A new method of observing weak extended x-ray sources with RHESSI

We present a new method, fan-beam modulation, for observing weak extended x-ray sources with the Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI). This space-based solar x-ray and gamma-ray telescope has much greater sensitivity than previous experiments in the 3-25 keV range, but is normally not well suited to detecting extended sources since their signal is not modulated by RHESSI's rotating grids. When the spacecraft is offpointed from the target source, however, the fan-beam modulation time-modulates the transmission by shadowing resulting from exploiting the finite thickness of the grids. In this paper we detail how the technique is implemented and verify its consistency with sources with clear known signals that have occurred during RHESSI offpointing: microflares and the Crab Nebula. In both cases the results are consistent with previous and complementary measurements. Preliminary work indicates that this new technique allows RHESSI to observe the integrated hard x-ray spectrum of weak extended sources on the quiet Sun.Comment: Publishe

Precision Power and Its Application to the Selection of Regression Sample Sizes

Because of contradictions among the various methods, sample size selection in multiple regression has been problematic. For example, how does one reconcile the difference between a 15: 1 subject-to-variable rule and a 30: 1 rule? The purpose of this paper is to analyze the advantages and disadvantages of the various methods of selecting sample sizes in regression. A discussion of the importance of cross-validity to prediction studies will be followed by descriptions of the three categories of sample size methods: cross-validation approaches, rules-of-thumb, and statistical power methods. A rationale will then be developed for the application of precision power to multiple regression, leading to the presentation, through multiple examples, of the precision power method for sample size selection in prediction studies

The classical capacity of quantum thermal noise channels to within 1.45 bits

We find a tight upper bound for the classical capacity of quantum thermal noise channels that is within $1/\ln 2$ bits of Holevo's lower bound. This lower bound is achievable using unentangled, classical signal states, namely displaced coherent states. Thus, we find that while quantum tricks might offer benefits, when it comes to classical communication they can only help a bit.Comment: Two pages plus a bi

Clustering Analyses of 300,000 Photometrically Classified Quasars--II. The Excess on Very Small Scales

We study quasar clustering on small scales, modeling clustering amplitudes using halo-driven dark matter descriptions. From 91 pairs on scales <35 kpc/h, we detect only a slight excess in quasar clustering over our best-fit large-scale model. Integrated across all redshifts, the implied quasar bias is b_Q = 4.21+/-0.98 (b_Q = 3.93+/-0.71) at ~18 kpc/h (~28 kpc/h). Our best-fit (real-space) power index is ~-2 (i.e., $\xi(r) \propto r^{-2}$), implying steeper halo profiles than currently found in simulations. Alternatively, quasar binaries with separation <35 kpc/h may trace merging galaxies, with typical dynamical merger times t_d~(610+/-260)m^{-1/2} Myr/h, for quasars of host halo mass m x 10^{12} Msolar/h. We find UVX quasars at ~28 kpc/h cluster >5 times higher at z > 2, than at z < 2, at the $2.0\sigma$ level. However, as the space density of quasars declines as z increases, an excess of quasar binaries (over expectation) at z > 2 could be consistent with reduced merger rates at z > 2 for the galaxies forming UVX quasars. Comparing our clustering at ~28 kpc/h to a \xi(r)=(r/4.8\Mpch)^{-1.53} power-law, we find an upper limit on any excess of a factor of 4.3+/-1.3, which, noting some caveats, differs from large excesses recently measured for binary quasars, at $2.2\sigma$. We speculate that binary quasar surveys that are biased to z > 2 may find inflated clustering excesses when compared to models fit at z < 2. We provide details of 111 photometrically classified quasar pairs with separations <0.1'. Spectroscopy of these pairs could significantly constrain quasar dynamics in merging galaxies.Comment: 12pages, 3 figures, 2 tables; uses amulateapj; accepted to Ap

Dynamic expressivity with static optimization for streaming languages

Developers increasingly use streaming languages to write applications that process large volumes of data with high throughput. Unfortunately, when picking which streaming language to use, they face a difficult choice. On the one hand, dynamically scheduled languages allow developers to write a wider range of applications, but cannot take advantage of many crucial optimizations. On the other hand, statically scheduled languages are extremely performant, but have difficulty expressing many important streaming applications. This paper presents the design of a hybrid scheduler for stream processing languages. The compiler partitions the streaming application into coarse-grained subgraphs separated by dynamic rate boundaries. It then applies static optimizations to those subgraphs. We have implemented this scheduler as an extension to the StreamIt compiler. To evaluate its performance, we compare it to three scheduling techniques used by dynamic systems (OS thread, demand, and no-op) on a combination of micro-benchmarks and real-world inspired synthetic benchmarks. Our scheduler not only allows the previously static version of StreamIt to run dynamic rate applications, but it outperforms the three dynamic alternatives. This demonstrates that our scheduler strikes the right balance between expressivity and performance for stream processing languages.National Science Foundation (U.S.) (CCF-1162444

Antagonism between Notch and bone morphogenetic protein receptor signaling regulates neurogenesis in the cerebellar rhombic lip

BACKGROUND: During the embryonic development of the cerebellum, neurons are produced from progenitor cells located along a ventricular zone within dorsal rhombomere 1 that extends caudally to the roof plate of the fourth ventricle. The apposition of the caudal neuroepithelium and roof plate results in a unique inductive region termed the cerebellar rhombic lip, which gives rise to granule cell precursors and other glutamatergic neuronal lineages. Recently, we and others have shown that, at early embryonic stages prior to the emergence of granule cell precursors (E12), waves of neurogenesis in the cerebellar rhombic lip produce specific hindbrain nuclei followed by deep cerebellar neurons. How the induction of rhombic lip-derived neurons from cerebellar progenitors is regulated during this phase of cerebellar development to produce these temporally discrete neuronal populations while maintaining a progenitor pool for subsequent neurogenesis is not known. RESULTS: Employing both gain- and loss-of-function methods, we find that Notch1 signaling in the cerebellar primordium regulates the responsiveness of progenitor cells to bone morphogenetic proteins (BMPs) secreted from the roof plate that stimulate the production of rhombic lip-derived neurons. In the absence of Notch1, cerebellar progenitors are depleted during the early production of hindbrain neurons, resulting in a severe decrease in the deep cerebellar nuclei that are normally born subsequently. Mechanistically, we demonstrate that Notch1 activity prevents the induction of Math1 by antagonizing the BMP receptor-signaling pathway at the level of Msx2 expression. CONCLUSION: Our results provide a mechanism by which a balance between neural induction and maintenance of neural progenitors is achieved in the rhombic lip throughout embryonic development

Burn wound classification model using spatial frequency-domain imaging and machine learning.

Accurate assessment of burn severity is critical for wound care and the course of treatment. Delays in classification translate to delays in burn management, increasing the risk of scarring and infection. To this end, numerous imaging techniques have been used to examine tissue properties to infer burn severity. Spatial frequency-domain imaging (SFDI) has also been used to characterize burns based on the relationships between histologic observations and changes in tissue properties. Recently, machine learning has been used to classify burns by combining optical features from multispectral or hyperspectral imaging. Rather than employ models of light propagation to deduce tissue optical properties, we investigated the feasibility of using SFDI reflectance data at multiple spatial frequencies, with a support vector machine (SVM) classifier, to predict severity in a porcine model of graded burns. Calibrated reflectance images were collected using SFDI at eight wavelengths (471 to 851&nbsp;nm) and five spatial frequencies (0 to 0.2  mm  -  1). Three models were built from subsets of this initial dataset. The first subset included data taken at all wavelengths with the planar (0  mm  -  1) spatial frequency, the second comprised data at all wavelengths and spatial frequencies, and the third used all collected data at values relative to unburned tissue. These data subsets were used to train and test cubic SVM models, and compared against burn status 28 days after injury. Model accuracy was established through leave-one-out cross-validation testing. The model based on images obtained at all wavelengths and spatial frequencies predicted burn severity at 24&nbsp;h with 92.5% accuracy. The model composed of all values relative to unburned skin was 94.4% accurate. By comparison, the model that employed only planar illumination was 88.8% accurate. This investigation suggests that the combination of SFDI with machine learning has potential for accurately predicting burn severity