Searching the Sky with CONFIGR-STARS

SyNAPSE program of the Defense Advanced Projects Research Agency (HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, a National Science Foundation Science of Learning Center (SBE-0354378)CONFIGR-STARS, a new methodology based on a model of the human visual system, is developed for registration of star images. The algorithm first applies CONFIGR, a neural model that connects sparse and noisy image components. CONFIGR produces a web of connections between stars in a reference starmap or in a test patch of unknown location. CONFIGR-STARS splits the resulting, typically highly connected, web into clusters, or "constellations." Cluster geometry is encoded as a signature vector that records edge lengths and angles relative to the cluster’s baseline edge. The location of a test patch cluster is identified by comparing its signature to signatures in the codebook of a reference starmap, where cluster locations are known. Simulations demonstrate robust performance in spite of image perturbations and omissions, and across starmaps from different sources and seasons. Further studies would test CONFIGR-STARS and algorithm variations applied to very large starmaps and to other technologies that may employ geometric signatures. Open-source code, data, and demos are available from http://techlab.bu.edu/STARS/

Speaker Normalization Methods for Vowel Cognition: Comparative Analysis Using Neural Network and Nearest Neighbor Classifiers

Intrinsic and extrinsic speaker normalization methods are systematically compared using a neural network (fuzzy ARTMAP) and L1 and L2 K-Nearest Neighbor (K-NN) categorizers trained and tested on disjoint sets of speakers of the Peterson-Barney vowel database. Intrinsic methods include one nonscaled, four psychophysical scales (bark, bark with endcorrection, mel, ERB), and three log scales, each tested on four combinations of F0 , F1, F2, F3. Extrinsic methods include four speaker adaptation schemes, each combined with the 32 intrinsic methods: centroid subtraction across all frequencies (CS), centroid subtraction for each frequency (CSi), linear scale (LS), and linear transformation (LT). ARTMAP and KNN show similar trends, with K-NN performing better, but requiring about ten times as much memory. The optimal intrinsic normalization method is bark scale, or bark with endcorrection, using the differences between all frequencies (Diff All). The order of performance for the extrinsic methods is LT, CSi, LS, and CS, with fuzzy ARTMAP performing best using bark scale with Diff All; and K-NN choosing psychophysical measures for all except CSi.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (AFOSR-90-0083, ONR-N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225

Results of the 2007-2008 Illinois Hunter Harvest Survey

Grant/Contract No: W-112-R-17A random sample of 3,000 potential hunters was selected from among 2007 Illinois Habitat Stamp and hunting license purchasers. An 8-page questionnaire was successfully mailed to 2,906 of the 3,000 individuals. We received 1,625 returned questionnaires, 1,620 of which were usable, for a 56% response rate. Comparisons of harvest estimates from the 2006-2007 season to the 2007-2008 season suggest harvest decreased for 10 game animal categories (rabbit, quail, pheasant, dove, gray partridge, fox squirrel, gray squirrel, red fox, gray fox, and opossum), and increased for 3 categories (woodcock, raccoon, and coyote). License sales increased 0.4% from 282,000 sold for the 2006 season to 283,000 sold in the 2007 season. This represents a recovery in license sales of less than 1% since the lowest level on record (since 1938) in 2005. Low license sales may be related to increasing difficulty in finding land to hunt. Resident hunters pursuing deer (61%) and small game species (51%) reported little difficulty in gaining access to hunting lands. However, all other seasons were associated with more difficulty in accessing land. Efforts should be undertaken to ensure that hunters are aware of the locations of public hunting lands associated with these seasons. Few residents reported leasing land (4%) or hiring outfitters (1%) to access land.INHS Technical Report Prepared for Illinois Department of Natural Resources; Illinois Natural History Surve

The how and why of lncRNA function: An innate immune perspective.

Next-generation sequencing has provided a more complete picture of the composition of the human transcriptome indicating that much of the "blueprint" is a vastness of poorly understood non-protein-coding transcripts. This includes a newly identified class of genes called long noncoding RNAs (lncRNAs). The lack of sequence conservation for lncRNAs across species meant that their biological importance was initially met with some skepticism. LncRNAs mediate their functions through interactions with proteins, RNA, DNA, or a combination of these. Their functions can often be dictated by their localization, sequence, and/or secondary structure. Here we provide a review of the approaches typically adopted to study the complexity of these genes with an emphasis on recent discoveries within the innate immune field. Finally, we discuss the challenges, as well as the emergence of new technologies that will continue to move this field forward and provide greater insight into the biological importance of this class of genes. This article is part of a Special Issue entitled: ncRNA in control of gene expression edited by Kotb Abdelmohsen

From extinction learning to anxiety treatment: mind the gap

Laboratory models of extinction learning in animals and humans have the potential to illuminate methods for improving clinical treatment of fear-based clinical disorders. However, such translational research often neglects important differences between threat responses in animals and fear learning in humans, particularly as it relates to the treatment of clinical disorders. Specifically, the conscious experience of fear and anxiety, along with the capacity to deliberately engage top-down cognitive processes to modulate that experience, involves distinct brain circuitry and is measured and manipulated using different methods than typically used in laboratory research. This paper will identify how translational research that investigates methods of enhancing extinction learning can more effectively model such elements of human fear learning, and how doing so will enhance the relevance of this research to the treatment of fear-based psychological disorders.Published versio

Injection of clarity needed?

The legal status of children who stay in hospital for three months or longer gives rise to considerable confusion among managers in social services and social work departments. And the number of young people affected is significant. NHS statistics for the year ending 31 March 2000 suggest that in England around 2,800 children aged 0-19 on admission were discharged after spending more than two months in hospital, as were more than 500 children in Scotland. (A small number of these would have been discharged as adults.) A two-year study, commissioned by the Joseph Rowntree Foundation1 and carried out by the universities of Stirling, Durham, Newcastle and York, investigated the numbers, characteristics and circumstances of children and young people with complex needs who spend long periods in health care settings. Interviews were conducted in England and Scotland with 11 social services or health managers responsible for these children. The findings show a worrying degree of uncertainty about the position of young people who find themselves in a hospital or other health care setting for at least three months. One social services manager believed such children become looked after under the terms of the Children Act 1989. Another said children are not formally looked after but nevertheless receive the same services and safeguards as those who are. One Scottish social work manager did not know whether children going into health care settings for short-term (respite) care are looked after or not. And discussion with the research team's advisory group indicated that the confusion is not confined to our fieldwork areas

Notes on the early-type components of W Cep, o Cet, CH Cyg, AR Mon, and BL Tel

Low resolution IUE spectra in both spectral regions are used to clarify the nature of the warmer components of several binary systems. The W Cep, the primary of which is a luminous K-type supergiant, shows an ultraviolet absorption spectrum of type B0 or B1; this system is heavily reddened. The hot companion of Mira (o Cet) is surprisingly faint in the short wavelength region, but it excites a rich emission spectrum from the surrounding gas. The ultraviolet active M7 giant CH Cyg is shown to be a binary with a hot companion. This system was also observed at high resolution and shows variable Fe II emission and well-separated circumstellar and interstellar absorptions within the broad Mg II emission profiles. The eclipsing binaries AR Mon and BL Tel are shown not to have hot companions

Interpersonal Emotion Regulation Questionnaire (IERQ): scale development and psychometric characteristics

Despite the popularity of emotion regulation in the contemporary literature, research has almost exclusively focused on only intrapersonal processes, whereas much less attention has been placed in interpersonal emotion regulation processes. In order to encourage research on interpersonal emotion regulation, we present a series of 4 studies to develop the Interpersonal Emotion Regulation Questionnaire (IERQ). The final scale consists of 20 items with 4 factors containing 5 items each. The 4 factors are: Enhancing Positive Affect; Perspective Taking; Soothing; and Social Modeling. The scale shows excellent psychometric characteristics. Implications for future research are discussed.R01 MH078308 - NIMH NIH HHS; R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH