Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Energy Dissipation from Kelvin–Helmholtz Instability Induced Turbulence in Oscillating Coronal Loops

Kelvin–Helmholtz instability induced turbulence is one promising mechanism by which loops in the solar corona can be heated by MHD waves. In this Letter we present an analytical model of the dissipation rate of Kelvin–Helmholtz instability induced turbulence εD, finding it scales as the wave amplitude (d) to the third power (εD ∝ d3). Based on the concept of steady-state turbulence, we expect the turbulence heating throughout the volume of the loop to match the total energy injected through its footpoints. In situations where this holds, the wave amplitude has to vary as the cube-root of the injected energy. Comparing the analytic results with those of simulations shows that our analytic formulation captures the key aspects of the turbulent dissipation from the numerical work. Applying this model to the observed characteristics of decayless kink waves we predict that the amplitudes of these observed waves are insufficient to turbulently heat the solar corona.
Application of the BPM Strategy to the Management of the COPD Clinical Process

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of death according to the World Health Organization (WHO). Like any chronic disease, the clinical process of COPD affects the patient’s life. Currently, clinical processes are inefficient, causing a loss of quality of life for patients and a high economic increase in the costs borne by family members, health systems and society. This paper presents a new approach to the redesign of the process for the management of COPD based on the use of the strategy of continuous improvement Business Process Management. This approach aims to improve the efficiency of the management of chronic diseases such as COPD, while achieving a higher quality of life and patient satisfaction.
A network approach to item response data: Development and applications of latent space item response models

We propose a novel network approach to item response data with advantages over existing approaches, both non-network approaches and network approaches. Our proposal is motivated by the observation that in some educational assessment settings it is not credible that all items with the same difficulty have the same response probability for all respondents with the same ability. An example is an educational assessment where unobserved heterogeneity is present, arising from unobserved variables such as cultural background and upbringing of students, the quality of mentorship and other forms of emotional and professional support received by students, and other unobserved variables that may affect response probabilities. To do justice to such item response data, we offer a fresh perspective, viewing item response data as a bipartite network, consisting of links between respondents on the one hand and items on the other hand. We assume that both items and respondents are embedded in a common latent space, with the probability of a correct response decreasing as a function of the distance between the respondent's and the item's position in the latent space. The resulting latent space network approach to item response data is simpler than existing network approaches, and helps derive insightful diagnostic information on items as well as respondents. We provide ample empirical evidence to demonstrate the usefulness of our proposed approach.
Joint inference on extreme expectiles for multivariate heavy-tailed distributions

The notion of expectiles, originally introduced in the context of testing for homoscedasticity and conditional symmetry of the error distribution in linear regression, induces a law-invariant, coherent and elicitable risk measure that has received a significant amount of attention in actuarial and financial risk management contexts. A number of recent papers have focused on the behaviour and estimation of extreme expectile-based risk measures and their potential for risk management. Joint inference of several extreme expectiles has however been left untouched; in fact, even the inference of a marginal extreme expectile turns out to be a difficult problem in finite samples. We investigate the simultaneous estimation of several extreme marginal expectiles of a random vector with heavy-tailed marginal distributions. This is done in a general extremal dependence model where the emphasis is on pairwise dependence between the margins. We use our results to derive accurate confidence regions for extreme expectiles, as well as a test for the equality of several extreme expectiles. Our methods are showcased in a finite-sample simulation study and on real financial data.
Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

Abstract An electroencephalogram (EEG)-based brain–computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior

Traditional models of category learning in psychology focus on representation at the category level as opposed to the stimulus level, even though the two are likely to interact. The stimulus representations employed in such models are either hand-designed by the experimenter, inferred circuitously from human judgments, or borrowed from pretrained deep neural networks that are themselves competing models of category learning. In this work, we extend classic prototype and exemplar models to learn both stimulus and category representations jointly from raw input. This new class of models can be parameterized by deep neural networks (DNN) and trained end-to-end. Following their namesakes, we refer to them as Deep Prototype Models, Deep Exemplar Models, and Deep Gaussian Mixture Models. Compared to typical DNNs, we find that their cognitively inspired counterparts both provide better intrinsic fit to human behavior and improve ground-truth classification.
Typed-based Declassiﬁcation for Free

This work provides a study to demonstrate the potential of using oﬀ-the-shelf programming languages and their theories to build sound language-based-security tools. Our study focuses on information ﬂow security encompassing declassiﬁcation policies that allow us to express ﬂexible security policies needed for practical requirements. We translate security policies, with declassiﬁcation, into an interface for which an unmodiﬁed standard typechecker can be applied to a source program—if the program typechecks, it provably satisﬁes the policy. Our proof reduces security soundness -with declassiﬁcation-to the mathematical foundation of data abstraction, Reynolds’ abstraction theorem.
Smooth Deformation Field-based Mismatch Removal in Real-time

This paper studies the mismatch removal problem, which may serve as the subsequent step of feature matching. Non-rigid deformation makes it difficult to remove mismatches because no parametric transformation can be found. To solve this problem, we first propose an algorithm based on the re-weighting and 1-point RANSAC strategy (R1P-RNSC), which is a parametric method under a reasonable assumption that the non-rigid deformation can be approximately represented by multiple locally rigid transformations. R1P-RNSC is fast but suffers from a drawback that the local smoothing information cannot be taken into account. Then, we propose a non-parametric algorithm based on the expectation maximization algorithm and dual quaternion (EMDQ) representation to generate the smooth deformation field. The two algorithms compensate for the drawbacks of each other. Specifically, EMDQ needs good initial values provided by R1P-RNSC, and R1P-RNSC needs EMDQ for refinement. Experimental results with real-world data demonstrate that the combination of the two algorithms has the best accuracy compared to other state-of-the-art methods, which can handle up to 85% of outliers in real-time. The ability to generate dense deformation field from sparse matches with outliers in real-time makes the proposed algorithms have many potential applications, such as non-rigid registration and SLAM.
Hybrid Discriminative-Generative Training via Contrastive Learning

Contrastive learning and supervised learning have both seen significant progress and success. However, thus far they have largely been treated as two separate objectives, brought together only by having a shared neural network. In this paper we show that through the perspective of hybrid discriminative-generative training of energy-based models we can make a direct connection between contrastive learning and supervised learning. Beyond presenting this unified view, we show our specific choice of approximation of the energy-based loss outperforms the existing practice in terms of classification accuracy of WideResNet on CIFAR-10 and CIFAR-100. It also leads to improved performance on robustness, out-of-distribution detection, and calibration.
Rapid parameter estimation of gravitational waves from binary neutron star coalescence using focused reduced order quadrature

Rapid parameter estimation of gravitational waves from binary neutron star coalescence, in particular accurate sky localisation in minutes after the initial detection stage, is crucial for the success of multi-messenger observations. One of the techniques to speed up the parameter estimation, which has been applied for the production analysis of the LIGO-Virgo collaboration, is reduced order quadrature (ROQ). While it speeds up parameter estimation significantly, the time required is still on the order of hours. Focusing on the fact that the parameter-estimation follow-up can be tuned with the information available at the detection stage, we improve the ROQ technique and develop a new technique, which we designate focused reduced order quadrature (FROQ). We find that FROQ speeds up the parameter estimation by a factor of $\mathcal{O}(10^3)$ to $\mathcal{O}(10^4)$ and enables providing accurate source properties such as the location of a source in several tens of minutes after detection.
Age of Information: An Introduction and Survey

We summarize recent contributions in the broad area of age of information (AoI). In particular, we describe the current state of the art in the design and optimization of low-latency cyberphysical systems and applications in which sources send time-stamped status updates to interested recipients. These applications desire status updates at the recipients to be as timely as possible; however, this is typically constrained by limited system resources. We describe AoI timeliness metrics and present general methods of AoI evaluation analysis that are applicable to a wide variety of sources and systems. Starting from elementary single-server queues, we apply these AoI methods to a range of increasingly complex systems, including energy harvesting sensors transmitting over noisy channels, parallel server systems, queueing networks, and various single-hop and multi-hop wireless networks. We also explore how update age is related to MMSE methods of sampling, estimation and control of stochastic processes. The paper concludes with a review of efforts to employ age optimization in cyberphysical applications.
Predicting the Number of Future Events

Abstract This article describes prediction methods for the number of future events from a population of units associated with an on-going time-to-event process. Examples include the prediction of warranty returns and the prediction of the number of future product failures that could cause serious threats to property or life. Important decisions such as whether a product recall should be mandated are often based on such predictions. Data, generally right-censored (and sometimes left truncated and right-censored), are used to estimate the parameters of a time-to-event distribution. This distribution can then be used to predict the number of events over future periods of time. Such predictions are sometimes called within-sample predictions and differ from other prediction problems considered in most of the prediction literature. This article shows that the plug-in (also known as estimative or naive) prediction method is not asymptotically correct (i.e., for large amounts of data, the coverage probability always fails to converge to the nominal confidence level). However, a commonly used prediction calibration method is shown to be asymptotically correct for within-sample predictions, and two alternative predictive-distribution-based methods that perform better than the calibration method are presented and justified. Supplementary materials for this article are available online.
Practical strategies for large missing data in dementia diagnosis

Accurate computational models for clinical decision support systems require clean and reliable data, but in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. Many popular methods of handling missing data are unsuitable for handling such missing test data. This work addresses the problem by evaluating multiple imputation and classification workflows based not only on diagnostic classification accuracy but also computational cost. In particular, we focus on dementia diagnosis due to long time delays, high variability, high attrition rates and lack of practical data imputation strategies in its diagnostic pathway. We identified and replicated the extreme missingness structure of data from a memory clinic on a larger open dataset, with the original complete data acting as ground truth. Overall, we found that computational cost, but not accuracy, varies widely for various imputation and classification approaches. Particularly, we found that iterative imputation on the training dataset combined with a reduced-feature classification model provides the best approach, compromising speed and accuracy. Taken together, this work has elucidated important factors to be considered when developing or maintaining a dementia diagnostic support system, which can be generalized to other clinical or medical domains, particularly with extreme data missingness.
Removal of Organic Dyes by Using Cellulose-Chitosan-Nanowires Hybrid Composition Beads

Organic dyes are mostly used in textile, paper, plastic, and other industries. These industries can be toxic and harmful to environment and human. Adsorption is the most efficient method to control wastewater that contains dyes. Cellulose-Chitosan beads was mainly used in the adsorption. To increase and enhance the organic dyes removal capacity of these beads, titanium dioxide nanowires (TiO2Nw) as used to achieve novel Cs-Cell-nanowires hybrid composites. Ultralong hydroxyapatite nanowires was prepared and used to enhance the mechanical and thermal properties of beads. On the other hand, the beads composition was prepared with spherical white shape. Internal structure of beads and other composition were studied by using Attenuated Total Reflectance (ATR) spectroscopy, scanning electron microscopy (SEM), lazer diffraction (LD), thermal analysis (TG, DSC, DTG, and DTA). The beads are tested with five different organic dyes which include Methyl blue (MB), Acid alizarin Black (AAB) as cationic dyes, and Methyl orang (MO), Methyl red (MR) and Eriochrome black T (EBT) as anionic dyes. The results indicate that the beads have the ability to remove MB, EBT,AAB but do not have the ability to remove MO and MR. So, the Cell-CS- HAPNw- TiO2Nw beads can be considered as the promising adsorption for MB, EBT, and AAB from aqueous solutions.
Photo Voltaic Properties of Ag2O/ Si Heterojunction Device: Effect of Substrate Conductivity

The chemical bath deposition method was employed to prepare Ag2O films at specific preparation conditions. Silicon substrate with different conductivity was used to study their effect on the electrical and photovoltaic properties of the prepared devices. The current-voltage properties reveal that the ideality factor was (2.4 and 3.36) for psi and n-si substrate respectively. The obtained device efficiency is 0.35 while maximum detectivity was 0.3 cm.Hz1/2W-1.
Effect of Friction Stir Processing Parameters on the Properties of AA7075-T73/Al2O3 Surface Metal Matrix Composite

Friction Stir Processing (FSP) technology was wielded to output the Al7075/ Al2O3 surface composite. The effects parameters of processing method on particle distribution have been studied. The microstructure and mechanical characteristics of the samples were examined using the optical microscope, SEM and hardness examination. Acquired consequences, showed that Al2O3 particles were in a good interior distribution inside the basement. This technique produced excellent bonding between the surface composite and the base material. On other hand the surface hardness was increased about 25% as compared with the substrate. In addition, grain matrix refinement and enhanced particle distribution were obtained after each FSP pass. Also the dispersion of Al2O3 particles in the stirred area became more homogeneous and the average hardness improved by increasing the number of passes.
Study of Coconut Charcoal Powder and Egg Shell Powder as Partial Replacement to Fine Aggregates and Cement in Load Bearing Concrete Bricks

Waste disposal is one of the issues that plague the society. Philippines is an agricultural country due to its strategic location in the tropics. Because of this nature, the agriculture sector tends to produce a huge amount of waste that if not disposed properly could pose a threat to the society and to the environment. With the advancement of technology, it has been a challenge for researchers to improve the quality of construction materials that is used in the industry. Studies have been performed on the possibilities of incorporating agricultural wastes to various construction materials as a form of waste diversion. In order to address the problem with agricultural wastes as well as to improve the property of construction materials, a study on coconut waste and eggshell waste was conducted as a constituent in concrete brick production. In this study, the density, compressive strength as well as the water absorption of concrete bricks with coconut charcoal powder (CCP) and eggshell powder (ESP) as replacement for fine aggregates and cement was conducted. Different mix ratios (0%, 2.5%, 5%, 10% CCP with a constant value of 5% ESP in each mix ratio) were carried out and the results were compared to a control sample. From the results of the density test, the concrete bricks with 5% CCP and 5% ESP was the lightest at a density of 1678.184 kg/m3, it also showed the least water absorption at 8.58% and exhibited the highest compressive strength at 17.5 MPa.
Effect of Doping Alumina by Nano Mn on the Sensing Applications

In this work, Nano Mn was deposited onto micro Alumina (Al2O3) films of 200 nm thicknesses using the wet impregnation method. The topography, morphology, chemical and structural characteristics of the grown Nanofilms were investigated using atomic force microscopy (AFM), XRD, Atomic absorption spectroscopy (AAS) and Field Emission Scanning Electron Microscopy (FESEM). The obtained results demonstrate that the structure of the proposed film was monocrystalline. The deposited Al2O3 Nanofilm was highly transparent and amorphous in nature. The values of the energy bandgap and the refractive index obtained by the OTS were found to be in a very good agreement with the values of the bulk (Al2O3). The surface morphology of the films was found to be smooth with no defects. Progressive increase in the range of 4 3.2 nm to 4.53 nm in the RMS roughness of the proposed films was observed with the increase in the annealing temperature.
Measuring Web Latency and Rendering Performance: Method, Tools, and Longitudinal Dataset

This paper presents Webget, a measurement tool that measures Web quality of service (QoS) metrics, including the DNS lookup time, time to first byte (TTFB), and the download time. Webget also captures Web complexity metrics, such as the number and the size of objects that make up the website. We deploy the Webget test to measure the Web performance of Google, YouTube, and Facebook from 182 SamKnows probes. Using a 3.5-year-long (January 2014–July 2017) dataset, we show that the DNS lookup time of these popular content delivery networks (CDNs) and the download time of Google have improved over time. We also show that the TTFB toward Facebook exhibits worse performance than the Google CDN. Moreover, we show that the number and the size of objects are not the only factors that affect the Web download time. We observe that these webpages perform differently across regions and service providers. We also developed a Web measurement system, Web performance and rendering (WePR) that measures the same Web QoS and complexity metrics as Webget, but it also captures the Web quality of experience metrics, such as rendering time. WePR has a distributed architecture where the component that measures the Web QoS and complexity metrics is deployed on the SamKnows probe, while the rendering time is calculated on a central server. We measured the rendering performance of four websites. We show that in 80% of the cases, the rendering time of the websites is faster than the downloading time. The source code of the WePR system and the dataset is made publicly available.
Effects of feeding raw or extruded linseed on the ruminal ecosystem of sheep

Abstract Polyunsaturated fatty acids affect bacterial and protozoal population, inducing important modifications in rumen metabolism. The aim of this study was to investigate the effect of linseed extrusion on in situ ruminal degradability and microbial number and distribution. Six ruminally fistulated sheep were divided in 3 groups and fed one of the following diets according to a replicated Latin square design: (a) control, based on mixed hay and maize grains; (b) as in (a) plus 130 g of grounded raw linseed; (c) as in (b) except that the linseed was extruded. Extrusion decreased linseed dry matter and fat degradabilities. There was a marked reduction of total protozoal population in sheep fed supplemented diets. No effects were observed between groups on bacterial concentration, hay dry matter and NDF in situ degradabilities.
Inverse problem for a multi-term fractional differential equation

Abstract Inverse problem for a family of multi-term time fractional differential equation with non-local boundary conditions is studied. The spectral operator of the considered problem is non-self-adjoint and a bi-orthogonal set of functions is used to construct the solution. The operational calculus approach has been used to obtain the solution of the multi-term time fractional differential equations. Integral type over-determination condition is considered for unique solvability of the inverse problem. Different estimates of multinomial Mittag-Leffler functions alongside Banach fixed point theorem are used to prove the unique local existence of the solution of the inverse problem. Stability of the solution of the inverse problem on the given datum is established.