The Milky Way Galaxy as a Strong Gravitational Lens

We study the gravitational lensing effects of spiral galaxies by taking a model of the Milky Way and computing its lensing properties. The model is composed of a spherical Hernquist bulge, a Miyamoto-Nagai disc and an isothermal halo. As a strong lens, a spiral galaxy like the Milky Way can give rise to four different imaging geometries. They are (i) three images on one side of the galaxy centre (`disc triplets'), (ii) three images with one close to the centre (`core triplets'), (iii) five images and (iv) seven images. Neglecting magnification bias, we show that the core triplets, disc triplets and fivefold imaging are roughly equally likely. Even though our models contain edge-on discs, their image multiplicities are not dominated by disc triplets. The halo has a small effect on the caustic structure, the time delays and brightnesses of the images. The Milky Way model has a maximum disc (i.e., the halo is not dynamically important in the inner parts). Strong lensing by nearly edge-on disc galaxies breaks the degeneracy between the relative contribution of the disc and halo to the overall rotation curve. If a spiral galaxy has a sub-maximum disc, then the astroid caustic shrinks dramatically in size, whilst the radial caustic shrinks more modestly. This causes changes in the relative likelihood of the image geometries, specifically (i) core triplets are now 9/2 times more likely than disc triplets, (ii) the cross section for threefold imaging is reduced by a factor of 2/3, whilst (iii) the cross section for fivefold imaging is reduced by 1/2. Although multiple imaging is less likely (the cross sections are smaller), the average total magnification is greater.Comment: MNRAS, in pres

Distributed Stochastic Optimization of the Regularized Risk

Many machine learning algorithms minimize a regularized risk, and stochastic optimization is widely used for this task. When working with massive data, it is desirable to perform stochastic optimization in parallel. Unfortunately, many existing stochastic optimization algorithms cannot be parallelized efficiently. In this paper we show that one can rewrite the regularized risk minimization problem as an equivalent saddle-point problem, and propose an efficient distributed stochastic optimization (DSO) algorithm. We prove the algorithm's rate of convergence; remarkably, our analysis shows that the algorithm scales almost linearly with the number of processors. We also verify with empirical evaluations that the proposed algorithm is competitive with other parallel, general purpose stochastic and batch optimization algorithms for regularized risk minimization

Sigma Model BPS Lumps on Torus

We study doubly periodic Bogomol'nyi-Prasad-Sommerfield (BPS) lumps in supersymmetric CP^{N-1} non-linear sigma models on a torus T^2. Following the philosophy of the Harrington-Shepard construction of calorons in Yang-Mills theory, we obtain the n-lump solutions on compact spaces by suitably arranging the n-lumps on R^2 at equal intervals. We examine the modular invariance of the solutions and find that there are no modular invariant solutions for n=1,2 in this construction.Comment: 15 pages, 3 figures, published versio

WordRank: Learning Word Embeddings via Robust Ranking

Embedding words in a vector space has gained a lot of attention in recent years. While state-of-the-art methods provide efficient computation of word similarities via a low-dimensional matrix embedding, their motivation is often left unclear. In this paper, we argue that word embedding can be naturally viewed as a ranking problem due to the ranking nature of the evaluation metrics. Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses. The performance of WordRank is measured in word similarity and word analogy benchmarks, and the results are compared to the state-of-the-art word embedding techniques. Our algorithm is very competitive to the state-of-the- arts on large corpora, while outperforms them by a significant margin when the training set is limited (i.e., sparse and noisy). With 17 million tokens, WordRank performs almost as well as existing methods using 7.2 billion tokens on a popular word similarity benchmark. Our multi-node distributed implementation of WordRank is publicly available for general usage.Comment: Conference on Empirical Methods in Natural Language Processing (EMNLP), November 1-5, 2016, Austin, Texas, US

Sine-Gordon Soliton on a Cnoidal Wave Background

The method of Darboux transformation, which is applied on cnoidal wave solutions of the sine-Gordon equation, gives solitons moving on a cnoidal wave background. Interesting characteristics of the solution, i.e., the velocity of solitons and the shift of crests of cnoidal waves along a soliton, are calculated. Solutions are classified into three types (Type-1A, Type-1B, Type-2) according to their apparent distinct properties.Comment: 11 pages, 5 figures, Contents change

Numerical simulation of super-square patterns in Faraday waves

We report the first simulations of the Faraday instability using the full three-dimensional Navier-Stokes equations in domains much larger than the characteristic wavelength of the pattern. We use a massively parallel code based on a hybrid Front-Tracking/Level-set algorithm for Lagrangian tracking of arbitrarily deformable phase interfaces. Simulations performed in rectangular and cylindrical domains yield complex patterns. In particular, a superlattice-like pattern similar to those of [Douady & Fauve, Europhys. Lett. 6, 221-226 (1988); Douady, J. Fluid Mech. 221, 383-409 (1990)] is observed. The pattern consists of the superposition of two square superlattices. We conjecture that such patterns are widespread if the square container is large compared to the critical wavelength. In the cylinder, pentagonal cells near the outer wall allow a square-wave pattern to be accommodated in the center

Dry Matter Production and Nutritive Value of Wild Alfalfa

Alfalfa grows wild in some parts of Korea, but specific information is lacking as to its agronomic characteristics, nutritive value and dry matter production potential. The objective of this study was to evaluate the usefulness of wild alfalfa (Medicago sativa L) as a forage. Wild alfalfa and Vernal were field sown at Keongsan, Keongbuk in the spring of 1995. Emergence for Vernal was better than for wild alfalfa. It was observed that the flowering date of the wild alfalfa was delayed by 8 days. Regrowth of Vernal was better than that of the wild alfalfa at each harvesting. After the last harvesting date, September 22, there was no regrowth of the wild alfalfa, but regrowth of Vernal measured 37cm. Weed infestation in the wild alfalfa plots was higher than in the Vernal plots. The dry matter yields per hectare were significantly(P\u3c0.05) higher for Vernal than for the wild alfalfa

Semiconducting-to-metallic photoconductivity crossover and temperature-dependent Drude weight in graphene

We investigated the transient photoconductivity of graphene at various gate-tuned carrier densities by optical-pump terahertz-probe spectroscopy. We demonstrated that graphene exhibits semiconducting positive photoconductivity near zero carrier density, which crosses over to metallic negative photoconductivity at high carrier density. Our observations are accounted for by considering the interplay between photo-induced changes of both the Drude weight and the carrier scattering rate. Notably, we observed multiple sign changes in the temporal photoconductivity dynamics at low carrier density. This behavior reflects the non-monotonic temperature dependence of the Drude weight, a unique property of massless Dirac fermions