Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Ignoring Correlated Measurement Error in Structural Equation Models

The implications of ignoring correlated error on parameter estimates in some simple structural equation models are examined. Ignoring correlated measurement error not only affected the acceptability of a model based on goodness-of-fit measures, but also affected the parameter estimation. Ignoring within-construct correlated error does not affect the estimates of the structural parameter but overestimates the associated measurement parameters. It is shown analytically and empirically that ignoring positive between-construct correlated error overestimates the structural parameter linking the two constructs. The effects become more pronounced with decreasing reliability in measurement.
Robust Radar Waveform Optimization Under Target Interpulse Fluctuation and Practical Constraints via Sequential Lagrange Dual Approximation

This correspondence considers the problem of robust radar waveform design against target interpulse fluctuation with constraints on the transmit energy, similarity, and signal dynamic range. We aim to make the transmit waveform and receive filter robust with respect to the target amplitude vector (TAV) uncertainties by enhancing the worst-case signal-to-noise-ratio (SNR) over the modeled ellipsoidal set. First of all, the filter is directly solved leveraging on the existence of saddle point condition, which leads to a suitable simplification of the original non-convex problem. Then, to handle the resultant max–min problem, the sequential Lagrange dual approximation procedure is devised. Specifically, we update the transmit waveform via solving a sequence of max–min approximate problems, which are proven to be hidden convex relying on the theory of Lagrange duality. Finally, the filter is synthesized after solving a quadratical constraint quadratic programming problem based on the optimized waveform. Numerical results highlight the robustness of the proposed design, which guarantees an enhanced worst-case SNR with respect to TAV uncertainties.
Test results of 14 kVA superconducting transformer with Bi-2223/Ag windings

Single phase 14 kVA, 400 V/200 V superconducting transformer with Bi/2223/Ag winding immersed in liquid nitrogen and with laminated ferromagnetic core was designed and manufactured. The core of the transformer was at room temperature, both windings were placed in a nonmetallic toroidal cryostat. The aim of the work was to verify the calculated values of critical currents and AC losses in primary and secondary windings which were based on short sample measurements. Standard short circuit test, open circuit test and load test were performed and evaluated. To reduce AC losses of primary and secondary winding laminated ferromagnetic rings attached to both ends of the windings were designed. Their influence on the radial magnetic field component in the windings was computed. The resulting effect of the rings on AC losses was verified experimentally. The issue of AC losses is discussed in another contribution of these proceedings. The residual basic parameters are presented here.
Interpretable Graph Convolutional Network for Multi-View Semi-Supervised Learning

As real-world data become increasingly heterogeneous, multi-view semi-supervised learning has garnered widespread attention. Although existing studies have made efforts towards this and achieved decent performance, they are restricted to shallow models and how to mine deeper information from multiple views remains to be investigated. As a recently emerged neural network, Graph Convolutional Network (GCN) exploits graph structure to propagate label signals and has achieved encouraging performance, and it has been widely employed in various fields. Nonetheless, research on solving multi-view learning problems via GCN is limited and lacks interpretability. To address this gap, in this paper we propose a framework termed Interpretable Multi-view Graph Convolutional Network (IMvGCN). We first combine the reconstruction error and Laplacian embedding to formulate a multi-view learning problem that explores the original space from feature and topology perspectives. In light of a series of derivations, we establish a potential connection between GCN and multi-view learning, which holds significance for both domains. Furthermore, we propose an orthogonal normalization method to guarantee the mathematical connection, which solves the intractable problem of orthogonal constraints in deep learning. In addition, the proposed framework is applied to the multi-view semi-supervised learning task. Comprehensive experiments demonstrate the superiority of our proposed method over other state-of-the-art methods.
Ultrafast carrier dynamics and saturable absorption of solution-processable few-layered graphene oxide

Ultrafast carrier dynamics and saturable absorption of few-layered graphene oxide, well-dispersed in organic solvent, are studied using femtosecond pump-probe and Z-scan techniques. The results demonstrate that few-layered graphene oxide has a fast energy relaxation of hot carriers and strong saturable absorption, which is comparable with that of reduced graphene oxide. Fast carrier relaxation combined with well solution processing capability arises from the large fraction of sp2 carbon atom inside the few-layered graphene oxide sheet together with oxidation mainly existing at the edge areas. This superiority of few-layered graphene oxide will facilitate potential applications of graphene for ultrafast photonics.
Modified Vertex Support Algorithm: A New approach for approximation of Minimum vertex cover

Graph related problems mostly belong to NP class and minimum vertex cover is one of them. Minimum vertex cover is focus point for researchers since last decade due to its vast areas of application. In this research paper we have presented a modified form of approximation algorithm for minimum vertex cover which makes use of data structure proposed already named vertex support. We changed the way of selection slightly from vertex support algorithm, vertices attached to minimum support node play very critical role in selection of vertices for minimum vertex cover and we used this in our algorithm. Using our approach we managed to reduce worst case approximation ratio of VSA which is 1.583 to 1.064, this is very major change in providing results with simplicity. Results are also compared with MDG and NOVAC in order to demonstrate the efficiency of selecting vertices in this manner. Simplicity in design can help in applying it in time restricted environments.
Morphological relationships among populations support a single taxonomic unit for the North American Gray Wolf

The gray Wolf (Canis lupus) is viewed as one of the most diverse mammal species. In north America, the diversity of its forms is debated, with views conflicting on subspecies designation. The present study aimed to reinvestigate the skull morphometric variation among north American populations while attempting to unveil underlying causal factors. A large sample of vouchered museum skulls, collected from 12 ecogeographical populations spanning the north American range of the species, was examined and 21 craniodental characters were measured. Skull shape showed within-population variations but provided evidence for a high morphological affinity among populations. Allometric analyses also pointed to similar evolutionary paths among populations. However, significant size-related differentiation was revealed within and among populations. Skull size could be related to three insulin-like growth factor-1 gene (IGF-1) alleles. Ecological conditions that should determine prey type and availability accounted for most of the skull size variation. In contrast, no evidence of geographical isolation of populations was detected. The results support the existence of a single morphological pool of north American gray wolf populations that could be equated with one taxonomic unit. This study raises again the question of the diversity of forms in this species in north America and calls into question the validity of previously recognized species and subspecies based on genetics and morphology.
Existence and stability of accelerating optical solutions

Optical solitons exist, in a strict sense, in simple nonlinear models governed by the integrable nonlinear Schrodinger (NLS) equation. They also exist, in a wider sense, in more realistic models governed by non-integrable evolution equations. Accelerating soliton is the name coined to describe steady profile solutions, existing in some of those nonlinear optical models, that evolve along parabolic trajectories, in either a time-space or 2-dimensional space coordinate system. In this thesis, we focus on three different optical models possessing accelerating solitons, namely, propagation of beams in photorefractive media with diffusion, pulse propagation in fibre links using sliding-frequency filters and pulse propagation governed by the NLS equation with intrapulse Raman scattering. In each case, an ordinary differential equation (ODE) is obtained from the partial differential equation (PDE) through a similarity variable reduction. Then, we investigate parameter ranges for which solutions exist and their normal mode stability. Integration of the PDE is also used to confirm acceleration regime and stability properties. The three accelerating solitons have the common characteristic that the asymptotics of both the ODE and the stability eigenvalue problem involve solutions to Airy equations. This characteristic allows us to use Airy functions and their asymptotic properties in our analytic and numerical evaluations. It also implies that, in using Evans function ideas in a stability analysis, a significant modification is necessary. The asymptotics must be described in terms of appropriate recessive choices of Airy functions. This leads to a novel modification of the Evans function method, which is then applied in the search for eigenvalues, allowing the identification of unstable and internal (oscillatory) modes when they exist. The prediction of these modes is shown to agree with direct numerical integration of the PDEs.
The asymptotically periodic behavior of the solutions of some linear functional equations

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A note on the Lax pairs for Painlevéequations

For the classical Painleveequations, besides the method of similarity reduction of Lax pairs for integrable partial differential equations, two ways are known for Lax pair generation. The first is based on the confluence procedure in Fuchs' linear ODE with four regular singularities isomonodromy deformation which is governed by the sixth Painleveequation. The second method treats the hypergeometric equation and confluent hypergeometric equations as the isomonodromy deformation equations for the triangular systems of ODEs, in whose non-triangular extensions give rise to the Lax pairs for the Painleveequations. The theory of integrable integral operators suggests a new way of Lax pair generation for the classical Painleveequations. This method involves a special kind of gauge transformation that is applied to linear systems which are exactly solvable in terms of the classical special functions. Some of the Lax pairs we introduce are known, others are new. The question of gauge equivalence of different Lax pairs for the Painleveequations is considered as well.
The Cyclostrophic Adjustment of Vortices with Application to Tropical Cyclone Modification

Abstract The cyclostrophic and hydrostatic adjustment of simple one-layer and multilayer vortex flows to the local removal and/or redistribution of mass and angular momentum are studied, and a detailed physical interpretation of the dynamics of adjustment is given for the one-layer model. The calculations provide insight into possible responses of tropical cyclones to modification by cloud seeding and facilitate an appraisal of the Simpson-Malkus modification hypothesis. Calculations for two- and three-layer models show that the maximum tangential velocity is increased whether or not mass transfer takes place predominantly inside or outside the radius at which the maximum occurs, and the central surface pressure decreases due to subsidence at one or both interface levels. However, the magnitude of these effects are comparatively small in relation to the strengths of the induced meridional circulation and corresponding changes in tangential wind speed outside the core, at, or beyond, the radii at which mas.
PairingNet: A Multi-Frame Based Vehicle Trajectory Prediction Deep Learning Network

The traffic infrastructure of a city requires evaluation and improvement through a large amount of data analysis. The construction and laborious work of traditional methods make computer vision flourish in traffic analysis. Among different computer vision technologies for intelligent transportation system, one of the most important algorithms is multiple object tracking (MOT). At present, MOT used in traffic analysis has several shortcomings, such as lack of the output of vehicle speed and movement direction, and the consideration of single factor in trajectory tracking. These limitations have affected the results of traffic analysis. This research proposes an end-to-end deep learning network, called PairingNet. In addition to retaining the function and accuracy of the original detection network, PairingNet integrates the calculation of vehicle trajectory into the network through the feature fusion of consecutive images, which is introduced to predict the movement direction and speed of the vehicle. These additional features can be used to better track the trajectories of vehicles. In addition, a pipeline is designed to reduce the loading latency incurred by using the consecutive frames as the input for PairingNet. The experiment results indicate that the vehicle identification of PairingNet reaches 96% accuracy, surpassing the original YOLOv3 as the backbone structure, and reaches a near 100% accuracy rate in the predicted vehicle position. Moreover, with the pipeline process, the inference speed of PairingNet is very close to the original YOLOv3. In MOT results, the MOTA of PairingNet also has a high performance of 91%.
Vitamin A Deficiency and Its Association with Visceral Adiposity in Women

Body adiposity is associated with increased metabolic risk, and evidence indicates that vitamin A is important in regulating body fat. The aim of this study was to evaluate serum concentrations of vitamin A and its association with body adiposity in women with the recommended intake of vitamin A. A cross-sectional study was designed with 200 women divided into four groups according to Body Mass Index (BMI): normal weight (NW), overweight (OW), class I obesity (OI), and class 2 obesity (OII). The cut-off points to assess inadequate participants were retinol < 1.05 µmol/L and β-carotene < 40 µg/dL. Body adiposity was assessed through different parameters and indexes, including waist circumference (WC), waist-to-height ratio (WHtR), hypertriglyceridemic waist (HW), lipid accumulation product (LAP), Visceral Adiposity Index (VAI), and Body Adiposity Index (BAI). It was observed that 55.5% of women had low serum concentrations of β-carotene (34.9 ± 13.8 µmol/L, p < 0.001) and 43.5% had low concentrations of retinol (0.71 ± 0.3 µmol/L, p < 0.001). Women classified as OI and OII had lower mean values of β-carotene (OI—35.9 ± 4.3 µg/dL: OII—32.0 ± 0.9 µg/dL [p < 0.001]). IAV showed significant negative correlation with retinol (r = −0.73, p < 0.001). Vitamin A deficiency is associated with excess body adiposity in women with the recommended intake of vitamin. Greater body adiposity, especially visceral, was correlated with reduced serum concentrations of vitamin A.
Activation of ventral tegmental area vesicular GABA transporter (Vgat) neurons alleviates social defeat stress-induced anxiety in APP/PS1 mice

Introduction Alzheimer’s disease (AD) is a progressive neurodegenerative disease that results in cognitive impairment and is often accompanied by anxiety. In this study, we investigated whether the activation of VTAVgat neurons could reduce anxiety in APP/PS1 mice. We hypothesized that acute social defeat stress (SDS) would lead to anxiety in APP/PS1 mice, and that the activation of VTAVgat neurons would alleviate this anxiety. Methods We exposed APP/PS1 mice to acute SDS and assessed anxiety using the open field test and elevated plus-arm test. Activated VTAVgat neurons was tested by cfos staining. Sleep quality was detected using electroencephalogram after SDS or non-SDS procedure. Sleep duration, sleep latency, and non-rapid eye movement (NREM) percentage were analyzed. VTAVgat neurons were chemogenetically activated by deschloroclozapine. Results Our results showed that acute SDS led to anxiety in APP/PS1 mice, as evidenced by increased anxiety-related behaviors in the open field and elevated plus-arm tests. Activation of VTAVgat neurons by SDS led to an increase in sleep duration, primarily due to a decrease in sleep latency and an increase in NREMs. However, the quality of sleep was poor. Chemogenetical activation of VTAVgat neurons improved sleep quality and relieved SDS-induced anxiety. Furthermore, the anxiety state correlated negatively with sleep duration and NREM percentage and correlated positively with theta power density in APP/PS1 mice. Discussion Our study provides evidence that the activation of VTAVgat neurons alleviates SDS-induced anxiety in APP/PS1 mice, suggesting that poor sleep quality may exacerbate anxiety in AD. These findings may have important implications for the treatment of anxiety in AD, as targeting VTAVgat neurons could be a potential therapeutic approach.
Generalized case–cohort sampling

A class of cohort sampling designs, including nested case–control, case–cohort and classical case–control designs involving survival data, is studied through a unified approach using Cox’s proportional hazards model. By finding an optimal sample reuse method via local averaging, a closed form estimating function is obtained, leading directly to the estimators of the regression parameters that are relatively easy to compute and are more efficient than some commonly used estimators in case–cohort and nested case–control studies. A semiparametric efficient estimator can also be found with some further computation. In addition, the class of sampling designs in this study provides a variety of sampling options and relaxes the restrictions of sampling schemes that are currently available.
Clinical outcomes of cryptozoospermic patients undergoing surgical sperm retrieval

Introduction Cryptozoospermia is defined by the World Health Organization (WHO) as the presence of isolated sperm cell in the ejaculate only identified after an extended microscopic search or after being pelleted. Although the number of spermatozoa is usually sufficient for intracytoplasmic sperm injection (ICSI), ICSI fails due to poor sperm quality in some cases. Contention remains regarding whether testicular sperm offers any advantage in this unique situation. At our tertiary referral center, we will offer patients a surgical sperm retrieval via conventional or microdissection testicular sperm extraction (microTESE) for men with cryptozoospermia and failed ICSI, or where ejaculated specimens are immotile or insufficient for ICSI. In this study, we sought to describe our experience and evaluate the predictors of success in cryptozoospermic patients who had microTESE at our center. Methods We retrospectively reviewed our electronic medical records for all patients with cryptozoospermia who underwent microTESE between 2007- 2021 for failed ICSI with ejaculated sperm or sperm quality deemed to be of insufficient quality for ICSI (e.g., nonmotile sperm). We evaluated demographics, preoperative lab results, pathology results, sperm retrieval rate (SRR) and ICSI outcomes. Results 28 cryptozoospermic patients were identified. These patients underwent 37 unique microTESE. 22 of these men had failed previous ICSI treatment with ejaculated sperm, while the other 6 patients had ejaculated sperm with non-suitable quality for ICSI. None had genetic abnormalities. Successful retrieval of motile sperm suitable for ICSI was achieved in in 30 micro TESE procedures (SRR: 81.0%). 14 out of 28 patients (50%) who underwent embryo transfer had positive pregnancy result, and 12/28 patients (42.8%) had successful live birth. The most common pathological pattern was hypospermatogenesis found in 65.3% (17/26). Fibrosis pathology was significantly higher in the negative pregnancy group. There were no postoperative complications noted. Disscussion The use of testicular sperm in cryptozoospermic men with failed prior ICSI using ejaculated sperm has a high rate of pregnancy and live birth. While still controversial, our results suggest that surgical sperm retrieval is a viable option for these men with minimal risk of complications.
Search for ultra-high energy photons and neutrinos using Telescope Array surface detector

We search for ultra-high energy photons by analyzing geometrical properties of shower fronts of events registered by the Telescope Array surface detector. By making use of an event-by-event statistical method, we derive upper limits on the absolute flux of primary photons with energies above 1019 eV, 1019.5 eV and above 1020 eV based on the three years data from Telescope Array surface detector (May 2008 – May 2011). We report the results of down-going neutrino search based on the analysis of very inclined events.
Probabilistic representations of matrix variate skew normal models

Abstract Azzalini [Scand. J. Stat. 12 (1985), 171–178] first introduced the skew normal distribution family with a shape parameter λ, and then extended this family by adding an additional shape parameters ξ. Basic properties of these two families were studied. Henze [Scand. J. Stat. 13 (1986), 271–275] gave the probabilistic representations for these two families by interpreting it as the linear combination of a normal random variable with another normal random variable truncated at the origin and several properties were illustrated. Chen and Gupta [Statistics 39 (2005), no. 3, 247–253] extended the skew normal distribution family to the matrix variate and proposed the moment generating function and the quadratic form of the matrix variate skew normal models. Motivated by these results, we first study the probabilistic representation for the matrix variate skew normal models and several properties. Then we define the extended skew normal model of the matrix variate, and give the probabilistic representation for this family and its extension.