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74
Marginalized kernels between labeled graphs
 Proceedings of the Twentieth International Conference on Machine Learning
, 2003
"... A new kernel function between two labeled graphs is presented. Feature vectors are defined as the counts of label paths produced by random walks on graphs. The kernel computation finally boils down to obtaining the stationary state of a discretetime linear system, thus is efficiently performed by s ..."
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Cited by 144 (14 self)
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A new kernel function between two labeled graphs is presented. Feature vectors are defined as the counts of label paths produced by random walks on graphs. The kernel computation finally boils down to obtaining the stationary state of a discretetime linear system, thus is efficiently performed by solving simultaneous linear equations. Our kernel is based on an infinite dimensional feature space, so it is fundamentally different from other string or tree kernels based on dynamic programming. We will present promising empirical results in classification of chemical compounds. 1 1.
Remarks on input to output stability
 Proceedings of the IEEE Conference on Decision and Control
, 1999
"... This paper deals with concepts of output stability. Inspired in part by regulator theory, several variants are considered, which differ from each other in the requirements imposed upon transient behavior. The main results provide a comparison among the various notions, all of which specialize to inp ..."
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Cited by 54 (14 self)
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This paper deals with concepts of output stability. Inspired in part by regulator theory, several variants are considered, which differ from each other in the requirements imposed upon transient behavior. The main results provide a comparison among the various notions, all of which specialize to input to state stability (iss) when the output equals the complete state.
Design and implementation of a sensor network system for vehicle tracking and autonomous interception
 In Proc. EWSN
, 2005
"... networked system of distributed sensor nodes that detects an uncooperative agent called the evader and assists an autonomous robot called the pursuer in capturing the evader. PEG requires services such as leader election, routing, network aggregation, and closed loop control. Instead of using genera ..."
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Cited by 51 (14 self)
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networked system of distributed sensor nodes that detects an uncooperative agent called the evader and assists an autonomous robot called the pursuer in capturing the evader. PEG requires services such as leader election, routing, network aggregation, and closed loop control. Instead of using general purpose distributed system solutions for these services, we employ wholesystem analysis and rely on spatial and physical properties to create simple and efficient mechanisms. We believe this approach advances sensor network design, yielding pragmatic solutions that leverage physical properties to simplify design of embedded distributed systems. We deployed PEG on a 400 square meter field using 100 sensor nodes, and successfully intercepted the evader in all runs. While implementing PEG, we confronted practical issues such as node breakage, packaging decisions, in situ debugging, network reprogramming, and system reconfiguration. We discuss the approaches we took to cope with these issues and share our experiences in deploying a large sensor network system. I.
Krylov Subspace Techniques for ReducedOrder Modeling of Nonlinear Dynamical Systems
 Appl. Numer. Math
, 2002
"... Means of applying Krylov subspace techniques for adaptively extracting accurate reducedorder models of largescale nonlinear dynamical systems is a relatively open problem. There has been much current interest in developing such techniques. We focus on a bilinearization method, which extends Kry ..."
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Cited by 50 (3 self)
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Means of applying Krylov subspace techniques for adaptively extracting accurate reducedorder models of largescale nonlinear dynamical systems is a relatively open problem. There has been much current interest in developing such techniques. We focus on a bilinearization method, which extends Krylov subspace techniques for linear systems. In this approach, the nonlinear system is first approximated by a bilinear system through Carleman bilinearization. Then a reducedorder bilinear system is constructed in such a way that it matches certain number of multimoments corresponding to the first few kernels of the VolterraWiener representation of the bilinear system. It is shown that the twosided Krylov subspace technique matches significant more number of multimoments than the corresponding oneside technique.
Stability criteria for switched and hybrid systems
 SIAM Review
, 2007
"... The study of the stability properties of switched and hybrid systems gives rise to a number of interesting and challenging mathematical problems. The objective of this paper is to outline some of these problems, to review progress made in solving these problems in a number of diverse communities, an ..."
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Cited by 34 (4 self)
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The study of the stability properties of switched and hybrid systems gives rise to a number of interesting and challenging mathematical problems. The objective of this paper is to outline some of these problems, to review progress made in solving these problems in a number of diverse communities, and to review some problems that remain open. An important contribution of our work is to bring together material from several areas of research and to present results in a unified manner. We begin our review by relating the stability problem for switched linear systems and a class of linear differential inclusions. Closely related to the concept of stability are the notions of exponential growth rates and converse Lyapunov theorems, both of which are discussed in detail. In particular, results on common quadratic Lyapunov functions and piecewise linear Lyapunov functions are presented, as they represent constructive methods for proving stability, and also represent problems in which significant progress has been made. We also comment on the inherent difficulty of determining stability of switched systems in general which is exemplified by NPhardness and undecidability results. We then proceed by considering the stability of switched systems in which there are constraints on the switching rules, through both dwell time requirements and state dependent switching laws. Also in this case the theory of Lyapunov functions and the existence of converse theorems is reviewed. We briefly comment on the classical Lur’e problem and on the theory of stability radii, both of which contain many of the features of switched systems and are rich sources of practical results on the topic. Finally we present a list of questions and open problems which provide motivation for continued research in this area.
Projection Frameworks for Model Reduction of Weakly . . .
, 2000
"... In this paper we present a generalization of popular linear model reduction methods, such as Lanczos and Arnoldibased algorithms based on rational approximation, to systems whose response to interesting external inputs can be described by a few terms in a functional series expansion such as a Volt ..."
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Cited by 23 (1 self)
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In this paper we present a generalization of popular linear model reduction methods, such as Lanczos and Arnoldibased algorithms based on rational approximation, to systems whose response to interesting external inputs can be described by a few terms in a functional series expansion such as a Volterra series. The approach allows automatic generation of macromodels that include frequencydependent nonlinear effects.
Semidefinite Programming Duality and Linear TimeInvariant Systems
, 2003
"... Several important problems in control theory can be reformulated as semidefinite programming problems, i.e., minimization of a linear objective subject to Linear Matrix Inequality (LMI) constraints. From convex optimization duality theory, conditions for infeasibility of the LMIs as well as dual opt ..."
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Cited by 21 (2 self)
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Several important problems in control theory can be reformulated as semidefinite programming problems, i.e., minimization of a linear objective subject to Linear Matrix Inequality (LMI) constraints. From convex optimization duality theory, conditions for infeasibility of the LMIs as well as dual optimization problems can be formulated. These can in turn be reinterpreted in control or system theoretic terms, often yielding new results or new proofs for existing results from control theory. We explore such connections for a few problems associated with linear timeinvariant systems. 1
Iterative Learning Control  Analysis, Design, and Experiments
, 2000
"... In many industrial robot applications it is a fact that the robot is programmed to do the same task repeatedly. By observing the control error in the di#erent iterations of the same task it becomes clear that it is actually highly repetitive. Iterative Learning Control (ILC) allows to iteratively co ..."
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Cited by 19 (3 self)
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In many industrial robot applications it is a fact that the robot is programmed to do the same task repeatedly. By observing the control error in the di#erent iterations of the same task it becomes clear that it is actually highly repetitive. Iterative Learning Control (ILC) allows to iteratively compensate for and, hence, remove this repetitive error. In the thesis
Neural Systems as Nonlinear Filters
, 2000
"... Experimental data show that biological synapses behave quite differently from the symbolic synapses in all common artificial neural network models. Biological synapses are ..."
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Cited by 19 (6 self)
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Experimental data show that biological synapses behave quite differently from the symbolic synapses in all common artificial neural network models. Biological synapses are
Gamps: Compressing multi sensor data by grouping and amplitude scaling
 In: ACM SIGMOD. (2009
"... We consider the problem of collectively approximating a set of sensor signals using the least amount of space so that any individual signal can be efficiently reconstructed within a given maximum (L∞) error ε. The problem arises naturally in applications that need to collect large amounts of data fr ..."
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Cited by 15 (0 self)
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We consider the problem of collectively approximating a set of sensor signals using the least amount of space so that any individual signal can be efficiently reconstructed within a given maximum (L∞) error ε. The problem arises naturally in applications that need to collect large amounts of data from multiple concurrent sources, such as sensors, servers and network routers, and archive them over a long period of time for offline data mining. We present GAMPS, a general framework that addresses this problem by combining several novel techniques. First, it dynamically groups multiple signals together so that signals within each group are correlated and can be maximally compressed jointly. Second, it appropriately scales the amplitudes of different signals within a group and compresses them within the maximum allowed reconstruction error bound. Our schemes are polynomial time O(α, β) approximation schemes, meaning that the maximum (L∞) error is at most αε and it uses at most β times the optimal memory. Finally, GAMPS maintains an index so that various queries can be issued directly on compressed data. Our experiments on several realworld sensor datasets show that GAMPS significantly reduces space without compromising the quality of search and query. Categories and Subject Descriptors