72,678 research outputs found
EZ-AG: Structure-free data aggregation in MANETs using push-assisted self-repelling random walks
This paper describes EZ-AG, a structure-free protocol for duplicate
insensitive data aggregation in MANETs. The key idea in EZ-AG is to introduce a
token that performs a self-repelling random walk in the network and aggregates
information from nodes when they are visited for the first time. A
self-repelling random walk of a token on a graph is one in which at each step,
the token moves to a neighbor that has been visited least often. While
self-repelling random walks visit all nodes in the network much faster than
plain random walks, they tend to slow down when most of the nodes are already
visited. In this paper, we show that a single step push phase at each node can
significantly speed up the aggregation and eliminate this slow down. By doing
so, EZ-AG achieves aggregation in only O(N) time and messages. In terms of
overhead, EZ-AG outperforms existing structure-free data aggregation by a
factor of at least log(N) and achieves the lower bound for aggregation message
overhead. We demonstrate the scalability and robustness of EZ-AG using ns-3
simulations in networks ranging from 100 to 4000 nodes under different mobility
models and node speeds. We also describe a hierarchical extension for EZ-AG
that can produce multi-resolution aggregates at each node using only O(NlogN)
messages, which is a poly-logarithmic factor improvement over existing
techniques
Correlative Microscopy of Morphology and Luminescence of Cu porphyrin aggregates
Transfer of energy and information through molecule aggregates requires as
one important building block anisotropic, cable-like structures. Knowledge on
the spatial correlation of luminescence and morphology represents a
prerequisite in the understanding of internal processes and will be important
for architecting suitable landscapes. In this context we study the morphology,
fluorescence and phosphorescence of molecule aggregate structures on surfaces
in a spatially correlative way. We consider as two morphologies, lengthy
strands and isotropic islands. It turns out that phosphorescence is quite
strong compared to fluorescence and the spatial variation of the observed
intensities is largely in line with the amount of dye. However in proportion,
the strands exhibit more fluorescence than the isotropic islands suggesting
weaker non-radiative channels. The ratio fluorescence to phosphorescence
appears to be correlated with the degree of aggregation or internal order. The
heights at which luminescence saturates is explained in the context of
attenuation and emission multireflection, inside the dye. This is supported by
correlative photoemission electron microscopy which is more sensitive to the
surface region. The lengthy structures exhibit a pronounced polarization
dependence of the luminescence with a relative dichroism up to about 60%,
revealing substantial perpendicular orientation preference of the molecules
with respect to the substrate and parallel with respect to the strands
Temporal and Spatial Classification of Active IPv6 Addresses
There is striking volume of World-Wide Web activity on IPv6 today. In early
2015, one large Content Distribution Network handles 50 billion IPv6 requests
per day from hundreds of millions of IPv6 client addresses; billions of unique
client addresses are observed per month. Address counts, however, obscure the
number of hosts with IPv6 connectivity to the global Internet. There are
numerous address assignment and subnetting options in use; privacy addresses
and dynamic subnet pools significantly inflate the number of active IPv6
addresses. As the IPv6 address space is vast, it is infeasible to
comprehensively probe every possible unicast IPv6 address. Thus, to survey the
characteristics of IPv6 addressing, we perform a year-long passive measurement
study, analyzing the IPv6 addresses gleaned from activity logs for all clients
accessing a global CDN.
The goal of our work is to develop flexible classification and measurement
methods for IPv6, motivated by the fact that its addresses are not merely more
numerous; they are different in kind. We introduce the notion of classifying
addresses and prefixes in two ways: (1) temporally, according to their
instances of activity to discern which addresses can be considered stable; (2)
spatially, according to the density or sparsity of aggregates in which active
addresses reside. We present measurement and classification results numerically
and visually that: provide details on IPv6 address use and structure in global
operation across the past year; establish the efficacy of our classification
methods; and demonstrate that such classification can clarify dimensions of the
Internet that otherwise appear quite blurred by current IPv6 addressing
practices
Temporal Feature Selection with Symbolic Regression
Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the underlying drivers of a target variable. In this research we propose a novel feature learning technique in which Symbolic regression is endowed with a ``Range Terminal\u27\u27 that allows it to explore functions of the aggregate of variables over time. We test the Range Terminal on a synthetic data set and a real world data in which we predict seasonal greenness using satellite derived temperature and snow data over a portion of the Arctic. On the synthetic data set we find Symbolic regression with the Range Terminal outperforms standard Symbolic regression and Lasso regression. On the Arctic data set we find it outperforms standard Symbolic regression, fails to beat the Lasso regression, but finds useful features describing the interaction between Land Surface Temperature, Snow, and seasonal vegetative growth in the Arctic
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