2,184 research outputs found
Project RISE: Recognizing Industrial Smoke Emissions
Industrial smoke emissions pose a significant concern to human health. Prior
works have shown that using Computer Vision (CV) techniques to identify smoke
as visual evidence can influence the attitude of regulators and empower
citizens to pursue environmental justice. However, existing datasets are not of
sufficient quality nor quantity to train the robust CV models needed to support
air quality advocacy. We introduce RISE, the first large-scale video dataset
for Recognizing Industrial Smoke Emissions. We adopted a citizen science
approach to collaborate with local community members to annotate whether a
video clip has smoke emissions. Our dataset contains 12,567 clips from 19
distinct views from cameras that monitored three industrial facilities. These
daytime clips span 30 days over two years, including all four seasons. We ran
experiments using deep neural networks to establish a strong performance
baseline and reveal smoke recognition challenges. Our survey study discussed
community feedback, and our data analysis displayed opportunities for
integrating citizen scientists and crowd workers into the application of
Artificial Intelligence for social good.Comment: Technical repor
Unsupervised Path Representation Learning with Curriculum Negative Sampling
Path representations are critical in a variety of transportation
applications, such as estimating path ranking in path recommendation systems
and estimating path travel time in navigation systems. Existing studies often
learn task-specific path representations in a supervised manner, which require
a large amount of labeled training data and generalize poorly to other tasks.
We propose an unsupervised learning framework Path InfoMax (PIM) to learn
generic path representations that work for different downstream tasks. We first
propose a curriculum negative sampling method, for each input path, to generate
a small amount of negative paths, by following the principles of curriculum
learning. Next, \emph{PIM} employs mutual information maximization to learn
path representations from both a global and a local view. In the global view,
PIM distinguishes the representations of the input paths from those of the
negative paths. In the local view, \emph{PIM} distinguishes the input path
representations from the representations of the nodes that appear only in the
negative paths. This enables the learned path representations to encode both
global and local information at different scales. Extensive experiments on two
downstream tasks, ranking score estimation and travel time estimation, using
two road network datasets suggest that PIM significantly outperforms other
unsupervised methods and is also able to be used as a pre-training method to
enhance supervised path representation learning.Comment: This paper has been accepted by IJCAI-2
Thermal excitation of plasmons for near-field thermophotovoltaics
The traditional approaches of exciting plasmons consist of using electrons
(eg: electron energy loss spectroscopy) or light (Kretchman and Otto geometry)
while more recently plasmons have been excited even by single photons. A
different approach: thermal excitation of a plasmon resonance at high
temperatures using alternate plasmonic media was proposed by S. Molesky et.al.,
Opt. Exp. 21.101, A96-A110, (2013). Here, we show how the long-standing search
for a high temperature narrow band near-field emitter for thermophotovoltaics
can be fulfilled by high temperature plasmonics. We also describe how to
control Wein's displacement law in the near-field using high temperature
epsilon-near-zero metamaterials. Finally, we show that our work opens up an
interesting direction of research for the field of slow light: thermal emission
control
Modeling whole-tree carbon assimilation rate using observed transpiration rates and needle sugar carbon isotope ratios
• Understanding controls over plant–atmosphere CO2 exchange is important for quantifying carbon budgets across a range of spatial and temporal scales. In this study, we used a simple approach to estimate whole-tree CO2 assimilation rate (ATree) in a subalpine forest ecosystem. • We analysed the carbon isotope ratio (δ13C) of extracted needle sugars and combined it with the daytime leaf-to-air vapor pressure deficit to estimate tree water-use efficiency (WUE). The estimated WUE was then combined with observations of tree transpiration rate (E) using sap flow techniques to estimate ATree. Estimates of ATree for the three dominant tree species in the forest were combined with species distribution and tree size to estimate and gross primary productivity (GPP) using an ecosystem process model. • A sensitivity analysis showed that estimates of ATree were more sensitive to dynamics in E than δ13C. At the ecosystem scale, the abundance of lodgepole pine trees influenced seasonal dynamics in GPP considerably more than Engelmann spruce and subalpine fir because of its greater sensitivity of E to seasonal climate variation. • The results provide the framework for a nondestructive method for estimating whole-tree carbon assimilation rate and ecosystem GPP over daily-to weekly time scales
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