Tidal debris from high-velocity collisions as fake dark galaxies: A numerical model of VirgoHI21

High speed collisions, although current in clusters of galaxies, have long been neglected, as they are believed to cause little damages to galaxies, except when they are repeated, a process called harassment. In fact, they are able to produce faint but extended gaseous tails. Such low-mass, starless, tidal debris may become detached and appear as free floating clouds in the very deep HI surveys that are currently being carried out. We show in this paper that these debris possess the same apparent properties as the so-called "Dark Galaxies", objects originally detected in HI, with no optical counterpart, and presumably dark matter dominated. We present a numerical model of the prototype of such Dark Galaxies - VirgoHI21 -, that is able to reproduce its main characteristics: the one-sided tail linking it to the spiral galaxy NGC 4254, the absence of stars, and above all the reversal of the velocity gradient along the tail originally attributed to rotation motions caused by a massive dark matter halo and which we find to be consistent with simple streaming motions plus projection effects. According to our numerical simulations, this tidal debris was expelled 750 Myr ago during a fly-by at 1100 km/s of NGC 4254 by a massive companion which should now lie at a projected distance of about 400 kpc. A candidate for the intruder is discussed. The existence of galaxies that have never been able to form stars had already been challenged based on theoretical and observational grounds. Tidal collisions, in particular those occurring at high speed, provide a much more simple explanation for the origin of such putative Dark Galaxies.Comment: 13 pages, 6 figures, accepted for publication in Ap

The turbomachine blading design using S2-S1 approach

The boundary conditions corresponding to the design problem when the blades being simulated by the bound vorticity distribution are presented. The 3D flow is analyzed by the two steps S2 - S1 approach. In the first step, the number of blades is supposed to be infinite, the vortex distribution is transformed into an axisymmetric one, so that the flow field can be analyzed in a meridional plane. The thickness distribution of the blade producing the flow channel striction is taken into account by the modification of metric tensor in the continuity equation. Using the meridional stream function to define the flow field, the mass conservation is satisfied automatically. The governing equation is deduced from the relation between the azimuthal component of the vorticity and the meridional velocity. The value of the azimuthal component of the vorticity is provided by the hub to shroud equilibrium condition. This step leads to the determination of the axisymmetric stream sheets as well as the approximate camber surface of the blade. In the second step, the finite number of blades is taken into account, the inverse problem corresponding to the blade to blade flow confined in each stream sheet is analyzed. The momentum equation implies that the free vortex of the absolute velocity must be tangential to the stream sheet. The governing equation for the blade to blade flow stream function is deduced from this condition. At the beginning, the upper and the lower surfaces of the blades are created from the camber surface obtained from the first step with the assigned thickness distribution. The bound vorticity distribution and the penetrating flux conservation applied on the presumed blade surface constitute the boundary conditions of the inverse problem. The detection of this flux leads to the rectification of the geometry of the blades

Shear thinning in dilute and semidilute solutions of polystyrene and DNA

The viscosity of dilute and semidilute unentangled DNA solutions, in steady simple shear flow, has been measured across a range of temperatures and concentrations. For polystyrene solutions, measurements of viscosity have been carried out in the semidilute unentangled regime, while results of prior experimental measurements in the dilute regime have been used for the purpose of data analysis, and for comparison with the behaviour of DNA solutions. Interpretation of the shear rate dependence of viscosity in terms of suitably defined non-dimensional variables, is shown to lead to master plots, independent of temperature and concentration, in each of the two concentration regimes. In the case of semidilute unentangled solutions, defining the Weissenberg number in terms of a concentration dependent large scale relaxation time is found not to lead to data collapse across different concentrations. On the other hand, the use of an alternative relaxation time, with the concentration dependence of a single correlation blob, suggests the existence of universal shear thinning behaviour at large shear rates.Comment: 24 pages, 13 figures, supplementary material (see ancillary directory), to appear in Journal of Rheolog

PassGAN: A Deep Learning Approach for Password Guessing

State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., "password123456") and leet speak (e.g., "password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode.Comment: This is an extended version of the paper which appeared in NeurIPS 2018 Workshop on Security in Machine Learning (SecML'18), see https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pd

Magnetic properties of Gd_xY_{1-x}Fe_2Zn_{20}: dilute, large, S\textbf {S} moments in a nearly ferromagnetic Fermi liquid

Single crystals of the dilute, rare earth bearing, pseudo-ternary series, Gd_xY_{1-x}Fe_2Zn_{20} were grown out of Zn-rich solution. Measurements of magnetization, resistivity and heat capacity on Gd_xY_{1-x}Fe_2Zn_{20} samples reveal ferromagnetic order of Gd^{3+} local moments across virtually the whole series ($x \geq 0.02$). The magnetic properties of this series, including the ferromagnetic ordering, the reduced saturated moments at base temperature, the deviation of the susceptibilities from Curie-Weiss law and the anomalies in the resistivity, are understood within the frame work of dilute, $\textbf {S}$ moments (Gd^{3+}) embedded in a nearly ferromagnetic Fermi liquid (YFe_2Zn_{20}). The s-d model is employed to further explain the variation of $T_{\mathrm{C}}$ with x as well as the temperature dependences of of the susceptibilities

Fast Forwarding with Network Processors

Forwarding is a mechanism found in many network operations. Although a regular workstation is able to perform forwarding operations it still suffers from poor performances when compared to dedicated hardware machines. In this paper we study the possibility of using Network Processors (NPs) to improve the capability of regular workstations to forward data. We present a simple model and an experimental study demonstrating that even though NPs are less powerful than Host Processors (HPs) they can forward data more efficiently than HPs in some specific cases

Electrical conductivity beyond linear response in layered superconductors under magnetic field

The time-dependent Ginzburg-Landau approach is used to investigate nonlinear response of a strongly type-II superconductor. The dissipation takes a form of the flux flow which is quantitatively studied beyond linear response. Thermal fluctuations, represented by the Langevin white noise, are assumed to be strong enough to melt the Abrikosov vortex lattice created by the magnetic field into a moving vortex liquid and marginalize the effects of the vortex pinning by inhomogeneities. The layered structure of the superconductor is accounted for by means of the Lawrence-Doniach model. The nonlinear interaction term in dynamics is treated within Gaussian approximation and we go beyond the often used lowest Landau level approximation to treat arbitrary magnetic fields. The I-V curve is calculated for arbitrary temperature and the results are compared to experimental data on high-$T_{c}$ superconductor YBa$_{2}$Cu$_{3}$O$$.Comment: 8 pages, 3 figure