Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Image Database Using Independent Principal Component Analysis

The paper presents a modified approach of Principal Component Analysis (PCA) for an automatic classification of image database. Principal components are the distinctive or peculiar features of an image. PCA also holds information regarding the structure of data. PCA can be applied to all training images of different classes together forming universal subspace or to an individual image forming an object subspace . But if PCA is applied independently on the different classes of objects, the main direction will be different for them. Thus, they can be used to construct a classifier which uses them to make decisions regarding the class. Also the dimension reduction of feature vector is possible. Initially training image set is chosen for each class. PCA, using eigen vector decomposition, is applied to an individual class forming an individual and independent eigenspace for that class. If there are n classes of training images, we get n eigenspaces. The dimension of eigenspace depends upon the number of selected eigen vectors. Each training image is projected on the corresponding eigenspace giving its feature vector. Thus n sets of training feature vectors are produced. In testing phase, new image is projected on all eigenspaces forming n feature vectors. These feature vectors are compared with training feature vectors in corresponding eigenspace. Feature vector nearest to new image in each eigenspace is found out. Classification of new image is accomplished by comparing the distances between the nearest feature vector and training image feature vector in each eigenspace. Two distance criteria such as Euclidean and Manhattan distance are used. The system is tested on COIL-100 database. Performance is tested and tabulated for different sizes of training image database.
FV-DGNN: A Distance-Based Graph Neural Network for Finger Vein Recognition

As a promising biometric identification technology, finger vein recognition has gained considerable attention in the field of information security due to its inherent advantages, such as living body recognition, noncontact operation, and high security. However, the existing models often focus on pairwise matching of low-contrast infrared finger vein images, overlooking the underlying relationships among the matching information. To address this limitation, we propose a graph neural network (GNN) model that captures the distance-based interrelation between multiple pairs of samples. Specifically, we design an architecture to obtain a binary finger vein mask image, which guides the model to capture high-level features of finger vein regions while ignoring noises behind nonfinger vein regions. Moreover, a distance-based GNN architecture, which models the distance distribution between multiple pairs of finger vein images by fusing the distance information propagated along edges, is proposed to determine the matching degree between each pair of images. Furthermore, to further expedite the proposed model in application, the depthwise separable convolution layer is adopted in the encoder component of a convolutional neural network (CNN) architecture to reduce the parameters significantly. Extensive experimental results on three public databases have verified the effectiveness of our proposed model.
Effects of different phenylcapsaicin doses on neuromuscular activity and mechanical performance in trained male subjects: a randomized, triple-blinded, crossover, placebo-controlled trial

Objective: This study aimed to examine the effects of phenylcapsaicin (PC) supplementation on strength performance and neuromuscular activity in young trained male subjects. Materials and methods: A total of 25 trained subjects [full-squat (SQ) one repetition maximum (1RM) = 125.6 ± 21.0 kg] were enrolled in this randomized, triple-blinded, crossover, placebo-controlled trial. The subjects performed a first session and a post-24 h session for each condition. In the first session, the subjects ingested a high dose of PC (HD, 2.5 mg), a low dose (LD, 0.625 mg), or a placebo (PLA). Their performance in SQ was assessed under a 3% × 8 × 70% 1RM protocol in the first session. Their performances in countermovement jump (CMJ), SQ with 60% 1RM, and isometric squat were measured before and after the SQ protocol in both sessions. The neural activity of the vastus lateralis (VL) and vastus medialis (VM) was recorded via surface electromyography (EMG) and averaged in both sessions. Results: Significant differences between the conditions were reported for lifting velocity, velocity loss, and the 60% load in dynamic SQ (p range = 0.02–0.04). Electrical changes were not identified for any outcome, although neural activity changed across time (p range ≤0.001–0.006). A significant condition × time effect was observed in CMJ compared to PLA (p ≤0.001) and LD (p ≤0.001). Intra-set analyses revealed higher velocities in HD compared to those in LD (p = 0.01) and PLA (p range = 0.004–0.008). Conclusion: Therefore, PC may improve the strength performance and attenuate the mechanical fatigue induced by resistance training in SQ and CMJ exercises.
Predictors of inflammatory arthritis among new rheumatology referrals: a cross-sectional study

Abstract Objectives Early diagnosis and treatment of inflammatory arthritis (IA) is essential to optimize disease control. We aimed to identify variables that distinguish IA from non-inflammatory arthropathy by performing a cross-sectional study of rheumatology referral letters and visit records. Further work describes time to assessment and documentation of variables within referral letters. Methods We reviewed rheumatology referral letters and new patient visits over a 6-month period. The diagnosis of IA was based on the clinical judgement of the assessing rheumatologist. IA diagnoses included RA, SpAs, unspecified IA, PMR, crystalline arthropathies and remitting seronegative symmetrical synovitis with pitting oedema. Univariate analysis was performed for each variable. Multivariable logistic regression was performed on statistically significant variables. Results Of 697 patients referred for arthralgia, 25.7% were diagnosed with IA. Variables predictive of IA included tenderness and swelling on examination and ≥1 h of morning stiffness. Increasing arthralgia duration, fatigue and brain fog were negative predictors. The median time from referral to IA diagnosis was 55 days and 20.7% of these patients were seen within 6 weeks. Among referral letters, documentation of arthralgia duration, morning stiffness or joint examination findings was uncommon (31%, 20.5% and 56.7%, respectively). Conclusion We identified positive and negative predictors of IA. Referral letters often missed key information required for the triaging process. Future efforts will be directed towards build a triaging tool to improve the referral quality and capture of those patients with IA who need earlier access to rheumatology care.
Simulating the 21-cm signal during the Cosmic Dawn

The anticipated radio telescope SKA is expected to detect the 21-cm signal from the Cosmic Dawn, allowing us to probe the astrophysical processes of this previously unobserved era. The 21-cm differential brightness temperature fluctuations from the Cosmic Dawn are driven by early inhomogeneous heating of the neutral intergalactic medium and variations in Lyman-alpha photon density. Inhomogeneous heating is driven by high energy, X-ray photons which have long mean free paths and thus penetrate deep into the neutral intergalactic medium. Lyman-α fluctuations depend on the soft, UV photons from these sources redshifting into Lyman-α resonance. In this thesis I present a large-volume (349Mpc comoving) suite of fully numerical radiative transfer simulations of this epoch. The simulations include the effects of helium ionisation, secondary ionisations and multi-frequency heating in order to include different types of X-ray sources (high mass X-ray binaries sources and QSO sources) in addition to black body stellar sources and Lyman-alpha fluctuations, which are added as a post-processing step. In our simulations X-ray sources are able to contribute significantly to early heating of the neutral IGM. Different X-ray models produce varying lengths and morphologies of the transition from absorption to emission. When the results are smoothed to the expected resolution of SKA1-Low the mean, rms, skewness, kurtosis and power spectra of the 21-cm differential brightness temperature are notably different for each X-ray model. These rms fluctuations for each heating model are well above the expected noise for deep integrations which suggests direct imaging of X-ray heating during the Cosmic Dawn should be possible. The presence of QSOs greatly affects the non-Gaussianity, suggesting higher order statistics may be a good observational probe of rare X-ray sources. This e_ect is decreased if the Lyman-α background is built up late. We conclude by discussing ongoing and future work on the topic.
SCHOLARS IN THE MAKING: UNRAVELLING THE PUBLICATION QUEST THROUGH A COMMUNITY OF WRITING PRACTICE

ABSTRACTAlthough many studies have been conducted to investigate how graduate students navigate the publication endeavor as a graduation requirement, a scant number have explicitly focused on how Master's students manage to do so through their participation in a community of practice (CoP). This study set out to bridge this gap by examining the transformation experienced by Indonesian Master's students of English education in terms of their knowledge and practice in writing for scholarly publication after joining a writing group. This study employed a qualitative, descriptive case study approach. Data were collected through a questionnaire, in-depth interviews, and artifact analysis from 15 students and analyzed using Content Analysis. Findings indicate that after joining the writing group, the students reported increased skills in writing scholarly articles and more knowledge of the publication process. This study suggests the establishment of communities of writing practice in graduate programs to assist students in addressing the mounting pressure of scholarly publication, either as degree conferment or career preparation and advancement. ABSTRAKMeskipun segudang penelitian telah dilakukan untuk menyelidiki bagaimana mahasiswa pascasarjana menavigasi upaya publikasi sebagai syarat kelulusan, hanya sedikit penelitian yang berfokus secara khusu pada bagaimana mahasiswa magister berhasil melakukannya melalui keikutsertaan mereka dalam komunitas praktik (CoP). Penelitian ini bertujuan untuk menjembatani kesenjangan tersebut dengan mengkaji transformasi yang dialami oleh mahasiswa magister pendidikan bahasa Inggris di Indonesia terkait pengetahuan dan praktik menulis untuk publikasi ilmiah setelah bergabung dengan kelompok menulis. Penelitian ini menggunakan pendekatan kualitatif deskriptif studi kasus. Data dikumpulkan melalui angket, wawancara mendalam, dan analisis artefak dari 15 mahasiswa dan dianalisis dengan Content Analysis. Temuan menunjukkan bahwa setelah bergabung dengan kelompok menulis, para mahasiswa melaporkan peningkatan keterampilan dalam menulis artikel ilmiah dan pengetahuan yang lebih baik tentang proses publikasi. Studi ini menyarankan pembentukan komunitas praktik menulis dalam program pascasarjana untuk membantu mahasiswa mengatasi tekanan berat dalam publikasi ilmiah, baik sebagai persyaratan untuk mendapatkan gelar atau persiapan dan kemajuan karir.How to Cite: Fithriani, R. , Rahman Hz, B.I. , Kurniati, E.Y. (2023). Scholars in the Making: Unravelling the Publication Quest Through a Community of Writing Practice. IJEE (Indonesian Journal of English Education), 10(1), 221-238. doi:10.15408/ijee.v10i1. 31457.
Intent-Aware Radio Resource Scheduling in a RAN Slicing Scenario Using Reinforcement Learning

Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization. In this scenario, radio resource scheduling (RRS) is responsible for dealing with scarce and limited frequency spectrum resources available at the RAN domain while fulfilling the slice intents. The wide variety of scenarios supported in 5G and beyond 5G networks makes the RRS problem in RAN slicing scenario a significant challenge. This paper proposes an intent-aware reinforcement learning method to perform the RRS function in a RAN slicing scenario. The slice’s quality of service intents is described in a common intent model in a service-level agreement. The proposed method tries to prevent intent faults by making the management of radio resources available among slices. This method uses slices’ and user equipment network metrics in the observation space. The proposed method is evaluated under different network conditions and outperforms different baselines considering the slices’ intents fulfillment.
Plant Effectiveness of Acorus calamus, Pistia stratiotes, Azolla pinnata as Hyperaccumulator Candidate of Phytoremediation Agent for Copper (Cu) Absorption

Metals are harmful pollutants because they cannot be broken down by living organisms. An excess of metal can become toxic. The metal in question is copper (Cu). This research aimed to determine the phytoremediation effectiveness of Acorus calamus, Pistia stratiotes, and Azolla pinnata as candidates for Cu metal absorption. This investigation employed a wholly random factorial design with two factors: plant absorbent materials comprised of three plant species (Acorus calamus, Pistia stratiotes, and Azolla pinnata) and absorption concentrations of 2 and 5 ppm. The ANOVA test was used to analyze the data, followed by the average difference test. According to the results of this study, three plants—Acorus calamus, Pistia stratiotes, and Azolla pinnata—are capable of reducing Cu metal in water. The Acorus calamus plant can reduce Cu concentrations in water from 2 ppm to 96.85% and from 5 ppm to 96.80%. Pistia stratiotes plants can reduce Cu level in water from 2 ppm to 96.50% and 5 ppm to 99.94% at concentrations of 2 and 5 ppm, respectively. The Azolla pinnata plant can reduce Cu level in water from 2 ppm to 98.50% and from 5 ppm to 96.54% at concentrations of 2 ppm and 5 ppm, respectively. The highest BCF value at a concentration of 2 ppm is in the roots of Azolla pinnata plants, at 6.77 mg/kg, followed by the leaves, at 8.88 mg/kg. The maximum BCF value at a concentration of 5 ppm for Pistia stratiotes plants is 2.26 mg/kg for the roots and 2.46 mg/kg for the foliage. The greatest concentration of TF at 2 ppm in Azolla pinnata is 1.31 mg/kg, while the maximum concentration at 5 ppm in Acorus calamus is 1.98 mg/kg
Characterizing Neuronal Circuits with Spike-triggered Non-negative Matrix Factorization

Neuronal circuits formed in the brain are complex with intricate connection patterns. Such a complexity is also observed in the retina as a relatively simple neuronal circuit. A retinal ganglion cell receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required that can decipher these components in a systematic manner. Recently a method termed spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using the retinal ganglion cell as a model system, we show that STNMF can detect various biophysical properties of upstream bipolar cells, including spatial receptive fields, temporal filters, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.
Second hand smoke: ambient racial harassment at work

– Like “second hand smoke,” the harmful repercussions of racial harassment may extend well beyond the target to impact others at work. This study seeks to examine the “second hand smoke effect”, or ambient racial harassment, which involves exposure to racial harassment aimed at others. The paper examines race differences in awareness of racial harassment and explored work and health‐related outcomes associated with exposure to racial harassment. It also examines organizational tolerance for harassment as a moderator of these relationships. , – A diverse sample of 245 employees from three data sources were surveyed. One data source involved White and Black employees in the same organization; the others worked in a variety of organizations across the USA. , – Whites were less likely than Blacks to be aware of racial harassment, even when employed in the same workplace. However, awareness of racial harassment predicted negative job attitudes and psychological strain for both Whites and Blacks. These relationships were amplified by perceptions of organizational tolerance for racial harassment. , – The study documents ramifications of ambient racial harassment and illuminates a racial divide in awareness of harassment at work that may exacerbate racial conflict and prevent needed organizational change. , – The paper extends the construct of ambient racial harassment by measuring a range of overt and subtle forms that vary in type and intensity, and by examining the role of organizational tolerance for racial harassment as a moderator of the relationship between ambient racial harassment and work and health‐related outcomes.
Development and Validation of Nomogram Models for Postoperative Pneumonia in Adult Patients Undergoing Elective Cardiac Surgery

Background: Postoperative pneumonia (POP) is a frequent complication following cardiac surgery, related to increased morbidity, mortality and healthcare costs. The objectives of this study were to investigate the risk factors associated with POP in adults undergoing elective cardiac surgery and to develop and validate nomogram models. Methods: We conducted a multicenter retrospective study in four cardiac centers in China. Adults operated with elective open-heart surgery from 2016 to 2020 were included. Patients were randomly allocated to training and validation sets by 7:3 ratio. Demographics, comorbidities, laboratory data, surgical factors, and postoperative outcomes were collected and analyzed. Risk factors for POP were identified by univariate and multivariate analysis. Nomograms were constructed based on the multivariate logistic regression models and were evaluated with calibration, discrimination and decision curve analysis. Results: A total of 13,380 patients meeting the criteria were included and POP developed in 882 patients (6.6%). The mortality was 2.0%, but it increased significantly in patients with POP (25.1 vs. 0.4%, P < 0.001). Using preoperative and intraoperative variables, we constructed a full nomogram model based on ten independent risk factors and a preoperative nomogram model based on eight preoperative factors. Both nomograms demonstrated good calibration, discrimination, and were well validated. The decision curves indicated significant clinical usefulness. Finally, four risk intervals were defined for better clinical application. Conclusions: We developed and validated two nomogram models for POP following elective cardiac surgery using preoperative and intraoperative factors, which may be helpful for individualized risk evaluation and prevention decisions.
An additive–multiplicative mean model for panel count data with dependent observation and dropout processes

This paper discusses regression analysis of panel count data with dependent observation and dropout processes. For the problem, a general mean model is presented that can allow both additive and multiplicative effects of covariates on the underlying point process. In addition, the proportional rates model and the accelerated failure time model are employed to describe possible covariate effects on the observation process and the dropout or follow‐up process, respectively. For estimation of regression parameters, some estimating equation‐based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for estimating a covariance matrix of the proposed estimator and a model checking procedure is also provided. Results from an extensive simulation study indicate that the proposed methodology works well for practical situations, and it is applied to a motivating set of real data.
Constraining Cosmology with Weak Gravitational Lensing

3 Dedication . 4 Acknowledgments . 5 List of Tables . 8 List of Figures . 9 1 Gravitational Lensing and Cosmology . 14 1.1 Curvature of Spacetime . 16 1.2 Strong Lensing . 19 1.3 Weak Lensing . 23 1.3.1 Shear . 24 1.3.2 Clusters . 27 1.3.3 Cosmic Shear . 31 1.4 Dark Energy . 33 1.5 Lensing & Cosmology . 36 2 Weak Lensing Formalism . 39 2.1 Theory . 40 2.1.1 Geometrically Thin Lenses . 41 2.1.2 Shear & Magnification . 45 2.2 Measurement . 48 2.2.1 Observations . 49 2.2.1.1 Strategy . 49 2.2.1.2 Data Reduction . 50 2.2.1.3 Co-addition . 53 2.2.2 Shape Measurements . 55 2.2.3 Modeling the Point Spread Function . 60 2.2.3.1 KSB+ . 62 2.2.3.2 Computing {P,P} . 66 2.2.3.3 Constructing g from P . 67 2.3 Application: Cluster Mass Reconstruction . 69 2.3.1 The Problem of γ Inversion . 70 2.3.2 Direct Inversion & Parametric Mass Models . 71 2.4 Summary . 73
Young's modulus reconstruction using a constrained Gauss-Newton method

We present an ongoing work on the application of the GaussNewton method for solving an inverse elasticity problem. The motivation is to provide an inverse reconstruction technique for computing the spatial distribution of Young’s modulus within human tissues. The idea consist on deriving a model of the tissue using the finite element method, affine on the Young’s modulus, and finally solving a nonlinearly constrained least squares problem that includes the measured forces and displacements from experimental data.
Cloudy with a chance of starlight : coupling of smoothed particle hydrodynamics and Monte Carlo radiative transfer for the study of ionising stellar feedback

"This thesis received funding from the European Research Council for the FP7 ERC advanced grant project ECOGAL. The presented work used the DiRAC Complexity system, operated by the University of Leicester IT Services, which forms part of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment is funded by BIS National E-Infrastructure capital grant ST/K000373/1 and STFC DiRAC Operations grant ST/K0003259/1. DiRAC is part of the National E-Infrastructure. " -- Acknowledgements
Comparing Bayesian regularization and cross-validated early-stopping for streamflow forecasting with ANN models

Two popular techniques for generalizing artificial neural network (ANN) models are the Bayesian regularization (BR) and the cross-validated early stopping (CVES) methods. The comparison between the two methods and the no-stop training method (NST, in which the training is stopped after 1000 epochs) for forecasting one-step ahead discharges based on univariate daily and monthly streamflow time series of three rivers (the Yellow River in China, and the Rhine and the Danube rivers in Europe), shows that both methods perform generally better than NST when the ratio (R) of training sample size to the number of weights in the network is less than 20. But the advantage of the two techniques over NST is not guaranteed. In several cases they fail even when the ratio R < 10, especially the CVES method. On the other hand, comparison between the BR and CVES method shows that BR outperforms CVES in most cases, and it even outperforms the NST method in several cases when the ratio R is larger than 30. Furthermore, the performance of BR is very stable. whereas the performance of CVES is highly unstable. The evaluation result for making 1- to 5-day ahead streamflow forecasts for the upper Yellow River based on multiple explanatory variables further confirms the advantage of the BR method.