Miracle-dz/UniFAR-FTUs huggingface dataset:
Backtesting value-at-risk accuracy: a simple new test

This paper proposes a new test of value-at-risk (VAR) validation. Our test exploits the idea that the sequence of VAR violations (hit function) ‐ taking value 1 ‐ α, if there is a violation, and ‐α otherwise ‐ for a nominal coverage rate α verifies the properties of a martingale difference if the model used to quantify risk is adequate (Berkowitz et al., 2005). More precisely, we use the multivariate portmanteau statistic of Li and McLeod (1981) ‐ extension to the multivariate framework of the test of Box and Pierce (1970) ‐ to jointly test the absence of autocorrelation in the vector of hit sequences for various coverage rates considered as relevant for the management of extreme risks. We show that this shift to a multivariate dimension appreciably improves the power properties of the VAR validation test for reasonable sample sizes.
SYSTEMATIC REVIEW ON ISSUES AND CHALLENGES OF PRE-SERVICE ENGLISH TEACHERS IN MALAYSIA

This systematic review focuses on issues and challenges related to pre-service English teachers (PSETs) in Malaysia for the past decade. Even though improving English language teachers’ quality is a primary agenda in the Malaysian Education Blueprint 2013-2025, review studies documenting the recent issues and developments of pre-service teachers who are just joining the teaching profession are still lacking. Combining Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) with published review guidelines, the review utilised seven databases to look for current research. Subsequently, the final search yielded twenty articles for the qualitative synthesis using ATLAS.ti. It has been determined that the PSETs in Malaysia experience issues and challenges in terms of (1) teaching competency; (2) professional development; (3) support; (4) the disparity between theories and classroom practices; (5) classroom management and (6) transition stage in becoming a teacher. This study concludes that future teacher education needs to prepare trainee teachers to be more resilient and adaptable to new environments, challenges and unforeseen circumstances. Finally, several recommendations were highlighted for further studies.
DETERMINANTS OF BEHAVIOR INTENTION TO ADOPT PEER-TO-PEER LENDING SERVICES AMONG INDONESIA MSMES

An alternative for Micro Small and Medium-Sized Enterprises (MSMEs) against the barriers of financial inclusion is peer-to-peer (P2P) lending services. Despite its effectiveness in advancing financial inclusion, the factors that influence its adoption among MSMEs are unique. The purpose of this study is to analyze the factors influencing the intention of Indonesia MSMEs to adopt P2P lending services using the unified theory of acceptance and use of technology (UTAUT) 2 model. The results of this study show that five variables significantly affect MSMEs including performance expectancy, social influence, price value hedonic motivation, and effort expectancy. Meanwhile, the result shows that the factors of facilitating conditions and habits were not significant. This study contributes a reflective insight on the determinants of the P2P lending adoption among MSMEs in a developing country.
Wealth and welfare across generations

Although older generations have substantially more wealth than their recent predecessors did at the same age, younger generations do not. Bringing together UK data on those born between the 1930s and 1980s and a lifecycle model of saving, I quantify whether this is due to changes in preferences or changes in the circumstances each generation has faced. Changing circumstances can rationalise slowing generationon-generation wealth growth. I find no evidence that later-born generations are less patient. I quantify the implications of changing circumstances for consumption and welfare. Later-born generations are predicted to have higher consumption, despite accumulating no greater wealth, than their predecessors because their earnings are more ‘backloaded’, they have fewer children, and face lower taxation.
BIAS CORRECTION IN GROUP SEQUENTIAL ANALYSIS WITH CORRELATED DATA

This paper focuses on the bias of the group sequential estimate of treat- ment effect for correlated data using the generalized estimating equation (GEE) method and the Lan and DeMets alpha-spending function. Linear and logistic re- gressions are used to examine (a) the magnitude of the bias of a sequential estimate with correlated data; (b) the influence of the true correlation structure on bias. A bias-corrected sequential estimate is proposed using a Brownian motion approxi- mation and numerical simulation. Logistic regression is used to illustrate and to assess the performance of the proposed method.
POLARIZATION, CONFORMAL INVARIANTS, AND BROWNIAN MOTION

The polarization P(A) of a closed or open set A ⊂ C is defined as follows: If z, z ∈ A then z, z ∈ P(A) . If neither z, z ∈ A then neither z, z ∈ P(A) . If exactly one of z = x + iy , z belongs to A then x+ i|y| ∈ P(A) and x− i|y| / ∈ P(A) . We prove theorems that describe the behaviour of harmonic measure, Green function, Robin function, Brownian motion and extremal length under polarization. These theorems, combined with an approximation technique due to Dubinin, lead to a new proof of symmetrization results of Baernstein.
Structure, bonding, and possible superhardness of CrB4

By electron and x-ray diffractionwe establish that the CrB4 compound discovered over 40 years ago crystallizes in the oP 10 (Pnnm) structure, in disagreement with previous experiments but in agreement with a recent first-principles prediction. The 3D boron network in this structure is a distorted version of the rigid carbon sp(3) network proposed recently for the high-pressure C-4 allotrope. Our systematic density functional theory analysis of the electronic, structural, and elastic properties in ten related transition metal TMB4 tetraborides (TM = Ti, V, Cr, Mn, Fe and Y, Zr, Nb, Mo, Tc) identifies CrB4 as the prime candidate to be a superhard material. In particular, the compound's calculated weakest shear and tensile stresses exceed 50 GPa, and its Vickers hardness is estimated to be 48 GPa. We compare the reported and estimated Vickers hardness for notable (super) hard materials and find that the CrB4 calculated value is exceptionally high for a material synthesizable under standard ambient-pressure conditions.
Scalable Undersized Dataset RF Classification: Using convolutional multistage training

Unlike areas such as computer vision and speech recognition, where convolutional and recurrent neural network (NN)-based approaches have proven effective due to the nature of the respective areas of application, deep learning (DL) still lacks a general approach suitable for the unique nature and challenges of radiofrequency (RF) systems such as radar, signals intelligence, electronic warfare, and communications. Existing approaches face problems in robustness, consistency, efficiency, repeatability, and scalability. Some of the main challenges in radiofrequency (RF) sensing such as radar target identification are the difficulty and cost of obtaining data. Hundreds to thousands of samples per class are typically used when training for classifying signals into 2–12 classes with reported accuracy ranging from 87% to 99%, where accuracy generally decreases with more classes added. In this article, we present a new DL approach based on multistage training (MST) and demonstrate it on RF sensing signal classification. We consistently achieve more than 99% accuracy for up to 17 diverse classes using only 11 samples per class for training, a substantial improvement over standard DL approaches.
Potential predictability of Indian summer monsoon rainfall in NCEP CFSv2

The potential predictability of the Indian summer monsoon rainfall (ISMR), soil moisture, and sea surface temperature (SST) is explored in the latest version of the NCEP Climate Forecast System (CFSv2) retrospective forecast at five different lead times. The focus of this study is to find out the sensitivity of the potential predictability of the ISMR to the initial condition through analysis of variance technique (ANOVA), information‐based measure, including relative entropy (RE), mutual information (MI), and classical perfect model correlation. In general, the all methods show an increase in potential predictability with a decrease in lead time. Predictability is large over the Pacific Ocean basin as compared to that of the Indian Ocean basin. However, over the Indian land region the potential predictability increases from lead‐4 to lead‐2 and then decreases at lead‐1 followed by again increase at lead‐0. While the actual ISMR prediction skill is highest at lead‐3 forecast (second highest at lead‐1), the potential predictability is highest at lead‐2. It is found that highest and second highest actual prediction skill of the ISMR in CFSv2 is due to the combined effects of initial Eurasian snow and SST over Indian, west Pacific and eastern equatorial Pacific Ocean region. While the teleconnection between the ISMR and El Niño‐Southern Oscillation is too strong, the ISMR and Indian Ocean dipole have completely out of phase relation in the model as compared to the observation. Furthermore, the actual prediction skill of the ISMR is now very close to the potential predictability limit. Therefore, in order to improve the ISMR prediction skill further, development of model physics as well as improvements in the initial conditions is required.
Constant Scalar Curvature and Extremal Kahler Metrics on Blow ups

Extremal Kahler metrics were introduced by E. Calabi as best representatives of a given Kahler class of a complex compact manifold, these metrics are critical points of the L 2 norm of the scalar curvature function. In this paper, we report some joint works with C. Arezzo and M. Singer concerning the construction of extremal Kahler metrics on blow ups at finitely many points of Kahler manifolds which already carry an extremal Kahler metric. In particular, we give sufficient conditions on the number and locations of the blown up manifold points for the blow up to carry an extremal Kahler metric.
Mathematical modelling and applications of spherical catadioptric omnidirectional view sensors

Electronic sensors form an essential component in a robotic ecosystem. In modern configurations, a visual input provides a multitude of information that describes the environment via a mere measurement on the light intensities and their colours. In this doctoral work, the attention is stressed on a new type of visual sensor that is gradually seeing adoptions in various applications. It is known as the “catadioptric omnidirectional view sensor”, and is constructed using a rotationally symmetrical mirror and a perspective camera. The relevant studies on the catadioptric omnidirectional view sensor had begun several decades ago. However, the spherical mirror variant of the sensor was found to be less attended by the research community despite its advantage in cost effectiveness. This is due to the absence of a convenient feature called the “single view point”. Therefore, the study in this work investigates the possibilities of the spherical mirror variant, mostly on its practical usages in robotics applications. There are four themes in this study. First, the mapping relationship of the point correspondences between an image and the environment was derived from the study of the caustic behaviour of the sensor itself. With the said mapping relationship, it becomes possible to utilise it for various useful applications that largely involve 3-dimensional reconstruction, depth analysis and view rectification. In the subsequent work, a view rectification procedure commonly known as “unwrapping” was developed using the mapping relationship for the spherical mirror variant. The unwrapping procedure is a crucial technique that converts a catadioptric omnidirectional view image into a perspective view image. It therefore simplifies various application frameworks by bridging omnidirectional view input with existing image processing procedures and computer vision techniques. The proposed approach eliminates several dependencies on ground truth measurement and introduces a post-error correction procedure that requires only a single checker-box pattern image.
Computation of incompressible flow over three-dimensional cavities.

This paper investigates unsteady incompressible flow over cavities. Previous research in incompressible cavity-flow has included flow inside and past a 2-dimensional cavity, and flow inside a 3-dimensional cavity, driven by a moving lid. The present research is focused on incompressible flow past a 3-dimensional cavity. This involves the complex interaction between the external flow and the re-circulating flow within the cavity. A CFD approach, based on the unsteady Navier-Stokes equations for 3-dimensional incompressible flow, was used in the study. The cavity was assumed to be rectangular in geometry, and the flow was assumed to be laminar with a varying Reynolds number. Typical results of the computation are presented. These results reveal some useful and interesting flow phenomena, such as the Taylor-Goitler longitudinal (TGL) vortices and Kelvin-Helmholtz instability.
Development of an Experimentally Validated Non-linear Viscoelastic Viscoplastic Model for a Novel Fuel Cell Membrane Material

The proton exchange membrane (PEM) is a key component in proton exchange membrane fuel cells (PEMFCs). During standard fuel cell operation, the PEM degrades due to cyclic hygrothermal loads, resulting in performance loss or total failure. Improvement of current PEM materials and development of cheaper, more durable materials is essential to the commercialization of PEMFC technology, which may provide an attractive alternative energy source for transportation. This dissertation investigates a new PEM material which is a blend of sulfonated perfluorocyclobutane (PFCB) and polyvinylidene fluoride (PVDF). Hereafter referred to as PFCB/PVDF, this polymer blend was developed by General Motors CompanyTM as a potential replacement for the current benchmark PEM, the DuPontTM product Nafion®. The PFCB/PVDF blend is less costly to manufacture than standard PEM materials and investigations into its long-term mechanical durability are ongoing. Specifically, this document discusses the experimental and analytical work performed in the material characterization, constitutive expression development, and implementation of that expression into uniaxial and biaxial finite element geometries. Extension of the model to time-varying temperature and moisture conditions is also explored. The uniaxial finite element model uses a non-linear viscoelastic viscoplastic (NLVE-VP) constitutive expression with parameters determined from uniaxial creep and recovery experiments at a single environmental condition. Validation tests show that this model accurately predicts results from uniaxial tension experiments, such as stress relaxation, force ramp, and multistep creep and recovery, to stresses of 8 MPa and strains approaching 15%, which is the maximum hygrothermal strain expected in an operating fuel cell. The biaxial finite element model combines the NLVE-VP constitutive expression with the geometry of a pressure-loaded blister experiment, which better approximates fuel cell membrane constraints. Results from the biaxial model are compared to experimental results. The model accurately predicts strain in the blister test but predicts stresses that differ from those estimated from blister curvature. Additionally, it is found that both the non-linear viscoelastic and viscoplastic parameters are functions of the operating environment. Future experimental work is needed to characterize that dependence before the constitutive model is used to simulate the response of the PFCB/PVDF blend to fuel cell operating conditions.
A Deep Learning approach for fire object detection in Autonomous vehicles

In recent years, object detection has emerged as a crucial component of many popular consumer applications, including video surveillance and security systems, mobile text recognition, etc. The potential for autonomous vehicles (AVs) to increase driver satisfaction and decrease fatalities and injuries in traffic accidents has attracted a lot of interest in recent years. Object detection is critical to autonomous driving infrastructure. Autonomous automobiles need precise environmental interpretation to drive safely. Locating and identifying these things in real time is a significant challenge, but deep learning-based object detectors play a crucial role in this endeavour. In this paper, a prototype of the autonomous vehicle controlled by a microcontroller for fire object detection is proposed. Localization of the fire object in a picture using the deep learning model is performed using the live video feed from the camera installed on the prototype remote-operated car. The gadget may also pick up on specifics and alert the user. Experimental findings showed that the suggested prototype with deep learning architecture recognised and alerted devastating fires with high speed and accuracy in diverse weather conditions—sunny or overcast, day or night.
Conformal Anomaly and Counterterms in Designer Gravity

We construct concrete counterterms of the Balasubramanian-Kraus type for Einstein-scalar theories with designer gravity boundary conditions in AdS$_{4}$, so that the total action is finite on-shell and satisfy a well defined variational principle for an arbitrary scalar field potential. We focus on scalar fields with the conformal mass, $m^{2}=-2l^{-2}$, and show that the holographic mass matches the Hamiltonian mass for any boundary conditions. We compute the conformal anomaly of the dual field theory in the generic case, as well as when there exist logarithmic branches of non-linear origin. As expected, the conformal anomaly vanishes for the boundary conditions that are AdS invariant. When the anomaly does not vanish, the dual stress tensor describes a thermal gas with an equation of state related to the boundary conditions of the scalar field. When the anomaly vanishes, we recover the dual theory of a massless thermal gas. As an application of the formalism, we consider a general family of exact hairy black hole solutions that, for some particular values of the parameters in the moduli potential, contains solutions of four-dimensional gauged $\mathcal{N}=8$ supergravity and its $\omega$-deformation. Using the AdS/CFT duality dictionary, they correspond to triple trace deformations of the dual field theory.
Causes of the unprecedented freezing disaster in January 2008 and its possible association with the global warming.

The unprecedented disaster of low temperature, persistent rain, snow and ice storms, causing widespread freezing in the Yangtze River basin and South China in January 2008 is not a local or regional event, but a part of the chain events of large-scale low temperature and snow storms in the same period in Asia. Its severity and impacts were most significant in South China. The event was characterized by three major features: (1) snowfall, freezing rain and rainfall, the three forms of precipitation coexisted with freezing rain being the dominant factor responsible for this disaster; (2) low temperature, rain and snow, and freezing rain exhibited extremely great intensity, with record-breaking measurements logged down for 8 meteorological elements based on the statistics made by China National Climate Center and the provincial meteorological services in the Yangtze River basin and South China; (3) the disastrous weathers persisted for an exceptionally long time period, unrecorded before in the observation history of China. There is no single cause for this event. It may be resulted from multiple factors that superimpose on and interweave with one another at the right time and place. Among them, the La Nina situation is a climate background that provided conducive conditions for the invasion of cold air into South China; the persistent anomaly of the atmospheric circulation in Eurasia is the direct cause for 4 rounds of cold air invasion into South China; and the northward transport of warm and moist airflows from the Bay of Bengal and the South China Sea finally warranted the formation of the freezing rain and snow storms and their prolonged dominance in the southern part of China. A preliminary discussion of a possible association of this disastrous event with the global warming is presented. This event may be viewed as a short-term, regional perturbation to the global warming trend. There is not any possibility for this event to divert the long-term trend and the global pattern of the global warming.
A representation for the limiting random variable of a branching process with infinite mean and some related problems

It is known that for a Bienaymé– Galton–Watson process {Zn } whose mean m satisfies 1 < m < ∞, the limiting random variable in the strong limit theorem can be represented as a random sum of i.i. d. random variables and hence that convergence rate results follow from a random sum central limit theorem. This paper develops an analogous theory for the case m = ∞ which replaces ‘sum' by ‘maximum'. In particular we obtain convergence rate results involving a limiting extreme value distribution. An associated estimation problem is considered.
Equilibrium and stability analysis of cellular neural networks

Some sufficient conditions for the existence of stable equilibrium points in cellular neural networks (CNNs), described by space-invariant templates, are presented, Such conditions exhibit the following advantages: (1) they exploit the key property of CNNs, i.e. the local interactions among the cells; (2) they can be verified by a simple examination of the template elements; (3) they give rise to constraints, that in most cases cover wider parameter sets than those available in the CNN literature. Extensive simulations show that the sufficient conditions also guarantee the network stability. This is in agreement with the fact that, so far, no example of unstable CNNs, possessing a stable equilibrium point, has been shown.
Time-variant displacement structure and interpolation problems

Derives a new recursive solution for a general time-variant interpolation problem of the Hermite-Fejer type, based on a fast algorithm for the recursive triangular factorization of time-variant structured matrices. The solution follows from studying the properties of an associated cascade system and leads to a triangular array implementation of the recursive algorithm. The system can be drawn as a cascade of first-order lattice sections, where each section is composed of a rotation matrix followed by a storage element and a tapped-delay filter. Such cascades always have certain blocking properties, which can be made equivalent to the interpolation conditions. The authors also illustrate the application of the algorithm to problems in adaptive filtering, model validation, robust control, and analytic interpolation theory. >
Triple patterning lithography layout decomposition using end-cutting

Abstract. Triple patterning lithography (TPL) is one of the most promising techniques in the 14-nm logic node and beyond. Conventional LELELE type TPL technology suffers from native conflict and overlapping problems. Recently, as an alternative process, TPL with end-cutting (LELE-EC) was proposed to overcome the limitations of LELELE manufacturing. In the LELE-EC process, the first two masks are LELE type double patterning, while the third mask is used to generate the end-cuts. Although the layout decomposition problem for LELELE has been well studied in the literature, only a few attempts have been made to address the LELE-EC layout decomposition problem. We propose a comprehensive study for LELE-EC layout decomposition. Layout graph and end-cut graph are constructed to extract all the geometrical relationships of both input layout and end-cut candidates. Based on these graphs, integer linear programming is formulated to minimize the conflict and the stitch numbers. The experimental results demonstrate the effectiveness of the proposed algorithms.