Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGo With the Flow, on Jupiter and Snow. Coherence From Model-Free Video Data without Trajectories
356
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Viewing a data set such as the clouds of Jupiter, coherence is readily apparent to human observers, especially the Great Red Spot, but also other great storms and persistent structures. There are now many different definitions and perspectives mathematically describing coherent structures, but we will take an image processing perspective here. We describe an image processing perspective inference of coherent sets from a fluidic system directly from image data, without attempting to first model underlying flow fields, related to a concept in image processing called motion tracking. In contrast to standard spectral methods for image processing which are generally related to a symmetric affinity matrix, leading to standard spectral graph theory, we need a not symmetric affinity which arises naturally from the underlying arrow of time. We develop an anisotropic, directed diffusion operator corresponding to flow on a directed graph, from a directed affinity matrix developed with coherence in mind, and corresponding spectral graph theory from the graph Laplacian. Our methodology is not offered as more accurate than other traditional methods of finding coherent sets, but rather our approach works with alternative kinds of data sets, in the absence of vector field. Our examples will include partitioning the weather and cloud structures of Jupiter, and a local to Potsdam, N.Y. lake-effect snow event on Earth, as well as the benchmark test double-gyre system.
rate research
Read More
Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic-scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.
This work presents an analysis of ocean wave data including rogue waves. A stochastic approach based on the theory of Markov processes is applied. With this analysis we achieve a characterization of the scale dependent complexity of ocean waves by means of a Fokker-Planck equation, providing stochastic information of multi-scale processes. In particular we show evidence of Markov properties for increment processes, which means that a three point closure for the complexity of the wave structures seems to be valid. Furthermore we estimate the parameters of the Fokker-Planck equation by parameter-free data analysis. The resulting Fokker-Planck equations are verified by numerical reconstruction. This work presents a new approach where the coherent structure of rogue waves seems to be integrated into the fundamental statistics of complex wave states.
We present a Bayesian dynamical inference method for characterizing cardiorespiratory (CR) dynamics in humans by inverse modelling from blood pressure time-series data. This new method is applicable to a broad range of stochastic dynamical models, and can be implemented without severe computational demands. A simple nonlinear dynamical model is found that describes a measured blood pressure time-series in the primary frequency band of the CR dynamics. The accuracy of the method is investigated using surrogate data with parameters close to the parameters inferred in the experiment. The connection of the inferred model to a well-known beat-to-beat model of the baroreflex is discussed.
Particle picking is currently a critical step in the cryo-EM single particle reconstruction pipeline. Despite extensive work on this problem, for many data sets it is still challenging, especially for low SNR micrographs. We present the KLT (Karhunen Loeve Transform) picker, which is fully automatic and requires as an input only the approximated particle size. In particular, it does not require any manual picking. Our method is designed especially to handle low SNR micrographs. It is based on learning a set of optimal templates through the use of multi-variate statistical analysis via the Karhunen Loeve Transform. We evaluate the KLT picker on publicly available data sets and present high-quality results with minimal manual effort.
Concussion and repeated exposure to mild traumatic brain injury are risks for athletes in many sports. While direct head impacts are analyzed to improve the detection and awareness of head acceleration events so that an athletes brain health can be appropriately monitored and treated. However, head accelerations can also be induced by impacts with little or no head involvement. In this work we evaluated if impacts that do not involve direct head contact, such as being pushed in the torso, can be sufficient in collegiate American football to induce head accelerations comparable to direct head impacts. Datasets of impacts with and without direct head contact were collected and compared. These datasets were gathered using a state-of-the-art impact detection algorithm embedded in an instrumented mouthguard to record head kinematics. Video analysis was used to differentiate between impact types. In total, 15 impacts of each type were used in comparison, with clear video screenshots available to distinguish each impact type. Analysis of the kinematics showed that the impacts without direct head contact achieved similar levels of linear and angular accelerations during impact compared to those from direct head impacts. Finite element analyses using the median and peak kinematic signals were used to calculate maximum principal strain of the brain. Statistical analysis revealed that no significant difference was found between the two datasets based on a Bonferroni-adjusted p-value threshold of 0.017 , with the exception of peak linear acceleration. Impacts without direct head contact showed higher mean values of peak linear acceleration values of 17.6 g compared to the direct-head impact mean value of 6.1g. These results indicated that impacts other than direct head impacts could still produce meaningful kinematic loads in the head and as such should be included in athlete health monitoring.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
