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Randall D. Kamien
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We propose and compare different strategies to construct dynamic density functional theories (DDFTs) for inhomogeneous polymer systems close to equilibrium from microscopic simulation trajectories. We focus on the systematic construction of the mobility coefficient, $Lambda(r,r)$, which relates the thermodynamic driving force on monomers at position $r$ to the motion of monomers at position $r$. A first approach based on the Green-Kubo formalism turns out to be impractical because of a severe plateau problem. Instead, we propose to extract the mobility coefficient from an effective characteristic relaxation time of the single chain dynamic structure factor. To test our approach, we study the kinetics of ordering and disordering in diblock copolymer melts. The DDFT results are in very good agreement with the data from corresponding fine-grained simulations.
In this work, I calculate the $p_perp$ resolved spectra for the three stages of the textit{bottom-up} scenario, which are comparable to the thermal contribution, particularly at higher values of the saturation scale $Q_S^2$. Analytical solutions are obtained by including a parametrization of scaling solutions from far-from-equilibrium classical statistical lattice simulations into a small angle kinetic rate. Furthermore, a theoretically motivated ansatz is used to account for near-collinear enhancement of the low-$p_perp$ radiation. The system is phenomenologically constrained using the charge hadron multiplicities from LHC and RHIC as in previous parametric estimates and fair agreement with the data available for photons was found. I find that for this realistic set of parameters, the contribution from the thermalizing glasma dominates the excess photons.
Extending chip performance beyond current limits of miniaturisation requires new materials and functionalities that integrate well with the silicon platform. Germanium fits these requirements and has been proposed as a high-mobility channel material,[1] a light emitting medium in silicon-integrated lasers,[2,3] and a plasmonic conductor for bio-sensing.[4,5] Common to these diverse applications is the need for homogeneous, high electron densities in three-dimensions (3D). Here we use a bottom-up approach to demonstrate the 3D assembly of atomically sharp doping profiles in germanium by a repeated stacking of two-dimensional (2D) high-density phosphorus layers. This produces high-density (10^19 to 10^20 cm-3) low-resistivity (10^-4 Ohmcm) metallic germanium of precisely defined thickness, beyond the capabilities of diffusion-based doping technologies.[6] We demonstrate that free electrons from distinct 2D dopant layers coalesce into a homogeneous 3D conductor using anisotropic quantum interference measurements, atom probe tomography, and density functional theory.
We study the flow of elongated grains (wooden pegs of length $L$=20 mm with circular cross section of diameter $d_c$=6 and 8 mm) from a silo with a rotating bottom and a circular orifice of diameter $D$. In the small orifice range ($D/d<5$) clogs are mostly broken by the rotating base, and the flow is intermittent with avalanches and temporary clogs. Here $dequiv(frac{3}{2}d_c^2L)^{1/3}$ is the effective grain diameter. Unlike for spherical grains, for rods the flow rate $W$ clearly deviates from the power law dependence $Wpropto (D-kd)^{2.5}$ at lower orifice sizes in the intermittent regime, where $W$ is measured in between temporary clogs only. Instead, below about $D/d<3$ an exponential dependence $Wpropto e^{kappa D}$ is detected. Here $k$ and $kappa$ are constants of order unity. Even more importantly, rotating the silo base leads to a strong -- more than 50% -- decrease of the flow rate, which otherwise does not depend significantly on the value of $omega$ in the continuous flow regime. In the intermittent regime, $W(omega)$ appears to follow a non-monotonic trend, although with considerable noise. A simple picture, in terms of the switching from funnel flow to mass flow and the alignment of the pegs due to rotation, is proposed to explain the observed difference between spherical and elongated grains. We also observe shear induced orientational ordering of the pegs at the bottom such that their long axes in average are oriented at a small angle $langlethetarangle approx 15^circ$ to the motion of the bottom.
Despite of the recent progress in agents that learn through interaction, there are several challenges in terms of sample efficiency and generalization across unseen behaviors during training. To mitigate these problems, we propose and apply a first-order Meta-Learning algorithm called Bottom-Up Meta-Policy Search (BUMPS), which works with two-phase optimization procedure: firstly, in a meta-training phase, it distills few expert policies to create a meta-policy capable of generalizing knowledge to unseen tasks during training; secondly, it applies a fast adaptation strategy named Policy Filtering, which evaluates few policies sampled from the meta-policy distribution and selects which best solves the task. We conducted all experiments in the RoboCup 3D Soccer Simulation domain, in the context of kick motion learning. We show that, given our experimental setup, BUMPS works in scenarios where simple multi-task Reinforcement Learning does not. Finally, we performed experiments in a way to evaluate each component of the algorithm.
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