Symmetric Exploration in Combinatorial Optimization is Free!

Recently, deep reinforcement learning (DRL) has shown promise in solving combinatorial optimization (CO) problems. However, they often require a large number of evaluations on the objective function, which can be time-consuming in real-world scenarios. To address this issue, we propose a "free" technique to enhance the performance of any deep reinforcement learning (DRL) solver by exploiting symmetry without requiring additional objective function evaluations. Our key idea is to augment the training of DRL-based combinatorial optimization solvers by reward-preserving transformations. The proposed algorithm is likely to be impactful since it is simple, easy to integrate with existing solvers, and applicable to a wide range of combinatorial optimization tasks. Extensive empirical evaluations on NP-hard routing optimization, scheduling optimization, and de novo molecular optimization confirm that our method effortlessly improves the sample efficiency of state-of-the-art DRL algorithms. Our source code is available at https://github.com/kaist-silab/sym-rd.Comment: 20 pages (including 9 pages of the appendix), 12 figure

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Energy Management Control Strategy for Saving Trip Costs of Fuel Cell/Battery Electric Vehicles

Fuel cell vehicles (FCVs) should control the energy management between two energy sources for fuel economy, using the stored energy in a battery or generation of energy through a fuel cell system. The fuel economy for an FCV includes trip costs for hydrogen consumption and the lifetime of two energy sources. This paper proposes an implementable energy management control strategy for an FCV to reduce trip costs. The concept of the proposed control strategy is first to analyze the allowable current of a fuel cell system from the optimal strategies for various initial battery state of charge (SOC) conditions using dynamic programming (DP), and second, to find a modulation ratio determining the current of a fuel cell system for driving a vehicle using the particle swarm optimization method. The control strategy presents the on/off moment of a fuel cell system and the proper modulation ratio of the turned-on fuel cell system with respect to the battery SOC and the power demand. The proposed strategy reduces trip costs in real-time, similar to the DP-based optimal strategy, and more than the simple energy control strategy of switching a fuel cell system on/off at the battery SOC boundary conditions even for long-term driving cycles

Dental Adhesion Enhancement On Zirconia Inspired By Mussel\u27S Priming Strategy Using Catechol

Zirconia has recently become one of the most popular dental materials in prosthodontics being used in crowns, bridges, and implants. However, weak bonding strength of dental adhesives and resins to zirconia surface has been a grand challenge in dentistry, thus finding a better adhesion to zirconia is urgently required. Marine sessile organisms such as mussels use a unique priming strategy to produce a strong bonding to wet mineral surfaces; one of the distinctive chemical features in the mussel\u27s adhesive primer proteins is high catechol contents among others. In this study, we pursued a bioinspired adhesion strategy, using a synthetic catechol primer applied to dental zirconia surfaces to study the effect of catecholic priming to shear bond strength. Catechol priming provided a statistically significant enhancement (p \u3c 0.05) in shear bond strength compared to the bonding strength without priming, and relatively stronger bonding than commercially available zirconia priming techniques. This new bioinspired dental priming approach can be an excellent addition to the practitioner\u27s toolkit to improve dental bonding to zirconia

Dental Adhesion Enhancement on Zirconia Inspired by Mussel’s Priming Strategy Using Catechol

Zirconia has recently become one of the most popular dental materials in prosthodontics being used in crowns, bridges, and implants. However, weak bonding strength of dental adhesives and resins to zirconia surface has been a grand challenge in dentistry, thus finding a better adhesion to zirconia is urgently required. Marine sessile organisms such as mussels use a unique priming strategy to produce a strong bonding to wet mineral surfaces; one of the distinctive chemical features in the mussel’s adhesive primer proteins is high catechol contents among others. In this study, we pursued a bioinspired adhesion strategy, using a synthetic catechol primer applied to dental zirconia surfaces to study the effect of catecholic priming to shear bond strength. Catechol priming provided a statistically significant enhancement (p < 0.05) in shear bond strength compared to the bonding strength without priming, and relatively stronger bonding than commercially available zirconia priming techniques. This new bioinspired dental priming approach can be an excellent addition to the practitioner’s toolkit to improve dental bonding to zirconia

Feature Sensitive Out-of-Core Chartification of Large Polygonal Meshes

Abstract. Mesh chartification is an important tool for processing meshes in various applications. In this paper, we present a novel feature sensitive mesh chartification technique that can handle huge meshes with limited main memory. Our technique adapts the mesh chartification approach using Lloyd-Max quantization to out-of-core processing. While the previous approach updates chartification globally at each iteration of Lloyd-Max quantization, we propose a local update algorithm where only a part of the chartification is processed at a time. By repeating the local updates, we can obtain a chartification of a huge mesh that cannot fit into the main memory. We verify the accuracy of the serialized local updates by comparing the results with the global update approach. We demonstrate that our technique can successfully process huge meshes for applications, such as mesh compression, shape approximation, and remeshing.

Displaced subdivision surfaces of animated meshes

This paper proposes a novel technique for converting a given animated mesh into a series of displaced subdivision surfaces. Instead of independently converting each mesh frame in the animated mesh, our technique produces displaced subdivision surfaces that share the same topology of the control mesh and a single displacement map. We first propose a conversion framework that enables sharing the same control mesh topology and a single displacement map among frames, and then present the details of the components in the framework. Each component is specifically designed to minimize the shape conversion errors that can be caused by enforcing a single displacement map. The resulting displaced subdivision surfaces have a compact representation, while reproducing the details of the original animated mesh. The representation can also be used for efficient rendering on modern graphics hardware that supports accelerated rendering of subdivision surfaces. (C) 2011 Elsevier Ltd. All rights reserved.X1143sciescopu