Activity-aware Stress Sensory System

Continuous stress monitoring may able to help analyzing and enhance the awareness of an individual on their stress patterns and provide more reliable data information for physicians in interventions. In the past years research, studies on mental stress sensory system were limited inside laboratory environment. However, excluding the effects of physical activity can be impractical while developing a wearable stress sensory system for daily use. In this project, effects of external factors from environment on Galvanic Skin Response (GSR) measurements and integration of several stress sensory system were studied. Electrocardiogram (ECG), GSR, and Activity Recognition System (ARS) were studied under different physical activities: sitting, standing, lying and walking. It is showed from the studies that an overall accuracy of 94.7% in ARS is achieved by using two sensor node system (at thigh and ankle each) which an improvement of 27.3% from using single sensor node system. It is further demonstrated that ARS could help improve in accuracy of wearable stress sensory system

Estimating sample-specific regulatory networks

Biological systems are driven by intricate interactions among the complex array of molecules that comprise the cell. Many methods have been developed to reconstruct network models of those interactions. These methods often draw on large numbers of samples with measured gene expression profiles to infer connections between genes (or gene products). The result is an aggregate network model representing a single estimate for the likelihood of each interaction, or "edge," in the network. While informative, aggregate models fail to capture the heterogeneity that is represented in any population. Here we propose a method to reverse engineer sample-specific networks from aggregate network models. We demonstrate the accuracy and applicability of our approach in several data sets, including simulated data, microarray expression data from synchronized yeast cells, and RNA-seq data collected from human lymphoblastoid cell lines. We show that these sample-specific networks can be used to study changes in network topology across time and to characterize shifts in gene regulation that may not be apparent in expression data. We believe the ability to generate sample-specific networks will greatly facilitate the application of network methods to the increasingly large, complex, and heterogeneous multi-omic data sets that are currently being generated, and ultimately support the emerging field of precision network medicine

The Final Fate of Binary Neutron Stars: What Happens After the Merger?

The merger of two neutron stars usually produces a remnant with a mass significantly above the single (nonrotating) neutron star maximum mass. In some cases, the remnant will be stabilized against collapse by rapid, differential rotation. MHD-driven angular momentum transport eventually leads to the collapse of the remnant's core, resulting in a black hole surrounded by a massive accretion torus. Here we present simulations of this process. The plausibility of generating short duration gamma ray bursts through this scenario is discussed.Comment: 3 pages. To appear in the Proceedings of the Eleventh Marcel Grossmann Meeting, Berlin, Germany, 23-29 July 2006, World Scientific, Singapore (2007

Workspace Optimization Techniques to Improve Prediction of Human Motion During Human-Robot Collaboration

Understanding human intentions is critical for safe and effective human-robot collaboration. While state of the art methods for human goal prediction utilize learned models to account for the uncertainty of human motion data, that data is inherently stochastic and high variance, hindering those models' utility for interactions requiring coordination, including safety-critical or close-proximity tasks. Our key insight is that robot teammates can deliberately configure shared workspaces prior to interaction in order to reduce the variance in human motion, realizing classifier-agnostic improvements in goal prediction. In this work, we present an algorithmic approach for a robot to arrange physical objects and project "virtual obstacles" using augmented reality in shared human-robot workspaces, optimizing for human legibility over a given set of tasks. We compare our approach against other workspace arrangement strategies using two human-subjects studies, one in a virtual 2D navigation domain and the other in a live tabletop manipulation domain involving a robotic manipulator arm. We evaluate the accuracy of human motion prediction models learned from each condition, demonstrating that our workspace optimization technique with virtual obstacles leads to higher robot prediction accuracy using less training data.Comment: International Conference on Human-Robot Interactio

Activity-aware Stress Sensory System

Continuous stress monitoring may able to help analyzing and enhance the awareness of an individual on their stress patterns and provide more reliable data information for physicians in interventions. In the past years research, studies on mental stress sensory system were limited inside laboratory environment. However, excluding the effects of physical activity can be impractical while developing a wearable stress sensory system for daily use. In this project, effects of external factors from environment on Galvanic Skin Response (GSR) measurements and integration of several stress sensory system were studied. Electrocardiogram (ECG), GSR, and Activity Recognition System (ARS) were studied under different physical activities: sitting, standing, lying and walking. It is showed from the studies that an overall accuracy of 94.7% in ARS is achieved by using two sensor node system (at thigh and ankle each) which an improvement of 27.3% from using single sensor node system. It is further demonstrated that ARS could help improve in accuracy of wearable stress sensory system

Relativistic Magnetohydrodynamics In Dynamical Spacetimes: Numerical Methods And Tests

Many problems at the forefront of theoretical astrophysics require the treatment of magnetized fluids in dynamical, strongly curved spacetimes. Such problems include the origin of gamma-ray bursts, magnetic braking of differential rotation in nascent neutron stars arising from stellar core collapse or binary neutron star merger, the formation of jets and magnetized disks around newborn black holes, etc. To model these phenomena, all of which involve both general relativity (GR) and magnetohydrodynamics (MHD), we have developed a GRMHD code capable of evolving MHD fluids in dynamical spacetimes. Our code solves the Einstein-Maxwell-MHD system of coupled equations in axisymmetry and in full 3+1 dimensions. We evolve the metric by integrating the BSSN equations, and use a conservative, shock-capturing scheme to evolve the MHD equations. Our code gives accurate results in standard MHD code-test problems, including magnetized shocks and magnetized Bondi flow. To test our code's ability to evolve the MHD equations in a dynamical spacetime, we study the perturbations of a homogeneous, magnetized fluid excited by a gravitational plane wave, and we find good agreement between the analytic and numerical solutions.Comment: 22 pages, 15 figures, accepted for publication in Phys. Rev.

Discovery of A New Retrograde Trans-Neptunian Object: Hint of A Common Orbital Plane for Low Semi-Major Axis, High Inclination TNOs and Centaurs

Although the majority of Centaurs are thought to have originated in the scattered disk, with the high-inclination members coming from the Oort cloud, the origin of the high inclination component of trans-Neptunian objects (TNOs) remains uncertain. We report the discovery of a retrograde TNO, which we nickname "Niku", detected by the Pan-STARRS 1 Outer Solar System Survey. Our numerical integrations show that the orbital dynamics of Niku are very similar to that of 2008 KV$_{42}$ (Drac), with a half-life of $\sim 500$ Myr. Comparing similar high inclination TNOs and Centaurs ($q > 10$ AU, $a 60^\circ$), we find that these objects exhibit a surprising clustering of ascending node, and occupy a common orbital plane. This orbital configuration has high statistical significance: 3.8-$\sigma$. An unknown mechanism is required to explain the observed clustering. This discovery may provide a pathway to investigate a possible reservoir of high-inclination objects.Comment: 18 pages, 4 figures, 1 table, accepted for publication in ApJ Letter