On the direct evaluation of the equilibrium distribution of clusters by simulation. II

We clarify some of the subtle issues surrounding the observational cluster method, a simulation technique for studying nucleation. The validity of the method is reaffirmed here. The condition of the compact cluster limit is quantified and its implications are elucidated in terms of the correct enumeration of configuration space

On the direct evaluation of the equilibrium distribution of clusters by simulation

An expression is derived that relates the average population of a particular type of cluster in a metastable vapor phase of volume Vtot to the probability, estimated by simulation, of finding this cluster in a system of volume V taken inside Vtot, where V<<Vtot. Correct treatment of the translational free energy of the cluster is crucial for this purpose. We show that the problem reduces to one of devising the proper boundary condition for the simulation. We then verify the result obtained previously for a low vapor density limit [J. Chem. Phys. 108, 3416 (1998)]. The difficulty implicit in our recent calculation [J. Chem. Phys. 110, 5249 (1999)], in which the approach in the former was generalized to higher vapor densities, is shown to be resolved by a method already suggested in that paper

Phase separation in solutions with specific and nonspecific interactions.

Protein solutions, which tend to be thermodynamically stable under physiological conditions, can demix into protein-enriched and protein-depleted phases when stressed. Using a lattice-gas model of proteins with both isotropic and specific, directional interactions, we calculate the critical conditions for phase separation for model proteins with up to four patches via Monte Carlo simulations and statistical associating fluid theory. Given a fixed specific interaction strength, the critical value of the isotropic energy, which accounts for dispersion forces and nonspecific interactions, measures the stability of the solution with respect to nonspecific interactions. Phase separation is suppressed by the formation of protein complexes, which effectively passivate the strongly associating sites on the monomers. Nevertheless, we find that protein models with three or more patches can form extended aggregates that phase separate despite the assembly of passivated complexes, even in the absence of nonspecific interactions. We present a unified view of the critical behavior of model fluids with anisotropic interactions, and we discuss the implications of these results for the thermodynamic stability of protein solutions.Protein solutions, which tend to be thermodynamically stable under physiological conditions, can demix into protein-enriched and protein-depleted phases when stressed. Using a lattice-gas model of proteins with both isotropic and specific, directional interactions, we calculate the critical conditions for phase separation for model proteins with up to four patches via Monte Carlo simulations and statistical associating fluid theory. Given a fixed specific interaction strength, the critical value of the isotropic energy, which accounts for dispersion forces and nonspecific interactions, measures the stability of the solution with respect to nonspecific interactions. Phase separation is suppressed by the formation of protein complexes, which effectively passivate the strongly associating sites on the monomers. Nevertheless, we find that protein models with three or more patches can form extended aggregates that phase separate despite the assembly of passivated complexes, even in the absence of nonspecific interactions. We present a unified view of the critical behavior of model fluids with anisotropic interactions, and we discuss the implications of these results for the thermodynamic stability of protein solutions.This is the final published version, which can also be found on the publisher's website at: http://scitation.aip.org/content/aip/journal/jcp/140/20/10.1063/1.4878836 © 2014 AIP Publishing LL

Motional diminishing of optical activity: a novel method for studying molecular dynamics in liquids and plastic crystals

Molecular dynamics calculations and optical spectroscopy measurements of weakly active infrared modes are reported. The results are qualitatively understood in terms of the "motional diminishing" of IR lines, a process analogous to the motional narrowing of a nuclear magnetic resonance (NMR) signal. In molecular solids or liquids where the appropriate intramolecular resonances are observable, motional diminishing can be used to study the fluctuations of the intermolecular interactions having time scales of 1psec to 100psec.Comment: RevTeX in LaTeX file, 12 preprint pages, 4 ps figures included. Also available from http://insti.physics.sunysb.edu/~mmartin/pubs.html Accepted for publication in Chem. Phys. Let

Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways

Quantifying and encoding occupants' preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. This paper presents a low-complexity approach for lane-change initiation and planning to facilitate highly automated driving on freeways. Conditions under which human drivers find different manoeuvres desirable are learned from naturalistic driving data, eliminating the need for an engineered objective function and incorporation of expert knowledge in form of rules. Motion planning is formulated as a finite-horizon optimisation problem with safety constraints. It is shown that the decision model can replicate human drivers' discretionary lane-change decisions with up to 92% accuracy. Further proof of concept simulation of an overtaking manoeuvre is shown, whereby the actions of the simulated vehicle are logged while the dynamic environment evolves as per ground truth data recordings.Comment: 6 pages, 8 figures, The 2020 IEEE 92nd Vehicular Technology Conferenc

Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments using Robust MPC

Automated vehicles are increasingly getting mainstreamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, overtake, etc.) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway, motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: (i) it is free from nonconvex collision avoidance constraints, (ii) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion, and (iii) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for highspeed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment

Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models

Heterogeneity in Alzheimer's disease progression contributes to the ongoing failure to demonstrate efficacy of putative disease-modifying therapeutics that have been trialed over the past two decades. Any treatment effect present in a subgroup of trial participants (responders) can be diluted by non-responders who ideally should have been screened out of the trial. How to identify (screen-in) the most likely potential responders is an important question that is still without an answer. Here, we pilot a computational screening tool that leverages recent advances in data-driven disease progression modeling to improve stratification. This aims to increase the sensitivity to treatment effect by screening out non-responders, which will ultimately reduce the size, duration, and cost of a clinical trial. We demonstrate the concept of such a computational screening tool by retrospectively analyzing a completed double-blind clinical trial of donepezil in people with amnestic mild cognitive impairment (clinicaltrials.gov: NCT00000173), identifying a data-driven subgroup having more severe cognitive impairment who showed clearer treatment response than observed for the full cohort

Trajectory planning for autonomous high-speed overtaking in structured environments using robust MPC

Automated vehicles are increasingly getting main-streamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, and overtake) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway and motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: 1) it is free from non-convex collision avoidance constraints; 2) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion; and 3) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for high-speed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment

Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data

Monitoring drivers' mental workload facilitates initiating and maintaining safe interactions with in-vehicle information systems, and thus delivers adaptive human machine interaction with reduced impact on the primary task of driving. In this paper, we tackle the problem of workload estimation from driving performance data. First, we present a novel on-road study for collecting subjective workload data via a modified peripheral detection task in naturalistic settings. Key environmental factors that induce a high mental workload are identified via video analysis, e.g. junctions and behaviour of vehicle in front. Second, a supervised learning framework using state-of-the-art time series classifiers (e.g. convolutional neural network and transform techniques) is introduced to profile drivers based on the average workload they experience during a journey. A Bayesian filtering approach is then proposed for sequentially estimating, in (near) real-time, the driver's instantaneous workload. This computationally efficient and flexible method can be easily personalised to a driver (e.g. incorporate their inferred average workload profile), adapted to driving/environmental contexts (e.g. road type) and extended with data streams from new sources. The efficacy of the presented profiling and instantaneous workload estimation approaches are demonstrated using the on-road study data, showing $F_{1}$ scores of up to 92% and 81%, respectively.Comment: Accepted for IEEE Transactions on Intelligent Vehicle

Determinacy and indeterminacy of games played on complete metric spaces

Schmidt's game is a powerful tool for studying properties of certain sets which arise in Diophantine approximation theory, number theory, and dynamics. Recently, many new results have been proven using this game. In this paper we address determinacy and indeterminacy questions regarding Schmidt's game and its variations, as well as more general games played on complete metric spaces (e.g. fractals). We show that except for certain exceptional cases, these games are undetermined on Bernstein sets