5,455 research outputs found
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 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., ), 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 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
. 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
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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 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 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
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