Owenmclaughlin5685

Accordingly, we posit that the proposed higher order robust principal component analysis based approach filters out the background phase-amplitude coupling activity and predominantly captures the event-related phase-amplitude coupling dynamics to provide insight into the spatially distributed brain networks across different frequency bands.It is common to believe that passengers are more adversely affected by motion sickness than drivers. However, no study has compared passengers and drivers' neural activities and drivers experiencing motion sickness (MS). Therefore, this study attempts to explore brain dynamics in motion sickness among passengers and drivers. Eighteen volunteers participated in simulating the driving winding road experiment while their subjective motion sickness levels and electroencephalogram (EEG) signals were simultaneously recorded. Independent Component Analysis (ICA) was employed to isolate MS-related independent components (ICs) from EEG. Furthermore, comodulation analysis was applied to decompose spectra of interest ICs, related to MS, to find the specific spectra-related temporally independent modulators (IMs). The results showed that passengers' alpha band (8-12 Hz) power increased in correlation with the MS level in the parietal, occipital midline and left and right motor areas, and drivers' alpha band (8-12 Hz) power showed relatively smaller increases than those in the passenger. Further, the results also indicate that the enhanced activation of alpha IMs in the passenger than the driver is due to a higher degree of motion sickness. In conclusion, compared to the driver, the passenger experience more conflicts among multimodal sensory systems and demand neuro-physiological regulation.Existing GAN-based multi-view face synthesis methods rely heavily on "creating" faces, and thus they struggle in reproducing the faithful facial texture and fail to preserve identity when undergoing a large angle rotation. In this paper, we combat this problem by dividing the challenging large-angle face synthesis into a series of easy small-angle rotations, and each of them is guided by a face flow to maintain faithful facial details. In particular, we propose a Face Flow-guided Generative Adversarial Network (FFlowGAN) that is specifically trained for small-angle synthesis. The proposed network consists of two modules, a face flow module that aims to compute a dense correspondence between the input and target faces. It provides strong guidance to the second module, face synthesis module, for emphasizing salient facial texture. We apply FFlowGAN multiple times to progressively synthesize different views, and therefore facial features can be propagated to the target view from the very beginning. All these multiple executions are cascaded and trained end-to-end with a unified back-propagation, and thus we ensure each intermediate step contributes to the final result. Extensive experiments demonstrate the proposed divide-and-conquer strategy is effective, and our method outperforms the state-of-the-art on four benchmark datasets qualitatively and quantitatively.Panoptic segmentation (PS) is a complex scene understanding task that requires providing high-quality segmentation for both thing objects and stuff regions. Previous methods handle these two classes with semantic and instance segmentation modules separately, following with heuristic fusion or additional modules to resolve the conflicts between the two outputs. This work simplifies this pipeline of PS by consistently modeling the two classes with a novel PS framework, which extends a detection model with an extra module to predict category- and instance-aware pixel embedding (CIAE). CIAE is a novel pixel-wise embedding feature that encodes both semantic-classification and instance-distinction information. At the inference process, PS results are simply derived by assigning each pixel to a detected instance or a stuff class according to the learned embedding. Our method not only demonstrates fast inference speed but also the first one-stage method to achieve comparable performance to two-stage methods on the challenging COCO benchmark.Multi-label image recognition is a practical and challenging task compared to single-label image classification. However, previous works may be suboptimal because of a great number of object proposals or complex attentional region generation modules. In this paper, we propose a simple but efficient two-stream framework to recognize multi-category objects from global image to local regions, similar to how human beings perceive objects. To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible. Our method can efficiently and effectively recognize multi-class objects with an affordable computation cost and a parameter-free region localization module. Over three benchmarks on multi-label image classification, our method achieves new state-of-the-art results with a single model only using image semantics without label dependency. In addition, the effectiveness of the proposed method is extensively demonstrated under different factors such as global pooling strategy, input size and network architecture. Code has been made available at https//github.com/gaobb/MCAR.This paper addresses the challenge of Multispectral (MS) document image segmentation, which is an essential step for subsequent document image analysis. Most previous studies have focused only on binary (text/non-text) separation. They also rely on handcrafted features and techniques dedicated to conventional images that do not take advantage of MS images' spectral richness. In this work, we reformulate this task as a source separation problem, whereby we target the blind decomposition of entire MS document images via a new orthogonal nonnegative matrix factorization (ONMF). On the one hand, we incorporate orthogonality constraint as a Riemannian optimization on the Stiefel manifold. On the other hand, based on which factor we impose the orthogonality constraint, i.e., either on the endmember matrix, abundance matrix, or both, we propose three ONMF models to investigate this issue and determine which model is more suitable for this study. Minimizing the three models subject to nonnegativity and orthogonality constraints simultaneously is very challenging. Therefore, we extend the alternating direction method of multipliers scheme to solve them. We evaluated our models on synthetic Hyperspectral (HS) images and real-world MS document images. The experimental results confirm the effectiveness of the proposed models and demonstrate their generalization power compared to state-of-the-art techniques.The paper presents a study of special material properties of the single crystalline material Ca3TaGa3Si2O14 (CTGS=Catangasite). The comparatively highly ordered crystal structure and acceptable piezoelectric strength make it a candidate for microacoustic applications under extreme conditions. Obviously, low-loss dynamic behavior is typical for this crystal which consequently enables high temperature use. As a particular challenge the behavior at GHz frequencies is investigated here. For that, HBAR (High overtone Bulk Acoustic wave Resonator) type measurements in the range of 1⃛6 GHz are performed. The selection of 5 distinctive propagation directions for exclusively pure or quasi-longitudinal modes enables to derive the dynamic viscosities from the quality factors of HBAR results. The observed frequency dependences exhibit Akhiezer behavior as the predominant loss mechanism in the cases examined.The determination of complex elastic, piezoelectric, and dielectric coefficients of piezoelectric ceramics is important for precision engineering devices. Here, a novel method for determining the optimal material coefficients is presented. This method minimizes the average relative error in the values of conductance, susceptance, resistance, and reactance obtained from the one-dimensional model in the IEEE Standard (ANSI/IEEE Std 176-1987) and the experimental measurements of the first and second radial modes. The Poisson's ratio is assumed to be a complex number in addition to the elastic, piezoelectric and dielectric coefficients in the present method. The global minimum of the average relative error is found by searching the minimum among all local minima of the average relative error, which are obtained with the Levenberg-Marquardt modification of Newton's method from randomly chosen initial conditions. The optimal material coefficients of an APC 850 disk and an APC 855 disk are calculated with this method. The uncertainties in the optimal material coefficients are evaluated by calculating the minimum average relative error when the real or imaginary part of each coefficient is prescribed.The use of super-resolution ultrasound (SR-US) imaging greatly improves visualization of microvascular structures, but clinical adoption is limited by long imaging times. This method depends on detecting and localizing isolated microbubbles (MBs), forcing the use of a dilute contrast agent concentration. Contrast-enhanced ultrasound (CEUS) image acquisition times as long as minutes arise as the localization of thousands of MBs are acquired to form a complete SR-US image. In this paper, we explore the use of nonlinear CEUS strategies using nonlinear fundamental contrast pulse sequencing (CPS) to increase the contrast-to-tissue ratio (CTR) and compare MB detection effectiveness to linear B-mode CEUS imaging. The CPS compositions of amplitude modulation (AM), pulse inversion (PI), and a combination of the two (AMPI) were studied. A simulation study combined the Rayleigh-Plesset-Marmottant (RPM) model of MB characteristics and a nonlinear tissue model using the k-Wave toolbox for MATLAB (MathWorks Inc.). Validati and was shown to shorten image acquisition time by an average of 33.3 to 76.7% when combining one or more CPS sequences.Image stitching is a prominent challenge in medical imaging, where the limited field-of-view captured by single images prohibits holistic analysis of patient anatomy. The barrier that prevents straight-forward mosaicing of 2D images is depth mismatch due to parallax. In this work, we leverage the Fourier slice theorem to aggregate information from multiple transmission images in parallax-free domains using fundamental principles of X-ray image formation. The details of the stitched image are subsequently restored using a novel deep learning strategy that exploits similarity measures designed around frequency, as well as dense and sparse spatial image content. Our work provides evidence that reconstruction of orthographic mosaics is possible with realistic motions of the C-arm involving both translation and rotation. We also show that these orthographic mosaics enable metric measurements of clinically relevant quantities directly on the 2D image plane.In this paper, we present novel objectives for one-class learning, which we collectively refer to as Generalized One-class Discriminative Subspaces (GODS). Our key idea is to learn a pair of complementary classifiers to flexibly bound the one-class data distribution, where the data belongs to the positive half-space of one of the classifiers in the complementary pair and to the negative half-space of the other. selleck inhibitor To avoid redundancy while allowing non-linearity in the classifier decision surfaces, we design each classifier as an orthonormal frame and learn these frames via jointly optimizing for two objectives, namely i) to minimize the distance between the two frames, and ii) to maximize the margin between the frames and the data. The learned frames will thus characterize a piecewise linear decision surface allowing for efficient inference, while our objectives seek to bound the data within a minimal volume that maximizes the decision margin, thereby robustly capturing the data distribution. We explore several variants of our formulation under different constraints on the constituent classifiers, including kernelized feature maps.