A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

A two-sample Bayesian t-test for microarray data

BACKGROUND: Determining whether a gene is differentially expressed in two different samples remains an important statistical problem. Prior work in this area has featured the use of t-tests with pooled estimates of the sample variance based on similarly expressed genes. These methods do not display consistent behavior across the entire range of pooling and can be biased when the prior hyperparameters are specified heuristically. RESULTS: A two-sample Bayesian t-test is proposed for use in determining whether a gene is differentially expressed in two different samples. The test method is an extension of earlier work that made use of point estimates for the variance. The method proposed here explicitly calculates in analytic form the marginal distribution for the difference in the mean expression of two samples, obviating the need for point estimates of the variance without recourse to posterior simulation. The prior distribution involves a single hyperparameter that can be calculated in a statistically rigorous manner, making clear the connection between the prior degrees of freedom and prior variance. CONCLUSION: The test is easy to understand and implement and application to both real and simulated data shows that the method has equal or greater power compared to the previous method and demonstrates consistent Type I error rates. The test is generally applicable outside the microarray field to any situation where prior information about the variance is available and is not limited to cases where estimates of the variance are based on many similar observations

Capillary-gravity wave resistance in ordinary and magnetic fluids

Wave resistance is the drag force associated to the emission of waves by a moving disturbance at a fluid free surface. In the case of capillary-gravity waves it undergoes a transition from zero to a finite value as the speed of the disturbance is increased. For the first time an experiment is designed in order to obtain the wave resistance as a function of speed. The effect of viscosity is explored, and a magnetic fluid is used to extend the available range of critical speeds. The threshold values are in good agreement with the proposed theory. Contrary to the theoretical model, however, the measured wave resistance reveals a non monotonic speed dependence after the threshold.Comment: 12 pages, 4 figures, 1 table, submitted to Physical Review Letter

Stratospheric Data Analysis System (STRATAN)

A state of the art stratospheric analyses using a coupled stratosphere/troposphere data assimilation system is produced. These analyses can be applied to stratospheric studies of all types. Of importance to this effort is the application of the Stratospheric Data Analysis System (STRATAN) to constituent transport and chemistry problems

Microwave Temperature Profiler Mounted in a Standard Airborne Research Canister

Many atmospheric research aircraft use a standard canister design to mount instruments, as this significantly facilitates their electrical and mechanical integration and thereby reduces cost. Based on more than 30 years of airborne science experience with the Microwave Temperature Profiler (MTP), the MTP has been repackaged with state-of-the-art electronics and other design improvements to fly in one of these standard canisters. All of the controlling electronics are integrated on a single 4 ~5-in. (.10 ~13- cm) multi-layer PCB (printed circuit board) with surface-mount hardware. Improved circuit design, including a self-calibrating RTD (resistive temperature detector) multiplexer, was implemented in order to reduce the size and mass of the electronics while providing increased capability. A new microcontroller-based temperature controller board was designed, providing better control with fewer components. Five such boards are used to provide local control of the temperature in various areas of the instrument, improving radiometric performance. The new stepper motor has an embedded controller eliminating the need for a separate controller board. The reference target is heated to avoid possible emissivity (and hence calibration) changes due to moisture contamination in humid environments, as well as avoiding issues with ambient targets during ascent and descent. The radiometer is a double-sideband heterodyne receiver tuned sequentially to individual oxygen emission lines near 60 GHz, with the line selection and intermediate frequency bandwidths chosen to accommodate the altitude range of the aircraft and mission

Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models

1. Modern species distribution models account for spatial autocorrelation in order to obtain unbiased statistical inference on the effects of covariates, to improve the model's predictive ability through spatial interpolation and to gain insight in the spatial processes shaping the data. Somewhat analogously, hierarchical approaches to community-level data have been developed to gain insights into community-level processes and to improve species-level inference by borrowing information from other species that are either ecologically or phylogenetically related to the focal species. 2. We unify spatial and community-level structures by developing spatially explicit joint species distribution models. The models utilize spatially structured latent factors to model missing covariates as well as species-to-species associations in a statistically and computationally effective manner. 3. We illustrate that the inclusion of the spatial latent factors greatly increases the predictive performance of the modelling approach with a case study of 55 species of butterfly recorded on a 10 km × 10 km grid in Great Britain consisting of 2609 grid cells

Pollination by nocturnal Lepidoptera, and the effects of light pollution: A review

1. Moths (Lepidoptera) are the major nocturnal pollinators of flowers. However, their importance and contribution to the provision of pollination ecosystem services may have been under-appreciated. Evidence was identified that moths are important pollinators of a diverse range of plant species in diverse ecosystems across the world. 2. Moth populations are known to be undergoing significant declines in several European countries. Among the potential drivers of this decline is increasing light pollution. The known and possible effects of artificial night lighting upon moths were reviewed, and suggest how artificial night lighting might in turn affect the provision of pollination by moths. The need for studies of the effects of artificial night lighting upon whole communities of moths was highlighted. 3. An ecological network approach is one valuable method to consider the effects of artificial night lighting upon the provision of pollination by moths, as it provides useful insights into ecosystem functioning and stability, and may help elucidate the indirect effects of artificial light upon communities of moths and the plants they pollinate. 4. It was concluded that nocturnal pollination is an ecosystem process that may potentially be disrupted by increasing light pollution, although the nature of this disruption remains to be tested

Bucking the trend: the diversity of Anthropocene ‘winners’ among British moths

An appreciation of how some species are becoming more common despite unprecedented anthropogenic pressures could offer key insights for mitigating the global biodiversity crisis.  Research to date has largely focused on declining species, while species that are becoming more common have received relatively little attention. Macro-moths in Great Britain are well-studied and species-rich, making them an ideal group for addressing this knowledge gap. Here, we examine changes in 51 successful species between 1968 and 2016 using 4.5 million occurrence records and a systematic monitoring dataset. We employ 3D graphical analysis to visualise long-term multidimensional trends in prevalence (abundance and range) and use vector autoregression models to test whether past values of local abundance are useful for predicting changes in the extent of occurrence. The responses of Anthropocene winners are heterogeneous, suggesting multiple drivers are responsible. Changes in range and local abundance frequently occur intermittently through time, demonstrating the value of long-term, continuous monitoring. There is significant diversity among the winners themselves, which include widespread generalists, habitat specialists, and recent colonists. We offer brief discussion of possible causal factors and the wider ecosystem implications of these trends

Bacterial genotyping by 16S rRNA mass cataloging

BACKGROUND: It has recently been demonstrated that organism identifications can be recovered from mass spectra using various methods including base-specific fragmentation of nucleic acids. Because mass spectrometry is extremely rapid and widely available such techniques offer significant advantages in some applications. A key element in favor of mass spectrometric analysis of RNA fragmentation patterns is that a reference database for analysis of the results can be generated from sequence information. In contrast to hybridization approaches, the genetic affinity of any unknown isolate can in principle be determined within the context of all previously sequenced 16S rRNAs without prior knowledge of what the organism is. In contrast to the original RNase T(1 )cataloging method, when digestion products are analyzed by mass spectrometry, products with the same base composition cannot be distinguished. Hence, it is possible that organisms that are not closely related (having different underlying sequences) might be falsely identified by mass spectral coincidence. We present a convenient spectral coincidence function for expressing the degree of similarity (or distance) between any two mass-spectra. Trees constructed using this function are consistent with those produced by direct comparison of primary sequences, demonstrating that the inherent degeneracy in mass spectrometric analysis of RNA fragments does not preclude correct organism identification. RESULTS: Neighbor-joining trees for important bacterial pathogens were generated using distances based on mass spectrometric observables and the spectral coincidence function. These trees demonstrate that most pathogens will be readily distinguished using mass spectrometric analyses of RNA digestion products. A more detailed, genus-level analysis of pathogens and near relatives was also performed, and it was found that assignments of genetic affinity were consistent with those obtained by direct sequence comparisons. Finally, typical values of the coincidence between organisms were also examined with regard to phylogenetic level and sequence variability. CONCLUSION: Cluster analysis based on comparison of mass spectrometric observables using the spectral coincidence function is an extremely useful tool for determining the genetic affinity of an unknown bacterium. Additionally, fragmentation patterns can determine within hours if an unknown isolate is potentially a known pathogen among thousands of possible organisms, and if so, which one