195 research outputs found
Signal Space CoSaMP for Sparse Recovery with Redundant Dictionaries
Compressive sensing (CS) has recently emerged as a powerful framework for
acquiring sparse signals. The bulk of the CS literature has focused on the case
where the acquired signal has a sparse or compressible representation in an
orthonormal basis. In practice, however, there are many signals that cannot be
sparsely represented or approximated using an orthonormal basis, but that do
have sparse representations in a redundant dictionary. Standard results in CS
can sometimes be extended to handle this case provided that the dictionary is
sufficiently incoherent or well-conditioned, but these approaches fail to
address the case of a truly redundant or overcomplete dictionary. In this paper
we describe a variant of the iterative recovery algorithm CoSaMP for this more
challenging setting. We utilize the D-RIP, a condition on the sensing matrix
analogous to the well-known restricted isometry property. In contrast to prior
work, the method and analysis are "signal-focused"; that is, they are oriented
around recovering the signal rather than its dictionary coefficients. Under the
assumption that we have a near-optimal scheme for projecting vectors in signal
space onto the model family of candidate sparse signals, we provide provable
recovery guarantees. Developing a practical algorithm that can provably compute
the required near-optimal projections remains a significant open problem, but
we include simulation results using various heuristics that empirically exhibit
superior performance to traditional recovery algorithms
Spectral Clustering: An Empirical Study of Approximation Algorithms and its Application to the Attrition Problem
Clustering is the problem of separating a set of objects into groups (called clusters) so that objects within the same cluster are more similar to each other than to those in different clusters. Spectral clustering is a now well-known method for clustering which utilizes the spectrum of the data similarity matrix to perform this separation. Since the method relies on solving an eigenvector problem, it is computationally expensive for large datasets. To overcome this constraint, approximation methods have been developed which aim to reduce running time while maintaining accurate classification. In this article, we summarize and experimentally evaluate several approximation methods for spectral clustering. From an applications standpoint, we employ spectral clustering to solve the so-called attrition problem, where one aims to identify from a set of employees those who are likely to voluntarily leave the company from those who are not. Our study sheds light on the empirical performance of existing approximate spectral clustering methods and shows the applicability of these methods in an important business optimization related problem
Structured Sparsity: Discrete and Convex approaches
Compressive sensing (CS) exploits sparsity to recover sparse or compressible
signals from dimensionality reducing, non-adaptive sensing mechanisms. Sparsity
is also used to enhance interpretability in machine learning and statistics
applications: While the ambient dimension is vast in modern data analysis
problems, the relevant information therein typically resides in a much lower
dimensional space. However, many solutions proposed nowadays do not leverage
the true underlying structure. Recent results in CS extend the simple sparsity
idea to more sophisticated {\em structured} sparsity models, which describe the
interdependency between the nonzero components of a signal, allowing to
increase the interpretability of the results and lead to better recovery
performance. In order to better understand the impact of structured sparsity,
in this chapter we analyze the connections between the discrete models and
their convex relaxations, highlighting their relative advantages. We start with
the general group sparse model and then elaborate on two important special
cases: the dispersive and the hierarchical models. For each, we present the
models in their discrete nature, discuss how to solve the ensuing discrete
problems and then describe convex relaxations. We also consider more general
structures as defined by set functions and present their convex proxies.
Further, we discuss efficient optimization solutions for structured sparsity
problems and illustrate structured sparsity in action via three applications.Comment: 30 pages, 18 figure
Dismissive and deceptive car dealerships create barriers to electric vehicle adoption at the point of sale
This study investigates the role of car dealerships in the electrification of passenger transport, namely their sales advice about the purchase and use of electric vehicles (EVs). Because most consumers do not have pre-existing knowledge of EVs, and current market conditions favour petrol and diesel vehicles, car dealership experiences may strongly influence EV purchasing decisions. Here we show that car dealerships pose a significant barrier at the point of sale due to a perceived lack of business case viability in relation to petrol and diesel vehicles. In 126 shopping experiences at 82 car dealerships across Denmark, Finland, Iceland, Norway, and Sweden, we find dealers were dismissive of EVs, misinformed shoppers on vehicle specifications, omitted EVs from the sales conversation and strongly oriented customers towards petrol and diesel vehicle options. Dealer’s technological orientation, willingness to sell, and displayed knowledge of EVs were the main contributors to likely purchase intentions. These findings combined with expert interviews suggest that government and industry signalling affect sales strategies and purchasing trends. Policy and business strategies that address barriers at the point of sale are needed to accelerate EV adoption
Reunifying from behind bars: A quantitative study of the relationship between parental incarceration, service use, and foster care reunification
Incarcerated parents attempting to reunify with their children in foster care can find it difficult to complete the activities on their court-ordered case plans, such as drug treatment services and visitation with children. Although much has been written regarding the obstacles that are likely to interfere with reunification for incarcerated parents, very little quantitative research has examined the topic. This study uses secondary data to examine the incarceration experiences and reunification outcomes of a sample of 225 parents in one large urban California county. In multivariate analysis controlling for problems and demographics, incarcerated parents were less likely to reunify with their children; however, service use appeared to mediate this relationship, as the negative association between incarceration and reunification did not persist when service use was included as a variable in the model. Suggestions are made for policy and practice changes to improve reunification outcomes for this population of parents.
Efficient Sparse Coding in Early Sensory Processing: Lessons from Signal Recovery
Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representations in an efficient way. We argue that higher level overcompleteness becomes computationally tractable by imposing sparsity on synaptic activity and we also show that such structural sparsity can be facilitated by statistics based decomposition of the stimuli into typical and atypical parts prior to sparse coding. Typical parts represent large-scale correlations, thus they can be significantly compressed. Atypical parts, on the other hand, represent local features and are the subjects of actual sparse coding. When applied on natural images, our decomposition based sparse coding model can efficiently form overcomplete codes and both center-surround and oriented filters are obtained similar to those observed in the retina and the primary visual cortex, respectively. Therefore we hypothesize that the proposed computational architecture can be seen as a coherent functional model of the first stages of sensory coding in early vision
Understanding and measuring child welfare outcomes
The new Children\u27s and Family Services Reviews (CFSR) process focuses on the effectiveness of services to children and families by measuring client outcomes. This article reviews the research literature related to child welfare outcomes in order to provide a context for federal accountability efforts. It also summarizes the 2001 federal mandate to hold states accountable for child welfare outcomes and describes California\u27s response to this mandate. Implications of the outcomes literature review and measurement problems in the CFSR process suggest CSFR measures do not always capture meaningful outcomes. Recommendations for change are made
Precision Tests of the Standard Model
30 páginas, 11 figuras, 11 tablas.-- Comunicación presentada al 25º Winter Meeting on Fundamental Physics celebrado del 3 al 8 de MArzo de 1997 en Formigal (España).Precision measurements of electroweak observables provide stringent tests of the Standard Model structure and an accurate determination of its parameters. An overview of the present experimental status is presented.This work has been supported in part
by CICYT (Spain) under grant No. AEN-96-1718.Peer reviewe
Low Complexity Regularization of Linear Inverse Problems
Inverse problems and regularization theory is a central theme in contemporary
signal processing, where the goal is to reconstruct an unknown signal from
partial indirect, and possibly noisy, measurements of it. A now standard method
for recovering the unknown signal is to solve a convex optimization problem
that enforces some prior knowledge about its structure. This has proved
efficient in many problems routinely encountered in imaging sciences,
statistics and machine learning. This chapter delivers a review of recent
advances in the field where the regularization prior promotes solutions
conforming to some notion of simplicity/low-complexity. These priors encompass
as popular examples sparsity and group sparsity (to capture the compressibility
of natural signals and images), total variation and analysis sparsity (to
promote piecewise regularity), and low-rank (as natural extension of sparsity
to matrix-valued data). Our aim is to provide a unified treatment of all these
regularizations under a single umbrella, namely the theory of partial
smoothness. This framework is very general and accommodates all low-complexity
regularizers just mentioned, as well as many others. Partial smoothness turns
out to be the canonical way to encode low-dimensional models that can be linear
spaces or more general smooth manifolds. This review is intended to serve as a
one stop shop toward the understanding of the theoretical properties of the
so-regularized solutions. It covers a large spectrum including: (i) recovery
guarantees and stability to noise, both in terms of -stability and
model (manifold) identification; (ii) sensitivity analysis to perturbations of
the parameters involved (in particular the observations), with applications to
unbiased risk estimation ; (iii) convergence properties of the forward-backward
proximal splitting scheme, that is particularly well suited to solve the
corresponding large-scale regularized optimization problem
Brazil in the Era of Fascism: The “New State” of Getúlio Vargas
The New State established in Brazil by Getúlio Vargas (1937–1945) is the most important case of the institutionalisation of a dictatorship of the fascism era in Latin America. During this time, an impressive spectrum of authoritarian regimes was established, some of which were very instable and poorly institutionalised, while others were more consolidated. Roger Griffin coined the concept of para-fascism for some of them, and the “New State” of Getúlio Vargas in Brazil is a paradigmatic case. In this essay, we analyse the processes of institutional reform in 1930s Brazil paying particular attention to how domestic political actors look at institutional models of fascism and corporatism.info:eu-repo/semantics/publishedVersio
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