The distribution of species range size: a stochastic process

The major role played by environmental factors in determining the geographical range sizes of species raises the possibility of describing their long-term dynamics in relatively simple terms, a goal which has hitherto proved elusive. Here we develop a stochastic differential equation to describe the dynamics of the range size of an individual species based on the relationship between abundance and range size, derive a limiting stationary probability model to quantify the stochastic nature of the range size for that species at steady state, and then generalize this model to the species-range size distribution for an assemblage. The model fits well to several empirical datasets of the geographical range sizes of species in taxonomic assemblages, and provides the simplest explanation of species-range size distributions to date

Monad Bundles in Heterotic String Compactifications

In this paper, we study positive monad vector bundles on complete intersection Calabi-Yau manifolds in the context of E8 x E8 heterotic string compactifications. We show that the class of such bundles, subject to the heterotic anomaly condition, is finite and consists of about 7000 models. We explain how to compute the complete particle spectrum for these models. In particular, we prove the absence of vector-like family anti-family pairs in all cases. We also verify a set of highly non-trivial necessary conditions for the stability of the bundles. A full stability proof will appear in a companion paper. A scan over all models shows that even a few rudimentary physical constraints reduces the number of viable models drastically.Comment: 35 pages, 4 figure

Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge. VQA is a task of significant importance for research in artificial intelligence, given its multimodal nature, clear evaluation protocol, and potential real-world applications. The performance of deep neural networks for VQA is very dependent on choices of architectures and hyperparameters. To help further research in the area, we describe in detail our high-performing, though relatively simple model. Through a massive exploration of architectures and hyperparameters representing more than 3,000 GPU-hours, we identified tips and tricks that lead to its success, namely: sigmoid outputs, soft training targets, image features from bottom-up attention, gated tanh activations, output embeddings initialized using GloVe and Google Images, large mini-batches, and smart shuffling of training data. We provide a detailed analysis of their impact on performance to assist others in making an appropriate selection.Comment: Winner of the 2017 Visual Question Answering (VQA) Challenge at CVP

Exploring Positive Monad Bundles And A New Heterotic Standard Model

A complete analysis of all heterotic Calabi-Yau compactifications based on positive two-term monad bundles over favourable complete intersection Calabi-Yau threefolds is performed. We show that the original data set of about 7000 models contains 91 standard-like models which we describe in detail. A closer analysis of Wilson-line breaking for these models reveals that none of them gives rise to precisely the matter field content of the standard model. We conclude that the entire set of positive two-term monads on complete intersection Calabi-Yau manifolds is ruled out on phenomenological grounds. We also take a first step in analyzing the larger class of non-positive monads. In particular, we construct a supersymmetric heterotic standard model within this class. This model has the standard model gauge group and an additional U(1)_{B-L} symmetry, precisely three families of quarks and leptons, one pair of Higgs doublets and no anti-families or exotics of any kind.Comment: 48 page

Do Balance Demands Induce Shifts in Visual Proprioception in Crawling Infants?

The onset of hands-and-knees crawling during the latter half of the first year of life heralds pervasive changes in a range of psychological functions. Chief among these changes is a clear shift in visual proprioception, evident in the way infants use patterns of optic flow in the peripheral field of view to regulate their postural sway. This shift is thought to result from consistent exposure in the newly crawling infant to different patterns of optic flow in the central field of view and the periphery and the need to concurrently process information about self-movement, particularly postural sway, and the environmental layout during crawling. Researchers have hypothesized that the demands on the infant's visual system to concurrently process information about self-movement and the environment press the infant to differentiate and functionalize peripheral optic flow for the control of balance during locomotion so that the central field of view is freed to engage in steering and monitoring the surface and potentially other tasks. In the current experiment, we tested whether belly crawling, a mode of locomotion that places negligible demands on the control of balance, leads to the same changes in the functional utilization of peripheral optic flow for the control of postural sway as hands-and-knees crawling. We hypothesized that hands-and-knees crawlers (n = 15) would show significantly higher postural responsiveness to movements of the side walls and ceiling of a moving room than same-aged pre-crawlers (n = 19) and belly crawlers (n = 15) with an equivalent amount of crawling experience. Planned comparisons confirmed the hypothesis. Visual-postural coupling in the hands-and-knees crawlers was significantly higher than in the belly crawlers and pre-crawlers. These findings suggest that the balance demands associated with hands-and-knees crawling may be an important contributor to the changes in visual proprioception that have been demonstrated in several experiments to follow hands-and-knees crawling experience. However, we also consider that belly crawling may have less potent effects on visual proprioception because it is an effortful and attention-demanding mode of locomotion, thus leaving less attentional capacity available to notice changing relations between the self and the environment

Electronic Tuning of Mixed Quinoidal‐Aromatic Conjugated Polyelectrolytes: Direct Ionic Substitution on Polymer Main‐Chains

The synthesis of conjugated polymers with ionic substituents directly bound to their main chain repeat units is a strategy for generating strongly electron-accepting conjugated polyelectrolytes, as demonstrated through the synthesis of a series of ionic azaquinodimethane (iAQM) compounds. The introduction of cationic substituents onto the quinoidal para-azaquinodimethane (AQM) core gives rise to a strongly electron-accepting building block, which can be employed in the synthesis of ionic small molecules and conjugated polyelectrolytes (CPEs). Electrochemical measurements alongside theoretical calculations indicate notably low-lying LUMO values for the iAQMs. The optical band gaps measured for these compounds are highly tunable based on structure, ranging from 2.30 eV in small molecules down to 1.22 eV in polymers. The iAQM small molecules and CPEs showcase the band gap reduction effects of combining the donor-acceptor strategy with the bond-length alternation reduction strategy. As a demonstration of their utility, the iAQM CPEs so generated were used as active agents in photothermal therapy

Learning to infer: RL-based search for DNN primitive selection on Heterogeneous Embedded Systems

Deep Learning is increasingly being adopted by industry for computer vision applications running on embedded devices. While Convolutional Neural Networks' accuracy has achieved a mature and remarkable state, inference latency and throughput are a major concern especially when targeting low-cost and low-power embedded platforms. CNNs' inference latency may become a bottleneck for Deep Learning adoption by industry, as it is a crucial specification for many real-time processes. Furthermore, deployment of CNNs across heterogeneous platforms presents major compatibility issues due to vendor-specific technology and acceleration libraries. In this work, we present QS-DNN, a fully automatic search based on Reinforcement Learning which, combined with an inference engine optimizer, efficiently explores through the design space and empirically finds the optimal combinations of libraries and primitives to speed up the inference of CNNs on heterogeneous embedded devices. We show that, an optimized combination can achieve 45x speedup in inference latency on CPU compared to a dependency-free baseline and 2x on average on GPGPU compared to the best vendor library. Further, we demonstrate that, the quality of results and time "to-solution" is much better than with Random Search and achieves up to 15x better results for a short-time search

Fermion kinetics in the Falicov-Kimball limit of the three-band Emery model

The three-band Emery model is reduced to a single-particle quantum model of Falicov-Kimball type, by allowing only up-spins to hop, and forbidding double occupation by projection. It is used to study the effects of geometric obstruction on mobile fermions in thermodynamic equilibrium. For low hopping overlap, there appears a plateau in the entropy, due to charge correlations, and related to real-space disorder. For large overlap, the equilibrium thermopower susceptibility remains anomalous, with a sign opposite to the one predicted from the single-particle density of states. The heat capacity and non-Fermi liquid response are discussed in the context of similar results in the literature. All results are obtained by evaluation of an effective single-particle free-energy operator in closed form. The method to obtain this operator is described in detail.Comment: New calculations, method explained in detail, 16 pages, 9 figure

Machine-learning the string landscape

We propose a paradigm to apply machine learning various databases which have emerged in the study of the string landscape. In particular, we establish neural networks as both classifiers and predictors and train them with a host of available data ranging from Calabi–Yau manifolds and vector bundles, to quiver representations for gauge theories, using a novel framework of recasting geometrical and physical data as pixelated images. We find that even a relatively simple neural network can learn many significant quantities to astounding accuracy in a matter of minutes and can also predict hithertofore unencountered results, whereby rendering the paradigm a valuable tool in physics as well as pure mathematics