Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound object fields in air reconstructed using digital phase conjugation

A scanning laser Doppler vibrometer is used to record 2D ultrasound fields in air. The laser light of the vibrometer traverses the sound field to and from a rigid reflector and determines the velocity field, a quantity proportional to the sound pressure rate, in each scanned point relative to the sound source. The object sound is the scattered field from objects outside the recording area. Digital reconstruction using phase conjugation (time reversal) of the object sound field is then performed and the original object field intensity and phase is reconstructed.
Amplitude modulation in particle-laden atmospheric surface layers

Abstract Particle effects on the amplitude modulation are investigated in this study based on observational data with various mass loading acquired from long-term measurements of aeolian sandstorms in high-Reynolds-number ($Re_{\tau }\sim O(10^6)$) near-neutral atmospheric surface layers. In both particle-laden and unladen flows, in addition to the positive top–down modulation behaviour in the logarithmic region, a significant modulation effect that exists for some specific motions is also found for the single-point amplitude modulation. The most energetic turbulent motions exhibit the strongest modulation effect, and the modulating signals do not change with the small-scale motions being modulated. In particle-laden flows, the length of the most energetic structure is almost constant, thus the scales of the modulating signal and carrier signal are hardly affected by particles. However, the addition of particles changes the distribution of energy between multi-scale turbulent motions. The kinetic energy of the large-scale component is less enhanced than the total kinetic energy by particles. This leads to a reduced energy proportion of the large-scale component and an augmented one of the small-scale component. Moreover, the particles produce a large damping in the degree of the amplitude modulation and move down the positions of the modulating signals and carrier signals corresponding to the strongest inter-layer modulation, but the damping is weakened with the wall-normal distance due to the decreased mass loading. This study may provide a more general insight into the modulation mechanism between multi-scale turbulent motions and the effect of particles on turbulence.
Lane Detection Based on Improved Canny Detector and Least Square Fitting

The video images of road monitoring system contain noise, which blurs the difference between the lane and the background. The lane detection algorithm based on traditional Canny edge detector hardly detects the single-pixel lane accurately and it produces pseudo lane. The paper proposes an effective lane detection method based on improved Canny edge detector and least square fitting. The proposed method improves the dual-threshold selection of traditional Canny detector by using the histogram concavity analysis, which sets the optimal threshold automatically. The least square method is used to fit the feature points of detected edges to accurate and single-pixel wide lane. Experimental results show that the proposed method detects the lane of video images accurately in the noise environment.
Nonlinear magnetic field calculation using dipole approximation

A program for calculating static magnetic fields using an integral method for the iron contribution to the field is presented. To calculate the magnetic coefficients one essentially uses a simple dipole approximation. The developed formalism is applied to compute the magnetic field of toroidally shaped iron geometries. Using this formalism a program tractable on microcomputers was written with a discretization of the iron in up to 1600 elements. The results agree well with those obtained with the program TOSCA. >
Finite Element Solution in Fluid Mechanics Using Exponential Function Based Interpolation

The wave problem of finite element solution of one dimensional stationary convection diffusion equation is analyzed. The reason for wave is the poor continuity of linear Lagrange shape function. By using exponential function based interpolation (EFBI), the results of finite element solution are compared with that of the analytical solution. The results indicate that EFBI is effective to deal with the problem of numerical wave. The shape function of three dimensional EFBI is derived and analyzed. Morphological analysis shows that EFBI has good differentiability and adaptability.
Intercomparison between GOME Ozone Profiles Retrieved by Neural Network Inversion Schemes and ILAS Products

Abstract The Global Ozone Monitoring Experiment (GOME) is a spectrometer boarded on the European Space Agency (ESA) Remote Sensing Satellite (ERS)-2, which measures, in a nadir-viewing geometry, the solar radiation that is scattered by the earth’s atmosphere. Measurements are performed in the wavelength range of 240–790 nm, with a spectral resolution of 0.2–0.4 nm. Vertical ozone profiles can be retrieved from observations of the backscattered light using neural network inversion schemes, which are able to provide real-time estimations with an accuracy that is comparable with the accuracy obtained by means of traditional optimal estimation (OE) methods, which are high-computation demanding and generally offline. In this study, neural network–estimated profiles have been compared to the ozone profiles that are retrieved by the Improved Limb Atmospheric Spectrometer (ILAS) boarded on the Japanese Advanced Earth Observing Satellite (ADEOS). From November 1996 to June 1997 more than 3000 coincident profiles h.
COVID-19 Supply Chain Risks and Environmental Impact Assessment

This article describes the risks and environmental impacts associated with COVID-19 supply chain activities from production of vaccines to the ultimate disposal of personal protective equipment (PPE). The benefits and harm to the environment arising from preventive measures such as lockdown, wearing masks, travel restrictions, and hand washing are described. Three tools are used in the evaluation: (a) evaluation of risks in the supply chain using failure modes and effects analysis (FMEA), (b) evaluation of risks due to social behavior, using the tool RIKA and (c) environmental impact assessment. Risk communication and risk mitigation strategies are described. COVID-19 presents us an ideal opportunity to learn from the benefits and adopt measures to minimize environmental harm.
A periodic adaptive current controller for torque ripple reduction in uncertain permanent magnet synchronous motor drives

This paper addresses an adaptive control approach to achieve high-performance response in phase currents and to minimize torque ripples in uncertain permanent magnet synchronous motor drives. In this manner, the electrical part of the permanent magnet synchronous motor drive is considered completely uncertain by taking the inherent characteristic of the parameters into account. A periodic adaptive controller is formulated in order to achieve the torque ripple reduction in the presence of the time-varying periodic uncertainties. In the sense that the periodic uncertainties appearing in permanent magnet synchronous motor drives change by the angular position, a change of the time variable is applied in the formulation of the proposed adaptive controller, and the stability analysis is conducted accordingly utilizing a Lyapunov–Krasovskii functional. Extensive numerical simulations are successfully performed for various operation points to validate the effectiveness of the proposed method.
Polarization image fusion algorithm based on improved PCNN

The polarization detection technique provides polarization information of objects which conventional detection techniques are unable to obtain. In order to fully utilize of obtained polarization information, various polarization imagery fusion algorithms have been developed. In this research, we proposed a polarization image fusion algorithm based on the improved pulse coupled neural network (PCNN). The improved PCNN algorithm uses polarization parameter images to generate the fused polarization image with object details for polarization information analysis and uses the matching degree M as the fusion rule. The improved PCNN fused image is compared with fused images based on Laplacian pyramid (LP) algorithm, Wavelet algorithm and PCNN algorithm. Several performance indicators are introduced to evaluate the fused images. The comparison showed the presented algorithm yields image with much higher quality and preserves more detail information of the objects.
LIKELIHOOD BASED STATISTICAL METHODS FOR ESTIMATING HIV INCIDENCE RATE

Estimation of current levels of human immunodeficiency virus (HIV) incidence is essential for monitoring the impact of an epidemic, determining public health priorities, assessing the impact of interventions and for planning purposes. However, there is often insufficient data on incidence as compared to prevalence. A direct approach is to estimate incidence from longitudinal cohort studies. Although this approach can provide direct and unbiased measure of incidence for settings where the study is conducted, it is often too expensive and time consuming. An alternative approach is to estimate incidence from cross sectional survey using biomarkers that distinguish between recent and non-recent/longstanding infections. The original biomarker based approach proposes the detection of HIV-1 p24 antigen in the pre-seroconversion period to identify persons with acute infection for estimating HIV incidence. However, this approach requires large sample sizes in order to obtain reliable estimates of HIV incidence because the duration of antigenemia before antibody detection is short, about 22.5 days. Subsequently, another method that involves dual antibody testing system was developed. In stage one, a sensitive test is used to diagnose HIV infection and a less sensitive test such is used in the second stage to distinguish between long standing infections and recent infections among those who tested positive for HIV in stage one. The question is: how do we combine this data with other relevant information, such as the period an individual takes from being undetectable by a less sensitive test to being detectable, to estimate incidence? The main objective of this thesis is therefore to develop likelihood based methods
Computing the Moore–Penrose inverse of a matrix with a Computer Algebra System

In this paper Derive functions are provided for the computation of the Moore–Penrose inverse of a matrix, as well as for solving systems of linear equations by means of the Moore–Penrose inverse. Making it possible to compute the Moore–Penrose inverse easily with one of the most commonly used Computer Algebra Systems–and to have the blueprint to write such a function in other Computer Algebra Systems or in a matrix programming language such as Gauss–may promote the use of generalized inverses in the teaching of linear algebra.
Existence of a positive solution of an elliptic equation on RN

We prove the existence of a positive solution for the Schrödinger-type equation on RN,−Δu + V(x)u = f(u) for u ∈ H1(RN), provided that The result is obtained in two cases. (a) lim|x|→ ∞V(x) = V (∞) ∈ (0,∞), f is subcritical and supt>0 2F(t)t−2 < V (∞). (b) There exist V > 0 and R* > 0 such that f(t)t−1 < V ∞ ≤ V (x) for all t > 0 and |x| ≥ R*.
An H I Aperture Synthesis Mosaic of the Large Magellanic Cloud

We present the results of an H I aperture synthesis mosaic of the Large Magellanic Cloud (LMC), made by combining data from 1344 separate pointing centers using the Australia Telescope Compact Array (ATCA). The resolution of the mosaicked images is 1. ′0 (15 pc, using a distance to the LMC of 50 kpc). This mosaic, with a spatial resolution 15 times higher than that which had been previously obtained, emphasizes the turbulent and fractal structure of the ISM on the small scale, resulting from the dynamical feedback of the star formation processes with the ISM. The structure of the neutral atomic ISM in the LMC is dominated by H I filaments combined with numerous shells and holes. On the large scale, the H I disk appears to be remarkably symmetric and to have a well-organized and orderly, if somewhat complex, rotational field. The bulk of the H I resides in a disk 7.3 kpc in diameter. The mass of the disk component of the LMC is 2.5 × 109 M☉, and the upper limit to all mass within a radius of 4 kpc is ~3.5 × 109 M☉.
Nonlinear guided waves coupled nonlinearly in a planar GaAs/GaAlAs multiple quantum well structure

A nonlinear guided‐wave concept and nonlinear coupled‐wave equations are used to study numerically the coupling characteristics of two planar waveguides in a GaAs/GaAlAs multiple quantum well structure with self‐defocusing nonlinearities. Both the mode‐intensity‐dependent critical power and the coupling length are calculated for the first time using the nonlinear field distributions. An optically controlled modulation/switching behavior is predicted.
A GENERALIZED NORMAL MEAN-VARIANCE MIXTURE FOR RETURN PROCESSES IN FINANCE

Time-changed Brownian motions are extensively applied as mathematical models for asset returns in Finance. Time change is interpreted as a switch from calendar time to trade-related business time. Time-changed Brownian motions can be generated by infinitely divisible normal mixtures. The standard multivariate mixtures assume a common mixing variable. This corresponds to a multidimensional return process with a unique change of time for all assets under exam. The economic counterpart is uniqueness of trade or business time, which is not in line with empirical evidence. In this paper we propose a new multivariate definition of normal mixtures with a flexible dependence structure, based on the economic intuition of both a common and an idiosyncratic component of business time. We analyze both the distribution and the related process. We use the above construction to introduce a multivariate generalized hyperbolic process with generalized hyperbolic margins. We conclude with a stock market example to show the ease of calibration of the model.
The Design of Fast and Lightweight Resemblance Detection for Efficient Post-Deduplication Delta Compression

Post-deduplication delta compression is a data reduction technique that calculates and stores the differences of very similar but non-duplicate chunks in storage systems, which is able to achieve a very high compression ratio. However, the low throughput of widely used resemblance detection approaches (e.g., N-Transform) usually becomes the bottleneck of delta compression systems due to introducing high computational overhead. Generally, this overhead mainly consists of two parts: ① calculating the rolling hash byte by byte across data chunks and ② applying multiple transforms on all of the calculated rolling hash values. In this article, we propose Odess, a fast and lightweight resemblance detection approach, that greatly reduces the computational overhead for resemblance detection while achieving high detection accuracy and a high compression ratio. Odess first utilizes a novel Subwindow-based Parallel Rolling (SWPR) hash method using Single Instruction Multiple Data [1] (SIMD) to accelerate calculation of rolling hashes (corresponding to the first part of the overhead). Odess then uses a novel Content-Defined Sampling method to generate a much smaller proxy hash set from the whole rolling hash set and quickly applies transforms on this small hash set for resemblance detection (corresponding to the second part of the overhead). Evaluation results show that during the stage of resemblance detection, the Odess approach is ∼31.4× and ∼7.9× faster than the state-of-the-art N-Transform and Finesse (a recent variant of N-Transform [39]), respectively. When considering an end-to-end data reduction storage system, the Odess-based system’s throughput is about 3.20× and 1.41× higher than the N-Transform- and Finesse-based systems’ throughput, respectively, while maintaining the high compression ratio of N-Transform and achieving ∼1.22× higher compression ratio over Finesse.
Unsupervised Domain Adaptation by Causal Learning for Biometric Signal-based HCI

Biometric signal based human-computer interface (HCI) has attracted increasing attention due to its wide application in healthcare, entertainment, neurocomputing, and so on. In recent years, deep learning-based approaches have made great progress on biometric signal processing. However, the state-of-the-art (SOTA) approaches still suffer from model degradation across subjects or sessions. In this work, we propose a novel unsupervised domain adaptation approach for biometric signal-based HCI via causal representation learning. Specifically, three kinds of interventions on biometric signals (i.e., subjects, sessions, and trials) can be selected to generalize deep models across the selected intervention. In the proposed approach, a generative model is trained for producing intervened features that are subsequently used for learning transferable and causal relations with three modes. Experiments on the EEG-based emotion recognition task and sEMG-based gesture recognition task are conducted to confirm the superiority of our approach. An improvement of +0.21% on the task of inter-subject EEG-based emotion recognition is achieved using our approach. Besides, on the task of inter-session sEMG-based gesture recognition, our approach achieves improvements of +1.47%, +3.36%, +1.71%, and +1.01% on sEMG datasets including CSL-HDEMG, CapgMyo DB-b, 3DC, and Ninapro DB6, respectively. The proposed approach also works on the task of inter-trial sEMG-based gesture recognition and an average improvement of +0.66% on Ninapro databases is achieved. These experimental results show the superiority of the proposed approach compared with the SOTA unsupervised domain adaptation methods on HCIs based on biometric signal.
Viscous flow in a channel with periodic cross-bridging fibres: exact solutions and Brinkman approximation

A general solution of the three-dimensional Stokes equations is developed for the viscous flow past a square array of circular cylindrical fibres confined between two parallel walls. This doubly periodic solution, which is an extension of the theory developed by Lee & Fung (1969) for flow around a single fibre, successfully describes the transition in behaviour from the Hele-Shaw potential flow limit (aspect ratio B [Lt ] 1) to the viscous two-dimensional limiting case (B [Gt ] 1, Sangani & Acrivos 1982) for the hydrodynamic interaction between the fibres. These results are also compared with the solution of the Brinkman equation for the flow through a porous medium in a channel. This comparison shows that the Brinkman approximation is very good when B > 5, but breaks down when B [les ] O(1). A new interpolation formula is proposed for this last regime. Numerical results for the detailed velocity profiles, the drag coefficient f, and the Darcy permeability Kp are presented. It is shown that the velocity component perpendicular to the parallel walls is only significant within the viscous layers surrounding the fibres, whose thickness is of the order of half the channel height B′. One finds that when the aspect ratio B > 5, the neglect of the vertical velocity component vz can lead to large errors in the satisfaction of the no-slip boundary conditions on the surfaces of the fibres and large deviations from the approximate solution in Lee (1969), in which vz and the normal pressure field are neglected. The numerical results show that the drag coefficient of the fibrous bed increases dramatically when the open gap between adjacent fibres Δ′ becomes smaller than B′. The predictions of the new theory are used to examine the possibility that a cross-bridging slender fibre matrix can exist in the intercellular cleft of capillary endothelium as proposed by Curry & Michel (1980).
Lymphocyte activation gene 3 (Lag3) supports Foxp3+ Treg cell function by restraining c-Myc-dependent aerobic glycolysis

Lymphocyte activation gene 3 (Lag3) has emerged as the next-generation immune checkpoint molecule due to its ability to inhibit effector T cell activity. Foxp3+ regulatory T (Treg) cells, a master regulator of immunity and tolerance, also highly express Lag3. While Lag3 is thought to be necessary for Treg cell-mediated regulation of immunity, the precise roles and underlying mechanisms remain largely elusive. In this study, we report that Lag3 is indispensable for Treg cells to control autoimmune inflammation. Utilizing a newly generated Treg cell specific Lag3 mutant mouse model, we found that these animals are highly susceptible to autoimmune diseases, suggesting defective Treg cell function. Genome wide transcriptome analysis further uncovered that Lag3 mutant Treg cells upregulated genes involved in metabolic processes. Mechanistically, we found that Lag3 limits Treg cell expression of Myc, a key regulator of aerobic glycolysis. We further found that Lag3-dependent Myc expression determines Treg cells’ metabolic programming as well as the in vivo function to suppress autoimmune inflammation. Taken together, our results uncovered a novel function of Lag3 in supporting Treg cell suppressive function by regulating Myc-dependent metabolic programming.
Far-infrared rotational emission by carbon monoxide

Accurate theoretical collisional excitation rates are used to determine the emissivities of CO rotational lines 10 to the 4th power/cu cm n(H2), 100 K T 2000 K, and J 50. An approximate analytic expression for the emissitivities which is valid over most of this region is obtained. Population inversions in the lower rotational levels occur for densities n(H2) approximately 10 (to the 3rd to 5th power)/cu cm and temperatures T approximately 50 K. Interstellar shocks observed edge on are a potential source of millimeter wave CO maser emission. The CO rotational cooling function suggested by Hollenbach and McKee (1979) is verified, and accurate numerical values given. Application of these results to other linear molecules should be straightforward.
Deep evolution of MADS-box genes in Archaeplastida

MADS-box genes represent a paneukaryotic gene family encoding transcription factors. Given its importance for essential functions in plants, animals and fungi, such as development of organ identity and mating type determination, the phylogeny of MADS-box genes is of great biological interest. It has been well established that a gene duplication in the stem group of extant eukaryotes generated two clades of MADS-box genes, termed Type I and Type II genes. Almost all Type II genes of land plants contain a keratin-like (K) domain in addition to the family-defining, DNA-binding MADS (M) domain and are also termed MIKC-type genes. Due to a lack of sampling of MADS-box genes in Archaeplastida (rhodophytes, glaucophytes, chlorophytes, and streptophytes) except land plants, the deep evolution of MADS-box genes in plants remains poorly understood, however. Here we use the genomic and transcriptomic ressources that have become available in recent years to answer longstanding questions of MADS-box gene evolution in Archaeplastida. Our results reveal that archaeplastid algae likely do not harbour Type I MADS-box genes. However, rhodophytes, glaucophytes, prasinodermophytes and chlorophytes possess Type II MADS-box genes without a K domain. Type II MADS-box genes with a K domain are found only in streptophytes. This corroborates previous views that some Type II gene acquired a K domain in the stem group of extant streptophytes, generating MIKC-type genes. Interestingly, we found both variants of Type II genes - with (MIKC) and without a K domain - in streptophyte algae, but not in land plants (embryophytes), suggesting that Type II genes without a K domain (ancestral Type II genes) were lost in the stem group of land plants. Our data reveal that the deep evolution of MADS-box genes in “plants” (Archaeplastida) was more complex than has previously been thought.