Miracle-dz/UniFAR-FTUs huggingface dataset:
The Principle and Application of Deep Learning Algorithm

Deep learning is emerging as a multilayer neural network dimensionality reduction algorithm, the neural network model with multiple deep hidden layer through the formation of the high dimensional data of input layer feature extraction, to find a low dimensional nested data structure, the formation of more abstract effective executives said. Starting from the basic principle of deep learning algorithm, introduces the basic structure of single-layer network restricted Boltzmann deep learning algorithm and its training process. Finally, an example is given to illustrate the performance improvement of deep learning technology applied to handwritten digit recognition, and the deep learning technology is briefly summarized.
Data-Driven Spatio-Temporal Analysis of Curbside Parking Demand: A Case-Study in Seattle

Due to rapid expansion of urban areas in recent years, management of curbside parking has become increasingly important. To mitigate congestion, while meeting a city's diverse needs, performance-based pricing schemes have received a significant amount of attention. However, several recent studies suggest location, time-of-day, and awareness of policies are the primary factors that drive parking decisions. In light of this, we provide an extensive data-driven study of the spatio-temporal characteristics of curbside parking. This work advances the understanding of where and when to set pricing policies, as well as where to target information and incentives to drivers looking to park. Harnessing data provided by the Seattle Department of Transportation, we develop a Gaussian mixture model based technique to identify zones with similar spatial parking demand as quantified by spatial autocorrelation. In support of this technique, we introduce a metric based on the repeatability of our Gaussian mixture model to investigate temporal consistency.
Qualitative Exploration on Children's Interactions in Telepresence Robot Assisted Language Learning

The purpose of this study was to explore children and robot interaction in distant language learning environments using three different video-conferencing technologies — two traditional screen-based videoconference technologies and a telepresence robot. One American and six Korean elementary school students participated in our case study. We relied on narratives of one-on-one interviews and observation of nonverbal cues in robot assisted language learning. Our findings suggest that participants responded more positively to interactions via a telepresence robot than to two screen-based video-conferencings, with many citing a stronger sense of immediacy during robot-mediated communications
PENERAPAN PRINCIPAL COMPONENT ANALYSIS (PCA) DALAM PENENTUAN FAKTOR DOMINAN YANG MEMPENGARUHI PRESTASI BELAJAR SISWA (Studi Kasus : SMK Raksana 2 Medan)

Abstract - School is one of the facilities in the implementation and development of education. SMK Raksana 2 Medan is one of the best schools in North Sumatra. With differing abilities, students differ in their level of achievement. There are several factors that greatly affect student learning achievement, among others: talent, interest, student motivation, parental motivation, home learning facilities, quality teaching from teachers, school facilities, extracurricular, additional tutoring and student association. This study aims to simplify and reduce these factors so as to obtain dominant factors that influence student learning achievement by using Principal Component Analysis (PCA). The study was conducted by collecting data through questionnaires to respondents or research samples. Finally to obtain 3 factors: The main factor (PC1) has an eigenvalue of 3.11 with a variance of 31%. The supporting factor (PC2) has an eigenvalue of 1.50 with a variance of 15%. The additional factor (PC3) has an eigenvalue of 1.16 with a variance of 12%. The overall factor gives a cumulative diversity proportion of 57.70%, meaning that the three factors according to the perceptions of students who are respondents in this study can influence student achievement at SMK Raksana 2 Medan at 57.70%. Keywords - Education, Achievement, Principal Component Analysis, Eigenvalue, Factor Analysis Abstrak - Sekolah merupakan salah satu sarana dalam penyelenggaraan dan pengembangan pendidikan. SMK Raksana 2 Medan merupakan salah satu sekolah yang memiliki banyak prestasi terbaik di Sumatera Utara. Dengan berbeda-bedanya kemampuan siswa maka berbeda pula tingkat prestasinya. Terdapat beberapa faktor yang sangat mempengaruhi prestasi belajar siswa, antara lain: bakat, minat, motivasi siswa, motivasi orang tua, fasilitas belajar dirumah, kualitas pengajaran dari guru, fasilitas sekolah, ekstrakullikuler, les tambahan dan pergaulan siswa. Penelitian ini bertujuan untuk menyederhanakan dan mereduksi faktor – faktor tersebut sehingga memperoleh faktor dominan yang mempengaruhi prestasi belajar siswa dengan menggunakan metode analisis Principal Component Analysis (PCA). Penelitian dilakukan dengan mengumpulkan data melalui kuesioner kepada responden atau sampel penelitian yaitu siswa/i kelas X dan XI SMK Raksana 2 Medan, Sehingga diperoleh 3 faktor yaitu: faktor utama (PC1) memiliki nilai eigenvalue sebesar 3.11 dengan jumlah varians sebesar 31%. Faktor pendukung (PC2) memiliki nilai eigenvalue sebesar 1.50 dengan jumlah varians sebesar 15%. Faktor tambahan (PC3) memiliki nilai eigenvalue sebesar 1.16 dengan jumlah varians sebesar 12%. Keseluruhan faktor memberikan proporsi keragaman kumulatif sebesar 57.70%, artinya ketiga faktor tersebut menurut persepsi siswa/i yang menjadi responden dalam penelitian ini dapat mempengaruhi prestasi belajar siswa/i di SMK Raksana 2 Medan sebesar 57.70%. Kata Kunci - Pendidikan, Prestasi, Principal Component Analysis, Nilai Eigen, Faktor Analisis
Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model

Strongly coupled data assimilation (SCDA) views the Earth as one unified system. This allows observations to have an instantaneous impact across boundaries such as the air‐sea interface when estimating the state of each individual component. Operational prediction centers are moving toward Earth system modeling for all forecast timescales, ranging from days to months. However, there have been few studies that examine fundamental aspects of SCDA and the transition from traditional approaches that apply data assimilation only to a single component, whether forecasts were derived from a coupled model or an uncoupled forced model. The SCDA approach is examined here in detail using numerical experiments with a simple coupled atmosphere‐ocean quasi‐geostrophic model. The impact of coupling is explored with respect to its impact on the Lyapunov spectrum and on data assimilation system stability. Different data assimilation methods are compared within the context of SCDA, including the 3‐D and 4‐D Variational methods, the ensemble Kalman filter, and the hybrid gain method. The impact of observing system coverage is also investigated. We find that SCDA is generally superior to weakly coupled or uncoupled approaches. Dynamically defined background error covariance estimates are essential for SCDA to achieve an accurate coupled state estimate as the observing system becomes sparser. As a clarification of seemingly contradictory findings from previous studies, it is shown that ocean observations can adequately constrain atmospheric state estimates provided that the analysis‐observing frequency is sufficiently high and the ensemble size determining the background error covariance is sufficiently large.
Time Domain Electromagnetic Transient Analysis of Aerial Nonuniform Transmission Lines Excited by an Incident Electromagnetic Field

In this paper a model for Nonuniform Transmission Lines for electromagnetic transient analysis that incorporates frequency dependency of electrical parameters, variation of line electrical parameters with respect to distance, and distributed excitations due to incident electromagnetic fields is presented. The model is developed using the Method of Characteristics in the actual physical domain instead of the Modal Domain; this simplifies the mathematical development and the final equations. Moreover, the equations of the resultant model are valid either for two-conductor lines or for multiconductor lines without any change; this can be an advantage when the computer programming language considers a scalar as a 1x1 matrix. The proposed numerical model is developed under the hypothesis that the dielectric surrounding the conductors is homogenous. It is shown that in this case the characteristic curves of a Nonuniform Transmission Line become straight lines. Finally, the model is validated by comparison with results obtained using the Numerical Laplace Transform method.
Artificial perches increase bird-mediated seed rain in agricultural fallow area in southern Brazil

Abstract. One of the main barriers to restoration is the arrival of diaspores in degraded areas. However, this process can be hampered in open areas without trees in the landscape. For that, artificial perches are used to attract and provide a landing area for avian seed dispersers, to enhance seed rain. Our objective was to evaluate the effect of the distance of artificial perches in relation to a forest fragment on the diversity and composition of seed rain in an agricultural fallow area, including alien invasive plant species. We also aimed to record and characterize the bird species that potentially act as seed dispersers. Thus, we used artificial perches at three different distances from a forest fragment (5, 25, and 50 m). Four seed traps were arranged under the perches at each distance, and four control seed traps were interspersed with these and distanced at 7.5 m. Furthermore, we placed four seed traps inside the forest fragment at 5 m from the edge. We also carried out 80 h of focal observation of the avifauna that used artificial perches. A total of 24 655 seeds were sampled across all treatments. There was a significant difference in seed abundance and richness between artificial perches, control seed traps, and forest seed traps. Seed deposition increased with distance from the forest fragment (50, 25, and 5 m). An ordination procedure indicated the formation of three plant seed communities, with the forest community being most distinct. The invasive exotic species Pittosporum undulatum (Australian cheesewood) was the third most abundant in the seed rain. We observed 24 bird species from 12 families using artificial perches. The Tyrannidae family was the most represented. We showed that artificial perches are efficient structures for attracting birds, increasing the richness and abundance of seed species. Artificial perches at 25 and 50 m were more efficient possibly due to the provision of greater visibility for birds. Therefore, artificial perches are efficient in increasing seed rain in the fallow area but should be used with caution in landscapes with the presence of alien species. These findings contribute to increasing knowledge about overcoming the first barrier to ecological restoration, which is the arrival of diaspores in degraded areas, and showing the importance of birds in this process.
Application fuzzy multi-attribute decision analysis method to prioritize project success criteria

Project success is a foundation for project owner to manage and control not only for the current project but also for future potential projects in construction companies. However, identifying the key success criteria for evaluating a particular project in real practice is a challenging task. Normally, it depends on a lot of factors, such as the expectation of the project owner and stakeholders, triple constraints of the project (cost, time, quality), and company’s mission, vision, and objectives. Traditional decision-making methods for measuring the project success are usually based on subjective opinions of panel experts, resulting in irrational and inappropriate decisions. Therefore, this paper introduces a multi-attribute decision analysis method (MADAM) for weighting project success criteria by using fuzzy Analytical Hierarchy Process approach. It is found that this method is useful when dealing with imprecise and uncertain human judgments in evaluating project success criteria. Moreover, this researc.
Dual-wavelength cross absorption modulation theory based on graphene

The cross absorption modulation effect on monolayer undoped graphene is analyzed theoretically under ultrafast dual-wavelength pulses. A four-level system based on the two-level system with redistribution of electrons in the intraband relaxation and Pauli exclusion principle of interband transition is established to analyze this effect. By adjusting the modulation intensity, modulation wavelength and intraband relaxation time, the variations in output characteristics are taken into consideration: signal saturable absorption is decreased with the enhancement of modulation intensity; shorter modulation wavelength leads to larger modulation depth in high modulation intensity region; with the increasement of modulation wavelength, the modulation saturation intensity is decreased by nearly one order of magnitude. Additionally, the growth of intraband relaxation time also leads to a sharp decline in modulation saturation intensity; the modulation depth of signal light can be considerably adjusted by doping graphene. The simulation results may provide theoretical support in experimental design such as controllable saturable absorber, variable synchronous pulse laser and flexible all-optical modulator.
Decomposição dos diferenciais de rendimentos entre os trabalhadores brasileiros por quantis e categorias ocupacionais

Analisar os diferenciais de rendimentos entre as ocupacoes dos trabalhadores brasileiros e o foco central deste trabalho. Em virtude da vasta diversidade de ocupacoes catalogadas pelo IBGE (cerca de 800) e para permitir tratamento estatistico em modelagem econometrica, aplica-se o principio teorico do esquema de classes de Erickson, Goldthorpe e Portocarrero (1979) e Goldthorpe, Llewellyn e Payne (1987) para reduzi-las em apenas sete. A abordagem metodologica baseia-se em equacoes mincerianas quantilicas, estimadas em varios estratos da distribuicao de rendimentos dos trabalhadores, sobre a qual e feita uma decomposicao para verificar o hiato entre os rendimentos dos individuos com atributos inerentes distintos, bem como entre aqueles residentes em regioes mais e menos desenvolvidas do pais. Os resultados das estimacoes ratificam a importância da analise desagregada em estratos ocupacionais e por quantis, uma vez que os efeitos das variaveis explicativas sao distintos ao longo das distribuicoes de rendimentos e entre as ocupacoes. Em relacao as lacunas existentes entre as distribuicoes de rendimentos, verifica-se um glass ceiling effect em algumas ocupacoes, ou seja, a defasagem e maior no topo da distribuicao. Entretanto, na maioria dos casos ocorre um sticky floor effect, isto e, o hiato e maior na parte inferior da distribuicao. Alem disso, diferente das lacunas entre generos, que sao decorrentes apenas da remuneracao as caracteristicas dos individuos, o que e entendido como discriminacao, os diferenciais de rendimentos entre racas e regioes tambem sao decorrentes das diferentes caracteristicas dos trabalhadores.
Synthesizing Loop Invariants Through a Multiplayer Game

Human computation and crowdsourcing have been successfully proposed and applied to solve problems that are difficult to solve due to either the limitations of current computing technology or the existence of only a few experts. A common way to solve problems through human computation and crowdsourcing systems is to convert them into interactive games that target a wide audience of non-experts. Leveraging humans’ innate ability to recognize patterns, a few systems have solved the problem of synthesizing loop invariants through single-player puzzle games.This thesis proposes to apply human computation to synthesize loop invariants by having players find them through a multiplayer game. We explain the challenges involved in designing such games and explore the advantages multiplayer games offer over single-player games in terms of players’ efficiency of solving problems and the level of fun they have.
Influence of N2, O2, and H2 admixtures on the electron power balance and neutral gas heating in microwave Ar plasmas at atmospheric pressure

A combination of optical emission and absorption spectroscopy of argon 2p –1s transitions (Paschen notation) combined with collisional-radiative (CR) modelling of argon 2p states was used to characterize microwave argon plasmas at atmospheric pressure in presence of N2, O2, and H2 admixtures. In particular, the neutral gas temperature (obtained from the broadening of argon 2p 2–1s2 and 2p 3–1s2 emission lines), the number density of argon 1s5 atoms (obtained from absorption spectroscopy of the argon 2p 9–1s5 transition using a tunable laser diode), the electron temperature (obtained from the comparison between measured and simulated argon 2p -to-1s relative line emission intensities), and the electron density (obtained from the Stark broadening of the Hβ line and argon relative line emission intensities) were recorded as a function of the axial distance along the microwave plasma column. The results show that, for a given position in the plasma and a higher amount of admixture in the nominally pure argon plasma, the neutral gas temperature increases and the electron number density decreases, while the electron temperature and the population of argon metastable atoms first decreases and then increases at higher concentrations. With such information, a detailed analysis of the electron power balance was performed. It is found that less than 1% of the admixture in the argon plasma already absorbed more than 80% of the microwave power. Part of this energy is used for neutral gas heating, mostly through electron-impact excitation of rotational levels.
Prediction of protein subcellular localization using support vector machine with the choice of proper kernel

The prediction of subcellular locations of proteins can provide useful hints for revealing their functions as well as for understanding the mechanisms of some diseases and, finally, for developing novel drugs. As the number of newly discovered proteins has been growing exponentially, laboratory-based experiments to determine the location of an uncharacterized protein in a living cell have become both expensive and time-consuming. Consequently, to tackle these challenges, computational methods are being developed as an alternative to help biologists in selecting target proteins and designing related experiments. However, the success of protein subcellular localization prediction is still a complicated and challenging problem, particularly when query proteins may have multi-label characteristics, i.e. their simultaneous existence in more than one subcellular location, or if they move between two or more different subcellular locations as well. At this point, to get rid of this problem, several types of subcellular localization prediction methods with different levels of accuracy have been proposed. The support vector machine (SVM) has been employed to provide potential solutions for problems connected with the prediction of protein subcellular localization. However, the practicability of SVM is affected by difficulties in selecting its appropriate kernel as well as in selecting the parameters of that selected kernel. The literature survey has shown that most researchers apply the radial basis function (RBF) kernel to build a SVM based subcellular localization prediction system. Surprisingly, there are still many other kernel functions which have not yet been applied in the prediction of protein subcellular localization. However, the nature of this classification problem requires the application of different kernels for SVM to ensure an optimal result. From this viewpoint, this paper presents the work to apply different kernels for SVM in protein subcellular localization prediction to find out which kernel is the best for SVM. We have evaluated our system on a combined dataset containing 5447 single-localized proteins (originally published as part of the Höglund dataset) and 3056 multi-localized proteins (originally published as part of the DBMLoc set). This dataset was used by Briesemeister et al. in their extensive comparison of multilocalization prediction system. The experimental results indicate that the system based on SVM with the Laplace kernel, termed LKLoc, not only achieves a higher accuracy than the system using other kernels but also shows significantly better results than those obtained from other top systems (MDLoc, BNCs, YLoc+). The source code of this prediction system is available upon request.
A Mini-Review on Functional Near-Infrared Spectroscopy (fNIRS): Where Do We Stand, and Where Should We Go?

This mini-review is aimed at briefly summarizing the present status of functional near-infrared spectroscopy (fNIRS) and predicting where the technique should go in the next decade. This mini-review quotes 33 articles on the different fNIRS basics and technical developments and 44 reviews on the fNIRS applications published in the last eight years. The huge number of review articles about a wide spectrum of topics in the field of cognitive and social sciences, functional neuroimaging research, and medicine testifies to the maturity achieved by this non-invasive optical vascular-based functional neuroimaging technique. Today, fNIRS has started to be utilized on healthy subjects while moving freely in different naturalistic settings. Further instrumental developments are expected to be done in the near future to fully satisfy this latter important aspect. In addition, fNIRS procedures, including correction methods for the strong extracranial interferences, need to be standardized before using fNIRS as a clinical tool in individual patients. New research avenues such as interactive neurosciences, cortical activation modulated by different type of sport performance, and cortical activation during neurofeedback training are highlighted.
Climate Models: How to Assess Their Reliability

ABSTRACT The paper discusses modelling uncertainties in climate models and how they can be addressed based on physical principles as well as based on how the models perform in light of empirical data. We argue that the reliability of climate models can be judged by three kinds of standards: striking confirmation, supplementing independent causal arguments, and judging the causal core of models by establishing model robustness. We also use model robustness for delimiting confirmational holism. We survey recent results of climate modelling and identify salient results that fulfil each of the three standards. Our conclusion is that climate models can be considered reliable for some qualitative gross features and some long-term tendencies of the climate system as well as for quantitative aspects of some smaller-scale mechanisms. The adequacy of climate models for other purposes is less convincing. Among the latter are probability estimates, in particular, those concerning rare events. On the whole, climate models suffer from important deficits and are difficult to verify, but are still better confirmed and more reliable than parts of the methodological literature suggest.