On the Information Rates of the Plenoptic Function

The {\it plenoptic function} (Adelson and Bergen, 91) describes the visual information available to an observer at any point in space and time. Samples of the plenoptic function (POF) are seen in video and in general visual content, and represent large amounts of information. In this paper we propose a stochastic model to study the compression limits of the plenoptic function. In the proposed framework, we isolate the two fundamental sources of information in the POF: the one representing the camera motion and the other representing the information complexity of the "reality" being acquired and transmitted. The sources of information are combined, generating a stochastic process that we study in detail. We first propose a model for ensembles of realities that do not change over time. The proposed model is simple in that it enables us to derive precise coding bounds in the information-theoretic sense that are sharp in a number of cases of practical interest. For this simple case of static realities and camera motion, our results indicate that coding practice is in accordance with optimal coding from an information-theoretic standpoint. The model is further extended to account for visual realities that change over time. We derive bounds on the lossless and lossy information rates for this dynamic reality model, stating conditions under which the bounds are tight. Examples with synthetic sources suggest that in the presence of scene dynamics, simple hybrid coding using motion/displacement estimation with DPCM performs considerably suboptimally relative to the true rate-distortion bound.Comment: submitted to IEEE Transactions in Information Theor

Empirical Transition Matrix of Multi-State Models: The etm Package

Multi-State models provide a relevant framework for modelling complex event histories. Quantities of interest are the transition probabilities that can be estimated by the empirical transition matrix, that is also referred to as the Aalen-Johansen estimator. In this paper, we present the R package etm that computes and displays the transition probabilities. etm also features a Greenwood-type estimator of the covariance matrix. The use of the package is illustrated through a prominent example in bone marrow transplant for leukaemia patients.

Hypsometry, Volume and Physiography of the Arctic Ocean and Their Paleoceanographic Implications

Recent analyses of the International Bathymetric Chart of the Arctic Ocean (IBCAO) grid model include: Hypsometry (the distribution of surface area at various depths); ocean volume distribution; and physiographic provinces [Jakobsson 2002; Jakobsson et al., in press]. The present paper summarizes the main results from these recent studies and expands on the paleoceanographic implications for the Arctic Ocean, which in this work is defined as the broad continental shelves of the Barents, Kara, Laptev, East Siberian and Chukchi Seas, the White Sea and the narrow continental shelves of the Beaufort Sea, the Arctic continental margins off the Canadian Arctic Archipelago and northern Greenland. This, the Worlds smallest ocean, is a virtually land-locked ocean that makes up merely 2.6 % of the area, and 1.0 % of the volume, of the entire World Ocean. The continental shelf area, from the coastline out to the shelf break, comprises as much as 52.9 % of the total area in the Arctic Ocean, which is significantly larger in comparison to the rest of the world oceans where the proportion of shelves, from the coastline out to the foot of the continental slope, only ranges between about 9.1 % and 17.7 %. In Jakobsson [2002], the seafloor area and water volume were calculated for different depths starting from the present sea level and progressing in increments of 10 m to a depth of 500 m, and in increments of 50 m from 550 m down to the deepest depth within each of the analyzed Arctic Ocean seas. Hypsometric curves expressed as simple histograms of the frequencies in different depth bins were presented, along with depth plotted against cumulative area for each of the analyzed seas. The derived hypsometric curves show that most of the Arctic Ocean shelf seas besides the Barents Sea, Beaufort Sea and the shelf off northern Greenland have a similar shape with the largest seafloor area between 0 and 50 m. The East Siberian and Laptev seas, in particular, show area distributions concentrated in this shallow depth range, and together with the Chukchi Sea they form a large flat shallow shelf province comprising as much as 22 Besides being the world’s smallest ocean with the by far largest shelf area in proportion to its size, the Arctic Ocean is unique in terms of its physiographic setting. The Fram Strait is the only real break in the barrier of vast continental shelves enclosing the Arctic Ocean. The second largest physiographic province after the continental shelves consists of ridges, which is in contrast to the rest of the World’s oceans where abyssal plains dominate. As much as 15.8 % of the area is underlain by ridges indicating the profound effect they have on ocean circulation

Arctic Ocean Physiography

The first order physiographic provinces of the Arctic Ocean has been defined using the recently updated International Bathymetric Chart of the Arctic Ocean (IBCAO) grid model as the main database and a semi-quantitative approach. The first step in our classification of physiographic provinces is an evaluation of seafloor gradients contained in a slope model that was derived from the IBCAO grid. The slope information reveals certain seafloor process-related features, which add to the bathymetric information. Using interactive 3D-visualization, the slope and bathymetric information were simultaneously analyzed and certain slope intervals of the Arctic Ocean seafloor were found to generally characterize major physiographic provinces. This information was used for the initial classification, although in certain locations gradual changes in bottom inclination made it difficult to detect transitions between some physiographic provinces, as for example, the transition between continental rise and slope, as well as between the rise and abyssal plain. In these cases some manual intervention was required guided by generated bathymetric profiles. The areas of the provinces we classified are individually calculated, and their morphologies are subsequently discussed in the context of the geologic evolution of the Arctic Ocean Basin as described in the published literature. In summary, our study: provides a physiographic classification of the Arctic Ocean sea floor according to the most up-to-date bathymetric model and addresses the geologic origin of the prominent features as well as provides areal computations of the defined first order physiographic provinces and of the most prominent second-order features

Aortic pulse wave velocity measurement via heart sounds and impedance plethysmography

Full abstract in the manuscript

Estimating Consumption Economies of Scale, Adult Equivalence Scales, and Household Bargaining Power

How much income would a woman living alone require to attain the same standard of living that she would have if she were married? What percentage of a married couple’s expenditures are controlled by the husband? How much money does a couple save on consumption goods by living together versus living apart? We propose and estimate a collective model of household behavior that permits identification and estimation of concepts such as these. We model households in terms of the utility functions of its members, a bargaining or social welfare function, and a consumption technology function. We demonstrate generic nonparametric identification of the model, and hence of equivalence scales, consumption economies of scale, household members’ bargaining power and other related concepts.consumer demand; collective model; adult equivalence scales; household bargaining; economies of scale; demand systems; bargaining power; Barten scales

The Application of the Uniform Declaratory Judgments Act in Montana

The Application of the Uniform Declaratory Judgments Act in Montan