RAM-Efficient External Memory Sorting

In recent years a large number of problems have been considered in external memory models of computation, where the complexity measure is the number of blocks of data that are moved between slow external memory and fast internal memory (also called I/Os). In practice, however, internal memory time often dominates the total running time once I/O-efficiency has been obtained. In this paper we study algorithms for fundamental problems that are simultaneously I/O-efficient and internal memory efficient in the RAM model of computation.Comment: To appear in Proceedings of ISAAC 2013, getting the Best Paper Awar

EDUCATION AND LABOUR MARKET OUTCOMES: EVIDENCE FROM INDIA

The impact of education on labour market outcomes is analysed using data from various rounds of the National Sample Survey of India. Occupational destination is examined using both multinomial logit analyses and structural dynamic discrete choice modelling. The latter approach involves the use of a novel approach to constructing a pseudo-panel from repeated cross-section data, and is particularly useful as a means of evaluating policy impacts over time. We find that policy to expand educational provision leads initially to an increased takeup of education, and in the longer term leads to an increased propensity for workers to enter non-manual employment.

EDUCATION AND LABOUR MARKET OUTCOMES: EVIDENCE FROM BRAZIL

The effect of education on labour market outcomes is analysed using both survey and administrative data from The Brazilian PNAD and RAIS-MIGRA series, respectively. Occupational destination is examined using both multinomial logit analyses and structural dynamic discrete choice modelling. The latter approach is particularly useful as a means of evaluating policy impacts over time. We find that policy to expand educational provision leads initially to an increased take-up of education, and in the longer term leads to an increased propensity for workers to enter non-manual employment.

Studies in matter antimatter separation and in the origin of lunar magnetism

Antimatter experiments of the University of Santa Clara are investigated. Topics reported include: (1) planetary geology, (2) lunar Apollo magnetometer experiments, and (3) Roche limit of a solid body

Size versus truthfulness in the house allocation problem

We study the House Allocation problem (also known as the Assignment problem), i.e., the problem of allocating a set of objects among a set of agents, where each agent has ordinal preferences (possibly involving ties) over a subset of the objects. We focus on truthful mechanisms without monetary transfers for finding large Pareto optimal matchings. It is straightforward to show that no deterministic truthful mechanism can approximate a maximum cardinality Pareto optimal matching with ratio better than 2. We thus consider randomized mechanisms. We give a natural and explicit extension of the classical Random Serial Dictatorship Mechanism (RSDM) specifically for the House Allocation problem where preference lists can include ties. We thus obtain a universally truthful randomized mechanism for finding a Pareto optimal matching and show that it achieves an approximation ratio of eovere-1. The same bound holds even when agents have priorities (weights) and our goal is to find a maximum weight (as opposed to maximum cardinality) Pareto optimal matching. On the other hand we give a lower bound of 18 over 13 on the approximation ratio of any universally truthful Pareto optimal mechanism in settings with strict preferences. In the case that the mechanism must additionally be non-bossy, an improved lower bound of eovere-1 holds. This lower bound is tight given that RSDM for strict preference lists is non-bossy. We moreover interpret our problem in terms of the classical secretary problem and prove that our mechanism provides the best randomized strategy of the administrator who interviews the applicants

Studies in matter antimatter separation and in the origin of lunar magnetism

A progress report, covering lunar and planetary research is introduced. Data cover lunar ionospheric models, lunar and planetary geology, and lunar magnetism. Wind tunnel simulations of Mars aeolian problems and a comparative study of basaltic analogs of Lunar and Martial volcanic features was discussed

Experimental Signatures of Anomaly Induced DCC Formation

We discuss characteristic experimental signatures related to the formation of domains of disoriented chiral condensate (DCC) triggered by the axial anomaly in relativistic heavy ion collisions. We predict that the enhancement of the fraction of neutral pions compared to all pions depends on the angle of emission with respect to the scattering plane and is concentrated at small transverse momentum and small rapidity in the center-of-mass frame. The anisotropy with respect to the reaction plane is also observable in the inclusive photon distribution.Comment: 11 pages, 4 figures, REVTEX, discussion on photon distribution added, one figure adde

Electron Impact Excitation Cross Sections for Hydrogen-Like Ions

We present cross sections for electron-impact-induced transitions n --> n' in hydrogen-like ions C 5+, Ne 9+, Al 12+, and Ar 17+. The cross sections are computed by Coulomb-Born with exchange and normalization (CBE) method for all transitions with n < n' < 7 and by convergent close-coupling (CCC) method for transitions with n 2s and 1s --> 2p are presented as well. The CCC and CBE cross sections agree to better than 10% with each other and with earlier close-coupling results (available for transition 1 --> 2 only). Analytical expression for n --> n' cross sections and semiempirical formulae are discussed.Comment: RevTeX, 5 pages, 13 PostScript figures, submitted to Phys. Rev.

A tight lower bound instance for k-means++ in constant dimension

The k-means++ seeding algorithm is one of the most popular algorithms that is used for finding the initial $k$ centers when using the k-means heuristic. The algorithm is a simple sampling procedure and can be described as follows: Pick the first center randomly from the given points. For $i > 1$, pick a point to be the $i^{th}$ center with probability proportional to the square of the Euclidean distance of this point to the closest previously $(i-1)$ chosen centers. The k-means++ seeding algorithm is not only simple and fast but also gives an $O(\log{k})$ approximation in expectation as shown by Arthur and Vassilvitskii. There are datasets on which this seeding algorithm gives an approximation factor of $\Omega(\log{k})$ in expectation. However, it is not clear from these results if the algorithm achieves good approximation factor with reasonably high probability (say $1/poly(k)$). Brunsch and R\"{o}glin gave a dataset where the k-means++ seeding algorithm achieves an $O(\log{k})$ approximation ratio with probability that is exponentially small in $k$. However, this and all other known lower-bound examples are high dimensional. So, an open problem was to understand the behavior of the algorithm on low dimensional datasets. In this work, we give a simple two dimensional dataset on which the seeding algorithm achieves an $O(\log{k})$ approximation ratio with probability exponentially small in $k$. This solves open problems posed by Mahajan et al. and by Brunsch and R\"{o}glin.Comment: To appear in TAMC 2014. arXiv admin note: text overlap with arXiv:1306.420