Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial–Spectral Total Variation Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising

Several bandwise total variation (TV) regularized low-rank (LR)-based models have been proposed to remove mixed noise in hyperspectral images (HSIs). These methods convert high-dimensional HSI data into 2-D data based on LR matrix factorization. This strategy introduces the loss of useful multiway structure information. Moreover, these bandwise TV-based methods exploit the spatial information in a separate manner. To cope with these problems, we propose a spatial–spectral TV regularized LR tensor factorization (SSTV-LRTF) method to remove mixed noise in HSIs. From one aspect, the hyperspectral data are assumed to lie in an LR tensor, which can exploit the inherent tensorial structure of hyperspectral data. The LRTF-based method can effectively separate the LR clean image from sparse noise. From another aspect, HSIs are assumed to be piecewisely smooth in the spatial domain. The TV regularization is effective in preserving the spatial piecewise smoothness and removing Gaussian noise. These facts inspire the integration of the LRTF with TV regularization. To address the limitations of bandwise TV, we use the SSTV regularization to simultaneously consider local spatial structure and spectral correlation of neighboring bands. Both simulated and real data experiments demonstrate that the proposed SSTV-LRTF method achieves superior performance for HSI mixed-noise removal, as compared to the state-of-the-art TV regularized and LR-based methods.
Accelerated PDE's for efficient solution of regularized inversion problems

We further develop a new framework, called PDE Acceleration, by applying it to calculus of variations problems defined for general functions on $\mathbb{R}^n$, obtaining efficient numerical algorithms to solve the resulting class of optimization problems based on simple discretizations of their corresponding accelerated PDE's. While the resulting family of PDE's and numerical schemes are quite general, we give special attention to their application for regularized inversion problems, with particular illustrative examples on some popular image processing applications. The method is a generalization of momentum, or accelerated, gradient descent to the PDE setting. For elliptic problems, the descent equations are a nonlinear damped wave equation, instead of a diffusion equation, and the acceleration is realized as an improvement in the CFL condition from $\Delta t\sim \Delta x^{2}$ (for diffusion) to $\Delta t\sim \Delta x$ (for wave equations). We work out several explicit as well as a semi-implicit numerical schemes, together with their necessary stability constraints, and include recursive update formulations which allow minimal-effort adaptation of existing gradient descent PDE codes into the accelerated PDE framework. We explore these schemes more carefully for a broad class of regularized inversion applications, with special attention to quadratic, Beltrami, and Total Variation regularization, where the accelerated PDE takes the form of a nonlinear wave equation. Experimental examples demonstrate the application of these schemes for image denoising, deblurring, and inpainting, including comparisons against Primal Dual, Split Bregman, and ADMM algorithms.
SEFRON: A New Spiking Neuron Model With Time-Varying Synaptic Efficacy Function for Pattern Classification

This paper presents a new time-varying long-term Synaptic Efficacy Function-based leaky-integrate-and-fire neuRON model, referred to as SEFRON and its supervised learning rule for pattern classification problems. The time-varying synaptic efficacy function is represented by a sum of amplitude modulated Gaussian distribution functions located at different times. For a given pattern, the SEFRON’s learning rule determines the changes in the amplitudes of weights at selected presynaptic spike times by minimizing a new error function reflecting the differences between the desired and actual postsynaptic firing times. Similar to the gamma-aminobutyric acid-switch phenomenon observed in a biological neuron that switches between excitatory and inhibitory postsynaptic potentials based on the physiological needs, the time-varying synapse model proposed in this paper allows the synaptic efficacy (weight) to switch signs in a continuous manner. The computational power and the functioning of SEFRON are first illustrated using a binary pattern classification problem. The detailed performance comparisons of a single SEFRON classifier with other spiking neural networks (SNNs) are also presented using four benchmark data sets from the UCI machine learning repository. The results clearly indicate that a single SEFRON provides a similar generalization performance compared to other SNNs with multiple layers and multiple neurons.
Novel Feature Selection Algorithm for Thermal Prediction Model

Demand for more computing power grows steadily, which leads to increase the integration density of modern processors that rise thermal hotspots. A proactive thermal management algorithm tries to avoid exceeding a thermal threshold by exploiting the control decisions and using the thermal prediction model. In this paper, we proposed a new approach to build a thermal prediction model for a multicore processor using a multilayer perceptron (MLP). We generate a data set composed of system state samples gathered during Standard Performance Evaluation Corporation benchmark execution. Thereafter, a compilation method is applied to extract the new features from the data set. These features are categorized into two behavioral and reflective groups. The first group allows the model to track the current thermal behavior of the processor, whereas the second one helps the model to predict the control response temperature. Finally, a smaller set of input features is selected by a new proposed feature selection algorithm. The evaluation shows that the mean prediction error of the proposed model is about 0.5 °C–0.6 °C with 0.6 °C–0.7 °C standard deviations from 2- to 5-s prediction distances. The results show the superiority of our model and feature selection algorithm in comparison with the counterparts.
YoungMito 2018: Report on the 1st International Mitochondria Meeting for Young Scientists

The 1st International Mitochondria Meeting for Young Scientists (International YoungMito 2018) was held at Hotel Co‐op Inn Kyoto in Kyoto, Japan, from 20 to 22 April 2018. The meeting was attended by 130 mitochondrial researchers from 15 countries. International YoungMito 2018 was the first international mitochondria meeting held in Japan organized by and for young mitochondrial researchers. Over the 3‐day period, there were 28 oral presentations including two keynote lectures, 20 presentations from invited speakers, and six short talks selected from abstract submissions. Many different topics were covered including quality control pathways acting against mitochondrial stresses, mitochondrial dynamics, protein/lipid transport, cristae organization, respiration/ATP synthesis, mtDNA maintenance, mitochondrial disease models, and pharmacological approaches. In addition, we had 64 posters, a number which represented almost half of all attendees. Thanks to the cutting‐edge information and high‐quality unpublished data that were presented, there were many lively discussions during oral and poster sessions that continued into the coffee breaks, lunchtime, and nighttime discussions. The 1st international YoungMito meeting was successful in promoting intellectual exchange among all participants, facilitating collaborations beyond national boundaries, and closed with great success. It was a great pleasure that many participants were looking forward to the next YoungMito meeting.
Improving RealSense by Fusing Color Stereo Vision and Infrared Stereo Vision for the Visually Impaired

The introduction of RGB-D sensor has attracted attention from researchers majored in computer vision. With real-time depth measurement provided by RGB-D sensor, a better navigational assistance than traditional aiding tools can be offered for visually impaired people. However, nowadays RGB-D sensor usually has a limited detecting range, and fails in performing depth measurement on objects with special surfaces, such as absorbing, specular, and transparent surfaces. In this paper, a novel algorithm using two RealSense R200 simultaneously to build a short-baseline color stereo vision system is developed. This algorithm enhances depth estimation by fusing color stereo depth map and original RealSense depth map, which is obtained by infrared stereo vision. Moreover, the minimum range is decreased by up to 84.6%, from 650mm to 100mm. We anticipate out algorithm to provide better assistance for visually impaired individuals.
Towards Resource-Aware Hybrid Camera Systems

We investigate how hybrid camera systems---stationary and mobile cameras---allow to improve the observability of mobile objects and/or locations considering limited resources. This Static Camera System with Mobile robots (SCSM) allows mobile cameras to observe targets where no stationary cameras are deployed, where they fail, or their field of view (FOV) is blocked. By assigning dynamic priorities to targets, the self-coordination and control of the SCSM becomes an assignment problem. Furthermore, the coordination of the available resources, especially for the mobile part of the SCSM, is considered. We adapt a market-based approach based on dynamic clustering. The SCSM is evaluated through simulation studies with a graphical simulator for visual sensor networks (VSNs). The results show that the SCSM can achieve up to 30% higher observability of targets compared to a stationary camera system. Moreover, the resource consumption can be distributed among the cameras with the dynamic clustering protocol to not burden all cameras having the same object in their FOV.
A Novel 3G/LTE Data Assortment System in Conveyance Networks-A review

Vehicular circumstantial networks (VANETs) are a special case of mobile ad hoc networks (MANETs). The distinctive characteristics of VANETs embrace high-speed conveyance nodes and vital variability in node density. Collection information from VANETs is vital in watching, dominant, and managing road traffic. However, economical assortment of the required information is difficult because vehicles are incessantly moving and generating a significant quantity of events and information. The main focus of this paper is on vehicle information assortment victimization 3G/LTE. Initially, a comparison of proactive and reactive information assortment schemes is conducted victimization simulation. The results show that proactive schemes manufacture the lowest delay and information measure usage however the best ratio. To efficiently use the offered information measure, Associate in nursing adaptation information assortment scheme is developed and represented. This adaptation information. Assortment scheme is predicated on a proactive theme victimization variable polling periods looking on the vehicle positions within the network and travel time to supply correct traffic and period of time data to the Traffic Management Center (TMC). Simulation results, using taxi traces in Qatar, show that the planned rule consumes an acceptable quantity of megabytes (≈31 MB) per month when the fundamental polling interval is about to 10s. moreover, traffic simulation results demonstrate that the planned theme features a minimum impact on delay and period of time estimates (relative error less than 2.5%), however will manufacture vital degradations in fuel consumption and emission estimates if computations are created at the TMC (relative error larger than 28%and65%, respectively). These Errors are often eliminated if computations are done on the vehicle.
Concept Drift Detection via Improved Deep Belief Network

One of the issues raised in streaming data is concept drift detection. In fact, the process of concept drift comes from natural tendency events in the real world to change over time. For example, in data receiving from credit card transactions, detect when transactions rise suddenly, can help in identifying the fraud. In this paper regards to the importance of concept drift in streaming data, a solution to accurate diagnosis and timely is presented. This solution is based on ensemble algorithm and “streaming ensemble algorithm” (SEA) algorithm that SEA algorithm is used as one of the most commonly stream algorithms. This approach uses a deep belief network as a basic model in the SEA algorithm. In the method which is presented in this paper, we used the change of classification error on new data for concept drift detection. Analyzing the results shows that the proposed method compared with similar algorithms, in addition to a significant reduction in the runtime, improved F_measure criteria.
Artificial Intelligence Based Solarbot

A chatbot (Also known as Chatterbox) is a computer program which conducts communication between a human and machine, commonly through aural or textual methods. Chatbots are becoming popular since they have the potential to save any individual's hassle and time by automating mundane errands. Chatbots have been witnessing an era of highest demand powered by either a set of responses and rules that are already predefined or artificial intelligence method such as Natural Language Processor. Chatbots are often built-in into the chat systems, for example automated online assistants, which gives them the power for small talk or informal conversations secluded to the scopes of their prime expert systems. Solar power in India is rapid developing industry. As of December 2017 the country's solar power had 17.05 GW total capacities. It has been seen that using solar power is better than using electricity as it is an unlimited and renewable source. More and more people prefer using solar energy, they are being more curious about the uses of solar panels. They find it difficult to acquire information related to solar panels and their facilities. To ease their work a Solarbot can be designed that would provide them with all the necessary information they need quickly and without any hassle. The chatbot provides an interface as a platform for communication between the bot and the client. For pattern matching database is used for storing data and as per for functions and procedures. Pattern matching looks for definite patterns for words in the user's input, compares it with the database and responds to the consumer. A system that would provide facilities to the user very quickly and with least amount of work would be very beneficial.
Intercalibration of Cross-Track Scanners in GPM Constellation

This paper discusses the intersatellite radiometric calibration for microwave sounders in support of NASA’s Global Precipitation Measurement (GPM) mission. This paper is an extension of previous GPM Intersatellite Calibration Working Group work, which assesses the robustness of the Central Florida Remote Sensing Laboratory “double difference” technique for sounders intercalibration. In this paper, we will analyze the intercalibration between the Advanced Technology Microwave Sounder on board the National Polar-orbiting Operational Environmental Satellite System Preparatory Project, the Microwave Humidity Sounder on board the MetOp-A and MetOp-B and NOAA-18 and NOAA-19, and the Sondeur Atmosphérique du Profil d’Humidité Intertropicale par Radiométrie sounder onboard the Megha-Tropiques satellite. The sensitivity of the calibration to the geophysical parameters and incidence angles is investigated using two years data. Here, it is observed that a reliable intercalibration can be made only when the path lengths of the two sensors are similar, so when matching the scan angles for the two sensors, the sensitivity of the biases upon the water vapor profiles is significantly reduced.
Mental illness in primary care: a narrative review of patient, GP and population factors that affect prescribing rates

Background Mental illness poses a large and growing disease burden worldwide. Its management is increasingly provided by primary care. The prescribing of psychotropic drugs in general practice has risen in recent decades, and variation in prescribing rates has been identified by a number of studies. It is unclear which factors lead to this variation. Aim To describe the variables that cause variation in prescribing rates for psychotropic drugs between general practices. Methods A narrative review was conducted in January 2018 by searching electronic databases using the PRISMA statement. Studies investigating causal factors for variation in psychotropic prescribing between at least two general practice sites were eligible for inclusion. Results Ten studies met the inclusion criteria. Prescribing rates varied considerably between practices. Positive associations were found for many variables, including social deprivation, ethnicity, patient age and gender, urban location, co-morbidities, chronic diseases and GP demographics. However studies show conflicting findings, and no single regression model explained more than 57% of the variation in prescribing rates. Discussion There is no consensus on the factors that most predict prescribing rates. Most research was conducted in countries with central electronic databases, such as the United Kingdom; it is unclear whether these findings apply in other healthcare systems. More research is needed to determine the variables that explain prescribing rates for psychotropic medications.
Medium-Transparent Packet-Based Fronthauling for 5G Hot-Spot Networks

In order to meet the traffic density requirement and cope with the technical challenges of hot-spot scenarios exhibiting extreme user density in very confined geographical areas, the fifth generation (5G) networks must undergo radical technological and architectural innovations that support a broad solution portfolio. This solution mix includes spectrum expansion to the millimeter Wave (mmWave) band, massive Multiple-Input Multiple-Output antennas and network densification through remote radio head deployment. Since the current Common Public Radio Interface-based mobile fronthaul cannot cope with massive mmWave multi-Gbps traffic streams, it is imperative to introduce a novel converged 5G architecture, specifically designed to facilitate mmWave access to massive amounts of users. To this end, a Medium Transparent Medium Access Control (MT-MAC) protocol has been proposed, designed to operate over a converged mmWave analog Fiber-Wireless (FiWi) fronthaul. MT-MAC allows for fast and direct negotiation of wavelength, radio frequency and time resources between the base band unit and the mmWave users, while offering fast on-demand link formation. However, its performance has not yet been evaluated under 5G heavy-traffic scenarios, as the ones previously described. Hence, in this paper, we investigate the performance of an MT-MAC-enabled fronthaul and report on its suitability for mmWave hot-spot 5G access networks.
Denoising of Iris Image Using Stationary Wavelet Transform

In Therapeutic images, the analysis activities, for example, object recognition and feature extraction will assume the key part. These errands will end up troublesome if the pictures are defiled with noises. Currently the development of complex algorithms became a new research area. Creating Image denoising calculations is a troublesome errand since fine points of interest in a medical picture implanting analytic data ought not be devastated amid commotion evacuation. A considerable lot of the wavelet based denoising calculations utilize DWT (Discrete Wavelet Transform) in the deterioration stage which is experiencing shift variance and lack of directionality. To overthrow this in this paper we are proposing the denoising strategy which utilizes Undecimated Wavelet Transform to break down the picture and we played out the shrinkage task to wipe out the noise from the picture. In the shrinkage step we utilized semi - soft and stein thresholdingfunctions alongside customary hard and soft thresholding functions and confirmed the reasonableness of various wavelet families for the denoising of therapeutic pictures. The outcomes demonstrated that the denoised picture utilizing SWT (Stationary Wavelet Transform) have a superior harmony amongst smoothness and exactness than the DWT. To survey the nature of denoised pictures, the different measurements we utilized are MSE (Mean Squared Error), SSIM (Structural similarity index measure), UQI (Universal Quality Index), PSNR (Peak signal-to-noise ratio), AD (Average Difference), ST(Structural Content), MD (Maximum Difference). NAD (Normalized Absolute Error).
A novel biomarker of laminin turnover is associated with disease progression and mortality in chronic kidney disease

Background Patients with chronic kidney disease (CKD) have increased risk of development of end-stage renal disease (ESRD) and early mortality. Fibrosis is the central pathogenic process in CKD and is caused by dysregulated extracellular matrix (ECM) remodeling. The laminin γ1 chain (LAMC1) is a core structural protein present in the basement membrane of several organs, including the kidneys. We hypothesized that dysregulation of LAMC1 remodeling could be associated with a higher risk of adverse clinical outcomes in patients with CKD. Methods A novel immunoassay targeting LG1M, a specific MMP-9-generated neo-epitope fragment of LAMC1, was developed and used to measure the levels of the fragment in urine and serum from 492 patients from the Renal Impairment in Secondary Care (RIISC) study, a prospective cohort of patients with high-risk CKD. Patients were monitored for a median follow-up time of 3.5 years. Associations between serum and urine LG1M levels and progression of CKD at 12 months were assessed by a multivariable logistic regression model. The association with ESRD or mortality was assessed by Kaplan-Meier survival curves and Cox proportional hazards regression. Results Forty-six (11%) of the 416 patients who reached 12-month follow-up had progression of CKD; during the study follow-up, 125 patients (25.4%) developed ESRD and 71 patients (14.4%) died. Serum and urine levels of LG1M correlated with baseline eGFR (r = -0.43, p<0.0001 and r = -0.17, p = 0.0002, respectively). Serum levels of LG1M were higher in patients with one-year progression of CKD compared to those who did not progress (p<0.01). Baseline serum levels of LG1M were associated with development of ESRD (HR 3.2, 95% CI 1.99–5.2 for patients in the highest LG1M tertile compared to patient in the lowest tertile). Baseline urinary levels of LG1M (uLG1M) were significantly associated with mortality (HR 5.0, 95% CI 2.8–8.9, p<0.0001 for patients in the highest LG1M tertile compared to patients in the lowest tertile). Urine LG1M was retained in the model for prediction of mortality (HR per standard deviation of uLG1M: 1.01, 95% CI 1.00–1.02, p = 0.001). Conclusions LG1M, a marker of basement membrane remodeling, is increased in serum and urine of patients with CKD and levels are associated with one-year disease progression, development of ESRD, and mortality.