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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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	<rdf:li rdf:resource="http://arxiv.org/abs/1302.7080"/>
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  </channel><item rdf:about="https://arxiv.org/abs/1906.05746">
    <title>[1906.05746] Nonlinear System Identification via Tensor Completion</title>
    <dc:date>2022-03-04T11:21:16+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.05746</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Function approximation from input and output data pairs constitutes a fundamental problem in supervised learning. Deep neural networks are currently the most popular method for learning to mimic the input-output relationship of a general nonlinear system, as they have proven to be very effective in approximating complex highly nonlinear functions. In this work, we show that identifying a general nonlinear function y=f(x1,…,xN) from input-output examples can be formulated as a tensor completion problem and under certain conditions provably correct nonlinear system identification is possible. Specifically, we model the interactions between the N input variables and the scalar output of a system by a single N-way tensor, and setup a weighted low-rank tensor completion problem with smoothness regularization which we tackle using a block coordinate descent algorithm. We extend our method to the multi-output setting and the case of partially observed data, which cannot be readily handled by neural networks. Finally, we demonstrate the effectiveness of the approach using several regression tasks including some standard benchmarks and a challenging student grade prediction task.
]]></description>
<dc:subject>approximation models-and-modes representation optimization system-identification tensors to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4cc8017689df/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
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<item rdf:about="https://arxiv.org/abs/2003.07798">
    <title>[2003.07798] Pressio: Enabling projection-based model reduction for large-scale nonlinear dynamical systems</title>
    <dc:date>2021-10-23T06:50:26+00:00</dc:date>
    <link>https://arxiv.org/abs/2003.07798</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This work introduces Pressio, an open-source project aimed at enabling leading-edge projection-based reduced order models (ROMs) for large-scale nonlinear dynamical systems in science and engineering. Pressio provides model-reduction methods that can reduce both the number of spatial and temporal degrees of freedom for any dynamical system expressible as a system of parameterized ordinary differential equations (ODEs). We leverage this simple, expressive mathematical framework as a pivotal design choice to enable a minimal application programming interface (API) that is natural to dynamical systems. The core component of Pressio is a C++11 header-only library that leverages generic programming to support applications with arbitrary data types and arbitrarily complex programming models. This is complemented with Python bindings to expose these C++ functionalities to Python users with negligible overhead and no user-required binding code. We discuss the distinguishing characteristics of Pressio relative to existing model-reduction libraries, outline its key design features, describe how the user interacts with it, and present two test cases -- including one with over 20 million degrees of freedom -- that highlight the performance results of Pressio and illustrate the breath of problems that can be addressed with it.
]]></description>
<dc:subject>nonlinear-dynamics approximation numerical-methods rather-interesting dynamical-systems to-understand system-identification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:69ce0d534dcf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-understand"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
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<item rdf:about="https://arxiv.org/abs/1802.09904">
    <title>[1802.09904] Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism</title>
    <dc:date>2019-11-25T23:47:41+00:00</dc:date>
    <link>https://arxiv.org/abs/1802.09904</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Complex data usually results from the interaction of objects produced by different generating mechanisms. Here we introduce a universal, unsupervised and parameter-free model-oriented approach, based upon the seminal concept of algorithmic probability, that decomposes an observation into its most likely algorithmic generative sources. Our approach uses a causal calculus to infer model representations. We demonstrate its ability to deconvolve interacting mechanisms regardless of whether the resultant objects are strings, space-time evolution diagrams, images or networks. While this is mostly a conceptual contribution and a novel framework, we provide numerical evidence evaluating the ability of our methods to separate data from observations produced by discrete dynamical systems such as cellular automata and complex networks. We think that these separating techniques can contribute to tackling the challenge of causation, thus complementing other statistically oriented approaches.
]]></description>
<dc:subject>machine-learning dynamical-systems inverse-problems rather-interesting system-identification to-simulate to-try to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:aa810e1dd392/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-try"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
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<item rdf:about="https://www.sciencedirect.com/science/article/pii/S2210650218300208?dgcid=coauthor">
    <title>Alignment-based genetic programming for real life applications - ScienceDirect</title>
    <dc:date>2019-01-19T13:17:52+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/S2210650218300208?dgcid=coauthor</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A recent discovery has attracted the attention of many researchers in the field of genetic programming: given individuals with particular characteristics of alignment in the error space, called optimally aligned, it is possible to reconstruct a globally optimal solution. Furthermore, recent preliminary experiments have shown that an indirect search consisting of looking for optimally aligned individuals can have benefits in terms of generalization ability compared to a direct search for optimal solutions. For this reason, defining genetic programming systems that look for optimally aligned individuals is becoming an ambitious and important objective. Nevertheless, the systems that have been introduced so far present important limitations that make them unusable in practice, particularly for complex real-life applications. In this paper, we overcome those limitations, and we present the first usable alignment-based genetic programming system, called nested alignment genetic programming (NAGP). The presented experimental results show that NAGP is able to outperform two of the most recognized state-of-the-art genetic programming systems on four complex real-life applications. The predictive models generated by NAGP are not only more effective than the ones produced by the other studied methods but also significantly smaller and thus more manageable and interpretable.

]]></description>
<dc:subject>symbolic-regression genetic-programming hybrid-methods system-identification have-read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:7f84383f1550/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:symbolic-regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hybrid-methods"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:have-read"/>
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<item rdf:about="http://arxiv.org/abs/1607.01067">
    <title>[1607.01067] Exact Recovery of Chaotic Systems from Highly Corrupted Data</title>
    <dc:date>2016-08-15T13:13:28+00:00</dc:date>
    <link>http://arxiv.org/abs/1607.01067</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Learning the governing equations in dynamical systems from time-varying measurements is of great interest across different scientific fields. This task becomes prohibitive when such data is moreover highly corrupted, for example, due to the recording mechanism failing over unknown intervals of time. When the underlying system exhibits chaotic behavior, such as sensitivity to initial conditions, it is crucial to recover the governing equations with high precision. In this work, we consider continuous time dynamical systems x˙=f(x) where each component of f:ℝd→ℝd is a multivariate polynomial of maximal degree p; we aim to identify f exactly from possibly highly corrupted measurements x(t1),x(t2),…,x(tm). As our main theoretical result, we show that if the system is sufficiently ergodic that this data satisfies a strong central limit theorem (as is known to hold for chaotic Lorenz systems), then the governing equations f can be exactly recovered as the solution to an ℓ1 minimization problem -- even if a large percentage of the data is corrupted by outliers. Numerically, we apply the alternating minimization method to solve the corresponding constrained optimization problem. Through several examples of 3D chaotic systems and higher dimensional hyperchaotic systems, we illustrate the power, generality, and efficiency of the algorithm for recovering governing equations from noisy and highly corrupted measurement data.]]></description>
<dc:subject>nonlinear-dynamics system-identification regression nudge-targets consider:rediscovery consider:looking-to-see consider:horse-races</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3b2a267edeef/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
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<item rdf:about="http://arxiv.org/abs/1604.04198">
    <title>[1604.04198] Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies</title>
    <dc:date>2016-06-20T12:11:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1604.04198</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this article, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach for nonlinear system identification. The EPFES is derived from the frequently-employed state-space model, where the relevant information of the nonlinear system is captured by an unknown state vector. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary elitist-particle selection which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this article, we propose two advancements of the previously-published elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter for two completely different scenarios: First, we consider the so-called univariate nonstationary growth model with time-variant latent state variable, where the evolutionary selection of elitist particles is evaluated for non-recursively calculated particle weights. Second, the problem of nonlinear acoustic echo cancellation is addressed in a simulated scenario with speech as input signal: By using long-term fitness measures, we highlight the efficacy of the well-generalizing EPFES in estimating the nonlinear system even for large search spaces. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.
]]></description>
<dc:subject>machine-learning system-identification inverse-problems nonlinear-dynamics Bayesian-modeling statistics nudge-targets consider:looking-to-see consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6fd22b991e30/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Bayesian-modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
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<item rdf:about="http://arxiv.org/abs/1605.06973">
    <title>[1605.06973] Inverse Problems for Matrix Exponential in System Identification: System Aliasing</title>
    <dc:date>2016-06-06T11:25:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1605.06973</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This note addresses identification of the A-matrix in continuous time linear dynamical systems on state-space form. If this matrix is partially known or known to have a sparse structure, such knowledge can be used to simplify the identification. We begin by introducing some general conditions for solvability of the inverse problems for matrix exponential. Next, we introduce "system aliasing" as an issue in the identification of slow sampled systems. Such aliasing give rise to non-unique matrix logarithms. As we show, by imposing additional conditions on and prior knowledge about the A-matrix, the issue of system aliasing can, at least partially, be overcome. Under conditions on the sparsity and the norm of the A-matrix, it is identifiable up to a finite equivalence class.
]]></description>
<dc:subject>nonlinear-dynamics system-identification dimension-reduction statistics algorithms inference nudge-targets consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:49664a5aab54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dimension-reduction"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1509.03580#">
    <title>[1509.03580] Discovering governing equations from data: Sparse identification of nonlinear dynamical systems</title>
    <dc:date>2016-05-17T23:50:24+00:00</dc:date>
    <link>http://arxiv.org/abs/1509.03580#</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The ability to discover physical laws and governing equations from data is one of humankind's greatest intellectual achievements. A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technological achievements, including aircraft, combustion engines, satellites, and electrical power. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing physical equations from measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized, time-varying, or externally forced systems.
]]></description>
<dc:subject>system-identification nonlinear-dynamics via:arthegall rather-interesting compressed-sensing inference nudge-targets consider:representation consider:making-it-a-primitive</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:60f965015ae2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:arthegall"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:compressed-sensing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:making-it-a-primitive"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1403.7175">
    <title>[1403.7175] Low-Rank and Low-Order Decompositions for Local System Identification</title>
    <dc:date>2014-08-01T11:38:51+00:00</dc:date>
    <link>http://arxiv.org/abs/1403.7175</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As distributed systems increase in size, the need for scalable algorithms becomes more and more important. We argue that in the context of system identification, an essential building block of any scalable algorithm is the ability to estimate local dynamics within a large interconnected system. We show that in what we term the "full interconnection measurement" setting, this task is easily solved using existing system identification methods. We also propose a promising heuristic for the "hidden interconnection measurement" case, in which contributions to local measurements from both local and global dynamics need to be separated. Inspired by the machine learning literature, and in particular by convex approaches to rank minimization and matrix decomposition, we exploit the fact that the transfer function of the local dynamics is low-order, but full-rank, while the transfer function of the global dynamics is high-order, but low-rank, to formulate this separation task as a nuclear norm minimization.
]]></description>
<dc:subject>system-identification modeling modularity distributed-systems what-is-this-I-don't-even system-of-professions</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:81dd1146c348/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modularity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:distributed-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:what-is-this-I-don't-even"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-of-professions"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1308.5261">
    <title>[1308.5261] The Network Observability Problem: Detecting nodes and connections and the role of graph symmetries</title>
    <dc:date>2014-02-26T11:33:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1308.5261</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Reconstructing the connections between the nodes of a network is a problem of fundamental importance in the study of neuronal and genetic networks. An underlying related problem is that of observability, i.e., identifying the conditions under which such a reconstruction is possible. In this paper we consider observability of complex dynamical networks,for which we aim at identifying both node and edge states. We use a graphical approach, which we apply to both the Node Inference Diagram (NID) and the Node Edge Inference Diagram (NEID) of the network. We investigate the relationship between the observability of the NID and that of the NEID network representations and conclude that the latter can be derived from the former, under general assumptions. We further consider the effects of graph symmetries on observability and we show how a minimal set of outputs can be selected to obtain observability in the presence of graph symmetries.
]]></description>
<dc:subject>network-theory models algorithms system-identification inference interesting nudge-targets philosophy-of-science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:20486663fac8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:philosophy-of-science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.1896">
    <title>[1304.1896] Parenclitic networks' representation of data sets</title>
    <dc:date>2013-04-25T00:09:27+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.1896</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Of the different ways of representing a multi-unit system, the one afforded by networks is among the most elegant and general. Endowing a system with a network representation requires defining nodes and links connecting them. Often physical or virtual relationships between the elements of the system, e.g. anatomic brain fibres or hyper-links between the pages of a web site, constrain the way a link is defined. When such relationships are not clearly apparent, yet functional links can be built as long as time evolving variables are associated to each node, as e.g. the time evolution of a stock price, or of brain activity in a given region. We propose a third, novel, method which allows treating collections of isolated, possibly heterogeneous, scalars, e.g. sets of biomedical tests, as networked systems. The method builds a network where each node represents a feature, while each pairing quantifies the deviation between those two features and the corresponding typical relationship between them within a studied population. Topological characteristics can then be used to extract important information about the system. In particular, atypical or pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. Insofar as a network representation of each instance or subject is constructed with reference to the population to which he is compared, this technique is by its very nature a difference seeker. We apply the method to unveil the importance of specific genes in the response of a plant, the {\it Arabidopsis thaliana}, to osmotic stress. The most important genes turned out to be the nodes with highest centrality in the reconstructed networks, such that, when they are knocked out, different phenotypes appear...
]]></description>
<dc:subject>statistics out-of-the-box network-theory covariance-somehow bioinformatics inference algorithms nudge-targets representation interesting because-cshalizi-will-know-what-we-should-think-of-it system-identification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:05dda374bb09/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:out-of-the-box"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:covariance-somehow"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:because-cshalizi-will-know-what-we-should-think-of-it"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1302.7080">
    <title>[1302.7080] Parameter Identification of Induction Motor Using Modified Particle Swarm Optimization Algorithm</title>
    <dc:date>2013-03-03T12:42:24+00:00</dc:date>
    <link>http://arxiv.org/abs/1302.7080</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper presents a new technique for induction motor parameter identification. The proposed technique is based on a simple startup test using a standard V/F inverter. The recorded startup currents are compared to that obtained by simulation of an induction motor model. A Modified PSO optimization is used to find out the best model parameter that minimizes the sum square error between the measured and the simulated currents. The performance of the modified PSO is compared with other optimization methods including line search, conventional PSO and Genetic Algorithms. Simulation results demonstrate the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the modified PSO over other optimization techniques.]]></description>
<dc:subject>classification system-identification inverse-problems algorithms agent-based PSO nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cf93f1acb3b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:classification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:agent-based"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:PSO"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.4648">
    <title>[1206.4648] Two-Manifold Problems with Applications to Nonlinear System Identification</title>
    <dc:date>2012-07-02T22:14:54+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.4648</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Recently, there has been much interest in spectral approaches to learning manifolds---so-called kernel eigenmap methods. These methods have had some successes, but their applicability is limited because they are not robust to noise. To address this limitation, we look at two-manifold problems, in which we simultaneously reconstruct two related manifolds, each representing a different view of the same data. By solving these interconnected learning problems together, two-manifold algorithms are able to succeed where a non-integrated approach would fail: each view allows us to suppress noise in the other, reducing bias. We propose a class of algorithms for two-manifold problems, based on spectral decomposition of cross-covariance operators in Hilbert space, and discuss when two-manifold problems are useful. Finally, we demonstrate that solving a two-manifold problem can aid in learning a nonlinear dynamical system from limited data."]]></description>
<dc:subject>statistics inverse-problems system-identification algorithms nudge-targets benchmarking</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c30747f15a18/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:benchmarking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1109.0573">
    <title>[1109.0573] Phase Retrieval via Matrix Completion</title>
    <dc:date>2011-10-04T13:42:49+00:00</dc:date>
    <link>http://arxiv.org/abs/1109.0573</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["This paper considers the fundamental problem of recovering a general signal, an image for example, from the magnitude of its Fourier transform. This problem, also known as phase retrieval, arises in many applications and has challenged engineers, physicists, and mathematicians for decades. Its origin comes from the fact that detectors can often times only record the squared modulus of the Fresnel or Fraunhofer diffraction pattern of the radiation that is scattered from an object. In such settings, one cannot measure the phase of the optical wave reaching the detector and, therefore, much information about the scattered object or the optical field is lost since, as is well known, the phase encodes a lot of the structural content of the image we wish to form."]]></description>
<dc:subject>image-processing inverse-problems signal-processing system-identification frequency-space algorithms nudge-targets numerical-methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9773207acbd9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:signal-processing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:system-identification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:frequency-space"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:numerical-methods"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>