Static Background Research Articles

Seasonal variation and geomagnetic storm index effects on the proton flux response in the South Atlantic Anomaly by test particle simulations

In order to estimate the proton flux variations occurring in the South Atlantic Anomaly (SAA) caused by the geodipole tilting angle and the Dst index variations, we performed three dimensional relativistic test particle simulations to calculate the trajectories of high-energy protons (70–180 MeV), where the static background magnetic field was calculated by the Tsyganenko models T01/TS05 combined with IGRF-12. As various parameters characterize the SAA proton flux, the study considered the maximum proton flux and the area of the SAA. Among many different parameters that define the space weather conditions, we considered the geodipole tilting angle and the Dst index. The numerical results showed that (1) the proton flux intensity was increased in the SAA for small geodipole tilting angles, which was confirmed by observations, and that (2) the proton flux intensity was also increased for significantly low Dst index.

Unsupervised Monocular Depth Perception: Focusing on Moving Objects

As a flexible passive 3D sensing means, unsupervised learning of depth from monocular videos is becoming an important research topic. It utilizes the photometric errors between the target view and the synthesized views from its adjacent source views as the loss instead of the difference from the ground truth. Occlusion and scene dynamics in real-world scenes still adversely affect the learning, despite significant progress made recently. In this paper, we show that deliberately manipulating photometric errors can efficiently deal with these difficulties better. We first propose an outlier masking technique that considers the occluded or dynamic pixels as statistical outliers in the photometric error map. With the outlier masking, the network learns the depth of objects that move in the opposite direction to the camera more accurately. To the best of our knowledge, such cases have not been seriously considered in the previous works, even though they pose a high risk in applications like autonomous driving. We also propose an efficient weighted multi-scale scheme to reduce the artifacts in the predicted depth maps. Extensive experiments on the KITTI dataset and additional experiments on the Cityscapes dataset have verified the proposed approach’s effectiveness on depth or ego-motion estimation. Furthermore, for the first time, we evaluate the predicted depth on the regions of dynamic objects and static background separately for both supervised and unsupervised methods. The evaluation further verifies the effectiveness of our proposed technical approach and provides some interesting observations that might inspire future research in this direction.

Integrated Hybrid Data Assimilation for an Ensemble Kalman Filter

Abstract Hybrid ensemble–variational assimilation methods that combine static and flow-dependent background error covariances have been widely applied for numerical weather predictions. The commonly used hybrid assimilation methods compute the analysis increment using a variational framework and update the ensemble perturbations by an ensemble Kalman filter (EnKF). To avoid the inconsistencies that result from performing separate variational and EnKF systems, two integrated hybrid EnKFs that update both the ensemble mean and ensemble perturbations by a hybrid background error covariance in the framework of EnKF are proposed here. The integrated hybrid EnKFs approximate the static background error covariance by use of climatological perturbations through augmentation or additive approaches. The integrated hybrid EnKFs are tested in the Lorenz05 model given different magnitudes of model errors. Results show that the static background error covariance can be sufficiently estimated by climatological perturbations with an order of hundreds. The integrated hybrid EnKFs are superior to the traditional hybrid assimilation methods, which demonstrates the benefit to update ensemble perturbations by the hybrid background error covariance. Sensitivity results reveal that the advantages of the integrated hybrid EnKFs over traditional hybrid assimilation methods are maintained with varying ensemble sizes, inflation values, and localization length scales. Significance Statement Data assimilation is critical for providing the best possible initial condition for forecast and improving the numerical weather predictions. The hybrid ensemble–variational data assimilation method has been widely adopted and developed by many operational centers. The hybrid ensemble–variational assimilation method combines the advantages of ensemble and variational methods and minimizes the weaknesses of the two methods, and thus it outperforms the stand-alone variational and ensemble assimilation methods. The hybrid ensemble–variational assimilation method often computes the control analysis using a variational solver with hybrid background error covariances, but generates the ensemble perturbations by an ensemble Kalman filter (EnKF) system with pure flow-dependent background error covariances. The inconsistencies that result from performing separate variational and EnKF systems can lead to suboptimality in the hybrid ensemble–variational assimilation method. Therefore, integrated hybrid EnKF methods that utilize the framework of an EnKF to update both the ensemble mean and ensemble perturbations by the hybrid background error covariance, are proposed. The integrated hybrid EnKFs use climatological ensemble perturbations to approximate the static background error covariance. The integrated hybrid EnKFs are superior to the traditional hybrid ensemble–variational assimilation methods by producing smaller errors, and the advantages are persistent with varying assimilation parameters.

Entanglement entropy of inhomogeneous XX spin chains with algebraic interactions

We introduce a family of inhomogeneous XX spin chains whose squared couplings are a polynomial of degree at most four in the site index. We show how to obtain an asymptotic approximation for the Rényi entanglement entropy of all such chains in a constant magnetic field at half filling by exploiting their connection with the conformal field theory of a massless Dirac fermion in a suitably curved static background. We study the above approximation for three particular chains in the family, two of them related to well-known quasi-exactly solvable quantum models on the line and the third one to classical Krawtchouk polynomials, finding an excellent agreement with the exact value obtained numerically when the Rényi parameter α is less than one. When α ≥ 1 we find parity oscillations, as expected from the homogeneous case, and show that they are very accurately reproduced by a modification of the Fagotti-Calabrese formula. We have also analyzed the asymptotic behavior of the Rényi entanglement entropy in the non-standard situation of arbitrary filling and/or inhomogeneous magnetic field. Our numerical results show that in this case a block of spins at each end of the chain becomes disentangled from the rest. Moreover, the asymptotic approximation for the case of half filling and constant magnetic field, when suitably rescaled to the region of non-vanishing entropy, provides a rough approximation to the entanglement entropy also in this general case.

Improving RGB-D SLAM in dynamic environments using semantic aided segmentation

SummaryMost conventional simultaneous localization and mapping (SLAM) approaches assume the working environment to be static. In a highly dynamic environment, this assumption divulges the impediments of a SLAM algorithm that lack modules that distinctively attend to dynamic objects despite the inclusion of optimization techniques. This work exploits such environments and reduces the effects of dynamic objects in a SLAM algorithm by separating features belonging to dynamic objects and static background using a generated binary mask image. While the features belonging to the static region are used for performing SLAM, the features belonging to non-static segments are reused instead of being eliminated. The approach employs deep neural network or DNN-based object detection module to obtain bounding boxes and then generates a lower resolution binary mask image using depth-first search algorithm over the detected semantics, characterizing the segmentation of the foreground from the static background. In addition, the features belonging to dynamic objects are tracked into consecutive frames to obtain better masking consistency. The proposed approach is tested on both publicly available dataset as well as self-collected dataset, which includes both indoor and outdoor environments. The experimental results show that the removal of features belonging to dynamic objects for a SLAM algorithm can significantly improve the overall output in a dynamic scene.

Instabilities of Scalar Fields around Oscillating Stars

The behavior of fundamental fields in strong gravity or nontrivial environments is important for our understanding of nature. This problem has interesting applications in the context of dark matter, of dark energy physics, or of quantum field theory. The dynamics of fundamental fields has been studied mainly in static or stationary backgrounds, whereas most of our Universe is dynamic. In this Letter we investigate "blueshift" and parametric instabilities of scalar fields in dynamical backgrounds, which can be triggered (for instance) by oscillating stars in scalar-tensor theories of gravity. We discuss possible implications of our results, which include constraints on an otherwise hard-to-access parameter space of scalar-tensor theories.

Calibration for multivariate Lévy-driven Ornstein-Uhlenbeck processes with applications to weak subordination

Consider a multivariate Lévy-driven Ornstein-Uhlenbeck process where the stationary distribution or background driving Lévy process is from a parametric family. We derive the likelihood function assuming that the innovation term is absolutely continuous. Two examples are studied in detail: the process where the stationary distribution or background driving Lévy process is given by a weak variance alpha-gamma process, which is a multivariate generalisation of the variance gamma process created using weak subordination. In the former case, we give an explicit representation of the background driving Lévy process, leading to an innovation term which is a discrete and continuous mixture, allowing for the exact simulation of the process, and a separate likelihood function. In the latter case, we show the innovation term is absolutely continuous. The results of a simulation study demonstrate that maximum likelihood numerically computed using Fourier inversion can be applied to accurately estimate the parameters in both cases.

On cosmological expansion and local physics

We find an exact convergence in the local dynamics described by two supposedly antagonistic approaches applied at the local, solar system scale: one starting from an expanding universe perspective such as FLRW, the other based on a local model ignoring any notion of expansion, such as static Schwarzschild dS. Both models are in complete agreement when the local effects of the expansion are circumscribed to the presence of the cosmological constant. We elaborate on the relevant role of static backgrounds like the Schwarzschild-dS metric in standard form as the most proper coordinatizations to describe physics at the local scale. We also elaborate on the popular expanding 3-space picture—to be distinguished from that of the expanding universe—and point out the confusion of scales which is typically associated with it. Finally, making use of an old and too often forgotten relativistic kinematical invariant, we address some remaining misunderstandings on space expansion, cosmological and gravitational redshifts. As a byproduct we propose a unique and unambiguous prescription to match the local and cosmological expression of a specific observable.

Gravitational multipole renormalization

We compute the effect of scattering gravitational radiation off the static background curvature, up to second order in Newton constant, known in literature as tail and tail-of-tail processes, for generic electric and magnetic multipoles. Starting from the multipole expansion of composite compact objects, and as expected due to the known electric quadrupole case, both long- and short-distance (UV) divergences are encountered. The former disappears from properly defined observables, the latter are renormalized and their associated logarithms give rise to a classical renormalization group flow. UV divergences alert for incompleteness of the multipolar description of the composite source, and are expected not to be present in a UV-complete theory, as explicitly derived in literature for the case of conservative dynamics. Logarithmic terms from tail-of-tail processes associated to generic magnetic multipoles are computed in this work for the first time.

New Insights into Decoupled Cation and Anion Transport and Dynamic Heterogeneity in a Diethyl(methyl)(isobutyl)phosphonium Hexafluorophosphate Organic Ionic Plastic Crystal.

Organic ionic plastic crystals (OIPCs) are an emerging family of materials with demonstrated applications in electrochemical devices such as lithium/sodium ion batteries, dye-sensitized solar cells, and hydrogen fuel cells. Herein, we present direct evidence of anion diffusion through a relatively static background of a cation lattice in an ionic plastic crystal compound, [P122i4][PF6], in an elevated temperature solid phase. We found all anions are diffusive, whereas only a small population of cations is diffusive. Two anion populations were identified with diffusion coefficients differing by 2 orders of magnitude. The slow-diffusing anion is attributed to the plastic crystal region where the cation forms a relative static background, allowing anions to diffuse possibly through a defect-assisted hopping mechanism.

A comprehensive approach to modern aspects in sunflower selection

According to scientists of the NAAS network, the genetic potential of sunflower hybrids of Ukrainian selection is used in production by no more than 30%. Due to the reduction of the rotation period in crop rotation, crop losses from diseases increased from 10-15% to 35%, and in some favorable years for the development of diseases, losses only from rot are 70% or more. Phytopathological studies were performed on a stationary artificial infectious background of the Institute of Oilseeds NAAS during 2017-2020. We studied the biological resistance to major fungal diseases of new promising lines and hybrids of sunflower breeding institutions of NAAS (Institute of Plant Breeding Yur`eva). Synthetic artificial infectious background was formed during 16 years with forced introduction of infection during sowing of selection genotypes of sunflower. To assess the resistance of sunflower varieties to dry rot and embelization, the infectious background began to be created in 2012. Infectious material was developed in the immunity laboratory of the Institute of Oilseeds of NAAS by increasing pure cultures by the method of V.Y. White Assessment of resistance of sunflower to a number of diseases in the field was carried out according to the method of VP Mistletoe. Sowing was carried out with a seeding rate of 55 thousand / ha. According to the results of field and laboratory studies, sunflower lines and hybrids were identified, which were characterized by the duration of the growing season of the phase "seedlings-physiological maturity" in hybrids 95-110 days, in lines 85-105 days. The selected linear samples had a yield higher than the linear standard (ZL42 with a yield of 0.97 t/ha), and the studied sterile lines had a yield from 1.76 (+0.79) to 1.96 (+0.99) t/ha. Pollen fertility restorer lines had a yield at the level of 0.36 (-0.05) - 0.55 (+0.14) t/ha, when forming the yield of the control line ZL260V - 0.41 t / ha. Created hybrids based on these lines (parental components) formed a yield: Firstborn 3.34 t / ha, Tour - 3.80 t / ha, which is higher than the control hybrid Darius from 0.4 to 0.86 t / ha, respectively. The weight of 1000 seeds of sterile analogues varied in the range of 55.2 g - 56.4 g. In the lines of pollen fertility restorers in the range of 29.4-35.0 g. In the presented hybrids, the average weight of 1000 seeds was in the range of 57.0-60,1 g, while the standard Darius formed a mass of 1000 seeds within 49.0 g. The selected samples had a high percentage of fat accumulation in the seeds. This figure ranged from 49.76 (ZL96A) to 51.12% (ZL72A) among sterile analogues, while the standard sample ZL42A had an oil content of 49.26%. New selected sunflower hybrids were marked by fat content in seeds at the level of 51.88 (Prize) and 52.12% (Firstborn), while the hybrid - standard Darius also had a fairly high level of oil content, namely 48.13%. The presented lines and their hybrid combinations for the study period (2017-2020) combined relatively high resistance to powdery mildew, dry rot, embelisation and high economic performance. According to the results of a comprehensive assessment, a number of hybrids were identified. Among them are Tour, Prize and Firstborn. With such indicators, the presented hybrids are able to compete with dignity in the sunflower seed market.

Auditory selective attention and speech-in-noise in cochlear implants

Auditory selective attention is a crucial mechanism for comprehending speech in noise. We investigated if cochlear implant (CI) users exhibit auditory selective attention that involves modulation of neural responses to target speech and if such attentional ability predicts their speech-in-noise performance. In our experiment, designed to assess the strength of attentional modulation, participants with normal hearing and with CI were given a pre-stimulus visual cue that directed their attention to either of two sequences in stationary background noise and asked to select a deviant syllable. We hypothesized that the amplitude of event-related-potential (ERP) would be greater when the syllable is attended if either group is capable of employing auditory selective attention and that the difference of ERP amplitude between attended and unattended trials predicts the performance in a speech-in-noise task. Our analysis showed that the amplitude of ERPs for the attended syllable was greater than that for the unattended syllable with the CI subjects, exhibiting that attention modulates CI users’ cortical responses to sounds. Moreover, the strength of attentional modulation showed a significant correlation with the same CI users’ speech-in-noise performance. These results show that attentional modulation provides a valuable neural marker for predicting CI users’ success in real-world communications.

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

Sports video analysis has received much attention as it turned to be a hot research area in the field of image processing. This motivation offers opportunities that develop fascinating applications supported by analysis of different sports, especially soccer. Ball identification, in soccer images, is an essential task not only for goal-scoring but also for performance evaluation. However, ball detection suffers from several hurdles such as occlusions, fast-moving objects, shadows, poor lighting, color contrast, and other static background objects. Although several ball detection techniques have been introduced such as Frame Difference, Mixture of Gaussian (MoG), Optical Flow, and so forth; ball detection in soccer games is still an open research area. In this paper, a new Fuzzy Based Ball Detection (FB2D) strategy is proposed for identifying the ball through a set of image sequences extracted from a soccer match video. FB2D can accurately identify the ball even if it is attached to the white lines drawn on the playground or partially occluded behind players. FB2D is compared to recent ball detection techniques. Experimental results show that FB2D outperforms recent detection techniques as it introduces both the highest level of detection accuracy in the testing stage and the lowest possible error.

Background Noise Filtering and Clustering With 3D LiDAR Deployed in Roadside of Urban Environments

Traffic information collection is an important foundation for intelligent transportation systems. In this paper, 3D Light Detection And Ranging (LiDAR) is deployed in the roadside of urban environments to collect vehicle and pedestrian information. A background filtering algorithm, including a mean background modeling to build a background map and a background difference method to filter static background noise points, is proposed for roadside fixed LiDAR facilities. Background points are filtered through the difference between data frames and a multi-level background map, and then there are still a small number of noise points. Aiming to reduce the noise points, a hierarchical maximum density clustering of applications with noise (HMDCAN) algorithm, utilizing both density clustering and hierarchical clustering, is proposed to effectively achieve both noise point filtering and target recognition. We verify our methods in a facility with a 16-channel LiDAR in which background filtering and target recognition are tested with different scenarios, and the accuracy rate is over 97%.

Odd-parity stability of black holes in Einstein-aether gravity

In Einstein-aether theory, we study the stability of black holes against odd-parity perturbations on a spherically symmetric and static background. For odd-parity modes, there are two dynamical degrees of freedom arising from the tensor gravitational sector and aether vector field. We derive general conditions under which neither ghosts nor Laplacian instabilities are present for these dynamical fields. We apply these results to concrete black hole solutions known in the literature and show that some of those solutions can be excluded by the violation of stability conditions. The exact Schwarzschild solution present for ${c}_{13}={c}_{14}=0$, where ${c}_{i}$'s are the four coupling constants of the theory with ${c}_{ij}={c}_{i}+{c}_{j}$, is prone to Laplacian instabilities along the angular direction throughout the horizon exterior. However, we find that the odd-parity instability of high radial and angular momentum modes is absent for black hole solutions with ${c}_{13}={c}_{4}=0$ and ${c}_{1}\ensuremath{\ge}0$.

Detecting and Characterising Small-Scale Brightenings in Solar Imaging Data

Observations of small-scale brightenings in the low solar atmosphere can provide valuable constraints on possible heating and heat transport mechanisms. We present a method for the detection and analysis of brightenings, and demonstrate its application to time-series imagery of the Interface Region Imaging Spectrograph (IRIS) in the extreme ultraviolet (EUV). The method is based on spatio-temporal band-pass filtering, adaptive thresholding and centroid tracking, and records an event’s spatial position, duration, total brightness and maximum brightness. Spatial area, brightness, and position are also recorded as functions of time throughout the event’s lifetime. Detected brightenings can fragment, or merge, over time – thus the number of distinct regions constituting a brightening event is recorded over time, and the maximum number of regions recorded as N_{mathit{frag}}, which is a simple measure of an event’s coherence or spatial complexity. A test is made on a synthetic datacube composed of a static background based on IRIS data, Poisson noise and approx 10^{4} randomly-distributed, moving, small-scale Gaussian brightenings. Maximum brightness, total brightness, area, and duration follow power-law distributions, and the results show the range over which the method can successfully extract information. The test shows that the recorded maximum brightness of an event is a reliable measure for the brightest and most accurately detected events, with an error of 6%. Event area, duration and speed are generally underestimated by around 15% and have an uncertainty of 20–30%. The total brightness is underestimated by 30%, and has an uncertainty of 30%. Applying this detection method to real IRIS quiet-sun data spanning 19 minutes over a 54.40''times 55.23'' field of view (FOV) yields 2997 detections, 1340 of these detections either remain un-fragmented or fragment to two distinct regions at least once during their lifetime (N_{mathit{frag}}le 2), equating to an event density of 3.96times 10^{-4} arcsec−2 s−1. The method will be used for a future large-scale statistical analysis of several quiet-sun (QS) data sets from IRIS, other EUV imagers, and other types of data including Halpha and visible photospheric imagery.

Research on Improvement of Background Modeling and Detection Method Based on Frame Difference

In order to improve the phenomena such as false edge and void in static background detection of frame difference method and the problems of slow background update and poor adaptability in background difference method, an improved research method based on frame difference background modeling detection method is proposed in this paper. The method first obtains two frame difference images by four frame difference method, carries on the closed operation, and respectively adds with the original image, after adding the two images to carry on the pixel value comparison and the choice, can get the background image. The experimental results show that the algorithm can effectively avoid the appearance of ghost, void and shadow, and adapt to the environment better. Compared with the traditional methods, the algorithm has better real-time performance and higher accuracy.

A Multiscale Approach to Timescale Analysis: Isolating Diel Signals from Solute Concentration Time Series.

Solute concentration time series reflect hydrological and biological drivers through various frequencies, phases, and amplitudes of change. Untangling these signals facilitates the understanding of dynamic ecosystem conditions and transient water quality issues. A case in point is the inference of biogeochemical processes from diel solute concentration variations. This analysis requires approaches capable of isolating subtle diel signals from background variability at other scales. Conventional time series analyses typically assume stationary or deterministic background variability; however, most rivers do not respect such niceties. We developed a time-series filtering method that uses empirical mode decomposition to decompose a measured solute concentration time series into intrinsic mode frequencies. Based on externally supplied mechanistic knowledge, we then filter these modes by periodicity, phase, and coherence with neighboring days. This method is tested on three synthetic series that incorporate environmental variability and sensor noise and on a year of 15 min sampled concentration time series from three hydrologically and ecologically distinct rivers in the eastern United States. The proposed method successfully isolated signals in the measured data sets that corresponded with variability in gross primary productivity. The strength the diel signal isolated through this method was smaller compared to the true signal in the synthetic series; however, uncertainty analysis showed that the process-model-based estimates derived from these signals were similar to other inference methods. This signal decomposition method retains information that can be used for further process modeling while making different assumptions about the data than Fourier and wavelet analyses.

Pressure Response Optimization of an Eddy Current-Driven Flyer Plate Valve for the ITER Shattered Pellet Injection System

One technique for mitigating disruptions in a tokamak is shattered pellet injection (SPI). SPI is a process in which a large solid pellet consisting of deuterium, neon, or argon is desublimated in a pipe gun barsrel and launched downstream. Pellets are shattered just before entering the plasma by an impact with an angled tube. Injection of these materials into the plasma radiates stored thermal energy, limits current decay rates, suppresses the generation of runaway electrons, and dissipates runaway electrons if necessary. A critical element of the SPI system is a fast-acting valve that releases high-pressure gas to dislodge and accelerate pellets directly, or indirectly via a mechanical punch. A prototype valve sized for the ITER SPI system has been designed and fabricated. A pulsed high-voltage power supply energizes the valve’s internal magnetic coil, which induces eddy currents in the adjacent flyer plate resulting in a repulsive force between the flyer plate and the coil. The flyer plate action lifts a valve seat, allowing high-pressure gas to flow from the valve plenum to the downstream (breech) location of the pellet or mechanical punch. All of the valve’s internal components are designed to operate in ITER-level static background magnetic fields. A study was conducted to optimize the downstream pressure response for a range of valve sizes and operating pressures. In particular, the study analyzes the breech pressure response associated with varying plenum pressures as well as varying breech volumes. A computational fluid dynamics simulation was built in STAR-CCM+ and validated against data from laboratory experiments. The resulting simulation outputs, in the form of downstream responses for a variety of initial plenum pressures and breech volumes, will be used as a complement to experimental data to ensure the pressure pulse is suitable for pellet survivability. These data, combined with studies on pellet shear strength and shock response, will be applied to optimization of overall operating parameters of the ITER SPI system.

Motion: enhancing signals and concealing cues.

ABSTRACTAnimal colour patterns remain a lively focus of evolutionary and behavioural ecology, despite the considerable conceptual and technical developments over the last four decades. Nevertheless, our current understanding of the function and efficacy of animal colour patterns remains largely shaped by a focus on stationary animals, typically in a static background. Yet, this rarely reflects the natural world: most animals are mobile in their search for food and mates, and their surrounding environment is usually dynamic. Thus, visual signalling involves not only animal colour patterns, but also the patterns of animal motion and behaviour, often in the context of a potentially dynamic background. While motion can reveal information about the signaller by attracting attention or revealing signaller attributes, motion can also be a means of concealing cues, by reducing the likelihood of detection (motion camouflage, motion masquerade and flicker-fusion effect) or the likelihood of capture following detection (motion dazzle and confusion effect). The interaction between the colour patterns of the animal and its local environment is further affected by the behaviour of the individual. Our review details how motion is intricately linked to signalling and suggests some avenues for future research. This Review has an associated Future Leader to Watch interview with the first author.