How closely do baryons follow dark matter on large scales?

We investigate the large-scale clustering and gravitational interaction of baryons and dark matter (DM) over cosmic time using a set of collisionless N-body simulations. Both components, baryons and DM, are evolved from distinct primordial density and velocity power spectra as predicted by early-universe physics. We first demonstrate that such two-component simulations require an unconventional match between force and mass resolution (i.e. force softening on at least the mean particle separation scale). Otherwise, the growth on any scale is not correctly recovered because of a spurious coupling between the two species at the smallest scales. With these simulations, we then demonstrate how the primordial differences in the clustering of baryons and DM are progressively diminished over time. In particular, we explicitly show how the BAO signature is damped in the spatial distribution of baryons and imprinted in that of DM. This is a rapid process, yet it is still not fully completed at low redshifts. On large scales, the overall shape of the correlation function of baryons and DM differs by 2% at z = 9 and by 0.2% at z = 0. The differences in the amplitude of the BAO peak are approximately a factor of 5 larger: 10% at z = 9 and 1% at z = 0. These discrepancies are, however, smaller than effects expected to be introduced by galaxy formation physics in both the shape of the power spectrum and in the BAO peak, and are thus unlikely to be detected given the precision of the next generation of galaxy surveys. Hence, our results validate the standard practice of modelling the observed galaxy distribution using predictions for the total mass clustering in the Universe.Comment: 9 pages, 6 figures. Replaced with version published in MNRA

The Relationship between Service Learning and Deep Learning

Data analysis from the 2012 National Survey of Student Engagement (NSSE) found that students at Indiana University-Purdue University Indianapolis (IUPUI) who participated in one or more service learning courses had higher mean scores on all three measures of deep learning. These include higher order learning, integrative learning, and reflective learning

Nilpotence in normed MGL-modules

We establish a motivic version of the May Nilpotence Conjecture: if E is a normed motivic spectrum that satisfies $E \wedge HZ \simeq 0$, then also $E \wedge MGL \simeq 0$. In words, motivic homology detects vanishing of normed modules over the algebraic cobordism spectrum.Comment: 17 page

Self-regulation of the endothelin receptor system in choriocarcinoma cells

AbstractThe human trophoblast secretes endothelin-1 (ET-1) and expresses ET receptors. The present study tested whether the transformed BeWo, JAR and JEG-3 choriocarcinoma cells: (1) secrete endothelin-1 (ET-1); (2) express both ET-A and ET-B receptor subtypes; and (3) have the potential to allow for autologous regulation of ET-receptor proteins. The cells were cultured for 24/48 h with or without 10% FCS and, in experiments on receptor regulation, with ET-1 (5–20 nM and 10 μM). ET-1 secretion was measured by RIA and receptor levels by immunoblotting. All cell types secreted ET-1 albeit at different levels and sensitivity to FCS. All cell lines expressed both ET-A (JEG-3>BeWo=JAR) and ET-B (JEG-3=JAR>BeWo) receptor subtypes, which could be up- and downregulated depending on ET-1 concentration, culture time and FCS presence. It is concluded that BeWo, JAR and JEG-3 choriocarcinoma cells secrete ET-1 and express both ET-A and ET-B receptor subtypes. The receptor levels can be regulated by ET-1. This provides the molecular basis for an autocrine system with the potential of autologous regulation of yet unidentified ET-1-induced functions

Biometric Verification Secure Against Malicious Adversaries

Biometric verification has been widely deployed in current authentication solutions as it proves the physical presence of individuals. To protect the sensitive biometric data in such systems, several solutions have been developed that provide security against honest-but-curious (semi-honest) attackers. However, in practice attackers typically do not act honestly and multiple studies have shown drastic biometric information leakage in such honest-but-curious solutions when considering dishonest, malicious attackers. In this paper, we propose a provably secure biometric verification protocol to withstand malicious attackers and prevent biometric data from any sort of leakage. The proposed protocol is based on a homomorphically encrypted log likelihood-ratio-based (HELR) classifier that supports any biometric modality (e.g. face, fingerprint, dynamic signature, etc.) encoded as a fixed-length real-valued feature vector and performs an accurate and fast biometric recognition. Our protocol, that is secure against malicious adversaries, is designed from a protocol secure against semi-honest adversaries enhanced by zero-knowledge proofs. We evaluate both protocols for various security levels and record a sub-second speed (between $0.37$s and $0.88$s) for the protocol against semi-honest adversaries and between $0.95$s and $2.50$s for the protocol secure against malicious adversaries.Comment: This is a complete reworking and major expansion of our paper arXiv:1705.09936 * Reworking of original semi-honest protocol and its security proof * Major expansions: tailored zero-knowledge proofs; efficient variant of original protocol that we prove secure against malicious adversaries; extensive experimental evaluation using three different datasets; in-depth comparison with related wor