U.S. Coast Guard Boat Recovery Simulation at NASA Ames Vertical Motion Simulator

The Boat Recovery Simulation was a collaboration between the U.S. Coast Guard and NASA. The experiment was conducted at the NASA Ames Vertical Motion Simulator (VMS). The goals were to (1) design a VMS experiment that can accurately simulate the motion of high sea conditions and to (2) collect data for the U.S. Coast Guard on human performance related to small boat recovery operations. The experiment setup included a software operation model designed around empirical boat position data; a replica boat section manufactured to incorporate real-world task elements; and the means to collect objective and subjective data from human participants. The VMS provided a viable testbed to assess certified U.S. Coast Guard crewmembers task performance while in motion

Query Resolution for Conversational Search with Limited Supervision

In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is often underspecified due to zero anaphora, topic change, or topic return. Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution. In this paper, we model the query resolution task as a binary term classification problem: for each term appearing in the previous turns of the conversation decide whether to add it to the current turn query or not. We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We propose a distant supervision method to automatically generate training data by using query-passage relevance labels. Such labels are often readily available in a collection either as human annotations or inferred from user interactions. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape

The RootScope: A Simple High-Throughput Screening System For Quantitating Gene Expression Dynamics In Plant Roots

Background: High temperature stress responses are vital for plant survival. The mechanisms that plants use to sense high temperatures are only partially understood and involve multiple sensing and signaling pathways. Here we describe the development of the RootScope, an automated microscopy system for quantitating heat shock responses in plant roots.Results: The promoter of Hsp17.6 was used to build a Hsp17.6(p):GFP transcriptional reporter that is induced by heat shock in Arabidopsis. An automated fluorescence microscopy system which enables multiple roots to be imaged in rapid succession was used to quantitate Hsp17.6p: GFP response dynamics. Hsp17.6(p):GFP signal increased with temperature increases from 28 degrees C to 37 degrees C. At 40 degrees C the kinetics and localization of the response are markedly different from those at 37 degrees C. This suggests that different mechanisms mediate heat shock responses above and below 37 degrees C. Finally, we demonstrate that Hsp17.6(p):GFP expression exhibits wave like dynamics in growing roots.Conclusions: The RootScope system is a simple and powerful platform for investigating the heat shock response in plants

Why surplus structure is not superfluous

The idea that gauge theory has `surplus' structure poses a puzzle: in one much discussed sense, this structure is redundant; but on the other hand, it is also widely held to play an essential role in the theory. In this paper, we employ category-theoretic tools to illuminate an aspect of this puzzle. We precisify what is meant by `surplus' structure by means of functorial comparisons with equivalence classes of gauge fields, and then show that such structure is essential for any theory that represents a rich collection of physically relevant fields which are `local' in nature

IMPROVING INTERACTION BETWEEN TECHNICAL AND CONTRACTING PERSONNEL AT NAVAL SURFACE WARFARE CENTER, PHILADELPHIA DIVISION

Organizational policy changes in contracting have created a competency gap in the pre-award phase contracting activities for acquisition professionals working in technical departments seeking procurement of products or services. Considering current contracting policies and perceived knowledge gaps in the technical community with respect to contract development, the goal of this research is to examine the organizational structure and policies of Naval Surface Warfare Center, Philadelphia Division (NSWCPD) to ensure maximum efficacy of the contracting process. The primary objective of this research is to determine what organizational policies, capability gaps, or other challenges hinder communication and collaboration between the technical and contracting departments at NSWCPD. A secondary objective of this research is to determine what initiatives can be taken at NSWCPD to improve interaction between these two working groups and to document a comprehensive strategic plan for implementation. The research team found several areas worth addressing to improve the efficacy of contract artifact development at NSWCPD. Recommendations are provided to address the communication gap, including establishing an integrated product team to improve the quality of contracting artifacts and networking amongst those teams through working groups, and to capitalize on modern technologies to ensure the production of contracting artifacts are up to speed with current technology.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages

nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms. Specifically, the package allows users to code models in the BUGS language, and it allows users to write algorithms that can be applied to any appropriate model. In this paper, we introduce the nimbleSMC R package. nimbleSMC contains algorithms for state-space model analysis using sequential Monte Carlo (SMC) techniques that are built using nimble. We first provide an overview of state-space models and commonly-used SMC algorithms. We then describe how to build a state-space model in nimble and conduct inference using existing SMC algorithms within nimbleSMC. SMC algorithms within nimbleSMC currently include the bootstrap filter, auxiliary particle filter, ensemble Kalman filter, IF2 method of iterated filtering, and a particle Markov chain Monte Carlo (MCMC) sampler. These algorithms can be run in R or compiled into C++ for more efficient execution. Examples of applying SMC algorithms to linear autoregressive models and a stochastic volatility model are provided. Finally, we give an overview of how model-generic algorithms are coded within nimble by providing code for a simple SMC algorithm. This illustrates how users can easily extend nimble's SMC methods in high-level code