Polymer Translocation througha Pore in a Membrane

We construct a new statistical physical model of polymer translocation through a pore in a membrane treated as the diffusion process across a free energy barrier. We determine the translocation time in terms of chain flexibility yielding an entropic barrier, as well as in terms of the driving mechanisms such as transmembrane chemical potential difference and Brownian ratchets. It turns out that, while the chemical potential differences induce pronounced effects on translocation due to the long-chain nature of the polymer, the ratchets suppress this effect and chain flexibility.Comment: 4 pages, 5 figures, published in Phys. Rev. Lett. 77, 783(1996

Internal Stresses and Formation of Switchable Nanowires at Thin Silica Film Edge

At vertical edges, thin films of silicon oxide (SiO_{2-x}) contain semiconductive c-Si layered nanocrystals (Si NC) embedded in and supported by an insulating g-SiO2 matrix. Tour et al. have shown that a trenched thin film geometry enables the NC to form switchable nanowires (SNW) when trained by an applied field. The field required to form SNW decreases rapidly within a few cycles, or by annealing at 600 C in even fewer cycles, and is stable to 700C. Here we describe the intrinsic evolution of Si NC and SNW in terms of the competition between internal stresses and electro-osmosis. The analysis relies heavily on experimental data from a wide range of thin film studies, and it explains why a vertical edge across the planar Si-SiOx interface is necessary to form SNW. The discussion also shows that the formation mechanisms of Si NC and Si/SiO_{2-x} SNW are intrinsic and result from optimization of nanowire conductivity in the presence of residual host misfit stresses

Monte Carlo likelihood inference for missing data models

We describe a Monte Carlo method to approximate the maximum likelihood estimate (MLE), when there are missing data and the observed data likelihood is not available in closed form. This method uses simulated missing data that are independent and identically distributed and independent of the observed data. Our Monte Carlo approximation to the MLE is a consistent and asymptotically normal estimate of the minimizer $\theta^*$ of the Kullback--Leibler information, as both Monte Carlo and observed data sample sizes go to infinity simultaneously. Plug-in estimates of the asymptotic variance are provided for constructing confidence regions for $\theta^*$. We give Logit--Normal generalized linear mixed model examples, calculated using an R package.Comment: Published at http://dx.doi.org/10.1214/009053606000001389 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Learning to Represent Haptic Feedback for Partially-Observable Tasks

The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks.Comment: IEEE International Conference on Robotics and Automation (ICRA), 201

A decoupled recursive approach for constrained flexible multibody system dynamics

A variational-vector calculus approach is employed to derive a recursive formulation for dynamic analysis of flexible multibody systems. Kinematic relationships for adjacent flexible bodies are derived in a companion paper, using a state vector notation that represents translational and rotational components simultaneously. Cartesian generalized coordinates are assigned for all body and joint reference frames, to explicitly formulate deformation kinematics under small deformation kinematics and an efficient flexible dynamics recursive algorithm is developed. Dynamic analysis of a closed loop robot is performed to illustrate efficiency of the algorithm

Low Q^2 Weak Mixing Angle Measurements and Rare Higgs Decays

A weighted average weak mixing angle theta_W derived from relatively low Q^2 experiments is compared with the Standard Model prediction obtained from precision measurements. The approximate 1.8 sigma discrepancy is fit with an intermediate mass (~ 10-35 GeV) "dark" Z boson Z_d, corresponding to a U(1)_d gauge symmetry of hidden dark matter, which couples to our world via kinetic and Z-Z_d mass mixing. Constraints on such a scenario are obtained from precision electroweak bounds and searches for the rare Higgs decays H -> Z Z_d -> 4 charged leptons at the LHC. The sensitivity of future anticipated low Q^2 measurements of sin^2 theta_W(Q^2) to intermediate mass Z_d is also illustrated. This dark Z scenario can provide interesting concomitant signals in low energy parity violating measurements and rare Higgs decays at the LHC, over the next few years.Comment: Version to appear in PR