Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified value-function-approximation for synchronous policy iteration with single-critic configuration for nonlinear optimal control

Abstract This study proposes a modified value-function-approximation (MVFA) and investigates its use under a single-critic configuration based on neural networks (NNs) for synchronous policy iteration (SPI) to deliver compact implementation of optimal control online synthesis for control-affine continuous-time nonlinear systems. Existing single-critic algorithms require stabilising critic tuning laws while eliminating actor tuning. This paper thus studies alternative single-critic realisation aiming to relax the needs for stabilising mechanisms in the critic tuning law. Optimal control laws are determined from the Hamilton–Jacobi–Bellman equality by solving for the associated value function via SPI in a single-critic configuration. Different from other existing single-critic methods, an MVFA is proposed to deal with closed-loop stability during online learning. Gradient-descent tuning is employed to adjust the critic NN parameters in the interests of not complicating the problem. Parameters convergence and closed-loop system states stability are examined. The proposed MVFA approach yields an alternative single-critic SPI method with uniformly ultimately bounded NN parameter convergence and asymptotic closed-loop system states stability throughout the process of online learning without the need for stabilising mechanisms in the tuning law for critic NN. The proposed approach is verified via simulations.
The Role of Nursing Consultant in Iran: A Qualitative Study

Background: Nursing consultation is one of the important roles of nurses and is very effective in improving care results. But this role is unknown in Iran and there are no respective organizational positions. The purpose of this study is to examine the factors affecting the position of this role. Materials and Methods: This study, as a qualitative research was conducted from April to December 2017 using content analysis approach. We performed semi-structured in-depth interviews with 23 participants who were selected using purposeful sampling and were asked to describe the factors affecting the organizational position of nurse consultant. Results: The mean (standard deviation) age of the participants was 44.5 years (10.68). 279 meaning units, 39 codes, 6 subcategories and two categories were obtained in this essay. The two main categories include necessity for nursing consultation role with two subcategories (nursing consultation role as one of the main roles in nursing, and the need for nursing consultation in healthcare system in Iran), and the obstacles of nursing consultation role with four subcategories (healthcare system problems, problems associated with physicians, problems associated with nurses and problems associated with patients' culture). Conclusions: Considering the importance of nursing consultation role in improving the health of the patients, the policymakers should take into account the consultant role of nurses and they should understand the importance of and the need for this role. As such, we hope that managers and policymakers create a role position by removing the obstacles and considering the necessity of this role.
Incorporating brand variability into classification of edible oils by Raman spectroscopy

Two‐hundred and fifteen Raman spectra of 15 edible oils or blends of edible oils from 53 samples spanning multiple brands purchased over 3 years were investigated using a genetic algorithm for spectral pattern recognition. Using a hierarchical approach to classification, the 15 edible oils could be divided into two groups based on their degree of unsaturation. While edible oils from any particular batch within a class are well clustered and can be differentiated from other varieties of edible oils that are also from a single source, incorporating uncontrolled variability from sources (by purchasing edible oils under different brand names) and seasons (by purchasing edible oils over a 3‐year period) presented a far more challenging classification problem for edible oils within the same group. The between‐source and yearly variability within one class of edible oils is often comparable to differences between the average spectra of the different varieties of edible oils, thereby preventing either a reliable classification of the edible oils or the detection of adulterants in an edible oil if a single model, spanning all sources and years of oils, is to be constructed. The novelty of this study arises from the incorporation of edible oils gathered systematically over the span of 3 years, introducing a heretofore unseen variance to the chemical compositions of the edible oils that are being classified. This is the first time that many different edible oils and commercially available brands thereof have been classified simultaneously.
Design of the 1.5 and 2.5 Bit MDAC - application opportunities

The paper presents design and simulation of the most important stage in pipelined analog-to-digital converter (ADC) so-called multiplying digital-to-analog converter (MDAC). The MDAC with 1,5 and 2,5 bit of resolution were designed using CMOS 0.7 μm technology. The both types of MDAC were compared and the results are also presented. All stages were proposed utilizing Cadence design software.
A Spectral Filter Array Camera for Clinical Monitoring and Diagnosis: Proof of Concept for Skin Oxygenation Imaging

The emerging technology of spectral filter array (SFA) cameras has great potential for clinical applications, due to its unique capability for real time spectral imaging, at a reasonable cost. This makes such cameras particularly suitable for quantification of dynamic processes such as skin oxygenation. Skin oxygenation measurements are useful for burn wound healing assessment and as an indicator of patient complications in the operating room. Due to their unique design, in which all pixels of the image sensor are equipped with different optical filters, SFA cameras require specific image processing steps to obtain meaningful high quality spectral image data. These steps include spatial rearrangement, SFA interpolations and spectral correction. In this paper the feasibility of a commercially available SFA camera for clinical applications is tested. A suitable general image processing pipeline is proposed. As a ’proof of concept’ a complete system for spatial dynamic skin oxygenation measurements is developed and evaluated. In a study including 58 volunteers, oxygenation changes during upper arm occlusion were measured with the proposed SFA system and compared with a validated clinical device for localized oxygenation measurements. The comparison of the clinical standard measurements and SFA results show a good correlation for the relative oxygenation changes. This proposed processing pipeline for SFA cameras shows to be effective for relative oxygenation change imaging. It can be implemented in real time and developed further for absolute spatial oxygenation measurements.
Chaos in Human Brain Phase Transition

From a theoretical equation, modeling the dynamic of the time-dependent coefficients of the first and the second Karhunen-Loeve (KL) expansion of a superconducting quantum interference device (SQUID) signal, chaotic phase transition has been studied in the human brain. Through numerical investigations, the bifurcation diagram and the dynamic of Lyapunov exponent have been plotted. These diagrams reveal that throughout the variation of the control parameter here the frequency of the acoustic stimulus, the brain bifurcates from chaotic states to periodic or to quasiperiodic one. Also a chaotic phase portrait of the KL modes and its corresponding Poincaré section have been plotted. The origin of chaos in the human brain could be due to the self-organizing processes of nonequilibrium phase transition occurring in the electrochemical physiological phenomena of the complex nerve cells and neural assembly. Besides, the occurrence of chaos in the absence of stimuli has been remarked and thought to be due to the fact that an intrinsic brain could be chaotic. Moreover it has been found that the range of frequency for which the brain is forced to behave periodically could be harmful to the thinking process.
Deep‐learning‐based depth estimation from light field images

A novel deep-learning-based depth estimation method for light field images is introduced. The proposed method employs a novel neural network design to estimate the disparity of each pixel based on block patches extracted from epipolar plane images. The network output is further refined based on filtering and denoising algorithms. Experimental results demonstrate an average improvement of 34.35 % in root mean squared error (RMSE) and 49.44 % in mean squared error over machine learning-based state-of-the-art methods.
Selective frequency conversion with coupled time-modulated cavities

Time dependence in photonic structures leads to a number of intriguing and useful phenomena such as optical isolation, topological states of light, and frequency conversion. Here we present a mechanism to achieve efficient and selective frequency conversion using a system of two time-modulated cavities. This setup allows us to fine-tune the conversion process by controlling important parameters such as the intercavity coupling and the external excitation frequency. Both symmetric and asymmetric (up- or down-conversion) outputs can be targeted at will. We describe the processes extensively, with for example a leading role for the dynamic modes of the coupled system, the Floquet modes.
Realization of comprehensive detection algorithm of electrocardiogram signal at application layer electrocardiogram monitoring internet of thing

The invention discloses the realization of a comprehensive detection algorithm of an electrocardiogram signal at the application layer of an electrocardiogram monitoring internet of thing, belonging to the technical field of the internet of thing. The invention mainly solves the problem of comprehensive detection of each wave shape component in the application layer of the electrocardiogram monitoring internet of thing. The invention builds a series of detection methods, detection standards and threshold parameters based on the wavelet transform technique, provides a detection method based on the wavelet transform, the Hilbert transform and the improved envelope transform to the electrocardiogram signal, and detects and identifies the specific shape and position of the QRS wave group, the P wave and the T wave. The detection algorithm provided in the invention has better robustness, and can preferably restrain or eliminate external factors of baseline drift, high-frequency interference and the like. The electrocardiogram signal comprehensive detection algorithm has the false detection rate of 0.89% through the MATLAB simulation and the MIT-BIH data base mark comparison.
Data-Driven Multivariate Signal Denoising Using Mahalanobis Distance

A novel multivariate signal denoising method is presented that computes Mahalanobis distance measure at multiple data scales obtained from multivariate empirical mode decomposition (MEMD) algorithm. That enables joint multichannel data denoising directly in multidimensional space <inline-formula><tex-math notation="LaTeX">$\mathcal {R}^N$</tex-math></inline-formula> where input signal resides, by employing interval thresholding on multiple data scales in <inline-formula><tex-math notation="LaTeX">$\mathcal {R}^N$</tex-math></inline-formula>. We provide theoretical justification of using Mahalanobis distance at multiple scales obtained from MEMD and prove that the proposed method is able to incorporate inherent correlation between multiple data channels in the denoising process. The performance of the proposed method is verified on a range of synthetic and real world signals.
Driving solar coronal MHD simulations on high-performance computers

ABSTRACT The quality of today's research is often tightly limited to the available computing power and scalability of codes to many processors. For example, tackling the problem of heating the solar corona requires a most realistic description of the plasma dynamics and the magnetic field. Numerically solving such a magneto-hydrodynamical (MHD) description of a small active region (AR) on the Sun requires millions of computation hours on current high-performance computing (HPC) hardware. The aim of this work is to describe methods for an efficient parallelisation of boundary conditions and data input/output (IO) strategies that allow for a better scaling towards thousands of processors (CPUs). The Pencil Code is tested before and after optimisation to compare the performance and scalability of a coronal MHD model above an AR. We present a novel boundary condition for non-vertical magnetic fields in the photosphere, where we approach the realistic pressure increase below the photosphere. With that, magnetic flux bundles become narrower with depth and the flux density increases accordingly. The scalability is improved by more than one order of magnitude through the HPC-friendly boundary conditions and IO strategies. This work describes also the necessary nudging methods to drive the MHD model with observed magnetic fields from the Sun's photosphere. In addition, we present the upper and lower atmospheric boundary conditions (photospheric and towards the outer corona), including swamp layers to diminish perturbations before they reach the boundaries. Altogether, these methods enable more realistic 3D MHD simulations than previous models regarding the coronal heating problem above an AR – simply because of the ability to use a large amount of CPUs efficiently in parallel.
Non-Hermitian Wave Transport with Applications to Limiters and Isolators

Non-Hermitian wave transport has attracted a lot of attention during the last ten years, mainly because of its applications in on-chip photonic circuitry. The underlying principle of this subfield of wave transport is the violation (induced or spontaneous) of the time reversal symmetry via lossy mechanisms. When other symmetries are also involved in the design of such circuits (e.g. chiral, mirror, etc.), novel phenomena, such as topologically protected states, might emerge. In this thesis, we utilize time reversal symmetry and its violation along with other symmetries in order to propose a new type of reflective photonic limiters and asymmetric valves. The former family of devices are useful for protection of sensitive sensors from high power/fluence incident radiation, while the latter guides light in a directional fashion, thus providing the basic element for on chip isolators, circulators etc.
Applications and applicability of Social Cognitive Theory in information science research

Social Cognitive Theory is a theory that derives from Psychology yet has been adopted in other interdisciplinary subject areas, including information science. The origins and key concepts of Social Cognitive Theory are presented, and the value of its contributions to information science research analysed, with particular reference to research into information-seeking behaviour and use and knowledge sharing. Prior applications of Social Cognitive Theory in information science research are related to a study of employee-led workplace learning and innovative work behaviour that has been designed to create new insight on: (1) workplace information literacy; (2) knowledge management; and (3) the relationship between information behaviours and innovation processes. It is anticipated that this research will also extend understandings of Social Cognitive Theory as a valuable tool for theory development across a range of domains that focus on learning processes.
A Novel Hedgehog Waveguide and its Application in Designing a Phase Shifter Compatible With Hollow Waveguide Technology

A new Hedgehog waveguide, consisting of a bed of nails embedded in a host rectangular hollow waveguide, is proposed and investigated as a promising state-of-the-art low-loss waveguide for millimeter-wave frequency bands. The proposed Hedgehog waveguide gets its name from its electromagnetic behavior. As hedgehogs root through hedges and other undergrowth in search of their favorite food, the proposed waveguide root through its embedded bed of nails. When we choose a host waveguide technology, it is worthwhile spending some time weighing up the pros and cons of the various types of waveguides on offer. The proposed Hedgehog waveguide is extremely low loss and is compatible with the hollow waveguide technology, which gives the ability to develop different components such as low-loss flat phase response phase shifters. In this paper, the proposed Hedgehog waveguide is analytically investigated, and a transition to the hollow waveguide is designed. Moreover, the low-loss nature of the designed Hedgehog waveguide is compared with the ridge gap waveguide, substrate-integrated waveguide (SIW), hollow waveguide, and microstrip line. Finally, the proposed waveguide is designed, simulated, and fabricated. The simulated and measured results show a good agreement, which validates the proposed concept.
Models of nonlinear acoustics viewed as approximations of the Kuznetsov equation

We relate together different models of non linear acoustic in thermo-elastic media as the Kuznetsov equation, the Westervelt equation, the Khokhlov-Zabolotskaya-Kuznetsov (KZK) equation and the Nonlinear Progressive wave Equation (NPE) and estimate the time during which the solutions of these models keep closed in the \begin{document}$ L^2 $\end{document} norm. The KZK and NPE equations are considered as paraxial approximations of the Kuznetsov equation. The Westervelt equation is obtained as a nonlinear approximation of the Kuznetsov equation. Aiming to compare the solutions of the exact and approximated systems in found approximation domains the well-posedness results (for the Kuznetsov equation in a half-space with periodic in time initial and boundary data) are obtained.
Could this be a fiction? Bitcoin forecasts most tradable currency pairs better than ARFIMA

In this paper, we attempt to exploit any inherent useful information in Bitcoin to predict the future path of the most tradable currency pairs in the world. We also verify whether the forecast outcomes can compare favourably with the time series model such as the fractionally integrated autoregressive moving average (ARFIMA) model. We follow the Lewellen (2004) and Westerlund and Narayan (2102, 2015) approaches that account for any statistical effect that could bias the regression estimates. Our results suggest that Bitcoin is a good predictor of the selected currency pairs and more importantly, its forecast results outperform the time series model judging by the Diebold and Mariano test regardless of the data sample and forecast horizon. Although, recent evidence in the literature seems to suggest that the Bitcoin bubble will soon burst, its connection with the considered currency pairs may be exploited while it lasts.
Robust Estimation of Scatter Matrix, Random Matrix Theory and an Application to Spectrum Sensing

Robust Estimation of Scatter Matrix, Random Matrix Theory and Applications to Spectrum Sensing Zhedong Liu The covariance estimation is one of the most critical tasks in multivariate statistical analysis. In many applications, reliable estimation of the covariance matrix, or scatter matrix in general, is required. The performance of the classical maximum likelihood method relies a great deal on the validity of the model assumption. Since the assumptions are often approximately correct, many robust statistical methods have been proposed to be robust against the deviation from the model assumptions. M-estimator is an important class of robust estimator of the scatter matrix. The properties of these robust estimators under high dimensional setting, which means the number of dimensions has the same order of magnitude as the number of observations, is desirable. To study these, random matrix theory is a very important tool. With high dimensional properties of robust estimators, we introduced a new method for blind spectrum sensing in cognitive radio networks.
Tree-based methods for clustering time series using domain-relevant attributes

ABSTRACT We propose two methods for time-series clustering that capture temporal information (trend, seasonality, autocorrelation) and domain-relevant cross-sectional attributes. The methods are based on model-based partitioning (MOB) trees and can be used as automated yet transparent tools for clustering large collections of time series. We address the challenge of using common time-series models in MOB by instead utilising least squares regression. We propose two methods. The single-step method clusters series using trend, seasonality, lags and domain-relevant cross-sectional attributes. The two-step method first clusters by trend, seasonality and cross-sectional attributes, and then clusters the residuals by autocorrelation and domain-relevant attributes. Both methods produce clusters interpretable by domain experts. We illustrate our approach by considering one-step-ahead forecasting and compare to autoregressive integrated moving average (ARIMA) models for forecasting many Wikipedia pageviews time series. The tree-based approach produces forecasts on par with ARIMA, yet is significantly faster and more efficient, thereby suitable for large collections of time-series. The simple parametric forecasting models allow for interpretable time-series clusters.
Combinatorial Design of Low-Degradation Linear Arrays in Interferometric Aperture Synthesis Radiometers

Radiometric sensitivity is a crucially important performance metric of interferometric aperture synthesis radiometers (IASRs). When synthesizing array geometries for optimum sensitivity performance, the minimum-degradation or low-degradation arrays (MDAs or LDAs) are employed to fulfill requirements of baseline coverage and redundancy distribution. This paper presents a study on combinatorial design of low-degradation linear arrays (LDLAs), based on a concept of multiple difference basis (MDB). Such a deterministic method can attain a satisfactory radiometric sensitivity performance in short computation time and can be easily applied in large array synthesis, compared with stochastic design methods (e.g., simulated annealing). Furthermore, the resultant LDLAs also have advantages of angular resolution and system reliability to element wreckage. Representative experiments are carried out to demonstrate the significance of the proposed MDB-based technique for designing LDLAs.
UMA ANÁLISE DA TRANSMISSÃO DE VOLATILIDADE ENTRE MERCADO DOS EUA E AMÉRICA LATINA ATRAVÉS DE UMA MODELAGEM DCC-GARCH

Este estudo investiga as transmissões de volatilidades entre os índices Standard Poor’s 500 (S&P 500) dos Estados Unidos e os índices IBOVESPA, MERVAL, IGBVL e IPSA, representando a América Latina, com o intuito de observar se um mercado desenvolvido afeta os em desenvolvimento. Sendo assim, o índice S&P 500 que é considerado um índice de referência global, possui grande relevância para o sistema financeiro e é de grande importância o conhecimento das transmissões para outros índices. Com base em uma modelagem DCC-GARCH e uma amostragem de dados diários dos últimos 10 anos foi observado a existência de correlação entre os índices avaliados.Palavras-chave: DCC-GARCH; EGARCH; volatilidade; ações; índice; S&P 500; IBOVESPA;