Three levels of metric for evaluating wayfinding

Three levels of virtual environment (VE) metric are proposed, based on: (1) users’ task performance (time taken, distance traveled and number of errors made), (2) physical behavior (locomotion, looking around, and time and error classification), and (3) decision making (i.e., cognitive) rationale (think aloud, interview and questionnaire). Examples of the use of these metrics are drawn from a detailed review of research into VE wayfinding. A case study from research into the fidelity that is required for efficient VE wayfinding is presented, showing the unsuitability in some circumstances of common metrics of task performance such as time and distance, and the benefits to be gained by making fine-grained analyses of users’ behavior. Taken as a whole, the article highlights the range of techniques that have been successfully used to evaluate wayfinding and explains in detail how some of these techniques may be applied

Resilience of multi-photon entanglement under losses

We analyze the resilience under photon loss of the bi-partite entanglement present in multi-photon states produced by parametric down-conversion. The quantification of the entanglement is made possible by a symmetry of the states that persists even under polarization-independent losses. We examine the approach of the states to the set of states with a positive partial transpose as losses increase, and calculate the relative entropy of entanglement. We find that some bi-partite distillable entanglement persists for arbitrarily high losses.Comment: 5 pages, 3 figures, title changed, minor typographic errors correcte

Statistics of Lead Changes in Popularity-Driven Systems

We study statistical properties of the highest degree, or most popular, nodes in growing networks. We show that the number of lead changes increases logarithmically with network size N, independent of the details of the growth mechanism. The probability that the first node retains the lead approaches a finite constant for popularity-driven growth, and decays as N^{-phi}(ln N)^{-1/2}, with phi=0.08607..., for growth with no popularity bias.Comment: 4 pages, 4 figures, 2 column revtex format. Minor changes in response to referee comments. For publication in PR

Atherosusceptible Shear Stress Activates Endoplasmic Reticulum Stress to Promote Endothelial Inflammation.

Atherosclerosis impacts arteries where disturbed blood flow renders the endothelium susceptible to inflammation. Cytokine activation of endothelial cells (EC) upregulates VCAM-1 receptors that target monocyte recruitment to atherosusceptible regions. Endoplasmic reticulum (ER) stress elicits EC dysregulation in metabolic syndrome. We hypothesized that ER plays a central role in mechanosensing of atherosusceptible shear stress (SS) by signaling enhanced inflammation. Aortic EC were stimulated with low-dose TNFα (0.3 ng/ml) in a microfluidic channel that produced a linear SS gradient over a 20mm field ranging from 0-16 dynes/cm2. High-resolution imaging of immunofluorescence along the monolayer provided a continuous spatial metric of EC orientation, markers of ER stress, VCAM-1 and ICAM-1 expression, and monocyte recruitment. VCAM-1 peaked at 2 dynes/cm2 and decreased to below static TNFα-stimulated levels at atheroprotective-SS of 12 dynes/cm2, whereas ICAM-1 rose to a maximum in parallel with SS. ER expansion and activation of the unfolded protein response also peaked at 2 dynes/cm2, where IRF-1-regulated VCAM-1 expression and monocyte recruitment also rose to a maximum. Silencing of PECAM-1 or key ER stress genes abrogated SS regulation of VCAM-1 transcription and monocyte recruitment. We report a novel role for ER stress in mechanoregulation at arterial regions of atherosusceptible-SS inflamed by low-dose TNFα

Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs

Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable. In this paper, we apply deep neural networks to the nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We consider problems of estimating macroscopic quantities (e.g., the queue at an intersection) at a lane level. First-principles modeling at the lane scale has been a challenge due to complexities in modeling social behaviors like lane changes, and those behaviors' resultant macro-scale effects. Following domain knowledge that upstream/downstream lanes and neighboring lanes affect each others' traffic flows in distinct ways, we apply a form of neural attention that allows the neural network layers to aggregate information from different lanes in different manners. Using a microscopic traffic simulator as a testbed, we obtain results showing that an attentional neural network model can use information from nearby lanes to improve predictions, and, that explicitly encoding the lane-to-lane relationship types significantly improves performance. We also demonstrate the transfer of our learned neural network to a more complex road network, discuss how its performance degradation may be attributable to new traffic behaviors induced by increased topological complexity, and motivate learning dynamics models from many road network topologies.Comment: To appear at 2019 IEEE Conference on Intelligent Transportation System

Singular components of spectral measures for ergodic Jacobi matrices

For ergodic 1d Jacobi operators we prove that the random singular components of any spectral measure are almost surely mutually disjoint as long as one restricts to the set of positive Lyapunov exponent. In the context of extended Harper's equation this yields the first rigorous proof of the Thouless' formula for the Lyapunov exponent in the dual regions.Comment: to appear in the Journal of Mathematical Physics, vol 52 (2011

Lamb Shift in Muonic Hydrogen

The Lamb shift in muonic hydrogen continues to be a subject of experimental and theoretical investigation. Here my older work on the subject is updated to provide a complementary calculation of the energies of the 2p-2s transitions in muonic hydrogen.Comment: 15 pages, no figures. 2 small misprints corrected. Published in Phys. Rev.

Local rectification of heat flux

We present a chain-of-atoms model where heat is rectified, with different fluxes from the hot to the cold baths located at the chain boundaries when the temperature bias is reversed. The chain is homogeneous except for boundary effects and a local modification of the interactions at one site, the "impurity". The rectification mechanism is due here to the localized impurity, the only asymmetrical element of the structure, apart from the externally imposed temperature bias, and does not rely on putting in contact different materials or other known mechanisms such as grading or long-range interactions. The effect survives if all interaction forces are linear except the ones for the impurity.Comment: 5 pages, 5 figure