Using a GIS for Real Estate Market Analysis: The Problem of Spatially Aggregated Data

Many databases used for real estate market analysis are not available at the address level. For example, information on employment and unemployment may be available only for labor market areas; and Census data is typically tabulated for blocks or higher levels of spatial aggregation. A Geographic Information System (GIS) associates these spatially aggregated data with the geographical center of the area. This poses special problems when we use a GIS to evaluate linkages between supply and demand. This article presents some solutions to this problem; methods that are relatively easy to implement on a GIS are emphasized. A GIS can be used to calculate a theoretical average travel distance to the population in the geographical area. We propose ways to determine when these theoretical distances are inadequate approximations; and we provide alternatives for these situations.

Tracing the Peculiar Dark Matter Structure in the Galaxy Cluster CL 0024+17 with Intracluster Stars and Gas

ICL is believed to originate from the stars stripped from cluster galaxies. They are no longer gravitationally bound to individual galaxies, but to the cluster, and their smooth distribution potentially makes them serve as much denser tracers of the cluster dark matter than the sparsely distributed cluster galaxies. We present our study of the ICL in Cl 0024+17 using both ACS and Subaru data, where we previously reported discovery of a ringlike dark matter structure with gravitational lensing. The ACS images provide much lower sky levels than ground data, and enable us to measure relative variation of surface brightness reliably. This analysis is repeated with the Subaru images to examine if consistent features are recovered despite different reduction scheme and instrumental characteristics. We find that the ICL profile clearly resembles the peculiar mass profile, which stops decreasing at r~50" (~265 kpc) and slowly increases until it turns over at r~75" (~397 kpc). This feature is seen in both ACS and Subaru images for nearly all available passband images while the features are stronger in red filters. The consistency across different filters and instruments strongly rules out the possibility that the feature might come from any residual, uncorrected calibration errors. In addition, our re-analysis of the cluster X-ray data shows that the peculiar mass structure is also indicated by a non-negligible bump in the intracluster gas profile when the geometric center of the dark matter ring, not the peak of the X-ray emission, is chosen as the center of the radial bin. The location of the gas ring is closer to the center by ~15" (~80 kpc), raising an interesting possibility that the ring-like structure is expanding and the gas ring is lagging behind perhaps because of the ram pressure if both features in mass and gas share the same dynamical origin.Comment: Accepted to ApJ for publicatio

Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust

Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.Comment: J. Y. Shin, N. Tajbakhsh, R. T. Hurst, C. B. Kendall, and J. Liang. Automating carotid intima-media thickness video interpretation with convolutional neural networks. CVPR 2016, pp 2526-2535; N. Tajbakhsh, J. Y. Shin, R. T. Hurst, C. B. Kendall, and J. Liang. Automatic interpretation of CIMT videos using convolutional neural networks. Deep Learning for Medical Image Analysis, Academic Press, 201

Metrology Camera System of Prime Focus Spectrograph for Subaru Telescope

The Prime Focus Spectrograph (PFS) is a new optical/near-infrared multi-fiber spectrograph designed for the prime focus of the 8.2m Subaru telescope. The metrology camera system of PFS serves as the optical encoder of the COBRA fiber motors for the configuring of fibers. The 380mm diameter aperture metrology camera will locate at the Cassegrain focus of Subaru telescope to cover the whole focal plane with one 50M pixel Canon CMOS sensor. The metrology camera is designed to provide the fiber position information within 5{\mu}m error over the 45cm focal plane. The positions of all fibers can be obtained within 1s after the exposure is finished. This enables the overall fiber configuration to be less than 2 minutes.Comment: 10 pages, 12 figures, SPIE Astronomical Telescopes and Instrumentation 201

High-capacity preconscious processing in concurrent groupings of colored dots.

Grouping is a perceptual process in which a subset of stimulus components (a group) is selected for a subsequent-typically implicit-perceptual computation. Grouping is a critical precursor to segmenting objects from the background and ultimately to object recognition. Here, we study grouping by color. We present subjects with 300-ms exposures of 12 dots colored with the same but unknown identical color interspersed among 14 dots of seven different colors. To indicate grouping, subjects point-click the remembered centroid ("center of gravity") of the set of homogeneous dots, of heterogeneous dots, or of all dots. Subjects accurately judge all of these centroids. Furthermore, after a single stimulus exposure, subjects can judge both the heterogeneous and homogeneous centroids, that is, subjects simultaneously group by similarity and by dissimilarity. The centroid paradigm reveals the relative weight of each dot among targets and distractors to the underlying grouping process, offering a more detailed, quantitative description of grouping than was previously possible. A change detection experiment reveals that conscious memory contains less than two dots and their locations, whereas an ideal detector would have to perfectly process at least 15 of 26 dots to match the subjects' centroid judgments-indicating an extraordinary capacity for preconscious grouping. A different color set yielded identical results. Grouping theories that rely on predefined feature maps would fail to explain these results. Rather, the results indicate that preconscious grouping is automatic, flexible, and rapid, and a far more complex process than previously believed