Tracking a Large Number of Objects from Multiple Views

We propose a multi-object multi-camera framework for tracking large numbers of tightly-spaced objects that rapidly move in three dimensions. We formulate the problem of finding correspondences across multiple views as a multidimensional assignment problem and use a greedy randomized adaptive search procedure to solve this NP-hard problem efficiently. To account for occlusions, we relax the one-to-one constraint that one measurement corresponds to one object and iteratively solve the relaxed assignment problem. After correspondences are established, object trajectories are estimated by stereoscopic reconstruction using an epipolar-neighborhood search. We embedded our method into a tracker-to-tracker multi-view fusion system that not only obtains the three-dimensional trajectories of closely-moving objects but also accurately settles track uncertainties that could not be resolved from single views due to occlusion. We conducted experiments to validate our greedy assignment procedure and our technique to recover from occlusions. We successfully track hundreds of flying bats and provide an analysis of their group behavior based on 150 reconstructed 3D trajectories.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

On the Origin of Power Laws in Internet Topologies

Recent empirical studies have shown that Internet topologies exhibit power laws of the form for the following relationships: (P1) outdegree of node (domain or router) versus rank; (P2) number of nodes versus outdegree; (P3) number of node pairs y = x^α within a neighborhood versus neighborhood size (in hops); and (P4) eigenvalues of the adjacency matrix versus rank. However, causes for the appearance of such power laws have not been convincingly given. In this paper, we examine four factors in the formation of Internet topologies. These factors are (F1) preferential connectivity of a new node to existing nodes; (F2) incremental growth of the network; (F3) distribution of nodes in space; and (F4) locality of edge connections. In synthetically generated network topologies, we study the relevance of each factor in causing the aforementioned power laws as well as other properties, namely diameter, average path length and clustering coefficient. Different kinds of network topologies are generated: (T1) topologies generated using our parametrized generator, we call BRITE; (T2) random topologies generated using the well-known Waxman model; (T3) Transit-Stub topologies generated using GT-ITM tool; and (T4) regular grid topologies. We observe that some generated topologies may not obey power laws P1 and P2. Thus, the existence of these power laws can be used to validate the accuracy of a given tool in generating representative Internet topologies. Power laws P3 and P4 were observed in nearly all considered topologies, but different topologies showed different values of the power exponent α. Thus, while the presence of power laws P3 and P4 do not give strong evidence for the representativeness of a generated topology, the value of α in P3 and P4 can be used as a litmus test for the representativeness of a generated topology. We also find that factors F1 and F2 are the key contributors in our study which provide the resemblance of our generated topologies to that of the Internet.

An Invariant Representation for Matching Trajectories across Uncalibrated Video Streams

We introduce a view-point invariant representation of moving object trajectories that can be used in video database applications. It is assumed that trajectories lie on a surface that can be locally approximated with a plane. Raw trajectory data is first locally approximated with a cubic spline via least squares fitting. For each sampled point of the obtained curve, a projective invariant feature is computed using a small number of points in its neighborhood. The resulting sequence of invariant features computed along the entire trajectory forms the view invariant descriptor of the trajectory itself. Time parametrization has been exploited to compute cross ratios without ambiguity due to point ordering. Similarity between descriptors of different trajectories is measured with a distance that takes into account the statistical properties of the cross ratio, and its symmetry with respect to the point at infinity. In experiments, an overall correct classification rate of about 95% has been obtained on a dataset of 58 trajectories of players in soccer video, and an overall correct classification rate of about 80% has been obtained on matching partial segments of trajectories collected from two overlapping views of outdoor scenes with moving people and cars.

Web Mediators for Accessible Browsing

We present a highly accurate method for classifying web pages based on link percentage, which is the percentage of text characters that are parts of links normalized by the number of all text characters on a web page. K-means clustering is used to create unique thresholds to differentiate index pages and article pages on individual web sites. Index pages contain mostly links to articles and other indices, while article pages contain mostly text. We also present a novel link grouping algorithm using agglomerative hierarchical clustering that groups links in the same spatial neighborhood together while preserving link structure. Grouping allows users with severe disabilities to use a scan-based mechanism to tab through a web page and select items. In experiments, we saw up to a 40-fold reduction in the number of commands needed to click on a link with a scan-based interface, which shows that we can vastly improve the rate of communication for users with disabilities. We used web page classification and link grouping to alter web page display on an accessible web browser that we developed to make a usable browsing interface for users with disabilities. Our classification method consistently outperformed a baseline classifier even when using minimal data to generate article and index clusters, and achieved classification accuracy of 94.0% on web sites with well-formed or slightly malformed HTML, compared with 80.1% accuracy for the baseline classifier.

Distributed Placement of Service Facilities in Large-Scale Networks

The effectiveness of service provisioning in largescale networks is highly dependent on the number and location of service facilities deployed at various hosts. The classical, centralized approach to determining the latter would amount to formulating and solving the uncapacitated k-median (UKM) problem (if the requested number of facilities is fixed), or the uncapacitated facility location (UFL) problem (if the number of facilities is also to be optimized). Clearly, such centralized approaches require knowledge of global topological and demand information, and thus do not scale and are not practical for large networks. The key question posed and answered in this paper is the following: "How can we determine in a distributed and scalable manner the number and location of service facilities?" We propose an innovative approach in which topology and demand information is limited to neighborhoods, or balls of small radius around selected facilities, whereas demand information is captured implicitly for the remaining (remote) clients outside these neighborhoods, by mapping them to clients on the edge of the neighborhood; the ball radius regulates the trade-off between scalability and performance. We develop a scalable, distributed approach that answers our key question through an iterative reoptimization of the location and the number of facilities within such balls. We show that even for small values of the radius (1 or 2), our distributed approach achieves performance under various synthetic and real Internet topologies that is comparable to that of optimal, centralized approaches requiring full topology and demand information.

The Synthesis of Arbitrary Stable Dynamics in Non-linear Neural Networks II: Feedback and Universality

We wish to construct a realization theory of stable neural networks and use this theory to model the variety of stable dynamics apparent in natural data. Such a theory should have numerous applications to constructing specific artificial neural networks with desired dynamical behavior. The networks used in this theory should have well understood dynamics yet be as diverse as possible to capture natural diversity. In this article, I describe a parameterized family of higher order, gradient-like neural networks which have known arbitrary equilibria with unstable manifolds of known specified dimension. Moreover, any system with hyperbolic dynamics is conjugate to one of these systems in a neighborhood of the equilibrium points. Prior work on how to synthesize attractors using dynamical systems theory, optimization, or direct parametric. fits to known stable systems, is either non-constructive, lacks generality, or has unspecified attracting equilibria. More specifically, We construct a parameterized family of gradient-like neural networks with a simple feedback rule which will generate equilibrium points with a set of unstable manifolds of specified dimension. Strict Lyapunov functions and nested periodic orbits are obtained for these systems and used as a method of synthesis to generate a large family of systems with the same local dynamics. This work is applied to show how one can interpolate finite sets of data, on nested periodic orbits.

A unified framework for measuring preferences for schools and neighborhoods

This paper develops a framework for estimating household preferences for school and neighborhood attributes in the presence of sorting. It embeds a boundary discontinuity design in a heterogeneous residential choice model, addressing the endogeneity of school and neighborhood characteristics. The model is estimated using restricted-access Census data from a large metropolitan area, yielding a number of new results. First, households are willing to pay less than 1 percent more in house prices - substantially lower than previous estimates - when the average performance of the local school increases by 5 percent. Second, much of the apparent willingness to pay for more educated and wealthier neighbors is explained by the correlation of these sociodemographic measures with unobserved neighborhood quality. Third, neighborhood race is not capitalized directly into housing prices; instead, the negative correlation of neighborhood percent black and housing prices is due entirely to the fact that blacks live in unobservably lower-quality neighborhoods. Finally, there is considerable heterogeneity in preferences for schools and neighbors, with households preferring to self-segregate on the basis of both race and education. © 2007 by The University of Chicago. All rights reserved.

Ecological homogenization of urban USA

A visually apparent but scientifically untested outcome of land-use change is homogenization across urban areas, where neighborhoods in different parts of the country have similar patterns of roads, residential lots, commercial areas, and aquatic features. We hypothesize that this homogenization extends to ecological structure and also to ecosystem functions such as carbon dynamics and microclimate, with continental-scale implications. Further, we suggest that understanding urban homogenization will provide the basis for understanding the impacts of urban land-use change from local to continental scales. Here, we show how multi-scale, multidisciplinary datasets from six metropolitan areas that cover the major climatic regions of the US (Phoenix, AZ; Miami, FL; Baltimore, MD; Boston, MA; Minneapolis-St Paul, MN; and Los Angeles, CA) can be used to determine how household and neighborhood characteristics correlate with land-management practices, land-cover composition, and landscape structure and ecosystem functions at local, regional, and continental scales. © The Ecological Society of America.

Whole genome sequencing identifies circulating Beijing-lineage Mycobacterium tuberculosis strains in Guatemala and an associated urban outbreak.

Limited data are available regarding the molecular epidemiology of Mycobacterium tuberculosis (Mtb) strains circulating in Guatemala. Beijing-lineage Mtb strains have gained prevalence worldwide and are associated with increased virulence and drug resistance, but there have been only a few cases reported in Central America. Here we report the first whole genome sequencing of Central American Beijing-lineage strains of Mtb. We find that multiple Beijing-lineage strains, derived from independent founding events, are currently circulating in Guatemala, but overall still represent a relatively small proportion of disease burden. Finally, we identify a specific Beijing-lineage outbreak centered on a poor neighborhood in Guatemala City.

Modeling the Effects of Habitat Fragmentation on the Movements of the Mongolian Gazelle in the Eastern Mongolian Steppe

The Mongolian gazelle, Procapra gutturosa, resides in the immense and dynamic ecosystem of the Eastern Mongolian Steppe. The Mongolian Steppe ecosystem dynamics, including vegetation availability, change rapidly and dramatically due to unpredictable precipitation patterns. The Mongolian gazelle has adapted to this unpredictable vegetation availability by making long range nomadic movements. However, predicting these movements is challenging and requires a complex model. An accurate model of gazelle movements is needed, as rampant habitat fragmentation due to human development projects - which inhibit gazelles from obtaining essential resources - increasingly threaten this nomadic species. We created a novel model using an Individual-based Neural Network Genetic Algorithm (ING) to predict how habitat fragmentation affects animal movement, using the Mongolian Steppe as a model ecosystem. We used Global Positioning System (GPS) collar data from real gazelles to “train” our model to emulate characteristic patterns of Mongolian gazelle movement behavior. These patterns are: preferred vegetation resources (NDVI), displacement over certain time lags, and proximity to human areas. With this trained model, we then explored how potential scenarios of habitat fragmentation may affect gazelle movement. This model can be used to predict how fragmentation of the Mongolian Steppe may affect the Mongolian gazelle. In addition, this model is novel in that it can be applied to other ecological scenarios, since we designed it in modules that are easily interchanged.

La ruptura de una economía moral y las deslegitimación de las deudas hipotecarias

In Spain, during the recent housing bubble, purchasing a home seemed the most advantageous strategy to access housing, and there was a wide social consensus about the unavoidability of mortgage indebtedness. However, such consensus has been challenged by the financial and real-estate crisis. The victims of home repossessions have been affected by the transgression of several principles, such as the fair compensation for effort and sacrifice, the prioritisation of basic needs over financial commitments, the possibility of a second chance for over-indebted people, or the State's responsibility to guarantee its citizens' livelihood. Such principles may be understood as part of a moral economy, and their transgression has resulted in the emergence of a social movement, the Plataforma de Afectados por la Hipoteca (PAH), that is questioning the legitimacy of mortgage debts. The article reflects on the extent to which the perception of over-indebtedness and evictions as unfair situations can have an effect on the reproduction of the political-economic system, insofar the latter is perceived as able or unable to repair injustice.

Boron abundances in B-type stars: A test of rotational depletion during main-sequence evolution

Boron abundances have been derived for seven main-sequence B- type stars from Hubble Space Telescope STIS spectra around the B III lambda2066 line. In two stars, boron appears to be undepleted with respect to the presumed initial abundance. In one star, boron is detectable but is clearly depleted. In the other four stars, boron is undetectable, implying depletions of 1-2 dex. Three of these four stars are nitrogen enriched, but the fourth shows no enrichment of nitrogen. Only rotationally induced mixing predicts that boron depletions are unaccompanied by nitrogen enrichments. The inferred rate of boron depletion from our observations is in good agreement with these predictions. Other boron-depleted nitrogen-normal stars are identified from the literature. In addition, several boron- depleted nitrogen-rich stars are identified, and while all fall on the boron-nitrogen trend predicted by rotationally induced mixing, a majority have nitrogen enrichments that are not uniquely explained by rotation. The spectra have also been used to determine iron group (Cr, Mn, Fe, and Ni) abundances. The seven B-type stars have near-solar iron group abundances, as expected for young stars in the solar neighborhood. We have also analyzed the halo B-type star PG 0832 + 676. We find [Fe/H] = -0.88 +/- 0.10, and the absence of the B III line gives the upper limit [B/H] <-2.5. These and other published abundances are used to infer the star's evolutionary status as a post-asymptotic giant branch star.

Quantum homogenization for continuous variables: Realization with linear optical elements

Recently Ziman et al. [Phys. Rev. A 65, 042105 (2002)] have introduced a concept of a universal quantum homogenizer which is a quantum machine that takes as input a given (system) qubit initially in an arbitrary state rho and a set of N reservoir qubits initially prepared in the state xi. The homogenizer realizes, in the limit sense, the transformation such that at the output each qubit is in an arbitrarily small neighborhood of the state xi irrespective of the initial states of the system and the reservoir qubits. In this paper we generalize the concept of quantum homogenization for qudits, that is, for d-dimensional quantum systems. We prove that the partial-swap operation induces a contractive map with the fixed point which is the original state of the reservoir. We propose an optical realization of the quantum homogenization for Gaussian states. We prove that an incoming state of a photon field is homogenized in an array of beam splitters. Using Simon's criterion, we study entanglement between outgoing beams from beam splitters. We derive an inseparability condition for a pair of output beams as a function of the degree of squeezing in input beams.

Galactic Edge Clouds I. Molecular Line Observations and Chemical Modelling of Edge Cloud 2

Edge Cloud 2 (EC2) is a molecular cloud, about 35 pc in size, with one of the largest galactocentric distances known to exist in the Milky Way. We present observations of a peak CO emission region in the cloud and use these to determine its physical characteristics. We calculate a gas temperature of 20 K and a density of n(H2)~10^4 cm-3. Based on our CO maps, we estimate the mass of EC2 at around 10^4 Msolar and continuum observations suggest a dust-to-gas mass ratio as low as 0.001. Chemical models have been developed to reproduce the abundances in EC2, and they indicate that heavy element abundances may be reduced by a factor of 5 relative to the solar neighborhood (similar to dwarf irregular galaxies and damped Lya systems), very low extinction (A_V <4 mag) due to a very low dust-to-gas mass ratio, an enhanced cosmic-ray ionization rate, and a higher UV field compared to local interstellar values. The reduced abundances may be attributed to the low level of star formation in this region and are probably also related to the continuing infall of primordial (or low-metallicity) halo gas since the Milky Way formed. Finally, we note that shocks from the old supernova remnant GSH 138-01-94 may have determined the morphology and dynamics of EC2.