Assessing and countering reaction attacks against post-quantum public-key cryptosystems based on QC-LDPC codes

Code-based public-key cryptosystems based on QC-LDPC and QC-MDPC codes are promising post-quantum candidates to replace quantum vulnerable classical alternatives. However, a new type of attacks based on Bob's reactions have recently been introduced and appear to significantly reduce the length of the life of any keypair used in these systems. In this paper we estimate the complexity of all known reaction attacks against QC-LDPC and QC-MDPC code-based variants of the McEliece cryptosystem. We also show how the structure of the secret key and, in particular, the secret code rate affect the complexity of these attacks. It follows from our results that QC-LDPC code-based systems can indeed withstand reaction attacks, on condition that some specific decoding algorithms are used and the secret code has a sufficiently high rate.Comment: 21 pages, 2 figures, to be presented at CANS 201

Analysis of reaction and timing attacks against cryptosystems based on sparse parity-check codes

In this paper we study reaction and timing attacks against cryptosystems based on sparse parity-check codes, which encompass low-density parity-check (LDPC) codes and moderate-density parity-check (MDPC) codes. We show that the feasibility of these attacks is not strictly associated to the quasi-cyclic (QC) structure of the code but is related to the intrinsically probabilistic decoding of any sparse parity-check code. So, these attacks not only work against QC codes, but can be generalized to broader classes of codes. We provide a novel algorithm that, in the case of a QC code, allows recovering a larger amount of information than that retrievable through existing attacks and we use this algorithm to characterize new side-channel information leakages. We devise a theoretical model for the decoder that describes and justifies our results. Numerical simulations are provided that confirm the effectiveness of our approach

Mass accretion rates of clusters of galaxies: CIRS and HeCS

We use a new spherical accretion recipe tested on N-body simulations to measure the observed mass accretion rate (MAR) of 129 clusters in the Cluster Infall Regions in the Sloan Digital Sky Survey (CIRS) and in the Hectospec Cluster Survey (HeCS). The observed clusters cover the redshift range of $0.01<z<0.30$ and the mass range of $\sim 10^{14}-10^{15} {h^{-1}~\rm{M_\odot}}$. Based on three-dimensional mass profiles of simulated clusters reaching beyond the virial radius, our recipe returns MARs that agree with MARs based on merger trees. We adopt this recipe to estimate the MAR of real clusters based on measurements of the mass profile out to $\sim 3R_{200}$. We use the caustic method to measure the mass profiles to these large radii. We demonstrate the validity of our estimates by applying the same approach to a set of mock redshift surveys of a sample of 2000 simulated clusters with a median mass of $M_{200}= 10^{14} {h^{-1}~\rm{M_{\odot}}}$ as well as a sample of 50 simulated clusters with a median mass of $M_{200}= 10^{15} {h^{-1}~\rm{M_{\odot}}}$: the median MARs based on the caustic mass profiles of the simulated clusters are unbiased and agree within $19\%$ with the median MARs based on the real mass profile of the clusters. The MAR of the CIRS and HeCS clusters increases with the mass and the redshift of the accreting cluster, which is in excellent agreement with the growth of clusters in the $\Lambda$CDM model.Comment: 25 pages, 19 figures, 7 table

Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks

Prediction of one-dimensional protein structures such as secondary structures and contact numbers is useful for the three-dimensional structure prediction and important for the understanding of sequence-structure relationship. Here we present a new machine-learning method, critical random networks (CRNs), for predicting one-dimensional structures, and apply it, with position-specific scoring matrices, to the prediction of secondary structures (SS), contact numbers (CN), and residue-wise contact orders (RWCO). The present method achieves, on average, $Q_3$ accuracy of 77.8% for SS, correlation coefficients of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS prediction is comparable to other state-of-the-art methods, and that of the CN prediction is a significant improvement over previous methods. We give a detailed formulation of critical random networks-based prediction scheme, and examine the context-dependence of prediction accuracies. In order to study the nonlinear and multi-body effects, we compare the CRNs-based method with a purely linear method based on position-specific scoring matrices. Although not superior to the CRNs-based method, the surprisingly good accuracy achieved by the linear method highlights the difficulty in extracting structural features of higher order from amino acid sequence beyond that provided by the position-specific scoring matrices.Comment: 20 pages, 1 figure, 5 tables; minor revision; accepted for publication in BIOPHYSIC

Chemical enrichment of the complex hot ISM of the Antennae Galaxies: II. Physical properties of the hot gas and supernova feedback

We investigate the physical properties of the interstellar medium (ISM) in the merging pair of galaxies known as The Antennae (NGC 4038/39), using the deep coadded ~411 ks Chandra ACIS-S data set. The method of analysis and some of the main results from the spectral analysis, such as metal abundances and their variations from ~0.2 to ~20-30 times solar, are described in Paper I (Baldi et al. submitted). In the present paper we investigate in detail the physics of the hot emitting gas, deriving measures for the hot-gas mass (~10^ M_sun), cooling times (10^7-10^8 yr), and pressure (3.5x10^-11-2.8x10^-10 dyne cm^-2). At least in one of the two nuclei (NGC 4038) the hot-gas pressure is significantly higher than the CO pressure, implying that shock waves may be driven into the CO clouds. Comparison of the metal abundances with the average stellar yields predicted by theoretical models of SN explosions points to SNe of Type II as the main contributors of metals to the hot ISM. There is no evidence of any correlation between radio-optical star-formation indicators and the measured metal abundances. Although due to uncertainties in the average gas density we cannot exclude that mixing may have played an important role, the short time required to produce the observed metal masses (<=2 Myr) suggests that the correlations are unlikely to have been destroyed by efficient mixing. More likely, a significant fraction of SN II ejecta may be in a cool phase, in grains, or escaping in hot winds. In each case, any such fraction of the ejecta would remain undetectable with soft X-ray observations.Comment: 29 pages, 6 figures, accepted by the Astrophysical Journa

Effects of putrescine, cadaverine, spermine, spermidine and β-phenylethylamine on cultured bovine mammary epithelial cells

A bovine mammary epithelial cell line (BME-UV1) and three-dimensional collagen primary bovine organoids were used to evaluate the effects of cadaverine, putrescine, spermine, spermidine and β-phenylethylamine on mammary epithelial cells. Each biogenic amine was diluted in several concentrations (0-50 mM in BME-UV1 and 0-4 mM in primary bovine organoids) in the appropriate saline solution for the cell culture considered. In order to determine the activity of each compound tritiated thymidine incorporation was used. At low concentrations, all amines induced cell proliferation in both cultures. In BME-UV1, spermine significantly inhibited cell proliferation (P<0.001), while the other amines inhibited at higher concentrations (50mM). In primary bovine organoids, β−phenylethylamine significantly (P<0.001) inhibited cell proliferation at 4 mM. Organoids cultured in the presence of all amines, except β-phenylethylamine, had stellate projections indicating intense cell proliferation. Proliferation of mammary epithelial cells was stimulated at low concentrations, while at high concentrations it was inhibited. Our results suggested that the effects of each compound on mammary epithelial cells could be related to the compound itself and not to mediating by the bovine amino oxidase, responsible of the formation of toxic metabolites

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Mg/Ti multilayers: structural, optical and hydrogen absorption properties

Mg-Ti alloys have uncommon optical and hydrogen absorbing properties, originating from a "spinodal-like" microstructure with a small degree of chemical short-range order in the atoms distribution. In the present study we artificially engineer short-range order by depositing Pd-capped Mg/Ti multilayers with different periodicities and characterize them both structurally and optically. Notwithstanding the large lattice parameter mismatch between Mg and Ti, the as-deposited metallic multilayers show good structural coherence. Upon exposure to H2 gas a two-step hydrogenation process occurs, with the Ti layers forming the hydride before Mg. From in-situ measurements of the bilayer thickness L at different hydrogen pressures, we observe large out-of-plane expansions of the Mg and Ti layers upon hydrogenation, indicating strong plastic deformations in the films and a consequent shortening of the coherence length. Upon unloading at room temperature in air, hydrogen atoms remain trapped in the Ti layers due to kinetic constraints. Such loading/unloading sequence can be explained in terms of the different thermodynamic properties of hydrogen in Mg and Ti, as shown by diffusion calculations on a model multilayered systems. Absorption isotherms measured by hydrogenography can be interpreted as a result of the elastic clamping arising from strongly bonded Mg/Pd and broken Mg/Ti interfaces