536 research outputs found
Data Dissemination in Unified Dynamic Wireless Networks
We give efficient algorithms for the fundamental problems of Broadcast and
Local Broadcast in dynamic wireless networks. We propose a general model of
communication which captures and includes both fading models (like SINR) and
graph-based models (such as quasi unit disc graphs, bounded-independence
graphs, and protocol model). The only requirement is that the nodes can be
embedded in a bounded growth quasi-metric, which is the weakest condition known
to ensure distributed operability. Both the nodes and the links of the network
are dynamic: nodes can come and go, while the signal strength on links can go
up or down.
The results improve some of the known bounds even in the static setting,
including an optimal algorithm for local broadcasting in the SINR model, which
is additionally uniform (independent of network size). An essential component
is a procedure for balancing contention, which has potentially wide
applicability. The results illustrate the importance of carrier sensing, a
stock feature of wireless nodes today, which we encapsulate in primitives to
better explore its uses and usefulness.Comment: 28 pages, 2 figure
Coupled ocean-atmosphere dynamics of the 2017 extreme coastal El Niño.
In March 2017, sea surface temperatures off Peru rose above 28 °C, causing torrential rains that affected the lives of millions of people. This coastal warming is highly unusual in that it took place with a weak La Niña state. Observations and ocean model experiments show that the downwelling Kelvin waves caused by strong westerly wind events over the equatorial Pacific, together with anomalous northerly coastal winds, are important. Atmospheric model experiments further show the anomalous coastal winds are forced by the coastal warming. Taken together, these results indicate a positive feedback off Peru between the coastal warming, atmospheric deep convection, and the coastal winds. These coupled processes provide predictability. Indeed, initialized on as early as 1 February 2017, seasonal prediction models captured the extreme rainfall event. Climate model projections indicate that the frequency of extreme coastal El Niño will increase under global warming
A Lagrangian Dual-based Theory-guided Deep Neural Network
The theory-guided neural network (TgNN) is a kind of method which improves
the effectiveness and efficiency of neural network architectures by
incorporating scientific knowledge or physical information. Despite its great
success, the theory-guided (deep) neural network possesses certain limits when
maintaining a tradeoff between training data and domain knowledge during the
training process. In this paper, the Lagrangian dual-based TgNN (TgNN-LD) is
proposed to improve the effectiveness of TgNN. We convert the original loss
function into a constrained form with fewer items, in which partial
differential equations (PDEs), engineering controls (ECs), and expert knowledge
(EK) are regarded as constraints, with one Lagrangian variable per constraint.
These Lagrangian variables are incorporated to achieve an equitable tradeoff
between observation data and corresponding constraints, in order to improve
prediction accuracy, and conserve time and computational resources adjusted by
an ad-hoc procedure. To investigate the performance of the proposed method, the
original TgNN model with a set of optimized weight values adjusted by ad-hoc
procedures is compared on a subsurface flow problem, with their L2 error, R
square (R2), and computational time being analyzed. Experimental results
demonstrate the superiority of the Lagrangian dual-based TgNN.Comment: 12 pages, 10 figure
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