Miracle-dz/UniFAR-FTUs huggingface dataset:
Developers perception on the severity of test smells: an empirical study

Unit testing is an essential component of the software development life-cycle. A developer could easily and quickly catch and fix software faults introduced in the source code by creating and running unit tests. Despite their importance, unit tests are subject to bad design or implementation decisions, the so-called test smells. These might decrease software systems quality from various aspects, making it harder to understand, more complex to maintain, and more prone to errors and bugs. Many studies discuss the likely effects of test smells on test code. However, there is a lack of studies that capture developers perceptions of such issues. This study empirically analyzes how developers perceive the severity of test smells in the test code they develop. Severity refers to the degree to how a test smell may negatively impact the test code. We selected six open-source software projects from GitHub and interviewed their developers to understand whether and how the test smells affected the test code. Although most of the interviewed developers considered the test smells as having a low severity to their code, they indicated that test smells might negatively impact the project, particularly in test code maintainability and evolution. Also, detecting and removing test smells from the test code may be positive for the project.
Finite-size effects and thermodynamic limit in one-dimensional Janus fluids

The equilibrium properties of a Janus fluid made of two-face particles confined to a one-dimensional channel are revisited. The exact Gibbs free energy for a finite number of particles N is exactly derived for both quenched and annealed realizations. It is proved that the results for both classes of systems tend in the thermodynamic limit (N → ∞) to a common expression recently derived (Maestre and Santos 2020 J. Stat. Mech. 063217). The theoretical finite-size results are particularized to the Kern–Frenkel model and confirmed by Monte Carlo simulations for quenched and (both biased and unbiased) annealed systems.
PhotoNs-GPU: A GPU accelerated cosmological simulation code

We present a GPU-accelerated cosmological simulation code, PhotoNs-GPU, based on an algorithm of Particle Mesh Fast Multipole Method (PM-FMM), and focus on the GPU utilization and optimization. A proper interpolated method for truncated gravity is introduced to speed up the special functions in kernels. We verify the GPU code in mixed precision and different levels of theinterpolated method on GPU. A run with single precision is roughly two times faster than double precision for current practical cosmological simulations. But it could induce an unbiased small noise in power spectrum. Compared with the CPU version of PhotoNs and Gadget-2, the efficiency of the new code is significantly improved. Activated all the optimizations on the memory access, kernel functions and concurrency management, the peak performance of our test runs achieves 48% of the theoretical speed and the average performance approaches to ∼35% on GPU.
Fast and Scalable Image Search For Histology

The expanding adoption of digital pathology has enabled the curation of large repositories of histology whole slide images (WSIs), which contain a wealth of information. Similar pathology image search offers the opportunity to comb through large historical repositories of gigapixel WSIs to identify cases with similar morphological features and can be particularly useful for diagnosing rare diseases, identifying similar cases for predicting prognosis, treatment outcomes, and potential clinical trial success. A critical challenge in developing a WSI search and retrieval system is scalability, which is uniquely challenging given the need to search a growing number of slides that each can consist of billions of pixels and are several gigabytes in size. Such systems are typically slow and retrieval speed often scales with the size of the repository they search through, making their clinical adoption tedious and are not feasible for repositories that are constantly growing. Here we present Fast Image Search for Histopathology (FISH), a histology image search pipeline that is infinitely scalable and achieves constant search speed that is independent of the image database size while being interpretable and without requiring detailed annotations. FISH uses self-supervised deep learning to encode meaningful representations from WSIs and a Van Emde Boas tree for fast search, followed by an uncertainty-based ranking algorithm to retrieve similar WSIs. We evaluated FISH on multiple tasks and datasets with over 22,000 patient cases spanning 56 disease subtypes. We additionally demonstrate that FISH can be used to assist with the diagnosis of rare cancer types where sufficient cases may not be available to train traditional supervised deep models. FISH is available as an easy-to-use, open-source software package (https://github.com/mahmoodlab/FISH).
Recent Progress in Solar Atmospheric Neutrino Searches with IceCube

Cosmic-rays interacting with nucleons in the solar atmosphere produce a cascade of particles that give rise to a flux of high-energy neutrinos and gamma-rays. Fermi has observed this gamma-ray flux; however, the associated neutrino flux has escaped observation. In this contribution, we put forward two strategies to detect these neutrinos, which, if seen, would push forward our understanding of the solar atmosphere and provide a new testing ground of neutrino properties. First, we will extend the previous analysis, which used high-energy through-going muon events collected in the years of maximum solar activity and yielded only flux upper limits, to include data taken during the solar minimum from 2018 to 2020. Extending the analysis to the solar minimum is important as the gamma-ray data collected during past solar cycles indicates a possible enhancement in the high-energy neutrino flux. Second, we will incorporate sub-TeV events and include contributions from all neutrino flavors. These will improve our analysis sensitivity since the solar atmospheric spectrum is soft and, due to oscillation, contains significant contributions of all neutrino flavors. As we will present in this contribution, these complementary strategies yield a significant improvement in sensitivity, making substantial progress towards observing this flux.
Mapping Human Muscle Force to Supernumerary Robotics Device for Overhead Task Assistance

Supernumerary Robotics Device (SRD) is an ideal solution to provide robotic assistance in overhead manual manipulation. Since two arms are occupied for the overhead task, it is desired to have additional arms to assist us in achieving other subtasks such as supporting the far end of a long plate and pushing it upward to fit in the ceiling. In this study, a method that maps human muscle force to SRD for overhead task assistance is proposed. Our methodology is to utilize redundant DoFs such as the idle muscles in the leg to control the supporting force of the SRD. A sEMG device is worn on the operator's shank where muscle signals are measured, parsed, and transmitted to SRD for control. In the control aspect, we adopted stiffness control in the task space based on torque control at the joint level. We are motivated by the fact that humans can achieve daily manipulation merely through simple inherent compliance property in joint driven by muscles. We explore to estimate the force of some particular muscles in humans and control the SRD to imitate the behaviors of muscle and output supporting forces to accomplish the subtasks such as overhead supporting. The sEMG signals detected from human muscles are extracted, filtered, rectified, and parsed to estimate the muscle force. We use this force information as the intent of the operator for proper overhead supporting force. As one of the well-known compliance control methods, stiffness control is easy to achieve using a few of straightforward parameters such as stiffness and equilibrium point. Through tuning the stiffness and equilibrium point, the supporting force of SRD in task space can be easily controlled. The muscle force estimated by sEMG is mapped to the desired force in the task space of the SRD. The desired force is transferred into stiffness or equilibrium point to output the corresponding supporting force.
Demystifying Neural Language Models' Insensitivity to Word-Order

Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations have shown that the state-of-the-art models in several language tasks may have a unique way to understand the text that could seldom be explained with conventional syntax and semantics. In this paper, we investigate the insensitivity of natural language models to word-order by quantifying perturbations and analysing their effect on neural models’ performance on language understanding tasks in GLUE benchmark. To-wards that end, we propose two metrics — the D irect N eighbour D isplacement (DND) and the I ndex D isplacement C ount (IDC) — that score the local and global ordering of tokens in the perturbed texts and observe that perturbation functions found in prior literature affect only the global ordering while the local ordering remains relatively unperturbed. We propose perturbations at the granularity of sub-words and characters to study the correlation between DND, IDC and the performance of neural language models on natural language tasks. We ﬁnd that neural language models — pretrained and non-pretrained Transformers, LSTMs, and Convolutional architectures — require local ordering more so than the global ordering of tokens. The proposed metrics and the suite of perturbations allow a systematic way to study the (in)sensitivity of neural language understanding models to varying degree of perturbations.
The interpretation of endobronchial ultrasound image using 3D convolutional neural network for differentiating malignant and benign mediastinal lesions

The purpose of this study is to differentiate malignant and benign mediastinal lesions by using the three-dimensional convolutional neural network through the endobronchial ultrasound (EBUS) image. Compared with previous study, our proposed model is robust to noise and able to fuse various imaging features and spatiotemporal features of EBUS videos. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a diagnostic tool for intrathoracic lymph nodes. Physician can observe the characteristics of the lesion using grayscale mode, doppler mode, and elastography during the procedure. To process the EBUS data in the form of a video and appropriately integrate the features of multiple imaging modes, we used a time-series three-dimensional convolutional neural network (3D CNN) to learn the spatiotemporal features and design a variety of architectures to fuse each imaging mode. Our model (Res3D_UDE) took grayscale mode, Doppler mode, and elastography as training data and achieved an accuracy of 82.00% and area under the curve (AUC) of 0.83 on the validation set. Compared with previous study, we directly used videos recorded during procedure as training and validation data, without additional manual selection, which might be easier for clinical application. In addition, model designed with 3D CNN can also effectively learn spatiotemporal features and improve accuracy. In the future, our model may be used to guide physicians to quickly and correctly find the target lesions for slice sampling during the inspection process, reduce the number of slices of benign lesions, and shorten the inspection time.
Closing the B-tree vs. LSM-tree Write Amplification Gap on Modern Storage Hardware with Built-in Transparent Compression

This paper studies the design of B-tree that can take full advantage of modern storage hardware with built-in transparent compression. Recent years have witnessed significant interest in applying log-structured merge tree (LSM-tree) as an alternative to B-tree. The current consensus is that, compared with B-tree, LSM-tree has distinct advantages in terms of storage space efficiency and write amplification. This paper argues that one should revisit this belief upon the arrival of storage hardware with built-in transparent compression. Advanced storage appliances~(e.g., all-flash array) and emerging computational storage drives perform hardware-based lossless data compression, transparent to OS and user applications. Beyond straightforwardly reducing the physical storage cost difference between B-tree and LSM-tree, such modern storage hardware brings new opportunities to innovate B-tree implementation in order to largely reduce its write amplification. As the first step to explore the potential, this paper presents three simple design techniques (i.e., deterministic page shadowing, localized page modification logging, and sparse redo logging) that can leverage such modern storage hardware to significantly reduce the B-tree write amplification. We implemented these design techniques and carried out experiments on a commercial storage drive with built-in transparent compression. The results show that the proposed design techniques can reduce the B-tree write amplification by over 10x. Compared with RocksDB (a popular key-value store built upon LSM-tree), the implemented B-tree can achieve similar or even smaller write amplification and physical storage space usage.
Artificial Intelligence Hybrid Deep Learning Model for Groundwater Level Prediction Using MLP-ADAM

Groundwater is the largest storage of freshwater resources, which serves as the major inventory for most of the human consumption through agriculture, industrial, and domestic water supply. In the fields of hydrological, some researchers applied a neural network to forecast rainfall intensity in space-time and introduced the advantages of neural networks compared to numerical models. Then, many researches have been conducted applying data-driven models. Some of them extended an Artificial Neural Networks (ANNs) model to forecast groundwater level in semi-confined glacial sand and gravel aquifer under variable state, pumping extraction and climate conditions with significant accuracy. In this paper, a multi-layer perceptron is applied to simulate groundwater level. The adaptive moment estimation optimization algorithm is also used to this matter. The root mean squared error, mean absolute error, mean squared error and the coefficient of determination ( 2 R ) are used to evaluate the accuracy of the simulated groundwater level. Total value of 2 R and RMSE are 0.9458 and 0.7313 respectively which are obtained from the model output. Results indicate that deep learning algorithms can demonstrate a high accuracy prediction. Although the optimization of parameters is insignificant in numbers, but due to the value of time in modelling setup, it is highly recommended to apply an optimization algorithm in modelling. Keywords—Hybrid deep learning model; Groundwater; MLP; ADAM
Blind Room Parameter Estimation Using Multiple Multichannel Speech Recordings

Knowing the geometrical and acoustical parameters of a room may benefit applications such as audio augmented reality, speech dereverberation or audio forensics. In this paper, we study the problem of jointly estimating the total surface area, the volume, as well as the frequency-dependent reverberation time and mean surface absorption of a room in a blind fashion, based on two-channel noisy speech recordings from multiple, unknown source-receiver positions. A novel convolutional neural network architecture leveraging both single- and inter-channel cues is proposed and trained on a large, realistic simulated dataset. Results on both simulated and real data show that using multiple observations in one room significantly reduces estimation errors and variances on all target quantities, and that using two channels helps the estimation of surface and volume. The proposed model outperforms a recently proposed blind volume estimation method on the considered datasets.
Discovering 3D Parts from Image Collections

Reasoning 3D shapes from 2D images is an essential yet challenging task, especially when only single-view images are at our disposal. While an object can have a complicated shape, individual parts are usually close to geometric primitives and thus are easier to model. Furthermore, parts provide a mid-level representation that is robust to appearance variations across objects in a particular category. In this work, we tackle the problem of 3D part discovery from only 2D image collections. Instead of relying on manually annotated parts for supervision, we propose a self-supervised approach, latent part discovery (LPD). Our key insight is to learn a novel part shape prior that allows each part to fit an object shape faithfully while constrained to have simple geometry. Extensive experiments on the synthetic ShapeNet, PartNet, and real-world Pascal 3D+ datasets show that our method discovers consistent object parts and achieves favorable reconstruction accuracy compared to the existing methods with the same level of supervision. Our project page with code is at https://chhankyao.github. io/lpd/.
Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Survey

With the advancements in computer technology, there is a rapid development of intelligent systems to understand the complex relationships in data to make predictions and classifications. Artificail Intelligence based framework is rapidly revolutionizing the healthcare industry. These intelligent systems are built with machine learning and deep learning based robust models for early diagnosis of diseases and demonstrates a promising supplementary diagnostic method for frontline clinical doctors and surgeons. Machine Learning and Deep Learning based systems can streamline and simplify the steps involved in diagnosis of diseases from clinical and image-based data, thus providing significant clinician support and workflow optimization. They mimic human cognition and are even capable of diagnosing diseases that cannot be diagnosed with human intelligence. This paper focuses on the survey of machine learning and deep learning applications in across 16 medical specialties, namely Dental medicine, Haematology, Surgery, Cardiology, Pulmonology, Orthopedics, Radiology, Oncology, General medicine, Psychiatry, Endocrinology, Neurology, Dermatology, Hepatology, Nephrology, Ophthalmology, and Drug discovery. In this paper along with the survey, we discuss the advancements of medical practices with these systems and also the impact of these systems on medical professionals.
Supernovae producing unbound binaries and triples

The fraction of stars that are in binaries or triples at the time of stellar death and the fraction of these systems that survive the supernova explosion are crucial constraints for evolution models and predictions for gravitational wave source populations. These fractions are also subject to direct observational determination. Here, we search 10 supernova remnants containing compact objects with proper motions for unbound binaries or triples using Gaia EDR3 and new statistical methods and tests for false positives. We confirm the one known example of an unbound binary, HD 37424 in G180.0−01.7, and find no other examples. Combining this with our previous searches for bound and unbound binaries, and assuming no bias in favour of finding interacting binaries, we find that 72.0 per cent (52.2–86.4 per cent, 90 per cent confidence) of supernova producing neutron stars are not binaries at the time of explosion, 13.9 per cent (5.4–27.2 per cent) produce bound binaries, and 12.5 per cent (2.8–31.3 per cent) produce unbound binaries. With a strong bias in favour of finding interacting binaries, the medians shift to 76.0 per cent were not binaries at death, 9.5 per cent leave bound binaries, and 13.2 per cent leave unbound binaries. Of explosions that do not leave binaries, ${\lt}18.9{{\ \rm per\ cent}}$ can be fully unbound triples. These limits are conservatively for $M\gt 5\, \mathrm{M}_\odot$ companions, although the mass limits for some individual systems are significantly stronger. At birth, the progenitor of PSR J0538+2817 was probably a 13–$19\, \mathrm{M}_\odot$ star, and at the time of explosion, it was probably a Roche limited, partially stripped star transferring mass to HD 37424 and then producing a Type IIL or IIb supernova.
Similarity and symmetry measures based on fuzzy descriptors of image objects' composition

The paper describes a method for measuring the similarity and symmetry of an image annotated with boundingboxes indicating image objects. The latter representation became popular recently due to the rapid development offast and efficient deep-learning-based object-detection methods. The proposed approach allows for comparing setsof bounding boxes to estimate the degree of similarity of their underlying images. It is based on the fuzzy approachthat uses the fuzzy mutual position (FMP) matrix to describe spatial composition and relations between boundingboxes within an image. A method of computing the similarity of two images described by their FMP matrices isproposed and the algorithm of its computation. It outputs the single scalar value describing the degree of content-based image similarity. By modifying the method’s parameters, instead of similarity, the reflectional symmetry ofobject composition may also be measured. The proposed approach allows for measuring differences in objects’composition of various intensities. It is also invariant to translation and scaling and – in case of symmetry detection– position and orientation of the symmetry axis. A couple of examples illustrate the method.
New opportunities for axion dark matter searches in nonstandard cosmological models

We study axion dark matter production from a misalignment mechanism in scenarios featuring a general nonstandard cosmology. Before the onset of Big Bang nucleosynthesis, the energy density of the universe is dominated by a particle field ϕ described by a general equation of state ω. The ensuing enhancement of the Hubble expansion rate decreases the temperature at which axions start to oscillate, opening this way the possibility for axions heavier than in the standard window. This is the case for kination, or in general for scenarios with ω > 1/3. However, if ω < 1/3, as in the case of an early matter domination, the decay of ϕ injects additional entropy relative to the case of the standard model, diluting this way the preexisting axion abundance, and rendering lighter axions viable. For a misalignment angle 0.5 < θ_i < π/√(3), the usual axion window becomes expanded to 4 × 10-9 eV ≲ ma ≲ 2 × 10-5 eV for the case of an early matter domination, or to 2 × 10-6 eV ≲ ma ≲ 10-2 eV for the case of kination. Interestingly, the coupling axion-photon in such a wider range can be probed with next generation experiments such as ABRACADABRA, KLASH, ADMX, MADMAX, and ORGAN. Axion dark matter searches may therefore provide a unique tool to probe the history of the universe before Big Bang nucleosynthesis.