Control and Evaluation Methods for Multi-Mode Steering

A self-propelled agricultural sprayer was modified to enable both front and rear wheel steering through electrohydraulic control valves. These modifications, in conjunction with a digital controller, enabled the vehicle to be four-wheel steered in multiple modes. The research focused on modeling and evaluating the effect of multi-mode four-wheel steering on vehicle handling characteristics and vehicle performance of the sprayer. The multi-mode steering system was evaluated by driving the sprayer through specified paths in the different steering modes. The position and heading of the vehicle were measured for each mode using two dual frequency DGPS receivers. From the measure of vehicle posture, sprayer performance measures such as over/underspray and crop damage were assessed for each steering mode. Preliminary results show that drivers were able to take advantage of added maneuverability in headland turning procedures. Crab steering reduced the amount of area sprayed in error during lateral course adjustments. The steering and vehicle models yielded similar responses to steering inputs as experimental responses

Stories for Change

This compendium of nearly 50 best practices showcases the notable strategies that increase access to arts and culture for older adult and immigrant populations. Newcomers and older adults (65 +) are two of the fastest growing populations -- communities across the country are grappling with a demographic makeup that is increasingly diverse and proportionally older than in the past. Arts and cultural organizations have the opportunity to reach-out, to increase resources in the community, and to engage populations that are at risk for being overlooked."Stories for Change" is a compelling collection, brimming with new ideas brought to fruition by many types of organizations including: museums, libraries, community development organizations, theaters, orchestras, dance ensembles, area agencies on aging, transportation bureaus, parks, botanic gardens, universities, and more. Organizations that hope to enhance the lives of their older and immigrant residents can find approaches portrayed in these Stories that can be adapted to meet the needs of their communities.Best practices include the well-known Alzheimer's Project of the Museum of Modern Art, which has been adapted to museums around the country, and Circle of Care, a unique ride share program that partners young people with older adults to attend free arts performances in Boulder, Colorado. Stories are located in rural, mid-size, and metropolitan settings; many can be easily implemented, and do not require a major overhaul of staffing, operations, or an organization's mission

Policies and practices in supporting scientists’ public communication through training

Scientists are increasingly expected to engage in public communication, though they frequently report that they feel inadequately prepared for such activity. The necessary training for such activity has barely been discussed in the science communication literature. Drawing on country reports from the Monitoring Policy and Research Activities on Science in Society in Europe (MASIS) report, this paper reviews initiatives across Europe to support scientists’ public communication. It examines these within a framework that distinguishes between training oriented to dissemination or dialogue, and to capacity-building of scientists or professionalisation of science communicators. It traces the uneven spread and diverse character of such supports and identifies the four principal groups of policy actors who play distinct roles and, in the case of higher education institutions, sometimes internally contradictory roles. The paper draws on the authors’ own experiences to underline the value of communication training that is oriented to dialogue and stimulates reflexivity

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

This paper investigates using machine learning to rapidly develop empirical models suitable for system-level aircraft noise studies. In particular, machine learning is used to train a neural network to predict the noise spectra produced by a round jet near a surface over a range of surface lengths, surface standoff distances, jet Mach numbers, and observer angles. These spectra include two sources, jet-mixing noise and jet-surface interaction (JSI) noise, with different scale factors as well as surface shielding and reflection effects to create a multi- dimensional problem. A second model is then trained using data from three rectangular nozzles to include nozzle aspect ratio in the spectral prediction. The training and validation data are from an extensive jet-surface interaction noise database acquired at the NASA Glenn Research Center's Aero-Acoustic Propulsion Laboratory. Although the number of training and validation points is small compared a typical machine learning application, the results of this investigation show that this approach is viable if the underlying data are well behaved

Rotational spectrum of cis–cis HOONO

The pure rotational spectrum of cis-cis peroxynitrous acid, HOONO, has been observed. Over 220 transitions, sampling states up to J(')=67 and K-a(')=31, have been fitted with an rms uncertainty of 48.4 kHz. The experimentally determined rotational constants agree well with ab initio values for the cis-cis conformer, a five-membered ring formed by intramolecular hydrogen bonding. The small, positive inertial defect Delta=0.075667(60) amu A(2) and lack of any observable torsional splittings in the spectrum indicate that cis-cis HOONO exists in a well-defined planar structure at room temperature

Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge

Distributional models provide a convenient way to model semantics using dense embedding spaces derived from unsupervised learning algorithms. However, the dimensions of dense embedding spaces are not designed to resemble human semantic knowledge. Moreover, embeddings are often built from a single source of information (typically text data), even though neurocognitive research suggests that semantics is deeply linked to both language and perception. In this paper, we combine multimodal information from both text and image-based representations derived from state-of-the-art distributional models to produce sparse, interpretable vectors using Joint Non-Negative Sparse Embedding. Through in-depth analyses comparing these sparse models to human-derived behavioural and neuroimaging data, we demonstrate their ability to predict interpretable linguistic descriptions of human ground-truth semantic knowledge.Comment: Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 260-270. Brussels, Belgium, October 31 - November 1, 2018. Association for Computational Linguistic