REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes

One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales. To overcome this bottleneck, we present the REinforcement learning based Adaptive samPling (REAP) algorithm that aims to efficiently sample conformational space by learning the relative importance of each reaction coordinate as it samples the landscape. To achieve this, the algorithm uses concepts from the field of reinforcement learning, a subset of machine learning, which rewards sampling along important degrees of freedom and disregards others that do not facilitate exploration or exploitation. We demonstrate the effectiveness of REAP by comparing the sampling to long continuous MD simulations and least-counts adaptive sampling on two model landscapes (L-shaped and circular), and realistic systems such as alanine dipeptide and Src kinase. In all four systems, the REAP algorithm consistently demonstrates its ability to explore conformational space faster than the other two methods when comparing the expected values of the landscape discovered for a given amount of time. The key advantage of REAP is on-the-fly estimation of the importance of collective variables, which makes it particularly useful for systems with limited structural information

Competition and Oligopoly: A Case of UK Grocery Retailing

In this paper we develop a model of Bertrand price competition with uncertainty as to the number of bidders. The auction models predict retail price dispersion as an observable feature of price discrimination. The implications of the auction models are tested using a logit model on primary data. Some simulations of the logit model further enrich and capture critical states of chain-store rivalry. The findings show that consumer characteristics define type of store choice and that an auction model of price competition with uncertainty is an appropriate way to model retail grocery competition.

Matching Preclusion and Conditional Matching Preclusion Problems for Twisted Cubes

The matching preclusion number of a graph is the minimum number of edges whose deletion results in a graph that has neither perfect matchings nor almost-perfect matchings. For many interconnection networks, the optimal sets are precisely those induced by a single vertex. Recently, the conditional matching preclusion number of a graph was introduced to look for obstruction sets beyond those induced by a single vertex. It is defined to be the minimum number of edges whose deletion results in a graph with no isolated vertices that has neither perfect matchings nor almost-perfect matchings. In this paper, we find the matching preclusion number and the conditional matching preclusion number for twisted cubes, an improved version of the well-known hypercube. Moreover, we also classify all the optimal matching preclusion sets

Learning Hard Alignments with Variational Inference

There has recently been significant interest in hard attention models for tasks such as object recognition, visual captioning and speech recognition. Hard attention can offer benefits over soft attention such as decreased computational cost, but training hard attention models can be difficult because of the discrete latent variables they introduce. Previous work used REINFORCE and Q-learning to approach these issues, but those methods can provide high-variance gradient estimates and be slow to train. In this paper, we tackle the problem of learning hard attention for a sequential task using variational inference methods, specifically the recently introduced VIMCO and NVIL. Furthermore, we propose a novel baseline that adapts VIMCO to this setting. We demonstrate our method on a phoneme recognition task in clean and noisy environments and show that our method outperforms REINFORCE, with the difference being greater for a more complicated task

SI Engine Control in the Cold-Fast-Idle Period for Low HC Emissions and Fast Catalyst Light Off

The engine and its exhaust flow behaviors are investigated in a turbo-charged gasoline direct injection engine under simulated cold-fast-idle condition. The metrics of interest are the exhaust sensible and chemical enthalpy flows, and the exhaust temperature, all of which affect catalyst light off time. The exhaust sensible enthalpy flow is mainly a function of combustion phasing; the exhaust chemical enthalpy flow is mainly a function of equivalence ratio. High sensible and chemical enthalpy flow with acceptable engine stability could be obtained with retarded combustion and enrichment. When split injection is employed with one early and one later and smaller fuel pulse, combustion retards with early secondary injection in the compression stroke but advances with late secondary injection. Comparing gasoline to E85, the latter produces a lower exhaust temperature because of charge cooling effect and because of a faster combustion.Borg-Warner CorporationChrysler CorporationFord Motor CompanyGeneral Motors Corporatio