Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Foggy Image Restoration Under Hardy Space

The foggy image recovery is one of hot topics in the image processing. Considering that the haze images fade the colors and reduce the contrast in the process of defogging, in this paper, we propose a novel regularization method of image defogging based on Hardy space transformation. Our method uses model analysis and mathematical derivation to use the transformed pseudo-differential operator, which obtains more statistical features and defines the extent of image defogging. The effective comparison algorithm is preferred. Extensive quantitative and qualitative evaluations have been demonstrated that our algorithm improves effectively the defogging of real and simulated images.
Design of EMI Filters Using Multi-Objective Optimization

A multi-objective design procedure for EMI filters in power converters is proposed. In a first step, the results obtained by an automatic design of EMI filters for power electronic converters oriented to satisfy the electromagnetic compatibility standards and the power density constraint which impose the minimization of the filter volume and/or weight, are evaluated. In a second step, the power losses and the cost related to the EMI filter configurations are estimated. By using the dominance concept, the EMI filter designs which are part of the Pareto front are selected among a discrete set of feasible configurations. Therefore, the configurations of the Pareto front take into consideration the minimization of the volume, the power loss and the cost of the EMI filter. The design is mapped onto an objective function space in which the different filter implementations are evaluated and compared.
Fast Direct Solution of Electromagnetic Scattering with Modified HODLR Solver

An efficient fast direct solver of method of moments (MoM) is developed based on hierarchically off-diagonal low-rank (HODLR) structure. HODLR structure exploit that the off-diagonal matrices are rank deficient. In this paper, a modified method is proposed to construct off-diagonal matrices of HODLR structure. With admissible condition, the off-diagonal matrices are decomposed into far field interactions and near field interactions. The far field interactions and near field interactions are compressed into low-rank sub-matrices respectively. The off-diagonal matrices can be recompressed into low-rank matrices by aggregating all sub-matrices. Numerical results indicate the efficacy and accuracy of the proposed method compared with conventional methods.
Driving Style Analysis by Classifying Real-World Data with Support Vector Clustering

All drivers have their own habitual choice of driving behavior, causing variations in fuel consumption. It would be beneficial to classify these driving styles and extract the most economical and ecological driving patterns. However, driving style of each driver is not consistent and may vary within a single trip. Therefore, this paper proposes a novel technique to robustly classify driving style using the Support Vector Clustering approach, which attempts to differentiate the variations in individual's driving pattern and provides an objective driver classification. Real driving data of three human participants were collected using an instrumented vehicle. For data processing, each trip data were first segmented into separate event groups. Prominent factors were then extracted by applying Principal Component Analysis on both statistical and spectral features of all signals. Afterwards, Support Vector Clustering was performed to classify driving style during the trip. The trained classifier was used to indicate the driving pattern variations in percentage. The validity of the proposed method was evaluated using the jerk profile, where a high correlation was found between the classification results and jerk distributions. Moreover, a positive relation between fuel consumption and driving aggressivity was also confirmed. Furthermore, it was found that weather condition, time of the day and ultimately, the driver's eagerness, can cause significant variations in driving style.
Trial Reporting in Immuno-Oncology (TRIO): An American Society of Clinical Oncology-Society for Immunotherapy of Cancer Statement.

PURPOSE To develop recommendations for clinical trial reporting that address the unique efficacy, toxicity, and combination and sequencing aspects of immuno-oncology (IO) treatments. METHODS ASCO and the Society for Immunotherapy of Cancer (SITC) convened a working group that consisted of practicing medical oncologists, immunologists, clinical researchers, biostatisticians, and representatives from industry and government to develop Trial Reporting in Immuno-Oncology (TRIO) recommendations. These recommendations are based on expert consensus, given that existing data to support evidence-based recommendations are limited. CONCLUSION The TRIO recommendations are intended to improve the reporting of IO clinical trials and thus provide more complete evidence on the relative benefits and risks of an IO therapeutic approach. Given the rapid expansion of the number of IO clinical trials and ongoing improvements to the evidence base supporting the use of IO treatments in clinical care, these recommendations will likely need regular revision as the IO field develops.
Collaborative Edge Caching in Context-Aware Device-to-Device Networks

Caching content on the edge of a network can effectively localize traffic, reduce network latency, and improve network throughput. In this paper, we propose a posteriori caching mechanism rather than the mainstream apriority theory, in which the content placement strategy is determined based on the identical distribution of content popularity and user preference before the theoretical analysis of the placement gain is validated. We primarily investigate the optimal caching strategy subjected to the constraint of storage capacity in a heterogeneous network, where a macro base station (MBS), small base stations (SBSs), and user terminals (UTs) are integrated for proactive content storage in the physical layer. To maximize the local hit rate while reducing the transmission delay, we first analyze the optimal strategy by converting content placement into a 0–1 knapsack problem and address the optimization problem using the method of Lagrangian multipliers. We then determine the request probability for each user by exploiting the context information of the user's request history, user similarity, and social ties to achieve reasonably well-optimized caching performance. The caching policy is further optimized into a low-complexity heuristic algorithm with the knowledge of request probability and the optimal copy volumes. Finally, the simulation results show that the proposed cooperative caching algorithm improves the performance metric in terms of hit rate and transmission delay under different benchmarks.
Lung Nodule Classification Using Deep ConvNets on CT Images

Lung cancer is a hazardous disease which is the unrestricted growth of abnormal cells that can occur in one or both of the lungs. Lung tumors can be of two types such as benign and malignant. The survival rate of lung cancer depends upon early identification of lung nodules which is a crucial process. We propose a new method for Lung nodule classification from Lung CT images by using deep convolutional neural networks [19]. This method eliminates the need of manual feature extraction which is a feed back of previous works. The network is fed with raw lung CT images from publicly available LIDC-IDRI dataset. Here, the lung images are classified into three classes such as: non-nodules, nodules of size <3 mm and nodules of size >= 3 mm. This classification is achieved with the help of AlexNet which is a pre-trained convolutional neural network with the help of transfer learning methodology. This method successfully classified the lung CT images into three classes and achieved 98% accuracy with comparatively less false positive rates.
Recommendations for the Nomenclature of Cognitive Change Associated With Anaesthesia and Surgery—2018

Cognitive change affecting patients after anaesthesia and surgery has been recognised for more than 100 yr. Research into cognitive change after anaesthesia and surgery accelerated in the 1980s when multiple studies utilised detailed neuropsychological testing for assessment of cognitive change after cardiac surgery. This body of work consistently documented decline in cognitive function in elderly patients after anaesthesia and surgery, and cognitive changes have been identified up to 7.5 yr afterwards. Importantly, other studies have identified that the incidence of cognitive change is similar after non-cardiac surgery. Other than the inclusion of non-surgical control groups to calculate postoperative cognitive dysfunction, research into these cognitive changes in the perioperative period has been undertaken in isolation from cognitive studies in the general population. The aim of this work is to develop similar terminology to that used in cognitive classifications of the general population for use in investigations of cognitive changes after anaesthesia and surgery. A multispecialty working group followed a modified Delphi procedure with no prespecified number of rounds comprised of three face-to-face meetings followed by online editing of draft versions. Two major classification guidelines [Diagnostic and Statistical Manual for Mental Disorders, fifth edition (DSM-5) and National Institute for Aging and the Alzheimer Association (NIA-AA)] are used outside of anaesthesia and surgery, and may be useful for inclusion of biomarkers in research. For clinical purposes, it is recommended to use the DSM-5 nomenclature. The working group recommends that ‘perioperative neurocognitive disorders’ be used as an overarching term for cognitive impairment identified in the preoperative or postoperative period. This includes cognitive decline diagnosed before operation (described as neurocognitive disorder); any form of acute event (postoperative delirium) and cognitive decline diagnosed up to 30 days after the procedure (delayed neurocognitive recovery) and up to 12 months (postoperative neurocognitive disorder).
External Consensus in Networked Multi-Agent Systems with Random Network Delay

In this paper, the implementation of a prediction algorithm in solving an external consensus problem for heterogeneous networked multi-agent systems (NMAS), with bounded random network-induced delay is presented. In this work, a delay measurement signal generator (DMSG) is implemented; hence the random network delay is determined and compensated successfully. The developed strategy is simulated with single-input single-output (SISO) models for two dissimilar agents and validated through practical experiments with two different water level test rigs under intranet network connection. From the results obtained, the proposed controller shows promising results compared to the previous predecessor.
Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates.

BACKGROUND Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most complicated pharmacokinetic properties to be understood and predicted. However, experimental determination of the substrate binding, selectivity, sites and rates of metabolism is time- and recourse- consuming. In the phase I metabolism of foreign compounds (i.e., most of drugs), cytochrome P450 enzymes play a key role. To help develop drugs with proper ADME properties, computational models are highly desired to predict the ADME properties of drug candidates, particularly for drugs binding to cytochrome P450. OBJECTIVE This narrative review aims to briefly summarize machine learning techniques used in the prediction of the cytochrome P450 isoform specificity of drug candidates. RESULTS Both single-label and multi-label classification methods have demonstrated good performance on modelling and prediction of the isoform specificity of substrates based on their quantitative descriptors. CONCLUSION This review provides a guide for researchers to develop machine learning-based methods to predict the cytochrome P450 isoform specificity of drug candidates.
Systematic Evaluation of Microwave-Assisted Magnetic Recording

A micro-magnetic simulation model to evaluate microwave-assisted magnetic recording (MAMR) has been developed. Optimization of the spin torque oscillator with ac field strength, head geometry, and oscillating frequency is reported. Realistic recording conditions, including media properties and head parameters, are implemented to show the benefits and challenges of MAMR. We found the out-of-plane component of the ac magnetic field causes erasure when medium anisotropy is low for the system during the writing process even while ferromagnetic resonance is helping switching the recording media grains. The signal-to-noise ratio (SNR) of MAMR is calculated using ensemble waveform analysis with a pseudorandom bit sequence which mimics real user data. The SNR is compared under different recording media anisotropy field while maintaining the same linear density.
Predictive Control of a 27-level Asymmetric Multilevel Current Source Inverter

In this work a study of a 27 -level asymmetric multilevel current source inverter is developed. The presented structure is composed by three current source inverters that together are in a 9:3:1 ratio to produce a current waveform with multiple levels. A model predictive control scheme is developed for tracking the references for the output AC voltages, and to keep the asymmetric ratio between the DC currents even during transient conditions. Furthermore, an extra control goal is included in order to reduce the commutations of the inverter, and therefore reduce the power losses. In order to apply the predictive controller, the equations of the model to be analyzed are obtained systematically for continuous and discrete time domains. Simulations are carried out to validate the proposal and the integrity of the predictive control strategy.
Impact of overt diabetes diagnosed in pregnancy in a multi-ethnic cohort in Spain

Abstract Overt diabetes (ODM) is defined as women without diabetes meeting the criteria for diabetes at the first antenatal visit. The risk of obstetric complications increases linearly with maternal glycemia and poorer maternal-fetal outcomes than in gestational diabetes (GDM) may be expected. Studies focusing on ODM pregnancy outcomes are lacking. We aimed to analyze maternal characteristics and pregnancy outcomes in ODM women compared with those with GDM. A retrospective cohort study of women giving birth between January 2010 and April 2013 was conducted. Participants with pre-gestational diabetes were excluded. All women underwent screening for GDM at the 24th–28th weeks of gestation or at the first prenatal visit in those with risk factors. HbA1c and a fasting glucose were measured in GDM women to rule out ODM. Of the 5,633 women included, 572 (10%) were diagnosed with GDM and 50 (0.88%) with ODM. Almost 95% of ODM women were from ethnic minorities. After adjustment for confounding factors, ODM women showed increased rates of premature birth (23.1% vs. 6.7%, p < .001), emergent cesarean section (41.0% vs. 19.5%, p = .049), preeclampsia (22% vs. 3.7%, p < .001) and large-for-gestational-age babies (40.0% vs. 14.8%, p = .008) compared with GDM. In conclusion, ODM is associated with poorer obstetric outcomes than GDM and affects mainly women from ethnic minorities.
Natural History of Obesity Subphenotypes: Dynamic Changes Over Two Decades and Prognosis in the Framingham Heart Study

Context The natural histories of obesity subphenotypes are incompletely delineated. Objectives To investigate dynamic changes in obesity subphenotypes and associations with outcomes. Design, Setting, Participants, and Measurements Framingham Offspring Cohort participants (n = 4291) who attended the examination cycles 2 (1979 to 1983) to 7 (1998 to 2001), which included 26,508 participant observations. Obesity subphenotypes [metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy nonobese (MUNO), and metabolically unhealthy obese (MUO)] were ascertained based on metabolic health (<2 Adult Treatment Panel III criteria). The outcomes were subclinical cardiovascular disease (CVD), incident diseases [diabetes, hypertension, chronic kidney disease (CKD), CVD], and all-cause mortality. Results At baseline, 4% and 31% of participants exhibited the MHO and MUNO subphenotypes, respectively. Four-year probability of MHO participants becoming MUO was 43% in women and 46% in men. Compared with MHNO, MHO participants had 1.28-fold (95% CI, 0.85 to 1.93) and 1.92-fold (95% CI, 1.38 to 2.68) higher odds of subclinical CVD and coronary artery calcification, respectively; corresponding values for MUNO were 1.95 (1.54 to 2.47) and 1.92 (1.38 to 2.68). During follow-up (median of 14 years), 231 participants developed diabetes, 784 hypertension, 423 CKD, 639 CVD, and 1296 died. Compared with MHNO, MHO conferred higher risks of diabetes [hazard ratio (HR), 4.69; 95% CI, 2.21 to 9.96] and hypertension (HR, 2.21; 95% CI, 1.66 to 2.94). Compared with MUO, MHO conferred lower risks of diabetes (0.21; 0.12 to 0.39), CVD (0.64; 0.43 to 0.95), and CKD (0.44; 0.27 to 0.73), but similar hypertension, cardiovascular mortality, and overall mortality risks. Conclusion Over time, most MHO participants developed metabolic abnormalities and clinical disease. The MHO subphenotype is a harbinger of future risk.
Design and Analysis of Halbach Ironless Flywheel BLDC Motor/Generators

Halbach array-based permanent magnet (PM) brushless machines exhibit attractive features when compared with conventional PM brushless machines with normal segmented magnets, including the flux-concentration effect, essentially sinusoidal air-gap flux density due to PMs, and negligible cogging torque. However, as for a motor/generator used in flywheel, an external rotor composed of Halbach array and rotor back-iron is necessary to increase the inertia of flywheel system, which is different from the conventional external-rotor machines. In this paper, a Halbach array PM motor is studied compared with the radial magnet array. The relationship between the air-gap field distribution and the magnet array is discussed. Finally, the eddy current loss in PMs of the external-rotor ironless brushless dc motor is analyzed between the Halbach PM motor and the conventional PM motor.
Enhancement of Phase-Shifting Nonreciprocity in Metamaterial Lines With Comb-Shaped Capacitive Stubs on the Normally Magnetized Ferrite Substrate

In this paper, enhancement of phase-shifting nonreciprocity is proposed by using metamaterial lines with comb-shaped capacitive stubs on the normally magnetized ferrite substrate. The insertion of capacitive stubs enhances the effective permittivity, i.e., effective propagation distance in one propagation direction due to edge-guided mode propagation along the comb-shaped stub edges. The nonreciprocity of the proposed structure was numerically compared with those of other types of metamaterial lines, and transmission characteristics of the prototype circuit were measured and reproduced by the numerical simulations, verifying the enhancement.
An Analysis of Foot and Ankle Device Recalls by the Food and Drug Administration

Introduction Orthopaedic devices represent 12% of all medical device recalls. Products are approved through pre-market approval (PMA) or the 510(k) premarket notification process. No previous evaluation was found in the literature evaluating foot and ankle device recalls. The field of foot and ankle subspecialty has seen a rapid growth in innovation related to implants in recent years. Methods The Food and Drug Administration (FDA) Device Recall database was evaluated for all foot and ankle devices from 2007 through 2017 for the manufacturer, process of approval, type of implant, recall class, dates of initiation and termination of the recall, manufacturer determined reason, quantity affected, and distribution within the United States or internationally. Results A total of 161 products from 33 companies were identified with 158 (98.1%) approved through the 510(k) process. The most common reason for device recall was due to the device breaking intraoperatively or postoperatively. The average length of the recall was 487.5 days. Conclusions Device recall is not an uncommon event with the majority of products approved through the less demanding 510(k) process.
Minimally Biased Multipliers for Approximate Integer and Floating-Point Multiplication

Approximate multipliers enable the saving of area and power for implementation of many modern error-resilient compute-intensive applications. In this paper, we first propose a novel error-configurable minimally biased approximate integer multiplier (MBM) design. The proposed MBM design is devised by coupling a unique error-reduction mechanism with an approximate log based integer multiplier. Next, we propose an optimization (by removing leading-one detection and barrel shifting logic) of the MBM and a class of state-of-the-art approximate integer multipliers (DRUM and SSM), so that they can be efficiently used in approximate floating-point (FP) multipliers. Then, we propose a set of new approximate FP multipliers and we show that these FP multipliers lie on the Pareto front on the design spaces of area <italic>versus</italic> error and power <italic>versus</italic> error. We synthesize the designs using the TSMC 45-nm standard-cell library. Results show that the MBM integer design offers optimal points in the design space, offering up to 75% area reduction and 84% power reduction with <0.1% error bias, when compared with the accurate version. The proposed approximate FP multipliers offer better error-efficiency tradeoffs than traditional precision scaling. The FP design space can offer up to <inline-formula> <tex-math notation="LaTeX">$57 \times $ </tex-math></inline-formula> power and <inline-formula> <tex-math notation="LaTeX">$28 \times $ </tex-math></inline-formula> area improvement for less than 25% peak error, 7% mean error, and 4% error bias, when compared with the IEEE-754 single-precision FP multiplier. We also perform application-level evaluations of the proposed approximate integer and FP multipliers, showing that our proposed multipliers enable significant power and area reduction with minimal degradation in applications’ output quality.
Confidant Network and Quality of Life of Individuals Aged 50+: The Positive Role of Internet Use

Studies on the quality of life (QoL) of older adults have tended to focus on sociodemographic, economic, and health characteristics and, more recently, have analyzed the importance of confidant networks. The Internet has redesigned individuals' daily lives and has become one of the main means of communication. In addition to the aforementioned variables, research must also gauge the importance of this technology and how it can influence the relationship between confidant networks and QoL of older adults. This article aims to analyze the contribution of the Internet on the QoL of older adults as well as the manner that it affects the aforementioned relationship. The present study focuses on a sample of 1,906 individuals aged 50+ who are resident in Portugal and were surveyed in the context of the SHARE project (Survey of Health, Ageing and Retirement in Europe), wave 4. The results of the regression analyses reveal the importance of the Internet on the QoL of older adults. The data also underline the moderating role played by the Internet on the relationship between the confidant network and the QoL of individuals aged 50+, in particular the fact that this technology optimizes the positive impact of confidant networks on QoL. The results obtained thus reinforce the importance of policies aimed at the e-inclusion of older adults as a way to promote their QoL.