Assimilation of TES data from the Mars Global Surveyor scientifc mapping phase

The Thermal Emission Spectrometer (TES)aboard Mars Global Surveyor has produced data which cover almost two Martian years so far (during its scientific mapping phase). Thermal profiles for the atmosphere below 40 km and total dust opacities can be retrieved from TES nadir spectra and assimilated into a Mars general circulation model (MGCM), by using the assimilation techniques described in detail by Lewis et al. (2002). This paper describes some preliminary results from assimilations of temperature data from the period Ls=141°- 270° corresponding to late northern summer until winter solstice on Mars. Work in progress is devoted to assimilate both temperature and total dust opacity data for the full period for which they are already available

Data assimilation for the Martian atmosphere using MGS Thermal Emission Spectrometer observations

From the introduction: Given the quantity of data expected from current and forthcoming spacecraft missions to Mars, it is now possible to use data assimilation as a means of atmospheric analysis for the first time for a planet other than the Earth. Several groups have described plans to develop assimilation schemes for Mars [Banfield et al., 1995; Houben, 1999; Lewis and Read, 1995; Lewis et al., 1996, 1997; Zhang et al., 2001]. Data assimilation is a technique for the analysis of atmospheric observations which combines currently valid information with prior knowledge from previous observations and dynamical and physical constraints, via the use of a numerical model. Despite the number of new missions, observations of the atmosphere of Mars in the near future are still likely to be sparse when compared to those of the Earth, perhaps comprising one orbiter and a few surface stations at best at any one time. Data assimilation is useful as a means to extract the maximum information from such observations, both by a form of interpolation in space and time using model constraints and by the combination of information from different observations, e.g. temperature profiles and surface pressure measurements which may be irregularly distributed. The procedure can produce a dynamically consistent set of meteorological fields and can be used directly to test and to refine an atmospheric model against observations

Hierarchical Models for Independence Structures of Networks

We introduce a new family of network models, called hierarchical network models, that allow us to represent in an explicit manner the stochastic dependence among the dyads (random ties) of the network. In particular, each member of this family can be associated with a graphical model defining conditional independence clauses among the dyads of the network, called the dependency graph. Every network model with dyadic independence assumption can be generalized to construct members of this new family. Using this new framework, we generalize the Erd\"os-R\'enyi and beta-models to create hierarchical Erd\"os-R\'enyi and beta-models. We describe various methods for parameter estimation as well as simulation studies for models with sparse dependency graphs.Comment: 19 pages, 7 figure

Structural change of vortex patterns in anisotropic Bose-Einstein condensates

We study the changes in the spatial distribution of vortices in a rotating Bose-Einstein condensate due to an increasing anisotropy of the trapping potential. Once the rotational symmetry is broken, we find that the vortex system undergoes a rich variety of structural changes, including the formation of zig-zag and linear configurations. These spatial re-arrangements are well signaled by the change in the behavior of the vortex-pattern eigenmodes against the anisotropy parameter. The existence of such structural changes opens up possibilities for the coherent exploitation of effective many-body systems based on vortex patterns.Comment: 5 pages, 4 figure

Large Area Crop Inventory Experiment (LACIE). Intensive test site assessment report

There are no author-identified significant results in this report

Equilibration through local information exchange in networks

We study the equilibrium states of energy functions involving a large set of real variables, defined on the links of sparsely connected networks, and interacting at the network nodes, using the cavity and replica methods. When applied to the representative problem of network resource allocation, an efficient distributed algorithm is devised, with simulations showing full agreement with theory. Scaling properties with the network connectivity and the resource availability are found.Comment: v1: 7 pages, 1 figure, v2: 4 pages, 2 figures, simplified analysis and more organized results, v3: minor change

Children's Counterfactual Reasoning About Causally Overdetermined Events

In two experiments, one hundred and sixty-two 6- to 8-year-olds were asked to reason counterfactually about events with different causal structures. All events involved overdetermined outcomes in which two different causal events led to the same outcome. In Experiment 1, children heard stories with either an ambiguous causal relation between events or causally unrelated events. Children in the causally unrelated version performed better than chance and better than those in the ambiguous condition. In Experiment 2, children heard stories in which antecedent events were causally connected or causally disconnected. Eight-year-olds performed above chance in both conditions, whereas 6-year-olds performed above chance only in the connected condition. This work provides the first evidence that children can reason counterfactually in causally overdetermined contexts by age 8. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc

Replicated Bethe Free Energy: A Variational Principle behind Survey Propagation

A scheme to provide various mean-field-type approximation algorithms is presented by employing the Bethe free energy formalism to a family of replicated systems in conjunction with analytical continuation with respect to the number of replicas. In the scheme, survey propagation (SP), which is an efficient algorithm developed recently for analyzing the microscopic properties of glassy states for a fixed sample of disordered systems, can be reproduced by assuming the simplest replica symmetry on stationary points of the replicated Bethe free energy. Belief propagation and generalized SP can also be offered in the identical framework under assumptions of the highest and broken replica symmetries, respectively.Comment: appeared in Journal of the Physical Society of Japan 74, 2133-2136 (2005

Statistical physics-based reconstruction in compressed sensing

Compressed sensing is triggering a major evolution in signal acquisition. It consists in sampling a sparse signal at low rate and later using computational power for its exact reconstruction, so that only the necessary information is measured. Currently used reconstruction techniques are, however, limited to acquisition rates larger than the true density of the signal. We design a new procedure which is able to reconstruct exactly the signal with a number of measurements that approaches the theoretical limit in the limit of large systems. It is based on the joint use of three essential ingredients: a probabilistic approach to signal reconstruction, a message-passing algorithm adapted from belief propagation, and a careful design of the measurement matrix inspired from the theory of crystal nucleation. The performance of this new algorithm is analyzed by statistical physics methods. The obtained improvement is confirmed by numerical studies of several cases.Comment: 20 pages, 8 figures, 3 tables. Related codes and data are available at http://aspics.krzakala.or

Contextual Object Detection with a Few Relevant Neighbors

A natural way to improve the detection of objects is to consider the contextual constraints imposed by the detection of additional objects in a given scene. In this work, we exploit the spatial relations between objects in order to improve detection capacity, as well as analyze various properties of the contextual object detection problem. To precisely calculate context-based probabilities of objects, we developed a model that examines the interactions between objects in an exact probabilistic setting, in contrast to previous methods that typically utilize approximations based on pairwise interactions. Such a scheme is facilitated by the realistic assumption that the existence of an object in any given location is influenced by only few informative locations in space. Based on this assumption, we suggest a method for identifying these relevant locations and integrating them into a mostly exact calculation of probability based on their raw detector responses. This scheme is shown to improve detection results and provides unique insights about the process of contextual inference for object detection. We show that it is generally difficult to learn that a particular object reduces the probability of another, and that in cases when the context and detector strongly disagree this learning becomes virtually impossible for the purposes of improving the results of an object detector. Finally, we demonstrate improved detection results through use of our approach as applied to the PASCAL VOC and COCO datasets