Alfvenic waves in polar spicules

Context. For investigating spicules from the photosphere to coronal heights, the new Hinode/SOT long series of high resolution observations from Space taken in CaII H line emission offers an improved way to look at their remarkable dynamical behavior using images free of seeing effects. They should be put in the context of the huge amount of already accumulated material from ground-based instruments, including high- resolution spectra of off-limb spicules. Results. The surge-like behavior of solar polar region spicules supports the untwisting multi-component interpretation of spicules exhibiting helical dynamics. Several tall spicules are found with (i) upward and downward flows similar at lower and middle-levels, the rate of upward motion being slightly higher at high levels; (ii) the left and right-hand velocities are also increasing with height; (iii) a large number of multi-component spicules show shearing motion of both left-handed and right-handed senses occurring simultaneously, which might be understood as twisting (or untwisting) threads. The number of turns depends on the overall diameter of the structure made of components and changes from at least one turn for the smallest structure to at most two or three turns for surge-like broad structures; the curvature along the spicule corresponds to a low turn number similar to a transverse kink mode oscillation along the threads.Comment: 8 pages, 10 figures, Accepted in Astronomy and Astrophysic

EVALUATION OF REPEATED MEASURES OUTCOMES IN EPIDEMIOLOGY

Ph.DDOCTOR OF PHILOSOPH

Effect of Intensity Standardization on Deep Learning for WML Segmentation in Multi-Centre FLAIR MRI

Deep learning (DL) methods for white matter lesion (WML) segmentation in MRI suffer a reduction in performance when applied on data from a scanner or centre that is out-of-distribution (OOD) from the training data. This is critical for translation and widescale adoption, since current models cannot be readily applied to data from new institutions. In this work, we evaluate several intensity standardization methods for MRI as a preprocessing step for WML segmentation in multi-centre Fluid-Attenuated Inversion Recovery (FLAIR) MRI. We evaluate a method specifically developed for FLAIR MRI called IAMLAB along with other popular normalization techniques such as White-strip, Nyul and Z-score. We proposed an Ensemble model that combines predictions from each of these models. A skip-connection UNet (SC UNet) was trained on the standardized images, as well as the original data and segmentation performance was evaluated over several dimensions. The training (in-distribution) data consists of a single study, of 60 volumes, and the test (OOD) data is 128 unseen volumes from three clinical cohorts. Results show IAMLAB and Ensemble provide higher WML segmentation performance compared to models from original data or other normalization methods. IAMLAB & Ensemble have the highest dice similarity coefficient (DSC) on the in-distribution data (0.78 & 0.80) and on clinical OOD data. DSC was significantly higher for IAMLAB compared to the original data (p25mL: 0.77 vs. 0.71; 10mL<= LL<25mL: 0.66 vs. 0.61; LL<10mL: 0.53 vs. 0.52). The IAMLAB and Ensemble normalization methods are mitigating MRI domain shift and are optimal for DL-based WML segmentation in unseen FLAIR data

MODEM: a multi-agent hierarchical structure to model the human motor control system

In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a "gate selector” block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM

MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-Mixer

We propose a novel architecture called MLP-SRGAN, which is a single-dimension Super Resolution Generative Adversarial Network (SRGAN) that utilizes Multi-Layer Perceptron Mixers (MLP-Mixers) along with convolutional layers to upsample in the slice direction. MLP-SRGAN is trained and validated using high resolution (HR) FLAIR MRI from the MSSEG2 challenge dataset. The method was applied to three multicentre FLAIR datasets (CAIN, ADNI, CCNA) of images with low spatial resolution in the slice dimension to examine performance on held-out (unseen) clinical data. Upsampled results are compared to several state-of-the-art SR networks. For images with high resolution (HR) ground truths, peak-signal-to-noise-ratio (PSNR) and structural similarity index (SSIM) are used to measure upsampling performance. Several new structural, no-reference image quality metrics were proposed to quantify sharpness (edge strength), noise (entropy), and blurriness (low frequency information) in the absence of ground truths. Results show MLP-SRGAN results in sharper edges, less blurring, preserves more texture and fine-anatomical detail, with fewer parameters, faster training/evaluation time, and smaller model size than existing methods. Code for MLP-SRGAN training and inference, data generators, models and no-reference image quality metrics will be available at https://github.com/IAMLAB-Ryerson/MLP-SRGAN.Comment: 14 pages, 10 figure

A New Fractional Order Hold and Its Capability in Frequency Response and Zero Placement

This paper introduces a new holder with application on digital control systems. This holder is a combination of fractional order hold (FROH) and zero-order hold (ZOH) that has the capability of both holders and a frequency response better than both of ZOH and FROH. For the stability of zeros of the sampled system two theorems are stated and proved with the assumption that the sampling period is very small. Also simulation results are studied to show the effectiveness of the proposed holder and better performance results in comparison with ZOH and FROH

MODEM: a multi-agent hierarchical structure to model the human motor control system

Abstract In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a âgate selectorâ block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM)

Practice and principles of stereotactic body radiation therapy for spine and non-spine bone metastases

Radiotherapy is the dominant treatment modality for painful spine and non-spine bone metastases (NSBM). Historically, this was achieved with conventional low dose external beam radiotherapy, however, stereotactic body radiotherapy (SBRT) is increasingly applied for these indications. Meta-analyses and randomized clinical trials have demonstrated improved pain response and more durable tumor control with SBRT for spine metastases. However, in the setting of NSBM, there is limited evidence supporting global adoption and large scale randomized clinical trials are in need. SBRT is technically demanding requiring careful consideration of organ at risk tolerance, and strict adherence to technical requirements including immobilization, simulation, contouring and image-guidance procedures. Additional considerations include follow up practices after SBRT, with appropriate imaging playing a critical role in response assessment. Finally, there is renewed research into promising new technologies that may further refine the use of SBRT in both spinal and NSBM in the years to come