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    <title>Pinboard (Vaguery)</title>
    <link>https://pinboard.in/u:Vaguery/public/</link>
    <description>recent bookmarks from Vaguery</description>
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      <rdf:Seq>	<rdf:li rdf:resource="http://arxiv.org/abs/1303.2269"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1207.3437"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1110.1393"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0912.3513"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1005.1860"/>
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  </channel><item rdf:about="http://arxiv.org/abs/1303.2269">
    <title>[1303.2269] Feedback cooling, measurement errors, and entropy production</title>
    <dc:date>2013-03-24T21:24:31+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.2269</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The efficiency of a feedback mechanism depends on the precision of the measurement outcomes obtained from the controlled system. Accordingly, measurement errors affect the entropy production in the system. We explore this issue in the context of active feedback cooling by modeling a typical cold damping setup as a harmonic oscillator in contact with a heat reservoir and submitted to a velocity-dependent feedback force that reduces the random motion. We consider two models that distinguish whether the sensor continuously measures the position of the resonator or directly its velocity (in practice, an electric current). Adopting the standpoint of the controlled system, we identify the `entropy pumping' contribution that describes the entropy reduction due to the feedback control and that modifies the second law of thermodynamics. We also assign a relaxation dynamics to the feedback mechanism and compare the apparent entropy production in the system and the heat bath to the total entropy production in the super-system that includes the controller. In this context, entropy pumping reflects the existence of hidden degrees of freedom and the apparent entropy production satisfies fluctuation theorems associated to an effective Langevin dynamics.]]></description>
<dc:subject>entropy thermodynamics feedback-systems cybernetics-in-action control-systems dynamical-systems engineering-design it's-damping-but-at-least-it's-a-cold-damping</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:262f6344a829/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:entropy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:thermodynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feedback-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cybernetics-in-action"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-damping-but-at-least-it's-a-cold-damping"/>
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<item rdf:about="http://arxiv.org/abs/1207.3437">
    <title>[1207.3437] Robust Mission Design Through Evidence Theory and Multi-Agent Collaborative Search</title>
    <dc:date>2012-08-04T12:27:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.3437</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["In this paper, the preliminary design of a space mission is approached introducing uncertainties on the design parameters and formulating the resulting reliable design problem as a multiobjective optimization problem. Uncertainties are modelled through evidence theory and the belief, or credibility, in the successful achievement of mission goals is maximised along with the reliability of constraint satisfaction. The multiobjective optimisation problem is solved through a novel algorithm based on the collaboration of a population of agents in search for the set of highly reliable solutions. Two typical problems in mission analysis are used to illustrate the proposed methodology."]]></description>
<dc:subject>astronautics planning control-systems agent-based nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:2e705278770c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:astronautics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:planning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1110.1393">
    <title>[1110.1393] High-Precision Tuning of State for Memristive Devices by Adaptable Variation-Tolerant Algorithm</title>
    <dc:date>2011-10-10T12:40:02+00:00</dc:date>
    <link>http://arxiv.org/abs/1110.1393</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Using memristive properties common for the titanium dioxide thin film devices, we designed a simple write algorithm to tune device conductance at a specific bias point to 1% relative accuracy (which is roughly equivalent to 7-bit precision) within its dynamic range even in the presence of large variations in switching behavior. The high precision state is nonvolatile and the results are likely to be sustained for nanoscale memristive devices because of the inherent filamentary nature of the resistive switching. The proposed functionality of memristive devices is especially attractive for analog computing with low precision data. As one representative example we demonstrate hybrid circuitry consisting of CMOS summing amplifier and two memristive devices to perform analog multiply and accumulate computation, which is a typical bottleneck operation in information processing."]]></description>
<dc:subject>memristors engineering-design simulation control-systems nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:945f8126da45/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:memristors"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0912.3513">
    <title>[0912.3513] Stimulus-Dependent Suppression of Chaos in Recurrent Neural Networks</title>
    <dc:date>2010-08-12T23:01:41+00:00</dc:date>
    <link>http://arxiv.org/abs/0912.3513</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Neuronal activity arises from an interaction between ongoing firing generated spontaneously by neural circuits and responses driven by external stimuli. Using mean-field analysis, we ask how a neural network that intrinsically generates chaotic patterns of activity can remain sensitive to extrinsic input. We find that inputs not only drive network responses, they also actively suppress ongoing activity, ultimately leading to a phase transition in which chaos is completely eliminated. The critical input intensity at the phase transition is a non-monotonic function of stimulus frequency, revealing a "resonant" frequency at which the input is most effective at suppressing chaos even though the power spectrum of the spontaneous activity peaks at zero and falls exponentially. A prediction of our analysis is that the variance of neural responses should be most strongly suppressed at frequencies matching the range over which many sensory systems operate."
]]></description>
<dc:subject>chaos dynamical-systems neural-networks engineering-design emergent-design control-systems nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:58b8b40f62f7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:chaos"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:neural-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1005.1860">
    <title>[1005.1860] Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes</title>
    <dc:date>2010-05-24T21:02:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.1860</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions reliably. Large and rich sets of features can cause existing algorithms to overfit because of a limited number of samples. We address this shortcoming using $L_1$ regularization in approximate linear programming. Because the proposed method can automatically select the appropriate richness of features, its performance does not degrade with an increasing number of features. These results rely on new and stronger sampling bounds for regularized approximate linear programs. We also propose a computationally efficient homotopy method. The empirical evaluation of the approach shows that the proposed method performs well on simple MDPs and standard benchmark problems."
]]></description>
<dc:subject>nudge-targets dynamic-programming approximation algorithms control-systems linear-programming</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7f8057f9d46e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:linear-programming"/>
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