43,667 research outputs found
A Probabilistic Approach to Robust Optimal Experiment Design with Chance Constraints
Accurate estimation of parameters is paramount in developing high-fidelity
models for complex dynamical systems. Model-based optimal experiment design
(OED) approaches enable systematic design of dynamic experiments to generate
input-output data sets with high information content for parameter estimation.
Standard OED approaches however face two challenges: (i) experiment design
under incomplete system information due to unknown true parameters, which
usually requires many iterations of OED; (ii) incapability of systematically
accounting for the inherent uncertainties of complex systems, which can lead to
diminished effectiveness of the designed optimal excitation signal as well as
violation of system constraints. This paper presents a robust OED approach for
nonlinear systems with arbitrarily-shaped time-invariant probabilistic
uncertainties. Polynomial chaos is used for efficient uncertainty propagation.
The distinct feature of the robust OED approach is the inclusion of chance
constraints to ensure constraint satisfaction in a stochastic setting. The
presented approach is demonstrated by optimal experimental design for the
JAK-STAT5 signaling pathway that regulates various cellular processes in a
biological cell.Comment: Submitted to ADCHEM 201
Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 199
This bibliography lists 82 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1979
Selective pressures on genomes in molecular evolution
We describe the evolution of macromolecules as an information transmission
process and apply tools from Shannon information theory to it. This allows us
to isolate three independent, competing selective pressures that we term
compression, transmission, and neutrality selection. The first two affect
genome length: the pressure to conserve resources by compressing the code, and
the pressure to acquire additional information that improves the channel,
increasing the rate of information transmission into each offspring. Noisy
transmission channels (replication with mutations) gives rise to a third
pressure that acts on the actual encoding of information; it maximizes the
fraction of mutations that are neutral with respect to the phenotype. This
neutrality selection has important implications for the evolution of
evolvability. We demonstrate each selective pressure in experiments with
digital organisms.Comment: 16 pages, 3 figures, to be published in J. theor. Biolog
Optimizing Associative Information Transfer within Content-addressable Memory
Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe
Multimodal Network Alignment
A multimodal network encodes relationships between the same set of nodes in
multiple settings, and network alignment is a powerful tool for transferring
information and insight between a pair of networks. We propose a method for
multimodal network alignment that computes a matrix which indicates the
alignment, but produces the result as a low-rank factorization directly. We
then propose new methods to compute approximate maximum weight matchings of
low-rank matrices to produce an alignment. We evaluate our approach by applying
it on synthetic networks and use it to de-anonymize a multimodal transportation
network.Comment: 14 pages, 6 figures, Siam Data Mining 201
Clustering based on Random Graph Model embedding Vertex Features
Large datasets with interactions between objects are common to numerous
scientific fields (i.e. social science, internet, biology...). The interactions
naturally define a graph and a common way to explore or summarize such dataset
is graph clustering. Most techniques for clustering graph vertices just use the
topology of connections ignoring informations in the vertices features. In this
paper, we provide a clustering algorithm exploiting both types of data based on
a statistical model with latent structure characterizing each vertex both by a
vector of features as well as by its connectivity. We perform simulations to
compare our algorithm with existing approaches, and also evaluate our method
with real datasets based on hyper-textual documents. We find that our algorithm
successfully exploits whatever information is found both in the connectivity
pattern and in the features
The Feasibility of Counting Songbirds Using Unmanned Aerial Vehicles
Obtaining unbiased survey data for vocal bird species is inherently challenging due to observer biases, habitat coverage biases, and logistical constraints. We propose that combining bioacoustic monitoring with unmanned aerial vehicle (UAV) technology could reduce some of these biases and allow bird surveys to be conducted in less accessible areas. We tested the feasibility of the UAV approach to songbird surveys using a low-cost quadcopter with a simple, lightweight recorder suspended 8 m below the vehicle. In a field experiment using playback of bird recordings, we found that small variations in UAV altitude (it hovered at 28, 48, and 68 m) didn\u27t have a significant effect on detections by the recorder attached to the UAV, and we found that the detection radius of our equipment was comparable with detection radii of standard point counts. We then field tested our equipment, comparing songbird detections from our UAV-mounted recorder with standard point-count data from 51 count stations. We found that the number of birds per point on UAV counts was comparable with standard counts for most species, but there were significant underestimates for some—specifically, issues of song masking for a species with a low-frequency song, the Mourning Dove (Zenaida macroura); and underestimation of the abundance of a species that was found in very high densities, the Gray Catbird (Dumetella carolinensis). Species richness was lower on UAV counts (mean = 5.6 species point−1) than on standard counts (8.3 species point−1), but only slightly lower than on standard counts if nonaudible detections are omitted (6.5 species point−1). Excessive UAV noise is a major hurdle to using UAVs for bioacoustic monitoring, but we are optimistic that technological innovations to reduce motor and rotor noise will significantly reduce this issue. We conclude that UAV-based bioacoustic monitoring holds great promise, and we urge other researchers to consider further experimentation to refine techniques
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