KMOS Data Flow: Reconstructing Data Cubes in One Step

KMOS is a multi-object near-infrared integral field spectrometer with 24 deployable pick-off arms. Data processing is inevitably complex. We discuss specific issues and requirements that must be addressed in the data reduction pipeline, the calibration, the raw and processed data formats, and the simulated data. We discuss the pipeline architecture. We focus on its modular style and show how these modules can be used to build a classical pipeline, as well as a more advanced pipeline that can account for both spectral and spatial flexure as well as variations in the OH background. A novel aspect of the pipeline is that the raw data can be reconstructed into a cube in a single step. We discuss the advantages of this and outline the way in which we have implemented it. We finish by describing how the QFitsView tool can now be used to visualise KMOS data.Comment: Contribution to "Ground-based and Airborne Instrumentation for Astronomy III', SPIE 7735-254 (June 2010). High resolution version can be found at http://spiedl.or

Learning with Opponent-Learning Awareness

Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be extended to hierarchical RL, generative adversarial networks and decentralised optimisation. In all these settings the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable training or undesired final results. We present Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in the environment. The LOLA learning rule includes a term that accounts for the impact of one agent's policy on the anticipated parameter update of the other agents. Results show that the encounter of two LOLA agents leads to the emergence of tit-for-tat and therefore cooperation in the iterated prisoners' dilemma, while independent learning does not. In this domain, LOLA also receives higher payouts compared to a naive learner, and is robust against exploitation by higher order gradient-based methods. Applied to repeated matching pennies, LOLA agents converge to the Nash equilibrium. In a round robin tournament we show that LOLA agents successfully shape the learning of a range of multi-agent learning algorithms from literature, resulting in the highest average returns on the IPD. We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL. The method thus scales to large parameter and input spaces and nonlinear function approximators. We apply LOLA to a grid world task with an embedded social dilemma using recurrent policies and opponent modelling. By explicitly considering the learning of the other agent, LOLA agents learn to cooperate out of self-interest. The code is at github.com/alshedivat/lola

Development of 3D cellular silicone structures using reactive inkjet printing approach for energy absorbing application

Silicone cellular foams are well suited for energy absorbing applications due to their ability to undertake large deformations and absorb significant quantities of energy. However traditional methods for fabrication of cellular silicone are long and difficult and with no possibility of varying the density of pores. Having a fabrication method that allows controlling the structure hence mechanical properties of the silicone features is essential for expanding their application. This work investigates a method based on reactive inkjet printing approach to produce 3D silicone structures of which mechanical properties can be tailored by varying the process parameters and structure’s design. Printing parameters such as pressure, temperature, and pulse shape were investigated to optimize the process for SE1700 silicone material. The vinyl terminated part of SE1700 silicone with the addition of different solvents (vinyl terminated polydimethylsiloxane, silicone oil 10cP and 100cP) were evaluated for printability using rheology. The mechanical properties of printed films were assessed using dynamic mechanical analysis and tensile testing. The TGA and swelling study were performed to understand the change in sample’s properties in relation to different formulations. Silicone structures with different porosities were printed and the storage modulus, loss modulus and damping properties were investigated. The results showed that despite the high viscosity of silicone fluids, it is possible to employ reactive inkjet printing approach in order to obtain silicone features. It was also demonstrated that the capability to alter mechanical properties of printed silicone structures could be achieved using different process parameters and also

The Pathology of War Plans: The Lessons of 1914

The University Archives has determined that this item is of continuing value to OSU's history.The major European powers, Austria-Hungary , Britain , France , Germany , Italy , and Russia , developed war plans in the years prior to the August 1914 outbreak. These plans, all of them, proved to be seriously flawed. Six experts will present their analyses of the planning processes and the pathologies involved.Ohio State University. Mershon Center for International Security StudiesEvent webpage, streaming audio, phot

Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning

A fundamental question in any peer-to-peer ridesharing system is how to, both effectively and efficiently, dispatch user's ride requests to the right driver in real time. Traditional rule-based solutions usually work on a simplified problem setting, which requires a sophisticated hand-crafted weight design for either centralized authority control or decentralized multi-agent scheduling systems. Although recent approaches have used reinforcement learning to provide centralized combinatorial optimization algorithms with informative weight values, their single-agent setting can hardly model the complex interactions between drivers and orders. In this paper, we address the order dispatching problem using multi-agent reinforcement learning (MARL), which follows the distributed nature of the peer-to-peer ridesharing problem and possesses the ability to capture the stochastic demand-supply dynamics in large-scale ridesharing scenarios. Being more reliable than centralized approaches, our proposed MARL solutions could also support fully distributed execution through recent advances in the Internet of Vehicles (IoV) and the Vehicle-to-Network (V2N). Furthermore, we adopt the mean field approximation to simplify the local interactions by taking an average action among neighborhoods. The mean field approximation is capable of globally capturing dynamic demand-supply variations by propagating many local interactions between agents and the environment. Our extensive experiments have shown the significant improvements of MARL order dispatching algorithms over several strong baselines on the gross merchandise volume (GMV), and order response rate measures. Besides, the simulated experiments with real data have also justified that our solution can alleviate the supply-demand gap during the rush hours, thus possessing the capability of reducing traffic congestion.Comment: 11 pages, 9 figure

Brief Report: Excitatory and Inhibitory Brain Metabolites as Targets of Motor Cortex Transcranial Direct Current Stimulation Therapy and Predictors of Its Efficacy in Fibromyalgia

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110535/1/art38945.pd

Multimodal MRI as a diagnostic biomarker for amyotrophic lateral sclerosis

Objective Reliable biomarkers for amyotrophic lateral sclerosis ( ALS ) are needed, given the clinical heterogeneity of the disease. Here, we provide proof‐of‐concept for using multimodal magnetic resonance imaging ( MRI ) as a diagnostic biomarker for ALS . Specifically, we evaluated the added diagnostic utility of proton magnetic resonance spectroscopy ( MRS ) to diffusion tensor imaging ( DTI ). Methods Twenty‐nine patients with ALS and 30 age‐ and gender‐matched healthy controls underwent brain MRI which used proton MRS including spectral editing techniques to measure γ‐aminobutyric acid ( GABA ) and DTI to measure fractional anisotropy of the corticospinal tract. Data were analyzed using logistic regression, t ‐tests, and generalized linear models with leave‐one‐out analysis to generate and compare the resulting receiver operating characteristic ( ROC ) curves. Results The diagnostic accuracy is significantly improved when the MRS data were combined with the DTI data as compared to the DTI data only (area under the ROC curves ( AUC ) = 0.93 vs. AUC  = 0.81; P  = 0.05). The combined MRS and DTI data resulted in sensitivity of 0.93, specificity of 0.85, positive likelihood ratio of 6.20, and negative likelihood ratio of 0.08 whereas the DTI data only resulted in sensitivity of 0.86, specificity of 0.70, positive likelihood ratio of 2.87, and negative likelihood ratio of 0.20. Interpretation Combining multiple advanced neuroimaging modalities significantly improves disease discrimination between ALS patients and healthy controls. These results provide an important step toward advancing a multimodal MRI approach along the diagnostic test development pathway for ALS.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106065/1/acn330.pd

A jump-growth model for predator-prey dynamics: derivation and application to marine ecosystems

This paper investigates the dynamics of biomass in a marine ecosystem. A stochastic process is defined in which organisms undergo jumps in body size as they catch and eat smaller organisms. Using a systematic expansion of the master equation, we derive a deterministic equation for the macroscopic dynamics, which we call the deterministic jump-growth equation, and a linear Fokker-Planck equation for the stochastic fluctuations. The McKendrick--von Foerster equation, used in previous studies, is shown to be a first-order approximation, appropriate in equilibrium systems where predators are much larger than their prey. The model has a power-law steady state consistent with the approximate constancy of mass density in logarithmic intervals of body mass often observed in marine ecosystems. The behaviours of the stochastic process, the deterministic jump-growth equation and the McKendrick--von Foerster equation are compared using numerical methods. The numerical analysis shows two classes of attractors: steady states and travelling waves.Comment: 27 pages, 4 figures. Final version as published. Only minor change