Asymmetry measures for QSOs and companions

An asymmetry index is derived from ellipse-fitting to galaxy images, that gives weight to faint outer features and is not strongly redshift-dependent. These measures are made on a sample of 13 2MASS QSOs and their neighbour galaxies, and a control sample of field galaxies from the same wide-field imaging data. The QSO host galaxy asymmetries correlate well with visual tidal interaction indices previously published. The companion galaxies have somewhat higher asymmetry than the control galaxy sample, and their asymmetry is inversely correlated with distance from the QSO. The distribution of QSO-companion asymmetry indices is different from that for matched control field galaxies at the $\sim95$ significance level. We present the data and discuss this evidence for tidal and other disturbances in the vicinity of QSOs.Comment: 13 pages, 2 tables, 4 figures; to appear in A

Fast Mesh Refinement in Pseudospectral Optimal Control

Mesh refinement in pseudospectral (PS) optimal control is embarrassingly easy --- simply increase the order $N$ of the Lagrange interpolating polynomial and the mathematics of convergence automates the distribution of the grid points. Unfortunately, as $N$ increases, the condition number of the resulting linear algebra increases as $N^2$; hence, spectral efficiency and accuracy are lost in practice. In this paper, we advance Birkhoff interpolation concepts over an arbitrary grid to generate well-conditioned PS optimal control discretizations. We show that the condition number increases only as $\sqrt{N}$ in general, but is independent of $N$ for the special case of one of the boundary points being fixed. Hence, spectral accuracy and efficiency are maintained as $N$ increases. The effectiveness of the resulting fast mesh refinement strategy is demonstrated by using \underline{polynomials of over a thousandth order} to solve a low-thrust, long-duration orbit transfer problem.Comment: 27 pages, 12 figures, JGCD April 201

Guidelines for Affect Elicitation and Tracking in High Intensity VR Exergaming

This position paper provides an overview of advances made in affect elicitation and tracking. We provide guidelines forevoking underwhelming, overwhelming and optimal affective states and tracking the affective state using psychophysiologicalmeasurements in high intensity VR exergaming.We discuss the research challenges that need to be addressed to implement affective high intensity VR exergaming.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 66599

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Technical ReportThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Executive SummaryThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

Bipartite Graph Learning for Autonomous Task-to-Sensor Optimization

NPS NRP Project PosterThis study addresses the question of how machine learning/artificial intelligence can be applied to identify the most appropriate 'sensor' for a task, to prioritize tasks, and to identify gaps/unmet requirements. The concept of a bipartite graph provides a mathematical framework for task-to-sensor mapping by establishing connectivity between various high-level tasks and the specific sensors and processes that must be invoked to fulfil those tasks and other mission requirements. The connectivity map embedded in the bipartite graph can change depending on the availability/unavailability of resources, the presence of constraints (physics, operational, sequencing), and the satisfaction of individual tasks. Changes can also occur according to the valuation, re-assignment and re-valuation of the perceived task benefit and how the completion of a specific task (or group of tasks) can contribute to the state of knowledge. We plan to study how machine learning can be used to perform bipartite learning for task-to-sensor planning.Naval Special Warfare Command (NAVSPECWARCOM)N9 - Warfare SystemsThis research is supported by funding from the Naval Postgraduate School, Naval Research Program (PE 0605853N/2098). https://nps.edu/nrpChief of Naval Operations (CNO)Approved for public release. Distribution is unlimited.

T Cell Migration from Inflamed Skin to Draining Lymph Nodes Requires Intralymphatic Crawling Supported by ICAM-1/LFA-1 Interactions.

T cells are the most abundant cell type found in afferent lymph, but their migration through lymphatic vessels (LVs) remains poorly understood. Performing intravital microscopy in the murine skin, we imaged T cell migration through afferent LVs in vivo. T cells entered into and actively migrated within lymphatic capillaries but were passively transported in contractile collecting vessels. Intralymphatic T cell number and motility were increased during contact-hypersensitivity-induced inflammation and dependent on ICAM-1/LFA-1 interactions. In vitro, blockade of endothelial cell-expressed ICAM-1 reduced T cell adhesion, crawling, and transmigration across lymphatic endothelium and decreased T cell advancement from capillaries into lymphatic collectors in skin explants. In vivo, T cell migration to draining lymph nodes was significantly reduced upon ICAM-1 or LFA-1 blockade. Our findings indicate that T cell migration through LVs occurs in distinct steps and reveal a key role for ICAM-1/LFA-1 interactions in this process