6,555 research outputs found
Brief Announcement: Integrating Temporal Information to Spatial Information in a Neural Circuit
In this paper, we consider networks of deterministic spiking neurons, firing synchronously at discrete times. We consider the problem of translating temporal information into spatial information in such networks, an important task that is carried out by actual brains. Specifically, we define two problems: "First Consecutive Spikes Counting" and "Total Spikes Counting", which model temporal-coding and rate-coding aspects of temporal-to-spatial translation respectively. Assuming an upper bound of T on the length of the temporal input signal, we design two networks that solve two problems, each using O(log T) neurons and terminating in time T+1. We also prove that these bounds are tight
Current Trends in Child Abuse Prevention and Fatalities: The 2000 Fifty State Survey
This survey takes a closer look at two pressing needs. The first is the important role of prevention, specifically at the most effective prevention programs and how they are funded. The second need, based on feedback from PCA America Chapters and other prevention experts, is to better understand child fatalities and the kind of prevention strategies that can best reduce fatalities. This survey differs from previous surveys, as it did not attempt to gather data on child abuse and neglect reports or substantiations. Instead, the National Center on Child Abuse Prevention Research, the research arm of PCA America, is working with the National Child Abuse and Neglect Data System (NCANDS) and the Centers for Disease Control and Prevention (CDC) to ensure that our nation has the best systems available for gathering and tracking child maltreatment incidences. This report's highlights and findings are based on responses from 50 states and the District of Columbia, although all states have not responded to all questions. The results are reported in two main sections: Child Abuse and Neglect Prevention, and Child Maltreatment Fatalities. At the beginning of each are highlights followed by the complete findings for that section. Estimating procedures for child maltreatment fatalities should be used when interpreting the results for child maltreatment fatalities. In addition, throughout the document are references and links to sites containing additional information on the topics cited
AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video
Developing useful interfaces between brains and machines is a grand challenge
of neuroengineering. An effective interface has the capacity to not only
interpret neural signals, but predict the intentions of the human to perform an
action in the near future; prediction is made even more challenging outside
well-controlled laboratory experiments. This paper describes our approach to
detect and to predict natural human arm movements in the future, a key
challenge in brain computer interfacing that has never before been attempted.
We introduce the novel Annotated Joints in Long-term ECoG (AJILE) dataset;
AJILE includes automatically annotated poses of 7 upper body joints for four
human subjects over 670 total hours (more than 72 million frames), along with
the corresponding simultaneously acquired intracranial neural recordings. The
size and scope of AJILE greatly exceeds all previous datasets with movements
and electrocorticography (ECoG), making it possible to take a deep learning
approach to movement prediction. We propose a multimodal model that combines
deep convolutional neural networks (CNN) with long short-term memory (LSTM)
blocks, leveraging both ECoG and video modalities. We demonstrate that our
models are able to detect movements and predict future movements up to 800 msec
before movement initiation. Further, our multimodal movement prediction models
exhibit resilience to simulated ablation of input neural signals. We believe a
multimodal approach to natural neural decoding that takes context into account
is critical in advancing bioelectronic technologies and human neuroscience
Missing in action: framing race on prime-time television
This study analyzes the racial ideologies surrounding Asian/Pacific Islander Americans (APIAs) in prime-time television
Asian Americans and Pacific Islanders on TV
Representation in media matters–so why is prime time still so white
INTERDEPENDENCE OF AGRICULTURE AND TOURISM: QUANTIFYING THE VALUE OF THE AGRICULTURAL WORKING LANDSCAPE IN VERMONT
This study evaluates the impact of the agricultural working landscape on the Vermont tourist industry and state economy. Vermont is known for its scenery, especially its agricultural landscape. It has often been stated that Vermont's tourist industry, which represents 15% of the state's economy, depends upon this special landscape for its comparative advantage in the New England tourism market. However, Vermont's landscape is changing. The number of farms and acres of farmland have decreased significantly in the past several decades. State policy makers are grappling with the challenge of supporting and preserving both the farm and tourist economies in the face of regional and global competition. This study quantifies the impact of the agricultural working landscape on tourist demand in Vermont. Primary data were gathered through a survey of visitors to Vermont to determine how the disappearance of the agricultural landscape would affect their willingness to visit the state. Findings indicate that 84% of respondents value the agricultural landscape of Vermont and 58.5% of the respondents would be less likely to visit Vermont if there were very few farms. Knowing the level of this impact will help policy makers decide how much to invest in the preservation of farmland and marketing of farm visits and eco-tourism.Land Economics/Use, Resource /Energy Economics and Policy,
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