Quasiclassical Coarse Graining and Thermodynamic Entropy

Our everyday descriptions of the universe are highly coarse-grained, following only a tiny fraction of the variables necessary for a perfectly fine-grained description. Coarse graining in classical physics is made natural by our limited powers of observation and computation. But in the modern quantum mechanics of closed systems, some measure of coarse graining is inescapable because there are no non-trivial, probabilistic, fine-grained descriptions. This essay explores the consequences of that fact. Quantum theory allows for various coarse-grained descriptions some of which are mutually incompatible. For most purposes, however, we are interested in the small subset of ``quasiclassical descriptions'' defined by ranges of values of averages over small volumes of densities of conserved quantities such as energy and momentum and approximately conserved quantities such as baryon number. The near-conservation of these quasiclassical quantities results in approximate decoherence, predictability, and local equilibrium, leading to closed sets of equations of motion. In any description, information is sacrificed through the coarse graining that yields decoherence and gives rise to probabilities for histories. In quasiclassical descriptions, further information is sacrificed in exhibiting the emergent regularities summarized by classical equations of motion. An appropriate entropy measures the loss of information. For a ``quasiclassical realm'' this is connected with the usual thermodynamic entropy as obtained from statistical mechanics. It was low for the initial state of our universe and has been increasing since.Comment: 17 pages, 0 figures, revtex4, Dedicated to Rafael Sorkin on his 60th birthday, minor correction

Localized precipitation and runoff on Mars

We use the Mars Regional Atmospheric Modeling System (MRAMS) to simulate lake storms on Mars, finding that intense localized precipitation will occur for lake size >=10^3 km^2. Mars has a low-density atmosphere, so deep convection can be triggered by small amounts of latent heat release. In our reference simulation, the buoyant plume lifts vapor above condensation level, forming a 20km-high optically-thick cloud. Ice grains grow to 200 microns radius and fall near (or in) the lake at mean rates up to 1.5 mm/hr water equivalent (maximum rates up to 6 mm/hr water equivalent). Because atmospheric temperatures outside the surface layer are always well below 273K, supersaturation and condensation begin at low altitudes above lakes on Mars. In contrast to Earth lake-effect storms, lake storms on Mars involve continuous precipitation, and their vertical velocities and plume heights exceed those of tropical thunderstorms on Earth. Convection does not reach above the planetary boundary layer for lakes O(10^2) mbar. Instead, vapor is advected downwind with little cloud formation. Precipitation occurs as snow, and the daytime radiative forcing at the land surface due to plume vapor and storm clouds is too small to melt snow directly (<+10 W/m^2). However, if orbital conditions are favorable, then the snow may be seasonally unstable to melting and produce runoff to form channels. We calculate the probability of melting by running thermal models over all possible orbital conditions and weighting their outcomes by probabilities given by Laskar et al., 2004. We determine that for an equatorial vapor source, sunlight 15% fainter than at present, and snowpack with albedo 0.28 (0.35), melting may occur with 4%(0.1%) probability. This rises to 56%(12%) if the ancient greenhouse effect was modestly (6K) greater than today.Comment: Submitted to JGR Planet

Assessment of the probability of contaminating Mars

New methodology is proposed to assess the probability that the planet Mars will by biologically contaminated by terrestrial microorganisms aboard a spacecraft. Present NASA methods are based on the Sagan-Coleman formula, which states that the probability of contamination is the product of the expected microbial release and a probability of growth. The proposed new methodology extends the Sagan-Coleman approach to permit utilization of detailed information on microbial characteristics, the lethality of release and transport mechanisms, and of other information about the Martian environment. Three different types of microbial release are distinguished in the model for assessing the probability of contamination. The number of viable microbes released by each mechanism depends on the bio-burden in various locations on the spacecraft and on whether the spacecraft landing is accomplished according to plan. For each of the three release mechanisms a probability of growth is computed, using a model for transport into an environment suited to microbial growth

Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models

Structured additive regression provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random effects and further regression terms. The large flexibility of structured additive regression makes function selection a challenging and important task, aiming at (1) selecting the relevant covariates, (2) choosing an appropriate and parsimonious representation of the impact of covariates on the predictor and (3) determining the required interactions. We propose a spike-and-slab prior structure for function selection that allows to include or exclude single coefficients as well as blocks of coefficients representing specific model terms. A novel multiplicative parameter expansion is required to obtain good mixing and convergence properties in a Markov chain Monte Carlo simulation approach and is shown to induce desirable shrinkage properties. In simulation studies and with (real) benchmark classification data, we investigate sensitivity to hyperparameter settings and compare performance to competitors. The flexibility and applicability of our approach are demonstrated in an additive piecewise exponential model with time-varying effects for right-censored survival times of intensive care patients with sepsis. Geoadditive and additive mixed logit model applications are discussed in an extensive appendix

Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints

Action detection and temporal segmentation of actions in videos are topics of increasing interest. While fully supervised systems have gained much attention lately, full annotation of each action within the video is costly and impractical for large amounts of video data. Thus, weakly supervised action detection and temporal segmentation methods are of great importance. While most works in this area assume an ordered sequence of occurring actions to be given, our approach only uses a set of actions. Such action sets provide much less supervision since neither action ordering nor the number of action occurrences are known. In exchange, they can be easily obtained, for instance, from meta-tags, while ordered sequences still require human annotation. We introduce a system that automatically learns to temporally segment and label actions in a video, where the only supervision that is used are action sets. An evaluation on three datasets shows that our method still achieves good results although the amount of supervision is significantly smaller than for other related methods.Comment: CVPR 201

Voyager Mars planetary quarantine Basic math model report

Basic math model study of planetary quarantine effects on Voyager Mars missio