Miracle-dz/UniFAR-FTUs huggingface dataset:
On robustness in dimension determination in fused sliced inverse regression

The goal of su ﬃ cient dimension reduction (SDR) is to replace original p -dimensional predictors with a lower-dimensional linearly transformed predictor. The sliced inverse regression (SIR) (Li, Journal of the American Statistical Association , 86 , 316–342, 1991) is one of the most popular SDR methods because of its applicability and simple implementation in practice. However, SIR may yield di ﬀ erent dimension reduction results for di ﬀ erent numbers of slices and despite its popularity, is a clear deﬁcit for SIR. To overcome this, a fused sliced inverse regression was recently proposed. The study shows that the dimension-reduced predictors is robust to the numbers of the slices, but it does not investigate how robust its dimension determination is. This paper suggests a permutation dimension determination for the fused sliced inverse regression that is compared with SIR to investigate the robustness to the numbers of slices in the dimension determination. Numerical studies conﬁrm this and a real data example is presented.
Functional coeﬃcient time series models with trending regressors

ABSTRACT This paper studies a functional coeﬃcient time series model with trending regressors, where the coeﬃcients are unknown functions of time and random variables. We propose a local linear estimation method to estimate the unknown coeﬃcient functions, and establish the corresponding asymptotic theory under mild conditions. We also develop a test procedure to see if the functional coeﬃcients take particular parametric forms. For practical use, we further propose a Bayesian approach to select the bandwidths, and conduct several numerical experiments to examine the finite sample performance of our proposed local linear estimator and the test procedure. The results show that the local linear estimator works well and the proposed test has satisfactory size and power. In addition, our simulation studies show that the Bayesian bandwidth selection method performs better than the cross-validation method. Furthermore, we use the functional coeﬃcient model to study the relationship between consumption per capita and income per capita in United States, and it was shown that the functional coeﬃcient model with our proposed local linear estimator and Bayesian bandwidth selection method performs well in both in-sample fitting and out-of-sample forecasting.
The Impact of Deviant Social Media Influencers and Consumer Characteristics on Purchasing Counterfeit Goods

ABSTRACT Product counterfeiting is a large and global economic crime that causes significant economic, social and personal harms. Facilitated by the efficiency and convenience of e-commerce, finding and buying counterfeits have never been easier. Going beyond product listings on websites, counterfeiters are now using sophisticated marketing techniques to promote their illicit wares. This includes employing deviant social media influencers who violate social norms and the law to peddle counterfeit goods via channels such as YouTube and Instagram. However, very little is known about their market or their impact. Based on two UK surveys, the empirical research described in this article estimates the prevalence of consumers who purchase counterfeits because of SMI endorsements. We believe this is the first estimate of its kind anywhere. Further, the research finds that influencer marketing exploits characteristics of consumers that make them susceptible to the charms of the deviant SMIs: high susceptibility to the influence of trusted digital others, low risk awareness, high risk appetite, and prone to rationalizations that morally justify the purchases. The higher prevalence of these characteristics in young adults and males helps to explain why these demographic groups are the most likely to purchase endorsed counterfeits.
Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning

Cricket, especially the Twenty20 format, has maximum uncertainty, where a single over can completely change the momentum of the game. With millions of people following the Indian Premier League (IPL), developing a model for predicting the outcome of its matches is a real-world problem. A cricket match depends upon various factors, and in this work, the factors which significantly influence the outcome of a Twenty20 cricket match are identified. Each player's performance in the field is considered to find out the overall weight (relative strength) of the teams. A multivariate regression based solution is proposed to calculate points for each player in the league and the overall weight of a team is computed based on the past performance of the players who have appeared most for the team. Finally, a dataset is modeled based on the identified seven factors which influence the outcome of an IPL match. Six machine learning models were trained and used for predicting the outcome of each 2018 IPL match, 15 minutes before the gameplay, immediately after the toss. Three of the trained models were seen to be correctly predicting more than 40 matches, with Multilayer Perceptron outperforming all other models with an impressive accuracy of 71.66%.
An edge-suppressed points voting method for extracting rural residential areas from high spatial resolution images

ABSTRACT Extraction of rural residential areas from high spatial resolution remote sensing images is important for monitoring urban development and assessing disasters. In practice, plant rows in dry lands, field ridges and vegetable greenhouses around rural residential areas are easily confused in the images. To eliminate or reduce these confusions, an edge-suppressed points voting (ESPV) method is proposed. The rationale is that these nonrelated line-segment features have striking differences in perpendicular directional filtering responses. Comparison experiments indicate that ESPV can eliminate a lot of confusions and perform well in rural residential area extraction in accuracy aspect.
Magnetodynamic Finite Element Analysis Coupled With a Vector Hysteresis Model Applied to a Variable Flux Reluctance Machine

This article presents an extended magnetodynamic finite element modeling technique for 2-D time-dependent electromechanical problems with soft-magnetic laminated steels. The proposed modeling technique includes magnetic vector hysteresis, eddy-current, and excess field components in the system of equations instead of obtaining them in the post-processing. A transient finite element solver is coupled with the Jiles-Atherton vector hysteresis model, while the dynamic components, i.e. eddy current and excess field, are modeled in a weak formulation. The proposed method is experimentally verified using a laminated transformer core similar to TEAM problem 32. It is demonstrated that the proposed magnetodynamic model with vector hysteresis characteristics calculates the flux linkage and iron loss more accurately than magnetostatic and magnetodynamic models coupled with the single-valued magnetization curve. The proposed method estimates the iron loss with a discrepancy of less than 15% up to an excitation frequency of 1500 Hz when it is compared to the transformer core measurements. Later, the experimentally verified magnetodynamic model is employed to model a 48 V, 5 kW variable flux reluctance machine with 16 Nm peak torque under various excitation levels. The machine is tested in laboratory conditions utilizing a field-oriented control algorithm in motor mode at 1000 rpm rotor speed. The average percentage error of the magnetodynamic model with vector hysteresis characteristics is found to be 14% compared to the iron loss measurements while the magnetodynamic and magnetostatic models coupled with the single-valued curve exhibit 25% and 45% average percentage errors, respectively.
GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

The increased use of software applications has resulted in a surge in energy demand, particularly in data centers and IT infrastructures. As global energy consumption is projected to surpass supply by 2030, the need to optimize energy consumption in programming has become imperative. Our study explores the energy efficiency of various coding patterns and techniques in Python, with the objective of guiding programmers to a more informed and energy-conscious coding practices. The research investigates the energy consumption of a comprehensive range of topics, including data initialization, access patterns, structures, string formatting, sorting algorithms, dynamic programming and performance comparisons between NumPy and Pandas, and personal computers versus cloud computing. The major findings of our research include the advantages of using efficient data structures, the benefits of dynamic programming in certain scenarios that saves up to 0.128J of energy, and the energy efficiency of NumPy over Pandas for numerical calculations. Additionally, the study also shows that assignment operator, sequential read, sequential write and string concatenation are 2.2 times, 1.05 times, 1.3 times and 1.01 times more energy-efficient choices, respectively, compared to their alternatives for data initialization, data access patterns, and string formatting. Our findings offer guidance for developers to optimize code for energy efficiency and inspire sustainable software development practices, contributing to a greener computing industry.
Online Low-Light Sand-Dust Video Enhancement Using Adaptive Dynamic Brightness Correction and a Rolling Guidance Filter

Sand-dust videos obtained in a low-light environment are characterized by low contrast, nonuniform illumination, color cast, and considerable noise. To realize sand-dust removal and brightness enhancement simultaneously, this article proposes an online low-light sand-dust video enhancement method using adaptive dynamic brightness correction and a rolling guidance filter. The proposed dual-threshold interframe detection strategy involves two methods to treat low-light sand-dust video frames. The first method involves two components: an adaptive dynamic brightness correction algorithm to correct the color deviation of the low-light video frame and improve its brightness and a rolling guidance filter combined with guided image filtering to enhance the frame details. The second method enhances the quality of the incoming frame by reducing the amount of calculation. The first frame of the video is processed using the first method. The processing method of each subsequent frame is determined according to its interframe detection value with the buffer frame. Through qualitative and quantitative comprehensive experiments on low-light sand-dust images and videos, the performance of the proposed method is compared with those of state-of-the-art methods. The proposed method for frame quality improvement achieves the best visual effect in enhancing the quality of low-light sand-dust images, as indicated by the best objective evaluation indicators. Moreover, compared with the framewise enhancement method, the video processing efficiency associated with the dual-threshold interframe detection strategy is 2.77 times higher.
Comparing Predictive Effects of Sensory Processing and Executive Functions on Self-care Activities in Pre-school Children with Autism Spectrum Disorder

Background: Children with autism spectrum disorder (ASD) face some challenges with performing self-care. Many performance components, including sensory and cognitive functions, are effective in performing self-care. Objectives: This study aimed to compare the role of sensory processing and executive functions in performing self-care among preschool children with ASD. Methods: In this cross-sectional study, 70 children with ASD aged 3-6 years were selected and investigated by adopting convenience sampling. The instruments included the Short Sensory Profile-2 (SSP-2), Behavior Rating Inventory of Executive Function (BRIEF), and Pediatric Evaluation of Disability Inventory (PEDI). The data were analyzed using Pearson correlation and linear regression tests. Results: The results of the correlation revealed that executive functions (P≤0.01) and sensory processing (P ≤ 0.005) had a small yet significant inverse relationship with performing self-care activities. The results of stepwise linear regression showed that executive functions and sensory processing were equally capable of predicting self-care activities. Conclusions: It was recommended that therapists should pay enough attention to both executive functions and sensory processing in order to solve the self-care problems of preschool children with ASD. Due to the low correlation between variables, it was also suggested that a multifactorial approach to self-care activities should be employed as well as the role of other factors along with sensory processing and executive function should be considered.
Laser three-dimensional printing microchannel heat sink for high-power diode laser array

Abstract. To improve the heat management of high-power diode lasers, a microchannel heat sink is obtained, whose structure is optimized in method of numerical simulation. Following such a design, the microchannel heat sink is fabricated by nickel-based doping rare earth materials by laser three-dimensional (3-D) printing procedure. Since the noncorrosion property of such material has been preliminarily demonstrated by salt spray test, there is no necessity to plate the interior of the laser 3-D printing microchannel heat sink with gold. The coefficient of thermal expansion of such material is 11 ppm/K. The diode laser array (LDA) with 80-W cw output power, 2-mm cavity length, 100-μm emitter width, and 20% fill-factors is mounted on it for the thermal resistance test, and the result is 0.40 K/W. Moreover, the smile effect of the mounted LDA is merely 0.8 μm.
Quantum entropic regularization of matrix-valued optimal transport

This article introduces a new notion of optimal transport (OT) between tensor fields, which are measures whose values are positive semidefinite (PSD) matrices. This “quantum” formulation of optimal transport (Q-OT) corresponds to a relaxed version of the classical Kantorovich transport problem, where the fidelity between the input PSD-valued measures is captured using the geometry of the Von-Neumann quantum entropy. We propose a quantum-entropic regularization of the resulting convex optimization problem, which can be solved efficiently using an iterative scaling algorithm. This method is a generalization of the celebrated Sinkhorn algorithm to the quantum setting of PSD matrices. We extend this formulation and the quantum Sinkhorn algorithm to compute barycentres within a collection of input tensor fields. We illustrate the usefulness of the proposed approach on applications to procedural noise generation, anisotropic meshing, diffusion tensor imaging and spectral texture synthesis.
Nonlinear robust state feedback control system design for nonlinear uncertain systems

This paper presents a systematic approach to the design of a nonlinear robust dynamic state feedback controller for nonlinear uncertain systems using copies of the plant nonlinearities. The technique is based on the use of integral quadratic constraints and minimax linear quadratic regulator control, and uses a structured uncertainty representation. The approach combines a linear state feedback guaranteed cost controller and copies of the plant nonlinearities to form a robust nonlinear controller with a novel control architecture. A nonlinear state feedback controller is designed for a synchronous machine using the proposed method. The design provides improved stability and transient response in the presence of uncertainty and nonlinearity in the system and also provides a guaranteed bound on the cost function. An automatic voltage regulator to track reference terminal voltage is also provided by a state feedback equivalent robust nonlinear proportional integral controller. Copyright © 2016 John Wiley & Sons, Ltd.
Inversely Regulated Inflammation-Related Processes Mediate Anxiety–Obesity Links in Zebrafish Larvae and Adults

Anxiety and metabolic impairments are often inter-related, but the underlying mechanisms are unknown. To seek RNAs involved in the anxiety disorder–metabolic disorder link, we subjected zebrafish larvae to caffeine-induced anxiety or high-fat diet (HFD)-induced obesity followed by RNA sequencing and analyses. Notably, differentially expressed (DE) transcripts in these larval models and an adult zebrafish caffeine-induced anxiety model, as well as the transcript profiles of inherently anxious versus less anxious zebrafish strains and high-fat diet-fed versus standard diet-fed adult zebrafish, revealed inversely regulated DE transcripts. In both larval anxiety and obesity models, these included long noncoding RNAs and transfer RNA fragments, with the overrepresented immune system and inflammation pathways, e.g., the “interleukin signaling pathway” and “inflammation mediated by chemokine and cytokine signaling pathway”. In adulthood, overrepresented immune system processes included “T cell activation”, “leukocyte cell-cell adhesion”, and “antigen processing and presentation”. Furthermore, unlike adult zebrafish, obesity in larvae was not accompanied by anxiety-like behavior. Together, these results may reflect an antagonistic pleiotropic phenomenon involving a re-adjusted modulation of the anxiety–metabolic links with an occurrence of the acquired immune system. Furthermore, the HFD potential to normalize anxiety-upregulated immune-related genes may reflect the high-fat diet protection of anxiety and neurodegeneration reported by others.
HMM Based POS Tagging System for 8 Different Languages and Several Tagsets

We propose, in this paper,Part-Of-Speech (POS) tagging system is proposed which based on Hidden Markov Modal (HMM) for several languages. HMM is implemented using Viterbi algorithm on 8 languages English, Hindi, Telugu, Bangla (Bengali), Marathi, Standard Chinese, Portuguese and Spanish languages. The data for these languages were taken from the freely available corpora: Brown, NPS-Chat, Indiana, Sinica, Floresta and CESS-ESP Corpora. HMM is the most learning method used in many NLP applications, especially POS tagging. HMM taggerwas implemented, by other researchers,for a lot of languages where each one takeoneprivate language. system testing is done by splitting each corpus to 99% training and 1% testing. This testing is repeated for 10 times by changing the training and test data. The accuracies (average for all 10 tests) for English (using two tagsets of 40 tags and 472 tags), English (NPS corpus), Hindi, Telugu, Bangla or Bengali, Marathi, Standard Chinese, Portuguese (using two tagsets of 32 tags and 269 tags), and Spanish (using two tagsets of 14 tags and 289 tags) are (95.3%& 92.39%), 87.17%, 81.3%, 74.03%, 72.01%, 69
A novel solar photo voltaic powered drive for the SRM for irrigation purposes using a partial resonant AC link DC to a DC boost converter

In this study a novel drive for the Switched Reluctance Motor (SRM), powered by the solar photo voltaic (SPV) source, using a Partial Resonant Inverter (PRI) followed by a Doubler Rectifier (DR) for water pumping applications, has been proposed and validated. The PRI DR combination offers a large voltage gain that is required while using low-voltage SPV sources. The PRI increases the voltage level by resonance and also it exhibits zero voltage switching so that the switching losses are reduced. The proposed system also offers a sliding mode controller based on Maximum PowerPoint Tracking implemented in the PRI. The proposed system is a fixed torque variable speed application where the speed follows the solar irradiance. The proposed idea has been validated using simulations and experimental prototypes.
A NOVEL BIOMECHANICS-BASED APPROACH FOR PERSON RE-IDENTIFICATION BY GENERATING DENSE COLOR SIFT SALIENCE FEATURES

Currently, identifying humans using biomechanics-based approaches has gained a lot of significance for person re-identification. Biomechanics-based approaches use knee-hip angle–angle relationships and body movements for person re-identification. Generally, biomechanics of human walking and running is used for person re-identification. In fact, person re-identification is a complex and important task in academia as well as industry and remains an unsolved issue in the computer vision field. The subjects most commonly addressed regarding person re-identification include significant feature extraction that can function accurately with invariant appearance and robust classification. In this study, a significant color feature descriptor is proposed by combining dense color-SIFT and global convex hull salience region features. First convex hull boundary points are detected using the SIFT technique. Furthermore, it is extended with Grubb’s outlier test to eliminate the outlier points detected by SIFT and mark t.
Development, delivery, and evaluation of a training program for the early identification of autism: Monitoring of Social Attention, Interaction, and Communication

Introduction Early identification of Autistic children is an important precursor to diagnosis, and access to supports and services. Here we describe the training of the maternal and child health (MCH) workforce in the state of Victoria, Australia in the early identification of infants and toddlers with a high likelihood of autism. Methods In 2019, 1,428 MCH nurses completed early autism training held at venues across the state, with an additional 82 nurses completing online-only training. A training needs analysis enabled the research team to determine the workforce’s current skill and knowledge levels, and to identify knowledge gaps, training needs and workplace barriers. The professional development program, known as Monitoring of Social Attention, Interaction, and Communication (MoSAIC), comprised: online pre-workshop modules; a face-to-face instructor-led workshop, which included the use of the Social Attention and Communication-Revised (SACS-R) tool; and online post-workshop modules, which included a recording of a face-to-face workshop with all accompanying resources. This was the first time that the MCH workforce received this training package. Attendees were asked to complete a training satisfaction survey immediately following the face-to-face instructor-led workshop and a follow-up survey regarding their autism knowledge and SACS-R implementation 4–6 weeks after the workshop. Results Over 90% (n = 325) of MCH nurses who completed the training satisfaction survey agreed or strongly agreed with statements that the training was clear and of high quality. Most nurses also reported that the training was well-presented and that they would recommend it to a colleague. In the 6 months following the training, a total of 82,581 SACS-R assessments were conducted by the MCH workforce, reflecting that MCH nurses had successfully integrated SACS-R assessments into their work practice after receiving the early autism identification training. Discussion This study demonstrated that training on the early identification of autism can be successfully designed, customized, and delivered to a large primary healthcare workforce for universal developmental surveillance of autism.
Painlevé Analysis for (2 + 1) Dimensional Non-Linear Schrödinger Equation

This paper investigates a real version of a (2 + 1) dimensional nonlinear Schr? dinger equation through adoption of Painleve test by means of which the (2 + 1) dimensional nonlinear Schr? dinger equation is studied according to the Weiss et al. method and Kruskal’s simplification algorithms. According to Painleve test, it is found that the number of arbitrary functions required for explaining the Cauchy-Kovalevskaya theorem exist. Finally, the associated B? cklund transformation and bilinear form is directly obtained from the Painleve test.