Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we presentAgwan (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the Agwanmodel to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.

Ultra-Wideband Communications - An idea whose time has still yet to come? [Wireless Corner]

This paper presents a critical analysis of ultrawideband (UWB) and considers the turbulent journey it has had from the Federal Communications Commission's bandwidth allocation in 2002 to today. It analyzes the standards, the standoffs, and the stalemate in standardization activities and investigates the past and present research and commercial activities in realizing the UWB dream. In this paper, statistical evidence is presented to depict UWB's changing fortunes and is utilized as an indicator of future prominence. This paper reviews some of the opinions and remarks from commentators and analyzes predictions that were made. Finally, it presents possible ways forward to reignite the high-data-rate UWB standardization pursuit.

European urban spaces in crisis - the mapping of affective pracitices with Living with Difference: introduction

The introduction outlines the notion of urban space and crisis in Europe while taking into account the more recent protests and riots in different cities, in and beyond Europe. It is argued that the phenomen of protest is happening alongside the economic crisis underscoring an alternative political public civic spirit expressing to a certain degree the renaissance and timely making of, what might be called in the digital age, #œuvre. Its forces and emotional properties capture a political realm that unfolds as a globalized urban transnational public space, still progressing. Further, it introduces the collection of papers for the special themed feature. Five papers look at affective practices through a Continental European lens, which places the meaning of race, migration and intersecting identity angles at the centre of debates of individual encounters in public spaces. The final and sixth paper, written by Brenda Yeoh, looks through a Singapore/East Asia lens, and comments on the common European threats as well as on the historical specificity and implications of distinctive geo-political spaces for affective practices.

Seeking the New Normal? Troubled Spaces of Encountering Visible Differences in Warsaw

In times of globalisation and super-mobility, ideas of normality are in turmoil. In different societies in, across and beyond Europe, we face the challenge of undoing specific notions of normality and creating more inclusive societies with an open culture of learning to live with differences. The scope of
the paper is to introduce some findings on encounters with difference and negotiations of social values in relation to a growing visibility of difference after 1989 in Poland, on the background of a critique of normality/normalisation and normalcy.On the basis of interviews conducted inWarsaw, we investigate how normality/normalisation discourses of visible homosexuality and physical disability are incorporated into individual self-reflections and justifications of prejudices (homophobia and disabilism). More specifically we argue that there are moments of ‘cultural transgressions’ present in everyday practices towards ‘visible’sexual and (dis)ability difference.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

Violation of Bell inequalities in larger Hilbert spaces: robustness and challenges

We explore the challenges posed by the violation of Bell-like inequalities by d-dimensional systems exposed to imperfect state-preparation and measurement settings. We address, in particular, the limit of high-dimensional systems, naturally arising when exploring the quantum-to-classical transition. We show that, although suitable Bell inequalities can be violated, in principle, for any dimension of given subsystems, it is in practice increasingly challenging to detect such violations, even if the system is prepared in a maximally entangled state. We characterize the effects of random perturbations on the state or on the measurement settings, also quantifying the efforts needed to certify the possible violations in case of complete ignorance on the system state at hand.