1,490 research outputs found
Superconformal hypermultiplets
We present theories of N=2 hypermultiplets in four spacetime dimensions that are invariant under rigid or local superconformal symmetries. The target spaces of theories with rigid superconformal invariance are (4n)-dimensional {\it special} hyper-K\"ahler manifolds. Such manifolds can be described as cones over tri-Sasakian metrics and are locally the product of a flat four-dimensional space and a quaternionic manifold. The latter manifolds appear in the coupling of hypermultiplets to N=2 supergravity. We employ local sections of an Sp bundle in the formulation of the Lagrangian and transformation rules, thus allowing for arbitrary coordinatizations of the hyper-K\"ahler and quaternionic manifolds
The Horseshoe Estimator: Posterior Concentration around Nearly Black Vectors
We consider the horseshoe estimator due to Carvalho, Polson and Scott (2010)
for the multivariate normal mean model in the situation that the mean vector is
sparse in the nearly black sense. We assume the frequentist framework where the
data is generated according to a fixed mean vector. We show that if the number
of nonzero parameters of the mean vector is known, the horseshoe estimator
attains the minimax risk, possibly up to a multiplicative constant. We
provide conditions under which the horseshoe estimator combined with an
empirical Bayes estimate of the number of nonzero means still yields the
minimax risk. We furthermore prove an upper bound on the rate of contraction of
the posterior distribution around the horseshoe estimator, and a lower bound on
the posterior variance. These bounds indicate that the posterior distribution
of the horseshoe prior may be more informative than that of other one-component
priors, including the Lasso.Comment: This version differs from the final published version in pagination
and typographical detail; Available at
http://projecteuclid.org/euclid.ejs/141813426
A coarse-grained resource allocation model of carbon and nitrogen metabolism in unicellular microbes
Coarse-grained resource allocation models (C-GRAMs) are simple mathematical models of cell physiology, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models provides insights on optimal allocation of cellular resources and have explained experimentally observed cellular growth laws, but current models do not account for the uptake of compound sources of carbon and nitrogen. Here, we formulate a C-GRAM with nitrogen and carbon pathways converging on biomass production, with parametrizations accounting for respirofermentative and purely respiratory growth. The model describes the effects of the uptake of sugars, ammonium and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximized the growth rate. It robustly recovers cellular growth laws including the Monod law and the ribosomal growth law. Furthermore, we show how the growth-maximizing balance between carbon uptake, recycling, and excretion depends on the nutrient environment. Lastly, we find a robust linear correlation between the ribosome fraction and the abundance of amino acid equivalents in the optimal cell, which supports the view that simple regulation of translational gene expression can enable cells to achieve an approximately optimal growth state
Codebook-based Bayesian speech enhancement for nonstationary environments
In this paper, we propose a Bayesian minimum mean squared error approach for the joint estimation of the short-term predictor parameters of speech and noise, from the noisy observation. We use trained codebooks of speech and noise linear predictive coefficients to model the a priori information required by the Bayesian scheme. In contrast to current Bayesian estimation approaches that consider the excitation variances as part of the a priori information, in the proposed method they are computed online for each short-time segment, based on the observation at hand. Consequently, the method performs well in nonstationary noise conditions. The resulting estimates of the speech and noise spectra can be used in a Wiener filter or any state-of-the-art speech enhancement system. We develop both memoryless (using information from the current frame alone) and memory-based (using information from the current and previous frames) estimators. Estimation of functions of the short-term predictor parameters is also addressed, in particular one that leads to the minimum mean squared error estimate of the clean speech signal. Experiments indicate that the scheme proposed in this paper performs significantly better than competing method
Superconformal Hypermultiplets
We present theories of N=2 hypermultiplets in four spacetime dimensions that are invariant under rigid or local superconformal symmetries. The target spaces of theories with rigid superconformal invariance are (4n)-dimensional {\it special} hyper-Kähler manifolds. Such manifolds can be described as cones over tri-Sasakian metrics and are locally the product of a flat four-dimensional space and a quaternionic manifold. The latter manifolds appear in the coupling of hypermultiplets to N=2 supergravity. We employ local sections of an Sp bundle in the formulation of the Lagrangian and transformation rules, thus allowing for arbitrary coordinatizations of the hyper-Kähler and quaternionic manifolds
Wavenet based low rate speech coding
Traditional parametric coding of speech facilitates low rate but provides
poor reconstruction quality because of the inadequacy of the model used. We
describe how a WaveNet generative speech model can be used to generate high
quality speech from the bit stream of a standard parametric coder operating at
2.4 kb/s. We compare this parametric coder with a waveform coder based on the
same generative model and show that approximating the signal waveform incurs a
large rate penalty. Our experiments confirm the high performance of the WaveNet
based coder and show that the speech produced by the system is able to
additionally perform implicit bandwidth extension and does not significantly
impair recognition of the original speaker for the human listener, even when
that speaker has not been used during the training of the generative model.Comment: 5 pages, 2 figure
Two-channel speech denoising through minimum tracking
A blind two-channel interference reduction algorithm to suppress localised interferers in reverberant environments is presented. The algorithm requires neither knowledge of source positions nor a speech-free noise reference. The goal is to estimate the speech signal as observed at one of the microphones, without any additional filtering effects that are typical in convolutive blind source separation
Fission yeast obeys a linear size law under nutrient titration
Steady-state cell size and geometry depend on growth conditions. Here, we use an experimental setup based on continuous culture and single-cell imaging to study how cell volume, length, width and surface-to-volume ratio vary across a range of growth conditions including nitrogen and carbon titration, the choice of nitrogen source, and translation inhibition. Overall, we find cell geometry is not fully determined by growth rate and depends on the specific mode of growth rate modulation. However, under nitrogen and carbon titrations, we observe that the cell volume and the growth rate follow the same linear scaling
Delivery of crop pollination services is an insufficient argument for wild pollinator conservation
There is compelling evidence that more diverse ecosystems deliver greater benefits to people, and these ecosystem services have become a key argument for biodiversity conservation. However, it is unclear how much biodiversity is needed to deliver ecosystem services in a cost- effective way. Here we show that, while the contribution of wild bees to crop production is significant, service delivery is restricted to a limited subset of all known bee species. Across crops, years and biogeographical regions, crop-visiting wild bee communities are dominated by a small number of common species, and threatened species are rarely observed on crops. Dominant crop pollinators persist under agricultural expansion and many are easily enhanced by simple conservation measures, suggesting that cost- effective management strategies to promote crop pollination should target a different set of species than management strategies to promote threatened bees. Conserving the biological diversity of bees therefore requires more than just ecosystem-service-based arguments
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