Miracle-dz/UniFAR-FTUs huggingface dataset:
Divide-and-conquer for covariance matrix estimation

We propose a distributed computing framework, based on a divide and conquer strategy and hierarchical modeling, to accelerate posterior inference for high-dimensional Bayesian factor models. Our approach distributes the task of high-dimensional covariance matrix estimation to multiple cores, solves each subproblem separately via a latent factor model, and then combines these estimates to produce a global estimate of the covariance matrix. Existing divide and conquer methods focus exclusively on dividing the total number of observations n into subsamples while keeping the dimension p fixed. Our approach is novel in this regard: it includes all of the n samples in each subproblem and, instead, splits the dimension p into smaller subsets for each subproblem. The subproblems themselves can be challenging to solve when p is large due to the dependencies across dimensions. To circumvent this issue, we specify a novel hierarchical structure on the latent factors that allows for flexible dependencies across dimensions, while still maintaining computational efficiency. Our approach is readily parallelizable and is shown to have computational efficiency of several orders of magnitude in comparison to fitting a full factor model. We report the performance of our method in synthetic examples and a genomics application.
Biological Oscillator Synchronization with the Cellular Potts Model Biological Oscillator Synchronization with the Cellular Potts Model

Similar to the way in which neurons synchronize their firing in the brain, individual cells of certain single-celled species can synchronize their internal molecular clocks to those of their neighboring cells. We develop an abstract Cellular Potts Model (CPM) to analyze this oscillator synchronization with two-dimensional cells on a square lattice. Model assumptions and constraints are motivated by behavior in single-celled species of slime mold ( Dictyostelium discoideum ) and slime bacteria (myxobacteria). In the CPM framework, cell-cell adhesion, which is influenced by variable clock updates per time step, and cell movement are governed by the so-called Hamiltonian energy function. The effects on oscillator synchronization of the spatial attraction parameter and the neighboring clock coordination parameter are explored in this project. We find four distinct steady phase states which cells’ clocks converge to as a result of different parameter value combinations: global synchronization, local synchronization, incoherence, and anti-synchronization. Results include phase diagram, synchronization, and cell movement analysis. The findings of this project are applicable to pattern formation in cell cultures.
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach

Abstract. Soil organic carbon storage is a well-identified climate change mitigation solution. Quantification of the soil carbon storage in cropland for agricultural policy and offset carbon markets using in situ sampling would be excessively costly, especially at the intrafield scale. For this reason, comprehensive monitoring, reporting, and verification (MRV) of soil carbon and its explanatory variables at a large scale need to rely on hybrid approaches that combine remote sensing and modelling tools to provide the carbon budget components with their associated uncertainties at intrafield scale. Here, we present AgriCarbon-EO v1.0. 1: an end-to-end processing chain that enables the estimation of carbon budget components for major and cover crops at intrafield resolution (10 m) and regional extents (e.g. 10 000 km2) by assimilating remote sensing data (e.g. Sentinel-2 and Landsat8) in a physically based radiative transfer (PROSAIL) and agronomic models (SAFYE-CO2). The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines normalized importance sampling and look-up table generation. This approach propagates the uncertainties across the processing chain from the reflectances to the output variables. After a presentation of the chain, we demonstrate the accuracy of the estimates of AgriCarbon-EO through an application over winter wheat in the southwest of France during the cropping seasons from 2017 to 2019. We validate the outputs with flux tower data for net ecosystem exchange, biomass destructive samples, and combined harvester yield maps. Our results show that the scalability and uncertainty estimates proposed by the approach do not hinder the accuracy of the estimates (net ecosystem exchange, NEE: RMSE =1.68–2.38 gC m−2, R2=0.87–0.77; biomass: RMSE =11.34 g m−2, R2=0.94). We also show the added value of intrafield simulations for the carbon components through scenario testing of pixel and field simulations (biomass: bias =-47 g m−2, −39 % variability). Our overall analysis shows satisfying accuracy, but it also points out the need to represent more soil processes and include synthetic aperture radar data that would enable a larger coverage of AgriCarbon-EO. The paper's findings confirm the suitability of the choices made in building AgriCarbon-EO as a hybrid solution for an MRV scheme to diagnose agro-ecosystem carbon fluxes.
A Preliminary Study to Understand How Mainstream Accessibility and Digital Assistive Technologies Reaches People in Lower-and Middle-Income Countries

Access to information on digital platforms not only facilitates education, employment, entertainment, social interaction but also facilitates critical governmental services, ecommerce, healthcare services and entrepreneurship [1]. Article 9 of United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) enforces its signatories to commit to provide full accessibility to every citizen of the nation [2]. This has helped to spearhead accessibility directives such as the European Accessibility Act [3] that aims to improve the functioning of markets for accessible products and services. Such directives contribute to ensure that mainstream digital technologies (smartphones, computers etc.) are accessible for everyone and without being socially remarkable, they are able to assist in daily living. Additionally, there is evidence that improving access in mainstream technologies improves product experience and usability for everyone [4]. However, mainstream access has not been fully realized, leading to inferior opportunities for people with disabilities, a disparity which is more prominent in lower and middle-income countries [5].
Swimming Against the Current: DFT Imaging Revisited

—Use of the long ignored, but simple, DFT imaging technique is reexamined and compared in computational efﬁ-ciency and image quality to the more popular, but more complex, grid & FFT method. In spite of impressive advances in computer technology, the DFT technique is still orders of magnitude slower than grid & FFT for a very small EVLA test case and the difference is expected to increase dramatically with problem size for modern radio interferometer arrays. This is not the case for optical/IR interferometers where the data and image volumes are small enough that the computational cost of imaging (although not deconvolution) are insigniﬁcant. The images produced by the DFT and grid & FFT methods are essential indistinguishable except at the very edge of images where the anti-alias ﬁltering in grid & FFT is inadequate to completely suppress aliasing.
Teachers as Readers: Baseline Profile Types with Regard to Skills, Habits, and Dispositions

Abstract This paper shares findings drawn from data generated over a period of five years on self-reported reading skills, habits, and dispositions of pre-service and in-service K-12 teachers (primarily middle and high school) across content areas (N = 854). Using latent cluster analyses, we created a Teacher Reader Profile Type (TRPT) model that illuminates distinct differences between teachers in different content areas and across status (i.e. undergraduate v. graduate). Our results suggest five distinct TRPTs, each with relative strengths and areas for growth. We provide five recommendations for teacher education programs and policy makers to consider. In addition, the measurement tool in this study could be used for key assessment related to teachers as readers. Future research could focus on how to improve teacher progress toward reading maturity, and the extent to which TRPTs relate to their own students’ literacy development.
Evolution of the low-frequency pulse profile of PSR B2217+47

Abstract An evolution of the low-frequency pulse profile of PSR B2217+47 is observed during a six-year observing campaign with the LOFAR telescope at 150 MHz. The evolution is manifested as a new component in the profile trailing the main peak. The leading part of the profile, including a newly-observed weak component, is steady during the campaign. The transient component is not visible in simultaneous observations at 1500 MHz using the Lovell telescope, implying a chromatic effect. A variation in the dispersion measure of the source is detected in the same timespan. Precession of the pulsar and changes in the magnetosphere are investigated to explain the profile evolution. However, the listed properties favour a model based on turbulence in the interstellar medium (ISM). This interpretation is confirmed by a strong correlation between the intensity of the transient component and main peak in single pulses. Since PSR B2217+47 is the fourth brightest pulsar visible to LOFAR, we speculate that ISM-induced pulse profile evolution might be relatively common but subtle and that SKA-Low will detect many similar examples. In this scenario, similar studies of pulse profile evolution could be used in parallel with scintillation arcs to characterize the properties of the ISM.
Revitalizing Tourism and Hospitality Industry Through Smart Tourism Application (Beautiful Kenyir): A Case Study of Kenyir Lake, Malaysia

The COVID-19 had caused RM1mil loss for local Kenyir Lake tourism industry due to travel restrictions and interstate travel by domestic tourists. Kenyir Lake is located at Terengganu, one of the tourist destinations with multiple attractions that is rich with nature, beautiful islands and conservation and preservation of animals. Kenyir Lake is known as an ecotourism destination which has many attractions and activities such as fishing, caving, jungle trekking, houseboat overnight rides, and many more. Kenyir Lake has a huge potential to be known globally if the information of the Kenyir Lake is properly organized. To regenerate and revitalize our local tourism industry, a Smart Tourism mobile application named “Beautiful Kenyir” has been developed to encourage and promote the domestic tourism industry to the tourists. This research aims to identify the feedback on the importance of smart tourism application use on mobile phone. This smart tourism application is designed to attract locals and travelers to choose Kenyir Lake as one of the must-visit destinations. A cross-sectional survey of the 14-items instrument was adapted and distributed online to a total of 196 smartphone users in Klang Valley during the first week of April 2023. Most of the users responded with a high positive response toward this product. The findings supported the idea that smart tourism application can influence the intention to use. The result of the survey also improved our understanding and knowledge that this product can be one of the approaches to promote and market Kenyir Lake as a tourism destination. This smart tourism application provides brief information of the places of attractions, activities, restaurants, facilities booking, transportation, duty free shops, reviews, and rating just within one application. This study is beneficial for researchers, tourism agencies, ministries, local businesses, application developers and the public to engage in smart tourism while traveling.
Spatially inhomogeneous background rate estimators and uncertainty quantification for nonparametric Hawkes point process models of earthquake occurrences

Space–time Hawkes point process models for the conditional rate of earthquake occurrences traditionally make many parametric assumptions about the form of the triggering function for the rate of aftershocks following an earthquake. As an alternative, Marsan and Lengliné [Science 319 (2008) 1076–1079] developed a completely nonparametric method that provides an estimate of a homogeneous background rate for mainshocks, and a histogram estimate of the triggering function. At each step of the procedure the model estimates rely on computing the probability each earthquake is a mainshock or aftershock of a previous event. The focus of this paper is the improvement and assessment of Marsan and Lengliné’s method in the following ways: (a) the proposal of novel ways to incorporate a spatially inhomogeneous background rate; (b) adding error bars to the histogram estimates which quantify the sampling variability in the estimation of the underlying seismic process. A simulation study is designed to evaluate and validate the ability of our methods to recover the triggering function and spatially varying background rate. An application to earthquake data from the Tohoku District in Japan is discussed at the end, and the results are compared to a well-established parametric model of seismicity for this region.
Advanced Numerical Methods in Diffractive Optics and Applications to Periodic Photonic Nanostructures

In this thesis, we present the Fourier Modal Method for computing periodic nanophotonic systems. We investigate waveguides and cavities inside woodpile photonic crystals and compare our numerical results with experimental measurements. Also, the polarization properties of opal photonic crystals are investigated. Additionally, we report on our efforts in improving the convergence of the method by adaptive spatial resolution for arbitrarily shaped structures.
PlanetCam UPV/EHU: A Two-channel Lucky Imaging Camera for Solar System Studies in the Spectral Range 0.38–1.7 μm

We present PlanetCam UPV/EHU, an astronomical camera designed fundamentally for high-resolution imaging of Solar System planets using the “lucky imaging” technique. The camera observes in a wavelength range from 380 nm to 1.7 μm and the driving science themes are atmosphere dynamics and vertical cloud structure of Solar System planets. The design comprises two configurations that include one channel (visible wavelengths) or two combined channels (visible and short wave infrared) working simultaneously at selected wavelengths by means of a dichroic beam splitter. In this paper the camera components for the two configurations are described, as well as camera performance and the different tests done for the precise characterization of its radiometric and astrometric capabilities at high spatial resolution. Finally, some images of solar system objects are presented as well as photometric results and different scientific cases on astronomical targets.
A Randomized Algorithm for Minimum Cuts

A randomized algorithm is an algorithm that receives, in addition to its input, a stream of random bits which is used to make random choices. The random bits are assumed to be independent of the input. A salient feature is that repeated runs of a randomized algorithm with fixed input data will, in general, not produce the same result. You might be perplexed that such a lack of definiteness is desirable, but consider that this feature allows to transform deterministic algorithms with bad worst case behaviour into randomized algorithms that perform well with high probability on any input. I hope that the next example can convey that randomized algorithms are often simple and efficient.
WMF reduced‐order robust estimators for multisensor descriptor systems

For multisensor descriptor systems with uncertain noise variances, the weighted measurement fusion (WMF) reduced-order robust estimators are presented. The multisensor descriptor systems with known upper bounds of uncertain noise variances are defined as the worst-case conservative multisensor descriptor systems. For the worst-case conservative multisensor descriptor systems, the WMF reduced-order conservative Kalman estimators, including filter, predictor and smoother, are obtained, by applying the WMF method and the singular value decomposition method. However, the measurements of the conservative multisensor descriptor systems are unavailable. Replacing these unavailable conservative measurements by the actual and available measurements yields the actual WMF reduced-order robust estimators. Their robustness is proven by the dynamic error variance system method, i.e. the actual estimation error variances are not greater than those of conservative systems. Two simulation examples of four-sensor descriptor systems show the effectiveness of the presented algorithms.
An Improved Particle Swarm Optimization Algorithm

Niche is an important technique for multi-peak function optimization. When the particle swarm optimization (PSO) algorithm is used in multi-peak function optimization, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, an improved PSO algorithm based on niche technique is brought forward. PSO algorithm utilizes properties of swarm behavior to solve optimization problems rapidly. Niche techniques have the ability to locate multiple solutions in multimodal domains. The improved PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the niche technique. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on multi-peak function optimization.
Bayesian Inference for Bivariate Conditional Copula Models with Continuous or Mixed Outcomes

The main goal of this thesis is to develop Bayesian model for studying the inﬂuence of covariate on dependence between random variables. Conditional copula models are ﬂexible tools for modelling complex dependence structures. We construct Bayesian inference for the conditional copula model adapted to regression settings in which the bivariate outcome is continuous or mixed (binary and continuous) and the copula parameter varies with covariate values. The functional relationship between the copula parameter and the covariate is modelled using cubic splines. We also extend our work to additive models which would allow us to handle more than one covariate while keeping the computational burden within reasonable limits. We perform the proposed joint Bayesian inference via adaptive Markov chain Monte Carlo sampling. The deviance information criterion and cross-validated marginal log-likelihood criterion are employed for three model selection problems: 1) choosing the copula family that best ﬁts the data, 2) selecting the calibration function, i.e., checking if parametric form for copula parameter is suitable and 3) determining the number of independent variables in the additive model. The performance of the estimation and model selection techniques are investigated via simulations and demonstrated on two data sets: 1) Matched Multiple Birth and 2) Burn Injury. In which of interest is the inﬂuence of gestational age and maternal age on twin birth weights in the former data, whereas in the later data we are interested in investigating how patient’s age aﬀects the severity of burn injury and the probability of death. %%%%PhD
Enhancing light absorption in a thin film silicon tandem solar cell fabricated on a reactive ion etched nano-structured glass surface

The efficient capture and absorption of light are important aspects in improving the photovoltaic conversion efficiency of a solar cell. In a two-terminal tandem device, the output current density is limited by the lowest current-generating component sub-cell. We attempted by experiment to improve the current density of both sub-cells, using an inverted pyramid-type textured glass substrate. One of the two surfaces of the glass substrates was textured by semi-anisotropic reactive ion etching (RIE) with a 100 sccm flow rate of SF6 gas, and with 7 µm × 7 µm square-shaped etch masks, and obtained an optimum texture of base dimension ~7 µm, height ~8.5 µm, tip diameter ~75 nm and tip to tip separation between neighboring pyramids were about 9 µm. Tandem solar cells that were fabricated on these textured surfaces receive light from the base of these micro-pyramids, thereby enhancing light absorption in the active layers of the device. The shape of the texture and orientation of layers of the tandem solar cell ensured a longer optical path inside the solar cell, leading to higher optical absorption and an improvement in device performance. We observed an increase in the short circuit current density from 11.52 to 14.30 mA cm−2, and in the device efficiency from 11.97% to 14.22%.
Ink-jet printers go beyond paper

The network supports universities and businesses working in the ink-jet and display fields Every time you buy a new computer, you know that within months it will be superseded by a machine that is faster, cheaper and boasts even more memory. However, it is easy to forget that a similar revolution has quietly been taking place in the world of printing. Desktop printers, which now cost as little as $100, are marvels of scientific and technological achievement.
Visuospatial ability and student approach to learning as predictors of academic performance on written versus laboratory‐based assessments in human anatomy

As hours devoted to human anatomy curricula fall under threat and curricular delivery methods remain in flux, many new teaching innovations are emerging, which require comprehensive evaluation to ensure evidence‐based teaching is maintained. Although grades are the predominant measure of ‘learning’, alternative metrics can assess more nuanced and meaningful outcomes. Two common predictors of students' three‐dimensional understanding of the body and depth of learning are visuospatial abilities and approaches to learning, respectively. This study evaluated and compared the relative predictive power of these metrics on written and laboratory‐based assessments in a human anatomy course. Deep approaches to learning and visuospatial abilities were expected to positively correlate with overall performance, with visuospatial abilities being the more salient predictor, especially on laboratory‐based assessments. Additionally, visuospatial abilities were expected to positively correlate with deep learning approaches and negatively correlate with surface learning approaches. Multiple linear regression models controlling for covariates found that both visuospatial abilities (p = 0.049; p = 0.014) and deep learning approaches (p = 0.001; p = 0.001) were independent significant predictors of final and laboratory‐based grades, while only deep learning approaches were significantly predictive of written grades (p = 0.007). There was no significant relationship between visuospatial abilities and approaches to learning. Given these findings and the increased reliance on visuospatially demanding digital learning activities in anatomy, both metrics should be considered when evaluating the impact of teaching innovations. Further, educators should design learning resources and environments that train visuospatial abilities and promote deeper learning approaches to maximize students' success.