Continuous Shearlet Tight Frames

Based on the shearlet transform we present a general construction of continuous tight frames for $L^2(\mathbb{R}^2)$ from any sufficiently smooth function with anisotropic moments. This includes for example compactly supported systems, piecewise polynomial systems, or both. From our earlier results it follows that these systems enjoy the same desirable approximation properties for directional data as the previous bandlimited and very specific constructions due to Kutyniok and Labate. We also show that the representation formulas we derive are in a sense optimal for the shearlet transform

Parabolic Molecules

Anisotropic decompositions using representation systems based on parabolic scaling such as curvelets or shearlets have recently attracted significantly increased attention due to the fact that they were shown to provide optimally sparse approximations of functions exhibiting singularities on lower dimensional embedded manifolds. The literature now contains various direct proofs of this fact and of related sparse approximation results. However, it seems quite cumbersome to prove such a canon of results for each system separately, while many of the systems exhibit certain similarities. In this paper, with the introduction of the notion of {\em parabolic molecules}, we aim to provide a comprehensive framework which includes customarily employed representation systems based on parabolic scaling such as curvelets and shearlets. It is shown that pairs of parabolic molecules have the fundamental property to be almost orthogonal in a particular sense. This result is then applied to analyze parabolic molecules with respect to their ability to sparsely approximate data governed by anisotropic features. For this, the concept of {\em sparsity equivalence} is introduced which is shown to allow the identification of a large class of parabolic molecules providing the same sparse approximation results as curvelets and shearlets. Finally, as another application, smoothness spaces associated with parabolic molecules are introduced providing a general theoretical approach which even leads to novel results for, for instance, compactly supported shearlets

Definability and stability of multiscale decompositions for manifold-valued data

We discuss multiscale representations of discrete manifold-valued data. As it turns out that we cannot expect general manifold-analogues of biorthogonal wavelets to possess perfect reconstruction, we focus our attention on those constructions which are based on upscaling operators which are either interpolating or midpoint-interpolating. For definable multiscale decompositions we obtain a stability result

The surprising influence of late charged current weak interactions on Big Bang Nucleosynthesis

The weak interaction charged current processes ($\nu_e+n\leftrightarrow p+e^-$, $\bar\nu_e +p\leftrightarrow n+e^+$, $n\leftrightarrow p+e^-+\bar\nu_e$) interconvert neutrons and protons in the early universe and have significant influence on Big Bang Nucleosynthesis (BBN) light-element abundance yields, particulary that for $^{4}{\rm He}$. We demonstrate that the influence of these processes is still significant even when they operate well below temperatures $T\sim0.7\,{\rm MeV}$ usually invoked for "weak freeze-out," and in fact down nearly into the alpha-particle formation epoch ($T \approx 0.1\,{\rm MeV}$). This physics is correctly captured in commonly used BBN codes, though this late-time, low-temperature persistent effect of the isospin-changing weak processes, and the sensitivity of the associated rates to lepton energy distribution functions and blocking factors are not widely appreciated. We quantify this late-time influence by analyzing weak interaction rate dependence on the neutron lifetime, lepton energy distribution functions, entropy, the proton-neutron mass difference, and Hubble expansion rate. The effects we point out here render BBN a keen probe of any beyond-standard-model physics that alters lepton number/energy distributions, even subtly, in epochs of the early universe all the way down to near $T=100\,{\rm keV}$.Comment: 27 pages, 8 figure

Insights into neutrino decoupling gleaned from considerations of the role of electron mass

We present calculations showing how electron rest mass influences entropy flow, neutrino decoupling, and Big Bang Nucleosynthesis (BBN) in the early universe. To elucidate this physics and especially the sensitivity of BBN and related epochs to electron mass, we consider a parameter space of rest mass values larger and smaller than the accepted vacuum value. Electromagnetic equilibrium, coupled with the high entropy of the early universe, guarantees that significant numbers of electron-positron pairs are present, and dominate over the number of ionization electrons to temperatures much lower than the vacuum electron rest mass. Scattering between the electrons-positrons and the neutrinos largely controls the flow of entropy from the plasma into the neutrino seas. Moreover, the number density of electron-positron-pair targets can be exponentially sensitive to the effective in-medium electron mass. This entropy flow influences the phasing of scale factor and temperature, the charged current weak-interaction-determined neutron-to-proton ratio, and the spectral distortions in the relic neutrino energy spectra. Our calculations show the sensitivity of the physics of this epoch to three separate effects: finite electron mass, finite-temperature quantum electrodynamic (QED) effects on the plasma equation of state, and Boltzmann neutrino energy transport. The ratio of neutrino to plasma component energy scales manifests in Cosmic Microwave Background (CMB) observables, namely the baryon density and the radiation energy density, along with the primordial helium and deuterium abundances. Our results demonstrate how the treatment of in-medium electron mass (i.e., QED effects) could translate into an important source of uncertainty in extracting neutrino and beyond-standard-model physics limits from future high-precision CMB data.Comment: 32 pages, 8 figures, 1 table. Version accepted by Nuclear Physics

How degenerate is the parametrization of neural networks with the ReLU activation function?

Neural network training is usually accomplished by solving a non-convex optimization problem using stochastic gradient descent. Although one optimizes over the networks parameters, the main loss function generally only depends on the realization of the neural network, i.e. the function it computes. Studying the optimization problem over the space of realizations opens up new ways to understand neural network training. In particular, usual loss functions like mean squared error and categorical cross entropy are convex on spaces of neural network realizations, which themselves are non-convex. Approximation capabilities of neural networks can be used to deal with the latter non-convexity, which allows us to establish that for sufficiently large networks local minima of a regularized optimization problem on the realization space are almost optimal. Note, however, that each realization has many different, possibly degenerate, parametrizations. In particular, a local minimum in the parametrization space needs not correspond to a local minimum in the realization space. To establish such a connection, inverse stability of the realization map is required, meaning that proximity of realizations must imply proximity of corresponding parametrizations. We present pathologies which prevent inverse stability in general, and, for shallow networks, proceed to establish a restricted space of parametrizations on which we have inverse stability w.r.t. to a Sobolev norm. Furthermore, we show that by optimizing over such restricted sets, it is still possible to learn any function which can be learned by optimization over unrestricted sets.Comment: Accepted at NeurIPS 201