Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

Efficient and accurate set-based registration of time-separated aerial images

This paper addresses the task of time-separated aerial image registration. The ability to solve this problem accurately and reliably is important for a variety of subsequent image understanding applications. The principal challenge lies in the extent and nature of transient appearance variation that a land area can undergo, such as that caused by the change under illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several major novelties (i) unlike previous work on aerial image registration, we approach the problem using a set-based paradigm; (ii) we show how image space local, pair-wise constraints can be used to enforce a globally good registration using a constraints graph structure; (iii) we show how a simple holistic representation derived from raw aerial images can be used as a basic building block of the constraints graph in a manner which achieves both high registration accuracy and speed; (iv) lastly, we introduce a new and, to the best of our knowledge, the only data corpus suitable for the evaluation of set-based aerial image registration algorithms. Using this data set, we demonstrate (i) that the proposed method outperforms the state-of-the-art for pair-wise registration already, achieving greater accuracy and reliability, while at the same time reducing the computational cost of the task and (ii) that the increase in the number of available images in a set consistently reduces the average registration error, with a major difference already for a single additional image.

Rebound or resignation: developing a predictive model of return to subjective wellbeing set-point

Though subjective wellbeing (SWB) is generally stable and consistent over time, it can fall below its set-point in response to adverse life events. However, deviations from set-point levels are usually only temporary, as homeostatic processes operate to return SWB to its normal state. Given that income and close interpersonal relationships have been proposed as powerful external resources that are coincident with higher SWB, access to these resources may be an important predictor of whether or not a person is likely to recover their SWB following a departure from their set-point. Under the guiding framework of SWB Homeostasis Theory, this study considers whether access to a higher income and a committed partner can predict whether people who score lower than normal for SWB at baseline will return to normal set-point levels of SWB (rebound) or remain below the normal range (resigned) at follow-up. Participants were 733 people (53.3 % female) from the Australian Unity Longitudinal Wellbeing Study who ranged in age from 20 to 92 years (M = 59.65 years; SD = 13.15). Logistic regression analyses revealed that participants’ demographic characteristics were poor predictors of whether they rebounded or resigned. Consistent with homeostasis theory, the extent of departure from the proposed normal SWB set-point at baseline was significantly associated with rebound or resignation at time 2. These findings have implications for the way that SWB measures can be used in professional practice to identify people who are particularly vulnerable to depression and to guide the provision of appropriate and effective therapeutic interventions.

Connected P-percent coverage in wireless sensor networks based on degree constraint dominating set approach

In this paper, we propose an algorihm for conneced p-percent coverage probem in Wireless Sensor Networks(WSNs) to improve the over netork life time. In this work, we invstigae the p-pernt coverage problem(PCP) in WSNs which require % of n area should be monitored correctl and to find ou ny additional requirements of the connec p-percent coverge prom. We prose pDCDS algorith which is a learnin autmaton basd algorithm fr PCP pDCDS is a Degreconsained Connected Domining Se based algoithm whch detect the minimum numbe of des to monitor an area. The simulation results demonstrate hat pDCDS can remarkably improve the network lifetime.

Correlated differential privacy: hiding information in non-IID data set

Privacy preserving on data mining and data release has attracted an increasing research interest over a number of decades. Differential privacy is one influential privacy notion that offers a rigorous and provable privacy guarantee for data mining and data release. Existing studies on differential privacy assume that in a data set, records are sampled independently. However, in real-world applications, records in a data set are rarely independent. The relationships among records are referred to as correlated information and the data set is defined as correlated data set. A differential privacy technique performed on a correlated data set will disclose more information than expected, and this is a serious privacy violation. Although recent research was concerned with this new privacy violation, it still calls for a solid solution for the correlated data set. Moreover, how to decrease the large amount of noise incurred via differential privacy in correlated data set is yet to be explored. To fill the gap, this paper proposes an effective correlated differential privacy solution by defining the correlated sensitivity and designing a correlated data releasing mechanism. With consideration of the correlated levels between records, the proposed correlated sensitivity can significantly decrease the noise compared with traditional global sensitivity. The correlated data releasing mechanism correlated iteration mechanism is designed based on an iterative method to answer a large number of queries. Compared with the traditional method, the proposed correlated differential privacy solution enhances the privacy guarantee for a correlated data set with less accuracy cost. Experimental results show that the proposed solution outperforms traditional differential privacy in terms of mean square error on large group of queries. This also suggests the correlated differential privacy can successfully retain the utility while preserving the privacy.

Dominating set and network coding-based routing in wireless mesh networks

Wireless mesh networks are widely applied in many fields such as industrial controlling, environmental monitoring, and military operations. Network coding is promising technology that can improve the performance of wireless mesh networks. In particular, network coding is suitable for wireless mesh networks as the fixed backbone of wireless mesh is usually unlimited energy. However, coding collision is a severe problem affecting network performance. To avoid this, routing should be effectively designed with an optimum combination of coding opportunity and coding validity. In this paper, we propose a Connected Dominating Set (CDS)-based and Flow-oriented Coding-aware Routing (CFCR) mechanism to actively increase potential coding opportunities. Our work provides two major contributions. First, it effectively deals with the coding collision problem of flows by introducing the information conformation process, which effectively decreases the failure rate of decoding. Secondly, our routing process considers the benefit of CDS and flow coding simultaneously. Through formalized analysis of the routing parameters, CFCR can choose optimized routing with reliable transmission and small cost. Our evaluation shows CFCR has a lower packet loss ratio and higher throughput than existing methods, such as Adaptive Control of Packet Overhead in XOR Network Coding (ACPO), or Distributed Coding-Aware Routing (DCAR).

As mudanças na organização da produção na indústria gáfica do Grande ABC: um estudo sobre o segmento off set

O presente trabalho tem por objetivo caracterizar a indústria gráfica do ponto de vista da gestão, tecnologia, inovação e competição. A economia ao longo de sua história foi marcante por alguma situação peculiar que a caracterizasse. Em particular a partir da década de 1960 acompanhamos o crescimento em larga escala na região do Grande ABC onde, a indústria automobilística e seus fornecedores necessitaram de maior estrutura e apoio para as suas operações, provocando alterações nos modos de atuação das empresas industriais no país. Nessa mesma direção pelas necessidades criadas à época, a indústria gráfica na região do ABC também teve seu crescimento para atender a demanda e, criando o caráter da regionalidade e se fortalecendo economicamente. Entretanto a partir da década de 1980 com as crises econômicas e a abertura de mercado, houve uma redução nos postos de trabalho, mas as empresas também alteraram as formas de produzir. O trabalho de campo foi apoiado num referencial teórico baseado na revisão bibliográfica e um roteiro de entrevistas que orientou a coleta de dados. Na presente pesquisa foram realizadas entrevistas com os sujeitos relacionados e verificação de documentos. A análise realizada buscou confrontar os dados coletados e sistematizados com o quadro conceitual utilizado para a elaboração da pesquisa, possibilitando apurar as principais transformações verificadas no segmento estudado, bem como oferecendo embasamento para a tomada de decisão dos atores econômicos envolvidos com o desenvolvimento da indústria gráfica no Grande ABC.

Graphical models and point set matching

Point pattern matching in Euclidean Spaces is one of the fundamental problems in Pattern Recognition, having applications ranging from Computer Vision to Computational Chemistry. Whenever two complex patterns are encoded by two sets of points identifying their key features, their comparison can be seen as a point pattern matching problem. This work proposes a single approach to both exact and inexact point set matching in Euclidean Spaces of arbitrary dimension. In the case of exact matching, it is assured to find an optimal solution. For inexact matching (when noise is involved), experimental results confirm the validity of the approach. We start by regarding point pattern matching as a weighted graph matching problem. We then formulate the weighted graph matching problem as one of Bayesian inference in a probabilistic graphical model. By exploiting the existence of fundamental constraints in patterns embedded in Euclidean Spaces, we prove that for exact point set matching a simple graphical model is equivalent to the full model. It is possible to show that exact probabilistic inference in this simple model has polynomial time complexity with respect to the number of elements in the patterns to be matched. This gives rise to a technique that for exact matching provably finds a global optimum in polynomial time for any dimensionality of the underlying Euclidean Space. Computational experiments comparing this technique with well-known probabilistic relaxation labeling show significant performance improvement for inexact matching. The proposed approach is significantly more robust under augmentation of the sizes of the involved patterns. In the absence of noise, the results are always perfect.

The set of equilibria of first-price auctions

In this note I specify the class of functions that are equilibria of symmetric first-price auctions.

A linguagem de Oscar Niemeyer

Este trabalho tem como objetivo a descrição dos elementos que caracterizam a singularidade da linguagem arquitetônica de Oscar Niemeyer. Argumenta que a identificação de tais elementos passa pelo escrutínio de aspectos não visíveis da obra do arquiteto. A identificação foi possível a partir da análise de edifícios caracterizados pelo perfil curvilíneo e da construção de um modelo que associa os elementos compositivos utilizados por Niemeyer a uma Gramática de Formas. A utilização do modelo possibilitou revelar os princípios generativos - conjunto de regras, vocabulário e relações geométricas – que caracterizam o estilo – ou linguagem arquitetônica de Niemeyer. Ajudou ainda a demonstrar como a linguagem de Niemeyer associa de forma original, operações de transformação como rotação, reflexão, e translação a um vocabulário de curvas. A associação é parametrizada segundo um traçado regulador baseado na seção áurea. Em suas conclusões o trabalho sugere possibilidades de desenvolvimento desta gramática para todas as figuras utilizadas por Niemeyer e a aplicação de princípios generativos no ensino de arquitetura.