L∞ Error and Bandwidth Selection for Kernel Density Estimates of Large Data

Kernel density estimates are a robust way to reconstruct a continuous distribution from a discrete point set. Typically their effectiveness is measured either in L1 or L2 error. In this paper we investigate the challenges in using L ∞ (or worst case) error, a stronger measure than L1 or L2. We present efficient solutions to two linked challenges: how to evaluate the L ∞ error between two kernel density estimates and how to choose the bandwidth parameter for a kernel density estimate built on a subsample of a large data set. 1 1

Adaptive structure tensors and their applications

The structure tensor, also known as second moment matrix or Förstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This article wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the dataadaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered

Nonlocal similarity image filtering

Abstract. We exploit the recurrence of structures at different locations, orientations and scales in an image to perform denoising. While previous methods based on “nonlocal filtering ” identify corresponding patches only up to translations, we consider more general similarity transformations. Due to the additional computational burden, we break the problem down into two steps: First, we extract similarity invariant descriptors at each pixel location; second, we search for similar patches by matching descriptors. The descriptors used are inspired by scale-invariant feature transform (SIFT), whereas the similarity search is solved via the minimization of a cost function adapted from local denoising methods. Our method compares favorably with existing denoising algorithms as tested on several datasets.

Graph-based topic models for trajectory clustering in crowd videos

Probabilistic topic modelings, such as latent Dirichlet allocation (LDA) and correlated topic models (CTM), have recently emerged as powerful statistical tools for processing video content. They share an important property, i.e., using a common set of topics to model all data. However such property can be too restrictive for modeling complex visual data such as crowd scenes where multiple fields of heterogeneous data jointly provide rich information about objects and events. This paper proposes graph-based extensions of LDA and CTM, referred to as GLDA and GCTM, to learn and analyze motion patterns by trajectory clustering in a highly cluttered and crowded environment. Unlike previous works that relied on a scene prior, we apply a spatio-temporal graph (STG) to uncover the spatial and temporal coherence between the trajectories of crowd motion during the learning process. The presented models advance the conventional approaches by integrating a manifold-based clustering as initialization and iterative statistical inference as optimization. The output of GLDA and GCTM are mid-level features that represent the motion patterns used later to generate trajectory clusters. Experiments on three different datasets show the effectiveness of the approaches in trajectory clustering and crowd motion modeling

Industrial Structure and Political Outcomes: The Case of the 2016 US Presidential Election

This paper analyzes the US presidential election of 2016, examining the patterns of industrial structure and party competition in both the major party primaries and the general election. It attempts to identify the new, historically specific factors that led to the upheavals, especially the steady growth of a “dual economy” that locks more and more Americans out of the middle class. It draws extensively on a newly assembled, more comprehensive database to identify the specific political forces that coalesced around each candidate, including the various stages of the Trump campaign

The development and general morphology of the telencephalon of actinopterygian fishes: synopsis, documentation and commentary

The Actinopterygii or ray-finned fishes comprise, in addition to the large superorder of teleosts, four other superorders, namely the cladistians, the chondrosteans, the ginglymodes, and the halecomorphs, each with a limited number of species. The telencephalon of actinopterygian fishes differs from that in all other vertebrates in that it consists of a pair of solid lobes. Lateral ventricles surrounded by nervous tissue are entirely lacking. At the end of the nineteenth century, the theory was advanced that the unusual configuration of the forebrain in actinopterygians results from an outward bending or eversion of its lateral walls. This theory was accepted by some authors, rejected or neglected by others, and modified by some other authors. The present paper is based on the data derived from the literature, complemented by new observations on a large collection of histological material comprising specimens of all five actinopterygian superorders. The paper consists of three parts. In the first, a survey of the development of the telencephalon in actinopterygian fishes is presented. The data collected show clearly that an outward bending or eversion of the pallial parts of the solid hemispheres is the principal morphogenetic event in all five actinopterygian superorders. In all of these superorders, except for the cladistians, eversion is coupled with a marked thickening of the pallial walls. In the second part, some aspects of the general morphology of the telencephalon in mature actinopterygians are highlighted. It is pointed out that (1) the degree of eversion varies considerably among the various actinopterygian groups; (2) eversion leads to the transformation of the telencephalic roof plate into a wide membrane or tela choroidea, which is bilaterally attached to the lateral or ventrolateral aspect of the solid hemispheres; (3) the lines of attachment or taeniae of the tela choroidea form the most important landmarks in the telencephalon of actinopterygians, indicating the sites where the greatly enlarged ventricular surface of the hemispheres ends and its reduced meningeal surface begins; (4) the meningeal surface of the telencephalon shows in most actinopterygians bilaterally a longitudinally oriented sulcus externus, the depth of which is generally positively correlated with the degree of eversion; (5) a distinct lateral olfactory tract, occupying a constant topological position close to the taenia, is present in all actinopterygians studied; and (6) this tract is not homologous to the tract of the same name in the evaginated and inverted forebrains of other groups of vertebrates. In the third and final section, the concept that the structural organization of the pallium in actinopterygians can be fully explained by a simple eversion of its walls, and the various theories, according to which the eversion is complicated by extensive shifts of its constituent cell groups, are discussed and evaluated. It is concluded that there are no reasons to doubt that the pallium of actinopterygian fishes is the product of a simple and complete eversion

Working with pain : sustainable work participation of workers with chronic nonspecific musculoskeletal pain

Dit proefschrift is een van de eerste studies specifiek gericht op mensen die blijven werken met chronische pijn aan het bewegingsapparaat. Unieke kennis over duurzame arbeidsparticipatie van werknemers met chronische pijn werd vergaard. Doel van dit promotieonderzoek was meer inzicht te krijgen in de groep mensen die werkt met pijn en te achterhalen hoe zij in staat zijn ondanks hun klachten te blijven werken. In het proefschrift staan kenmerken en determinanten beschreven van werknemers die doorwerken met chronische pijn, waardoor een completer beeld is ontstaan van arbeidsparticipatie bij mensen met chronische pijn aan het bewegingsapparaat. Uit de vergelijking van mensen die doorwerken ondanks chronische pijn en mensen met arbeidsverzuim die in revalidatiebehandeling komen met chronische pijn blijkt dat deze groepen op diverse factoren significant verschillen. In het onderzoek werd onder andere aangetoond dat de motivatie voor werk, zelfmanagementvaardigheden en het belang dat wordt toegekend aan pijn, belangrijke factoren zijn die werken met chronische pijn faciliteren. Chronische pijn op zichzelf is vaak niet de reden voor arbeidsverzuim, maar meestal spelen persoonlijke- en omgevingsfactoren daarin een beslissende rol. Deze factoren kunnen dienen als aangrijpingspunt voor het verhogen van duurzame inzetbaarheid en preventie van arbeidsverzuim van mensen met chronische pijn aan het bewegingsapparaat. De effectieve manier waarop deelnemende werknemers in het onderzoek met hun pijn omgingen en productief bleven, kan anderen inspireren aan het werk te blijven. Daarnaast biedt het onderzoek een nieuw referentiekader voor de bedrijfs-, verzekerings-, en revalidatiegeneeskunde. This thesis was one of the first studies that focused specifically on people who continued work with chronic nonspecific musculoskeletal pain (CMP), and collected (identified) unique data concerning sustainable work participation of workers with CMP. It provides a large range of characteristics of workers with CMP who continued work despite pain, which has added to our understanding of sustainable work participation in people suffering from CMP. Comparison of workers who continued work with CMP with sick listed workers with CMP admitted for rehabilitation revealed that these groups differ significantly on several factors. In this thesis, evidence was found that the workers’ motivation to work, self-management skills, and the attributed importance of pain on their (working) lives are important factors to manage staying at work with CMP. It is recommended to be aware of the fact that CMP standing on itself is often not the reason for sick leave and disability, but regularly personal and environmental factors play an additional decisive role. Because these factors can be influenced, they offer opportunity to promote staying at work. In the process of guiding workers back to work, the results of the project ‘Working with pain’ may be used. The findings of this thesis potentially contribute to promotion of sustained work participation and prevention of sick-leave in workers with CMP. The effective way workers in this project coped with CMP and remained productive, may inspire others in their efforts to stay work. Finally, this thesis offers a new reference for rehabilitation-, occupational-,and insurance medicine.

Local Light Alignment for Multi-Scale Shape Depiction

International audienceMotivated by recent findings in the field of visual perception, we present a novel approach for enhancing shape depiction and perception of surface details. We propose a shading-based technique that relies on locally adjusting the direction of light to account for the different components of materials. Our approach ensures congruence between shape and shading flows, leading to an effective enhancement of the perception of shape and details while impairing neither the lighting nor the appearance of materials. It is formulated in a general way allowing its use for multiple scales enhancement in real-time on the GPU, as well as in global illumination contexts. We also provide artists with fine control over the enhancement at each scale

Statistical Computing on Non-Linear Spaces for Computational Anatomy

International audienceComputational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features usually extracted raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. We explain in this chapter how the Riemannian structure can provide a powerful framework to build generic statistical computing tools. We show that few computational tools derive for each Riemannian metric can be used in practice as the basic atoms to build more complex generic algorithms such as interpolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings