Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptives Online-Tuning für kontinuerliche Zustandsräume

Raytracing ist ein rechenintensives Verfahren zur Erzeugung photorealistischer Bilder. Durch die automatische Optimierung von Parametern, die Einfluss auf die Rechenzeit haben, kann die Erzeugung von Bildern beschleunigt werden. Im Rahmen der vorliegenden Arbeit wurde der Auto-Tuner libtuning um ein generalisiertes Reinforcement Learning-Verfahren erweitert, das in der Lage ist, bestimmte Charakteristika der zu zeichnenden Frames bei der Auswahl geeigneter Parameterkonfigurationen zu berucksichtigen. Die hierfur eingesetzte Strategie ist eine e -gierige Strategie, die fur die Exploration das Nelder-Mead-Verfahren zur Funktionsminderung aus libtuning verwendet. Es konnte gezeigt werden, dass eine Beschleunigung von bis zu 7,7 % in Bezug auf die gesamte Rechenzeit eines Raytracing-Anwendungsszenarios dieser Implementierung gegenuber der Verwendung von libtuning erzielt werden konnte.
Oscillating piston machine

【課題】走行特性を改善するために振動ピストン・エンジンを提供する。 【解決手段】ハウジング１２に対して固定されている回転軸３８のまわりを回転することができるとともに、回転軸３８に対して垂直な枢動軸４２のまわりで第１の終端位置（ＴＤＣ）と第２の終端位置（ＢＤＣ）の間で往復枢動運動を行うピストン３０が配置されている。 ピストン３０は、回転軸３８のまわりを回転するとき、制御カム６６に沿って走行する第１の走行要素５８を有しており、制御カム６６は、走行要素５８が制御カム６６に沿って行う回転運動から、ピストン３０の枢動運動を導き出すように構成されている。 ピストン３０の第１の終端位置（ＴＤＣ）から第２の終端位置（ＢＤＣ）への復帰を助ける復帰手段が、走行要素５８に加えてピストン３０と結合している。 【選択図】図３
Conducting Synchronous Assessment through Web Videoconference to Improve Online Learning: Case Outcomes with Nonparametric Analysis

: Online assessment has always been a challenge to online teaching. Educators have been exploring a variety of methods to perform online assessment. However, it appears that there is not enough work in the field focusing on online synchronous assessment. This paper presents two cases that demonstrate the design and implementation of using web videoconference for synchronous assessment in an educational research methods online course and an instructional video production online course. The purpose of the two cases was to explore whether or with what methods student online learning could be improved through synchronous assessment. Case outcomes were analyzed with nonparametric methods, and the results did show students’ improvement in their learning, specifically in their understanding and mastering of factual, conceptual, procedural and metacognitive knowledge. Methods, procedures, tips and cautions of conducting such videoconference-based synchronous assessment in online courses are discussed.
Graphon Games: A Statistical Framework for Network Games and Interventions

In this paper, we present a unifying framework for analyzing equilibria and designing interventions for large network games sampled from a stochastic network formation process represented by a graphon. To this end, we introduce a new class of infinite population games, termed graphon games, in which a continuum of heterogeneous agents interact according to a graphon, and we show that equilibria of graphon games can be used to approximate equilibria of large network games sampled from the graphon. This suggests a new approach for design of interventions and parameter inference based on the limiting infinite population graphon game. We show that, under some regularity assumptions, such approach enables the design of asymptotically optimal interventions via the solution of an optimization problem with much lower dimension than the one based on the entire network structure. We illustrate our framework on a synthetic data set and show that the graphon intervention can be computed efficiently and based solely on aggregated relational data.
Deep Adversarial Domain Adaptation Model for Bearing Fault Diagnosis

Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis. This model constructs an adversarial adaptation network to solve the commonly encountered problem in numerous real applications: the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE) is combined with representative feature learning for dimensionality reduction, and such a combination provides an unsupervised learning method to effectively acquire fault features. Meanwhile, domain adaptation and recognition classification are implemented using a Softmax classifier to augment classification accuracy. Second, the effects of the number of hidden layers in the stack autoencoder network, the number of neurons in each hidden layer, and the hyperparameters of the proposed fault diagnosis algorithm are analyzed. Third, comprehensive analysis is performed on real data to validate the performance of the proposed method; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability.
Dispersion Reduction for the Wave Propagation Problems Using a Coupled “FE-Meshfree” Triangular Element

As is known to all, there always exists the numerical dispersive effects of the standard finite element (FE) for the wave propagation problems and the corresponding FE solutions are usually unreliable in relatively high frequency range. In this work, a coupled “FE-Meshfree” element based on triangular mesh is introduced to reduce the dispersion effects for wave propagation problems. In this coupled element, the standard FE nodal shape functions are combined with the meshfree nodal shape functions to give a new hybrid nodal shape functions. As a result, both the individual advantages of the FE technique and the meshfree technique are strengthened by the present hybrid method. Through the dispersion analysis for the wave propagation problems, it is found that this coupled “FE-Meshfree” element could significantly reduce the numerical dispersive effects and it also have a higher tolerance to the mesh distortion than the other existing elements, hence the present method is quite promising to handle the general wave propagation problems in practical engineering application.
A COMPACT DIFFERENTIAL-FED UWB ANTENNA WITH BAND-NOTCHED CHARACTERISTICS

A compact differential-fed ultra-wideband (UWB) antenna with single band-notched characteristic is proposed in this paper. The antenna consists of a ground plane etched with an octagonal groove and two symmetrical hexagonal radiating patches. Return loss and isolation of the antenna can be effectively reduced by loading a rectangular stub on the ground plane and etching a semi-circular groove on the radiation patch. In order to achieve a controllable single band-notched characteristic, two pairs of quarter-wavelength stubs are introduced into the grounding plane. The antenna has lower cross polarization and stable gains than conventional single feed antennas. The measured impedance bandwidth with S11 ≤ −10 dB is 114% from 3GHz to 11 GHz, and the notch characteristic is realized in the 7.2–8.4 GHz band.
A Fast Semi-Supervised Clustering Framework for Large-Scale Time Series Data

Semi-supervised clustering algorithms have several limitations: 1) the computation complexity of them is very high, because calculating the similarity distances of pairs of examples is time-consuming; 2) traditional semi-supervised clustering methods have not considered how to make full use of must-link and cannot-link constraints. In the clustering, the contribution of a few pairwise constraints to the clustering performance is very limited, and some may negatively affect the outcome; and 3) these methods are not effective to handle high dimensional data, especially for time series data. Up to now, few work touched semi-supervised clustering on time series data. To efficiently cluster large-scale time series data, we first tackle contract time series clustering to produce the most accurate clustering results under a contracted time. We propose a semi-supervised time series clustering framework (STSC), which integrates a fast similarity measure and a constraint propagation approach. Based on the proposed framework, two valid semi-supervised clustering algorithms including fssK-means and fssDBSCAN are designed. Experiments on 11 datasets show that our proposed method is efficient and effective for clustering large-scale time series data.
Probabilistic Regression and Missing Values Compensation for P2P Lending

With the development of the Internet, peer-to-peer lending has become widely recognized and accepted by the public. P2P lending platforms are overgrowing around the world, which provides many conveniences to medium borrowers who have difficulty in traditional financial institutions. However, due to the qualification of P2P lending platforms and the lack of supervision, many of these platforms have no ability to disclose information to the authorities, which will increase industry risk and instability in the financial system. So, our work focuses on the missing data of P2P lending platforms from the perspective of predicting losing values. We highlight our work on dealing with missing values using Random Forest instead of traditional methods. After that, we applied the Gaussian Process Regression to explore the relationship between variables and got a conclusion that the loan balance of a particular platform can be predicted.
Evaluation of a Haptic Feedback System for Flight Envelope Protection

Modern fly-by-wire aircraft use flight envelope protection systems, whose actions are not always clear to pilots. To promote situation awareness, proximity to the limits of the flight envelope can be communicated using haptic feedback, by providing forces through the control device. Such a system was developed and this paper reports on the evaluation experiment. Professional pilots were invited to fly an Airbus A320 model in the Delft University of Technology Simona research simulator. A windshear and an icing scenario were flown using a full and degraded control law, with and without the haptic feedback system. The objective results show that the haptic feedback system does not lead to significant improvements in either performance or safety metrics, but also does not interfere with nominal pilot tasks. In the debriefing questionnaire, however, pilots expressed a clear preference for the haptic system. Recommendations for future research include the addition of visual support to complement the haptic cues, and the redesign of the scenarios to allow pilots more freedom in control.
Compton-thick active galactic nuclei from the 7 Ms observation in the Chandra Deep Field South

We present the X-ray spectroscopic study of the Compton-thick (CT) active galactic nuclei (AGN) population within the Chandra Deep Field South (CDF-S) by using the deepest X-ray observation to date, the Chandra 7 Ms observation of the CDF-S. We combined an optimized version of our automated selection technique and a Bayesian Monte Carlo Markov chains (MCMC) spectral fitting procedure, to develop a method to pinpoint and then characterize candidate CT AGN as less model dependent and/or data-quality dependent as possible. To obtain reliable automated spectral fits, we only considered the sources detected in the hard (2−8 keV) band from the CDF-S 2 Ms catalog with either spectroscopic or photometric redshifts available for 259 sources. Instead of using our spectral analysis to decide if an AGN is CT, we derived the posterior probability for the column density, and then we used it to assign a probability of a source being CT. We also tested how the model-dependence of the spectral analysis, and the spectral data quality, could affect our results by using simulations. We finally derived the number density of CT AGN by taking into account the probabilities of our sources being CT and the results from the simulations. Our results are in agreement with X-ray background synthesis models, which postulate a moderate fraction (25%) of CT objects among the obscured AGN population.
ALSDOC: ADAPTIVE LEARNING STYLE FOR DOCUMENT RETRIEVAL

Learning can be enhanced when its learning process aligned with learner learning styles. Individual has different learning preferences. The consideration of learner preferences in finding document can enable a learner to obtain knowledge easily and fast where it constantly changes and shift according to learning situation and environment. Thus, the incorporation of Learning Style (LS) with adaptive learning environment is one of a method to assist novice learner in finding a suitable document and enhance the e-learning system; while it can overcome the one-size-fitsfor-all problem. The LS identification is an important component to identify the learner learning style. There are two conventional techniques to identify learners learning style, which are questionnaires and computer-based detection technique. The questionnaire involves the learner to fill up a survey explicitly. However, existing of LS identification techniques facing some major drawbacks can lead the LS tool to become static nature. The questionnaire technique d facing issues such as questionnaire too long and took long time that can make learner lack of motivation to answer the question. Moreover, the result from the questionnaire technique is fixed because it only prompts a learner to do it only one at the time. Therefore, a few researchers employed computer-based detection techniques, which use a numerous computer algorithm to determined LS. However, these techniques suffer on cold start problem, required a large amount of training data and the existing computer-based detection were not focused document as learning material. Hence, there is a need to develop an adaptive learning style identification that uses document as a learning material. This paper presents the architecture for ALSDoc, an adaptive LS identification for document retrieval. In particular, this study explained how LS is adapt to the learner that allows the retrieval the suitable document that match their LS. Key Terms: Adaptive Learning Environment, Collaborative Learning, Learning Style, Information Retrieval, Document.
Classification of SAR and PolSAR images using deep learning: a review

ABSTRACT Advancement in remote sensing technology and microwave sensors explores the applications of remote sensing in different fields. Microwave remote sensing encompasses its benefits of providing cloud-free, all-weather images and images of day and night. Synthetic Aperture Radar (SAR) images own this capability which promoted the use of SAR and PolSAR images in land use/land cover classification and various other applications for different purposes. A review of different polarimetric decomposition techniques for classification of different regions is introduced in the paper. The general objective of the paper is to help researchers in identifying a deep learning technique appropriate for SAR or PolSAR image classification. The architecture of deep networks which ingest new ideas in the given area of research are also analysed in this paper. Benchmark datasets used in microwave remote sensing have been discussed and classification results of those data are analysed. Discussion on experimental results on one of the benchmark datasets is also provided in the paper. The paper discusses challenges, scope and opportunities in research of SAR/PolSAR images which will be helpful to researchers diving into this area.
Effective Electromagnetic Properties of Woven Fiber Composites for Shielding Applications

Composite materials reinforced with woven conductive fibers can be good candidates for electromagnetic shielding applications. Their light weight provides an important advantage over metallic alloys classically used in the automotive and aircraft industries. However, numerical modeling of composite-based large-scale structures, such as shielding enclosures, is rendered almost impossible by the heterogeneities at the microscopic scale. This problem is commonly addressed using electromagnetic homogenization methods. In this paper, we propose a homogenization technique based on finite element computations and inverse problem solving for estimating the effective properties of woven composites. The effect of electrical contacts between the fibers is also studied and its influence on the global behavior of the material is analyzed. The results are then shown to diverge from those obtained using analytical mixing rules over the frequency range of 1–40 GHz. Thus the suitability of these homogenization methods is discussed with respect to the studied frequency range and the nature of the microstructure.
Cellular Traffic Offloading via Link Prediction in Opportunistic Networks

With the emergence of affordable smart mobile devices (such as smartphones and tablets) running innovative applications have severely overloaded the cellular network. To cope with this issue, there have been many efforts to offload the traffic from the cellular network to other complement networks, for instance, Wi-Fi and device-to-device (D2D) communications. In this paper, we formulate the traffic offloading issue as a link prediction problem in opportunistic D2D network, which is targeted to alleviate the overburdened cellular network traffic and reduce the delay time. Considering the complexity of realistic networks, we employ three indexes of link prediction: common neighbors, Katz, and LRW index. To measure the performance of our proposed algorithm, we analyze it is offloading traffic capacity along with delay minimization among users in different networks. It is demonstrated that our proposed link prediction solution can efficiently offload up to 80% of the cellular traffic.
Direct integrators of modified multistep method for the solution of third order boundary value problem in ordinary differential equations

In this paper, we propose an efficient modified multistep method for direct solution of boundary value problems (BVPs) using multistep collocation approach. The continuous form was evaluated at grid and off-grid points to obtain the multiple finite difference schemes. The basic properties, such as order and error constants, zero stability and convergence analysis of the proposed methods were investigated. Numerical experiment were performed to show the efficiency of the method and the results were compared with the existing methods in the literature.
Nonlinear corrector for Reynolds‐averaged Navier‐Stokes equations

The scope of this paper is to present a nonlinear error estimation and correction for Navier‐Stokes and Reynolds‐averaged Navier‐Stokes equations. This nonlinear corrector enables better solution or functional output predictions at fixed mesh complexity and can be considered in a mesh adaptation process. After solving the problem at hand, a corrected solution is obtained by solving again the problem with an added source term. This source term is deduced from the evaluation of the residual of the numerical solution interpolated on the h/2 mesh. To avoid the generation of the h/2 mesh (which is prohibitive for realistic applications), the residual at each vertex is computed by local refinement only in the neighborhood of the considered vertex. One of the main feature of this approach is that it automatically takes into account all the properties of the considered numerical method. The numerical examples point out that it successfully improves solution predictions and yields a sharp estimate of the numerical error. Moreover, we demonstrate the superiority of the nonlinear corrector with respect to linear corrector that can be found in the literature.
The diurnal variation of polarization characteristics of the Earth treated as an exoplanet

The diurnal polarization variation of the Earth, treated as an exoplanet with an unresolved disk but resolvable from its host star, is presented in three wavelength bands centered at 490 nm, 670 nm and 865 nm respectively according to French satellite-borne PARASOL data. We aim to estimate disk-integrated polarization of the Earth with a phase angle of 55°. It is shown that: (1) the linear polarization signal and its variation are ascribed to the combination of surface feature distribution and atmospheric conditions acting as a variable polarimetric modulator; and (2) the polarimetric wavelength dependence is strong due to the atmospheric wavelength sensitivity. During the period when the PARASOL data were acquired, the cloud coverage ranged from 44.0% to 57.7%, and the polarimetric diurnal variation amplitude was within 1.8% in the 490 nm band, 1.3% in the 670 nm band and 1.5% in the 865 nm band.
Study of the crystal and electronic structures of (Bi1−xNdx)FeO3 compositions using Rietveld refinements and the maximum entropy method

Abstract In this work we present a methodology that combines the Rietveld structure refinement and the Maximum Entropy Method (MEM) to analyze the crystal and electronic structures, and the chemical bonding of the Bi1−xNdxFeO3 compositions for x = 0.025, 0.05, 0.075, 0.10, 0.15 and 0.20. A structural phase modification from rhombohedral R3c to orthorhombic (Pnma) was observed with the increase in the neodymium content. As an intermediate phase in the transition from R3c to Pnma, a monoclinic structure (C2/m) took place. Electron density calculations provided the observation of a total suppression of the covalent bonds between the bismuth and oxygen ions for x = 0.10 and the emergence of the covalent character between all the bonds for x = 0.20.
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma.

As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well. While methods have been developed to account for high-dimensional spatial objects, the setting where there are exceedingly large samples of spatial observations has had less attention. The variational autoencoder (VAE), an unsupervised generative model based on deep learning and approximate Bayesian inference, fills this void using a latent variable specification that is inferred jointly across the large number of samples. In this manuscript, we compare the performance of the VAE with a more classical ST method when analyzing longitudinal visual fields from a large cohort of patients in a prospective glaucoma study. Through simulation and a case study, we demonstrate that the VAE is a scalable method for analyzing ST data, when the goal is to obtain accurate predictions. R code to implement the VAE can be found on GitHub: this https URL.
Impact of material characteristics on the general optical behavior of perforated surface plasmon system

ABSTRACT In this paper, we studied the impact of material characteristics on the optical response of a perforated surface plasmon system as a general trend by employing dielectric function of metallic film. To this end, different artificial materials were modeled based on a single interband transition peak in the form of a Lorentzian function in the imaginary part of dielectric function as well as a Drude term for free electron feature. The impact of place, strength and broadening of interband peak of metallic film were evaluated on the optical transmission spectrum. Our results revealed that these peaks affect the excited modes in the form of a decrease in the intensity of transmitted beam at corresponding frequencies. This decrease was proportional to the strength and width of the peak, and interpreted as an optical dissipation coming from material specifics. The present study provides a better insight into how the optical transmission response of such systems can be tuned for a particular goal or application using material characteristics.