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We initiate the systematic computation of the heat-kernel coefficients for Laplacian operators obeying anisotropic dispersion relations in curved spacetime. Our results correctly reproduce the limit where isotropy is restored and special anisotropic cases considered previously in the literature. Subsequently, the heat kernel is used to derive the scalar-induced one-loop effective action and beta functions of Horava-Lifshitz gravity. We identify the Gaussian fixed point which is supposed to provide the UV completion of the theory. In the present setting, this fixed point acts as an infrared attractor for the renormalization group flow of Newton's constant and the high-energy phase of the theory is screened by a Landau pole. We comment on the consequences of these findings for the renormalizability of the theory. Comment: 32 pages, 2 figures

Electrical and thermal transport on a fractional quantum Hall edge are determined by topological quantities inherited from the corresponding bulk state. While electrical transport is the standard method for studying edges, thermal transport appears more challenging. Here, we show that the shot noise generated on the edge provides a fully electrical method to probe the edge structure. In the incoherent regime, the noise falls into three topologically distinct universality classes: charge transport is always ballistic while thermal transport is either ballistic, diffusive, or "antiballistic". Correspondingly, the noise either vanishes, decays algebraically or is constant up to exponentially small corrections in the edge length. Published version: 6+7 pages, 3+3 figures

In this paper we apply estimates of the norms of Sobolev extension operators to the spectral estimates of of the first nontrivial Neumann eigenvalue of the Laplace operator in non-convex extension domains. As a consequence we obtain a connection between resonant frequencies of free membranes and the smallest-circle problem (initially proposed by J.~J.~Sylvester in 1857). 12 pages

Yield Optimized Interpolated Superoscillations (YOIS) have been recently introduced as a means for possibly making the use of the phenomenon of superoscillation practical. In this paper we study how good is a superoscillation that is not optimal. Namely, by how much is the yield decreased when the signal departs from the optimal one. We consider two situations. One is the case where the signal strictly obeys the interpolation requirement and the other is when that requirement is relaxed. In the latter case the yield can be increased at the expense of deterioration of signal quality. An important conclusion is that optimizing superoscillations may be challenging in terms of the precision needed, however, storing and using them is not at all that sensitive. This is of great importance in any physical system where noise and error are inevitable. 22 pages, 18 figures. Accepted to J. Phys. A

Spatial modulations in the distribution of observed luminosities (computed using redshifts) of ~ 5 $\times$ 10$^5$ galaxies from the SDSS Data Release 7, probe the cosmic peculiar velocity field out to z ~ 0.1. Allowing for luminosity evolution, the r-band luminosity function, determined via a spline-based estimator, is well represented by a Schechter form with M$^{\star}$(z) - 5log$_{10}$h = -20.52 - 1.6(z - 0.1) $\pm$ 0.05 and $\alpha^{\star}$ = -1.1 $\pm$ 0.03. Bulk flows and higher velocity moments in two redshift bins, 0.02 < z < 0.07 and 0.07 < z < 0.22, agree with the predictions of the $\Lambda$CDM model, as obtained from mock galaxy catalogs designed to match the observations. Assuming a $\Lambda$CDM model, we estimate $\sigma_{8}$ $\approx$ 1.1 $\pm$ 0.4 for the amplitude of the linear matter power spectrum, where the low accuracy is due to the limited number of galaxies. While the low-z bin is robust against coherent photometric uncertainties, the bias of results from the second bin is consistent with the ~ 1% magnitude tilt reported by the SDSS collaboration. The systematics are expected to have a significantly lower impact in future datasets with larger sky coverage and better photometric calibration. Comment: 21 pages, 11 figures, accepted version

Power line communications (PLC) is the central communications technology for the realization of smart power grids. As the designated band for smart grid communications is the narrowband (NB) power line channel, NB-PLC has been receiving substantial attention in recent years. Narrowband power line channels are characterized by cyclic short-term variations of the channel transfer function (CTF) and strong noise with periodic statistics. In this work, modeling the CTF as a linear periodically time-varying filter and the noise as an additive cyclostationary Gaussian process, we derive the capacity of discrete-time NB-PLC channels. As part of the capacity derivation, we characterize the capacity achieving transmission scheme, which leads to a practical code construction that approaches capacity. The capacity derived in this work is numerically evaluated for several NB-PLC channel configurations taken from previous works, and the results show that the optimal scheme achieves a substantial rate gain over a previously proposed ad-hoc scheme. This gain is due to optimally accounting for the periodic properties of the channel and the noise. Comment: 46 pages, 4 figures. This work was presented in part at the IEEE International Conference on Communications (ICC), June 2015

ABSTRACT Linear polarization has been measured in several gamma-ray burst (GRB) afterglows. After a few days, polarization arises from the forward shock emission that depends on the post-shock magnetic field. The latter can originate both from compression of existing fields, here the interstellar medium (ISM) magnetic field, and from shock-generated instabilities. For short GRBs, previous modelling of the polarization arising from the forward shock considered a random field fully or partially confined to the shock plane. However, the ISM magnetic field likely consists of both random and ordered components. Here we study the impact of a more realistic magnetic field having both ordered and random components. We present our semi-analytical model and compute polarization curves arising for different magnetic field configurations. We find that the presence of an ordered component, even significantly weaker than the random one, has distinct signatures that could be detectable. In the presence of an ordered component not in the observer plane, we show that (i) for an observer inside the jet, the polarization angle θp either remains constant during all the afterglow phase or exhibits variations smaller than the 90° swing expected from a random component solely; (ii) for an off-axis observer, the polarization angle evolves from $\theta _\mathrm{ p}^{\max }$, before the jet break to its opposite after the jet break. We also find that the upper limit polarization for GRB 170817 requires a random field not fully confined to the shock plane and is compatible with an ordered component as large as half the random one.

We review the current status of the radioisotopes program at the Soreq Applied Research Accelerator Facility (SARAF), where we utilize an electrostatic-ion-beam trap and a magneto-optical trap for studying the nuclear $\beta$-decay from trapped radioactive atoms and ions. The differential energy spectra of $\beta$'s and recoil ions emerging from the decay is sensitive to beyond standard model interactions and is complementary to high energy searches. The completed facility SARAF-II will be one of the world's most powerful deuteron, proton and fast neutron sources, producing light radioactive isotopes in unprecedented amounts, needed for obtaining enough statistics for a high precision measurement.

We present $^{56}$Ni mass estimates for 110 normal Type II supernovae (SNe II), computed here from their luminosity in the radioactive tail. This sample consists of SNe from the literature, with at least three photometric measurements in a single optical band within 95-320 d since explosion. To convert apparent magnitudes to bolometric ones, we compute bolometric corrections (BCs) using 15 SNe in our sample having optical and near-IR photometry, along with three sets of SN II atmosphere models to account for the unobserved flux. We find that the $I$- and $i$-band are best suited to estimate luminosities through the BC technique. The $^{56}$Ni mass distribution of our SN sample has a minimum and maximum of 0.005 and 0.177 M$_{\odot}$, respectively, and a selection-bias-corrected average of $0.037\pm0.005$ M$_{\odot}$. Using the latter value together with iron isotope ratios of two sets of core-collapse (CC) nucleosynthesis models, we calculate a mean iron yield of $0.040\pm0.005$ M$_{\odot}$ for normal SNe II. Combining this result with recent mean $^{56}$Ni mass measurements for other CC SN subtypes, we estimate a mean iron yield $$36 per cent. We also find that the empirical relation between $^{56}$Ni mass and steepness parameter ($S$) is poorly suited to measure the $^{56}$Ni mass of normal SNe II. Instead, we present a correlation between $^{56}$Ni mass, $S$, and absolute magnitude at 50 d since explosion. The latter allows to measure $^{56}$Ni masses of normal SNe II with a precision around 30 per cent. Comment: 33 pages, 20 figures, 6 figures in appendix, accepted for publication to MNRAS

Recent work on adversarial learning has focused mainly on neural networks and domains where those networks excel, such as computer vision, or audio processing. The data in these domains is typically homogeneous, whereas heterogeneous tabular datasets domains remain underexplored despite their prevalence. When searching for adversarial patterns within heterogeneous input spaces, an attacker must simultaneously preserve the complex domain-specific validity rules of the data, as well as the adversarial nature of the identified samples. As such, applying adversarial manipulations to heterogeneous datasets has proved to be a challenging task, and no generic attack method was suggested thus far. We, however, argue that machine learning models trained on heterogeneous tabular data are as susceptible to adversarial manipulations as those trained on continuous or homogeneous data such as images. To support our claim, we introduce a generic optimization framework for identifying adversarial perturbations in heterogeneous input spaces. We define distribution-aware constraints for preserving the consistency of the adversarial examples and incorporate them by embedding the heterogeneous input into a continuous latent space. Due to the nature of the underlying datasets We focus on $\ell_0$ perturbations, and demonstrate their applicability in real life. We demonstrate the effectiveness of our approach using three datasets from different content domains. Our results demonstrate that despite the constraints imposed on input validity in heterogeneous datasets, machine learning models trained using such data are still equally susceptible to adversarial examples.