Miracle-dz/UniFAR-FTUs huggingface dataset:
A Current-Mode Four-Phase Synchronous Buck Converter With Dynamic Dead-Time Control

A current-mode four-phase synchronous buck converter with a dynamic dead-time control (DDTC) method is presented in this work. A brief analysis of the multiphase buck converter power efficiency in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is performed to provide design guidelines for minimizing power losses. In synchronous converters, the power efficiency can always be improved by optimizing the dead-time. Therefore, a gate driver with DDTC is designed to optimize the dead-time in every switching cycle, thereby improving power efficiency particularly under heavy load conditions. The proposed buck converter has the ability to deliver a maximum load current of 6.0 A at a typical output voltage of 1.2 V from a power supply of 3.0 V. A power efficiency improvement of over 1.0% is achieved when the load current is over 2.0 A, and an improvement of about 2.4% is obtained at a load current of 4.0 A. A peak power efficiency of 92.8% is measured at an output voltage of 1.8 V.
Schottky Gate Induced Threshold Voltage Instabilities in p-GaN Gate AlGaN/GaN HEMTs

We present detailed ON-state gate current characterization of Schottky gate p-GaN capped AlGaN/GaN high-electron-mobility transistors (HEMTs) on two distinct gate processes. The threshold voltage is monitored from <inline-formula> <tex-math notation="LaTeX">$10~\mathrm {\mu }\text{s}$ </tex-math></inline-formula> up to 100 s under positive gate bias stress and during recovery. The threshold voltage stability is affected by the balance between hole and electron current in the gate stack. More specifically, devices with uniform hole conduction across the p-GaN gate area demonstrate stable threshold voltage behavior up to <inline-formula> <tex-math notation="LaTeX">$V_{g}=5\,\mathrm {V}$ </tex-math></inline-formula>, whereas devices with a dominating gate perimeter electron conduction demonstrate larger instabilities. Finally, the threshold voltage stability during OFF-state pulsed stress is investigated and correlated to the excess gate-to-drain charge extracted from capacitance curves.
Data Evaluation and Enhancement for Quality Improvement of Machine Learning

Poor data quality has a direct impact on the performance of the machine learning system that is built on the data. As a demonstrated effective approach for data quality improvement, transfer learning has been widely used to improve machine learning quality. However, the “quality improvement” brought by transfer learning was rarely rigorously validated, and some of the quality improvement results were misleading. This article first exposed the hidden quality problem in the datasets used to build a machine learning system for normalizing medical concepts in social media text. The system was claimed to have achieved the best performance compared to existing work on a machine learning task. However, the results of our experiments showed that the “best performance” was due to the poor quality of the datasets and the defective validation process. To address the data quality issue and build a high-performance medical concept normalization system, we developed a transfer-learning-based strategy for data quality enhancement and system performance improvement. The results of the experiments showed a strong correlation between the quality of the datasets and the performance of the machine learning system. The results also demonstrated that a rigorous evaluation of data quality is necessary for guiding the quality improvement of machine learning. Therefore, we propose a data quality evaluation framework that includes the quality criteria and their corresponding evaluation approaches. The data validation process, the performance improvement strategy, and the data quality evaluation framework discussed in this article can be used for machine learning researchers and practitioners to build high-performance machine learning systems. The code and datasets used in this research are available in GitHub (https://github.com/haihua0913/dataEvaluationML).
Effective Prediction of Bug-Fixing Priority via Weighted Graph Convolutional Networks

With the increasing number of software bugs, bug fixing plays an important role in software development and maintenance. To improve the efficiency of bug resolution, developers utilize bug reports to resolve given bugs. Especially, bug triagers usually depend on bugs’ descriptions to suggest priority levels for reported bugs. However, manual priority assignment is a time-consuming and cumbersome task. To resolve this problem, recent studies have proposed many approaches to automatically predict the priority levels for the reported bugs. Unfortunately, these approaches still face two challenges that include words’ nonconsecutive semantics in bug reports and the imbalanced data. In this article, we propose a novel approach that graph convolutional networks (GCN) based on weighted loss function to perform the priority prediction for bug reports. For the first challenge, we build a heterogeneous text graph for bug reports and apply GCN to extract words’ semantics in bug reports. For the second challenge, we construct a weighted loss function in the training phase. We conduct the priority prediction on four open-source projects, including Mozilla, Eclipse, Netbeans, and GNU compiler collection. Experimental results show that our method outperforms two baseline approaches in terms of the F-measure by weighted average of 13.22%.
The Influence of Turbulence-Chemistry Interaction Modelling for Supersonic Combustion

Past studies of supersonic reacting hydrogen jets have shown that simple laminar chemistry provides comparable results to more complex turbulence-chemistry interaction modelling. This has led to the neglection of combustion modelling for many simulations of supersonic combustion conducted today. This paper aims to investigate the influence of turbulence-chemistry interaction modelling through the simulation of a more realistic combustor geometry, the SCHOLAR scramjet test case. Results from laminar chemistry and the assumed PDF combustion model are compared using two detailed chemical mechanisms. It is shown that the inclusion of combustion modelling has a minimal impact on the results obtained, possibly due to alteration of the turbulence energy spectrum from that expected under subsonic conditions. It is suggested that this leads to the smallest turbulent structures in the flow being larger than the flame thickness and therefore unable to enter the reaction zone. This is supported by analysis of the supersonic combustion regime, showing an expected flamelet-like behaviour.
Triplet-Graph Reasoning Network for Few-Shot Metal Generic Surface Defect Segmentation

Metal surface defect segmentation can play an important role in dealing with the issue of quality control during the production and manufacturing stages. There are still two major challenges in industrial applications. One is the case that the number of metal surface defect samples is severely insufficient, and the other is that the most existing algorithms can only be used for specific surface defects and it is difficult to generalize to other metal surfaces. In this work, a theory of few-shot metal generic surface defect segmentation is introduced to solve these challenges. Simultaneously, the Triplet-Graph Reasoning Network (TGRNet) and a novel dataset Surface Defects- $4^{i}$ are proposed to achieve this theory. In our TGRNet, the surface defect triplet (including triplet encoder and trip loss) is proposed and is used to segment background and defect area, respectively. Through triplet, the few-shot metal surface defect segmentation problem is transformed into few-shot semantic segmentation problem of defect area and background area. For few-shot semantic segmentation, we propose a method of multi-graph reasoning to explore the similarity relationship between different images. And to improve segmentation performance in the industrial scene, an adaptive auxiliary prediction module is proposed. For Surface Defects- $4^{i}$ , it includes multiple categories of metal surface defect images to verify the generalization performance of our TGRNet and adds the nonmetal categories (leather and tile) as extensions. Through extensive comparative experiments and ablation experiments, it is proved that our architecture can achieve state-of-the-art results.
Multi-Attention Ghost Residual Fusion Network for Image Classification

In order to achieve high-efficiency and high-precision multi-image classification tasks, a multi-attention ghost residual fusion network (MAGR) is proposed. MAGR is formed by cascading basic feature extraction network (BFE), ghost residual mapping network (GRM) and image classification network (IC). The BFE uses spatial and channel attention mechanisms to help the MAGR extract low-level features of the input image in a targeted manner. The GRM is formed by cascading 4 multi-branch group convolutional ghost residual blocks (MGR-Blocks). Each MGR-Block is cascaded by a dimension reducer and several ghost residual sub-networks (GRSs). The GRS integrates ghost convolution and residual connection, and the use of ghost convolution can significantly reduce parameters and achieve high-efficient classification. The GRS is a parallel convolution structure with 32 branches, which ensures that GRM has enough width to extract advanced features and extract as much feature information as possible, so as to obtain high-precision classification. The IC completes the aggregation of high-dimensional channel feature information, and then achieves a significant improvement in the classification accuracy of MAGR, by fusing the effective channel attention mechanism, global average pooling and SoftMax layer. Simulation experiment shows that MAGR has excellent classification capability while achieving high efficiency and lightweight. Compare with VGG16, the parameters of MAGR on CIFAR-10 is reduced by 94.8% while the classification accuracy is increased by 1.18%. Compare with MobileNetV2, the parameters of MAGR on CIFAR-100 is reduced by 33.9% while the classification accuracy is increased by 15.6%.
Fast 3D-HEVC Depth Intra Coding Based on Boundary Continuity

The encoding format of the 3D extension of high efficiency video coding (3D-HEVC) consists of a multiview color texture and an associated depth map. Because of the unique characteristics of the depth map, advanced coding techniques are designed for depth map coding at the expense of computational complexity. In this paper, fast algorithms are conceived to accelerate the intra coding time of the depth map based on boundary continuity. First, the proposed fast prediction unit (PU) mode decision reduces the number of conventional intra prediction modes based on calculating the total sum of squares (TSS) of the PU boundaries. Second, the proposed fast depth modeling mode (DMM) decision makes use of the variances of the boundary pixels to determine the execution of the DMM. Third, the proposed coding unit (CU) early termination algorithm decides whether to further split the current CU by utilizing the thresholds of the TSS and the rate-distortion cost (RD-cost). The experimental results show that the proposed algorithm provides better performance in terms of coding speed and bitrate than the algorithm in previous work. The coding time of the depth map is reduced by 56.08%, while the Bjøntegaard delta bitrate (BD-BR) is only increased by 0.32% for the synthesis view.
Performance Analysis of Distinct Feed-forward Neural Networks Structures on the AE Index Prediction

Space weather events, such as geomagnetic storms and substorms, can affect the operation of modern technological systems that mankind currently relies on, including electric power grids, spaceborne vehicles, radio wave communication and navigation systems. Therefore, any new information that helps us to have a better understanding of the space weather and their impact on our infrastructure and services is of the highest interest. Furthermore, monitoring and forecasting these disturbances are essential activities to avoid damage and losses. Over the past decades several different indices have been developed in order to monitor the space weather. One index commonly used by the scientific community to assess the level of geomagnetic activity in the auroral region is the AE (auroral electrojet) index, which reflects changes on the auroral electrojets due to the fluctuations in the solar wind convection electric field that are produced by variations in the solar wind velocity and interplanetary magnetic field (IMF). In this framework, this paper investigates the performance of distinct arrangements of feedforward artificial neural networks (ANNs) on the estimation of the hourly-averaged AE index aiming at monitoring and forecasting activities. We have analyzed the effect of the inclusion of different information related to the IMF, solar wind and their daily and seasonal variability using more than 10 years of data derived from the Advanced Composition Explorer (ACE) and Wind missions. Our analysis indicates that the inclusion of the temporal information improved the performance of the ANN. Tests on the most suitable arrangement during different periods during the year of 2012 revealed a good correlation between the estimated and the reference AE index values and indicate that the model could reproduce the component of the AE index that is directly driven by the solar wind. The ANN model was able to qualitatively depict long temporal variations of the hourly averaged AE index during periods of low, moderate and high geomagnetic activity.
Automated Data Accountability for Missions in Mars Rover Data

This paper proposes an automated solution system to assist with Real-Time Operations and automatically identify and report on issues with data transfer, archive, and manipulation throughout the Ground Data System (GDS) process. As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The prior state of GDS issue detection and resolution was largely manual. GDSAs receive email alerts when something goes wrong, but it's not always clear what the exact problem is or how to fix it. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implemented various supervised machine learning-based models and evaluate their accuracies to identify a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in near real time.
An Energy-Efficient Fine-Grained Deep Neural Network Partitioning Scheme for Wireless Collaborative Fog Computing

Fog computing is a potential solution for heterogeneous resource-constrained mobile devices to collaboratively operate deep learning-driven applications at the edge of the networks, instead of offloading the computations of these applications to the powerful cloud servers thanks to the latency reduction, decentralized structure, and privacy concerns. Compared to the mobile cloud computing concept where computation-intensive deep learning operations are offloaded to the powerful cloud servers, making use of the computing capabilities of resource-constrained devices can improve the delay performance and lessen the need for powerful servers to execute such applications by considering a collaborative fog computing scenario with deep neural network (DNN) partitioning. In this paper, we propose an energy-efficient fine-grained DNN partitioning scheme for wireless collaborative fog computing systems. The proposed scheme includes both layer-based partitioning where the DNN model is divided into layer by layer and horizontal partitioning where the input data of each layer operation is partitioned among multiple devices to encourage parallel computing. A convex optimization problem is formulated to minimize the energy consumption of the collaborative part of the system by optimizing the communication and computation parameters as well as the workload of each participating device and solved by using the primal-dual decomposition and Lagrange duality theory. As can be observed in the simulation results, the proposed optimized scheme makes a notable difference in the energy consumption compared to the non-optimized scenario where the workload distribution is equal for all participating devices but the communication and computation parameters are still optimized, so it is a quite challenging bound to be compared.
Control Loop Pairing and Interaction Analyses of the Falling Film Evaporator Process

The paper solves the control loop pairing problem of the falling evaporator process. To this end, we derive a dynamic input-output model by taking dominant time-delays into account. Besides classical methods to solve the loop pairing problem, such as Bristol’s relative gain array or the Niederlinski index, we apply modern approaches, namely, the participation matrix and Hankel interaction index array. Since the former only focus on multiloop single-input-single-output control, the latter additionally give recommendations regarding the structure of a multivariable controller. On the one hand, we show a feasible pairing for multiloop single-input-single-output process control but, on the other hand, we conclude a triangular structure of a multivariable controller, which may achieve better results due to the strong interaction of two control loops.
Multi-scale Pulmonary Nodule Detection by Fusion of Cascade R-CNN and FPN

Accurate pulmonary nodule detection in chest computed tomography (CT) is a crucial step in diagnosis and treatment of lung cancers. However, pulmonary nodules in CT images vary in size and are difficult to detect. In this paper, we proposed a new model fusing Cascade R-CNN and feature pyramid network (FPN) for multi-scale pulmonary nodule. In our model, ResNet is used as the backbone network for feature extraction. Meanwhile, to enhance the semantic features of each layer, the feature pyramid network is constructed by fusing the upper layer features with the lower layer features. Cascade R-CNN is introduced into our model for detection using different Intersection over Union (IoU) thresholds. To improve the average detection precision of small nodules, the Mosaic data are employed to augment the training set. Our model achieved 0.879 average precision (AP) on Lung Nodule Analysis 2016 (LUNA16) dataset, which shows that our model can effectively detect multi-scale pulmonary nodules, and the average precision on nodules of small sizes has increased significantly.
Experience, Attitude, and Perceived Barriers Toward Research Among Ophthalmology Residents in Saudi Arabia: A National Cross-Sectional Study

Introduction Research sets the foundation for evidence-based practice in medicine. Globally resident doctors in various specialties are facing major obstacles to accomplish high quality research projects. Understanding these obstacles may help residents achieve their maximum research potential. This current study was undertaken to document the experience, attitude, and perceived barriers toward research among Saudi ophthalmology residents. Methodology A specific questionnaire was developed and validated for the purpose of this study. The questionnaire was distributed online via email to actively enrolled residents in all five ophthalmology training programs in Saudi Arabia. Results Out of a total number of 193 ophthalmology residents in all five training programs, 147 responded to the questionnaire yielding a 76.1% response rate the mean age of participants was 27.6 ±1.8 and the number of males and females was almost equal. The vast majority [96.4%] have worked on at least one research project before starting residency training. Involvement was mainly in the phases of concept and design [72.5%], proposal preparation [85.9%], the three most frequent obstacles to conducting research projects for trainees were burden of other activities [4.27], lack of protected time for research [4.11] and too many regulations in obtaining ethical approval [3.67]. Discussion Our current study shows that ophthalmology residents understand the importance of clinical research, but they are facing a considerable number of barriers toward accomplishing high-quality research projects. Findings of our study may help program directors to address these barriers and improve the incorporation of research along with clinical training in residency curricula.
A Case Study on Major Constraints of Small Ruminants Management in Juba County Central Equatoria State South Sudan

A six month case study was conducted in Juba County South Sudan to identify major constraints encountering small ruminant management systems. A total of 45 respondents were administered with questionnaires in the study area. Secondary relevant data obtained from peer-reviewed journals, records and websites. Descriptive statistics and SSPS-20 were used in data analysis. Results revealed that 0Tmajor 0Tconstraints 0T of small ruminants management included inadequate knowledge and skills in improved management practices (19.2%), cost of drugs and vaccines (18.3%) and both pasture scarcity during the dry season and irregular demand for sheep and goat products showed 17.5% compared to inadequate herdsmen (0.8%). Internal parasitic infection showed 44.3% compared to pneumonia (32%) which is more common in both 0TNyarkenyi0T and Lirya Payams. The external parasitic infestations are commonly observed in 0TNyarkenyi0T compared to the other Payams due to poor management system. Livestock diseases were controlled by provision of drugs and vaccines (58.6%) and practiced by most of the farmers in 0TNyarkenyi0T and Rejaf Payams compared to Lirya Payam due to accessibility to veterinary services delivery. Further study is needed for identifying and bridging gap areas of veterinary education and extension services delivery to the farmers for sustainable development of sheep and goats production in Juba County South Sudan. 0T