Some Thoughts on Energy Conditions and Wormholes

This essay reviews some of the recent progress in the area of energy conditions and wormholes. Most of the discussion centers on the subject of ``quantum inequality'' restrictions on negative energy. These are bounds on the magnitude and duration of negative energy which put rather severe constraints on its possible macroscopic effects. Such effects might include the construction of wormholes and warp drives for faster-than-light travel, and violations of the second law of thermodynamics. Open problems and future directions are also discussed.Comment: 24 pages; to appear in the Proceedings of the Tenth Marcel Grossmann Meeting on General Relativity and Gravitatio

A New Galactic Wolf-Rayet Star in Centaurus

In this work I communicate the detection of a new Galactic Wolf-Rayet star (WR60a) in Centaurus. The H- and K-band spectra of WR60a, show strong carbon near-infrared emission lines, characteristic of Wolf-Rayet stars of the WC5-7 sub-type. Adopting mean absolute magnitude M$_K$ and mean intrinsic ($J-K_S$) and ($H-K_S$) colours, it was found that WR60a suffer a mean visual extinction of 3.8$\pm$1.3 magnitudes, being located at a probable heliocentric distance of 5.2$\pm$0.8 Kpc, which for the related Galactic longitude (l=312) puts this star probably in the Carina-Sagittarius arm at about 5.9 kpc from the Galactic center. I searched for clusters in the vicinity of WR60a, and in principle found no previously known clusters in a search radius region of several tens arc-minutes. The detection of a well isolated WR star induced us to seek for some still unknown cluster, somewhere in the vicinity of WR60a. From inspection of 5.8$\mu$m and 8.0$\mu$m Spitzer/IRAC GLIMPSE images of the region around the new WR star, it was found strong mid-infrared extended emission at about 13.5 arcmin south-west of WR60a. The study of the the H-K$_S$ colour distribution of point sources associated with the extended emission, reveals the presence of a new Galactic cluster candidate probably formed by at least 85 stars.Comment: 5 pages, 2 tables and 4 figures. Figure 4 is in low-resolution mode. The published on-line version of the paper can be obtained at http://www.isrn.com/journals/astro/2011/632850

Knowing How: A Computational Approach

With advances in Artificial Intelligences being achieved through the use of Artificial Neural Networks, we are now at the point where computers are able to do tasks that were previously only able to be accomplished by humans. These advancements must cause us to reconsider our previous understanding of how people come to know how to do a particular task. In order to unpack this question, I will first look to an account of knowing how presented by Jason Stanley in his book Know How. I will then look towards criticisms of this view before using evidence presented by the existence of Artificial Neural Networks to present a new view that addresses the problems present in Stanley’s work. Finally, I will argue that knowing how to do something is a matter of heuristics, or knowing certain shortcuts which approximate a solution to the task one is trying to accomplish

Does patience pay? : empirical testing of the option to delay accepting a tender offer in the U.S. banking sector

We examine the empirical predictions of a real option-pricing model using a large sample of data on mergers and acquisitions in the U.S. banking sector. We provide estimates for the option value that the target bank has in waiting for a higher bid instead of accepting an initial tender offer. We find empirical support for a model that estimates the value of an option to wait in accepting an initial tender offer. Market prices reflect a premium for the option to wait to accept an offer that has a mean value of almost 12.5% for a sample of 424 mergers and acquisitions between 1997 and 2005 in the U.S. banking industry. Regression analysis reveals that the option price is related to both the price to book market and the free cash flow of target banks. We conclude that it is certainly in the shareholders best interest if subsequent offers are awaited. JEL Classification: G34, C1

Interactive Music Generation with Positional Constraints using Anticipation-RNNs

Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for interactive and creative usages such as interactive music generation. This paper introduces a novel architecture called Anticipation-RNN which possesses the assets of the RNN-based generative models while allowing to enforce user-defined positional constraints. We demonstrate its efficiency on the task of generating melodies satisfying positional constraints in the style of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using the Anticipation-RNN is of the same order of complexity than sampling from the traditional RNN model. This fast and interactive generation of musical sequences opens ways to devise real-time systems that could be used for creative purposes.Comment: 9 pages, 7 figure