Miracle-dz/UniFAR-FTUs huggingface dataset:
Tests of heteroscedasticity and correlation in multivariate t regression models with AR and ARMA errors

Heteroscedasticity checking in regression analysis plays an important role in modelling. It is of great interest when random errors are correlated, including autocorrelated and partial autocorrelated errors. In this paper, we consider multivariate t linear regression models, and construct the score test for the case of AR(1) errors, and ARMA(s,d) errors. The asymptotic properties, including asymptotic chi-square and approximate powers under local alternatives of the score tests, are studied. Based on modified profile likelihood, the adjusted score test is also developed. The finite sample performance of the tests is investigated through Monte Carlo simulations, and also the tests are illustrated with two real data sets.
Atmospheric turbulence component of the innovation covariance

The innovation covariance is derived for general atmospheric turbulence conditions and for climatologies of upper‐level turbulence based on aircraft data. Error is defined in terms of the effective spatial filter of the forecast model to produce a consistent definition of measurement error and model error. Calculations of the atmospheric turbulence component (observation sampling‐error covariance) are performed for rawinsonde data and various forecast model resolutions. Significant contributions from the atmospheric turbulence field are produced, especially for the older forecast models. Copyright © 2008 Royal Meteorological Society
CEKD: Cross-Modal Edge-Privileged Knowledge Distillation for Semantic Scene Understanding Using Only Thermal Images

Semantic scene understanding using thermal images has received great attention due to the advantage that thermal imaging cameras could see in challenging illumination conditions. However, thermal images are lack of color information and the edges in thermal images are often blurred, making them not very suitable to be directly used by existing semantic segmentation networks that are designed with RGB images. To address this problem, we propose a cross-modal edge-privileged knowledge distillation framework, which utilizes a well-trained RGB-Thermal fusion-based semantic segmentation network with edge-privileged information as the teacher, to guide the training of a semantic segmentation network as the student. The student network only uses thermal images. The experimental results on a public dataset demonstrate that under the guidance of the teacher, the student network achieves superior performance over the state of the arts using only thermal images.
Nucleation and Arrest of Fluid‐Induced Aseismic Slip

Microseismicity associated with fluid pressurization in the subsurface occurs during fluid injection but can also be triggered after injection shut‐in. Understanding the extent and duration of the post‐injection microseismicity is critical to limit the risk of fluid‐induced seismicity and insure the safe utilization of the subsurface. Using theoretical and numerical techniques, we investigated how aseismic slip on a fault plane evolves and stops after a fluid pressurization event. We found that the locking mechanisms controlling the arrest of aseismic slip highly depend on the initial fault stress criticality and the pressurization duration. The absolute arrest time of fault aseismic slip after injection shut‐in is proportional to the pressurization duration and increases significantly with the initial fault stress criticality. Given that microseismicity can be triggered by aseismic slip, these results provide insights into the mechanics controlling the arrest of microseismicity after fluid pressurization as a milestone toward induced seismicity mitigation strategies.
Semantic Alignment Network for Multi-Modal Emotion Recognition

Modality alignment can maintain the consistency of semantics in multi-modal emotion recognition tasks, ensuring that features from different modalities accurately represent the emotion-related information in an encoding space. However, current alignment models either focus only on the local fusion of different modal representations or lack a mining process for unimodal specificity information. We design a Semantic Alignment network based on Multi-Spatial learning (SAMS) for multi-modal emotion recognition, which achieves local and global alignment between modalities using high-level emotion representations of different modalities as supervisory signals. SAMS builds a multi-spatial learning framework for each modality, and constructs a self-modal interaction module under this framework based on cross-modal semantic learning. SAMS provides two learning spaces for each modality, one to detect the affective information for a specific modality, and the other to learn semantic knowledge from other modalities. Subsequently, the features of these two spaces are aligned in temporal and utterance levels by homologous encoding and different target constraints. Based on the alignment characteristics of these two spaces, a self-modal interaction is built to investigate the fusion representation by exploring the global correlation between the alignment features in unimodal multi-spatial learning. In experiments, our proposed model yields consistent improvements on two standard multi-modal benchmarks, and outperforms state-of-the-art approaches. The code of our SAMS is available at: https://github.com/xiaomi1024/code_SAMS.
A joint spatial econometric model for regional FDI and output growth

Abstract This paper studies the joint dynamics of foreign direct investments (FDI) and output growth in European regions by using spatially augmented systems of equations modeling framework that incorporates third‐region and spillover effects. The joint framework is used to study the dynamic impacts of regional human capital endowments, which demonstrates the importance of explicitly accounting for an endogenous relationship. The relationship is highlighted in a stylized projection exercise, where the long‐run impacts are pronounced in Eastern Europe and capital cities. Overall, ignoring the relationship of regional economic performance and FDI distorts the implied transmission mechanism, which is of utmost importance for policy makers.
Frequency selectivity as a function of sound pressure level

Many hearing‐impaired listeners appear to have poor auditory frequency selectivity. However, the data are confounded by the fact that these listeners are usually tested at high SPLs. There is evidence that even in normal listeners frequency selectivity is diminished at high SPLs, though there is some question about the size of this level effect. In two different experiments directed at this issue, we found the decrease in normal selectivity at high SPLs to be small and highly subject dependent. In the first, psychophysical tuning curves were obtained from six listeners at probe levels 40 dB apart. The probe was set at 10 dB SL; the low‐level condition was run in quiet, and the high‐level condition in a background of intense wide‐band noise. The average tuning curve from the high‐level condition was a little wider, but one listener actually produced a narrower curve in this condition. In the second experiment we measured the threshold of a tone masked by noise with a cosinusoidal power spectrum. Threshold .
Linker Length Drives Heterogeneity of Multivalent Complexes of Hub Protein LC8 and Transcription Factor ASCIZ

LC8, a ubiquitous and highly conserved hub protein, binds over 100 proteins involved in numerous cellular functions, including cell death, signaling, tumor suppression, and viral infection. LC8 binds intrinsically disordered proteins (IDPs), and although several of these contain multiple LC8 binding motifs, the effects of multivalency on complex formation are unclear. Drosophila ASCIZ has seven motifs that vary in sequence and inter-motif linker lengths, especially within subdomain QT2–4 containing the second, third, and fourth LC8 motifs. Using isothermal-titration calorimetry, analytical-ultracentrifugation, and native mass-spectrometry of QT2–4 variants, with methodically deactivated motifs, we show that inter-motif spacing and specific motif sequences combine to control binding affinity and compositional heterogeneity of multivalent duplexes. A short linker separating strong and weak motifs results in stable duplexes but forms off-register structures at high LC8 concentrations. Contrastingly, long linkers engender lower cooperativity and heterogeneous complexation at low LC8 concentrations. Accordingly, two-mers, rather than the expected three-mers, dominate negative-stain electron-microscopy images of QT2–4. Comparing variants containing weak-strong and strong-strong motif combinations demonstrates sequence also regulates IDP/LC8 assembly. The observed trends persist for trivalent ASCIZ subdomains: QT2–4, with long and short linkers, forms heterogeneous complexes, whereas QT4–6, with similar mid-length linkers, forms homogeneous complexes. Implications of linker length variations for function are discussed.
Gauge natural prolongation of connections

The main result is the classification of all gauge bundle functors H on the category PBm(G) which admit gauge natural operators transforming principal connections on P → M into general connections on HP → M . We also describe all gauge natural operators of this type. Similar problems are solved for the prolongation of principal connections to HP → P . A special attention is paid to linear connections. Introduction. It is well known that various kinds of prolongations of smooth manifolds and geometric structures on them play an important role in many directions of differential geometry and its applications in mathematical physics. The aim of this paper is to study prolongation of principal connections, which has motivation in quantum mechanics, higher order dynamics, field theories and gauge theories of mathematical physics (see [3], [6], [13], [19]). Roughly speaking, by prolongation of connections we understand geometric constructions transforming a given connection on a manifold into a connection on some prolongation of this manifold. We point out that the theory of prolongation of geometric structures has its origins in the works of C. Ehresmann [4], [5]. Significant progress in that research started after the clarification of the role of jet calculus and natural operations in differential geometry [12]. Since that time these problems have been intensively studied by many authors (see e.g. [1], [7]–[12]). Let G be a finite-dimensional Lie group and denote by PBm(G) the category of principal G-bundles with m-dimensional bases and their local principal G-bundle isomorphisms over the identity isomorphism of G. It has been pointed out recently that prolongation of principal connections can be expressed in terms of gauge natural operators (see Section 1 below). The present paper is mainly devoted to the following problems: 2000 Mathematics Subject Classification: 58A05, 58A20.
Delay-Dependent Stability Analysis of Discrete Time Delay Systems with Actuator Saturation

This paper focuses on the study and the characterization of stability regions of discrete time systems with a time varying state delay subjected to actuator saturation through anti-windup strategies. Delay-dependent stability conditions are stated in the local as well as global context. An optimization procedure to maximize the estimate of domain of attraction is given. The proposed technique is illustrated by means of numerical examples.
Optical‐fiber microcoil waveguides and resonators and their applications for interferometry and sensing

In this work, recent progress in the theoretical and experimental studies of optical‐fiber microcoil waveguides and resonators, as well as their various applications are reviewed. In particular, the focus is set on sensing and interferometry applications. It is shown that due to its inherently low propagation and fiber‐coupling losses, fiber‐microcoil based sensors and interferometers offer substantial enhancement of sensitivity and compactness compared to other types of devices. Recent progress in the realization and experimental characterization of such structures is presented and the theoretical tools to analyze the impact of real‐world nonuniformities on the characteristics of fiber‐microcoil structures are provided.
The Robust Design Approach For Reducing Cogging Torque In Permanent Magnet Motors

This paper discusses the robust design method for reducing the cogging torque developed in permanent magnet motors and improving the N/S ratio (noise to signal ratio) between the cogging torque and average operational torque. The robust design method used is Dr. Taguchi's approach which is applied the first time to determine the optimum design parameters of a brushless DC spindle motor used in data storage systems. The results show that the robust design method is a very efficient and effective approach in designing disk drive spindle motors for high performance.
Ion beam diagnostics by means of plane Langmuir probes

An ion beam normally incident on a plane Langmuir probe in a plasma gives rise to an extra knee in the electron saturation part of the current‐voltage characteristics of the probe. Findings from a numerical investigation, together with some experimental results, are presented, confirming that the effect arises from a modulation of the electron collection current because of space‐charge effects from beam ions reflected in front of the probe. It is verified that beam energy, temperature, and, in principle, beam density can be derived from the position, relative shape, and size of the extra ion‐beam‐induced knee of the Langmuir characteristics.
Genome-Based Analysis of the Potential Bioactivity of the Terrestrial Streptomyces vinaceusdrappus Strain AC-40

Simple Summary The problem of antimicrobial resistance represents a real danger to mankind. There is a crucial necessity to search for new antibiotics. The current advances in sequencing techniques and bioinformatics represent a critical tool for finding out the hidden biosynthetic capabilities within bacterial genomes and finding out novel active secondary metabolites. In this work, we managed to recover the Streptomyces vinaceusdrappus strain AC-40, a rhizobacterium with potent and broad-spectrum antimicrobial activity. We made whole genome sequencing and genome mining for the genes and gene clusters related to beneficial traits such as the production of secondary metabolites and plant growth promotion. Abstract Streptomyces are factories of antimicrobial secondary metabolites. We isolated a Streptomyces species associated with the Pelargonium graveolens rhizosphere. Its total metabolic extract exhibited potent antibacterial and antifungal properties against all the tested pathogenic microbes. Whole genome sequencing and genome analyses were performed to take a look at its main characteristics and to reconstruct the metabolic pathways that can be associated with biotechnologically useful traits. AntiSMASH was used to identify the secondary metabolite gene clusters. In addition, we searched for known genes associated with plant growth-promoting characteristics. Finally, a comparative and pan-genome analysis with three closely related genomes was conducted. It was identified as Streptomyces vinaceusdrappus strain AC-40. Genome mining indicated the presence of several secondary metabolite gene clusters. Some of them are identical or homologs to gene clusters of known metabolites with antimicrobial, antioxidant, and other bioactivities. It also showed the presence of several genes related to plant growth promotion traits. The comparative genome analysis indicated that at least five of these gene clusters are highly conserved through rochei group genomes. The genotypic and phenotypic characteristics of S. vinaceusdrappus strain AC-40 indicate that it is a promising source of beneficial secondary metabolites with pharmaceutical and biotechnological applications.
An Equation-Based Dynamic Nonlinear Model of Metal-Oxide Arrester and Its SPICE Implementation

The existing models of the Metal-Oxide arresters (MOA) have difficulty covering the behavior prediction under surges with front time ranging from microseconds to nanoseconds. This brief presents an equation-based dynamic nonlinear model and its circuit implementation, which can describe the behavior of the MOA in a wide range of frequencies and amplitudes with the consideration of the piecewise nonlinearity and the dynamic nonlinearity characteristics. The model can be easily constructed based on the manufacturer’s data and a multiplication factor table for the dynamic characteristic. Two types of MOA used in the power distribution systems are tested to validate the proposed model. The simulated responses of MOAs are in good agreement with the measured results when the MOAs are applied to impulses with front time ranging from 20 ns to $30~\mu \text{s}$ . Both the errors of the peak voltage and the absorbed energy between simulated and measured results are less than 6%.
Correlation between saturation magnetization, bandgap, and lattice volume of transition metal (M=Cr, Mn, Fe, Co, or Ni) doped Zn1−xMxO nanoparticles

This work reports on transition metal doped ZnO nanoparticles and compares the effects doping with different transition metal ions has on the structural, optical, and magnetic properties. Zn1−xMxO (M=Cr, Mn, Fe, Co, or Ni) nanoparticles were prepared by a chemical process for x=0.02 and 0.05 in powder form. The powders where characterized by x-ray diffraction (XRD), spectrophotometry, and magnetometry. The Zn1−xMxO samples showed a strong correlation between changes in the lattice parameters, bandgap energy, and the ferromagnetic saturation magnetization. Unit cell volume and bandgap, determined from XRD and spectrophotometry respectively, were maximized with Fe doping and decreased as the atomic number of the dopant moved away from Fe. Bandgap was generally lower at x=0.05 than x=0.02 for all dopants. The saturation magnetization reached a maximum of 6.38 memu/g for Zn0.95Fe0.05O.
An automatic COVID-19 diagnosis from chest X-ray images using a deep trigonometric convolutional neural network

ABSTRACT With growing demands for diagnosing COVID-19 definite cases, employing radiological images, i.e., the chest X-ray, is becoming challenging. Deep Convolutional Neural Networks (DCNN) propose effective automated models to detect COVID_19 positive cases. In order to improve the total accuracy, this paper proposes using the novel Trigonometric Function (TF) instead of the existing gradient descendent-based training method for training fully connected layers to have a COVID-19 detector with parallel implementation ability. The designed model gets then benchmarked on a verified dataset denominated COVID-Xray-5k. The results get investigated by qualified research with classic DCNN, BWC, and MSAD. The results confirm that the produced detector can present competitive results compared to the benchmark detection models. The paper also examines the class activation map theory to detect the areas probably infected by the Covid-19 virus. As experts confirm, the obtained results get correlated with the clinical recognitions.
Asymptotic behaviour of the optimal filter of jump and slope jump processes

A piecewise constant process is considered which may contain a jump of random magnitude at a random time. The conditional density of the jump magnitude given noisy observations of the signal is derived, and the conditional density when only noisy observations of its integral can be measured. These densities are then analysed using large deviations theory and the asymptotical distributions for vanishing observation noise are characterised for times after the jump. The results suggest a surprisingly complicated behaviour of the conditional laws, which may be of some importance when assessing the performance of approximations to the optimal filter.
Dynamic calculation of the responsivity of monodomain fluxgate magnetometers

A model is presented describing the dynamic response of a single-domain fluxgate magnetometer over a wide range of operating conditions in terms of a single measurement of a hysteresis loop or the permeability of the fluxgate's ferromagnetic core. The model is based on the Landau-Lifshitz-Gilbert equation, which describes the dynamics of a coherently rotating ferromagnet. Measurements of the response of a permalloy thin-film fluxgate and a molybdenum-permalloy tube fluxgate demonstrate the accuracy and limitations of the model.
Crack propagation in disordered materials: how to decipher fracture surfaces

For a half-century, engineers know how to describe and predict the propagation of a crack in a model elastic homogeneous medium. The case of real materials is much more complex. Indeed, we do not know how to relate their lifetime or their resistance to their microstructure. To achieve such a prediction, understanding the role of the microstructural disorder on the behavior of a crack is determinant. Fracture surfaces represent a promising field of investigation to address this question. From the study of various disordered materials, we propose a statistical description of their roughness and determine to which extent their properties are dependent of the material. We show that fracture surfaces display an anisotropic scale invariant geometry characterized by two universal exponents. Glass ceramics is then studied because its microstructure can be tuned in a controlled manner. Their fracture surfaces display the same general anisotropic properties but with surprisingly low exponents independent of the detail of the ceramics microstructure. This suggests the existence of a second universality class in failure problems. Using finally theoretical tools from out-of-equilibrium statistical physics and fracture mechanics, we relate the statistical properties of fracture surfaces with the mechanisms occurring at the microscopic scale during the failure of a material. In particular, we show that the first class of fracture surfaces results from a failure involving damage processes while the second one results from a perfectly brittle failure.
Detection of latent fingerprints by near-infrared spectral imaging

Spectral imaging technology research is becoming more extensive in the field of examination of material evidence. Near-Infrared spectral imaging technology is an important part of the full spectrum of imaging technology. This paper finished the experiment contents of the Near-Infrared spectrum imaging method and image acquisition system Near-Infrared spectral imaging technology. The experiment of Near-Infrared spectral imaging method obtains the image set of the Near-Infrared spectrum, and formats a pseudo-color images to show the potential traces successfully by processing the set of spectral images; Near-Infrared spectral imaging technology explores the technology method of obtaining the image set of Near-Infrared spectrometer and image acquisition system, and extensive access to the Near-Infrared spectrum information of latent blood, stamp and smear fingerprints on common objects, and study the characteristics of the Near-Infrared spectrum. Near-Infrared spectroscopic imaging experiments explores a wide variety of Near-Infrared reflectance spectra of the object material curve and its Near-Infrared spectrum of imaging modalities, can not only gives a reference for choosing Near-Infrared wavelength to show the object surface potential traces of substances, but also gives important data for the Near-Infrared spectrum of imaging technology development.