Informational and Causal Architecture of Continuous-time Renewal and Hidden Semi-Markov Processes

We introduce the minimal maximally predictive models ({\epsilon}-machines) of processes generated by certain hidden semi-Markov models. Their causal states are either hybrid discrete-continuous or continuous random variables and causal-state transitions are described by partial differential equations. Closed-form expressions are given for statistical complexities, excess entropies, and differential information anatomy rates. We present a complete analysis of the {\epsilon}-machines of continuous-time renewal processes and, then, extend this to processes generated by unifilar hidden semi-Markov models and semi-Markov models. Our information-theoretic analysis leads to new expressions for the entropy rate and the rates of related information measures for these very general continuous-time process classes.Comment: 16 pages, 7 figures; http://csc.ucdavis.edu/~cmg/compmech/pubs/ctrp.ht

Cycle-Level Products in Equivariant Cohomology of Toric Varieties

In this paper, we define an action of the group of equivariant Cartier divisors on a toric variety X on the equivariant cycle groups of X, arising naturally from a choice of complement map on the underlying lattice. If X is nonsingular, this gives a lifting of the multiplication in equivariant cohomology to the level of equivariant cycles. As a consequence, one naturally obtains an equivariant cycle representative of the equivariant Todd class of any toric variety. These results extend to equivariant cohomology the results of Thomas and Pommersheim. In the case of a complement map arising from an inner product, we show that the equivariant cycle Todd class obtained from our construction is identical to the result of the inductive, combinatorial construction of Berline-Vergne. In the case of arbitrary complement maps, we show that our Todd class formula yields the local Euler-Maclarurin formula introduced in Garoufalidis-Pommersheim.Comment: 15 pages, to be published in Michigan Mathematical Journal; LaTe

Optimized Bacteria are Environmental Prediction Engines

Experimentalists have observed phenotypic variability in isogenic bacteria populations. We explore the hypothesis that in fluctuating environments this variability is tuned to maximize a bacterium's expected log growth rate, potentially aided by epigenetic markers that store information about past environments. We show that, in a complex, memoryful environment, the maximal expected log growth rate is linear in the instantaneous predictive information---the mutual information between a bacterium's epigenetic markers and future environmental states. Hence, under resource constraints, optimal epigenetic markers are causal states---the minimal sufficient statistics for prediction. This is the minimal amount of information about the past needed to predict the future as well as possible. We suggest new theoretical investigations into and new experiments on bacteria phenotypic bet-hedging in fluctuating complex environments.Comment: 7 pages, 1 figure; http://csc.ucdavis.edu/~cmg/compmech/pubs/obepe.ht

Prediction and Power in Molecular Sensors: Uncertainty and Dissipation When Conditionally Markovian Channels Are Driven by Semi-Markov Environments

Sensors often serve at least two purposes: predicting their input and minimizing dissipated heat. However, determining whether or not a particular sensor is evolved or designed to be accurate and efficient is difficult. This arises partly from the functional constraints being at cross purposes and partly since quantifying the predictive performance of even in silico sensors can require prohibitively long simulations. To circumvent these difficulties, we develop expressions for the predictive accuracy and thermodynamic costs of the broad class of conditionally Markovian sensors subject to unifilar hidden semi-Markov (memoryful) environmental inputs. Predictive metrics include the instantaneous memory and the mutual information between present sensor state and input future, while dissipative metrics include power consumption and the nonpredictive information rate. Success in deriving these formulae relies heavily on identifying the environment's causal states, the input's minimal sufficient statistics for prediction. Using these formulae, we study the simplest nontrivial biological sensor model---that of a Hill molecule, characterized by the number of ligands that bind simultaneously, the sensor's cooperativity. When energetic rewards are proportional to total predictable information, the closest cooperativity that optimizes the total energy budget generally depends on the environment's past hysteretically. In this way, the sensor gains robustness to environmental fluctuations. Given the simplicity of the Hill molecule, such hysteresis will likely be found in more complex predictive sensors as well. That is, adaptations that only locally optimize biochemical parameters for prediction and dissipation can lead to sensors that "remember" the past environment.Comment: 21 pages, 4 figures, http://csc.ucdavis.edu/~cmg/compmech/pubs/piness.ht

Feasibility Study of SDAS Instrumentation's Ability to Identify Mobile Launcher (ML)/Crawler-Transporter (CT) Modes During Rollout Operations

The Space Launch System (SLS) and its Mobile Launcher (ML) will be transported to the launch pad via the Crawler-Transporter (CT) system. Rollout (i.e., transportation) loads produce structural loads on the integrated SLS/Orion Multi-Purpose Crew Vehicle (MPCV) launch vehicle which are of a concern with respect to fatigue. As part of the risk reduction process and in addition to the modal building block test approach that has been adopted by the SLS Program, acceleration data will be obtained during rollout for use in modal parameter estimation. There are several occurrences where the ML/CT will be transported either into the Vertical Assembly Building (VAB) or to the launch pad and back without the SLS stack as part of the Kennedy Space Center (KSC) Exploration Ground Systems (EGS) Integrated Test and Checkout (ITCO). NASA KSC EGS has instrumentation installed on both the ML and CT to record data during rollout, at the launch pad, and during liftoff. The EGS instrumentation on the ML, which includes accelerometers, is referred to as the Sensor Data Acquisition System (SDAS). The EGS instrumentation on the CT, which also includes accelerometers, is referred to as the CT Data Acquisition System (CTDAS). The forces and accelerations applied to the ML and CT during a rollout event will be higher than any of the planned building block modal tests. This can be very beneficial in helping identify nonlinear behavior in the structure. Developing modal parameters from the same test hardware in multiple boundary conditions and under multiple levels of excitation is a key step in developing a well correlated FEM. The purpose of this study was three fold. First, determine the target modes of the ML/CT in its rollout configuration. Second, determine if the test degrees of freedom (DOF) corresponding to the layout of the SDAS/CTDAS accelerometers (i.e. position and orientation) is sufficient to identify the target modes. Third, determine if the Generic Rollout Forcing Functions (GRFF's) is sufficient for identifying the ML/CT target modes accounting for variations in CT speed, modal damping, and sensor/ambient background noise levels. The finding from the first part of this study identified 28 target modes of the ML/CT rollout configuration based upon Modal Effective Mass Fractions (MEFF) and engineering judgement. The finding from the second part of this study showed that the SDAS/CTDAS accelerometers (i.e. position and orientation) are able to identify a sufficient number of the target modes to support model correlation of the ML/CT FEM. The finding from the third part of this study confirms the GRFFs sufficiently excite the ML/CT such that varying quantities of the defined target modes should be able to be extracted when utilizing an Experimental Modal Analysis (EMA) Multi-Input Multi-Output (MIMO) analysis approach. An EMA analysis approach was used because Operational Modal Analysis (OMA) tools were not available and the GRFFs were sufficiently uncorrelated. Two key findings from this third part of the study are that the CT speed does not show a significant impact on the ability to extract the modal parameters and that keeping the ambient background noise observed at each accelerometer location at or below 30 grms is essential to the success of this approach

Abundance stratification in Type Ia supernovae - V. SN 1986G bridging the gap between normal and subluminous SNe Ia

A detailed spectroscopic analysis of SN 1986G has been performed. SN 1986G `bridges the gap' between normal and sub luminous type Ia supernova (SNe Ia). The abundance tomography technique is used to determine the abundance distribution of the elements in the ejecta. SN 1986G was found to be a low energy Chandrasekhar mass explosion. Its kinetic energy was 70% of the standard W7 model (0.9x10$^{51}$erg). Oxygen dominates the ejecta from the outermost layers down to $\sim$9000kms$^{-1}$ , intermediate mass elements (IME) dominate from $\sim$ 9000kms$^{-1}$ to $\sim$ 3500kms$^{-1}$ with Ni and Fe dominating the inner layers $<\sim$ 3500kms$^{-1}$. The final masses of the main elements in the ejecta were found to be, O=0.33M, IME=0.69M, stable NSE=0.21M, $^{56}$Ni=0.14M. An upper limit of the carbon mass is set at C=0.02M. The spectra of SN1986G consist of almost exclusively singly ionised species. SN1986G can be thought of as a low luminosity extension of the main population of SN Ia, with a large deflagration phase that produced more IMEs than a standard SN Ia.Comment: Accepted for publication in MNRAS, update

The long-term evolution of photoevaporating transition discs with giant planets

Photo-evaporation and planet formation have both been proposed as mechanisms responsible for the creation of a transition disc. We have studied their combined effect through a suite of 2d simulations of protoplanetary discs undergoing X-ray photoevaporation with an embedded giant planet. In a previous work we explored how the formation of a giant planet triggers the dispersal of the inner disc by photo-evaporation at earlier times than what would have happened otherwise. This is particularly relevant for the observed transition discs with large holes and high mass accretion rates that cannot be explained by photo-evaporation alone. In this work we significantly expand the parameter space investigated by previous simulations. In addition, the updated model includes thermal sweeping, needed for studying the complete dispersal of the disc. After the removal of the inner disc the disc is a non accreting transition disc, an object that is rarely seen in observations. We assess the relative length of this phase, to understand if it is long lived enough to be found observationally. Depending on the parameters, especially on the X-ray luminosity of the star, we find that the fraction of time spent as a non-accretor greatly varies. We build a population synthesis model to compare with observations and find that in general thermal sweeping is not effective enough to destroy the outer disc, leaving many transition discs in a relatively long lived phase with a gas free hole, at odds with observations. We discuss the implications for transition disc evolution. In particular, we highlight the current lack of explanation for the missing non-accreting transition discs with large holes, which is a serious issue in the planet hypothesis.Comment: 11 pages, 5 figures; accepted by MNRA

THE GREEN REVOLUTION: ITS IMPACT ON TRADE AND AGRICULTURAL POLICY IN DEVELOPED NATIONS

Agricultural and Food Policy, International Relations/Trade,