Transportability, distributability and rehosting experience with a kernel operating system interface set

For the past two years, PRC has been transporting and installing a software engineering environment framework, the Automated Product control Environment (APCE), at a number of PRC and government sites on a variety of different hardware. The APCE was designed using a layered architecture which is based on a standardized set of interfaces to host system services. This interface set called the APCE Interface Set (AIS), was designed to support many of the same goals as the Common Ada Programming Support Environment (APSE) Interface Set (CAIS). The APCE was developed to provide support for the full software lifecycle. Specific requirements of the APCE design included: automation of labor intensive administrative and logistical tasks: freedom for project team members to use existing tools: maximum transportability for APCE programs, interoperability of APCE database data, and distributability of both processes and data: and maximum performance on a wide variety of operating systems. A brief description is given of the APCE and AIS, a comparison of the AIS and CAIS both in terms of functionality and of philosophy and approach and a presentation of PRC's experience in rehosting AIS and transporting APCE programs and project data. Conclusions are drawn from this experience with respect to both the CAIS efforts and Space Station plans

The localization sequence for the algebraic K-theory of topological K-theory

We prove a conjecture of Rognes by establishing a localization cofiber sequence of spectra, K(Z) to K(ku) to K(KU) to Sigma K(Z), for the algebraic K-theory of topological K-theory. We deduce the existence of this sequence as a consequence of a devissage theorem identifying the K-theory of the Waldhausen category of Postnikov towers of modules over a connective A-infinity ring spectrum R with the Quillen K-theory of the abelian category of finitely generated pi_0(R)-modules.Comment: Updated final version. Small change in definition of S' construction and correction to the proof of 2.

Evolution of Magnetic and Superconducting Fluctuations with Doping of High-Tc Superconductors

Electronic Raman scattering from high- and low-energy excitations was studied as a function of temperature, extent of hole doping, and energy of the incident photons in Bi_2Sr_2CaCu_2O_{8 \pm \delta} superconductors. For underdoped superconductors, short range antiferromagnetic (AF) correlations were found to persist with hole doping, and doped single holes were found to be incoherent in the AF environment. Above the superconducting (SC) transition temperature T_c, the system exhibits a sharp Raman resonance of B_{1g} symmetry and energy of 75 meV and a pseudogap for electron-hole excitations below 75 meV, a manifestation of a partially coherent state forming from doped incoherent quasi particles. The occupancy of the coherent state increases with cooling until phase ordering at T_c produces a global SC state.Comment: 6 pages, 4 color figures, PDF forma

1031 Tenant in Common Exchanges: A Tic king Time Bomb at the Intersection of Real Estate, Securities, and Tax Law?

Those who follow economic trends know that investing in real estate has recently become a hotbed of activity. In response to the stock market\u27s unpredictability, investors have been drawn to the commercial real estate market in record numbers, seeking to capitalize on low interest rates coupled with the rising appreciation such properties have offered. In addition to the potential upside of such investments, many commercial property investors seek a tax deferral method for the capital gains they realized upon the sale of other previously owned property. Section 1031 of the Internal Revenue Code ( Section 1031 ), under specially defined circumstances, allows for deferral of the tax liability that would otherwise be imposed as a result of real property capital gains.3 For a variety of reasons which will be discussed, greater numbers of investors are choosing to take advantage of Section 1031\u27s tax liability deferral by purchasing fractional interests in commercial real property through 1031 Tenant-in-Common (\u27TIC\u27) exchanges. The 1031 TIC exchange is a relatively new investment vehicle that raises a number of novel legal issues. Primarily, whether such an arrangement should be considered a security under federal tax, federal securities, and state securities laws. The provisions of Section 1031 specifically exclude exchanges involving stocks, bonds, or notes 6 as well as other securities. Therefore, if a 1031 TIC exchange is deemed to be a security, it is questionable whether the arrangement would then meet the requirements of Section 1031 and entitle the investor to a tax deferral benefit. The Internal Revenue Service is aware of the issue of whether, or under what circumstances, a TIC may constitute a security that may not be exchanged under Section 1031, and is watching how matters develop as the TIC concept evolves in the Section 1031 context. This article will explore the burgeoning 1031 TIC industry, discuss the nuances of 1031 TIC exchanges, and provide an analysis of whether such transactions are in fact, securities, and if so, whether that status poses a problem for the taxpayer seeking the advantages of Section 1031

Concentration and Length Dependence of DNA Looping in Transcriptional Regulation

In many cases, transcriptional regulation involves the binding of transcription factors at sites on the DNA that are not immediately adjacent to the promoter of interest. This action at a distance is often mediated by the formation of DNA loops: Binding at two or more sites on the DNA results in the formation of a loop, which can bring the transcription factor into the immediate neighborhood of the relevant promoter. These processes are important in settings ranging from the historic bacterial examples (bacterial metabolism and the lytic-lysogeny decision in bacteriophage), to the modern concept of gene regulation to regulatory processes central to pattern formation during development of multicellular organisms. Though there have been a variety of insights into the combinatorial aspects of transcriptional control, the mechanism of DNA looping as an agent of combinatorial control in both prokaryotes and eukaryotes remains unclear. We use single-molecule techniques to dissect DNA looping in the lac operon. In particular, we measure the propensity for DNA looping by the Lac repressor as a function of the concentration of repressor protein and as a function of the distance between repressor binding sites. As with earlier single-molecule studies, we find (at least) two distinct looped states and demonstrate that the presence of these two states depends both upon the concentration of repressor protein and the distance between the two repressor binding sites. We find that loops form even at interoperator spacings considerably shorter than the DNA persistence length, without the intervention of any other proteins to prebend the DNA. The concentration measurements also permit us to use a simple statistical mechanical model of DNA loop formation to determine the free energy of DNA looping, or equivalently, the J-factor for looping

The electronic specific heat in the pairing pseudogap regime

When pairing correlations in a quasi two dimensional electron system induce a pseudogap in the single particle density of states, the specific heat must also contain a sizeable pair contribution. The theoretically calculated specific heat for such a system is compared to the experimental results of Loram and his collaborators for underdoped YBa_2Cu_3O_{6+x} and La_{2-x}Sr_{x}CuO_4 samples. The size and doping dependence of the extracted pseudogap energy scale for both materials is comparable to the values obtained from a variety of other experiments.Comment: 4 pages, 5 eps figure

Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning. We exploit memory-efficient backpropagation techniques, to reduce the memory complexity of network training from being linear in the network's depth, to being roughly constant $-$ permitting us to elongate deep architectures with negligible memory increase. We evaluate our methodology in the paradigm of Image Quality Transfer, whilst noting its potential application to various tasks that use deep learning. We study the impact of depth on accuracy and show that deeper models have more predictive power, which may exploit larger training sets. We obtain substantially better results than the previous state-of-the-art model with a slight memory increase, reducing the root-mean-squared-error by $13\%$. Our code is publicly available.Comment: Accepted in: MICCAI 201