to the scalar field theory of inflation. Our analysis also shows that the slow-roll conditions are realised in the bulk-viscosity-supported model of inflation. Finally, we examine the viability of our model by computing the inflationary observables, namely the spectral index and the tensor-to-scalar ratio of the curvature perturbations, and compare them with a number of different observations, finding good agreement in most cases.
PESTS: Persian_English Cross Lingual Corpus for Semantic Textual Similarity

One of the components of natural language processing that has received a lot of investigation recently is semantic textual similarity. In computational linguistics and natural language processing, assessing the semantic similarity of words, phrases, paragraphs, and texts is crucial. Calculating the degree of semantic resemblance between two textual pieces, paragraphs, or phrases provided in both monolingual and cross-lingual versions is known as semantic similarity. Cross lingual semantic similarity requires corpora in which there are sentence pairs in both the source and target languages with a degree of semantic similarity between them. Many existing cross lingual semantic similarity models use a machine translation due to the unavailability of cross lingual semantic similarity dataset, which the propagation of the machine translation error reduces the accuracy of the model. On the other hand, when we want to use semantic similarity features for machine translation the same machine translations should not be used for semantic similarity. For Persian, which is one of the low resource languages, no effort has been made in this regard and the need for a model that can understand the context of two languages is felt more than ever. In this article, the corpus of semantic textual similarity between sentences in Persian and English languages has been produced for the first time by using linguistic experts. We named this dataset PESTS (Persian English Semantic Textual Similarity). This corpus contains 5375 sentence pairs. Also, different models based on transformers have been fine-tuned using this dataset. The results show that using the PESTS dataset, the Pearson correlation of the XLM ROBERTa model increases from 85.87% to 95.62%.
Spectroscopic Follow-up of Gaia Exoplanet Candidates: Impostor Binary Stars Invade the Gaia DR3 Astrometric Exoplanet Candidates

In this paper, we report on the follow-up of six potential exoplanets detected with Gaia astrometry and provide an overview of what is currently known about the nature of the entire Gaia astrometric exoplanet candidate sample, 72 systems in total. We discuss the primary false-positive scenario for astrometric planet detections: binary systems with alike components that produce small photocenter motions, mimicking exoplanets. These false positives can be identified as double-lined binaries (SB2) through analysis of high-resolution spectra. Doing so we find that three systems, Gaia DR3 1916454200349735680, Gaia DR3 2052469973468984192, and Gaia DR3 5122670101678217728, are indeed near-equal-mass double-star systems rather than exoplanetary systems. The spectra of the other two analyzed systems, HD 40503 and HIP 66074, are consistent with the exoplanet scenario in that no second set of lines can be found in the time series of publicly available high-resolution spectra. However, their Gaia astrometric solutions imply radial-velocity semiamplitudes ∼3 (HD 40503) and ∼15 (HIP 66074) larger than what was observed with ground-based spectrographs. The Gaia astrometry orbital solutions and ground-based radial-velocity measurements exhibit inconsistencies in six out of a total of 12 exoplanet candidate systems where such data are available, primarily due to substantial differences between observed ground-based radial-velocity semiamplitudes and those implied by the Gaia orbits. We investigated various hypotheses as to why this might be the case, and although we found no clear perpetrator, we note that a mismatch in orbital inclination offers the most straightforward explanation.
Edit As You Wish: Video Description Editing with Multi-grained Commands

Automatically narrating a video with natural language can assist people in grasping and managing massive videos on the Internet. From the perspective of video uploaders, they may have varied preferences for writing the desired video description to attract more potential followers, e.g. catching customers' attention for product videos. The Controllable Video Captioning task is therefore proposed to generate a description conditioned on the user demand and video content. However, existing works suffer from two shortcomings: 1) the control signal is fixed and can only express single-grained control; 2) the video description can not be further edited to meet dynamic user demands. In this paper, we propose a novel Video Description Editing (VDEdit) task to automatically revise an existing video description guided by flexible user requests. Inspired by human writing-revision habits, we design the user command as a {operation, position, attribute} triplet to cover multi-grained use requirements, which can express coarse-grained control (e.g. expand the description) as well as fine-grained control (e.g. add specified details in specified position) in a unified format. To facilitate the VDEdit task, we first automatically construct a large-scale benchmark dataset namely VATEX-EDIT in the open domain describing diverse human activities. Considering the real-life application scenario, we further manually collect an e-commerce benchmark dataset called EMMAD-EDIT. We propose a unified framework to convert the {operation, position, attribute} triplet into a textual control sequence to handle multi-grained editing commands. For VDEdit evaluation, we adopt comprehensive metrics to measure three aspects of model performance, including caption quality, caption-command consistency, and caption-video alignment.
Multimessenger observations of core-collapse supernovae: Exploiting the standing accretion shock instability

The gravitational wave (GW) and neutrino signals from core-collapse supernovae (CCSNe) are expected to carry pronounced imprints of the standing accretion shock instability (SASI). We investigate whether the correlation between the SASI signatures in the GW and neutrino signals could be exploited to enhance the detection efficiency of GWs. We rely on a benchmark full-scale three-dimensional CCSN simulation with zero-age main sequence mass of $27\ M_\odot$. Two search strategies are explored: 1. ~the inference of the SASI frequency range and/or time window from the neutrino event rate detectable at the IceCube Neutrino Observatory; 2. ~the use of the neutrino event rate to build a matched filter template. We find that incorporating information from the SASI modulations of the IceCube neutrino event rate can increase the detection efficiency compared to standard GW excess energy searches up to $30\%$ for nearby CCSNe. However, we do not find significant improvements in the overall GW detection efficiency for CCSNe more distant than $1.5$~kpc. We demonstrate that the matched filter approach performs better than the unmodeled search method, which relies on a frequency bandpass inferred from the neutrino signal. The improved detection efficiency provided by our matched filter method calls for additional work to outline the best strategy for the first GW detection from CCSNe.
Nonequilibrium moderate deviations from hydrodynamics of simple exclusion processes

In this paper, we give the moderate deviation principle from the hydrodynamic limit of the simple exclusion process on $1$-dimensional torus starting from a nonequilibrium state, which extends the result given in Gao and Quastel (2003) about the case where the process starts from an equilibrium state. The exponential tightness of the scaled density field of the process and a replacement lemma play key roles in the proof of the main result. We utilize Grownwall's inequality and the upper bound of the large deviation principle given in Kipnis, Olla and Varadhan (1989) to prove above exponential tightness and replacement lemma respectively in the absence of the invariance of the initial distribution.
Can the Problem-Solving Benefits of Quality Diversity Be Obtained without Explicit Diversity Maintenance?

When using Quality Diversity (QD) optimization to solve hard exploration or deceptive search problems, we assume that diversity is extrinsically valuable. This means that diversity is important to help us reach an objective, but is not an objective in itself. Often, in these domains, practitioners benchmark their QD algorithms against single objective optimization frameworks. In this paper, we argue that the correct comparison should be made to multi-objective optimization frameworks. This is because single objective optimization frameworks rely on the aggregation of sub-objectives, which could result in decreased information that is crucial for maintaining diverse populations automatically. In order to facilitate a fair comparison between quality diversity and multi-objective optimization, we present a method that utilizes dimensionality reduction to automatically determine a set of behavioral descriptors for an individual, as well as a set of objectives for an individual to solve. Using the former, one can generate solutions using standard quality diversity optimization techniques, and using the latter, one can generate solutions using standard multi-objective optimization techniques. This allows for a level comparison between these two classes of algorithms, without requiring domain and algorithm specific modifications to facilitate a comparison.
Hybrid Feedback Control Design for Non-Convex Obstacle Avoidance

We develop an autonomous navigation algorithm for a robot operating in two-dimensional environments containing obstacles, with arbitrary non-convex shapes, which can be in close proximity with each other, as long as there exists at least one safe path connecting the initial and the target location. An instrumental transformation that modifies (virtually) the non-convex obstacles, in a non-conservative manner, is introduced to facilitate the design of the obstacle-avoidance strategy. The proposed navigation approach relies on a hybrid feedback that guarantees global asymptotic stabilization of a target location while ensuring the forward invariance of the modified obstacle-free workspace. The proposed hybrid feedback controller guarantees Zeno-free switching between the move-to-target mode and the obstacle-avoidance mode based on the proximity of the robot with respect to the modified obstacle-occupied workspace. Finally, we provide an algorithmic procedure for the sensor-based implementation of the proposed hybrid controller and validate its effectiveness via some numerical simulations.
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems

Commonsense reasoning is omnipresent in human communications and thus is an important feature for open-domain dialogue systems. However, evaluating commonsense in dialogue systems is still an open challenge. We take the first step by focusing on event commonsense that considers events and their relations, and is crucial in both dialogues and general commonsense reasoning. We propose ACCENT, an event commonsense evaluation metric empowered by commonsense knowledge bases (CSKBs). ACCENT first extracts event-relation tuples from a dialogue, and then evaluates the response by scoring the tuples in terms of their compatibility with the CSKB. To evaluate ACCENT, we construct the first public event commonsense evaluation dataset for open-domain dialogues.Our experiments show that ACCENT is an efficient metric for event commonsense evaluation, which achieves higher correlations with human judgments than existing baselines.
The Threshold Energy of Low Temperature Langevin Dynamics for Pure Spherical Spin Glasses

We study the Langevin dynamics for spherical $p$-spin models, focusing on the short time regime described by the Cugliandolo-Kurchan equations. Confirming a prediction of [Cugliandolo-Kurchan, Phys. Rev. Lett. 1993], we show the asymptotic energy achieved is exactly $E_{\infty}(p)=2\sqrt{\frac{p-1}{p}}$ in the low temperature limit. The upper bound uses hardness results for Lipschitz optimization algorithms and applies for all temperatures. For the lower bound, we prove the dynamics reaches and stays above the lowest energy of any approximate local maximum. In fact the latter behavior holds for any Hamiltonian obeying natural smoothness estimates, even with disorder-dependent initialization and on exponential time-scales.
Calculating potential energy surfaces with quantum computers by measuring only the density along adiabatic transitions

We show that chemically-accurate potential energy surfaces (PESs) can be generated from quantum computers by measuring the density along an adiabatic transition between different molecular geometries. In lieu of using phase estimation, the energy is evaluated by performing line-integration using the inverted TDDFT Kohn-Sham potential obtained from the time-varying densities. The accuracy of this method depends on the validity of the adiabatic evolution itself and the potential inversion process (which is theoretically exact but can be numerically unstable), whereas total evolution time is the defining factor for the precision of phase estimation. We examine the method with a one-dimensional system of two electrons for both the ground and first triplet state in first quantization, as well as the ground state of three- and four- electron systems in second quantization. It is shown that few accurate measurements can be utilized to obtain chemical accuracy across the full potential energy curve, with shorter propagation time than may be required using phase estimation for a similar accuracy. We also show that an accurate potential energy curve can be calculated by making many imprecise density measurements (using few shots) along the time evolution and smoothing the resulting density evolution. We discuss how one can generate full PESs using either sparse grid representations or machine learning density functionals where it is known that training the functional using the density (along with the energy) generates a more transferable functional than only using the energy. Finally, it is important to note that the method is able to classically provide a check of its own accuracy by comparing the density resulting from a time-independent Kohn-Sham calculation using the inverted potential, with the measured density.