Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual reality for pain management: an umbrella review

Background and objective Virtual reality is a promising pain control strategy for various pain conditions. This umbrella review of systematic reviews and meta-analyses aims to evaluate the analgesic effects of virtual reality. Methods We searched for the relevant reviews in Scopus, PubMed and Cochrane library. Our primary outcome was pain, with secondary outcomes including disability, general health status, patient satisfaction, depression, balance, fear of movement, and adverse events. The quality of included articles was evaluated using the AMSTAR-2 tool. Results 21 systematic reviews and meta-analyses with 274 studies and 17,680 patients were included in this review. All the reviews concluded benefits of virtual reality in managing pain conditions, including chronic and pain. Discussion and conclusions This umbrella review demonstrates successful application of virtual reality in pain control, including perioperative, periprocedural, and chronic pain settings. Virtual reality can be used as an alternative therapy for pain management in children and adults.
Secure Communications over Arbitrarily Varying Wiretap Channels

by Ebrahim MolavianJazi In this thesis, we consider a class of information protection problems requiring confidentiality (secrecy) from eavesdropping and integrity (reliability) from jamming in an information-theoretic context. For this purpose, we introduce the arbitrarily varying wire-tap channel (AVWTC) model consisting of a family of wiretap channels indexed by some state that is selected by the jammer in an arbitrary and time-varying manner. We study this model for traditional wiretap codes and randomized wiretap codes, and develop insights about the corresponding secrecy capacity and randomized-code secrecy capacity, respectively. We characterize a lower bound on the randomized-code secrecy capacity of the AVWTC that suggests the secrecy is mainly dictated by a worst-case scenario. Additionally, we establish two upper bounds on the randomized-code secrecy capacity that suggest a single bad “averaged” state can preclude secure communication, while certain superiorities of the legitimate receiver over the eavesdropper in all “averaged” states result in a positive secrecy capacity. Finally, we prove that the secrecy capacity of the AVWTC is either zero or equal to its randomized-code secrecy capacity. We show that a “two-layer” wiretap code is appropriate for combatting the two non-colluding jamming and eavesdropping adversaries. To Our Lady of Fatima and Her Peace-Bringing Son
Closed Loop Step test used for Process Identification and PID tuning controller by Genetic Algorithms

Los metodos de identificacion y diseno de controladores PID a partir de multiples puntos de la respuesta en frecuencia, presentan mejores resultados, en comparacion con los que consideran un solo punto de la respuesta en frecuencia. Los puntos identificados, los cuales se obtienen mediante el metodo del escalon en lazo cerrado, se utilizan para el diseno de controladores PID, y para modelar sistemas lineales mediante funcion de transferencia, proponiendo la estructura de un sistema de segundo orden mas un retardo. Ambos problemas son planteados como un problema de optimizacion no lineal de minimos cuadrados sin restricciones. El problema de optimizacion se resuelve mediante un algoritmo genetico simple.
ResearchEngineering Management—Article Data-Driven Discovery of Stochastic Differential Equations

Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identiﬁcation of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufﬁcient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efﬁcient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering ﬁelds for analysis, prediction, and decision making.
FASTING, BREAKFAST, CAFFEINE AND CAFFEINE WITHDRAWAL: EFFECTS ON ALERTNESS, RECALL AND ENCODING

Background: Extensive research on the behavioural effects of consuming breakfast and caffeine has led to the identification of sensitive tests. It has been suggested that the effects of breakfast on memory and alertness may reflect removal of the negative effects of fasting. Similarly, it has been argued that beneficial behavioural effects of caffeine may be due to reversal of caffeine withdrawal. The aim of the present research was to examine these mechanisms using tests which are known to be sensitive to the effects of breakfast and caffeine. Methods : The present study examined the effects of fasting and caffeine withdrawal over the course of an 8 hour period during the day. The participants were 65 males and 65 females, age range 18-30 years, mean age 23.5 years. Participants carried out a baseline test session and were then given breakfast cereal and caffeinated coffee (100 mg caffeine). Volunteers were then assigned to groups formed by combining fasting (fast/no fast) and caffeine withdrawal conditions (withdrawn/non-withdrawn). The effects of fasting and withdrawal were tested 8 hours after breakfast. Following this test volunteers were given caffeine (100 mg) or placebo, and breakfast cereal or no cereal. A final test session was completed an hour later. Results: The results showed that fasting was associated with reduced pre-test alertness and poorer recall of a list of words. Caffeine withdrawal had no significant effects on tests of post-task alertness and encoding. In the final session, caffeine increased post-task alertness and encoding of new information. This was observed in both withdrawn and non-withdrawn volunteers. Breakfast cereal increased pre-task alertness in both those who had fasted and those who had not. However, breakfast cereal only improved recall in the fasted group. Conclusions: Use of sensitive tests demonstrated behavioural effects of breakfast cereal and caffeine. Fasting led to reduced alertness and poorer recall and the effect on memory was removed by consuming cereal. The effects of caffeine were observed in both withdrawn and non-withdrawn volunteers.
Dynamic Cross-Sectional Factor Identification and Analysis of Consumer Perception towards Insurance Schemes in India

The insurance industry is an integral part of any modern economy, providing individuals and businesses with protection against a wide range of risks. However, the success of the industry depends largely on consumer perception towards insurance schemes. Consumer perception towards insurance schemes can have a significant impact on the demand for insurance products, the competitiveness of the insurance market, and the overall profitability of insurance companies.
Skin Lession Classification Using Image Processing *Note: Sub-titles are not captured in Xplore and should not be used

: Melanoma is a type of skin cancer with a high mortality rate. The different types of skin lesions result in an inaccurate diagnosis due to their high similarity. Accurate classification of the skin lesions in their early stages enables dermatologists to treat the patients and save their lives. This paper proposes a model for a highly accurate classification of skin lesions. The proposed model utilized the GLCM and Gabor features. Performance of SVM, KNN and Naïve Bayes Classifiers is evaluated. The latest well-known public challenge dataset, ISIC 2019, is used to test the ability of the proposed model to classify different kinds of skin lesions. The proposed model successfully classified the nine different classes of skin lesions, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, vascular lesion, and Squamous cell carcinoma. The achieved classification accuracy, using KNN and Gabor features are 96.07. The proposed model can detect images that do not belong to any one of the nine classes where these images are classified as unknown images.
A Model for Intrusion Detection in Cybersecurity Using Random Forest Algorithm

----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ABSTRACT Network Intrusion Detection Systems (NIDSs) are essential tools for the network system administrators to detect various security breaches inside an organization’s network. A NIDS monitors and analyzes the network traffic to and from the network devices of an organization and raises alarms if an intrusion is observed. The feature and the parameter passed into algorithm may reduce performance of the algorithm. The aim of the research is to develop a model in Cybersecurity using Random forest Algorithm. Machine learning approach was used as a tool for the developed model. The objective of the study is to select the best feature using Principal Component Analysis and tune the parameter using Sequential Mode Based Optimization. There were two data set used for the research. The test+ and the test-21 from NSL-KDD data set. The test+ dataset for the developed model shows 78% Accuracy.79% for Precision and 79% for Recall while for the existing model the accuracy was 88.5%, precision 82.6% and 96.2% Recall. In a similar result for test-21 dataset, the developed model shows 45% Accuracy, 55% Precision and 88.6% for Recall while in existing model 77%, 88.5% and 78.5% for Accuracy, Precision and Recall was gathered respectively. The developed model shows a low classification of 16% for normal packet and 84% classification for anomalies as seen in the confusion matrices. The model was able to identify anomalies with 84% Accuracy which is very good considering the focus on detecting suspicious packet.
An image reconstruction framework based on boundary voltages for ultrasound modulated electrical impedance tomography

A new image reconstruction framework based on boundary voltages is presented for ultrasound modulated electrical impedance tomography (UMEIT). Combining the electric and acoustic modalities, UMEIT reconstructs the conductivity distribution with more measurements with position information. The proposed image reconstruction framework begins with approximately constructing the sensitivity matrix of the imaging object with inclusion. Then the conductivity is recovered from the boundary voltages of the imaging object. To solve the nonlinear inverse problem, an optimization method is adopted and the iterative method is tested. Compared with that for electrical resistance tomography (ERT), the newly constructed sensitivity matrix is more sensitive to the inclusion, even in the center of the imaging object, and it contains more effective information about the inclusions. Finally, image reconstruction is carried out by the conjugate gradient algorithm, and results show that reconstructed images with higher quality can be obtained for UMEIT with a faster convergence rate. Both theory and image reconstruction results validate the feasibility of the proposed framework for UMEIT and confirm that UMEIT is a potential imaging technique.
SAR Deep Learning Sea Ice Retrieval Trained with Airborne Laser Scanner Measurements from the MOSAiC Expedition

Automated sea ice charting from Synthetic Aperture Radar (SAR) has been researched for more than a decade and still, we are not close to unlocking the full potential of automated solutions in terms of resolution and accuracy. The central complications arise from ground truth data not being readily available in the polar regions. In this paper, we build a dataset from 20 near coincident X-Band SAR acquisitions and as many Airborne Laser Scanner (ALS) measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), between October and May. This dataset is then used to 5 assess the accuracy and robustness of five machine learning based approaches, by deriving classes from the freeboard, surface roughness (standard deviation at 0 . 5 m correlation length) and reflectance. It is shown that there is only a weak correlation of the radar backscatter and the sea ice topography. Accuracies between 40% and 69% percent and robustnesses between 68% and 85% give a realistic insight into modern classifiers’ performance across a range of ice conditions over 8 months. It also marks the first time algorithms are trained entirely with labels from coincident measurements, allowing for a probabilistic class 10 retrieval. The results show that segmentation models able to learn from the class distribution significantly perform pixel-wise classification approaches.
How the recursive feature elimination affects the SVM and RF for relative wildfire susceptibility in a certain area? A study case in Northern Iran

This study aims to identify key fire factors via recursive feature elimination (RFE) to generate forest fire susceptibility map (FSM) using support vector machine (SVM), and random forest (RF) models. The fire zones were derived from MODIS satellite imagery from 2012 to 2017. Further validation of these data has been provided by field surveys and reviews of land records in rangelands and forests; a total of 326 fire points were
Distributed optimization for control and learning

Large scale multi-agent networked systems are becoming increasingly popular in industry and academia as they can be applied to represent systems in diverse application areas, such as intelligent surveillance and reconnaissance, mobile robotics, transportation networks and complex buildings. In such systems, issues related to control and learning have been significant technical challenges to affect system performance and overall cost. While centralized optimization approaches have been widely used by the engineering and computer science community, advanced and effective distributed optimization techniques have not been explored sufficiently and thoroughly in this regard. This study explores various categories of centralized and distributed optimization methods that have been applied or may be applicable for diverse engineering and science problems. The performance of centralized or distributed optimization schemes significantly depends on various factors including the types of objective functions, constraints, step sizes, and communication networks, etc. In this context, the focus of this dissertation is towards developing novel distributed optimization algorithms in order to solve challenging control and learning problems in various domains such as large-scale building energy systems and robotic networks. Specifically, we develop a generalized gossip-based subgradient method for solving distributed optimization problems in large-scale networked systems, e.g., larger-scale commercial building energy systems. Different from previous work, a user-defined control parameter is introduced to control a spectrum from globally optimal solution to suboptimal solutions and the trade-off between the solution accuracy and temporal convergence. We test and validate our proposed algorithm on a real testbed involving multiple zones incorporating a distributed control and sensing platform. In addition, we extend the distributed optimization to the deep learning area for solving an emerging topic, i.e., distributed deep learning, in fixed topology networks. While some previous work exists on this topic, the data parallelism and distributed computation are still not sufficiently explored. Therefore, we propose a
Efﬁcient XVA management: pricing, hedging and allocation

B anks must calculate and manage valuation adjustments (XVA) across their entire trading portfolio. XVA includes the effects of credit (CVA, DVA), funding (FVA, MVA) (Burgard & Kjaer 2013; Green & Kenyon 2015a), capital (KVA) (Green, Kenyon & Dennis 2014) and tax (TVA) (Kenyon & Green 2015). XVA management includes allocation, hedging and pricing. Allocation refers to the allocation of XVA, and XVA hedging costs, to desks. Hedging costs require the computation of both first-order sensitivities and second-order sensitivities, such as interest rate-credit cross-gamma. Incremental allocation is required for daily trading. Here, we provide an analytically rigorous method for managing XVA efficiently, which combines three elements: trade-level regression, analytic computation of sensitivities and global conditioning. Regression has been demonstrated in MVA to speed things up by oneto-two orders of magnitude, even on vanilla instruments for mediumsized portfolios (1,000–10,000 swaps) (Green & Kenyon 2015a). Analytic computation of sensitivities gives one-to-two orders of magnitude (Capriotti, Lee & Peacock 2011; Giles & Glasserman 2006) (the more sensitivities required, the more improvement). Global conditioning can give a 40:1 improvement for MVA. These are point examples; our contribution is to make these improvements systematically available and extend them generally across trading book XVA management. Technically, this paper generalises Green & Kenyon (2015a) from MVA to XVA, and adds sensitivities and allocation; it also makes explicit elements that are implicit in Green & Kenyon (2014). Regression-based CVA was developed in Cesari et al (2010). We apply regression to all trades; ie, including non-callable trades and European callable trades. Wang & Caflish (2009) used regressions for sensitivities, but were limited to the regression variables themselves. We cover all sensitivities by including sensitivities of the underlyings to hedging instruments via the chain rule. Sensitivities of underlyings to hedging instruments can be calculated using analytic derivatives (AD) (Broadie & Glasserman 1996) or adjoint algorithmic differentiation (AAD) (Capriotti, Lee & Peacock 2011) – we use A/AD as a label for both. Examples of allocation methods for capital and CVA include Tasche (2008). We extend to other XVAs and their sensitivities, which requires a general global conditioning approach to cover MVA when based on expected shortfall (ES) as well as CVA, DVA and FVA. The contribution of this paper is to show how to systematically combine trade-level regression, analytic computation of sensitivities and global conditioning to make XVA management computations radically more efficient. Each element alone is useful, but only in combination does the step-change in efficiency of computation and implementation appear. This methodology naturally handles wrong-way risk (see, for A. Trade-level regression example Basis functions x x x Coefficients of Regression equation basis functions f.x/
Inference for L-estimators of location using a bootstrap warping approach

Abstract In this note we propose a new semi-parametric bootstrap procedure for hypothesis tests about a statistical function and termed bootstrap warping. This procedure was motivated by empirical likelihood and bootstrap tilting techniques. The procedure is computationally efficient and has a fixed number of parameters. We show that the warping procedure has good type I error control and has monotone power as a function of sample size and shift alternatives.
Three principal components describe the spatiotemporal development of mesoscale ionospheric equivalent currents around substorm onsets

Abstract. Substorms are a commonly occurring but insufficiently understood form of dynamics in the coupled magnetosphere–ionosphere system, associated with space weather disturbances and auroras. We have used principal component analysis (PCA) to characterize the spatiotemporal development of ionospheric equivalent currents as observed by the International Monitor for Auroral Geomagnetic Effects (IMAGE) magnetometers during 28 substorm onsets identified by Frey et al. (2004). Auroral observations were provided by all-sky cameras. We found that the equivalent currents can typically be described by three components: a channel of poleward equivalent current (wedgelet), a westward electrojet (WEJ) associated with an auroral arc, and a vortex. The WEJ and vortex are located at the equatorward end of the channel, which has been associated with bursty bulk flows (BBFs) by previous studies. Depending on its polarity, the vortex either indents the WEJ and arc equatorward or bulges the WEJ poleward while winding the arc into an auroral spiral. In addition, there may be a background current system associated with the large-scale convection. The dynamics of the WEJ, vortex, and channel can describe up to 95 % of the variance of the time derivative of the equivalent currents during the examined 20 min interval. Rapid geomagnetic variations at the substorm onset location, which can drive geomagnetically induced currents (GICs) in technological conductor networks, are mainly associated with the oscillations of the WEJ, which may be driven by oscillations of the transition region between dipolar and tail-like field lines in the magnetotail due to the BBF impact. The results contribute to the understanding of substorm physics and to the understanding of processes that drive intense GICs.
Spatiotemporally consistent global dataset of the GIMMS Normalized Difference Vegetation Index (PKU GIMMS NDVI) from 1982 to 2020

Global products of remote sensing Normalized Difference Vegetation Index (NDVI) are critical to assessing the vegetation dynamic and its impacts and feedbacks on climate change from local to global scales. The previous versions of the Global Inventory Modelling and Mapping Studies (GIMMS) NDVI product derived from the Advanced Very High Resolution 10 Radiometer (AVHRR) provide global biweekly NDVI data starting from the 1980s, being a reliable long-term NDVI time series that has been widely applied in Earth and environmental sciences. However, the GIMMS NDVI products have several limitations (e.g., orbital drift and sensor degradation) and cannot provide continuous data for the future. In this study, we presented a machine learning model that employed massive high-quality and global-wide Landsat NDVI samples and a data consolidation method to generate a new version of the GIMMS NDVI product, i.e., PKU GIMMS NDVI (1982−2020), based 15 on AVHRR and Moderate-Resolution Imaging Spectroradiometer (MODIS) data. A total of 3.6 million Landsat NDVI samples that well spread across the globe were extracted for vegetation biomes in all seasons. The PKU GIMMS NDVI exhibits higher accuracy than its predecessor (GIMMS NDVI3g) in terms of R (0.975 over 0.942), mean absolute error (MAE: 0.033 over 0.074), and mean absolute percentage error (MAPE: 9% over 20%). Notably, PKU GIMMS NDVI effectively eliminates the evident orbital drift and sensor degradation effects in tropical areas. The consolidated PKU GIMMS NDVI has a high temporal 20 consistency with MODIS NDVI in describing vegetation trends (R = 0.962, MAE = 0.032, and MAPE = 6.5%). The PKU GIMMS NDVI product can potentially provide a more solid data basis for global change studies. The theoretical framework that employs Landsat data samples can facilitate the generation of remote sensing products for other land surface parameters.