Prototype selection for parameter estimation in complex models

Parameter estimation in astrophysics often requires the use of complex physical models. In this paper we study the problem of estimating the parameters that describe star formation history (SFH) in galaxies. Here, high-dimensional spectral data from galaxies are appropriately modeled as linear combinations of physical components, called simple stellar populations (SSPs), plus some nonlinear distortions. Theoretical data for each SSP is produced for a fixed parameter vector via computer modeling. Though the parameters that define each SSP are continuous, optimizing the signal model over a large set of SSPs on a fine parameter grid is computationally infeasible and inefficient. The goal of this study is to estimate the set of parameters that describes the SFH of each galaxy. These target parameters, such as the average ages and chemical compositions of the galaxy's stellar populations, are derived from the SSP parameters and the component weights in the signal model. Here, we introduce a principled approach of choosing a small basis of SSP prototypes for SFH parameter estimation. The basic idea is to quantize the vector space and effective support of the model components. In addition to greater computational efficiency, we achieve better estimates of the SFH target parameters. In simulations, our proposed quantization method obtains a substantial improvement in estimating the target parameters over the common method of employing a parameter grid. Sparse coding techniques are not appropriate for this problem without proper constraints, while constrained sparse coding methods perform poorly for parameter estimation because their objective is signal reconstruction, not estimation of the target parameters.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS500 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Report on OTHER proposals for SSPEX

The only unifying factor among the experiments discussed is that they are all unique Opportunities and/or Techniques for High-caliber Experimental Research (OTHER). Thirteen of the experiments are briefly described

Semi-supervised Learning for Photometric Supernova Classification

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods. Applied to supernova data simulated by Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95% Type Ia purity and 87% Type Ia efficiency on the spectroscopic sample, but only 50% Type Ia purity and 50% efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44% (1%) increase in purity to 72% (87%) and 30% (162%) increase in efficiency to 65% (84%) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5% improvement in Type Ia purity and 13% improvement in Type Ia efficiency.Comment: 16 pages, 11 figures, accepted for publication in MNRA

Bostonia. Volume 4

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Administration of a tropomyosin receptor kinase inhibitor attenuates sarcoma-induced nerve sprouting, neuroma formation and bone cancer pain

Pain often accompanies cancer and most current therapies for treating cancer pain have significant unwanted side effects. Targeting nerve growth factor (NGF) or its cognate receptor tropomyosin receptor kinase A (TrkA) has become an attractive target for attenuating chronic pain

A Novel Role for Wnt/Ca2+ Signaling in Actin Cytoskeleton Remodeling and Cell Motility in Prostate Cancer

Wnt signaling is a critical regulatory pathway in development and disease. Very little is known about the mechanisms of Wnt signaling in prostate cancer, a leading cause of death in men. A quantitative analysis of the expression of Wnt5A protein in human tissue arrays, containing 600 prostate tissue cores, showed >50% increase in malignant compared to benign cores (p<0.0001). In a matched pair of prostate cancer and normal cell line, expression of Wnt5A protein was also increased. Calcium waves were induced in prostate cells in response to Wnt5A with a 3 fold increase in Flou-4 intensity. The activity of Ca2+/calmodulin dependent protein kinase (CaMKII), a transducer of the non-canonical Wnt/Ca2+ signaling, increased by 8 fold in cancer cells; no change was observed in β-catenin expression, known to activate the canonical Wnt/β-catenin pathway. Mining of publicly available human prostate cancer oligoarray datasets revealed that the expression of numerous genes (e.g., CCND1, CD44) under the control of β-catenin transcription is down-regulated. Confocal and quantitative electron microscopy showed that specific inhibition of CaMKII in cancer cells causes remodeling of the actin cytoskeleton, irregular wound edges and loose intercellular architecture and a 6 and 8 fold increase in the frequency and length of filopodia, respectively. Conversely, untreated normal prostate cells showed an irregular wound edge and loose intercellular architecture; incubation of normal prostate cells with recombinant Wnt5A protein induced actin remodeling with a regular wound edge and increased wound healing capacity. Live cell imaging showed that a functional consequence of CaMKII inhibition was 80% decrease in wound healing capacity and reduced cell motility in cancer cells. We propose that non-canonical Wnt/Ca2+ signaling via CaMKII acts as a novel regulator of structural plasticity and cell motility in prostate cancer

CD36 Mediates the Innate Host Response to β-Amyloid

Accumulation of inflammatory microglia in Alzheimer's senile plaques is a hallmark of the innate response to β-amyloid fibrils and can initiate and propagate neurodegeneration characteristic of Alzheimer's disease (AD). The molecular mechanism whereby fibrillar β-amyloid activates the inflammatory response has not been elucidated. CD36, a class B scavenger receptor, is expressed on microglia in normal and AD brains and binds to β-amyloid fibrils in vitro. We report here that microglia and macrophages, isolated from CD36 null mice, had marked reductions in fibrillar β-amyloid–induced secretion of cytokines, chemokines, and reactive oxygen species. Intraperitoneal and stereotaxic intracerebral injection of fibrillar β-amyloid in CD36 null mice induced significantly less macrophage and microglial recruitment into the peritoneum and brain, respectively, than in wild-type mice. Our data reveal that CD36, a major pattern recognition receptor, mediates microglial and macrophage response to β-amyloid, and imply that CD36 plays a key role in the proinflammatory events associated with AD

Effect of an 860-m thick, cold, freshwater aquifer on geothermal potential along the axis of the eastern Snake River Plain, Idaho

A 1912-m exploration corehole was drilled along the axis of the eastern Snake River Plain, Idaho. Two temperature logs run on the corehole display an obvious inflection point at about 960 m. Such behavior is indicative of downward fluid flow in the wellbore. The geothermal gradient above 935 m is 4.5 °C/km, while the gradient is 72–75 °C/km from 980 to 1440 m. Projecting the higher gradients upward to where they intersect the lower gradient on the temperature logs places the bottom of the cold, freshwater Snake River Plain aquifer, which suppresses the geothermal gradient at this location, at least 860 m below the surface. The average heat flow for the corehole between 983 and 1550 m is 132 mW/m2. Although the maximum bottom-hole temperature extrapolated from a measured time–temperature curve was only 59.3 °C, geothermometers suggest an equilibrium temperature on the order of 125–140 °C based on a single fluid sample from 1070 m. Furthermore, below 960 m the basalt core shows obvious signs of alteration, including a distinct color change, the formation of smectite clay, and the presence of secondary minerals filling vesicles and fracture zones. This alteration boundary could act as an effective cap or seal for a hot-water geothermal system