Miracle-dz/UniFAR-FTUs huggingface dataset:
Spherical Harmonic Lighting

The rendering of objects under distant diffuse illumination has been considered. The illumination has been projected into spherical harmonic space and the coefficients are stored in the geometry to reconstruct the lighting. RenderMan has been the choice of platform for the implementation. Using a plugin, the existing functionality of RenderMan has been extended to make it capable of spherical harmonic lighting.
The Smart SD-WAN Architecture

In this paper, we propose the Smart Software-Defined Wide Area Network (SD-WAN) Architecture, which combined the traditional SD-WAN and Segment Routing over IPv6 (SRv6) to realize an end-to-end SRv6 tunnel connection. Compared to traditional SD-WAN, the Smart SD-WAN can integrate with the underlay network to realize the collaboration of underlay and overlay. In this way, many services such as the ability of underlay network slicing when subscribers have bought the underlay network slicing service can be used. Furthermore, the Smart SD-WAN can provide differentiated, guaranteed Service Level Agreement (SLA) service and integrate virtual network elements in the cloud to provide service function chain (SFC).
Assessing and Reassessing the Association of Comorbidities and Coinfections in COVID-19 Patients

Coronavirus disease 2019 (COVID-19) has posed an enormous global health and economic burden. To date, 324 million confirmed cases and over 5.5 million deaths have been reported. Several studies have reported comorbidities and coinfections associated with complicated and serious COVID-19 infections. Data from retrospective, prospective, case series, and case reports from various geographical locations were assessed, which included ~ 2300 COVID-19 patients with varying comorbidities and coinfection. We report that Enterobacterales with Staphylococcus aureus was the most while Mycoplasma pneumoniae was the least prevalent coinfection in COVID-19 patients with a comorbidity. In this order, hypertension, diabetes, cardiovascular disease, and pulmonary disease were the prevalent comorbidities observed in COVID-19 patients. There was a statistically significant difference in the prevalent comorbidities observed in patients coinfected with Staphylococcus aureus and COVID-19 and a statistically non-significant difference in the prevalent comorbidities in patients coinfected with Mycoplasma pneumoniae and COVID-19 as compared to similar infections in non-COVID-19 coinfection. We report a significant difference in the prevalent comorbidities recorded in COVID-19 patients with varying coinfections and varying geographic study regions. Our study provides informative data on the prevalence of comorbidities and coinfections in COVID-19 patients to aid in evidence-based patient management and care.
Assessing Psychological Morbidity in Cancer-Unaffected BRCA1/2 Pathogenic Variant Carriers: A Systematic Review

Female BRCA1/2 pathogenic variant carriers have an increased lifetime risk for breast and ovarian cancer. Cancer-unaffected women who are newly diagnosed with this pathogenic variant may experience psychological distress because of imminent health threat. No comprehensible review on psychological morbidity in cancer-unaffected BRCA1/2 pathogenic variant carriers is currently available. This review aims to give an overview about all available the studies in which psychological outcomes have been assessed in cancer-unaffected BRCA1/2 pathogenic variant carriers, whether as a primary outcome or secondary measurement. A systematic search across four databases (Web of Science, PubMed, ScienceDirect, and EBSCO) was conducted. Studies had to report on cancer-unaffected pathogenic variant carriers (exclusively or separately) and use a validated measure of psychological morbidity to be eligible. Measures were only included if they were used in at least three studies. The final review consisted of 45 studies from 13 countries. Distress measures, including anxiety and cancer worry, were most often assessed. Most studies found a peak of distress immediately after genetic test result disclosure, with a subsequent decline over the following months. Only some studies found elevated distress in carriers compared to non-carriers in longer follow-ups. Depression was frequently investigated but largely not found to be of clinical significance. Quality of life seemed to be largely unaffected by a positive genetic test result, although there was some evidence that younger women, especially, were less satisfied with their role functioning in life. Body image has been infrequently assessed so far, but the evidence suggested that there may be a decrease in body image after genetic test result disclosure that may decrease further for women who opt for a prophylactic mastectomy. Across all the outcomes, various versions of instruments were used, often limiting the comparability among the studies. Hence, future research should consider using frequently used instruments, as outlined by this review. Finally, while many studies included cancer-unaffected carriers, they were often not reported on separately, which made it difficult to draw specific conclusions about this population.
Probabilistic Sensitivity Analysis With Dependent Variables: Covariance‐Based Decomposition of Hydrologic Models

Variance‐based analysis has emerged as method of choice for quantifying the sensitivity of the output, y, of a scalar‐valued square‐integrable function, f ∈ L2( χ $\boldsymbol{\upchi }$ ), to its d ≥ 1 input variables, x = {x1, …, xd}, with support x∈χ⊆Rd $\mathbf{x}\in \boldsymbol{\upchi }\subseteq {\mathbb{R}}^{d}$ . The prototype of this approach, Sobol's method is a generalization of the analysis of variance (ANOVA) to d > 2 independent input variables and decomposes y, as sum of elementary functions of zeroth‐, first‐, second‐, up to dth‐order. This independence assumption is mathematically convenient but may not be borne out of the causal or correlational relationships between the x's. This paper is concerned with variance‐based sensitivity analysis (SA) for correlated input variables, for example, multivariate dependencies in a posterior parameter distribution. We use high‐dimensional model representation (HDMR) of Li et al. (2010, https://doi.org/10.1021/jp9096919), Li and Rabitz (2012, https://doi.org/10.1007/s10910-011-9898-0) and replace Sobol's elementary functions with so‐called component functions with unknown expansion coefficients to disentangle the structural, correlative and total contribution of input factors. We contrast the default HDMR methodology with cubic B‐splines and sequential coefficient estimation against its successor, HDMRext of Li and Rabitz (2012, https://doi.org/10.1007/s10910-011-9898-0), which uses polynomial component functions with an extended orthonormalized basis. Benchmark experiments confirm that HDMR and HDMRext parse out the structural and correlative contributions of input factors to the model output and infer an optimal experimental design with parameter correlation. Our last study applies HDMRext to probabilistic SA of a watershed model. The multivariate posterior parameter distribution supports model emulation and yields sensitivity indices that pertain to measured discharge data.
Fingersound: A Low-cost and Deployable Authentication System with Fingertip Sliding Sound

Fingerprint recognition technology is the most widely used technology in the field of biometrics and is also the preferred solution for mobile devices and the financial industry. However, traditional fingerprint recognition technology requires expensive sensors on the one hand and risks leaking fingerprint images on the other hand. In this paper, we propose Fingersound, a low-cost and deployable authentication system that utilizes the sound generated by finger sliding as an identity feature. We use a microphone array to record continuous sliding sound and extract multiple features in the frequency and time domains. Then we use various algorithms including a deep neural network to perform user authentication. We also design a mobile phone application that can interact with the embedded system to manage users and record authentication history. In our experiments, 100 participants used this system and achieved an equal error rate of 5.2%. Additionally, we investigate the system’s robustness within different sliding materials, texturesand under noise disturbances. We further demonstrate the resistance of Fingersound to replay attack.
A geometrical approach to wave-type solutions of excitable reaction-diffusion systems

We formulate the eikonal equation approximation for travelling waves in excitable reaction-diffusion systems, which have been proposed as models for a large number of biomedical situations. This formulation is particularly suited, in a natural way, to numerical solution by finite difference methods. We show how this solution is independent of the parametric variable used for expressing the eikonal equation, and how a reduction of dimensionality implies a major saving over the time taken to solve the original reaction-diffusion system. Neumann boundary conditions on reactants in the original system translate into a geometric constraint on the wave boundary itself. We show how this leads to geometrically stable stationary wave boundaries in appropriately shaped non-convex domains. This analytical prediction is verified by numerical solution of the eikonal equation on a domain which supports geometrically stable stationary wave boundary configurations. We show how the concepts of geometrical stability and wave-front stability relate to a problem where a bi-stable reaction-diffusion system has a stable stationary wave-front configuration.
BPNN Optimization With Genetic Algorithm For Classification of Tobacco Leaves With GLCM Extraction Features

Tobacco leaves are one of the agricultural commodities cultivated by Indonesian farmers. In their application in the field, there are many obstacles in tobacco leaf cultivation, one of which is declining tobacco quality caused by weather factors. In this study, a technology-based analysis step was carried out to determine the classification in determining the quality of tobacco leaves. The research was carried out by applying the classification optimization of the Backpropagation Artificial Neural Network Method and genetic algorithms to determine the weights obtained from extracting GLCM features. You can get the weight value from the genetic algorithm on the homogeneity variable from this analysis step. The variable gets a weight value of 1. The results of this study obtained a classification value with the Backpropagation Artificial Neural Network Method model getting an accuracy value of 53.50% at a hidden layer value of 2,4,5,7. For classification with the Artificial Neural Network Method, Backpropagation, which is optimized with genetic algorithms, you get an accuracy value of 64.50% at the 4th hidden layer value. From this study, the value of optimization accuracy increased by 11% after being optimized with genetic algorithms.
Experimental generation and dynamical reconfiguration of different circular optical lattices for applications in atom trapping

Different configurations of optical lattices with circular cylindrical geometry have been recently studied in the context of atom trapping from a theoretical viewpoint, giving rise to a number of proposed applications. A common problem for testing theoretical predictions is the difficulty in the experimental realization of some of the necessary optical potentials. Here we discuss the experimental generation of four different circular optical lattices in an efficient and simple way using a single spatial light modulator. Our approach allows switching between different light configurations with a time resolution given by the response time of the light modulator.
Maneuver control of satellite formation based on virtual structure

For certain space tasks which need to keep the formation during the maneuvers,the problem of three satellites formation control is solved using virtual structure.A virtual structure frame is presented.Three satellites are treated as a virtual rigid body during the maneuvers.The dynamics and kinematics controllers are designed.Dynamics controller with formation feedback realizes the control of formation velocity.An attitude sliding mode controller is presented with external interference.System stability is proved using the Lyapunov stability theorem and a numerical simulation is performed to verify the effectiveness of the method.
Solar cycle dependence of the heliospheric shape deduced from a global MHD simulation of the interaction process between a nonuniform time‐dependent solar wind and the local interstellar medium

The global structure of the solar wind/very local interstellar medium interaction is studied from a fully three-dimensional time-dependent magnetohydrodynamic model, in which the solar wind speed increases from 400 to 800 km/s in going from the ecliptic to pole and the heliolatitude of the low-high-speed boundary changes from 30° to 80° in going from the solar minimum to solar maximum. In addition, the interplanetary magnetic field (IMF) changes its polarity at the solar maximum. As a whole, the shapes of the terminal shock (TS) and heliopause (HP) are elongated along the solar polar axis owing to a high solar wind ram pressure over the poles. In the ecliptic plane, the heliospheric structure changes little throughout a solar cycle. The TS in this plane shows a characteristic bullet-shaped structure. In the polar plane, on the other hand, the shape of the TS exhibits many specific structures according to the stage of the solar cycle. These structures include the polygonal configuration of the polar TS seen around the solar minimum, the mesa- and terrace-shaped TSs in the high- and low-speed solar wind regions seen around the ascending phase, and the chimney-shaped TS in the high-speed solar wind region seen around the solar maximum. These structures are formed from different combinations of right-angle shock, oblique shock, and steep oblique shock so as to transport the heliosheath plasma most efficiently toward the heliotail (HT). In the HT, the hot and weakly-magnetized plasma from the high-heliolatitude TS invades as far as the ecliptic plane. A weakly time-dependent recirculation flow in the HT is a manifestation of invading flow. Distributions of magnetic field in the HT, which are a pile-up of the compressed IMF over several solar cycles, are modified by the flow from high-heliolatitude.
State-of-the-art modeling for unconventional gas recovery, part II: recent developments (1989-1994)

In, 1989, a survey of mathematical models which describe methane production from coal seams was presented at the SPE Joint Rocky Mountain Regional/Low Permeability Reservoirs Symposium in Denver, Colorado. This paper was later published in SPE Formation Evaluation Journal March 1991 issue. In this survey, 37 distinct models for coalbed methane production were reviewed in mathematical detail. Since that time, 15 additional mathematical approaches to forecasting reservoir performance, each with its own assumptions and limitations, have been developed. The objective of this paper is to discuss each of these recent models and to compare the assumptions, features, and limitations of these models with each other and with the original 37 models. Recommendations are also presented on the use of each model.
Iterative Difference Hard-Thresholding Algorithm for Sparse Signal Recovery

In this paper, a nonconvex surrogate function, namely, Laplace norm, is studied to recover the sparse signals. Firstly, we discuss the equivalence of the optimal solutions of <inline-formula><tex-math notation="LaTeX">$l_{0}$</tex-math></inline-formula>-norm minimization problem, Laplace norm minimization problem and regularization Laplace norm minimization problem. It is proved that the <inline-formula><tex-math notation="LaTeX">$l_{0}$</tex-math></inline-formula>-norm minimization problem can be solved by solving the regularization Laplace norm minimization problem if the certain conditions are satisfied. Secondly, an iterative difference hard-thresholding algorithm and its adaptive version algorithm are proposed to solve the regularization Laplace norm minimization problem. Finally, we provide some numerical experiments to test the performance of the adaptive iterative difference hard-thresholding algorithm, and the numerical results show that the adaptive iterative difference hard-thresholding algorithm performs better than some state-of-art methods in recovering the sparse signals.
Wavelet Characterizations for Anisotropic Besov Spaces

Abstract The goal of this paper is to provide wavelet characterizations for anisotropic Besov spaces. Depending on the anisotropy, appropriate biorthogonal tensor product bases are introduced and Jackson and Bernstein estimates are proved for two-parameter families of finite-dimensional spaces. These estimates lead to characterizations for anisotropic Besov spaces by anisotropy-dependent linear approximation spaces and lead further on to interpolation and embedding results. Finally, wavelet characterizations for anisotropic Besov spaces with respect to Lp-spaces with 0
Image alignment by parameter hypersurface learning

A new concept of image alignment method is introduced based on machine learning technique. The proposed method estimates image alignment parameters by using a regression hypersurface, which maps image feature space onto alignment parameter space. The regression hypersurface is obtained by learning an appropriate type of neural network from a large number of training samples, which consist of image features and corresponding alignment parameters. Compared with the previous method that utilises a regression hyperplane, this method achieved a significant improvement in convergence range to apply for alignment of rectangular region in long term image sequences in low frame rate.
Analysis Application of Big Data-based Analysis of Network Security and Intelligence

The use of big data expertise in network security and intelligence testing is important given the technology's rapid development. It is crucial to protect the overall security of the network since big data technologies may significantly increase the effectiveness of network security and intelligence analysis. An investigation has to be carried out on network security and big data-based intelligence analysis technology while using big data technology. In order to increase the security of the network operating environment as much as feasible, more research initiatives should be encouraged on network security and intelligent analysis techniques based on big data. The Computing power and network bandwidth are expanding at an ever-increasing rate due to the rapid development of communication and IT technology. As the Internet of Things (IoT) is developing at an increasingly rapid pace, along with the rate at which its data volume is growing. These have aided in the growth of the big data era. In order to analyze market trends and content based on the information acquired, big data technologies must be able to extract useful information from more complex and laborious data. The controls used to enforce security standards in computer networks are described in this study using a formal model referred to as security capabilities.
Physiological and Psychological Response to Acute Mental Stress in Female Patients Affected by Chronic Pulmonary Arterial Hypertension: An Explorative Controlled Pilot Trial

Little is known about physiological and psychological responses to mental stress in stable patients affected by pulmonary arterial hypertension (PAH). The current explorative controlled pilot study was conducted to investigate whether heart rate (HR) and perceived stress would differ during standardized mental stress testing in PAH patients compared to healthy subjects. Correlation analysis between HR, perceived stress, participants’ psychological status and performance on the mental stress task was also performed. The study included 13 female PAH patients (average age: 44.38 ± 10.88 years; average education: 14 ± 3.07 years; mean duration of illness: 9.15 ± 5.37 years) and 13 female controls similar in age (mean age: 47.85 ± 6.36 years) and education (15.92 ± 1.55 years). Participants performed a standardized 9 min mental stress test (computer based, adaptive math task). HR and perceived stress during the task were compared to resting baseline and correlated with psychological state and task performance. Both HR and perceived stress significantly increased during mental stress in a similar way in both groups. A significant correlation was found between HR and perceived stress. Our data show that moderate mental stress has a comparable effect on HR and perceived stress increase in stable PAH patients and control subjects.
New Directions in the Theory and Analysis of Musical Contour

Musical contour is one of the most general aspects of pitch perception, prior to the concept of pitch or pitch class, for it is grounded only in a listener's ability to hear pitches as relatively higher, equal, or lower, without discerning the exact differences between and among them. Yet, until rather recently, music theorists have paid little attention to contour relations. This is somewhat understandable since contour, especially melodic contour, remained largely an aspect of musical diction until the beginning of the twentieth century. But in much twentieth-century music, especially in the works of Edgard Varese, Iannis Xenakis, and Gy6rgy Ligeti, contour has been generalized beyond melody and may play an important structural role in a specific composition or repertoire.
Processing structured hypermedia - a matter of style

With the introduction of the World Wide Web in the early nineties, hypermedia has become the uniform interface to the wide variety of information sources available over the Internet. The full potential of the Web, however, can only be realized by building on the strengths of its underlying research fields. This book describes the areas of hypertext, multimedia, electronic publishing and the World Wide Web and points out fundamental similarities and differences in approaches towards the processing of information. It gives an overview of the dominant models and tools developed in these fields and describes the key interrelationships and mutual incompatibilities. In addition to a formal specification of a selection of these models, the book discusses the impact of the models described on the software architectures that have been developed for processing hypermedia documents. Two example hypermedia architectures are described in more detail: the DejaVu object-oriented hypermedia framework, developed at the VU, and CWI's Berlage environment for time-based hypermedia document transformations.