IPLIB (Image processing library) user's manual

IPLIB is a collection of HP FORTRAN 77 subroutines and functions that facilitate the use of a COMTAL image processing system driven by an HP-1000 computer. It is intended for programmers who want to use the HP 1000 to drive the COMTAL Vision One/20 system. It is assumed that the programmer knows HP 1000 FORTRAN 77 or at least one FORTRAN dialect. It is also assumed that the programmer has some familiarity with the COMTAL Vision One/20 system

Hybrid Stars

Recently there have been important developments in the determination of neutron star masses which put severe constraints on the composition and equation of state (EOS) of the neutron star matter. Here we study the effect of quark and nuclear matter mixed phase on mass radius relationship of neutron stars employing recent models from two classes of EOS's and discuss their implications.Comment: 3 pages LaTeX including 2 figures, macros included, Talk presented at the IX International Symposium on Particles, Strings and Cosmology (PASCOS'03), TIFR, Mumbai, India, January 3-8,2003. To appear in their proceeding

Radio faint AGN: a tale of two populations

We study the Extended Chandra Deep Field South (E-CDFS) Very Large Array sample, which reaches a flux density limit at 1.4 GHz of 32.5 microJy at the field centre and redshift ~ 4, and covers ~ 0.3 deg^2. Number counts are presented for the whole sample while the evolutionary properties and luminosity functions are derived for active galactic nuclei (AGN). The faint radio sky contains two totally distinct AGN populations, characterised by very different evolutions, luminosity functions, and Eddington ratios: radio-quiet (RQ)/radiative-mode, and radio-loud/jet-mode AGN. The radio power of RQ AGN evolves ~ (1+z)^2.5, similarly to star-forming galaxies, while the number density of radio-loud ones has a peak at ~ 0.5 and then declines at higher redshifts. The number density of radio-selected RQ AGN is consistent with that of X-ray selected AGN, which shows that we are sampling the same population. The unbiased fraction of radiative-mode RL AGN, derived from our own and previously published data, is a strong function of radio power, decreasing from ~ 0.5 at P_1.4GHz ~ 10^24 W/Hz to ~ 0.04$ at P_1.4GHz ~ 10^22 W/Hz. Thanks to our enlarged sample, which now includes ~ 700 radio sources, we also confirm and strengthen our previous results on the source population of the faint radio sky: star-forming galaxies start to dominate the radio sky only below ~ 0.1 mJy, which is also where radio-quiet AGN overtake radio-loud ones.Comment: 19 pages, 13 figures, accepted for publication in MNRA

Effect of the bound nucleon form factors on charged-current neutrino-nucleus scattering

We study the effect of bound nucleon form factors on charged-current neutrino-nucleus scattering. The bound nucleon form factors of the vector and axial-vector currents are calculated in the quark-meson coupling model. We compute the inclusive $^{12}$C($\nu_\mu,\mu^-$)$X$ cross sections using a relativistic Fermi gas model with the calculated bound nucleon form factors. The effect of the bound nucleon form factors for this reaction is a reduction of $\sim$8% for the total cross section, relative to that calculated with the free nucleon form factors.Comment: Latex, 11 pages, 3 figures, version to appear in Phys. Rev. C (Brief Report

Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)

Massive open online courses (MOOC) describe platforms where users with completely different backgrounds subscribe to various courses on offer. MOOC forums and discussion boards offer learners a medium to communicate with each other and maximize their learning outcomes. However, oftentimes learners are hesitant to approach each other for different reasons (being shy, don't know the right match, etc.). In this paper, we propose a reciprocal recommender system which matches learners who are mutually interested in, and likely to communicate with each other based on their profile attributes like age, location, gender, qualification, interests, etc. We test our algorithm on data sampled using the publicly available MITx-Harvardx dataset and demonstrate that both attribute importance and reciprocity play an important role in forming the final recommendation list of learners. Our approach provides promising results for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201