Decoupling of morphological disparity and taxic diversity during the adaptive radiation of anomodont therapsids

Adaptive radiations are central to macroevolutionary theory. Whether triggered by acquisition of new traits or ecological opportunities arising from mass extinctions, it is debated whether adaptive radiations are marked by initial expansion of taxic diversity or of morphological disparity (the range of anatomical form). If a group rediversifies following a mass extinction, it is said to have passed through a macroevolutionary bottleneck, and the loss of taxic or phylogenetic diversity may limit the amount of morphological novelty that it can subsequently generate. Anomodont therapsids, a diverse clade of Permian and Triassic herbivorous tetrapods, passed through a bottleneck during the end-Permian mass extinction. Their taxic diversity increased during the Permian, declined significantly at the Permo–Triassic boundary and rebounded during the Middle Triassic before the clade's final extinction at the end of the Triassic. By sharp contrast, disparity declined steadily during most of anomodont history. Our results highlight three main aspects of adaptive radiations: (i) diversity and disparity are generally decoupled; (ii) models of radiations following mass extinctions may differ from those triggered by other causes (e.g. trait acquisition); and (iii) the bottleneck caused by a mass extinction means that a clade can emerge lacking its original potential for generating morphological variety

Information-Theoretic Active Learning for Content-Based Image Retrieval

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval. Instead of combining different heuristics such as uncertainty, diversity, or density, our method is based on maximizing the mutual information between the predicted relevance of the images and the expected user feedback regarding the selected batch. We propose suitable approximations to this computationally demanding problem and also integrate an explicit model of user behavior that accounts for possible incorrect labels and unnameable instances. Furthermore, our approach does not only take the structure of the data but also the expected model output change caused by the user feedback into account. In contrast to other methods, ITAL turns out to be highly flexible and provides state-of-the-art performance across various datasets, such as MIRFLICKR and ImageNet.Comment: GCPR 2018 paper (14 pages text + 2 pages references + 6 pages appendix

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Molecular Gas in the Host Galaxy of a Quasar at Redshift z=6.42

Observations of the molecular gas phase in quasar host galaxies provide fundamental constraints on galaxy evolution at the highest redshifts. Molecular gas is the material out of which stars form; it can be traced by spectral line emission of carbon--monoxide (CO). To date, CO emission has been detected in more than a dozen quasar host galaxies with redshifts (z) larger 2, the record holder being at z=4.69. At these distances the CO lines are shifted to longer wavelengths, enabling their observation with sensitive radio and millimetre interferometers. Here we present the discovery of CO emission toward the quasar SDSS J114816.64+525150.3 (hereafter J1148+5251) at a redshift of z=6.42, when the universe was only 1/16 of its present age. This is the first detection of molecular gas at the end of cosmic reionization. The presence of large amounts of molecular gas (M(H_2)=2.2e10 M_sun) in an object at this time demonstrates that heavy element enriched molecular gas can be generated rapidly in the earliest galaxies.Comment: 12 pages, 2 figures. To appear in Nature, July, 200

Cosmic Ray Anomalies from the MSSM?

The recent positron excess in cosmic rays (CR) observed by the PAMELA satellite may be a signal for dark matter (DM) annihilation. When these measurements are combined with those from FERMI on the total ($e^++e^-$) flux and from PAMELA itself on the $\bar p/p$ ratio, these and other results are difficult to reconcile with traditional models of DM, including the conventional mSUGRA version of Supersymmetry even if boosts as large as $10^{3-4}$ are allowed. In this paper, we combine the results of a previously obtained scan over a more general 19-parameter subspace of the MSSM with a corresponding scan over astrophysical parameters that describe the propagation of CR. We then ascertain whether or not a good fit to this CR data can be obtained with relatively small boost factors while simultaneously satisfying the additional constraints arising from gamma ray data. We find that a specific subclass of MSSM models where the LSP is mostly pure bino and annihilates almost exclusively into $\tau$ pairs comes very close to satisfying these requirements. The lightest $\tilde \tau$ in this set of models is found to be relatively close in mass to the LSP and is in some cases the nLSP. These models lead to a significant improvement in the overall fit to the data by an amount $\Delta \chi^2 \sim 1/$dof in comparison to the best fit without Supersymmetry while employing boosts $\sim 100$. The implications of these models for future experiments are discussed.Comment: 57 pages, 31 figures, references adde

Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Reciprocity as a foundation of financial economics

This paper argues that the subsistence of the fundamental theorem of contemporary financial mathematics is the ethical concept ‘reciprocity’. The argument is based on identifying an equivalence between the contemporary, and ostensibly ‘value neutral’, Fundamental Theory of Asset Pricing with theories of mathematical probability that emerged in the seventeenth century in the context of the ethical assessment of commercial contracts in a framework of Aristotelian ethics. This observation, the main claim of the paper, is justified on the basis of results from the Ultimatum Game and is analysed within a framework of Pragmatic philosophy. The analysis leads to the explanatory hypothesis that markets are centres of communicative action with reciprocity as a rule of discourse. The purpose of the paper is to reorientate financial economics to emphasise the objectives of cooperation and social cohesion and to this end, we offer specific policy advice

A pilot dose-response study of the acute effects of haskap berry extract (Lonicera caerulea L.) on cognition, mood and blood pressure in older adults

Purpose Haskap (Lonicera caerulea L. or blue honeysuckle) is a plant native to the low-lying wet areas and mountains of Siberia and northeastern Asia, but is now cultivated in Canada. The dark blue berries are rich in anthocyanins, particularly cyanidin-3-O-glucoside. Previously, anthocyanin-rich fruits have been observed to benefit cognitive performance during the immediate postprandial period following a single acute dose. However, no study has currently examined the potential for haskap berries to influence cognitive performance. Here, we investigate the acute cognitive benefits of an anthocyanin-rich haskap berry extract. Methods A double-blind, counterbalanced, crossover intervention study compared the acute effects of three separate haskap berry extract doses, containing 100mg, 200mg, and 400mg anthocyanins, with a sugar-matched placebo. Participants were an opportunity sample of 20 older adults, aged 62-81 years. Measures of cognition, mood, and blood pressure were recorded at baseline and 1.5 hours postprandially. Results Compared to placebo, the 400mg dose elicited significantly lower diastolic blood pressure and heart rate. Both 200mg and 400mg doses elicited significantly higher word recall, with the 400mg dose also significantly improving word recognition scores, on an episodic memory task. However, mood, working memory and executive function task results were more equivocal. Conclusions The findings provide evidence for improvements in episodic memory and blood pressure following acute supplementation with haskap berry extract, with higher doses appearing most effective. The cognitive findings concur with previous literature that suggests episodic memory effects, and not executive function effects, are most prevalent in older adults following anthocyanin-rich berry supplementation. The blood pressure outcome is consistent with a vasodilatory mechanism of action

Algorithmic iteration for computational intelligence

Machine awareness is a disputed research topic, in some circles considered a crucial step in realising Artificial General Intelligence. Understanding what that is, under which conditions such feature could arise and how it can be controlled is still a matter of speculation. A more concrete object of theoretical analysis is algorithmic iteration for computational intelligence, intended as the theoretical and practical ability of algorithms to design other algorithms for actions aimed at solving well-specified tasks. We know this ability is already shown by current AIs, and understanding its limits is an essential step in qualifying claims about machine awareness and Super-AI. We propose a formal translation of algorithmic iteration in a fragment of modal logic, formulate principles of transparency and faithfulness across human and machine intelligence, and consider the relevance to theoretical research on (Super)-AI as well as the practical import of our results