The Lord of the Ring Road: A Review and Evaluation of Autonomous Control Policies for Traffic in a Ring Road

This study focuses on the comprehensive investigation of stop-and-go waves appearing in closed-circuit ring road traffic wherein we evaluate various longitudinal dynamical models for vehicles. It is known that the behavior of human-driven vehicles, with other traffic elements such as density held constant, could stimulate stop-and-go waves, which do not dissipate on the circuit ring road. Stop-and-go waves can be dissipated by adding automated vehicles (AVs) to the ring. Thorough investigations of the performance of AV longitudinal control algorithms were carried out in Flow, which is an integrated platform for reinforcement learning on traffic control. Ten AV algorithms presented in the literature are evaluated. For each AV algorithm, experiments are carried out by varying distributions and penetration rates of AVs. Two different distributions of AVs are studied. For the first distribution scenario, AVs are placed consecutively. Penetration rates are varied from 1 AV (5%) to all AVs (100%). For the second distribution scenario, AVs are placed with even distribution of human-driven vehicles in between any two AVs. In this scenario, penetration rates are varied from 2 AVs (10%) to 11 AVs (50%). Multiple runs (10 runs) are simulated to average out the randomness in the results. From more than 3,000 simulation experiments, we investigated how AV algorithms perform differently with varying distributions and penetration rates while all AV algorithms remained fixed under all distributions and penetration rates. Time to stabilize, maximum headway, vehicle miles traveled, and fuel economy are used to evaluate their performance. Using these metrics, we find that the traffic condition improvement is not necessarily dependent on the distribution for most of the AV controllers, particularly when no cooperation among AVs is considered. Traffic condition is generally improved with a higher AV penetration rate with only one of the AV algorithms showing a contrary trend. Among all AV algorithms in this study, the reinforcement learning controller shows the most consistent improvement under all distributions and penetration rates.

Memory effects and static disorder reduce information in single-molecule signals

A key theoretical challenge posed by single-molecule studies is the inverse problem of deducing the underlying molecular dynamics from the time evolution of low-dimensional experimental observables. Toward this goal, a variety of low-dimensional models have been proposed as descriptions of single-molecule signals, including random walks with or without conformational memory and/or with static or dynamics disorder. Differentiating among different models presents a challenge, as many distinct physical scenarios lead to similar experimentally observable behaviors such as anomalous diffusion and nonexponential relaxation. Here we show that information-theory-based analysis of single-molecule time series, inspired by Shannon's work studying the information content of printed English, can differentiate between Markov (memoryless) and non-Markov single-molecule signals and between static and dynamic disorder. In particular, non-Markov time series are more predictable and thus can be compressed and transmitted within shorter messages (i.e. have a lower entropy rate) than appropriately constructed Markov approximations, and we demonstrate that in practice the LZMA compression algorithm reliably differentiates between these entropy rates across several simulated dynamical models.

Static-Dynamic Hybridity in Dynamical Models of Cognition

Dynamical models of cognition have played a central role in recent cognitive science. In this paper, we consider a common strategy by which dynamical models describe their target systems neither as purely static nor as purely dynamic, but rather using a hybrid approach. This hybridity reveals how dynamical models involve representational choices that are important for understanding the relationship between dynamical and non-dynamical representations of a system.

Large silicic magma bodies and very large magnitude explosive eruptions

Over the last 20 years, new concepts have emerged into understanding the processes that lead to build up to large silicic explosive eruptions based on integration of geophysical, geochemical, petrological, geochronological and dynamical modelling. Silicic melts are generated within magma systems extending throughout the crust by segregation from mushy zones. Segregated melt layers become unstable and can assemble into ephemeral upper crustal magma chambers rapidly prior to eruption. In the next 10 years, we can expect major advances in dynamical models as well as in analytical and geophysical methods, which need to be underpinned in field research.

If time had no beginning: growth dynamics for past-infinite causal sets

We explore whether the growth dynamics paradigm of Causal Set Theory is compatible with past-infinite causal sets. We modify the Classical Sequential Growth dynamics of Rideout and Sorkin to accommodate growth "into the past" and discuss what form physical constraints such as causality could take in this new framework. We propose convex-suborders as the "observables" or "physical properties" in a theory in which causal sets can be past-infinite and use this proposal to construct a manifestly covariant framework for dynamical models of growth for past-infinite causal sets.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

Prediction of tropical cyclones by numerical models A review

ABSTRACT. This paper contains a review of some past and recent developments in cyclone track prediction problem by dynamical models. The early attempts aimed at predicting tropical cyclone motion by using simple barotropic models based on vertically integrated vorticity tendency equation. Barotropic models are still used operationally in some centres due to their simplicity. However, current emphasis is on advanced primitive equation models incorporating physical processes, like cumulus convection, which are necessary to account for a major component of the cyclone movement. An important aspect of cyclone prediction by dynamical models is prescription of a correctly analysed synthetic vortex in the initial fields for running a forecast model. Several approaches developed by various groups for generating synthetic vortex are discussed. Examples of some cases of track prediction by limited area model in IMD and by global models are illustrated.

The Far-away Blues: Exploring the Furthest Extents of the Boötes I Ultra-faint Dwarf Galaxy

Establishing the spatial extents and the nature of the outer stellar populations of dwarf galaxies is necessary for the determination of their total masses, current dynamical states, and past evolution. We here describe our investigation of the outer stellar content of the Boötes I ultra-faint dwarf galaxy, a satellite of the the Milky Way. We identify candidate member blue horizontal branch and blue straggler stars of Boötes I, both tracers of the underlying ancient stellar population, using a combination of multiband Pan-STARRS photometry and Gaia astrometry. We find a total of twenty-four candidate blue horizontal branch member stars with apparent magnitudes and proper motions consistent with membership of Boötes I, nine of which reside at projected distances beyond the nominal King profile tidal radius derived from earlier fits to photometry. We also identify four blue straggler stars of appropriate apparent magnitude to be at the distance of Boötes I, but all four are too faint to have high-quality astrometry from Gaia. The outer blue horizontal branch stars that we have identified confirm that the spatial distribution of the stellar population of Boötes I is quite extended. The morphology on the sky of these outer envelope candidate member stars is evocative of tidal interactions, a possibility that we explore further with simple dynamical models.