Miracle-dz/UniFAR-FTUs huggingface dataset:
Road traffic status discrimination method based on data fusion of multiple floating vehicles

In view of the fact that a single type of floating vehicle data can not fully reflect the traffic state of the road, this paper proposes a method to judge the urban traffic state based on multiple floating vehicle data. According to the operating characteristics of different vehicles, integrate the advantages and disadvantages of three common floating vehicles, fuse the data, and then use clustering algorithm to divide the traffic state, so as to more accurately reflect the real-time traffic state of urban roads. The experiment shows that this method has a good discrimination effect on the urban road traffic state. It can provide reference for urban road status discrimination and traffic management.
Vegetable price forecast based on improved WTLSSVR model

This article presents the implementation of the sparse twin least squares support vector regression (WTLSSVR) model, which is derived from the twin least squares support vector regression (TLSSVR) model. The WTLSSVR model achieves sparsity by eliminating sample points with small Lagrange multipliers.Our algorithm involves combining the LSSVR with the twin hyperplane algorithm, removing sample points with small Lagrange multipliers, and thinning the model to construct a sparse twin least squares support vector regression model. This approach enhances the predictive accuracy of current twin regressors, while maintaining excellent generalization performance.Therefore, the WTLSSVR is superior to the traditional LSSVR in generalization performance. In addition, compared with some other traditional algorithms, WTLSSVR has higher prediction accuracy, which is obvious when dealing with large-scale problems. To validate the proposed model’s soundness and practicality, we carried out numerous experiments, encompassing two UCI datasets and a genuine vegetable price dataset. The results of the experiments indicate that the suggested algorithm is efficacious, practical, and versatile.
Optimized procedure for high-throughput transcriptome profiling of small extracellular vesicles isolated from low volume serum samples

Abstract Objectives Small extracellular vesicles (EVs) contain various signaling molecules, thus playing a crucial role in cell-to-cell communication and emerging as a promising source of biomarkers. However, the lack of standardized procedures impedes their translation to clinical practice. Thus, we compared different approaches for high-throughput analysis of small EVs transcriptome. Methods Small EVs were isolated from 150 μL of serum. Quality and quantity were assessed by dynamic light scattering, transmission electron microscopy, and Western blot. Comparison of RNA extraction efficiency was performed, and expression of selected genes was analyzed by RT-qPCR. Whole transcriptome analysis was done using microarrays. Results Obtained data confirmed the suitability of size exclusion chromatography for isolation of small EVs. Analyses of gene expression showed the best results in case of samples isolated by Monarch Total RNA Miniprep Kit. Totally, 7,182 transcripts were identified to be deregulated between colorectal cancer patients and healthy controls. The majority of them were non-coding RNAs with more than 70 % being lncRNAs, while protein-coding genes represented the second most common gene biotype. Conclusions We have optimized the protocol for isolation of small EVs and their RNA from low volume of sera and confirmed the suitability of Clariom D Pico Assays for transcriptome profiling.
Finding shortest paths in a sequence of triangles in 3D by the method of orienting curves

Abstract We present an efficient algorithm for finding the shortest path joining two points in a sequence of triangles in three-dimensional space without planar unfolding. The concept of a funnel associated with a common edge along a sequence of triangles is introduced, that is similar to Lee and Preparata’s one in a simple polygon. The sequence of funnels associated with all common edges of the sequence is constructed and then the shortest path is determined by cusps of these funnels. Such funnels are determined iteratively to their associated edges by the Method of Orienting Curves, which was introduced by Phu [Ein konstruktives Lösungsverfahren für das Problem des Inpolygons kleinsten Umfangs von J. Steiner. Optimization. 1987;18:349–359]. The method consists of the concepts of final curves and orienting curves (the special cases of straightest geodesics). We then show that the shortest path from the cusp of a given funnel to the direct destination in the processed region of the funnel is determined by parts of orienting curves and a final curve. A numerical example for finding the shortest path joining two points in the sequence of triangles is presented and visualized by JavaView software.
Unconventional Imaging by Synthetic Aperture

Fig. 2: Single aperture: The known illumination cone in dependence of pinhole diameter 0.6 μm, 1 μm and 2 μm (from left to right), apertures 1, 0.65 and 0.32, respectively, wavelength 532 nm (simulation). To compare the pictures an adaptation of the scale of relative intensity is necessary (difference of two orders of magnitude). Small pinhole-diameters generate a large aperture but an essentially decreased throughput.
BISDU: A Bit-Serial Dot-Product Unit for Microcontrollers

Low-precision quantized neural networks (QNNs) reduce the required memory space, bandwidth, and computational power, and hence are suitable for deployment in applications such as IoT edge devices. Mixed-precision QNNs, where weights commonly have lower precision than activations or different precision is used for different layers, can limit the accuracy loss caused by low-bit quantization, while still benefiting from reduced memory footprint and faster execution. Previous multiple-precision functional units supporting 8-bit, 4-bit, and 2-bit SIMD instructions have limitations, such as large area overhead, under-utilization of multipliers, and wasted memory space for low and mixed bit-width operations. This article introduces BISDU, a bit-serial dot-product unit to support and accelerate execution of mixed-precision low-bit QNNs on resource-constrained microcontrollers. BISDU is a multiplier-less dot-product unit, with frugal hardware requirements (a population count unit and 2:1 multiplexers). The proposed bit-serial dot-product unit leverages the conventional logical operations of a microcontroller to perform multiplications, which enables efficient software implementations of binary (Xnor), ternary (Xor), and mixed-precision [W×A] (And) dot-product operations. The experimental results show that BISDU achieves competitive performance compared to two state-of-the-art units, XpulpNN and Dustin, when executing low-bit-width CNNs. We demonstrate the advantage that bit-serial execution provides by enabling trading accuracy against weight footprint and execution time. BISDU increases the area of the ALU by 68% and the ALU power consumption by 42% compared to a baseline 32-bit RISC-V (RV32IC) microcontroller core. In comparison, XpulpNN and Dustin increase the area by 6.9× and 11.1× and the power consumption by 3.8× and 5.97×, respectively. The bit-serial state-of-the-art, based on a conventional popcount instruction, increases the area by 42% and power by 32%, with BISDU providing a 37% speedup over it.
Trust-moderated information-likelihood. A multi-valued logics approach

This work's motivation is to evaluate an information's certainty based on a confirmation criterion and weighted by its source's credibility. To simplify matters in order to keep this paper legible, we will suppose we have an incoming flow of information, each of which is either a confirmation of a known information, a contradiction of a known information or is unknown. With each piece of information an external estimation of its source's credibility will be given. Due to the uncertainty regarding the evolution of information certainty and that of its source's credibility, we have chosen to represent both in a multi-valued logics formalism. In this way, the constraints we will put on credibility evolution will be expressed in the same formalism as the actual evolution.
Shrinkage estimation and selection for a logistic regression model

This paper considers the problem of variable selection and the esti- mation for a logistic regression model via shrinkage and three penalty methods. We develop a large sample theory for the shrinkage estimators including as- ymptotic distributional bias and risk. We show that if the shrinkage dimension exceeds two, the asymptotic risk of the shrinkage estimator is strictly less than the classical estimators for a wide class of models. This reduction holds glob- ally in the parameter space. Furthermore, we consider three different penalty estimators: the LASSO, adaptive LASSO, and SCAD and compare their rel- ative performance with the shrinkage estimators numerically. A Monte Carlo simulation study is conducted for different combinations of inactive predictors and the performance of each method is evaluated in terms of a simulated mean squared error. This study indicates that shrinkage method is comparable to the LASSO, adaptive LASSO, and SCAD when the number of inactive pre- dictors in the model is relatively large. A real data example is presented to illustrate the proposed methodologies.
On the Leslie matrices, Fibonacci sequences and population dynamics

Abstract This paper is devoted to an approach for establishing some explicit formulas for the entries of the powers of the Leslie matrix. Compact expressions for the vector of the population dynamics are derived and its asymptotic behavior is established. For this purpose, the entries of the powers of the Leslie matrix are computed with the aid of a recursive process of Fibonacci type. The tools used, through this study, repose predominately on the properties of a family of linear recursive sequences and their closed relation with specific Markov chains, and the associated row-stochastic matrices. Some special cases are discussed and illustrative examples are furnished.
The Kiloparsec-Scale Structure and Kinematics of High-Redshift Star-Forming Galaxies

We study the spatially resolved properties of star-forming galaxies at redshift z ~ 2 - 3 on scales ~ 1 kpc using a combination of morphological and kinematic analyses in an effort to characterize the major mechanisms of galaxy formation in the young universe. Using a sample of 216 galaxies which have been spectroscopically confirmed to lie between redshifts z = 1.8 - 3.4 in the GOODS-N field we demonstrate that rest-UV morphology (as seen by the Hubble Space Telescope) is statistically uncorrelated with physical properties such as star formation rate and is therefore unable to support the hypothesis that the prevalence of irregular morphologies indicates a high major merger fraction. Further, we present a sample of 13 galaxies observed with the OSIRIS integral field spectrograph and the Keck laser-guide star adaptive optics system which demonstrate the prevalence of high velocity dispersions ~ 80 km/s and generally little in the way of spatially resolved velocity gradients, inconsistent with favored rotating disk models. We discuss the implications of these results for galaxy formation models, including gas accretion via cold flows and gravitational instability of early gas-rich galactic disks. There is some evidence for a trend towards stronger rotational signatures in galaxies with more massive stellar populations.
Growth-Promoting Nitrogen Nutrition Affects Flavonoid Biosynthesis in Young Apple (Malus domestica Borkh.) Leaves

: Enhanced shoot growth and a decrease in flavonoid concentration in apple trees grown under high nitrogen (N) supply was observed in previous studies, along with increasing scab susceptibility of cultivar “Golden Delicious” after high N nutrition. Several hypotheses have suggested that there is a trade-off between primary and secondary metabolism because of competition for common substrates, but nothing is known about regulation at the enzyme level. In this study, a set of experiments was performed to elucidate the effect of N nutrition on the activities of key enzymes involved in flavonoid biosynthesis (phenylalanine ammonia-lyase [PAL], chalcone synthase/ chalcone isomerase [CHS/CHI}, flavanone 3-hydroxylase [FHT], flavonol synthase [FLS], dihydroflavonol 4-reductase [DFR]) and the accumulation of different groups of phenylpropanoids. The inhibition of flavonoid accumulation by high N nutrition could be confirmed, but the influence of N supply on the flavonoid enzymes CHS/CHI, FHT, DFR, and FLS was not evident. However, PAL activity seems to be downregulated, thus forming a bottle-neck resulting in a generally decreased flavonoid accumulation. Furthermore, the response of the scab-resistant cultivar “Rewe-na” to high N nutrition was not as strong as that of the susceptible cultivar “Golden Delicious”.
Accounting for Input Uncertainty in Discrete-Event Simulation

The primary objectives of this research are formulation and evaluation of a Bayes-ian approach for selecting input models in discrete-event stochastic simulation. This approach takes into account the model, parameter, and stochastic uncertainties that are inherent in most simulation experiments in order to yield valid predictive inferences about the output quantities of interest. We use prior information to specify the prior plausibility of each candidate input model that adequately fits the data, and to construct prior distributions on the parameters of each model. We combine prior information with the likelihood function of the data to compute the posterior model probabilities and the posterior parameter distributions using Bayes' rule. This leads to a Bayesian Simulation Replication Algorithm in which: (a) we estimate the parameter uncertainty by sampling from the posterior distribution of each model's parameters on selected simulation runs; (b) we estimate the stochastic uncertainty by multiple independent replications of those selected runs; and (c) we estimate model uncertainty by weighting the results of (a) and (b) using the corresponding posterior model probabilities. We also construct a confidence interval on the posterior mean response from the output of the algorithm, and we develop a replication allocation procedure that optimally allocates simulation runs to input models so as to minimize the variance of the mean estimator subject to a budget constraint on computer time. To assess the performance of the algorithm, we propose some evaluation criteria that are reasonable within both the Bayesian and frequentist paradigms. An experimental performance evaluation demonstrates the advantages of the Bayesian approach versus conventional frequentist techniques. To my wife C ¸ i˜gdem, my daughter Alya, and my parents Ammar and Radhia ii Biography he plans to pursue a career in industry as an operations research consultant. His research interests include simulation modeling, Bayesian statistics, and stochastic processes. for serving as advisors on my committee and for their constructive input. Finally, I would like to express my gratitude to many other people who supported me in this effort. My parents Ammar and Radhia, my sister Neila, and my parents-in-law Fehmi and Fikriye gave me encouragement when it was most needed. My brother Achraf and my friend Souheyl listened to hours of complaining through long distance calls. My daughter Alya endured my bad moods and absences with a smile. But most importantly C ¸ i˜gdem, my wife, did all of the above and did it unceasingly.
A Multi-band Single Shot Spectrometer for Observation of mm-Wave Bursts at Diamond Light Source

Micro-bunch instabilities (MBI) have been detected at many light sources across the world. The bursts produced as a result of this instability occur in the millimetre regime. More needs to be determined about the dynamics of MBI in order to confirm the simulations. Consequently, a single shot spectrometer has been created to investigate this instability at Diamond Light Source (DLS). Due to their low noise, ultra-fast response and excellent sensitivity, Schottky detector diodes make up this spectrometer. Currently, seven Schottky detectors are in place covering a range of 33-750 GHz. Unlike previous measurements carried out, each of the Schottky detectors has been characterised thus making the results obtained comparable to simulations. In this paper, we present the assessment of each Schottky detector in the spectrometer; the first results of the spectrometer’s use in the beam and future plans for the spectrometer.
Calculation of head related transfer functions in the proximity region using spherical harmonics decomposition: comparison with measurements and evaluation

This article describes a comparison between different compensation and range extrapolation methods to predict head related transfer functions (HRTFs) in the proximity of the head, and acoustic measurements of a HEAD acoustics dummy head in an anechoic room. An extensive set of left and right ear impulse responses was measured on a dense angular grid over an open sphere at eight different distances from the head, providing a total of 6920 measurement positions. In this study, HRTF data is represented in the spherical wave spectrum, turning the range extrapolation problem into an acoustic radiation problem. The radial extrapolation is calculated from a given set of HRTFs at a fixed distance, from near-field to far-field and vice versa. The prediction based on spherical harmonics is compared with methods such as the inverse distance attenuation coefficient, a nearfield compensation filter, and a geometrical approach for the selection of HRTFs. A spectral distortion measure is computed to quantify the similarity between predicted HRTFs and actual measurements. The data fit between predicted and measured HRTFs is correct for most of the different methods but the spherical harmonics method provides more convincing results especially for nearfield prediction at low and medium frequencies.
Propagation of a Finite‐Amplitude Elastic Pulse in a Bar of Berea Sandstone: A Detailed Look at the Mechanisms of Classical Nonlinearity, Hysteresis, and Nonequilibrium Dynamics

We study the propagation of a finite‐amplitude elastic pulse in a long thin bar of Berea sandstone. In previous work, this type of experiment has been conducted to quantify classical nonlinearity, based on the amplitude growth of the second harmonic as a function of propagation distance. To greatly expand on that early work, a noncontact scanning 3‐D laser Doppler vibrometer was used to track the evolution of the axial component of the particle velocity over the entire surface of the bar as functions of the propagation distance and source amplitude. With these new measurements, the combined effects of classical nonlinearity, hysteresis, and nonequilibrium dynamics have all been measured simultaneously. We show that the numerical resolution of the 1‐D wave equation with terms for classical nonlinearity and attenuation accurately captures the spectral features of the waves up to the second harmonic. However, for higher harmonics the spectral content is shown to be strongly influenced by hysteresis. This work also shows data which quantify not only classical nonlinearity but also the nonequilibrium dynamics based on the relative change in the arrival time of the elastic pulse as a function of strain and distance from the source. Finally, a comparison is made to a resonant bar measurement, a reference experiment used to quantify nonequilibrium dynamics, based on the relative shift of the resonance frequencies as a function of the maximum dynamic strain in the sample.
Interval Type-2 Fuzzy Integral Based Iris Recognition

Most of iris recognition system uses single matcher or classifier for decision making. In this paper, an Interval Type-2 Fuzzy Integral (IT2 FI) is proposed as a new approach to combine the match scores of three classifiers viz. fuzzy k-NN and two backpropagation neural networks with logsig and tansig transfer functions in order to improve the performance as well as robustness of the system. A comparison with other fusion rules viz. the sum rule, max-rule, product-rule and fuzzy integral, is also conducted. From the experimental results, it is observed that the proposed Interval Type-2 Fuzzy Integral based matching score fusion approach outperforms some of the existing fusion methods.
Extended Radial Point Interpolation Method for crack analysis in orthotropic media

Orthotropic materials are particular type of anisotropic materials; In contrast with isotropic materials, their properties depend on the direction in which they are measured. Orthotropic composite materials and their structures have been extensively used in a wide range of engineering applications. Studies on their physical behaviors under in-work loading conditions are essential. In this present, we apply an extended meshfree radial point interpolation method (XRPIM) for analyzing crack behaviour in 2D orthotropic materials models. The thin plate spline (TPS) radial basis function (RBF) is used for constructing the RPIM shape functions. Typical advantages of using RBF are the satisfaction of the Kronecker’s delta property and the high-order continuity. To calculate the stress intensity factors (SIFs), Interaction integral method with orthotropic auxiliary fields are used. Numerical examples are performed to show the accuracy of the approach; the results are compared with available refered results. Our numerical experiments have shown a very good performance of the present method.
Sympathetic NPY controls glucose homeostasis, cold tolerance, and cardiovascular functions in mice

Neuropeptide Y (NPY) is best known for its effects in the brain as an orexigenic and anxiolytic agent and in reducing energy expenditure. NPY is also co-expressed with Norepinephrine (NE) in sympathetic neurons. Although NPY is generally considered to modulate noradrenergic responses, its specific roles in autonomic physiology remain under-appreciated. Here, we show that sympathetic-derived NPY is essential for metabolic and cardiovascular regulation in mice. NPY and NE are co-expressed in 90% of prevertebral sympathetic neurons and only 43% of paravertebral neurons. NPY-expressing neurons primarily innervate blood vessels in peripheral organs. Sympathetic-specific deletion of NPY elicits pronounced metabolic and cardiovascular defects in mice, including reductions in insulin secretion, glucose tolerance, cold tolerance, pupil size, and an elevation in heart rate, while notably, however, basal blood pressure was unchanged. These findings provide new knowledge about target tissue-specific functions of NPY derived from sympathetic neurons and imply its potential involvement in metabolic and cardiovascular diseases.