MODELING STRATEGIC INTERACTIONS IN LAND-USE DECISION MODELS

Replaced with revised version of paper 11/18/02.Land Economics/Use,

Effects of Dredge Material Placement on Macroinvertebrate Communities: Phase 1

ID: 8809; issued October 1, 1998INHS Technical Report prepared for Rock Island District, US Army Corps of Engineer

Mental state estimation for brain-computer interfaces

Mental state estimation is potentially useful for the development of asynchronous brain-computer interfaces. In this study, four mental states have been identified and decoded from the electrocorticograms (ECoGs) of six epileptic patients, engaged in a memory reach task. A novel signal analysis technique has been applied to high-dimensional, statistically sparse ECoGs recorded by a large number of electrodes. The strength of the proposed technique lies in its ability to jointly extract spatial and temporal patterns, responsible for encoding mental state differences. As such, the technique offers a systematic way of analyzing the spatiotemporal aspects of brain information processing and may be applicable to a wide range of spatiotemporal neurophysiological signals

Defending America's Food Supply Against Terrorism: Who is Responsible? Who Should Pay?

Food Consumption/Nutrition/Food Safety, Q18, H44, H56,

How Should America's Anti-Terrorism Budget Be Allocated? Findings from a National Survey of Attitudes of U.S. Residents about Terrorism

U.S. residents are very concerned about future terrorist attacks and they are willing to commit substantial sums to prevent further terrorist acts. Protecting against another 9/11 style incident is important, but U.S. residents are more concerned about protecting the food supply system and preventing release of chemical or biological agents in public areas. On average respondents would allocate 13.3 percent more to protect the food supply chain and 12.0 percent more to protect against release of a toxic chemical or biological agent than they would to protect against another terrorist attack using hijacked aircraft. Approximately $5 billion is currently spent to protect civil aviation. The 2006 budget provided$8.6 billion of fiscal authority for programs protecting against all types of catastrophic terrorist incidents, including protection against radiological or nuclear incidents, as well as protecting the food supply and preventing chemical or biological attacks. No one would argue that decisions on the size and internal allocation of the nation's homeland security budget should be made on the basis of a public opinion survey, but this survey indicates that Americans would likely support additional spending to defend the food system and protect against release of a chemical or biological agent.Political Economy,

DCU System Report on the WMT 2017 Multi-modal Machine Translation Task

We report experiments with multi-modal neural machine translation models that incorporate global visual features in different parts of the encoder and decoder, and use the VGG19 network to extract features for all images. In our experiments, we explore both different strategies to include global image features and also how ensembling different models at inference time impact translations. Our submissions ranked 3rd best for translating from English into French, always improving considerably over an neural machine translation baseline across all language pair evaluated, e.g. an increase of 7.0–9.2 METEOR points

Multimodal neural machine translation for low-resource language pairs using synthetic data

In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a lowresource language pair, Hindi and English, using synthetic data. A threeway parallel corpus which contains bilingual texts and corresponding images is required to train a MNMT system with image features. However, such a corpus is not available for low resource language pairs. To address this, we developed both a synthetic training dataset and a manually curated development/test dataset for Hindi based on an existing English-image parallel corpus. We used these datasets to build our image description translation system by adopting state-of-theart MNMT models. Our results show that it is possible to train a MNMT system for low-resource language pairs through the use of synthetic data and that such a system can benefit from image features