Miracle-dz/UniFAR-FTUs huggingface dataset:
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks.

The presence of residual and dense neural networks which greatly promotes the development of image Super-Resolution(SR) have witnessed a lot of impressive results. Depending on our observation, although more layers and connections could always improve performance, the increase of model parameters is not conducive to launch application of SR algorithms. Furthermore, algorithms supervised by L1/L2 loss can achieve considerable performance on traditional metrics such as PSNR and SSIM, yet resulting in blurry and over-smoothed outputs without sufficient high-frequency details, namely low perceptual index(PI). Regarding the issues, this paper develops a perception-oriented single image SR algorithm via dual relativistic average generative adversarial networks. In the generator part, a novel residual channel attention block is proposed to recalibrate significance of specific channels, further increasing feature expression capabilities. Parameters of convolutional layers within each block are shared to expand receptive fields while maintain the amount of tunable parameters unchanged. The feature maps are subsampled using sub-pixel convolution to obtain reconstructed high-resolution images. The discriminator part consists of two relativistic average discriminators that work in pixel domain and feature domain, respectively, fully exploiting the prior that half of data in a mini-batch are fake. Different weighted combinations of perceptual loss and adversarial loss are utilized to supervise the generator to equilibrate perceptual quality and objective results. Experimental results and ablation studies show that our proposed algorithm can rival state-of-the-art SR algorithms, both perceptually(PI-minimization) and objectively(PSNR-maximization) with fewer parameters.
Randomized Exploration for Non-Stationary Stochastic Linear Bandits

We investigate two perturbation approaches to overcome conservatism that optimism based algorithms chronically suffer from in practice. The first approach replaces optimism with a simple randomization when using confidence sets. The second one adds random perturbations to its current estimate before maximizing the expected reward. For non-stationary linear bandits, where each action is associated with a $d$-dimensional feature and the unknown parameter is time-varying with total variation $B_T$, we propose two randomized algorithms, Discounted Randomized LinUCB (D-RandLinUCB) and Discounted Linear Thompson Sampling (D-LinTS) via the two perturbation approaches. We highlight the statistical optimality versus computational efficiency trade-off between them in that the former asymptotically achieves the optimal dynamic regret $\tilde{\mathcal{O}}( d ^{2/3}B_T^{1/3} T^{2/3})$, but the latter is oracle-efficient with an extra logarithmic gap in number of arms compared to minimax-optimal dynamic regret. In a simulation study, both empirically show the outstanding performance in tackling conservatism issue that Discounted LinUCB (D-LinUCB) struggles with.
On the geometric properties of finite mixture models

In this paper we relate the geometry of extremal points to properties of mixtures of distributions. For a mixture model in $\mathbb{R}^J$ we consider as a prior the mixing density given by a uniform draw of $n$ points from the unit $(J-1)$-simplex, with $J \leq n$. We relate the extrema of these $n$ points to a mixture model with $m \leq n$ mixture components. We first show that the extrema of the points can recover any mixture density in the convex hull of the the $n$ points via the Choquet measure. We then show that as the number of extremal points go to infinity the convex hull converges to a smooth convex body. We also state a Central Limit Theorem for the number of extremal points. In addition, we state the convergence of the sequence of the empirical measures generated by our model to the Choquet measure. We relate our model to a classical non-parametric one based on a Polya tree. We close with an application of our model to population genomics.
Mid-term clinical and functional outcomes of distal biceps tendon repair: A comparative study of two surgical fixation techniques

Objective: Acute rupture of the distal biceps tendon is relatively uncommon, and surgical repair is advocated for acute injuries to restore strength and function. Numerous techniques are described in the literature, with no true consensus regarding the optimal method of fixation. The aim of this study was to evaluate patient-reported outcome measures, clinical outcomes and complications for patients undergoing distal biceps tendon repair using two fixation techniques: cortical button and suture anchor. Methods: A retrospective single-unit case series of 51 cases (50 patients) underwent distal biceps tendon repair, comprising 19 cortical button and 32 suture anchor fixations. Patients were assessed using the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire and overall satisfaction. Clinical outcomes and complications were reviewed from patient records. Results: Thirty-eight (76.5%) patients responded to DASH questionnaires with a mean follow-up of 189 weeks (11.5–425 weeks). Mean DASH scores for cortical button and suture anchor groups were 6.2 (0–30.8) and 3.3 (0–16.7), respectively (p = 0.21). Eight patients (16%) reported lateral cutaneous neuropraxia in the early post-operative period. All but two of these patients experienced complete resolution at the final follow-up. One patient had heterotrophic ossification, which did not require any further intervention. There was one re-rupture (suture anchor), but the patient declined further surgical intervention. Conclusion: There were no statistically significant differences in the patient-reported outcome measures, overall satisfaction and complication rate between patients undergoing either method of fixation. Level of evidence: III
Estimated effectiveness of traveller screening to prevent international spread of 2019 novel coronavirus (2019-nCoV)

Traveller screening is being used to limit further global spread of 2019 novel coronavirus (nCoV) following its recent emergence. Here, we analyze the expected impact of different travel screening programs given remaining uncertainty around the values of key nCoV life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss around half of infected travellers. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. These findings emphasize the need for measures to track travellers who become ill after being missed by a travel screening program. We make our model available for interactive use so stakeholders can explore scenarios of interest using the most up-to-date information. We hope these findings contribute to evidence-based policy to combat the spread of nCoV, and to prospective planning to mitigate future emerging pathogens.
Comparative Analysis of Photovoltaic Faults and Performance Evaluation of its Detection Techniques

Faults detection and analysis in PV system are considered critical for ensuring safety and increasing output power of PV arrays. PV faults do not only reduce output power and efficiency but also lessen the working life time of a system. Most common and chronic PV faults are line to line, line to ground, shadowing fault, and arc fault while less common and acute faults are hotspot, degradation, bypass diode, and connection faults. Event of PV fault detection failures, such as most recent in Mount Holly, USA in 2011 evinced the improvement in current fault detection and mitigation techniques to shrink such failures. There are various limitations in the existing fault detection techniques, as identified in this paper, which may cause misdetection of the faults. This paper is focused on mathematical formulation of various PV faults and lead to the latter’s critical analysis in terms of efficiency, accuracy, complexity, and reliability. The presented work also helps to identify nature and causes of occurrence of a PV fault. This research work serves as a special set of references and recommendations for researchers and PV manufacturing industry to improve fault detection prospects in solar PV systems.
Detection of Srew Nut Images Based on Deep Transfer Learning Network

The rising of deep learning has great application value for the accurate recognition of industrial components. Based on Inception-V3 model, the network use suitably factorized convolutions and regularization to scale up networks, this method will make the added computation more efficient. When using transfer learning method on this modified neural network model, we locate the screw and nut with high recognition accuracy. This paper retrains the pre-trained weights fixed and tune some part of layers, we use a small learning rate to insure this model can learn the previously obtained knowledge, which can reduce the training time and solve the problem of insufficient datasets.
Heavy-Hitter Flow Identification in Data Centre Networks Using Packet Size Distribution and Template Matching

Data Centre Networks (DCNs) handle large volumes of data transmission that can consume a lot of bandwidth in short bursts or over prolonged periods of time. One class of traffic that constantly poses a challenge is Heavy-Hitter (HH) flows – large-volume flows that consume considerably more network resources than other flows combined. The identification of such flows is critical to prevent network congestion and overall network performance degradation. Most of the existing methods to identify HHs are based on thresholds, i.e., if the flow exceeds a predefined threshold, it will be marked as a HH; otherwise, it will be classified as a non-HH. However, these approaches present two significant issues. First, there is no consistent and accepted threshold that would reliably classify flows. Second, the existing threshold approaches use counters (duration, packets, and bytes); thus their accuracy depends on how complete the flow information is. In this paper, we address those issues using per-flow packet size distribution which can capture the behaviour and dynamics of network traffic flow more accurately than the counters in the early stage of the flow. We then propose the use of the template matching technique to identify HHs and achieved a classification accuracy of 96% using only the first 14 packets of a flow.
A Novel Cost-Effective AC-DC Power Supply for Indoor LED Lighting Applications

This paper proposes a novel AC-DC power supply with features of power-factor correction (PFC) and cost-effectiveness for indoor LED lighting applications. The proposed AC-DC power supply combines an inverse buck-boost converter with a lossless snubber, and also includes input-current shaping. An 1SW(60V/0.3A)-rated prototype power supply has been successfully developed and implemented for operating with an input utility-line voltage of 110 V. Satisfactory results have demonstrated the functionality of the presented AC-DC power supply and its suitability for indoor LED lighting applications.
Hybrid Analog-Digital Learning with Differential RRAM Synapses

Exploiting the analog properties of RRAM cells for learning is a compelling approach, but which raises important challenges in terms of CMOS overhead, impact of device imperfections and device endurance. In this work, we investigate a learning-capable architecture, based on the concept of Binarized Neural Networks, which addresses these three issues. It exploits the analog properties of the weak RESET in hafnium-oxide RRAM cells, but uses exclusively compact and low power digital CMOS. This approach requires no refresh process, is more robust to device imperfections than more conventional analog approaches, and we show that due to the reliance on weak RESETs, the devices show outstanding endurance that can withstand multiple learning processes.
Modified Estimation of Distribution Algorithm for Solving Flow-shop Scheduling Problem with Setup Times

To solve the flow-shop scheduling problem with sequence dependent setup times, the modified estimation of distribution algorithm was proposed. Considering the setup times dependent on sequence, multi-probability matrixes are employed in the probability model to denote the relative information about jobs in this problem. Meanwhile, the updating mechanism and an automatically adjusting method are improved to adapt to the new probability. A local search is designed to be able to jump out of the local optimum and improve the global optimization ability. Finally, simulation results are compared with other algorithms to demonstrate the effectiveness of the modified EDA.
Facial Expression Recognition based on Landmarks

Facial expression is presentation of people's emotion, which plays a vital role in person's daily communication. Therefore, facial expression recognition (FER) is becoming an increasingly significant task in contemporary society. Currently the majority of the proposed methods for facial expression recognition use deep convolutional neural networks (CNN) in a supervised learning fashion. In this paper, we try to answer two important questions. The first is that in human FER, what are the key features that mostly constitute an expression, if such features existƒ The second question is to find a way to increase the FER accuracy. To this end, we propose a new FER framework that relies solely on facial landmarks. In this framework, in the training process, we first extract facial landmarks, and feed them into a shallow network for recognition/classification. We show that just by using 68 facial landmark points, it's possible to achieve state-of-the-art FER results, thus opening the possibility to further study human emotion cognition process. On the other hand, our framework also produce better results than typical deep CNN-based methods with fast implementation.
Power Control Strategy for User-Centric in Cell-Free Massive MIMO

Cell-free (CF) massive multiple-input multiple-output (MIMO) system is a promising technology for 5G, where each mobile station (MS) is simultaneously served by all access points (APs). However, early research shows that only a fraction of APs are contributing in providing service for a MS. In this paper, we consider a user-centric (UC) approach for CF massive MIMO, where each AP only serves a limit number of MSs. We introduce a strategy to assign served MSs for each AP. Then a power control algorithm based on the assignment is proposed.
Life-Cycle Cost Analysis of Submarine Cable to Island Power Systems

Provincial Electricity Authority (PEA) has a submarine cable project to Koh Tao in Surat Thani Province, Thailand to increase the capacity, stability and support the growing demand for electricity in Koh Tao due to a high economic growth rate. At present, Koh Tao uses electricity from diesel power plants resulting in high costs in maintenance, fuel, and electricity, causing PEA to bear many losses each year. Investment in the construction of a distribution system linked by submarine cable is a project that uses a lot of investment cost in constructions and in each construction also affects the environment as well. Thus, there must be an effective method of calculating the investment value to ensure that the investment projects are appropriate and cost-effective throughout the life of the asset. This paper presents the analysis of the financial feasibility of the submarine cable project to ensure that it will have economic benefits and be effective throughout the asset utilization period with life-cycle cost analysis. In this paper, three alternatives are simulated, and the life-cycle cost (LCC) of all three alternatives are calculated. Then, the analysis is performed to find which options are worth the most investment. Common network asset indices methodology (CNAIM) is applied to determine the age of the submarine cable used in the project. In addition, this method can be used to find the probability of failure (PoF) of the submarine cable which is an important parameter and is used to calculate in the LCC model to achieve the purpose of this paper.
Educational and training levels and employment of the spinal cord injured patient.

: Questionnaire follow-up data were obtained on a sample of 745 male spinal cord injured veterans. Forty percent reported that they had improved their education subsequent to their disability. Postinjury education was not related to level of injury. Significant relationships between pre- and postinjury marital status and improvement in education were found for those who were single, separated, or divorced. An intact post-injury marriage was associated with improvement in education. Those with a higher educational status and those who improved their education following injury were more likely to have obtained employment following injury.
A Comparative Analysis of Classifiers in the Recognition of Packed Executables

Although the packing of executable binaries can be adopted with legitimate intent such as intellectual property protection and size reduction, malware developers utilize those tools to obfuscate their code and thus increase the complexity of static analysis. In order to recognize packed executables, the BinStat application was proposed. It is based on two major steps: the feature extraction, which involves the calculation of statistics and information theory properties from a given binary; and the classification, which adopts a decision tree learned from input features of packed and unpacked binaries previously known in order to classify new executables. The results obtained proved the effectiveness of the tool, but the choice of using only one classifier is arguably a weakness that we chose to improve on the present study. For that end, we rebuilt the training and test datasets and selected the following six classifiers to our analyses: classification and regression trees, random forest, k-nearest neighbors, naive Bayes, neural network and support vector machines. Our results show that the original decision tree algorithm adopted in BinStat (C5.0) is not the best choice for the proposed problem. Indeed, random forest, k-nearest neighbors and support vector machines achieved the best predictive performances.
Depth-Wise Asymmetric Bottleneck With Point-Wise Aggregation Decoder for Real-Time Semantic Segmentation in Urban Scenes

Semantic segmentation is a process of linking each pixel in an image to a class label, and is widely used in the field of autonomous vehicles and robotics. Although deep learning methods have already made great progress for semantic segmentation, they either achieve great results with numerous parameters or design lightweight models but heavily sacrifice the segmentation accuracy. Because of the strict requirements of real-world applications, it is critical to design an effective real-time model with both competitive segmentation accuracy and small model capacity. In this paper, we propose a lightweight network named DABNet, which employs Depth-wise Asymmetric Bottleneck (DAB) and Point-wise Aggregation Decoder (PAD) module to tackle the challenging real-time semantic segmentation in urban scenes. Specifically, the DAB module creates a sufficient receptive field and densely utilizes the contextual information, and the PAD module aggregates the feature maps of different scales to optimize performance through the attention mechanism. Compared with existing methods, our network substantially reduces the number of parameters but still achieves high accuracy with real-time inference ability. Extensive ablation experiments on two challenging urban scene datasets (Cityscapes and CamVid) have proved the effectiveness of the proposed approach in real-time semantic segmentation.
Exploring Important Motivation Drivers for Completion of Massive Open Online Courses by Bayesian Hierarchical Logistic Regression Model

Massive Open Online Courses (MOOCs) have provided over 9 thousand high-quality online courses to 81 million learners. MOOCs have brought about a revolution in education by eliminating the geographic and time constraints of traditional classrooms. It is also well-known that high dropout rates come with such massive user numbers. The retention rate of MOOCs may differ according to the various kinds of motivations that influence learners to finish courses. Few studies have taken into account the effect of user motivations on MOOCs' retention rates. This research involved comparisons between the effects of different variables on motivation as well as administrative variables in MOOCs by using Bayesian hierarchical logistic regression models. A Bayesian model allows explanatory variables that are not necessarily independent and treats missing values in variables within its method. This research also uses data from many course disciplines (instead of courses from the same discipline) and builds models for general data. The results show that hierarchical models performed better than non-hierarchical models, identifying the combinations of administrative independent variables and motivations that can contribute to the improvement of future architecture of MOOCs. Such improvements may help motivate targeting learners to actively engage in related courses on promotion.
Electromagnetic Influence on Electric Vehicles by Wireless Inductive Charging System

With the rapid development of automotive electromagnetic equipment, traditional electromagnetic compatibility (EMC) issues should get renewed to keep pace with increasingly complex car-electronics especially in electric vehicles (EVs). This work calculates the electromagnetic fields (EMFs) in several kinds of electronic components of an EV exposed to its wireless charging system working at the frequency of 30 kHz. An integrated real-size EV model is constructed and the numerical calculation is carried out by a finite element method (FEM). The simulation result provides a significant reference for authentic EMC of electric vehicles brought in by the inductive power transfer (IPT) system.
Multivariate Standard Addition Cobalt Electrochemistry Data Fusion for Determining Phosphate Concentration in Hydroponic Solution

Phosphate is one of the major elements affecting agricultural production. The accurate determination of phosphate concentration essential for plant growth, especially in a hydroponics system, allows regulating the balanced and suitable range set of nutrients to plants efficiently. This study proposed a data fusion model based on 70 samples for calibration and 30 samples for predicting concentrations of phosphate in an eggplant nutrient solution. Three multivariate analysis methods i.e. partial least squares model (PLS), Gaussian process regression (GPR), and artificial neural network (ANN) were studied and compared for their performance efficiencies. The results showed that combining the multivariate standard addition method (MSAM) in acquiring data from cobalt electrodes and ANN data fusion model came up with satisfactory outcomes. Both the method provided good performance with <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> values of 0.98 and 0.96, and the root mean square error (RMSE) of 50 and <inline-formula> <tex-math notation="LaTeX">$66\,\,{\mathrm {mg. \,\,L}}^{-1}$ </tex-math></inline-formula> respectively in calibration and evaluation tests. These values were much higher than those of conventional processing techniques. Moreover, the normal direct calibration method in acquisition signal from cobalt electrodes was also applied, which provided <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> values of 0.7 to 0.8. These high values are sufficient for development to measure phosphate concentration in hydroponic solutions.