Cellular trafficking of Sn-2 phosphatidylcholine prodrugs studied with fluorescence lifetime imaging and super-resolution microscopy

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In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?

It is often said that a deep learning model is "invariant" to some specific type of transformation. However, what is meant by this statement strongly depends on the context in which it is made. In this paper we explore the nature of invariance and equivariance of deep learning models with the goal of better understanding the ways in which they actually capture these concepts on a formal level. We introduce a family of invariance and equivariance metrics that allows us to quantify these properties in a way that disentangles them from other metrics such as loss or accuracy. We use our metrics to better understand the two most popular methods used to build invariance into networks: data augmentation and equivariant layers. We draw a range of conclusions about invariance and equivariance in deep learning models, ranging from whether initializing a model with pretrained weights has an effect on a trained model's invariance, to the extent to which invariance learned via training can generalize to out-of-distribution data.Comment: To appear at NeurIPS 202

Formulation and performance of variational integrators for rotating bodies

Variational integrators are obtained for two mechanical systems whose configuration spaces are, respectively, the rotation group and the unit sphere. In the first case, an integration algorithm is presented for Euler’s equations of the free rigid body, following the ideas of Marsden et al. (Nonlinearity 12:1647–1662, 1999). In the second example, a variational time integrator is formulated for the rigid dumbbell. Both methods are formulated directly on their nonlinear configuration spaces, without using Lagrange multipliers. They are one-step, second order methods which show exact conservation of a discrete angular momentum which is identified in each case. Numerical examples illustrate their properties and compare them with existing integrators of the literature

Real space tests of the statistical isotropy and Gaussianity of the WMAP CMB data

ABRIDGED: We introduce and analyze a method for testing statistical isotropy and Gaussianity and apply it to the WMAP CMB foreground reduced, temperature maps, and cross-channel difference maps. We divide the sky into regions of varying size and shape and measure the first four moments of the one-point distribution within these regions, and using their simulated spatial distributions we test the statistical isotropy and Gaussianity hypotheses. By randomly varying orientations of these regions, we sample the underlying CMB field in a new manner, that offers a richer exploration of the data content, and avoids possible biasing due to a single choice of sky division. The statistical significance is assessed via comparison with realistic Monte-Carlo simulations. We find the three-year WMAP maps to agree well with the isotropic, Gaussian random field simulations as probed by regions corresponding to the angular scales ranging from 6 deg to 30 deg at 68% confidence level. We report a strong, anomalous (99.8% CL) dipole ``excess'' in the V band of the three-year WMAP data and also in the V band of the WMAP five-year data (99.3% CL). We notice the large scale hemispherical power asymmetry, and find that it is not highly statistically significant in the WMAP three-year data (<~ 97%) at scales l <= 40. The significance is even smaller if multipoles up to l=1024 are considered (~90% CL). We give constraints on the amplitude of the previously-proposed CMB dipole modulation field parameter. We easily detect the residual foregrounds in cross-band difference maps at rms level <~ 7 \mu K (at scales >~ 6 deg) and limit the systematical uncertainties to <~ 1.7 \mu K (at scales >~ 30 deg).Comment: 20 pages, 20 figures; more tests added; updated to match the version to be published in JCA

Low NOx heavy fuel combustor concept program. Phase 1: Combustion technology generation

The viability of low emission nitrogen oxide (NOx) gas turbine combustors for industrial and utility application. Thirteen different concepts were evolved and most were tested. Acceptable performance was demonstrated for four of the combustors using ERBS fuel and ultralow NOx emissions were obtained for lean catalytic combustion. Residual oil and coal derived liquids containing fuel bound nitrogen (FBN) were also used at test fuels, and it was shown that staged rich/lean combustion was effective in minimizing the conversion of FBN to NOx. The rich/lean concept was tested with both modular and integral combustors. While the ceramic lined modular configuration produced the best results, the advantages of the all metal integral burners make them candidates for future development. An example of scaling the laboratory sized combustor to a 100 MW size engine is included in the report as are recommendations for future work

Baryogenesis through Collapsing String Loops in Gauged Baryon and Lepton Models

A scenario for the generation of the baryon asymmetry in the early Universe is proposed in which cosmic string loops, predicted by theories where the baryon and/or lepton numbers are gauged symmetries, collapse during the friction dominated period of string evolution. This provides a mechanism for the departure from thermal equilibrium necessary to have a nonvanishing baryon asymmetry. Examples of models are given where this idea can be implemented. In particular, the model with the gauge symmetry $SU(3)_{c}\otimes SU(2)_{L}\otimes U(1)_{Y}\otimes U(1)_{B} \otimes U(1)_{L}$ has the interesting feature where sphaleron processes do not violate the baryon and lepton numbers so that no wash out of any initial baryon asymmetry occurs at the electroweak scale.Comment: 21 pages, LaTeX, PURD-TH-93-09, SISSA 87/93/

From START to FINISH : the influence of osmotic stress on the cell cycle

Peer reviewedPublisher PD