Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings

Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the course of our experiment and how it affected the learning rate. We then zoom in on estimation uncertainty, which has been suggested to be a driving force in exploration, in spite of evidence of widespread aversion to ambiguity. Our data corroborate the latter. We discuss neural evidence that foreshadowed the ability of humans to distinguish between the three levels of uncertainty. Finally, we investigate the boundaries of human capacity to implement Bayesian learning. We repeat the experiment with different instructions, reflecting varying levels of structural uncertainty. Under this fourth notion of uncertainty, choices were no better explained by Bayesian updating than by (model-free) reinforcement learning. Exit questionnaires revealed that participants remained unaware of the presence of unexpected uncertainty and failed to acquire the right model with which to implement Bayesian updating

The MACHO Project Large Magellanic Cloud Variable Star Inventory. VIII. The Recent Star Formation History of the LMC from the Cepheid Period Distribution

We present an analysis of the period distribution of $\sim 1800$ Cepheids in the Large Magellanic Cloud, based on data obtained by the MACHO microlensing experiment and on a previous catalogue by Payne-Gaposchkin. Using stellar evolution and pulsation models, we construct theoretical period-frequency distributions that are compared to the observations. These models reveal that a significant burst of star formation has occurred recently in the LMC ($\sim 1.15\times 10^8$ years). We also show that during the last $\sim 10^8$ years, the main center of star formation has been propagating from SE to NW along the bar. We find that the evolutionary masses of Cepheids are still smaller than pulsation masses by $\sim 7$ % and that the red edge of the Cepheid instability strip could be slightly bluer than indicated by theory. There are $\sim 600$ Cepheids with periods below $\sim 2.5$ days cannot be explained by evolution theory. We suggest that they are anomalous Cepheids; a number of these stars are double-mode Cepheids

Primordial Black Holes: sirens of the early Universe

Primordial Black Holes (PBHs) are, typically light, black holes which can form in the early Universe. There are a number of formation mechanisms, including the collapse of large density perturbations, cosmic string loops and bubble collisions. The number of PBHs formed is tightly constrained by the consequences of their evaporation and their lensing and dynamical effects. Therefore PBHs are a powerful probe of the physics of the early Universe, in particular models of inflation. They are also a potential cold dark matter candidate.Comment: 21 pages. To be published in "Quantum Aspects of Black Holes", ed. X. Calmet (Springer, 2014

Stochastic population growth in spatially heterogeneous environments

Classical ecological theory predicts that environmental stochasticity increases extinction risk by reducing the average per-capita growth rate of populations. To understand the interactive effects of environmental stochasticity, spatial heterogeneity, and dispersal on population growth, we study the following model for population abundances in $n$ patches: the conditional law of $X_{t+dt}$ given $X_t=x$ is such that when $dt$ is small the conditional mean of $X_{t+dt}^i-X_t^i$ is approximately $[x^i\mu_i+\sum_j(x^j D_{ji}-x^i D_{ij})]dt$, where $X_t^i$ and $\mu_i$ are the abundance and per capita growth rate in the $i$-th patch respectivly, and $D_{ij}$ is the dispersal rate from the $i$-th to the $j$-th patch, and the conditional covariance of $X_{t+dt}^i-X_t^i$ and $X_{t+dt}^j-X_t^j$ is approximately $x^i x^j \sigma_{ij}dt$. We show for such a spatially extended population that if $S_t=(X_t^1+...+X_t^n)$ is the total population abundance, then $Y_t=X_t/S_t$, the vector of patch proportions, converges in law to a random vector $Y_\infty$ as $t\to\infty$, and the stochastic growth rate $\lim_{t\to\infty}t^{-1}\log S_t$ equals the space-time average per-capita growth rate \sum_i\mu_i\E[Y_\infty^i] experienced by the population minus half of the space-time average temporal variation \E[\sum_{i,j}\sigma_{ij}Y_\infty^i Y_\infty^j] experienced by the population. We derive analytic results for the law of $Y_\infty$, find which choice of the dispersal mechanism $D$ produces an optimal stochastic growth rate for a freely dispersing population, and investigate the effect on the stochastic growth rate of constraints on dispersal rates. Our results provide fundamental insights into "ideal free" movement in the face of uncertainty, the persistence of coupled sink populations, the evolution of dispersal rates, and the single large or several small (SLOSS) debate in conservation biology.Comment: 47 pages, 4 figure

Comparing angular and curved shapes in terms of implicit associations and approach/avoidance responses.

Most people prefer smoothly curved shapes over more angular shapes. We investigated the origin of this effect using abstract shapes and implicit measures of semantic association and preference. In Experiment 1 we used a multidimensional Implicit Association Test (IAT) to verify the strength of the association of curved and angular polygons with danger (safe vs. danger words), valence (positive vs. negative words) and gender (female vs. male names). Results showed that curved polygons were associated with safe and positive concepts and with female names, whereas angular polygons were associated with danger and negative concepts and with male names. Experiment 2 used a different implicit measure, which avoided any need to categorise the stimuli. Using a revised version of the Stimulus Response Compatibility (SRC) task we tested with a stick figure (i.e., the manikin) approach and avoidance reactions to curved and angular polygons. We found that RTs for approaching vs. avoiding angular polygons did not differ, even in the condition where the angles were more pronounced. By contrast participants were faster and more accurate when moving the manikin towards curved shapes. Experiment 2 suggests that preference for curvature cannot derive entirely from an association of angles with threat. We conclude that smoothly curved contours make these abstract shapes more pleasant. Further studies are needed to clarify the nature of such a preference

Observation of the Baryonic Flavor-Changing Neutral Current Decay Lambda_b -> Lambda mu+ mu-

We report the first observation of the baryonic flavor-changing neutral current decay Lambda_b -> Lambda mu+ mu- with 24 signal events and a statistical significance of 5.8 Gaussian standard deviations. This measurement uses ppbar collisions data sample corresponding to 6.8fb-1 at sqrt{s}=1.96TeV collected by the CDF II detector at the Tevatron collider. The total and differential branching ratios for Lambda_b -> Lambda mu+ mu- are measured. We find B(Lambda_b -> Lambda mu+ mu-) = [1.73+-0.42(stat)+-0.55(syst)] x 10^{-6}. We also report the first measurement of the differential branching ratio of B_s -> phi mu+ mu- using 49 signal events. In addition, we report branching ratios for B+ -> K+ mu+ mu-, B0 -> K0 mu+ mu-, and B -> K*(892) mu+ mu- decays.Comment: 8 pages, 2 figures, 4 tables. Submitted to Phys. Rev. Let

Spatial regularity of InAs-GaAs quantum dots: quantifying the dependence of lateral ordering on growth rate.

The lateral ordering of arrays of self-assembled InAs-GaAs quantum dots (QDs) has been quantified as a function of growth rate, using the Hopkins-Skellam index (HSI). Coherent QD arrays have a spatial distribution which is neither random nor ordered, but intermediate. The lateral ordering improves as the growth rate is increased and can be explained by more spatially regular nucleation as the QD density increases. By contrast, large and irregular 3D islands are distributed randomly on the surface. This is consistent with a random selection of the mature QDs relaxing by dislocation nucleation at a later stage in the growth, independently of each QD's surroundings. In addition we explore the statistical variability of the HSI as a function of the number N of spatial points analysed, and we recommend N > 10(3) to reliably distinguish random from ordered arrays

Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

Background Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF. Methods A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF. Results After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005). Conclusions The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF

Dopaminergic modulation of appetitive trace conditioning: the role of D1 receptors in medial prefrontal cortex

Rationale: Trace conditioning may provide a behavioural model suitable to examine the maintenance of ‘on line’ information and its underlying neural substrates. Objectives: Experiment la was run to establish trace conditioning in a shortened procedure which would be suitable to test the effects of dopamine (DA) D1 receptor agents administered by microinjection directly into the brain. Experiment lb examined the effects of the DA D1 agonist SKF81297 and the DA D1 antagonist SCH23390 following systemic administration in pre-trained animals. Experiment 2 went on to test the effects of systemically administered SKF81297 on the acquisition of trace conditioning. In experiment 3, SKF81297 was administered directly in prelimbic (PL) and infralimbic (IL) sub-regions of medial prefrontal cortex (mPFC) to compare the role of different mPFC sub-regions. Results: Whilst treatment with SCH23390 impaired motor responding and/or motivation, SKF81297 had relatively little effect in the pre-trained animals tested in experiment 1b. However, systemic SKF81297 depressed the acquisition function at the 2-s trace interval in experiment 2. Similarly, in experiment 3, SKF81297 (0.1 μg in 1.0 μl) microinjected into either PL or IL mPFC impaired appetitive conditioning at the 2-s trace interval. Conclusions: Impaired trace conditioning under SKF81297 is likely to be mediated in part (but not exclusively) within the IL and PL mPFC sub-regions. The finding that trace conditioning was impaired rather than enhanced under SKF81297 provides further evidence for the inverse U-function which has been suggested to be characteristic of mPFC DA function

An overview of knowledge sharing in new product development

This paper provides an overview of some of the issues in knowledge management related to the sharing of knowledge in new product development. Previous research and concepts reported by international researchers, and examples of the research projects carried out by the authors will be introduced. The paper first provides an overview of the history and importance of innovation and challenges in manufacturing. Then the importance of new product development in the sustainable success of manufacturing enterprises in the globalised business operations is discussed. The formalisation and modelling of product development processes will also be introduced. The concept and different definitions of knowledge management by previous researchers are then introduced, with further discussion on knowledge sharing. At this point, the authors’ research in knowledge sharing is also introduced. Finally, the trend of using social media and Enterprise 2 technologies in knowledge management and sharing is introduced using the recent research projects of the authors as examples