Miracle-dz/UniFAR-FTUs huggingface dataset:
Test effort estimation and prediction of traditional and rapid release models using machine learning algorithms

Recently, many software companies have shifted to shorter release cycles from the traditional multi-month release cycle. Evolution and transition of release cycles may affect the test effort in the system. This paper analyses 25 traditional releases containing 1210 classes and 69 rapid releases containing 2616 classes of four Open Source Java systems. Correlations between 48 Object Oriented metrics and 2 test metrics were evaluated to identify the best indicators of test effort. The results show that (i) correlation between OO and test metrics remain irrespective of release models, (ii) test effort required in Rapid Release (RR) models (shorter release cycles) is slightly more as compared to Traditional Release (TR) models, (iii) Out of 18 machine learning algorithms instance based machine learning algorithms IBK and K star followed by Multi-Layer Perceptron (MLP) and additive regression are able to predict the test effort accurately in classes.
Strategies in times of crisis—insights into the benthic foraminiferal record of the Palaeocene–Eocene Thermal Maximum

Climate change is predicted to alter temperature, carbonate chemistry and oxygen availability in the oceans, which will affect individuals, populations and ecosystems. We use the fossil record of benthic foraminifers to assess developmental impacts in response to environmental changes during the Palaeocene–Eocene Thermal Maximum (PETM). Using an unprecedented number of µ-computed tomography scans, we determine the size of the proloculus (first chamber), the number of chambers and the final size of two benthic foraminiferal species which survived the extinction at sites 690 (Atlantic sector, Southern Ocean, palaeodepth 1900 m), 1210 (central equatorial Pacific, palaeodepth 2100 m) and 1135 (Indian Ocean sector, Southern Ocean, palaeodepth 600–1000 m). The population at the shallowest site, 1135, does not show a clear response to the PETM, whereas those at the other sites record reductions in diameter or proloculus size. Temperature was similar at all sites, thus it is not likely to be the reason for differences between sites. At site 1210, small size coincided with higher chamber numbers during the peak event, and may have been caused by a combination of low carbonate ion concentrations and low food supply. Dwarfing at site 690 occurred at lower chamber numbers, and may have been caused by decreasing carbonate saturation at sufficient food levels to reproduce. Proloculus size varied strongly between sites and through time, suggesting a large influence of environment on both microspheric and megalospheric forms without clear bimodality. The effect of the environmental changes during the PETM was more pronounced at deeper sites, possibly implicating carbonate saturation. This article is part of a discussion meeting issue ‘Hyperthermals: rapid and extreme global warming in our geological past’.
Cooperative Correlational and Discriminative Ensemble Classifier Learning for Early Dementia Diagnosis Using Morphological Brain Multiplexes

Dementia alters the brain wiring on different levels. However, these changes might be subtle particularly in patients with early mild cognitive impairment (eMCI). Hence, developing accurate diagnostic techniques for eMCI identification is critical for early intervention to prevent the onset of Alzheimer’s disease (AD). There is a large body of machine-learning-based research developed for classifying different brain states (e.g., AD vs MCI) using neuroimaging data. These works can be fundamentally grouped into two categories. The first one uses correlational methods, such as canonical correlation analysis (CCA) and its variants, with the aim to identify most correlated features for diagnosis. The second one includes discriminative methods, such as feature selection methods and linear discriminative analysis (LDA) and its variants to identify brain features that discriminate between two brain states. However, existing methods examine these correlational and discriminative brain data independently, which overlooks the complementary information provided by both techniques, which could prove to be useful in data classification tasks. On the other hand, how early dementia affects cortical brain connections in morphology remains largely unexplored. To address these limitations, we propose a cooperative correlational and discriminative ensemble learning framework for eMCI diagnosis that leverages a brain network representation from multiple morphological networks, each derived from the cortical surface. Specifically, we devise the shallow convolutional brain multiplex (SCBM), which encodes both region-to-region and network-to-network relationships. Then, we represent each individual brain using a set of SCBMs, which are used to train an ensemble of CCA-SVM and LDA-based classifiers, cooperating to output the label for a new testing subject. Overall, our framework outperformed several state-of-the-art methods including independent correlational and discriminative methods.
Hybrid LSTM/MaxEnt Networks for Arabic Syntactic Diacritics Restoration

Restoring syntactic diacritics is an essential task for Arabic text-to-speech systems. Syntactic diacritic mark is defined as the last diacritic mark of the stem of a whitespace delimited word. It is usually assigned based on the syntax of the Arabic language. In this paper, we formulate the problem as a tagging problem and propose the use of long short-term memory (LSTM) networks to assign the syntactic diacritics for a sentence of Arabic words. These LSTM networks were augmented with sparse direct connections between the input and output layers of the tagger (i.e., maximum entropy (MaxEnt) connections). On the Arabic tree bank task, this hybrid LSTM/MaxEnt approach achieves competitive results to the state-of-the-art systems.
Segmentation of Integrated Circuit Layouts from Scan Electron Microscopy Images

One of the most important steps in the extraction of layout for reverse engineering of the integrated circuits (ICs) is the image segmentation of wires and vias from scan electron microscope (SEM) images. This segmentation is challenging due to the gigabytes of image data just for a single IC, image noise, and artefacts. Existing approaches rely on image intensity threshold-based methods but requires significant amount of manual user interactions to correct errors in segmentation. In this paper, we describe an image processing pipeline for segmenting IC layouts from SEM images. Our pipeline includes image normalization, image preprocessing, and segmentation. The segmentation results were compared using a custom-built comparison tool. The results showed, with the correct filters/methods selection, an increase in accuracy of the segmentation for all tested image sets.
Inhibition of Suv39H1 enhances transgenic IFNα-2b gene expression in Bcap-37 cells

Abstract The low expression of exogenous transferred gene limited the application of transgenic animal technology. Suppressor of variegation 3 ∼ 9 homolog 1(SUV39H1) gene plays a prominent role on repressive heterochromatin and transcription. To understand if exogenous transgenic gene expression was affected by SUV39H1 epigenetic modification, in this paper, the effective shRNA fragments targeting SUV39H1 gene were first screened, their roles on expression of exogenous transgenic genes were determined by using Bcap-37 cell line with stable expressing IFNα-2b gene as a model, the preliminary regulation mechanism of SUV39H1 gene was investigated. The results showed that the designed shRNA1/2 fragments of SUV39H1 gene had an obvious inhibition effect on the expression of SUV39H1 gene, reached 53.07 and 31.28%, respectively by qRT-PCR analysis. Compared with the control group, the expression of IFNα-2b gene in transgenic Bcap-37 cells infected with shRNA1 and 2 viruses significantly increased by 96.25 and 121.08%, respectively (p < 0.05). In addition, the expression of DNMT1, HDAC1 and G9a gene in the shRNA infected cells reduced significantly, and the expression of the HAT1 gene increased significantly (p < 0.05). The above results indicated that the expression of exogenous transgenic gene could be promoted by suppressing SUV39H1 gene at the cell level.
Low Resolution Sampling for Joint Millimeter-Wave MIMO Communication-Radar

We propose the deployment of millimeter-wave MIMO for joint vehicular high speed communication and high resolution radar sensing. To cope with the significant hardware complexity, we consider the use of low resolution analog-to-digital converters (ADC) while maintaining a separate radio-frequency chain per antenna. The system performance is analyzed in terms of Cramér Rao lower bound and achievable data rate, and compared to the ideal case with infinite resolution ADCs. Additionally, we study the impact of quantization on the trade-off between these performance metrics. Numerical results demonstrate the potential of the concept to meet the challenging requirements of next-generation vehicles.
Additive Manufacturing of a Soft Magnetic Rotor Active Part and Shaft for a Permanent Magnet Synchronous Machine

This paper describes the design, the additive manufacturing process and the testing of a soft magnetic rotor active part and shaft region for a permanent magnet synchronous machine (PMSM). In a first step, thebest possible ferromagnetic material was identified by investigating additively processed ferro-silicon and ferro-cobalt alloys which achieve both mechanic and electromagnetic requirements for PMSM. After this, a surface-mounted PMSM was chosen to serve as reference. Both the rotor active part as well as the shaft were made of a ferro-silicon alloy and produced generatively by using laser beam melting (LBM). To underline the high potential of additive manufacturing technologies in the field of electric machinery design, a supplemental feature was added to the rotor which only could be realized expediently due to the rapid development of metal-additive manufacturing. To be more specific, supplementary slots were added underneath the magnets, which-one per pole -contain short-circuited copper wires for increased self-sensing performance due to increased magnetic anisotropy. After assembling the additively manufactured rotor shafts into aconventionally laminated stator, the general functionality of the machine as well as the influence of the additional rotor coils were tested and compared with each other.
A novel framework for semantic segmentation with generative adversarial network

Semantic segmentation plays an important role in a series of high-level computer vision applications. However, the performance of Convolutional Neural Network (CNN) based segmentation models is currently influenced by higher order inconsistencies, which are mainly caused by the CNNs built-in invariance to spatial transformations and the independent prediction for each of pixel. In this paper, a novel framework, consisting of a segmentation network and a Generative Adversarial Network (GAN), is proposed to tackle this challenging problem by enforcing long-range spatial label contiguity. With the help of fully connected layers in the discriminator and adversarial training, the GAN model can evaluate the higher-order potentials loss. The motivation is that the GAN model provides an auxiliary higher-order potentials loss to the segmentation model, thus the segmentation model have the ability of correcting higher order inconsistencies. Extensive experiments on public benchmarking database demonstrate the effectiveness of the proposed method.
Probabilistic Low-Voltage State Estimation Using Analog-Search Techniques

Power systems are becoming more complex and the need for increased awareness at the lower voltage levels of the distribution grid requires new tools that provide a reliable and accurate estimation of the system state. This paper describes an innovative state estimation method for low voltage (LV) grids that analyses similarities between a real-time snapshot comprising only a subset of smart meters with real-time communications and fully observed system states present in historical data. Real-time estimates of voltage magnitudes are obtained by smoothing the most similar past snapshots with a data-driven methodology that does not relies on full knowledge of the grid topology and electrical characteristics. Moreover, the output of the LV state estimator is a conditional probability distribution obtained with kernel density estimation. The results show highly accurate estimation of voltage magnitude, even in a scenario characterized by a strong integration of photovoltaic (PV) microgeneration.
Enforcing Eigenvector Smoothness for a Compact DFT-Based Polynomial Eigenvalue Decomposition

A variety of algorithms have been developed to compute an approximate polynomial matrix eigenvalue decomposition (PEVD). As an extension of the ordinary EVD to polynomial matrices, the PEVD will generate paraunitary matrices that diagonalise a parahermitian matrix. While iterative PEVD algorithms that compute a decomposition in the time domain have received a great deal of focus and algorithmic improvements in recent years, there has been less research in the field of frequency-based PEVD algorithms. Such algorithms have shown promise for the decomposition of problems of finite order, but the state-of-the-art requires a priori knowledge of the length of the polynomial matrices required in the decomposition. This paper presents a novel frequency-based PEVD algorithm which can compute an accurate decomposition without requiring this information. Also presented is a new metric for measuring a function's smoothness on the unit circle, which is utilised within the algorithm to maximise eigenvector smoothness for a compact decomposition, such that the polynomial eigenvectors have low order. We demonstrate through the use of simulations that the algorithm can achieve superior levels of decomposition accuracy to a state-of-the-art frequency-based method.
Stochastic Fista Algorithms: So Fast ?

Motivated by challenges in Computational Statistics such as Penalized Maximum Likelihood inference in statistical models with intractable likelihoods, we analyze the convergence of a stochastic perturbation of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), when the stochastic approximation relies on a biased Monte Carlo estimation as it happens when the points are drawn from a Markov chain Monte Carlo (MCMC) sampler. We first motivate this general framework and then show a convergence result for the perturbed FISTA algorithm. We discuss the convergence rate of this algorithm and the computational cost of the Monte Carlo approximation to reach a given precision. Finally, through a numerical example, we explore new directions for a better understanding of these Proximal-Gradient based stochastic optimization algorithms.
Mobile Robot Motion Tracking Using Descriptor Matching and Sensor Fusion

This paper presents fast tracking of a mobile robots 2D pose in a plane using the open source computer vision library(OpenCV). This can be useful for setting up experiments to study mobile robot control, robot formation or conflict resolution. Here the feature detectors SIFT, AKAZE and ORB are tested for their speed and accuracy for tracking a robot on a plane of size 2.7m × 2.1m. To determine the accuracy that can be achieved they are compared against an edge-based template matching algorithm which has a known accuracy. First the accuracy vs detection time is studied on different size images. Then sensor fusion is studied by combining the extended Kalman filter (EKF) and unscented Kalman filter (UKF) with odometry to see what gains can be made. Root mean squared pose errors of less than 3mm in translation and less than 1 degree in heading are achieved at a object detection times of less than 50ms.
DDX39B promotes translation through regulation of pre-ribosomal RNA levels

ABSTRACT DDX39B, a DExD RNA helicase, is known to be involved in various cellular processes such as mRNA export, splicing and translation. Previous studies showed that the overexpression of DDX39B promotes the global translation but inhibits the mRNA export in a dominant negative manner. This presents a conundrum as to how DDX39B overexpression would increase the global translation if it inhibits the nuclear export of mRNAs. We resolve this by showing that DDX39B affects the levels of pre-ribosomal RNA by regulating its stability as well as synthesis. Furthermore, DDX39B promotes proliferation and colony forming potential of cells and its levels are significantly elevated in diverse cancer types. Thus, increase in DDX39B enhances global translation and cell proliferation through upregulation of pre-ribosomal RNA. This highlights a possible mechanism by which dysregulation of DDX39B expression could lead to oncogenesis.
Fully Integrated 94-GHz Dual-Polarized TX and RX Phased Array Chipset in SiGe BiCMOS Operating up to 105 °C

94-GHz dual-polarized phased array transmitter and receiver ICs in 130 nm BiCMOS technology are reported. The transmitter IC integrates 16 transmitter front ends with two independent outputs, a 1-to-16 power splitter, and an intermediate frequency (IF)-to-RF up-converter. The receiver IC integrates 32 front ends, two separate 16-to-1 power combiners, and two RF-to-IF down-converters, which can either support a 32-element phased array or a 16-element polarimetric phased array if connected to 16 dual-polarized antennas. Both transmitter and receiver ICs include IF/baseband circuitry, a frequency synthesizer with continuous lock detection, a temperature sensor, and digital circuitry, including serial interface and front-end memory. On-wafer TX measurements at 94 GHz taken at 25 °C show an IF-to-RF conversion gain of 35 dB, oP1dB of 4 dBm, <inline-formula> <tex-math notation="LaTeX">$P_{\text {SAT}}$ </tex-math></inline-formula> of 7.8 dBm per channel with a total power consumption of 3 W. On-wafer RX measurements at 94 GHz and 25 °C show a maximum RF-to-IF array conversion gain of 40 dB and an noise figure (NF) of 6 dB. The total RX power consumption varies from 3 to 4.6 W as it is configured from its minimum to its maximum RF front-end gain settings. RF phase shifting elements used in both TX and RX demonstrate a 1.6° phase resolution with 360° tuning range and gain variation (rms) lower than 0.5 dB. Phase-invariant gain tunability in TX and RX front ends has been achieved for independent tapering and beam steering, demonstrating the measured phase variation within ±0.5° for 10-dB gain tuning. Both TX and RX ICs are functional up to 105 °C, maintaining <inline-formula> <tex-math notation="LaTeX">$P_{\text {SAT}}>6.5$ </tex-math></inline-formula> dBm per channel in TX and NF < 12 dB in RX. The TX and RX ICs each have a chip size of 6.7 mm <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 5.6 mm.
Cryptococcal Meningitis and Tuberculous Meningitis Co-infection in HIV-Infected Ugandan Adults

Abstract We report 5 HIV-infected Ugandan adults with cryptococcal and tuberculous (TB) meningitis co-infection. All unmasked meningitis occurred within 5 weeks of starting HIV therapy. Xpert MTB/RIF Ultra facilitated prompt diagnosis; however, 60% in-hospital mortality occurred. TB meningitis coinfection prevalence was 0.8% (5/586) among cryptococcal meningitis, 2 during second cryptococcal episodes.
Phenotypic characterization of patients with early-onset high myopia due to mutations in COL2A1 or COL11A1: Why not Stickler syndrome?

Purpose Our previous study reported that 5.5% of probands with early-onset high myopia (eoHM) had mutations in COL2A1 or COL11A1. Why were the probands initially considered to have eoHM but not Stickler syndrome (STL)? Methods Probands and family members with eoHM and mutations in COL2A1 or COL11A1 were followed up and reexamined based on the criteria for STL. Further comprehensive examinations were conducted for patients with eoHM and mutations in COL2A1 or COL11A1 and controls with eoHM without mutations in COL2A1 or COL11A1. We performed comparisons between probands, affected family members with mutations in COL2A1 or COL11A1, and controls with eoHM without mutations in COL2A1 or COL11A1. Results Twelve probands (8.91±4.03 years) and 14 affected family members (37.00±11.18 years) with eoHM and mutations in COL2A1 or COL11A1, as well as 30 controls with eoHM but without mutations in COL2A1 or COL11A1, were recruited. Among them, 25.0% of probands and 50.0% of affected family members met the diagnostic criteria for STL after reexamination. Posterior vitreous detachment/foveal hypoplasia (PVD/FH), hypermobility of the elbow joint (HJ), and vitreous opacity were more frequent in patients with eoHM with mutations in COL2A1 or COL11A1 than in the controls (p = 1.40 × 10−5, 3.72 × 10-4, 2.30× 10-3, respectively). HJ was more common in the probands than in the affected family members (11/12 versus 3/14; p = 3.42 × 10-4), suggesting age-dependent manifestation. EoHM presented in all the probands and in 11/14 affected family members, suggesting that it is a more common indicator of STL than the previously described vitreoretinal abnormalities, especially in children. The rate of STL diagnosis could increase from 25.0% to 66.7% for probands and from 50.0% to 92.9% for affected family members if eoHM, PVD/FH, and HJ are added to the diagnostic criteria. Conclusions In summary, it is not easy to differentiate STL from eoHM with routine ocular examination in outpatient clinics. Awareness of atypical phenotypes and newly recognized signs may be of help in identifying atypical STL, especially in children at eye clinics.
Image thresholding segmentation of generalized fuzzy entropy based on double adaptive ant colony algorithm

A generalized fuzzy entropy based on double adaptive ant colony algorithm for image thresholding segmentation is proposed. The new algorithm first attempts to propose the adaptive pheromone concentration at the initial time and the adaptive global updating rules, which uses the double adaptive mechanism to automatically select the generalized fuzzy entropy parameters. The threshold of the image is obtained by introducing the parameters into the complement of the generalized fuzzy entropy, and then the optimal segmentation of the image is obtained. Compared with the existing image thresholding segmentation algorithms, in most cases, simulating results indicate that the new algorithm has less background information and clearer target information. In addition, it is superior to the existing algorithms in performance and greatly improves the stability and convergence speed. 6