Homogeneous Background Research Articles

Inflationary entanglement

We investigate the entanglement due to geometric corrections in particle creation during inflation. To do so, we propose a single-field inflationary scenario, nonminimally coupled to the scalar curvature of spacetime. We require particle production to be purely geometric, setting to zero the Bogoliubov coefficients and computing the $S$ matrix associated with spacetime perturbations, which are traced back to inflaton fluctuations. The corresponding particle density leads to a nonzero entanglement entropy whose effects are investigated at primordial time of Universe evolution. The possibility of modeling our particle candidate in terms of dark matter is discussed. The classical backreaction of inhomogeneities on the homogeneous dynamical background degrees of freedom is also studied and quantified in the slow-roll regime.

Probing quantum phase transition point by tuning an external anti trap.

Manipulation of ultracold atoms in optical lattices is one of the optimal ways to observe phase transitions of the Hubbard model which is useful in a variety of condensed-matter systems. Bosonic atoms in this model experience a phase transition from superfluids to Mott insulators by tuning systematic parameters. However, in conventional setups, phase transitions take place over a large range of parameters instead of one critical point due to the background inhomogeneity caused by the Gaussian shape of optical-lattice lasers. To probe the phase transition point more precisely in our lattice system, we apply a blue-detuned laser to compensate for this local Gaussian geometry. By inspecting the change of visibility, we find a sudden jump point at one particular trap depth of optical lattices, corresponding to the first appearance of Mott insulators in inhomogeneous systems. This provides a simple method to detect the phase transition point in such inhomogeneous systems. We believe it will be a useful tool for most cold atom experiments.

A Fast Algorithm of Cross-Correlated Contrast Source Inversion in Homogeneous Background Media

Nonlinear iterative inversion methods are computationally expensive, which leads to limited applications. In this paper, a fast algorithm of the cross-correlated contrast source inversion (CC-CSI) method is proposed. By assuming a homogeneous background medium, the task of solving systems of linear equations, the most time-consuming part of the CC-CSI method, has been efficiently implemented with several FFTs and IFFTs for transverse magnetic (TM), transverse electric (TE), and 3-D inversion, respectively. Theoretical analysis and real experiments demonstrate that the inversion efficiency of CC-CSI has been improved dramatically in comparison to the strategies of LU decomposition and the Biconjugate gradient algorithm. Specifically, the inversion speed of the proposed fast CC-CSI algorithm is at least one order of magnitude higher than using the latter two approaches in 3-D inversion, while the memory requirement of the proposed algorithm is intermediary and acceptable. Furthermore, the proposed fast scheme is also applicable to all the inversion methods which are related to computing of the electric fields directly from the contrast sources in homogeneous background media.

Global asymptotic dynamics of the cubic galileon interacting with dark matter

In this paper we perform a thorough dynamical systems analysis of the cubic galileon model non-minimally coupled to the dark matter. Three well-known classes of interacting models are considered where the energy exchange between the dark components is a function of the dark matter density and of the dark energy density: $Q_1=3\alpha H\rho_m$, $Q_2=3\beta\rho_m\dot{\phi}$ and $Q_3=3 \epsilon H \rho_\phi$, respectively. We are able to show the global asymptotic dynamics of the model for the exponential potential in a homogeneous and isotropic background. The cosmological implications of the proposed scenarios are explored and it is found that, in addition to the appearance of new equilibrium configurations that do not appear neither in the non-interacting cubic galileon model nor in the interacting quintessence model, there is a significant impact of the non-minimal coupling through modification of the stability properties of the critical points. The resulting cosmological scenario provides a bigbang origin of the cosmic expansion, an early transient stage of inflationary expansion, as well as matter-scaling late time stable state of the universe, among other solutions of lesser cosmological interest. This work extends previous studies of coupled dark energy to a broader class of gravitational theories.

Deep Autoencoder Thermography for Defect Detection of Carbon Fiber Composites

Infrared thermography (IRT) is an economical nondestructive testing technique for structural health monitoring of composite materials. However, the nonlinear nature of the thermographic data and the adverse effects of noise and inhomogeneous backgrounds prevent it from achieving satisfactory results. Most of the existing thermographc data analysis methods are supervised and/or linear, which therefore are not favorable for nonlinear feature extraction of unlabeled thermograms. In this work, a deep autoencoder thermography (DAT) method is proposed for detecting subsurface defects in composite materials. The multi-layer network structure of DAT can handle nonlinear temperature profiles, and the output of the intermediate hidden layer is visualized to highlight defects. The layer-by-layer feature visualization reveals how the model extracts defect features. A loss inflection point scheme is utilized to determine a suitable depth of the model. Moreover, a new quantitative index is proposed to compare the defect detectability of different methods.

New log-t-based CFAR detectors for a non-homogeneous Weibull Background

Based on new log-t-based detectors, we propose to improve the detection performances of the log-t-Constant False Alarm Rate (log-t-CFAR) detector for a non-homogeneous Weibull background. This paper is twofold. We first resort to the Automatic Constant False Censoring Rate (CFCR) algorithm, which guarantees an accurate rejection of an a priori unknown number of outliers. That is, we introduce two hybrid detectors by coupling the log-t-CFAR algorithm to the Maximum Likelihood-CFCR (MLE-CFCR) algorithm, yielding the H-MLE/log-t-CFAR detector, and to the Weber-Haykin Constant False Censoring Rate (WH-CFCR) algorithm, yielding the H-WH/log-t-CFAR detector. Then, based on the Variability Index (VI) as a background discriminator, we propose the Switching VI-log-t-CFAR (SVI-log-t-CFAR) detector. Thus, depending on the background heterogeneity, this detector has the capability to switch automatically to the appropriate detector; namely, the log-t-CFAR detector, in case of a homogeneous background, either one of the hybrid detectors, in case of the presence of outliers or the Automatic Edge Censoring log-t-CFAR (AEC-log-t-CFAR) detector, in case of the presence of a clutter edge. We assess the efficiency of these detectors through intensive Monte Carlo simulations. We show that, while no additional detection performances are observed in a homogeneous background, the new detectors exhibit a significant CFAR gain with respect to the log-t-CFAR detector in the presence of any inhomogeneity within the reference window.

Automatic Inhomogeneous Background Correction for Spatial Target Detection Image Based on Partition Processing

High-resolution imaging with wide field of view (FoV) ground-based telescopes is often affected by skylight background and noise due to the detector, resulting in an inhomogeneous background. In this paper, we propose an improved method for spatial image non-uniformity correction based on partition processing. First, an evaluation metric is introduced to evaluate the partition size and automatically iterate a suitable partition value for different scenarios based on the different operating conditions of the telescope. Then, we iteratively calculate the mean and variance in each partitioned region to extract the background of each partitioned region. Finally, after applying bilinear interpolation to the background extracted from each region, the inhomogeneous background is obtained and removed from the original image. The experiments on the simulated and real images show that the proposed method can effectively remove the inhomogeneous background of spatial images and meet the requirements of the real-time processing of high-resolution images under long exposure conditions.

Inversion of Wind and Temperature from Low SNR FPI Interferograms

The temperature and wind in the middle and upper atmosphere can be obtained by recording the Doppler shift and broadening of the airglow emission, which is reflected by the interference ring from a ground-based Fabry–Perot interferometer (FPI) system. FPI observations are highly susceptible to weather and the external environment, which seriously affect the signal-to-noise ratio (SNR) of FPI interferograms. An SNR can significantly increase errors in determining the center of the interferogram, leading to inaccurate wind and temperature inversions. The calculation shows that the wind inversion from the interferogram decreases and the temperature increases for larger central errors. In this paper, we propose the maximum standard deviation method (MSDM) with high accuracy and robustness to determine the interference ring center. The performance of the MSDM is better achieved by using more than 100 1D interferogram bins to determine the center of interferograms. The robustness of the MSDM is investigated by computing numerous simulated interferograms with white Gaussian noise and Poisson noise, and compared with the two algorithms of binarization and peak fitting, which are usually used to invert wind and temperature from the interference ring of FPI. The results show that MSDM has higher accuracy and robustness than the other two algorithms. We also simulate the distortion interferogram when the FPI may be illuminated by inhomogeneous background light, which can introduce additional errors in wind and temperature, and the MSDM still performs better. Finally, we invert the wind and temperature from the real airglow interferogram by the Kelan (38.7°N, 111.6°E) FPI, which shows that both the wind and temperature inverted by MSDM better agree well with the FPI product than the other two algorithms. Therefore, the MSDM helps to improve the accuracy and stability to invert the wind and temperature.

Analysis of background echotexture on automated breast ultrasound using BI-RADS and modified classification: Association with clinical features and mammographic density.

To analyze BE on ABUS using BI-RADS and a modified classification in association with mammographic density and clinical features. Menopausal status, parity, and family history of breast cancer were collected for 496 women who underwent ABUS and mammography. Three radiologists independently reviewed all ABUS BE and mammographic density. Statistical analyses including kappa statistics (κ) for interobserver agreement, Fisher's exact test, and univariate and multivariate multinomial logistic regression were performed. BE distribution between the two classifications and between each classification and mammographic density were associated (P < 0.001). BI-RADS homogeneous-fibroglandular (76.8%) and modified heterogeneous BE (71.3%, 75.7%, and 87.5% of mild, moderate, and marked heterogeneous background echotexture, respectively) tended to be dense. BE was correlated between BI-RADS homogeneous-fat and modified homogeneous background (95.1%) and between BI-RADS homogeneous-fibroglandular or heterogeneous (90.6%) and modified heterogeneous (86.9%) (P < 0.001). In multinomial logistic regression, age < 50 years was independently associated with heterogeneous BE (OR, 8.89, P=0.003, in BI-RADS; OR, 3.74; P=0.020 in modified classification). BI-RADS homogeneous-fat and modified homogeneous BE on ABUS was likely to be mammographically fatty. However, BI-RADS homogeneous-fibroglandular or heterogeneous BE might be classified as any modified BE. Younger age was independently associated with heterogeneous BE.

Numerical Study of the Influence of Collisions on Oscillations of the Electric Field for the Vlasov-Poisson-Landau Kinetic Equation

The paper is devoted to the numerical study of solutions to the Vlasov-Poisson-Landau kinetic equations for distribution functions with typical length such that , where stands for the Debye radius. It is also assumed that the Knudsen number , where denotes the mean free path of electrons. We use the standard model of plasma of electrons with a spatially homogeneous neutralizing background of motionless heavy ions. We study numerically the one-dimensional (1D) in space kinetic equation with the two-dimensional in velocity space (in three-dimensional spherical coordinates with axial symmetry) Landau collisional operator for small and different . The numerical results are presented and compared with appropriate solutions obtained earlier for the simplified BGK model. The results confirm numerically the existence of high-frequency oscillations of the electric field, which slowly decline due to collisions.

Single-Cell RNA Sequencing Reveals Cellular Heterogeneity in an Acral Amelanotic Melanoma After Immunotherapy Treatment.

Anti-programmed cell death ligand-1 (anti-PD-L1) immunotherapy is often used for advanced urothelial carcinoma and melanoma, including amelanotic melanoma, a relatively rare subtype with little to no pigment in the tumor cells. However, cellular heterogeneity of amelanotic melanoma during or after anti-PD-L1 immunotherapy treatments has not been described. To investigate cellular heterogeneity in acral amelanotic melanoma after immunotherapy exposure. We evaluated subtle visual changes of the melanoma by dermoscopy and performed a pathological examination to analyze the heterogeneity of microscopic morphological and immunohistochemistry changes. The cellular transcriptional heterogeneity and corresponding biological function profiles of the melanoma were determined by single-cell RNA sequencing (scRNA-seq). The dermoscopic examination revealed black globules and scar-like depigmentation areas against a homogeneous red background. Pigmented and amelanotic melanoma cells were observed microscopically. The pigmented cells were large and contained melanin granules expressing Melan-A and HMB45; the amelanotic cells were small and did not express HMB45. Ki-67 immunohistochemical staining revealed that the pigmented melanoma cells had a higher proliferative ability than the amelanotic cells. scRNA-seq identified three cell clusters: amelanotic cell cluster 1, amelanotic cell cluster 2, and pigmented cell cluster. Furthermore, a pseudo-time trajectory analysis showed that amelanotic cell cluster 2 originated from amelanotic cell cluster 1 and transformed into the pigmented melanoma cell cluster. The expression pattern of melanin synthesis-related and lysosome-endosome-related genes in different cell clusters supported the cell cluster transformation results. Also, upregulated expression of cell cycle genes indicated that the pigmented melanoma cells had a high proliferative ability. Coexisting amelanotic and pigmented melanoma cells indicated cellular heterogeneity in an acral amelanotic melanoma from a patient who underwent immunotherapy treatment. Additionally, the pigmented melanoma cells acquired a higher proliferative ability than the amelanotic melanoma cells.

Temporal localized states and square-waves in semiconductor micro-resonators with strong time-delayed feedback.

In this paper, we study the dynamics of a vertically emitting micro-cavity operated in the Gires-Tournois regime that contains a semiconductor quantum-well and that is subjected to strong time-delayed optical feedback and detuned optical injection. Using a first principle time-delay model for the optical response, we disclose sets of multistable dark and bright temporal localized states coexisting on their respective bistable homogeneous backgrounds. In the case of anti-resonant optical feedback, we identify square-waves with a period of twice the round-trip in the external cavity. Finally, we perform a multiple time scale analysis in the good cavity limit. The resulting normal form is in good agreement with the original time-delayed model.

High resolution acoustic tomography based on backpropagation of waves

A method of high-resolution acoustic tomography of scattering inhomogeneities based on the method of reverse time migration is proposed. An inhomogeneous background medium with a known distribution of refractive inhomogeneities is considered, where it is necessary to detect scattering inhomogeneities. Probing is carried out by broadband arbitrary waveforms. The method of reverse time migration is supplemented with linear filtering of the direct propagation field, which makes it possible to increase the resolution of reconstructed images. The results of numerical modeling and experiment are presented. Sounding was carried out in water at frequencies from 20 to 350 kHz using a signal with linear frequency modulation. The source of the ultrasonic signal was fixed and irradiated the objects under study in the water. The scattered signal was measured in a plane near the surface in water on a rectangular area with a step of less than 2 mm. Signal processing by the proposed method made it possible to reconstruct a three-dimensional image of scattering inhomogeneities with a resolution of about 7 mm. Comparison of the proposed method and the reverse time migration method showed that the proposed method allows obtaining a higher resolution.

Healthcare-Associated Bloodstream Infections Due to Multidrug-Resistant Acinetobacter baumannii in COVID-19 Intensive Care Unit: A Single-Center Retrospective Study.

Healthcare-associated infections are an emerging cause of morbidity and mortality in COVID-19 intensive care units (ICUs) worldwide, especially those caused by multidrug-resistant (MDR) pathogens. The objectives of this study were to assess the incidence of bloodstream infections (BSIs) among critically ill COVID-19 patients and to analyze the characteristics of healthcare-associated BSIs due to MDR Acinetobacter baumannii in an COVID-19 ICU. A single-center retrospective study was conducted at a tertiary hospital during a 5-month period. The detection of carbapenemase genes was performed by PCR and genetic relatedness by pulsed-field gel electrophoresis (PFGE) and multilocus-sequence typing. A total of 193 episodes were registered in 176 COVID-19 ICU patients, with an incidence of 25/1000 patient-days at risk. A. baumannii was the most common etiological agent (40.3%), with a resistance to carbapenems of 100%. The blaOXA-23 gene was detected in ST2 isolates while the blaOXA-24 was ST636-specific. PFGE revealed a homogeneous genetic background of the isolates. The clonal spread of OXA-23-positive A. baumannii is responsible for the high prevalence of MDR A. baumannii BSIs in our COVID-19 ICU. Further surveillance of resistance trends and mechanisms is needed along with changes in behavior to improve the implementation of infection control and the rational use of antibiotics.

Soft-collinear gravity with fermionic matter

We extend the effective field theory for soft and collinear gravitons to interactions with fermionic matter fields. The full theory features a local Lorentz symmetry in addition to the usual diffeomorphisms, which requires incorporating the former into the soft-collinear gravity framework. The local Lorentz symmetry gives rise to Wilson lines in the effective theory that strongly resemble those in SCET for non-abelian gauge interactions, whereas the diffeomorphisms can be treated in the same fashion as in the case of scalar matter. The basic structure of soft-collinear gravity, which features a homogeneous soft background field, giving rise to a covariant derivative and multipole-expanded covariant Riemann-tensor interactions, remains unaltered and generalises in a natural way to fermion fields.

Implicit Solutions of the Electrical Impedance Tomography Inverse Problem in the Continuous Domain with Deep Neural Networks.

Electrical impedance tomography (EIT) is a non-invasive imaging modality used for estimating the conductivity of an object Ω from boundary electrode measurements. In recent years, researchers achieved substantial progress in analytical and numerical methods for the EIT inverse problem. Despite the success, numerical instability is still a major hurdle due to many factors, including the discretization error of the problem. Furthermore, most algorithms with good performance are relatively time consuming and do not allow real-time applications. In our approach, the goal is to separate the unknown conductivity into two regions, namely the region of homogeneous background conductivity and the region of non-homogeneous conductivity. Therefore, we pose and solve the problem of shape reconstruction using machine learning. We propose a novel and simple jet intriguing neural network architecture capable of solving the EIT inverse problem. It addresses previous difficulties, including instability, and is easily adaptable to other ill-posed coefficient inverse problems. That is, the proposed model estimates the probability for a point of whether the conductivity belongs to the background region or to the non-homogeneous region on the continuous space Rd&cap;Ω with d∈{2,3}. The proposed model does not make assumptions about the forward model and allows for solving the inverse problem in real time. The proposed machine learning approach for shape reconstruction is also used to improve gradient-based methods for estimating the unknown conductivity. In this paper, we propose a piece-wise constant reconstruction method that is novel in the inverse problem setting but inspired by recent approaches from the 3D vision community. We also extend this method into a novel constrained reconstruction method. We present extensive numerical experiments to show the performance of the architecture and compare the proposed method with previous analytic algorithms, mainly the monotonicity-based shape reconstruction algorithm and iteratively regularized Gauss-Newton method.

Learn an Index Operator by CNN for Solving Diffusive Optical Tomography: A Deep Direct Sampling Method

In this work, we investigate the diffusive optical tomography (DOT) problem in the case that limited boundary measurements are available. Motivated by the direct sampling method (DSM) proposed in Chow et al. (SIAM J Sci Comput 37(4):A1658–A1684, 2015), we develop a deep direct sampling method (DDSM) to recover the inhomogeneous inclusions buried in a homogeneous background. In this method, we design a convolutional neural network to approximate the index functional that mimics the underling mathematical structure. The benefits of the proposed DDSM include fast and easy implementation, capability of incorporating multiple measurements to attain high-quality reconstruction, and advanced robustness against the noise. Numerical experiments show that the reconstruction accuracy is improved without degrading the efficiency, demonstrating its potential for solving the real-world DOT problems.

Perturbations in non-flat cosmology for f(T) gravity

The study of cosmological perturbation theory in f(T) gravity is a topic of great interest in teleparallel gravity since this is one of the simplest generalizations of the theory that modifies the teleparallel equivalent of general relativity. In this work, we explore the possibility of a non-flat FLRW background solution and perform perturbations for positively as well as negatively curved spatial geometries, together with a comparison to the flat case. We determine the generalized behaviour of the perturbative modes for this non-flat FLRW setting for arbitrary f(T) models, when the most general homogeneous and isotropic background tetrads are used. We also identify propagating modes in this setup, and relate this with the case of a flat cosmology.

Late-time constraints on modified Gauss-Bonnet cosmology

In this paper, we consider a gravitational action containing a combination of the Ricci scalar, R, and the topological Gauss-Bonnet term, G. Specifically, we study the cosmological features of a particular class of modified gravity theories selected by symmetry considerations, namely the $$f(R,G)= R^n G^{1-n}$$ model. In the context of a spatially flat, homogeneous and isotropic background, we show that the currently observed acceleration of the Universe can be addressed through geometry, hence avoiding de facto the shortcomings of the cosmological constant. We thus present a strategy to numerically solve the Friedmann equations in presence of pressureless matter and obtain the redshift behavior of the Hubble expansion rate. Then, to check the viability of the model, we place constraints on the free parameters of the theory by means of a Bayesian Monte Carlo method applied to late-time cosmic observations. Our results show that the f(R, G) model is capable of mimicking the low-redshift behavior of the standard $$\Lambda $$ CDM model. Finally, we investigate the energy conditions and show that, under suitable choices for the values of the cosmographic parameters, they are all violated when considering the mean value of n obtained from our analysis, as occurs in the case of a dark fluid.

Separable Paravector Orientation Tensors for Enhancing Retinal Vessels.

Robust detection of retinal vessels remains an unsolved research problem, particularly in handling the intrinsic real-world challenges of highly imbalanced contrast between thick vessels and thin ones, inhomogeneous background regions, uneven illumination, and complex geometries of crossing/bifurcations. This paper presents a new separable paravector orientation tensor that addresses these difficulties by characterizing the enhancement of retinal vessels to be dependent on a nonlinear scale representation, invariant to changes in contrast and lighting, responsive for symmetric patterns, and fitted with elliptical cross-sections. The proposed method is built on projecting vessels as a 3D paravector valued function rotated in an alpha quarter domain, providing geometrical, structural, symmetric, and energetic features. We introduce an innovative symmetrical inhibitory scheme that incorporates paravector features for producing a set of directional contrast-independent elongated-like patterns reconstructing vessel tree in orientation tensors. By fitting constraint elliptical volumes via eigensystem analysis, the final vessel tree is produced with a strong and uniform response preserving various vessel features. The validation of proposed method on clinically relevant retinal images with high-quality results, shows its excellent performance compared to the state-of-the-art benchmarks and the second human observers.