2,508 research outputs found
D-ADMM: A Communication-Efficient Distributed Algorithm For Separable Optimization
We propose a distributed algorithm, named Distributed Alternating Direction
Method of Multipliers (D-ADMM), for solving separable optimization problems in
networks of interconnected nodes or agents. In a separable optimization problem
there is a private cost function and a private constraint set at each node. The
goal is to minimize the sum of all the cost functions, constraining the
solution to be in the intersection of all the constraint sets. D-ADMM is proven
to converge when the network is bipartite or when all the functions are
strongly convex, although in practice, convergence is observed even when these
conditions are not met. We use D-ADMM to solve the following problems from
signal processing and control: average consensus, compressed sensing, and
support vector machines. Our simulations show that D-ADMM requires less
communications than state-of-the-art algorithms to achieve a given accuracy
level. Algorithms with low communication requirements are important, for
example, in sensor networks, where sensors are typically battery-operated and
communicating is the most energy consuming operation.Comment: To appear in IEEE Transactions on Signal Processin
Distributed Optimization With Local Domains: Applications in MPC and Network Flows
In this paper we consider a network with nodes, where each node has
exclusive access to a local cost function. Our contribution is a
communication-efficient distributed algorithm that finds a vector
minimizing the sum of all the functions. We make the additional assumption that
the functions have intersecting local domains, i.e., each function depends only
on some components of the variable. Consequently, each node is interested in
knowing only some components of , not the entire vector. This allows
for improvement in communication-efficiency. We apply our algorithm to model
predictive control (MPC) and to network flow problems and show, through
experiments on large networks, that our proposed algorithm requires less
communications to converge than prior algorithms.Comment: Submitted to IEEE Trans. Aut. Contro
Distributed Basis Pursuit
We propose a distributed algorithm for solving the optimization problem Basis
Pursuit (BP). BP finds the least L1-norm solution of the underdetermined linear
system Ax = b and is used, for example, in compressed sensing for
reconstruction. Our algorithm solves BP on a distributed platform such as a
sensor network, and is designed to minimize the communication between nodes.
The algorithm only requires the network to be connected, has no notion of a
central processing node, and no node has access to the entire matrix A at any
time. We consider two scenarios in which either the columns or the rows of A
are distributed among the compute nodes. Our algorithm, named D-ADMM, is a
decentralized implementation of the alternating direction method of
multipliers. We show through numerical simulation that our algorithm requires
considerably less communications between the nodes than the state-of-the-art
algorithms.Comment: Preprint of the journal version of the paper; IEEE Transactions on
Signal Processing, Vol. 60, Issue 4, April, 201
Revisiting Complex Moments For 2D Shape Representation and Image Normalization
When comparing 2D shapes, a key issue is their normalization. Translation and
scale are easily taken care of by removing the mean and normalizing the energy.
However, defining and computing the orientation of a 2D shape is not so simple.
In fact, although for elongated shapes the principal axis can be used to define
one of two possible orientations, there is no such tool for general shapes. As
we show in the paper, previous approaches fail to compute the orientation of
even noiseless observations of simple shapes. We address this problem. In the
paper, we show how to uniquely define the orientation of an arbitrary 2D shape,
in terms of what we call its Principal Moments. We show that a small subset of
these moments suffice to represent the underlying 2D shape and propose a new
method to efficiently compute the shape orientation: Principal Moment Analysis.
Finally, we discuss how this method can further be applied to normalize
grey-level images. Besides the theoretical proof of correctness, we describe
experiments demonstrating robustness to noise and illustrating the method with
real images.Comment: 69 pages, 20 figure
Perceived stress in obsessive-compulsive disorder is related with obsessive but not cmpulsive symptoms
Obsessiveācompulsive disorder (OCD) is achronic psychiatric disorder characterized by recurrent intrusive thoughts and/or repetitive compulsory behaviors. This psychiatric disorder is known to be stress responsive, as symptoms increase during periods of stress but also because stressful events may precede the onset of OCD. However, only a few and inconsistent reports have been published about the stress perception and the stress-response in these patients. Herein, we have characterized the correlations of OCD symptoms with basal serum cortisol levels and scores in a stress perceived questionnaire (PSS-10). The present data reveals that cortisol levels and the stress scores in the PSS-10 were significantly higher in OCD patients that in controls. Moreover, stress levels self-reported by patients using the PSS-10 correlated positively with OCD severity in the YaleāBrown ObsessiveāCompulsive Scale (YāBOCS). Interestingly, PSS-10 scores correlated with the obsessive component, but not with the compulsive component, of YāBOCS. These results confirm that stress is relevant in the context of OCD, particularly for the obsessive symptomatology.Pedro Morgado is supported by a fellowship āSFRH/SINTD/60129/2009ā funded by FCT ā Foundation for Science and Technology. Supported by FEDER funds through Operational program for competitive factors ā COMPETE and by national funds through FCT āFoundation for Science and Technology to project āPTDC/SAU-NSC/111814/2009.
Transition from endemic behavior to eradication of malaria due to combined drug therapies: an agent-model approach
We introduce an agent-based model describing a
susceptible-infectious-susceptible (SIS) system of humans and mosquitoes to
predict malaria epidemiological scenarios in realistic biological conditions.
Emphasis is given to the transition from endemic behavior to eradication of
malaria transmission induced by combined drug therapies acting on both the
gametocytemia reduction and on the selective mosquito mortality during parasite
development in the mosquito. Our mathematical framework enables to uncover the
critical values of the parameters characterizing the effect of each drug
therapy. Moreover, our results provide quantitative evidence of what is
empirically known: interventions combining gametocytemia reduction through the
use of gametocidal drugs, with the selective action of ivermectin during
parasite development in the mosquito, may actively promote disease eradication
in the long run. In the agent model, the main properties of human-mosquito
interactions are implemented as parameters and the model is validated by
comparing simulations with real data of malaria incidence collected in the
endemic malaria region of Chimoio in Mozambique. Finally, we discuss our
findings in light of current drug administration strategies for malaria
prevention, that may interfere with human-to-mosquito transmission process.Comment: 12 pages, 6 figure
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