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Subjects -> ASTRONOMY (Total: 94 journals)
 Showing 1 - 46 of 46 Journals sorted alphabetically Advances in Astronomy       (Followers: 49) Annual Review of Astronomy and Astrophysics       (Followers: 50) Annual Review of Earth and Planetary Sciences       (Followers: 67) Artificial Satellites       (Followers: 21) Astrobiology       (Followers: 11) Astronomical & Astrophysical Transactions: The Journal of the Eurasian Astronomical Society       (Followers: 7) Astronomical Review       (Followers: 5) Astronomische Nachrichten       (Followers: 4) Astronomy & Geophysics       (Followers: 49) Astronomy and Astrophysics       (Followers: 67) Astronomy and Computing       (Followers: 6) Astronomy Letters       (Followers: 22) Astronomy Reports       (Followers: 22) Astronomy Studies Development       (Followers: 15) Astroparticle Physics       (Followers: 10) Astrophysical Bulletin       (Followers: 4) Astrophysics       (Followers: 35) Astrophysics and Space Science       (Followers: 49) Astrophysics and Space Sciences Transactions (ASTRA)       (Followers: 60) Astropolitics: The International Journal of Space Politics & Policy       (Followers: 13) Celestial Mechanics and Dynamical Astronomy       (Followers: 15) Chinese Astronomy and Astrophysics       (Followers: 25) Colloid Journal       (Followers: 2) Comptes Rendus : Physique       (Followers: 2) Computational Astrophysics and Cosmology       (Followers: 6) Earth and Planetary Science Letters       (Followers: 142) Earth, Moon, and Planets       (Followers: 47) Earth, Planets and Space       (Followers: 77) EAS Publications Series       (Followers: 8) EPL Europhysics Letters       (Followers: 8) Experimental Astronomy       (Followers: 38) Expert Opinion on Astronomy and Astrophysics       (Followers: 8) Extreme Life, Biospeology & Astrobiology - International Journal of the Bioflux Society       (Followers: 4) Few-Body Systems       (Followers: 1) Foundations of Physics       (Followers: 40) Frontiers in Astronomy and Space Sciences       (Followers: 15) Galaxies       (Followers: 6) Globe, The       (Followers: 3) Gravitation and Cosmology       (Followers: 6) Icarus       (Followers: 71) International Journal of Advanced Astronomy       (Followers: 21) International Journal of Astrobiology       (Followers: 4) International Journal of Astronomy       (Followers: 22) International Journal of Astronomy and Astrophysics       (Followers: 36) International Journal of Satellite Communications Policy and Management       (Followers: 15) International Letters of Chemistry, Physics and Astronomy       (Followers: 8) ISRN Astronomy and Astrophysics       (Followers: 14) Journal for the History of Astronomy       (Followers: 20) Journal of Astrobiology & Outreach       (Followers: 5) Journal of Astronomical Instrumentation       (Followers: 3) Journal of Astrophysics       (Followers: 33) Journal of Astrophysics and Astronomy       (Followers: 58) Journal of Atmospheric and Solar-Terrestrial Physics       (Followers: 133) Journal of Geophysical Research : Planets       (Followers: 115) Journal of Geophysical Research : Space Physics       (Followers: 134) Journal of High Energy Astrophysics       (Followers: 25) Kinematics and Physics of Celestial Bodies       (Followers: 11) KronoScope       (Followers: 1) Macalester Journal of Physics and Astronomy       (Followers: 5) Monthly Notices of the Royal Astronomical Society       (Followers: 13) Monthly Notices of the Royal Astronomical Society : Letters       (Followers: 2) Nature Astronomy       (Followers: 14) New Astronomy       (Followers: 26) New Astronomy Reviews       (Followers: 19) Nonlinear Dynamics       (Followers: 19) NRIAG Journal of Astronomy and Geophysics       (Followers: 4) Physics of the Dark Universe       (Followers: 4) Planetary and Space Science       (Followers: 106) Planetary Science       (Followers: 52) Proceedings of the International Astronomical Union       (Followers: 2) Publications of the Astronomical Society of Australia       (Followers: 3) Publications of the Astronomical Society of Japan       (Followers: 4) Research & Reviews : Journal of Space Science & Technology       (Followers: 20) Research in Astronomy and Astrophysics       (Followers: 38) Revista Mexicana de Astronomía y Astrofísica       (Followers: 3) Science China : Physics, Mechanics & Astronomy       (Followers: 4) Science China Physics, Mechanics & Astronomy       (Followers: 4) Solar Physics       (Followers: 29) Solar System Research       (Followers: 15) Space Science International       (Followers: 117) Space Science Reviews       (Followers: 92) Space Weather       (Followers: 27) Transport and Aerospace Engineering       (Followers: 13) Universe       (Followers: 6)
Similar Journals
 Astronomy LettersJournal Prestige (SJR): 0.452 Citation Impact (citeScore): 1Number of Followers: 22      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1562-6873 - ISSN (Online) 1063-7737 Published by Springer-Verlag  [2469 journals]
• GX 339-4: Is the Hot-Flow Precession Model Consistent with the Infrared
Variability in Low-Mass X-ray Binaries'

Abstract: During accretion-induced outbursts, low-mass X-ray binaries demonstrate a complex behavior accompanied by a rapid change in their spectral and timing characteristics in the entire range of the electromagnetic spectrum — from radio to hard X-rays. So far there is no complete, self-consistent model describing all of the observed phenomena, including the rapid changes in visible and near-infrared fluxes and the correlation of these changes with the X-ray emission, while the observed evolution of outbursts is described in terms of the transition between characteristic states. Based on quasi-simultaneous optical, infrared, and X-ray observations, we show that the sharp decline in optical brightness near the low/hard-to-high/soft state transition is accompanied by an increase in the frequency of the observed X-ray quasi-periodic oscillations. This behavior can be explained in terms of the model in which both X-ray and optical emissions are produced by the synchrotron mechanism in a hot flow near a compact object.
PubDate: 2021-12-01

• Kinematic Properties of the GAIA EDR3 Catalogue

Abstract: We have obtained a solution of the Ogorodnikov–Milne stellar-kinematics equations based on the material of the entire Gaia EDR3 star catalogue containing 1.8 billion objects. In view of the insufficient accuracy of individual trigonometric parallaxes, we have also obtained an analogous solution based on a sub-catalogue of selected 98 million stars for which the parallaxes are known with an accuracy better than 1 $${\%}$$ . We have performed a detailed analysis of these solutions as a function of stellar magnitude and distance.
PubDate: 2021-12-01

• Populations of Ultraluminous X-ray Sources in Galaxies: Origin and
Evolution

Abstract: A model of the population of ultraluminous X-ray sources (ULXs) in binary systems with black hole (BH) accretors is constructed by hybrid population synthesis and is compared with the model of the population of ULXs with magnetized neutron stars (NSs) that can be observed as pulsating ULXs (Kuranov et al. 2020). A model of the formation of BHs whereby their mass is determined by the mass of the CO core immediately before its collapse ( $$M_{\mathrm{CO}}$$ ) and ‘‘delayed’’ and ‘‘rapid’’ collapse models (Fryer et al. 2012) are considered. The possible transiency of ULXs due to accretion disk instability is taken into account. The parameters and evolution of ULXs in galaxies with a constant star formation rate (SFR) and in those with an old stellar population after an instantaneous star formation burst are computed. The maximum number of ULXs with BHs ( $${\sim}10$$ ) is reached in galaxies with a stationary $$SFR=10M_{\odot}$$ yr $${}^{-1}$$   $${\sim}1$$ Gyr after the beginning of star formation. ULXs observed after the end of star formation are close binary systems in which BHs and/or NSs formed before the end of star formation, while long-lived donors with a mass $${\sim}M_{\odot}$$ continue to overflow their Roche lobes after its end or have filled their Roche lobes even later. Several Gyr after the end of star formation the number of ULXs in galaxies with a mass $$M_{G}=10^{10}M_{\odot}$$ is no more than 0.1, most of them are ULXs with NSs. Persistent sources with a Roche-lobe-overflowing optical star dominate in ULXs with NSs, irrespective of the adopted star formation model. The transient sources are an order of magnitude fewer. The ULXs accreting from the stellar wind of the optical component are an order of magnitude fewer than the sources with accretion via Roche lobe overflow.
PubDate: 2021-12-01

• Gamma-Ray Bursts: Multiwavelength Investigations and Models

Abstract: We present a brief history and the current status of research on gamma-ray bursts (GRBs), including observations at various GRB phases: the prompt emission, afterglow, supernova/kilonova, and the host galaxy. An overview of the known phenomenological features and theoretical models for GRBs is given. Problems, the solution of which is necessary for a successful study of the phenomenon of GRB, are discussed.
PubDate: 2021-12-01

• Transient AT2018cow: A Scenario with an Equatorial Disk

Abstract: To explain the presence of early X-ray emission from the object AT2018cow, we have studied a scenario with the presence of dense ejecta in the system in the equatorial plane. Based on a simplified one-dimensional model, we have developed a two-dimensional model with a dense equatorial disk in the system. As a result of two-dimensional radiation-hydrodynamics simulations with the multidimensional parallel FRONT code, we have shown that the shock wave propagates through such a disk more slowly than through the original low-density cloud and gives rise to a high-temperature region visible in X-rays in a certain range of viewing angles.
PubDate: 2021-11-01

• Orbits of Visual Double Stars from P. Couteau’s Catalogue Determined
with Gaia DR2 and Gaia EDR3 Data

Abstract: The orbits of ten stars discovered by P. Couteau have been determined for the first time: COU 99, 357, 610, 850, 1346, 1531, 1642, 1752, 1845, and 2459. For two of them (COU 850 and 1752) an arc of more than half a revolution is covered by observations; the orbits of the remaining stars have been determined from short arcs ( $$5^{\circ}{-}50^{\circ}$$ ). The orbits of COU 99, 610, 1346, 1531, and 1845 have been computed using the sums of the component masses estimated from photometric data and trigonometric parallaxes in the Gaia, Hipparcos, and Tycho catalogues. In addition, the orbits of COU 66, 169, 773, 1006, and 1897 have been improved.
PubDate: 2021-11-01

• On the Definition of the Astrocentric Coordinates in the Planetary Problem

Abstract: Two ways of defining the Poincaré astrocentric coordinates in the planetary problem are considered and compared. Both ways are shown to lead to the same system of equations of planetary motion after the elimination of the center of mass. Formulas for the transition from the osculating elements in the Poincaré coordinate system to the ordinary astrocentric osculating elements are given. The analytical apparatus constructed here is suitable for the practical application of perturbation methods, in particular, averaging methods.
PubDate: 2021-11-01

• Study of the Influence of the Background Neutral Component on the Dynamics
of the Envelope in Supernova Remnants

Abstract: The influence of neutrals on the development of multidimensional dense-shell instabilities in supernova remnants is investigated. We present a model in which the neutrals are described as a permanently present additional medium (with its own parameters: temperature, density, velocity, etc.) that interacts with the ion component through collisional processes. Within this model we have found that the presence of neutrals suppresses the growth of instabilities in the case where they dominate in the total density of the medium.
PubDate: 2021-11-01

• Evolution of Planetary Chaotic Zones in Planetesimal Disks

Abstract: Extensive numerical experiments on the long-term dynamics of planetesimal disks with planets in systems of single stars have been carried out. The planetary chaotic zone clearing timescales $$T_{\mathrm{cl}}$$ as a function of mass parameter $$\mu$$ (planet–star mass ratio) have been determined numerically with a high accuracy separately for the outer and inner parts of the chaotic zone. Diffusional components $${\propto}\mu^{-6/7}$$ and $${\propto}\mu^{-2}$$ have been revealed in the dependence $$T_{\mathrm{cl}}(\mu)$$ . The results obtained are discussed and interpreted in light of existing analytical theories based on the mean motion resonance overlap criterion and in comparison with previous numerical approaches to the problem.
PubDate: 2021-11-01

• TRGB Distances to Galaxies Based on Hubble Space Telescope (HST) Images in
One F814W Filter1

Abstract: We analyze the reasons for imaging in the $$V$$ and $$I$$ filters to measure the distances to galaxies by the TRGB method. We show the possibility of using the TRGB method if Hubble Space Telescope (HST) images only in one F814W ( $$I$$ ) filter are available. Based on archival HST images, we have carried out stellar photometry for 12 galaxies: NGC 1058, NGC 1365, NGC 1448, NGC 3079, NGC 3810, NGC 4321, NGC 4328, NGC 4485, NGC 4490, NGC 4651, NGC 6951, and NGC 7331, and measured their distances by the TRGB method. For many of the galaxies from this list such measurements have been made for the first time. A comparison of our results for galaxies with known TRGB measurements has shown good agreement.
PubDate: 2021-11-01

• Hydrodynamic Model of Decaying Radial Oscillations in RU Cam

Abstract: Calculations of stellar evolution up to the early white dwarf stage were carried out for stars with mass on the main sequence $$M_{0}=0.82$$ , $$0.85$$ , $$0.9M_{\odot}$$ and with initial abundances of helium and heavier elements $$Y=0.25$$ and $$Z=10^{-3}$$ , respectively. For each value of $$M_{0}$$ the AGB and post–AGB evolutionary phases were computed with three values of the mass loss parameter in the Blöcker formula: $$\eta_{\textrm{B}}=0.02$$ , $$0.05$$ , and $$0.1$$ . The variable star RU Cam with pulsation period $$\Pi\approx 22$$ day is shown to be in the post–AGB stage and the pulsation amplitude decrease in years 1962–1963 is due to movement of the star across the HR diagram beyond the pulsation instability region. Theoretical estimates of the mass and the luminosity of RU Cam are $$0.524\leq M\leq 0.532M_{\odot}$$ and $$2.20\times 10^{3}\leq L\leq 2.33\times 10^{3}L_{\odot}$$ , respectively. Hydrodynamic calculations of nonlinear stellar pulsations show that while the star approaches the instability boundary a significant reduction ( $${\approx}90\%$$ ) in the pulsation amplitude occurs for nearly two years with subsequent slow decay of low–amplitude oscillations. Solution of the equations of hydrodynamics with time–dependent inner boundary conditions describing evolutionary changes in the radius and the luminosity at the bottom of the pulsating envelope allows us to conclude that decay of radial oscillations in RU Cam is accompanied by the effect of oscillation hysteresis. In particular, the stage of large–amplitude limit cycle oscillations extends by $${\approx}12$$ yr and the subsequent stage of decaying small–amplitude oscilations spreads beyond the formal boundary of pulsation instability.
PubDate: 2021-11-01

• On a Possible Spicule Formation Mechanism in Quiet Regions of the Sun

Abstract: We consider the gravitational energy of the material falling from the corona as a source of energy for the formation of spicules in the solar chromosphere. We show that in the quiet chromosphere–corona transition region a downward-directed flow of material can generate an upward-traveling shock wave. Part of the energy of the falling material returns to the corona and contributes to its heating. We find the velocities of the falling material needed for this purpose. The corresponding range of velocities agrees well with present-day observations.
PubDate: 2021-10-01
DOI: 10.1134/S1063773721100030

• Observations of Candidates for Eclipsing Cataclysmic Variables with the
RTT-150 Telescope

Abstract: We present the results of our photometric and spectroscopic observations. We have studied four cataclysmic variable stars: 2XMM J183251.4-100106, MLS1809017 J224712+461815, MGAB-V330, and MGAB-V410. We have analyzed the observational data and determined the periods of the systems from our photometric data. The object MLS1809017 J224712+461815 has been classified as an SU UMa-type dwarf nova. The remaining three systems are eclipsing ones, with 2XMM J183251.4-100106 and MGAB-V410 showing signatures of polars. At the same time, for MGAB-V410 the orbital period $$P_{\textrm{orb}}=2\overset{\textrm{h}}{.}2$$ falls into the period gap for cataclysmic variables.
PubDate: 2021-10-01
DOI: 10.1134/S1063773721100042

• Determination of H II Region Metallicity in the Context of Estimating
the Primordial Helium Abundance

Abstract: The primordial $${}^{4}$$ He abundance (Y $${}_{p}$$ ) is one of the key characteristics of Primordial Nucleosynthesis processes that occurred in the first minutes after the Big Bang. Its value depends on the baryon/photon ratio $$\eta\equiv n_{b}/n_{\gamma}$$ , and is also sensitive to the relativistic degrees of freedom which affect the expansion rate of the Universe at the radiation-dominated era. At the moment, the most used method of the determination of Y $${}_{p}$$ is the study of the metal deficient H II regions located in blue compact dwarf galaxies (BCDs). In this paper, we discuss in detail various methods of the determination of H II region metallicity in the context of Y $${}_{p}$$ analyses. We show that some procedures used in the methods lead to biases in the metallicity estimates and underestimation of their uncertainties. We propose a modified method for the metallicity determination, as well as an additional criterion for selecting objects. We have selected 69 objects (26 objects with high quality spectra from the HeBCD+NIR database and 43 objects from the SDSS catalog), for which we estimate Y and O/H using the proposed method. We have estimated Y $${}_{p}=0.2470\pm 0.0020$$ which is one of the most accurate estimates obtained up to date. Its comparison with the value Y $${}_{p}=0.2470\pm 0.0002$$ obtained as a result of numerical modelling of Primordial Nucleosynthesis with the value of $$\Omega_{b}$$ taken from the analysis of the CMB anisotropy (Planck mission), is an important tool for studying the self-consistency of the Standard cosmological model (a possible discrepancy between these estimates could be an indicator of a new physics). The application of the proposed method allows one to more correctly estimate Y $${}_{p}$$ and the slope $$d$$ Y/ $$d$$ (O/H). Further analysis of the data from the SDSS catalog can significantly increase the statistics of objects for the regression analysis, which in turn can refine the Y $${}_{p}$$ estimate.
PubDate: 2021-10-01
DOI: 10.1134/S1063773721100054

• Magnetic Fields of Neutron Stars

Abstract: The birth function of neutron stars in magnetic field $$B$$ is estimated for two models of the evolution of radio pulsars corresponding to different directions of evolution of the inclination angle of the magnetic axis to the rotation axis $$\chi$$ (toward small angles $${\sim}0^{\circ}$$ or angles close to $$90^{\circ}$$ ). We show that for both models at magnetic fields $$B>10^{12}$$ G it is a power law with a break near $$10^{13}$$ G. At the same time, however, the number of born pulsars with a super-strong magnetic field $$B>10^{14}$$ G in the first model turns out to be smaller than that in the second one by an order of magnitude.
PubDate: 2021-10-01
DOI: 10.1134/S1063773721100017

• Orbital Maneuvering in the Vicinity of Collinear Libration Points Using
Light Pressure Forces

Abstract: The controlled motion of a spacecraft with a solar sail in interplanetary space near the collinear libration points $$L_{1}$$ and $$L_{2}$$ of the Sun–Earth system is investigated. The equations of the circular restricted three-body problem and their modifications are used to describe the orbital motion. We present a technique for constructing the orbital motion control laws and provide an estimate of the controllability region when solving the problem of stabilization or long-term spacecraft keeping in the vicinity of $$L_{1}$$ and $$L_{2}$$ . This technique is based on the solution of the control problem for a linearized model of the equations of orbital motion and the transfer of results to the nonlinear case. Such a technique for the construction of control laws extends the possibilities both in solving the problems of motion stabilization and in solving the problems of maneuvering in the vicinity of a collinear libration point. We estimate the controllability region for a spacecraft with a solar sail and present the trajectories of possible spacecraft motion that are provided by the solar sail orientation when flying from the vicinity of a collinear libration point. It is in these regions of space that the light pressure force can have a significant efficiency due to the relatively small magnitude of the gravitational force. As an example, we consider a model of a spacecraft with a solar sail whose sail area-to-mass ratio corresponds to the spacecraft from the implemented IKAROS project and is less than that of the CubeSat spacecraft from the implemented LightSail-2 project by an order of magnitude.
PubDate: 2021-10-01
DOI: 10.1134/S1063773721100066

• Optical Spectroscopy of Quasars Discovered by SRG/eROSITA with a 2.5-m
Telescope at the Caucasus Mountain Observatory of SAI MSU

Abstract: Based on observations with the transient double-beam spectrograph (TDS) at the 2.5-m telescope of the Caucasus Mountain Observatory of SAI MSU, we have determined the redshifts for 15 new quasars detected in X-rays by the eROSITA telescope of the SRG orbital observatory during the first three all-sky surveys. The redshifts of the sources lie in the range 0.5–4.156. We have estimated the masses of the central supermassive black holes and the bolometric luminosities for eight quasars from the width of the C IV 1549 Å emission line and the continuum flux at 1350 Å. For these objects we have also estimated the ratio of the X-ray luminosity to the Eddington limit to be a few percent. At the same time, the bolometric-to-Eddington luminosity ratio for them has turned out to be in a fairly narrow range, 30–40 $${\%}$$ . A large $$L_{\textrm{bol}}/L_{\textrm{Edd}}$$ ratio points to an active growth of the mass of the central black holes in these objects.
PubDate: 2021-10-01
DOI: 10.1134/S1063773721100029

• X-ray Observations of Historical Classical Nova Counterparts with eROSITA
Telescope Onboard the SRG Orbital Observatory during the All-Sky Survey

Abstract: The X-ray emission from the counterparts of historical classical novae (CNe) in our Galaxy is studied. For this purpose, we use data from three SRG/eROSITA sky surveys in the half of the sky for the processing of which the Russian SRG/eROSITA consortium is responsible. Out of 309 historical CNe, X-ray emission has been detected from 52 sources with 0.3–2.3 keV luminosities $$L_{X}\approx 10^{30}\sim 10^{34}$$ erg s $${}^{-1}$$ . Among them, two sources with supersoft spectra are associated with the post-nova supersoft X-ray emission. X-ray spectroscopy suggests that systems with magnetized white dwarfs (WDs) may account for some fraction in our sample of CN counterparts detected in X-rays. This suggestion will be checked during further SRG/eROSITA sky surveys. The CN counterparts represent a bona fide sample of accreting WDs with non-steady thermonuclear hydrogen burning on their surface, while their X-ray luminosity in quiescence is a good indicator of the accretion rate in a binary system. Using this fact, we have constructed the accretion rate distribution of WDs with non-steady hydrogen burning and compared it with the accretion rate distribution of known steady supersoft X-ray sources in our Galaxy and nearby external galaxies. There is a pronounced dichotomy between these two distributions—the CN counterparts and the steady supersoft sources are in different regions, in accordance with the predictions of the theory of thermonuclear hydrogen burning on the WD surface.
PubDate: 2021-09-01
DOI: 10.1134/S1063773721090048

• Three-Dimensional Kinematics of Classical Cepheids

Abstract: A linear Ogorodnikov–Milne model is applied to study the three-dimensional kinematics of classical Cepheids in the Milky Way. A sample of 832 classical Cepheids from Mróz et al. (2019) with distances, line-of-sight velocities, and proper motions from the Gaia DR2 catalogue is used. The Cepheid space velocities have been freed from the differential Galactic rotation found by us previously based on a nonlinear rotation model. Based on a complete Ogorodnikov–Milne model, involving the line-of-sight velocities and proper motions of stars, we have estimated the angular velocity of rotation around the Galactic $$y$$ axis, $$\Omega_{y}=0.64\pm 0.17$$ km s $${}^{-1}$$ kpc $${}^{-1}$$ . We think that this rotation is associated with the warp of the Galactic thin disk. Our calculations using only the proper motions of Cepheids under the assumption of no deformations due to the disk warp have shown the presence of a residual rotation around the $$y$$ axis with an angular velocity $$\Omega_{y}=0.54\pm 0.15$$ km s $${}^{-1}$$ kpc $${}^{-1}$$ and the presence of a positive rotation around the $$x$$ axis with an angular velocity $$\Omega_{x}=0.33\pm 0.10$$ km s $${}^{-1}$$ kpc $${}^{-1}$$ .
PubDate: 2021-09-01
DOI: 10.1134/S1063773721090012

• Host Stars of Planets on the Hertzsprung–Russell Diagram

Abstract: Our statistical analysis (based on Kolmogorov–Smirnov tests) of the mass and radius distributions of planets hosted by stars of different classes reveals their significant differences, depending on the class of host stars. These differences can be explained by (1) the long-term dynamical evolution of planetary systems, (2) the long-term physical evolution of host stars, and (3) the initial differences in the structure of systems. Kolmogorov–Smirnov tests show at a high significance level that during the transition of a star from the main sequence to the red giant branch its planetary system disintegrates, at least partially. We provide estimates of the dynamical disintegration timescale for various architectures of planetary systems. From a comparison of the estimates of the physical and dynamical evolution timescales we have concluded that the disintegration of the inner regions of planetary systems is mainly physical (absorption by the stellar atmosphere) rather than dynamical (breakup within the N-body problem) in nature. As regards the outer parts of planetary systems, their disintegration can be both purely dynamical (intrinsic disintegration, close encounters with neighboring stars) and mixed physical–dynamical (i.e., the physical destruction of the system’s inner component can trigger a dynamical breakup of the outer part) in nature.
PubDate: 2021-09-01
DOI: 10.1134/S1063773721080016

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# What are the formulas for the number of vertices, edges, faces, cells, 4-faces, …, $n$-faces, of convex regular polytopes in $n \geq 5$ dimensions?

I know that in dimension $n \geq 5$ there are only 3 kind of convex regular polytopes in each dimension: the $n$-simplex, the $n$-cube and the $n$-orthoplex.

I would like to know if there are formulas that give the number of vertices, edges, faces, cells, 4-faces, 5-faces, 6-faces, 7-faces, ..., $n$-faces, of each of these convex regular polytopes.

Also, I heard that convex regular polytopes in dimension $n \geq 5$ only have triangles or squares as their faces. Is that true?

And what about their cells? Are they also all necessarily tetrahedrons (3-simplex), cubes (3-cube) and octahedrons (3-orthoplex)?

And what about their $n$-faces?

The $n$-simplex has $\binom{n+1}{k+1}$ faces of dimension $k$.
Then $n$-cube has $2^{n-k}\binom{n}{k}$ faces of dimension $k$.
The $n$-crosspolytope (aka $n$-orthoplex) is dual to the $n$-cube, so it has $2^{k+1}\binom{n}{k+1}$ faces of dimension $k$ (the number of $(n-k-1)$-faces of the cube.)

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# 55.  A points A(-2, -2), B and C(2, -4) are collinear, prove that $AB=\frac{3}{7}AC$.

Dear Student,

The given question can be solved using distance formula.

Since it is given that points A,B and C are collinear, we need to find the length of AB and AC using distance formula.

Hence proved.

Hope it helps.

Regards

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Row Row Row your Boat - At which maximum speed?

The maximum speed of a rower boat is a classic example of the beauty of dimensional analysis. It was originally derived by McMahon (1971). Here it is brought again for the convenience of the Internet audience, together with updated rowing record data.

First, some assumptions (some are better than others).
1. Boats with different rowers are geometrically similar to each other.
2. The submerged volume of a boat, per rower, is the same and equal to $V_0$
3. The power supplied by a rower is always the same and equal to $P_{0}$
Second, we need to understand which physical processes govern the maximum speed of the rowing boats. The dominant drag force is that of friction with the water. Note that if the boat was larger or the speed faster, the typical Froude number would have been large. In such a case, the drag would have been governed by the generation of surface gravity waves (or "waves" for short). For a typical rowing boat, the Froude number is small and hence the effect of gravity waves is negligible.

Since the motion of the water is turbulent (there are vortices behind the boat), the frictional drag force is given by: $$F \sim\rho v^{2} \ell^2.$$ (This can be seen from dimensional analysis, the only "force" we can construct from the density of water $\rho$, the speed of the boat $v$ and its dimension $\ell$ is $\rho v^2 \ell^2$).

The power lost to drag is therefore: $$P_{drag} = F v \sim \rho v^3 \ell^{2}.$$ Since we assume geometric similarity, the size of the boat $\ell$ will be proportional to the submerged depth, which itself should be proportional to the cubic root of the submerged volume. Thus, $\ell \sim V^{1/3} \approx {(N V_0)}^{1/3}$.

On the other hand, the total power supplied by the rowers, $P_{rowers}$, should be proportional to the number of rowers $N$, each supplying a power of $P_0$

At steady state, the power of the rowers should be the same as the power lost to drag. We thus find:
$$P_{rowers} \approx P_{drag} ~~\Rightarrow ~~ N P_0 \sim \rho v^3 N^{2/3} {V_0}^{2/3} ,$$
or: $$v\sim {N^{1/9} P_0^{1/3} \over \rho^{1/3} V_0^{2/9}}.$$ In other words, the speed of an $N$ rower boat scales as $N^{1/9}$. The time it would take them to finish a race will therefore scale as $t \propto N^{-1/9}$.

Different type boats will of course have a different prefactor, since $V_0$ and $C_D$ (the drag coefficient, which is the prefactor in the drag equation) are somewhat different. But for a given type, we should see this scaling behavior.

What happens if there is a coxswain? In such a case, there is an extra person who does nothing (well, from a physical point of view), except add weight and volume. This implies that now, the power supplied by the $N$ rowers is the same, but the drag they need to overcome is larger:
$$N P_0 \sim \rho v^3 (N+1)^{2/3} {V_0}^{2/3} ,$$
or:
$$v \sim {N^{1/3} (N+1)^{-2/9} P_0^{1/3} \over \rho^{1/3} V_0^{2/9}} .$$
Both results, with and without a coxswain can be seen in the graph.

World Records

World Record for different classes of rower boats over 2000m races, as a function of number of rowers (as updated on Sep. 2004). Note that it is a Log-Log plot such that a power-law would appear as a straight line. The blue dots (and lines) are Men classes: Mnx, Mn-, LMnx, LMn-, which are rower boats without coxswains, while the green line is Mn+, i.e., with coxswains. n stands for the number of rowers. The short red dashed line is the prediction (up to a normalization) of t ~ N-1/9. The long red dashed line is the prediction when a coxswain is present (with the same normalization as without). The two lines asymptotically approach each other for large N.

Evidently from the graph, the predicted scaling of $t\propto N^{-1/9}$ for rower boats without coxswains, and $t \propto N^{-1/3} (N+1)^{2/9}$ with coxswains very nicely explains the observed world records. In fact, all are consistent with the prediction to within about 1.5 seconds or less!

Of course, given that we deliberately forgot all the prefactors (such as $C_D$), the absolute normalization can be estimated, up to an order of manitude. If we plug in typical values: $P_0 \approx 500 W$, $V_0 \approx 72 kg/ \rho_w$ and $\rho_w = 1000~kg/m^3$, we find that the normalization (i.e., the time for a one man rowing boat, over a D=2000m race) is:
$$t_1 = {D \over v} \sim { 2000m \times (1000 kg/m^3)^{1/3} (72/1000 m^3)^{2/9} \over (500W)^{1/3}} \approx 1400 s,$$
In reality, it is of course around 400s. The discrepancy is mostly because $C_D$, multiplying the drag force, is actually small, only a few percent (e.g., about 2%). Since $t \propto C_D^{1/3}$, a better estimate would be $t \sim 1400 s\times C_D^{1/3} \sim 400 s$ as observed.

Another interesting point. if one compares the world records for men and women, of the same class boats, it is evident that women records are about 8% longer. Since the time is proportional to $P^{-1/3}$, it implies that the best men rowers can deliver power from their muscles at a rate which about 8%*3 ~ 25% higher than the best women rowers. Men are about 25% stronger than women, but of course less pretty and often less smart as well.

Bibliography:
McMahon, T.A. 1971. "Rowing: A similarity analysis". Science 173:349.
Peterson, I, 1999. "Row Your Boat", science news online.

Blogroll

Sensible Climate
Physics and more Other


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# Factoring polynomial with 4 terms

I cant figure this out. $$\frac{x-5}{x^3-3x^2+7x-5}$$

I tried by grouping and got $$x^2(x-3)+1(7x-5)$$ for the denominator. I need to use partial fractions on this so I cant use that yet.

-
Take $x=1$. ${}{}$ –  Git Gud Mar 3 '13 at 19:28

Let $p(x)=x^3-3x^2+7x-5$. From the rational root test you know that the only possible rational roots of $p(x)$ are $\pm1$ and $\pm5$. Since $p(1)=0$, you know that $1$ is a root and therefore that $x-1$ is a factor, so divide it out to get $p(x)=(x-1)(x^2-2x+5)$. The discriminant of $x^2-2x+5$ is $(-2)^2-4\cdot1\cdot5<0$, so the quadratic factor is irreducible over the real numbers.
Clearly (why? Inspection...) , $\,x=1\,$ is a root, thus
$$x^3-3x^2+7x-5=(x-1)(x^2-2x+5)$$


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# Auto-Calibration

PHOTO BY SNAPWIRE

As the world is projected on our eyes, the objects in the world can be projected on the image plane of a camera. For this projection, the intrinsic and extrinsic matrix of the camera are required to render the image plane. In other words, if the shape of objects in the 3D world is already defined, estimating what it looks like on the image plane is related to these matrices.

Especially, finding out the intrinsic matrix is about camera calibration which can be done with or without some materials such as a chessboard. Simply put, it helps to decide which color is proper to be painted in each pixel of the image plane. I wanted to research this work for the 3D reconstruction project which is the one of my personal projects. For that, I needed some calibration methods processed automatically which are more technically called auto-calibration.

### Requirement

Before describing the details, I have to determine what information I will use if I do not want to use any added materials like a chessboard. I assumed that we already knew about the actual heights of humans in meters and the bounding boxes of them in camera frames. In most cases, people can be easily observed in the place where cameras are installed, for example, for detecting or tracking people in computer vision and graphics. That is why I chose the human information to calibrate cameras. More specifially, I assumed that the average height of people is 1.8 meters and the bounding boxes whose shpae is rectangle were already available. Moreover, the camera is considered being set on the flat ground.

### Problem Definition

Although we do not know about the tilt angle of the camera, it is required that the default camera coordinates is defined. As above, I set the coordinates as its z-axis is parallel with the ground and its y-axis is perpendicular to the ground. If the tilt angle is not zero, the z-axis which is the camera view vector intersects at a point on the ground. Let the tilt angle $\theta$, then the ground normal vector $n$ is $(0, -cos\theta, -sin\theta)^t$, so the ground plane equation is $H-ycos\theta-zsin\theta=0$ where $H$ is the camera height from the ground in meter. In addition, people detected are interpreted as two points which are the top point $T(x_t, y_t, z_t)$ and the bottom point $B(x_b, y_b, z_b)$ in 3D camera coordinates. In this case, the distance between $T$ and $B$ is the human height $h$ that we already defined as 1.8 meters. Finally, the intrinsic camera matrix $K$ can be defined as follows: \begin{aligned} K &= \left(\begin{array}{ccc} f & 0 & o_x \\ 0 & f & o_y \\ 0 & 0 & 1 \end{array}\right) \end{aligned}

where $f$ is the focal length, $o_x$ and $o_y$ are the half of the camera width and height. Meanwhile, these points are projected on the camera as $t(u_t, v_t)\cong KT$ and $b(u_b, v_b)\cong KB$. Now, $T$ and $B$ can be written as follows: \begin{aligned} T(x_t, y_t, z_t) &= \left(\frac{(u_t-o_x)(H-h)}{f sin\theta+(v_t-o_y) cos\theta}, \frac{(v_t-o_y)(H-h)}{f sin\theta+(v_t-o_y) cos\theta}, \frac{f(H-h)}{f sin\theta+(v_t-o_y) cos\theta}\right) \end{aligned} \\ \begin{aligned} B(x_b, y_b, z_b) &= \left(\frac{(u_b-o_x)H}{f sin\theta+(v_b-o_y) cos\theta}, \frac{(v_b-o_y)H}{f sin\theta+(v_b-o_y) cos\theta}, \frac{fH}{f sin\theta+(v_b-o_y) cos\theta}\right) \end{aligned}

### Cost Functions

The parameters to estimate about the unknown camera are the focal length, the tilt angle and the height from ground. The pan angle is always considered zero because the default camera coordinates can be set so that zero-pan angle is satisfied. The roll angle is ignored in this work.

#### 1. x-axis: the top point can be projected on the ground as the bottom one.

At first, we can imagine that the top and bottom points in 3D camera coordinates are transformed to the default camera coordinates whose tilt angle is zero. In the default camera coordinates, it is reasonable that the top point can be projected on the ground as the bottom one although it is possible that people do not always stand upright and the top and bottom points are distorted in camera view. Therefore, the $x$ components of the top and bottom points in the default camera coordinates should be the same, which allows the following cost function: $$$E_1(f, H, \theta) = \frac{(u_t-o_x)(H-h)}{f tan\theta+v_t-o_y}- \frac{(u_b-o_x)H}{f tan\theta+v_b-o_y}$$$

#### 2. y-axis: the top point is $h$-meter high from the bottom one.

Second, we already assume that the human height is $h$-meter high which is 1.8 meters, so it can be used for another cost function. The $y$ components of the top point in the default camera coordinate is $h$-meter higher than that of the bottom one. It induces the following cost function: $$$E_2(f, H, \theta) = \frac{(v_t-o_y)(H-h)}{f tan\theta+v_t-o_y} - \frac{(v_b-o_y)H}{f tan\theta+v_b-o_y} - h cos\theta$$$

#### 3. z-axis: the top and bottom points have the same depth.

Third,  it is natural that the top and bottom points in the default camera coordinates have the same depth although the human head can get ahead of its foot a little or vice versa. It means that the final cost function can be written as follows: $$$E_3(f, H, \theta) = \frac{(f-(v_t-o_y) tan\theta)(H-h)}{f tan\theta+v_t-o_y} - \frac{(f-(v_b-o_y) tan\theta)H}{f tan\theta+v_b-o_y}$$$

These three cost functions represent for only one human sample, but it can be applied for many samples using summation of each cost funcion, which is the more natural situation. When samples are summed, $L_1$ norm or $L_2$ norm can be used. Besides, it is not necessary that these cost functions should be used when the only one parameter is unknown. It can be solved through the simple calcuation, not the estimation because each cost function can be reformed for the unknown parameter although it will be more complex.


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# How to handle reflections when cutting metal with a laser?

I have heard of lasers being used to cut metal, however all laser cutting machines I've found can't cut metal, I'm guessing because it reflects the light?

What is different in the designs of such machines between those that can and can't cut metal? Is it just accepted that energy is lost in reflection, and has a safe way of absorbing reflections?

• They do try to pick wavelengths which the material in question is reasonably absorptive, tho' cost and reliability of various laser materials is a larger factor in design. – Carl Witthoft May 27 '16 at 13:06

A typical "hobbyist laser cutter" with about a 50W power rating won't get you very far cutting metal except for metal foil, but there is no big deal about cutting up to 1 inch thick mild steel plates or tubes with a few kilowatts of laser power. There are plenty of videos on YouTube.

Basically, you need enough power to cut quickly before the (high) thermal conductivity of the metal heats up with work piece and causes distortion, cracking, locked-in stress concentrations, etc. Aluminium needs higher power to cut than steel, because of its higher thermal conductivity.

Typical thermal conductivity of hobbyist materials like wood, plastics etc is less than 1 W/m.K. Stainless steel about 15, high and low carbon steels about 50, aluminium about 200, and copper about 400.

The price tag for say a 5kW laser cutter for "industrial-scale" metal cutting is in the £100,000 to £500,000 range, so you won't find many for sale on Ebay.

I know this is an old question, but I'd like to expand on the general info alephzero stated.

In my research for building my own metal cutting CNC laser, I've found that it takes at least a 150 watt CO2 laser to cut through even thin metals. This is also coupled with using a gas (N2 or O2) to either shield the material cut or to help it be cut. Thickness can also be a factor in whether a shield gas or a cutting gas is used.

http://am.co.za/laser/thickness

http://www.fabricatingandmetalworking.com/2012/11/nitrogen-vs-oxygenwhich-should-you-use-to-cut-steel/

Due to the different reflective nature of different metals, not all metals can be cut at the same power. Thin steel and stainless steel can be cut with a 150 watt laser, but much more powerful lasers are needed for more highly reflective materials, like aluminum and silver.

ND or ND:YAG lasers are commonly used for metal cutting, although they are much more expensive laser systems, and therefore much more expensive machines.

https://en.wikipedia.org/wiki/Laser_cutting

For my own machine, I've made it large (4'x8' capacity) and found that a 180 watt CO2 laser tube is between 6.5' and 7' long. Since my CNC frame is so big, I don't have to worry about that, though. The tube, if gotten directly from China can be as "little" as \$900-\$1000, but a more local USA dealer has them for \$1500-\$1600.

I've spent around \$8000 on my own machine and still don't have the tube yet, but that's not the £100,000 to £500,000 alephzero mentions. I've seen machines (from China) that were around \$40,000 with power ratings to do metal, but that's still not something you are likely to get with the change in your couch.

The thing about the reflections is that they definitely need to be managed. In too small of a laser cutter, the reflections will go back into the tube and basically set up a standing wave that eventually burns out the tube, since not enough of the light is absorbed or dispersed, exceeding the rated power of the tube for too long.

In a laser that can cut through metal, the laser can still be reflected into the tube, but only until it pierces the material. At that point, the angle of reflection moves any reflected laser to a different angle. That usually means the laser is reflected into the bottom the of laser cabinet and handled/absorbed there, if not used to heat the metal. By the time the laser hits something at that point, it is usually not focused enough to do any damage.

Also, many laser cutters have a full cabinet with windows that absorb the laser wavelength. In the one I have access to, it uses specially coated glass windows to absorb the laser. In the other one, it's Plexiglas/acrylic windows. Acrylic goggles/glasses are used when a laser cutter doesn't have a cabinet.


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# How to distinguish between the different frequency domains?

Sometimes the terms 'Fourier domain', 'complex frequency domain', 'Frequency domain' and 's domain' are used interchangeably.

For Laplace transforms, "s domain" will work as well, and I do believe they are interchangeable. I do not believe "Fourier domain" would be correct usage in this case. "s domain" may be preferred if you wish to emphasize that real values of $s$ have a clear aperiodic interpretation (exponentials in the time domain).


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# What is the “thing” which is trained in AI model training

I am a newbie in the fantastic AI world, I have started my learning recently. After a while, my understanding is, we need to feed in tremendous data to train a or many models.

Once the training is complete, we could take out the trained models and "plug in" to any other programming languages to use to detect things.

So my questions are:

1. What are the trained models? are they algorithms or a collection of parameters in a file?

2. What do they look like? e.g. file extensions

3. Especially, I want to find the trained models for detecting birds (the bird types do not matter). Are there any platforms for open-source/free online trained AI models??

Thank you!

This answer applies to Machine Learning (ML) part of AI, as that seems to be what you are asking about. Please bear in mind that AI is still a broad church, including many other techniques than ML. ML, including neural networks for deep learning, and Reinforcement Learning (RL) is only a subset of AI - some AI techniques are more focused on the algorithm than parameters.

1. What are the trained models? are they algorithms or a collection of parameters in a file?

In ML, the usual process is to feed data into parametric function (e.g. a neural network) and alter the parameters of it to "fit" the data. The main output of this is a collection of parameters and hyperparameters that describe the parametric function. So 90% of the time, when discussing the "trained model", it means the same thing as the collection of paramaters.

However, those parameters are of limited use without a library that can re-create the function from them. Parameters will be saved from a specific library and can be loaded back into that library easily. It is also possible for libraries to read or convert from models saved from other libraries, much like how different spreadsheet programs can read each others' files.

1. What do they look like? e.g. file extensions

This varies a lot, depending on which library was used. It is not possible to make a general statement. For example, Tensorflow can save variables to a "Checkpoint" file with .ckpt extension, but can get more sophisticated depending on how much of the model you want to export, and full models with whole structure will contain more than just the variables and have the .pb extension.

1. Especially, I want to find the trained models for detecting birds (the bird types do not matter). Are there any platforms for open-source/free online trained AI models??

There are a few places where you can find selections of pre-trained models. One such place is Tensorflow's Model Zoo and you might be interested in Tensorflow detection model zoo.

Other frameworks may also provide example code. For instance Caffe also has a "Model Zoo" (searching Model Zoo is a good starting strategy).

If you are working at the level of collecting model parameters and want to run these models yourself, you will need to learn a bit about each library, what language is used to work with it, and maybe follow some tutorial about how to use it. A few models will be packaged up with working scripts to use from the command line, but many are not and may take sometime and effort to get working.

When you have a specific detection target, you may be disappointed to find models that don't quite match what you want. For image classifying, it is common if you have a specialist need to take a pre-existing general model that has been trained on large dataset for weeks, and then "fine tune" it with your own image dataset for your purpose. Most NN libraries will have tutorials and examples of this fine tuning process.

1. What are the trained models? are they algorithms or a collection of parameters in a file?

"Model" could refer to the algorithm with or without a set of trained parameters. If you specify "trained model", the focus is on the parameters, but the algorithm is implicitly part of that, since without the algorithm, the parameters are just an arbitrary set of numbers.

1. What do they look like? e.g. file extensions

That very much depends on both the algorithm you're using and the specific implementation. A few simple examples might help clarify matters. Let's suppose that the problem we're trying to learn is the exclusive or (XOR) function:

a | b | a XOR b
--+---+---------
0 | 0 | 0
0 | 1 | 1
1 | 0 | 1
1 | 1 | 0

First, let's use a 2-layer neural net to learn it. We'll define our activation function to be a simple step function:

$$f(x) = \begin{cases} 1 & \text{if } x > 0.5 \\ 0 & \text{if } x \le 0.5 \end{cases}$$

(This is actually a terrible activation function for real neural nets since it's non-differentiable, but it makes the example clearer.)

Our model is:

$$h_0 = f(1\cdot a+1\cdot b + 0)\\ h_1 = f(0.5\cdot a + 0.5\cdot b + 0)\\ \,\;y = f(1\cdot h_0 - 1\cdot h_1 + 0)$$

Each step of this essentially draws a hyperplane and evaluates to 1 if the input is on one side of the hyperplane and 0 otherwise. In this particular case, h_0 tells us if either a or b is true. h_1 tells us if they're both true, and y tells us if exactly one of them is true, which is the exact definition of the XOR function.

Our parameters are the coefficients and biases (the offset added at the end of each expression):

$$\begin{bmatrix} 1 & 1 & 0 \\ 0.5 & 0.5 & 0 \\ 1 & 1 & 0 \\ \end{bmatrix}$$

They can be stored in a file in any way we want; all that matters is that the code that stores them and the code that reads them back agree on the format.

Now let's solve the same problem using a decision tree. For these, we traverse a tree, and at every node, ask a question about the input to decide which child to visit next. Ideally, each question will divide the space of possibilities exactly in half. Once we reach a leaf node, we know our answer.

In this diagram, we visit the right child iff the expression is true.

     a+b=2
/     \
a+b=0    0
/     \
0       1

In this case, the model and parameters are harder to separate. The only part of the model that isn't learned is "It's a tree". The expressions in each interior node, the structure of the tree, and the value of the leaf nodes are all learned parameters. As with the weights from the neural network, we can store these in any format we want to.

Both methods are learning the same problem, and actually find basically the same solution: a XOR b = (a OR b) AND NOT (a AND B). But the nature of the mathematical model we use depends on the method we choose, the parameters depend on what we train it on, the file format depends on the code we use to do it, and the line between model and parameter is fairly arbitrary; the math works out the same regardless of how we split it up. We could even write a program that tries different methods, and outputs a program that classifies inputs using the method that performed best. In this case, the model and parameters aren't separate at all.

1. Especially, I want to find the trained models for detecting birds (the bird types do not matter). Are there any platforms for open-source/free online trained AI models??

I don't know of any pretrained models that specifically recognize birds, but I'm not in image-recognition, so that doesn't mean much. If you're not averse to training your own model (using existing code), I believe the ImageNet dataset includes birds. AlexNet and LeNet would probably be good starting points for the model. Most if not all of the the state of the art image recognition models are based on convolutional networks, so you'll need a decent GPU to run them.


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X

# Séminaire de Géométrie

École polytechnique – Centre de Mathématiques Laurent Schwartz

10h30-12h00 – Dano Kim (Séoul)

"Canonical bundle formula and degenerating families of volume forms"

Abstract: For a degenerating family of projective manifolds, it is of fundamental interest to study the asymptotic behavior of the fiberwise mass of volume forms near singular fibers. We will discuss our main results where we determine the volume asymptotics (equivalently the asymptotics of $L^2$ metrics) in all base dimensions, which generalize numerous previous results in base dimension $1$. In the case of log Calabi-Yau fibrations, we establish a metric version of the canonical bundle formula in algebraic geometry: the $L^2$ metric carries the singularity described by the discriminant divisor and the moduli part line bundle has a singular hermitian metric with vanishing Lelong numbers. As consequences, we strengthen the semipositivity theorems in algebraic geometry due to Fujita, Kawamata and others for log Calabi-Yau fibrations, giving an entirely new simpler proof which does not use Hodge theory, i.e. difficult results (e.g. Cattani-Kaplan-Schmid) in the theory of variation of Hodge structure.


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# The Speed of Letters

Given a string as input, print a new string with each letter pushed to the right by its respective alphabet index.

We all know that A is a slow and Z is a fast letter. This means that Z gets shifted to the right by 25 spaces, A doesn't get shifted at all and B gets shifted by 1 space.

Your program only has to handle uppercase letters from A-Z, and no other characters, no whitespaces, no punctuation.

Note that if 2 or more letters fall onto the same space after shifting, the latest character will be used. (Example: BA ->  A)

## Examples

"AZ" -> "A                         Z"

"ABC" -> "A B C"

"ACE" -> "A  C  E"

"CBA" -> "  A"

"HELLOWORLD" -> "     E H    DLL   OLO   R  W"

## Rules

• This is , so the shortest code in any language bytes wins.
• Standard loopholes are forbidden.

• Input must be received as a string.

• You may print the result to stdout or return a string.
• A single trailing whitespace and/or newline is allowed.
• You may also use lowercase letters as input or output, but only use either case.
• Trailing whitespace okay? – Okx Oct 13 '17 at 13:56
• @Okx Yes, note my previous comment. – Ian H. Oct 13 '17 at 13:58
• What about a lot of trailing spaces? – Okx Oct 13 '17 at 13:59
• @Okx Forbidden, one is all you get. – Ian H. Oct 13 '17 at 13:59
• I assume we can use lowercase letters instead, right? – Mr. Xcoder Oct 13 '17 at 15:53

# Python 2, 81 bytes

t=[]
i=65
for c in input():t+=[' ']*26;t[ord(c)-i]=c;i-=1
printt[2::5].rstrip()

Try it online!

# MATL, 11 bytes

''jtfy65-+(

### Explanation

MATL indexing is 1-based. This golfing trick is used here. This other one cannot be used because we need an empty string, not an empty numeric array.

Consider input 'ACE' as an example. Stack contents are shown bottom to top.

''     % Push empty string
%   Stack: ''
j      % Input string
%   Stack: '', 'ACE'
t      % Duplicate
%   Stack: '', 'ACE', 'ACE'
f      % Indices of nonzero entries. Gives [1 2 ... n] where n is input length
%   Stack: '', 'ACE', [1 2 3]
y      % Duplicate from below
%   Stack: '', 'ACE', [1 2 3], 'ACE'
65     % Push 65
%   Stack: '', 'ACE', [1 2 3], 'ACE', 65
-      % Subtract, element-wise. Characters are converted to codepoints
%   Stack: '', 'ACE', [1 2 3], [0 2 4]
%   Stack: '', 'ACE', [1 4 7]
(      % Fill string '' with values 'ACE' at positions [1 4 7]. The original
% empty string is extended. Non-existing values are filled with char 0,
% which is displayed as space. Implicitly display
%   Stack: 'A  C  E'

• (: nice builtin – Erik the Outgolfer Oct 13 '17 at 14:39
• @EriktheOutgolfer I think it's similar to 05AB1E's ǝ? Ah, but that doesn't seem to vectorize over the second/third inputs – Luis Mendo Oct 13 '17 at 14:46
• Exactly why it's unique :p and also how MATL auto-fills with 0s and displays 0 as space. – Erik the Outgolfer Oct 13 '17 at 14:50
• @LuisMendo Nice answer. Out of curiosity, and this question is probably applicable to most stack based langs, when you write in MATL do you need to keep track of the stack (eg, in a line above the code, or a piece of paper, etc) as you compose code? Or has it become natural enough to you that you don't? – Jonah Oct 14 '17 at 1:20
• Heh - learned something new today; you can index into the same position twice without any problem in MATL(AB). I already started writing a complicated loop-based answer because I assumed it would give an error otherwise. – Sanchises Oct 15 '17 at 12:07

# R, 140133129 74 bytes

Saved a ton of bytes porting an ASCII value approach like everyone else. Sad I didn't think of it before :(

function(s){F[X-65+1:sum(X|1)]=X=utf8ToInt(s)
F[is.na(F)]=32
intToUtf8(F)}

Try it online!

function(s){o=rep(' ',(n=nchar(s))+25)
for(i in 1:n){k=substr(s,i,i)
o[x<-i+match(k,LETTERS)-1]=k
F=max(F,x)}
cat(o[1:F],sep='')}

Try it online!

generates a too-long list o of spaces, then iterates through s, replacing the values in o with the correct value and updating F, the position of the rightmost character. Then prints out the first F elements of o with no separators between them.

# 05AB1E, 20 16 bytes

-4 bytes thanks to Emigna

ð₄×svyAuykN+ǝ}ðÜ

Try it online!

• I think you can shorten to ð₄×svyAuykN+ǝ}ðÜ at least. Also, is there a guarantee that the input string shifted isn't larger than 1000 chars? If not, g₂+ð× should work. – Emigna Oct 13 '17 at 14:10

# JavaScript (ES6), 81 bytes

s=>[...s].map((c,i)=>a[i+parseInt(c,36)-10]=c,a=[])&&[...a].map(c=>c||" ").join

Somewhat builds off of Rick Hitchcock's incomplete answer but ended up rather different.

Places the characters into their respective index of an empty array, then uses array spread ([...a]) to turn the missing elements into undefined, allowing map to replace empty elements with a space.

## Test Cases

let f=
s=>[...s].map((c,i)=>a[i+parseInt(c,36)-10]=c,a=[])&&[...a].map(c=>c||" ").join

;["AZ", "ABC", "ACE", "CBA", "HELLOWORLD"]
.forEach(t=>console.log("${t}", "=>", "${f(t)}"))

• Very nice! I was about to post an 88-byte solution, but yours is much better. – Rick Hitchcock Oct 13 '17 at 19:45

## Perl 5, 42 bytes

41 bytes code + 1 for -p. The \x1bs in the code are literal escape characters.

Relies on ANSI escape sequences to position the cursor and therefore doesn't work on TIO.

s/./($-=-65+ord$&)?"\x1b[$-C$&\x1b[--$-D":$&/ge

### Usage

perl -pe 's/./($-=-65+ord$&)?"\x1b[$-C$&\x1b[--$-D":$&/ge' <<< 'HELLOWORLD'
E H    DLL   OLO   R  W

• Here's one that works on TIO but comes in at one byte more (41 bytes of code + 2 for -F): Try it online! – Xcali Oct 13 '17 at 18:03
• @Xcali You should post that so I can upvote it :) – Lynn Oct 13 '17 at 18:46
• @Xcali Agree with Lynn too more posts are great. I like language competition too! – Dom Hastings Oct 13 '17 at 21:08

# Java (OpenJDK 8), 207191189183178174173 170 bytes

s->{char i=0,l,c[]=new char[s.chars().map(j->j+s.lastIndexOf(j)).max().getAsInt()-64];for(;i<s.length();c[i+l-66]=l)l=s.charAt(i++);return"".valueOf(c).replace('',' ');}

Try it online!

# Perl 5, 41 + (-F) = 43 bytes

map$r[$i++-65+ord]=$_,@F;print$_||$"for@r  Try it online! Just for @lynn # brainfuck, 127 bytes ,[[-<+<+>>]----[----<<->>]<<-[[>]>++++>[-<[-]<+>>]<[-<++++++++>]<[<]>-]>[>]<[-]+[<]>-[[>]<+[<]>-]>.[-]>[>]<[[->>+<<]<]>,]>>[.>]  Try it online! ### Explanation ,[ Take input and start main loop [-<+<+>>] Make two copies of input byte ----[----<<->>]<<- Subtract 64 from one of them to get position in alphabet There are two zero cells between the input and the remaining output cells; we wish to move these zeroes to indicate where the letter is to be moved [ A number of times equal to the position in the alphabet: [>] Go to current position in output string >++++> Create 4 (as part of creating a space if needed) [-<[-]<+>>] Move output byte back two cells; zero the previous 4 if output existed <[-<++++++++>] Otherwise move a space (32) into that position <[<]>- Move back to counter and decrement ] >[>]<[-] Delete last moved byte to make room for input byte +[<]>- Initialize slot at 1 so it is always nonzero in this loop [[>]<+[<]>-] Move input byte into slot >.[-] Output next output byte and clear >[>]< Move to space vacated in preparation to remove gap (Moves to end instead if input was A; this causes no problems) [[->>+<<]<] Move values two cells right until zero reached >, Get into position and take another byte of input ] >>[.>] Output characters beyond end of input  # Proton, 78 bytes x=>{t=[' ']*26*(q=len(x))for i:0..q{t[i+ord(k=x[i])-65]=k}"".join(t).rstrip()}  Try it online! 69 bytes by porting Lynn's solution: x=>{t=[]i=65for k:x{t+=[' ']*26t[ord(k)-i]=k;i--}"".join(t).rstrip()} # Jelly, 20 bytes ØAiÐ€+JṬ€a"⁸Zḟ€0Ṫ€o⁶  Try it online! # Haskell, 90 88 bytes d=drop 1 [a]#s|s<"A"=a:d s|0<1=s b#s=(s++" ")!!0:d b#d s f(c:t)=['A'..c]#(' ':f t) f s=s  Try it online! • @Laikoni Oh dang, I misread that. Will fix... – Zgarb Oct 13 '17 at 21:04 • Well, it's no longer shorter. :/ – Zgarb Oct 13 '17 at 21:13 # Japt, 23 bytes ;iB ç Ng £=hX10nY+XnG U  Test it online! First attempt, may be improvable... • It looks like you should be able to save a byte by assigning the initial string to V instead of U: ethproductions.github.io/japt/… – Shaggy Oct 13 '17 at 21:02 # Wolfram Language (Mathematica), 76 bytes SparseArray[Reverse@MapIndexed[#&@@#2+LetterNumber@#-1->#&,#]]<>""/. 0->" "&  Takes a list of characters as input. This generates some error messages that are safe to ignore. I included Print and Character command in the footer of the TIO link for ease of use. (Character command simply converts a string to a list of characters) Try it online! • LetterNumber is a built-in for finding the position of a letter in the alphabet? Holy crap, that's ridiculous. – numbermaniac Oct 14 '17 at 6:40 # J, 37 31 bytes [](' '#~(1+>./)@])}(i.@#+65-~a.&i.) [](' '#~(1+>./)@])}#\-66-3&u:  -6 bytes thanks to FrownyFrog ### explanation The entire thing is a hook: [](' '#~(1+>./)@])} #\-66-3&u:  The right side calculates the new indexes for all the letters. The left side uses the gerund form of Amend } first to create a string of the necessary number of spaces: (' '#~(1+>./)@]) . And then to place each letter of the original string into its appropriate index within the all-space string. Try it online! • (i.@#+65-~a.&i.) -> (i.@#+65-~3&u:) -> (i.@#-65-3&u:) -> (#\-66-3&u:) – FrownyFrog Oct 13 '17 at 17:13 • You can drop the parentheses too. – FrownyFrog Oct 13 '17 at 17:18 • @FrownyFrog tyvm. i'd forgotten about both those golf tricks. – Jonah Oct 13 '17 at 17:52 • You can save 3 bytes with (]' '#~1+>./) – miles Oct 14 '17 at 1:28 • @miles. Nice. I need to make dyadic hooks part of my regular toolbox, I noticed you used them in that revision from this morning as well. – Jonah Oct 14 '17 at 1:49 # Haskell, 88 bytes foldr(\c->(((['B'..c]>>" ")++[c])#).(' ':))[] (x:r)#(y:t)|y>' '=y:r#t|1<3=x:r#t r#t=r++t  Try it online! ## Haskell, 88 bytes f s|q<-zipWith((+).fromEnum)s[0..]=[last$' ':[c|(c,i)<-zip s q,i==p]|p<-[65..maximum q]]

Try it online!

q is the list of the final indices of the letters of the input string (with an offset of 65). Loop through all indices (starting at 65) and find all letters for it, prepending a space. Take the last.

• Yet another 88 byte Haskell solution, see here and here. – nimi Oct 14 '17 at 17:51

# C# (.NET Core), 117110 84 bytes

Saved 7 bytes thanks to Ayb4tu.

Changed return type from string to char[] to save 26 bytes.

n=>{int i=0,l=n.Length;var t=new char[l+26];for(;i<l;)t[i+n[i]-65]=n[i++];return t;}

Try it online!

• You can save 7 bytes by changing t[i+((int)n[i]-65)] to t[i+n[i]-65]. – Ayb4btu Oct 14 '17 at 9:28
• @Ayb4btu Thanks forgot that char -> int conversions are implicit. – Ian H. Oct 14 '17 at 9:37

# C# .NET, 89 Bytes 87 Bytes

-2 bytes thanks to Lan H.

f=>{var s=new char[f.Length+26];for(int i=0;i<f.Length;i++)s[f[i]+i-65]=f[i];return s;}

Try it Online!

• Welcome to the site! :) – DJMcMayhem Oct 16 '17 at 15:44
• Thanks! I hope it's ok to post multiple answer in the same language – Emiliano Oct 16 '17 at 16:07
• You can omit the curly brackets in your for-loop for -2 bytes. – Ian H. Oct 16 '17 at 20:50

# Kotlin, 207 bytes189 bytes187 bytes 177 bytes

fun main(){val i=(readLine()+" ".repeat(26)).toCharArray();for(x in(i.size-1) downTo 0){if(i[x]!=' '){i[x+i[x].toInt()-65]=i[x];i[x]=' '}};print(i.joinToString("").trim())}

If the leading blank should remain I would just call trimEnd() instead of trim().

Unminified:

fun main() {
val m = (readLine() + " ".repeat(26)).toCharArray()
for (x in (m.size - 1) downTo 0) {
if(m[x] != ' ') {
m[x + m[x].toInt() - 65] = m[x]
m[x] = ' '
}
}

print(m.joinToString("").trim())
}

Maybe Kotlin is not the best language for code golfing but I liked the challenge and I wanted to make myself more familiar with Kotlin's standard library.

# q/kdb+, 37 bytes

Solution:

@[max[1+m]#" ";m:!:[x#:]+.Q.A?x;:;]x:

Examples:

q)@[max[1+m]#" ";m:!:[x#:]+.Q.A?x;:;]x:"AZ"
"A                         Z"
q)@[max[1+m]#" ";m:!:[x#:]+.Q.A?x;:;]x:"ABC"
"A B C"
q)@[max[1+m]#" ";m:!:[x#:]+.Q.A?x;:;]x:"ACE"
"A  C  E"
q)@[max[1+m]#" ";m:!:[x#:]+.Q.A?x;:;]x:"CBA"
"  A"
q)@[max[1+m]#" ";m:!:[x#:]+.Q.A?x;:;]x:"HELLOWORLD"
"     E H    DLL   OLO   R  W"

Explanation:

I think this is the same idea as the J solution, calculate the correct indices for the input array and then assign them to an empty string of correct length:

@[max[1+m]#" ";m:til[count x]+.Q.A?x;:;]x: / ungolfed solution
x: / save input as x
@[            ;                     ; ;]   / apply[variable;indices;function;parameters]
:     / assignment
.Q.A?x       / location of x in uppercase alphabet
count x               / length of input
til[       ]              / range, 0..n
m:                          / save as m
max[   ]                                 / maximum of list
1+m                                  / m + 1
#" "                             / take " ", creates empty character list

# Jq 1.5, 91 bytes

reduce(explode|[.,keys]|transpose[]|.[1]+=.[0]-65)as[$c,$p]([];.[$p]=$c)|map(.//32)|implode

Expanded

  reduce(  explode         # convert string to array of ordinals
| [.,keys]        # [ [v0,v1,...], [0,1,2,...] ]
| transpose[]     # [ [v0,0], [v1,1], [v2,2]...]
| .[1]+=.[0]-65   # adjust position of each value
) as[$c,$p] (
[]
; .[$p]=$c               # store each value at its position
)
| map(.//32)               # map null values to spaces
| implode                  # convert back to string

Try it online!

# Charcoal, 16 bytes

Ｐ ＦＳ«Ｍ⌕αι→ιＭ⌕αι←

Try it online! Link is to verbose version of code. Explanation:

Ｐ                   Output a space to force the indent
Ｓ                Input string
Ｆ «               Loop over each letter
α            Uppercase letters predefined variable
⌕ ι           Find index of current letter
Ｍ   →          Move that many characters right
ι         Implicitly print the current letter
Ｍ⌕αι←    Move the same number of characters left

# APL (Dyalog), 26 bytes

Anonymous prefix lambda which takes the input string as argument and returns the output string. Assumes ⎕IO (Index Origin) 0, which is default on many systems.

{⍵@i⊢''↑⍨1+⌈/i←(⎕A⍳⍵)+⍳≢⍵}

Try it online!

{} anonymous lambda; ⍵ represents the argument

≢⍵ tally of the argument

⍳ than many ɩntegers (0…LengthOfArgument-1)

()+ plus:

⎕A⍳⍵ the indices of the argument in the uppercase Alphabet

i← strore in i (for indices)

⌈/ maximum (reduction)

1+ add one

''↑⍨ take that many characters from the empty string, padding with spaces as needed

⊢ yield that (serves to separate i from '')

⍵@i amend that with the argument letters at the i indices

# SOGL V0.12, 10 bytes

ā,{ZFWē+1ž

Try it Here!

Explanation:

ā           push an empty array
,{         for each char in the input
ZFW        get its index in the uppercase alphabet
ē+      add to that the 0-indexed counter
1ž    at [pop; 1] insert in the array the current character

# Pyth, 44 38 bytes

Striked out 44 is still 44 :(

Bloody Pyth beginner.

Saved 6 bytes thanks to @Mr. Xcoder.

K*d+lz26Vlz K=XK-C@zN-65N@zN;.WqeHdPZK

Try it online!

How?

K*d+lz26Vlz K=XK-C@zN-65N@zN;.WqeHdPZK          Full program

K*d+lz26                                        Assign a string consisting of
(26 + input.length) whitespaces to K
Vlz                                     For-loop from 0 to input.length
-C@zN-65N                       Calculate the index for the current letter
@zN                    The current letter
K=XK                                Insert the current letter into K at
position i
;                   End statement
.WqeHdPZK          While the last character of H is not a
whitespace, pop the last character off K

• 38 bytes: K*d+lz26Vlz K=XK-C@zN-65N@zN;.WqeHdPZK. WqeKd K=PK;K is replaced by .W (functional while) and its arguments of course, and FNrZlz can be replaced VrZlz, but rZ... means U..., and U is generated automatically by V. So FNrZlz becomes Vlz – Mr. Xcoder Oct 17 '17 at 10:55

# Batch, 418 331 bytes

Works with uppercase letters only and will take some seconds for longer strings.

Learned new tricks here, the character to ASCII value conversion using %=exitcodeAscii%. Also, if defined and "array" access using call. Also, golfing by almost 100 bytes was good batch code golf training.

Note the trailing space in set z=set.

@echo off
setlocal EnableDelayedExpansion
set z=set
%z%a=%1
:a
%z%v=64
:b
%z%/Av+=1
cmd/Cexit %v%
if %=exitcodeAscii% neq %a:~0,1% goto b
%z%/Ao=v+c
%z%a%o%=%a:~0,1%
if %o%. geq %m%. %z%m=%o%
%z%/Ac+=1
%z%a=%a:~1%
if %a%. neq . goto a
for /l %%n in (65,1,%m%)do (
if defined a%%n (call %z%r=%%r%%%%a%%n%%
)else %z%r=!r! )
echo %r%

# Ruby, 68 bytes

->s{r,w="",-66;s.bytes{|b|r[(b+w+=1).times{|a|r[a]||=" "}]=b.chr};r}

Try it online!

# IBM PC DOS8088 Assembly, 34 33 bytes

b403 cd10 be80 00ad 8bc8 32ed ac8b de02 d880 ebc4 8ad3 b402 cd10 b40e cd10 e2ec c3

Ungolfed (unassembled):

    MOV  AH, 03H        ; get current cursor position row into DH
INT  10H
MOV  SI, 80H        ; point SI to PSP
LODSW               ; fetch length into AL, increment SI to 82H
MOV  CX, AX         ; move to CX
XOR  CH, CH         ; clear CH
OUTPUT:
LODSB               ; load DS:SI into AL
MOV  BX, SI         ; input string offset into BX (so doesn't overwrite DH)
SUB  BL, 'A'+83H    ; convert to numeric val (A=0, Z=25)
MOV  DL, BL         ; DL is column number
MOV  AH, 02H        ; BIOS set cursor position function
INT  10H            ; move to column
MOV  AH, 0EH        ; BIOS display char function in AL
INT  10H            ; write to screen
LOOP OUTPUT

This is a complete PC DOS executable that takes the input string from the command line and prints the new "faster" version to the screen. Requires a minimum of DOS 1.0... hopefully you've got at least that.

## Output

A>SPEED.COM ABC
A B C
A>SPEED.COM AZ
A                         Z
A>SPEED.COM CBA
A
A>SPEED.COM HELLOWORLD
E H    DLL   OLO   R  W

# PHP, 127 123 bytes

function b($i){for($q=0;$q<strlen($i);$q++){$n[ord($i[$q])-65]=$i[$q];}while($x++<26){$m.=$n[$x-1]?$n[$x-1]:" ";}return\$m;}

Try it online

Had to fix a bug that wouldn't output 'A'...

• To who-ever downvoted: You probably mis-clicked the up-button. Please correct, or drop me a line on why the downvote... Tnx – steenbergh Oct 13 '17 at 21:47


text:
# Challenge Data

### Who are the high-frequency traders ? by authority

#### Description

##### Dates

Started on Jan. 4, 2021

##### Challenge context

Financial markets are made up of various types of market players, each with specific interests and hence heterogeneous behaviours. For regulators, it is important to know the different types of market players in order to better understand how their behaviour has an impact on the market. Since the emergence of High-Frequency Trading (HFT) [1] more than a decade ago, financial sector authorities as well as academics have widely studied the impact and influence on markets of these market players, who invest in powerful low-latency infrastructure to transact a large number of orders in fractions of a second [2].

HFTs as well as other market players submit orders to an electronic trading mechanism called a Limit Order Book (LOB): orders are requests to buy or sell a given quantity of an asset at a specified price, thus allowing buyers to be matched with sellers at a mutually agreed price. Market players can modify the price or the quantity of their orders as long as they remain in the LOB ; they also can cancel them (modification and cancellation are tagged as « events »).

For this challenge, we focus on equity markets, and since a given stock can be traded on multiple trading venues (market fragmentation), participants can choose to which venue they send an order (each trading venue has its own distinct LOB). Thanks to their advantage in terms of speed, HFTs, who seek direct market gain based on small price variations, often apply arbitrage strategies [3] between several trading venues. Besides, HFTs generally send more events into the LOB than other participants, or at least are able to send two events with « the shortest » delta time possible.

##### Challenge goals

The goal of the challenge is to classify traders within three categories, HFT, non HFT and MIX.

According to the AMF in-house expert-based classification, based on the knowledge that AMF has on the market players, market players are divided into three categories, HFT, MIX and non-HFT.

From a set of behavioural variables based on order and transaction data, the challenger is invited to predict the category to which a given participant belongs.

The proposed classification algorithm will then be applied to other data sources for which market players are currently not well known by the AMF.

##### Data description

Each market player $i$ (i.e. participant) is represented by a matrix $X_{i}$ , whose row $r_{a,t}$ provides a given market player’s behaviour variables calculated for a given stock $a$ and a certain trading date $t$ . Since all market players are not active every day nor on the whole scope of assets, the length of the matrices $X_i$ may vary from participant to participant. The columns of $X_i$ contain the features (detailed below).

The objective is to find the function $f$ such as $f(X_i )=y$ , where $y= ⟨HFT│MIX│Non-HFT⟩$ , in other words y refers to the market player’s category.

The y_train file as the y_test file contain the market players’ identification code ($Trader_1, Trader_2,$ etc.) and the category they belong to ($Trader_1 = HFT, Trader_2 = HFT, Trader_3 = MIX$ ). Participants falling under the $MIX$ category are those who can sometimes use HFT algorithms but not systematically.

The x_train file and the x_test file data contain both the equivalent of 1 month of data (x_train data are prior to x_test data). The scope of market players is roughly the same in the y_train and y_test data (but the market players’ identification code have been changed, so that it is not possible to find who is, for example, $Trader_1$ of the train (x and y) files, in the test (x and y) files.

### List of features

x_Train and x_test data exhibit in their rows the same 35 features calculated for a given market player i on a certain stock $Isin_i$ (whose identification code is an Isin) and a specific trading date $t$ :

1. #1 number of trading venues on which the market player trades ;
2. from all trading venues, statistics over the number of trades observed per second : #2 the mean, and,
#3 the max
3. statistics over the observed time delta between two trades on the trading venue TV_1[1] :
• #4 min,
• #5 median,
• #6 min
4. statistics over the observed time delta between two trades occurring on trading venue TV_1 and then on trading venue TV_2 :
• #7 min,
• #8 median,
• #9 min
5. statistics over the observed time delta between two trades occurring on trading venue TV_1 and then on trading venue TV_3 :
• #10 min,
• #11 median,
• #12 min
6. statistics over the observed time delta between two trades occurring on trading venue TV_1 and then on trading venue TV_4 :
• #13 min,
• #14 median,
• #15 min
7. #16 from all trading venues, number of seconds during the trading day where at least one trade of the market player i is observed
8. on trading venue TV_1, three ratios between the number of all types of events[2] sent to the LOB and :
• #17 the number of trades (OTR),
• #18 the number of cancellation-type event (OCR),
• #19 the number of modification-type event (OMR)
9. on trading venue TV_1, statistics over the observed time delta between two all-type events sent :
• #20 min,
• #21 mean,
• #22 10th percentile,
• #23 1st quartile,
• #24 median,
• #25 3rd quartile,
• #26 90th percentile,
• #27 max
10. on trading venue TV_1, statistics over the observed lifetime of cancelled orders :
• #28 min,
• #29 mean,
• #30 10th percentile,
• #31 1st quartile,
• #32 median,
• #33 3rd quartile,
• #34 90th percentile,
• #35 max

The features above are not detailed in the same order as in the challenge files.

### Example

For example (based on the columns' order in the train files : « Isin_1, Date_1, Trader_1, 5, 2.3, 10, … » means that for $Isin_1$ on $Date_1$ , we have observed that $Trader_1$ has an OTR equals to 5, an OCR equals to 2.3 and an OMR equals to 10.

Finally, the link between test and train data can be established thanks to the market player’s identification code.

[2] Events include both the transactions and the messages that market players can send to the LOB: new order, order modification or order cancellation.

##### Benchmark description

A basic random forest with additional rules based on threshold to determine what percentage of rows by maket players makes them fall into one of each category gave us a micro-averaged F1- score of ~90%. For this « naive » model we have considered that a market player whose :

• at least 85% of his rows are predicted as HFT is an HFT participant,
• at least 50% of his rows are predicted as MIX is a MIX participant,

Otherwise the model considers that the market player is a NON HFT.

#### Files

Files are accessible when logged in and registered to the challenge

#### The challenge provider

Market surveillance


text:
# Nuclear power: what material is the control rod made from?

My question:

What type of material is used in the control rod of a nuclear reactor? (Nuclear power plant)

• Welcome to chemistry.SE! If you had any questions about the policies of the site, visit the help center or take a tour. By the way, you could do a little research. e.g.: Your answer could be hidden in this wikipedia page about the control rod in nuclear power plants. – M.A.R. ಠ_ಠ Dec 31 '14 at 18:04

Control Rods: They have the ability to capture the slow neutrons and can control the chain reaction at any stage. Boron and Cadmium are the best absorbers of neutrons.

To start fusion in $\ce{^{238}U}$ we need very fast electrons having kinetic energies more than about 1 $MeV$.In a nuclear reactor, the probability that of a neutron will cause $\ce{^{235}U}$ to fission is more for a slow moving neutrons.Thus there is less probability that the fast neitrons produced in fission will cause other fission to occur and chain reaction will start. To slow down these neutrons (to initiate more fission events) in a nuclear reactor moderator is used which may be ordinary water or heavy water($\ce{D2O}$).

• 1) Usually if an answer is one or two sentences it might be best to post it as a comment. 2) Control rods can also be designed to capture fast neutrons. 3) Many other factors go into control rod material selection in addition to neutron capture cross-section, so pure boron and cadmium won't make for the best control rods. – ron Dec 31 '14 at 19:07
• 1)Try hovering your mouse over "add comment" to know what it specifically does, If not many users post short answers on this site, this should not be discouraged, [as another point :I wish not go my answer go waste as a comment ] 2)So What? To start fusion in $^{238}U$ we need very fast electrons having kinetic energies more than about 1 $MeV$.In a nuclear reactor, the probability that of a neutron will cause $^{235}U$ to fission is more for a slow moving neutrons.3) I didn't said pure elements, in either ways that is what you learn if you study chemistry – RE60K Dec 31 '14 at 19:19
• See here for a discussion on short answers. For items 2) and 3), I'm just telling you how I read what you wrote. – ron Dec 31 '14 at 20:00
• But one question, why slow down the chain reaction. Isn't it better if the chain reaction were faster then the amount of energy in a period grow – tor Jan 1 '15 at 9:56
• @tor the probability that of a neutron will cause 235U to fission is more for a slow moving neutrons – RE60K Jan 1 '15 at 9:56

There are various designs and materials for control rods, which are more technically correctly called control assemblies. The choice depends on the type and purpose of the reactor, the size of the core, the availability of other means for power control (e.g. boric acid in the reactor coolant, burnable poison rods, $\ce{UO2-Gd2O3}$ fuels, recirculation pumps, or thermal-hydraulic feedback), patent rights, and tradition.

The control assemblies of typical boiling water reactors (BWRs) are inserted from the bottom because, in a BWR, the neutron flux is larger in the lower part of the core and the space above the core is occupied by the water separator and the steam dryer. Therefore, the design should consider saving weight. BWRs usually use cross-shaped control assemblies. The four blades of one cruciform control assembly are inserted between four fuel assemblies. The blades are made of stainless steel and contain tubes that are filled with boron carbide ($\ce{B4C}$) as neutron-absorbing material. In addition, hafnium ($\ce{Hf}$) absorber may be placed in selected locations of selected control assemblies.

The control assemblies of typical pressurized water reactors (PWRs) are inserted from above. Each control assembly has multiple rod fingers, which enter openings at the upper end of a fuel assembly. These control assemblies are very heavy because, when the reactor is tripped, they have to quickly drop into the core against the pressure of the reactor coolant. The neutron absorbing material mainly is an $\ce{Ag-In-Cd}$ alloy (usually 80 % $\ce{Ag}$, 15 % $\ce{In}$, and 5 % $\ce{Cd}$) encapsulated in an austenitic steel cladding. These three neutron absorbers together effectively block the entire energy spectrum of fast, epithermal, and thermal neutrons. In other PWR core designs, the control rods are divided into two groups: so-called black assemblies made up of rods consisting of an $\ce{Ag-In-Cd}$ alloy in the lower section and of $\ce{B4C}$ in the upper part, and so-called grey assemblies containing absorber rods made of $\ce{Ag-In-Cd}$ alloy and steel.


text:
# What are the absolute extrema of f(x) =x/(x^2-x+1) in[0,3]?

May 27, 2016

Absolute minimum is $0$ (at $x = 0$) and absolute maximum is $1$ (at $x = 1$).

#### Explanation:

$f ' \left(x\right) = \frac{\left(1\right) \left({x}^{2} - x + 1\right) - \left(x\right) \left(2 x - 1\right)}{{x}^{2} - x + 1} ^ 2 = \frac{1 - {x}^{2}}{{x}^{2} - x + 1} ^ 2$

$f ' \left(x\right)$ is never undefined and is $0$ at $x = - 1$ (which is not in $\left[0 , 3\right]$) and at $x = 1$.

Testing the endpoints of the intevral and the critical number in the interval, we find:

$f \left(0\right) = 0$
$f \left(1\right) = 1$
$f \left(3\right) = \frac{3}{7}$

So, absolute minimum is $0$ (at $x = 0$) and absolute maximum is $1$ (at $x = 1$).


text:
Question Authors' Forum

Should students be penalised when the JOBE server is offline?

Should students be penalised when the JOBE server is offline?

We just did a test of our CodeRunner set-up. We have 2 JOBE servers sitting behind a load-balancer, and what we did is to put the system under some load, using a Jmeter script we had made, and then we restarted one of the two JOBE servers.

The kind of load we were simulating was about 100 responses being submitted per minute, and when the server restart happened, 8 qiuestion submissions got an error. I am pretty happy with that.

However, the point I wanted to raise was that when this happened, the student got marked wrong, as if they had submitted an incorrect response. It seems a bit unfair to penalise the students for this:

Would it not be fairer to treat this case differently? For example, in Moodle question system terms, we could treat this as an 'invalid' response, rather than an incorrect one. That way, students would not get a penalty, and they could just try again later when the server comes back.

This might be something to think about for CodeRunner version 3.2?

(It might also be good to re-word the message to suggest students try again a bit later.)

Re: Should students be penalised when the JOBE server is offline?

Thanks Tim. Yes, I agree that the student shouldn't be penalised when the Jobe server fails. I was vaguely aware that I should fix that some time but it never got to be a priority, since Jobe never fails :-)

I'll bump it up the priority list for the next version, and think about the error message too. In the meantime, a "regrade all", or regrade of affected students, does of course fix any such problems.

Richard

Re: Should students be penalised when the JOBE server is offline?

Thanks Richard.
Re: Should students be penalised when the JOBE server is offline?

At least students should be informed, if CodeRunner (to be exact: the jobe server)  is online and working correctly.

For this reason I put a java script into the announcements of my Moodle course:

News and announcements

<button type="button" id="cr_check">Checking if CodeRunner is running...</button>

You can only check in "CodeRunner"-questions, if "CodeRunner" is active !

The script is not optimal, because I don't know how to access the moodle settings in javascript, to retrieve the Jobe-URL (and API-Key if necessary).

Moreover it would be better to integrate such a green/red light into the CodeRunner plugin itself.

Re: Should students be penalised when the JOBE server is offline?

Hi Martin

I agree that the student shouldn't be penalised if the Jobe server is down, although I would also hope that such a situation is very rare. I can't recall any unplanning jobe outages on our production server for some years.

I thought I had dealt with the problem Tim raised, at least to the point of ensuring students don't get penalised. I just did a bit of brief testing and here's what I found:

1. If the question type (or a customised version of it) explicitly specifies in the Advanced Customisation section that the sandbox to use is jobesandbox, then if the Jobe server doesn't respond the student should see the something like the following, and they should not be penalised.

2.  If instead the sandbox is specified as DEFAULT then things are not so pretty. CodeRunner attempts to ask each sandbox in turn what languages it supports, in order to find the first sandbox that works with the current language. [Historically CodeRunner had several different sandboxes available, but Jobe has now replaced all but the deprecated IdeoneSandbox]. If the Jobe sandbox fails to respond to that request, an exception is thrown. This results in somewhat ugly output, but it shouldn't result in penalising the student.

So ... are students actually been penalised when your Jobe server is down, Martin? If so, can you clarify exactly what error messages are being generated. I'm also curious to know why you're having issues with the Jobe server - is this something I should know about it? I haven't heard of any significant stability issues with it.

Richard

Re: Should students be penalised when the JOBE server is offline?

Hi Richard,

There have been no unexpected outages (save the time in my holidays, when I shut down the server, that's located in my home office by now consuming my privately paid electricity :-) ).

I will move the jobe server to our school now, when school starts again.
But there may be times,  the jobe will be down or not reachable, e.g. the DSL-wire or the router/proxy in our school gets stuck or the mains is down for electricians working on the installations or the DDNS service has issues.

There may also be situations, when I shut down the server myself expecting no student to use my courses at the moment, but some do use them anyway - who knows (hope dies last).

I know, that if you select jobe as the sandbox, the red message "Unexpected error..." is displayed. But it appears after the student has tried to check in his solution. He could be disappointed or frustrated this way.  Better if he knows the outage before and does not try.
(Also I'm not sure, if my students will really read or understand the english message...)

That's why I created the small script (messages are German)

On start:

If jobe responds:

If connection fails:


text:
# Genetic Variation of the β2-Adrenoceptor

@article{Taylor2001GeneticVO,
title={Genetic Variation of the $\beta$2-Adrenoceptor},
author={D. Taylor and M. Kennedy},
journal={American Journal of Pharmacogenomics},
year={2001},
volume={1},
pages={165-174}
}
• Published 2001
• Biology
• American Journal of Pharmacogenomics
Asthma is a polygenic disease for which no clear genotype-phenotype relationships have emerged. In contrast, although not associated with the diagnosis of asthma per se, variant forms of the β2-adrenoceptor (β2-AR) gene (ADRB2) display functional effects that may be clinically relevant. Single nucleotide polymorphisms (SNPs) of ADBR2 are common and result in amino acid substitutions at positions 16, 27, and 164 of the receptor as well as position 19 of its 5′ upstream peptide. These SNPs… Expand
1 Citations

#### References

SHOWING 1-10 OF 64 REFERENCES


text:
On the Number of Cylindrical Shells

1 PRISME - Geometry, Algorithms and Robotics
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Given a set $P$ of $n$ points in three dimensions, a cylindrical shell or zone cylinder is formed by two cylindrical cylinders with the same axis such that all points of $P$ are between the two cylinders. We prove that the number of cylindrical shells enclosing $P$ passing through combinator- ially different subsets of $P$ has size $\Omega(n^3)$ and $O(n^4)$ (previous known bound was $O(n^5)$).
Keywords :
Document type :
Reports
Domain :

Cited literature [1 references]

https://hal.inria.fr/inria-00072353
Contributor : Rapport de Recherche Inria <>
Submitted on : Tuesday, May 23, 2006 - 8:30:13 PM
Last modification on : Saturday, January 27, 2018 - 1:31:26 AM
Long-term archiving on: : Sunday, April 4, 2010 - 11:05:11 PM

Identifiers

• HAL Id : inria-00072353, version 1

Citation

Olivier Devillers. On the Number of Cylindrical Shells. RR-4234, INRIA. 2001. ⟨inria-00072353⟩

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