129,938 research outputs found
Flow-directed PCA for monitoring networks
Measurements recorded over monitoring networks often possess spatial and temporal correlation inducing redundancies in the information provided. For river water quality monitoring in particular, flow-connected sites may likely provide similar information. This paper proposes a novel approach to principal components analysis to investigate reducing dimensionality for spatiotemporal flow-connected network data in order to identify common spatiotemporal patterns. The method is illustrated using monthly observations of total oxidized nitrogen for the Trent catchment area in England. Common patterns are revealed that are hidden when the river network structure and temporal correlation are not accounted for. Such patterns provide valuable information for the design of future sampling strategies
New Complexity Results and Algorithms for the Minimum Tollbooth Problem
The inefficiency of the Wardrop equilibrium of nonatomic routing games can be
eliminated by placing tolls on the edges of a network so that the socially
optimal flow is induced as an equilibrium flow. A solution where the minimum
number of edges are tolled may be preferable over others due to its ease of
implementation in real networks. In this paper we consider the minimum
tollbooth (MINTB) problem, which seeks social optimum inducing tolls with
minimum support. We prove for single commodity networks with linear latencies
that the problem is NP-hard to approximate within a factor of through
a reduction from the minimum vertex cover problem. Insights from network design
motivate us to formulate a new variation of the problem where, in addition to
placing tolls, it is allowed to remove unused edges by the social optimum. We
prove that this new problem remains NP-hard even for single commodity networks
with linear latencies, using a reduction from the partition problem. On the
positive side, we give the first exact polynomial solution to the MINTB problem
in an important class of graphs---series-parallel graphs. Our algorithm solves
MINTB by first tabulating the candidate solutions for subgraphs of the
series-parallel network and then combining them optimally
Distributed Robust Set-Invariance for Interconnected Linear Systems
We introduce a class of distributed control policies for networks of
discrete-time linear systems with polytopic additive disturbances. The
objective is to restrict the network-level state and controls to user-specified
polyhedral sets for all times. This problem arises in many safety-critical
applications. We consider two problems. First, given a communication graph
characterizing the structure of the information flow in the network, we find
the optimal distributed control policy by solving a single linear program.
Second, we find the sparsest communication graph required for the existence of
a distributed invariance-inducing control policy. Illustrative examples,
including one on platooning, are presented.Comment: 8 Pages. Submitted to American Control Conference (ACC), 201
Traffic jams and intermittent flows in microfluidic networks
We investigate both experimentally and theoretically the traffic of particles
flowing in microfluidic obstacle networks. We show that the traffic dynamics is
a non-linear process: the particle current does not scale with the particle
density even in the dilute limit where no particle collision occurs. We
demonstrate that this non-linear behavior stems from long range hydrodynamic
interactions. Importantly, we also establish that there exists a maximal
current above which no stationary particle flow can be sustained. For higher
current values, intermittent traffic jams form thereby inducing the ejection of
the particles from the initial path and the subsequent invasion of the network.
Eventually, we put our findings in the broader context of the transport
proccesses of driven particles in low dimension
Short-term plasticity as cause-effect hypothesis testing in distal reward learning
Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity
as to which stimuli, actions, and rewards are causally related. Only the
repetition of reward episodes helps distinguish true cause-effect relationships
from coincidental occurrences. In the model proposed here, a novel plasticity
rule employs short and long-term changes to evaluate hypotheses on cause-effect
relationships. Transient weights represent hypotheses that are consolidated in
long-term memory only when they consistently predict or cause future rewards.
The main objective of the model is to preserve existing network topologies when
learning with ambiguous information flows. Learning is also improved by biasing
the exploration of the stimulus-response space towards actions that in the past
occurred before rewards. The model indicates under which conditions beliefs can
be consolidated in long-term memory, it suggests a solution to the
plasticity-stability dilemma, and proposes an interpretation of the role of
short-term plasticity.Comment: Biological Cybernetics, September 201
Hamiltonian Monte Carlo Acceleration Using Surrogate Functions with Random Bases
For big data analysis, high computational cost for Bayesian methods often
limits their applications in practice. In recent years, there have been many
attempts to improve computational efficiency of Bayesian inference. Here we
propose an efficient and scalable computational technique for a
state-of-the-art Markov Chain Monte Carlo (MCMC) methods, namely, Hamiltonian
Monte Carlo (HMC). The key idea is to explore and exploit the structure and
regularity in parameter space for the underlying probabilistic model to
construct an effective approximation of its geometric properties. To this end,
we build a surrogate function to approximate the target distribution using
properly chosen random bases and an efficient optimization process. The
resulting method provides a flexible, scalable, and efficient sampling
algorithm, which converges to the correct target distribution. We show that by
choosing the basis functions and optimization process differently, our method
can be related to other approaches for the construction of surrogate functions
such as generalized additive models or Gaussian process models. Experiments
based on simulated and real data show that our approach leads to substantially
more efficient sampling algorithms compared to existing state-of-the art
methods
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