Index-Stratified Types

We present Tores, a core language for encoding metatheoretic proofs. The novel features we introduce are well-founded Mendler-style (co)recursion over indexed data types and a form of recursion over objects in the index language to build new types. The latter, which we call index-stratified types, are analogue to the concept of large elimination in dependently typed languages. These features combined allow us to encode sophisticated case studies such as normalization for lambda calculi and normalization by evaluation. We prove the soundness of Tores as a programming and proof language via the key theorems of subject reduction and termination

Efficient Grounding of Abstract Spatial Concepts for Natural Language Interaction with Robot Manipulators

Our goal is to develop models that allow a robot to understand natural language instructions in the context of its world representation. Contemporary models learn possible correspondences between parsed instructions and candidate groundings that include objects, regions and motion constraints. However, these models cannot reason about abstract concepts expressed in an instruction like, “pick up the middle block in the row of five blocks”. In this work, we introduce a probabilistic model that incorporates an expressive space of abstract spatial concepts as well as notions of cardinality and ordinality. The graph is structured according to the parse structure of language and introduces a factorisation over abstract concepts correlated with concrete constituents. Inference in the model is posed as an approximate search procedure that leverages partitioning of the joint in terms of concrete and abstract factors. The algorithm first estimates a set of probable concrete constituents that constrains the search procedure to a reduced space of abstract concepts, pruning away improbable portions of the exponentiallylarge search space. Empirical evaluation demonstrates accurate grounding of abstract concepts embedded in complex natural language instructions commanding a robot manipulator. The proposed inference method leads to significant efficiency gains compared to the baseline, with minimal trade-off in accuracy.United States. Army Research Laboratory. Robotics Consortium (Collaborative Technology Alliance Program)National Science Foundation (U.S.) (Grant No.1427547

The Effect of Turbulence Modeling on the Mixing Characteristics of Several Fuel Injectors at Hypervelocity Flow Conditions

CFD analysis is presented on the effects of turbulence modeling choices on the mixing characteristics and performance of three fuel injectors at hypervelocity flow conditions. The analyses were carried out with the VULCAN-CFD solver using Reynolds-Averaged Simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. The three injectors are the baseline configurations used in the experiments and represent three categories of injectors typically considered individually or in combination for fueling high-speed propulsive devices. The current work discusses the impact of the turbulence model and the turbulent Schmidt number on the mixing flow field behavior and the mixing performance as described by the one-dimensional values of the Mach number, total pressure recovery, and the mixing efficiency. Because planar laser induced fluorescence (PLIF) images are available from the EIMP experiments, the sensitivity of the synthetic LIF signal to turbulence modeling choices is also examined to determine whether PLIF can be extended beyond its intended qualitative visualization purpose and used to guide CFD turbulence model and parameter selections. It is found that the mixing performance, as quantified using mixing efficiency, exhibits a strong sensitivity to both turbulence model choice and turbulent Schmidt number value. However, the synthetic LIF signal only demonstrates a modest level of sensitivity, which suggests that PLIF is of limited use for guiding CFD turbulence model and parameter selections

Interstitial photothermal therapy generates durable treatment responses in neuroblastoma

Please click Download on the upper right corner to see the full descriptio

Pore Microstructure Impacts on Lithium Ion Transport and Rate Capability of Thick Sintered Electrodes

Increasing electrode thickness is one route to improve the energy density of lithium-ion battery cells. However, restricted Li+ transport in the electrolyte phase through the porous microstructure of thick electrodes limits the ability to achieve high current densities and rates of charge/discharge with these high energy cells. In this work, processing routes to mitigate transport restrictions were pursued. The electrodes used were comprised of only active material sintered together into a porous pellet. For one of the electrodes, comparisons were done between using ice-templating to provide directional porosity and using sacrificial particles during processing to match the geometric density without pore alignment. The ice-templated electrodes retained much greater discharge capacity at higher rates of cycling, which was attributed to improved transport properties provided by the processing. The electrodes were further characterized using an electrochemical model of the cells evaluated and neutron imaging of a cell containing the ice-templated pellet. The results indicate that significant improvements can be made to electrochemical cell properties via templating the electrode microstructure for situations where the rate limiting step includes ion transport limitations in the cell