Miracle-dz/UniFAR-FTUs huggingface dataset:
Regionalisation of the parameters of the log‐Pearson 3 distribution: a case study for New South Wales, Australia

The index flood method is widely used in regional flood frequency analysis (RFFA) but explicitly relies on the identification of ‘acceptable homogeneous regions’. This paper presents an alternative RFFA method, which is particularly useful when ‘acceptably homogeneous regions’ cannot be identified. The new RFFA method is based on the region of influence (ROI) approach where a ‘local region’ can be formed to estimate statistics at the site of interest. The new method is applied here to regionalize the parameters of the log‐Pearson 3 (LP3) flood probability model using Bayesian generalized least squares (GLS) regression. The ROI approach is used to reduce model error arising from the heterogeneity unaccounted for by the predictor variables in the traditional fixed‐region GLS analysis. A case study was undertaken for 55 catchments located in eastern New South Wales, Australia. The selection of predictor variables was guided by minimizing model error. Using an approach similar to stepwise regression, the best model for the LP3 mean was found to use catchment area and 50‐year, 12‐h rainfall intensity as explanatory variables, whereas the models for the LP3 standard deviation and skewness only had a constant term for the derived ROIs. Diagnostics based on leave‐one‐out cross validation show that the regression model assumptions were not inconsistent with the data and, importantly, no genuine outlier sites were identified. Significantly, the ROI GLS approach produced more accurate and consistent results than a fixed‐region GLS model, highlighting the superior ability of the ROI approach to deal with heterogeneity. This method is particularly applicable to regions that show a high degree of regional heterogeneity. Copyright © 2014 John Wiley & Sons, Ltd.
CurveCrafter: A System for Animated Curve Manipulation

Linework on 3D animated characters is an important aspect of stylized looks for films. We present CurveCrafter, a system allowing animators to create new lines on 3D models and to edit the shape and opacity of silhouette curves. Our tools allow users to draw, redraw, erase, edit and retime user created curves. Silhouette curves can have their shape edited or reverted, and their opacity erased or revealed. Our algorithm for propagating edits over tracked silhouette curves ensures temporal consistency even as curves expand and merge. Five professional animators used our system to animate lines on three shots with different characters. Additionally, the effects lead from the short film Pete used our system to more easily recreate edits on a film shot. CurveCrafter was able to successfully enhance the resulting animations with additional linework.
Topics on Large Deviations in Artificial Intelligence

Artificial intelligence has become one of the most important fields of study during the last decade. Applications include medical sciences, autonomous vehicles, finance and everyday life. Therefore the analysis of convergence and stability of these algorithms is of utmost importance. One way of analysing the stability and speed of convergence is by the large deviations theory. In large deviations theory, a rate function characterises the exponential rate of convergence of stochastic processes. For example, by evaluating the rate function for stochastic approximation algorithms for training neural networks, faster convergence can be achieved. This thesis consists of five papers that use ideas from large deviation theory to understand and improve specific machine-learning models. Paper I proves that a stochastic approximation algorithm satisfies the large deviation principle with a specific rate function. This class of stochastic approximation contains many interesting learning algorithms, such as stochastic gradient descent, persistent contrastive divergence and the Wang-Landau algorithm. Analysing the rate function from Paper I is not straightforward. In Paper II, we use tools from weak KAM theory to characterise the rate function. The rate function takes the form of a Lagrangian and can be evaluated by calculating the viscosity solution to the corresponding Hamilton-Jacobi equations. In Paper II, we also identify the projected Aubry set, a set of great importance when it comes to describing the viscosity solutions. Papers III, IV and V all involve Variational autoencoders (VAE
De novo protein repeat identification by probabilistic consistency

Motivation: An estimated 25% of all eukaryotic proteins contain repeats, which underlines the importance of duplication for evolving new protein functions. Internal repeats often correspond to structural or functional units in proteins. Methods capable of identifying repeated segments or domains at the sequence level can therefore assist in predicting domain structures, inferring hypotheses about function and mechanism, and investigating the evolution of proteins from smaller
Research on Inﬂuencing Factors of College Evaluation Results Based on Machine Learning Model

This paper introduces the machine learning model into the research of determining the factors on college evaluations. Taking the “double-high colleges” of the Ministry of Education as a research case, compared with support vector machines, decision trees and other models, Lasso-Logistic can more efﬁciently compressand select the key explanatory variables. The overall prediction accuracy of the model is nearly 80%. The results show that the landmark achievements of majors, teachers, and students are the core inﬂuencing factors, and factors such as school establishment time, number of teachers, student-teacher ratio and other factors have no substantial impact.
Paraoxonase 2 Deficiency Causes Mitochondrial Dysfunction in Retinal Pigment Epithelial Cells and Retinal Degeneration in Mice

Although AMD is a complex disease, oxidative stress is a crucial contributor to its development, especially in view of the higher oxygen demand of the retina. Paraoxonase 2 (PON2) is a ubiquitously and constitutively expressed antioxidant protein that is found intracellularly associated with mitochondrial membranes and modulates mitochondrial ROS production and function. The contribution of PON2 to AMD has not been studied to date. In this study, we examined the role of PON2 in AMD utilizing both in vitro and in vivo models of AMD with emphasis on mitochondrial function. Mitochondrial localization and regulation of PON2 following oxidative stress were determined in human primary cultured retinal pigment epithelium (hRPE) cells. PON2 was knocked down in RPE cells using siRNA and mitochondrial bioenergetics were measured. To investigate the function of PON2 in the retina, WT and PON2-deficient mice were administered NaIO3 (20 mg/kg) intravenously; fundus imaging, optical coherence tomography (OCT), electroretinography (ERG) were conducted; and retinal thickness and cell death were measured and quantified. In hRPE, mitochondrial localization of PON2 increased markedly with stress. Moreover, a time-dependent regulation of PON2 was observed following oxidative stress, with an initial significant increase in expression followed by a significant decrease. Mitochondrial bioenergetic parameters (basal respiration, ATP production, spare respiratory capacity, and maximal respiration) showed a significant decrease with oxidative stress, which was further exacerbated in the absence of PON2. NaIO3 treatment caused significant retinal degeneration, retinal thinning, and reduced rod and cone function in PON2-deficient mice when compared to WT mice. The apoptotic cells and active caspase 3 significantly increased in PON2-deficient mice treated with NaIO3, when compared to WT mice. Our investigation demonstrates that deficiency of PON2 results in RPE mitochondrial dysfunction and a decline in retinal function. These findings imply that PON2 may have a beneficial role in retinal pathophysiology and is worthy of further investigation.
Acute effects of moderate-intensity continuous physical exercise performed with different amounts of muscle mass on executive function in healthy young adults: a randomized trial

We examined the effect of amount of muscle mass involved in moderate-intensity continuous physical exercise on executive function. To this end, fifty-five participants completed two acute physical exercise sessions on an airbike ergometer using the upper and lower limbs simultaneously and only the upper limbs, and a resting control session in a randomized order. The physical exercise session lasted 30 min and was performed at moderate intensity (between 64 %-76 % of maximal heart rate evaluated in graded maximal exercise testing). Participants took the Stroop test (congruent and incongruent trials) before and after the sessions to assess executive performance. For the congruent trial, both physical exercise interventions improved executive function performance (pre vs. post, p-value = 0.002 and 0.003 for physical exercise with upper limbs and physical exercise with upper and lower limbs, respectively). Furthermore, executive function performance was higher after the physical exercise interventions than after the control session (p-value = 0.002 and 0.004 for physical exercise with upper limbs and physical exercise with upper and lower limbs, respectively). For the incongruent trial, both physical exercise interventions also improved executive function performance (pre vs. post, p-value < 0.001 for physical exercise with upper limbs and physical exercise with upper and lower limbs, respectively). However, there were no significant differences after both physical exercise interventions and resting control session (p-value = 0.175). Executive function (congruent trial) was positively impacted by acute aerobic physical exercise regardless of the amount of muscle mass involved (upper limbs or upper plus lower limbs). Therefore, we recommend aerobic physical exercise with less or more muscle mass involved to improve cognitive function.
Adaptive Histogram Equalization in Diabetic Retinopathy Detection

Diabetic retinopathy is a dangerous pathology that can ultimately lead to permanent blindness. Recent studies have proved the feasibility of automatic diagnosis systems, supporting specialists, from eye fundus images, based on deep networks. A careful image equalization turned out to be important in obtaining good performances. However, state-of-the-art algorithms for image equalization require expensive parameter tuning which may limit the adoption of such support systems in practice. In this paper, we propose a learning-based approach to adaptively select the right equalization parameters for each image. This approach allows significant reduction in the inference time without necessarily sacrificing the accuracy. A preliminary empirical evaluation confirms the advantages of the proposed method.
A study on characterization of stratospheric aerosol and gas parameters with the spacecraft solar occultation experiment

Spacecraft remote sensing of stratospheric aerosol and ozone vertical profiles using the solar occultation experiment has been analyzed. A computer algorithm has been developed in which a two step inversion of the simulated data can be performed. The radiometric data are first inverted into a vertical extinction profile using a linear inversion algorithm. Then the multiwavelength extinction profiles are solved with a nonlinear least square algorithm to produce aerosol and ozone vertical profiles. Examples of inversion results are shown illustrating the resolution and noise sensitivity of the inversion algorithms.
Soil physical properties and wheat performance under various preparatory tillage practices in a subtropical dry land of Pakistan.

Conventional methods of seed bed preparation involve intensive tillage which not only deteriorate the soil physical health but also increase input cost for resource poor farmers of developing countries. A field experiment was conducted to evaluate different less intensive tillage systems on soil physical properties and wheat yield. The tillage treatments applied at the time of wheat planting were minimum tillage (MT), rotavator (RT), disc plough (DP) and cultivator (CT) arranged in RCBD layout. Soil bulk density was lowest under DP at 0-15 cm and under MT at 15-30 cm soil depth. Consequently the highest total soil porosity was observed under DP at 0-15 cm depth and under MT and DP at 15-30 cm depth. Soil water content measured twelve times during crop period did not show significant difference among tillage treatments. Mean values of soil water content were 8.20% at 0-15 cm depth and 9.29 at 15-30 cm depth. Wheat biomass yield was higher under DP and MT than RT and CT. Grain yield was highest under DP followed by MT that was signifcnalty higher than CT. Regression analysis showed that variation in biomass and grain yield of wheat was mainly explained by the differences in bulk density with r 2 values of 0.566 and 0.623, respectively. The study indicates that minimum tillage and disc plough can be used as an alternative to conventional intensive tillage systems for better soil physical health and wheat crop in subtropical dry land conditions.
Global Ionospheric Electron Density from GNSS-POD Limb Measurements

GNSS-LEO radio links from Precise Orbital Determination (POD) and Radio Occultation (RO) antennas have been used increasingly in studying and monitoring the global ionospheric electron density $(N_{e})$. In this study we developed an optimal estimation (OE) method to retrieve Ne profiles from the slant total electron content $(hTEC)$ measurements acquired by the GNSS-POD links at negative elevation angles. The hmF2 and NmF2 from the OE retrieval are validated against ground-based ionosondes and radar observations, showing generally good agreements in NmF2 from all sites. Nighttime hmF2 measurements tend to agree better than the daytime when the ionosonde heights tend to be slightly lower. The OE algorithm has been applied to all GNSS-POD data acquired from the COSMIC-1 (2006-2019), COSMIC-2 (2019-present), and Spire (2019-present) constellations, showing a consistent ionospheric $N_{e}$ morphology. A detailed analysis of the frequency-wavenumber spectra is made for the $N_{e}$ variability at different heights. In the lower ionosphere (~150 km) we found significant spectral power in DE1, DW6, DW4, SW5, and SE4 wave components, in addition to well-known DW1, SW2 and DE3 waves. In the upper ionosphere (~450 km), additional wave components are still present, including DE4, DW4, DW6, SE4 and SW4. The co-exist of eastward and westward propagating WN4 components implies the presence of a stationary planetary wave4 (sPW4), as suggested by other earlier studies.
Determining bone position from wearable antennas using microwave imaging: A feasibility study

Imaging of the bone is an important clinical tool in detecting fractures, determining loss of bone density and in improving movement and gait analysis, with visualisation predominantly performed using ionising imaging. Microwave imaging, an alternative non-ionising imaging modality, has shown promising results in analysing bone density variation and in determining the presence of tears in joint tissues. In this study, we aimed to detect the location of the bone in the leg using radar-based microwave imaging. Confocal imaging algorithms were applied to scattering data acquired from simulated wearable antennas on a male human model without the use of a coupling liquid. We successfully detected both thigh and shin bones, with a localisation error of 2.5 cm, 0.91 cm and 1.34 cm for the femur, tibia and fibula centres respectively. Notably, these errors are in line with tumour detection errors. Our methodology and results illustrate a safe, easy-to-implement and simple pipeline using off-the-shelf antennas and algorithms to determine bone position using wearable sensors. We believe this technique has wide ranging applications, particularly in improving the accuracy of clinical movement analysis systems.
A model-agnostic meta-learning fault diagnosis method based on dynamic weighting

Motor is an important component in industrial production, so it is very important to detect and diagnose the motor fault in time. The performance of traditional deep learning methods is often limited by the amount of data, which leads to the problem of unreliable model performance in the face of few shot fault diagnosis problem. Meta-learning can use previous few shot learning experience to quickly guide a new few shot problem to build a reliable model. However, traditional meta-learning methods only focus on training the meta model by accumulating loss functions for different tasks, without considering the specific contribution from different tasks. Therefore, when some data is polluted by noise, the construction of meta model may be affected, thus affect the accuracy of fault diagnosis. Therefore, this paper aims to propose a meta-learning fault diagnosis method based on dynamic weighting to overcome the above problems. Different weights are assigned according to the size of the training tasks’ loss function, and smaller weights are assigned to tasks with larger loss functions, so as to realize dynamic weakening of the negative effects of unreliable tasks and build a meta model that can quickly adapt to powerful fault diagnosis performance. Experimental verification for motor fault diagnosis shows that the fault diagnosis accuracy of this method is improved significantly.
Artificial Intelligence in Contending Climate Change to Achieve Environmental Justice

The UN COP26 2021 climate warming summit provides an opportunity for globe officials to take action & offer immediate & substantial pledges to reduce excretion & control rising climate by 2050. Artificial intelligence (AI) is a rapidly expanding segment of computer intelligence that is projected to profoundly impact many parts of one's civilization. AI approaches are employed to evaluate massive volumes of unorganized and diverse data in order to uncover and utilize complicated and nuanced relationships among such data without the requirement for explicit analytical treatment of such relationships. These artificial intelligence tools are inevitable in order to grasp meaning of the constantly rising data flood and adapt to the tough growing requirements in climate change surveillance. This study focused just on concept of climatic justice, that entails eliminating the globe's nations' disparities inside the status of the environment and climatic. Though climatic change is a worldwide issue, it presents itself to differing degree in each country; therefore, the study's uniqueness is in proposing a novel strategy to attaining environmental protection in the world economy by making use of AI as a weapon to battle climate change.
Bidirectional frequency control and inertia provision with grid interties and HVDC links

A control approach for HVDC links and grid interties is presented that allows adjustable, bidirectional frequency control and synthetic inertia provision without the need for additional storage in the DC link. The amount of grid support can be adjusted easily and independently on each terminal. The control approach is just a simple extension to standard vector current control, but it is fully equivalent to virtual synchronous generator (VSG) control running on both terminals simultaneously.
Non-linear response of geocell reinforced dense granular layer over weak soil under circular loading

A modified Pasternak model has been proposed to predict the behavior of a circular footing resting on a geocell reinforced granular layer overlying soft soil. In this model, the geocell reinforced granular layer was considered as a Pasternak shear layer and the soft soil as a series of Winkler springs to predict the overall behavior. Both linear and non-linear responses of the geocell reinforced bed were considered in the analysis, which correspond to low- (≤1% of footing width) and high-footing settlements (>1% of footing diameter), respectively. An iterative finite difference scheme is employed for obtaining the solution for the governing differential equations and the results presented in non-dimensional form. Results from the present model were validated with independent experimental test data by comparing the load-deformation responses. The model parameters, inverse of normalized shear stiffness of the geocell (α2) and the inverse of normalized ultimate bearing capacity (μ), were varied for the parametric study. It was found that the shear stiffness of the reinforced granular bed, i.e. the product of shear modulus and height of the geocell in a granular bed, plays an important role in improving the performance of the reinforced bed. Design charts were presented in the form of improvement factors for practical range of shear layer width, shear stiffness of the geocell reinforcement, and ultimate bearing capacity of the soft soil.
Mathematical Modelling & Simulation of Synchronous Buck Converter and Analysis of Its Simulation Results

This paper presents development of mathematical model for synchronous buck converter and conventional buck converter. Mathematical modelling based MATLAB/SIMULINK model is developed and various simulations are carried out. The effect of parasitic i.e., DC Resistance (DCR) of inductor and Equivalent Series Resistance (ESR) of capacitor in case of practical synchronous buck converters is observed and analyzed. The performance of practical and ideal synchronous buck converter is also compared in terms of the output voltage and capacitor voltage.
Enhancement of Maximum Energy Product by $\alpha$-Fe Coating on the Side of Rectangular Sm(Fe0.8Co0.2)12 Nanoparticles

The demagnetization process of exchange-coupled Sm(Fe<inf>0.8</inf>Co<inf>0.2</inf>)<inf>12</inf>/<tex>$\alpha$</tex>-Fe nanocomposite magnet particles with a-Fe phase coated on the sides of rectangular Sm(Fe<inf>0.8</inf>Co<inf>0.2</inf>)<inf>12</inf> nanoparticles and the effect of Fe phase arrangement on their <tex>$(BH)_{\max}$</tex> are systematically investigated from micromagnetics calculations. The <tex>$(BH)_{\max}$</tex> of the reference model Sm(Fe<inf>0.8</inf>Co<inf>0.2</inf>)<inf>12</inf> particles without Fe layer was 630 kJ/m<sup>3</sup>. On the other hand, the <tex>$(BH)_{\max}$</tex> of the nanocomposite magnet particle model, in which the four sides of the hard phase are coated with a soft phase, was found to have the highest <tex>$(BH)_{\max}$</tex> of 678 kJ/m<sup>3</sup>. This result may provide a guideline for designing high-performance magnet particle materials while reducing the use of rare metals.
Electrothermal modeling of junctionless vertical Si nanowire transistors for 3D logic circuit design

This work presents a comprehensive analysis of electrothermal effects in emerging 3D vertical junctionless nanowire transistors (VNWFETs) using on-wafer measurements under a wide range of temperature and validated against numerical and compact model simulations. Experimental observations indicate an increase of the drain current with the temperature, conforming to the behavior of junctionless FETs. Multiphysics simulations reveal formation of temperature hot-spots that adversely affect thermal conductivity in smaller geometries. The VNWFET compact model was then modified to account for the underlying electro-thermal effects as well as dynamic self-heating. Model simulations and the experimental results at different measurement temperatures for different transistor geometries show good agreement. The developed SPICE-compatible compact model was then used for studying the impact of electrothermal effects on the performances of basic 3D logic circuits.