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Seminal Ideas in Integral Methods
"... When we write papers we claim that we are the author, even sole author, of the analysis we present. In this paper, I intend to show how farcical this claim is; every step in our analysis is usually the end result of work painstakingly done by our antecedents over the last 2 1/2 millennia. At ..."
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When we write papers we claim that we are the author, even sole author, of the analysis we present. In this paper, I intend to show how farcical this claim is; every step in our analysis is usually the end result of work painstakingly done by our antecedents over the last 2 1/2 millennia. At
Learning affordance concepts: some seminal ideas
"... Inspired by the pioneering work of J. J. Gibson, we provide a workable characterisation of the notion of affordance and we explore a possible architecture for an agent that is able to autonomously acquire affordance concepts. 1 1 ..."
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Cited by 1 (0 self)
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Inspired by the pioneering work of J. J. Gibson, we provide a workable characterisation of the notion of affordance and we explore a possible architecture for an agent that is able to autonomously acquire affordance concepts. 1 1
Feature detection with automatic scale selection
 International Journal of Computer Vision
, 1998
"... The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works ..."
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Cited by 713 (34 self)
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The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal
Regression Shrinkage and Selection Via the Lasso
 Journal of the Royal Statistical Society, Series B
, 1994
"... We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactl ..."
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Cited by 4055 (51 self)
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an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and treebased models are briefly described. Keywords: regression, subset
A message ferrying approach for data delivery in sparse mobile ad hoc networks
 In Proc. of ACM Mobihoc
, 2004
"... Mobile Ad Hoc Networks (MANETs) provide rapidly deployable and selfconfiguring network capacity required in many critical applications, e.g., battlefields, disaster relief and wide area sensing. In this paper we study the problem of efficient data delivery in sparse MANETs where network partitions ..."
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Cited by 496 (14 self)
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) approach to address the problem. MF is a mobilityassisted approach which utilizes a set of special mobile nodes called message ferries (or ferries for short) to provide communication service for nodes in the deployment area. The main idea behind the MF approach is to introduce non
Implications of rational inattention
 JOURNAL OF MONETARY ECONOMICS
, 2002
"... A constraint that actions can depend on observations only through a communication channel with finite Shannon capacity is shown to be able to play a role very similar to that of a signal extraction problem or an adjustment cost in standard control problems. The resulting theory looks enough like fa ..."
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Cited by 514 (10 self)
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A constraint that actions can depend on observations only through a communication channel with finite Shannon capacity is shown to be able to play a role very similar to that of a signal extraction problem or an adjustment cost in standard control problems. The resulting theory looks enough like familiar dynamic rational expectations theories to suggest that it might be useful and practical, while the implications for policy are different enough to be interesting.
Modeling and simulation of genetic regulatory systems: A literature review
 JOURNAL OF COMPUTATIONAL BIOLOGY
, 2002
"... In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between ..."
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Cited by 729 (15 self)
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In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rulebased formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.
A ResourceBased View of the Firm
 STRATEGIC MANAGEMENT JOURNAL, VOL. 5, NO. 2. (APR. JUN., 1984)
, 1984
"... ..."
Constrained model predictive control: Stability and optimality
 AUTOMATICA
, 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
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Cited by 696 (15 self)
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Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon openloop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and the first control in this sequence is applied to the plant. An important advantage of this type of control is its ability to cope with hard constraints on controls and states. It has, therefore, been widely applied in petrochemical and related industries where satisfaction of constraints is particularly important because efficiency demands operating points on or close to the boundary of the set of admissible states and controls. In this review, we focus on model predictive control of constrained systems, both linear and nonlinear and discuss only briefly model predictive control of unconstrained nonlinear and/or timevarying systems. We concentrate our attention on research dealing with stability and optimality; in these areas the subject has developed, in our opinion, to a stage where it has achieved sufficient maturity to warrant the active interest of researchers in nonlinear control. We distill from an extensive literature essential principles that ensure stability and use these to present a concise characterization of most of the model predictive controllers that have been proposed in the literature. In some cases the finite horizon optimal control problem solved online is exactly equivalent to the same problem with an infinite horizon; in other cases it is equivalent to a modified infinite horizon optimal control problem. In both situations, known advantages of infinite horizon optimal control accrue.
The Wealth of Networks: How Social Production Transforms Markets and
, 2006
"... This is a visionary book written by a man on a mission. It articulates one possible answer to the question of what might come after the proprietarybased knowledgebased economy that currently exists in advanced countries. Benkler is professor of law at Yale Law School and one of the most ardent pro ..."
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Cited by 678 (5 self)
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This is a visionary book written by a man on a mission. It articulates one possible answer to the question of what might come after the proprietarybased knowledgebased economy that currently exists in advanced countries. Benkler is professor of law at Yale Law School and one of the most ardent proponents of the open source movement and the information commons approach. He argues that a new form of economy might be emerging, i.e. the “networked information economy”, in which nonmarket and nonproprietary commonsbased peer production (i.e. “social production”) and exchange of information, knowledge and culture play a central role. This has become feasible because the capital required for social production and exchange in the networked information economy is relatively cheap and widely distributed. Much of the book argues the perceived advantages of the networked information economy from a multidisciplinary and liberal political perspective, and the numerous threats endangering the realisation of its potential. The incumbents of the existing proprietarybased “industrial information economy”, in particular Hollywood and the
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