Atmospheric contaminant sensor. Book 2: Appendices

Appendices containing equipment specifications and performance test data of the atmospheric contaminant sensor for submarines are presented

Displaying 3D images: algorithms for single-image random-dot

A new, simple, and symmetric algorithm can be implemented that results in higher levels of detail in solid objects than previously possible with autostereograms. In a stereoscope, an optical instrument similar to binoculars, each eye views a different picture and thereby receives the specific image that would have arisen naturally. An early suggestion for a color stereo computer display involved a rotating filter wheel held in front of the eyes. In contrast, this article describes a method for viewing on paper or on an ordinary computer screen without special equipment, although it is limited to the display of 3D monochromatic objects. (The image can be colored, say, for artistic reasons, but the method we describe does not allow colors to be allocated in a way that corresponds to an arbitrary coloring of the solid object depicted.) The image can easily be constructed by computer from any 3D scene or solid object description

Effects of Differential Rotation on the Maximum Mass of Neutron Stars

The merger of binary neutron stars is likely to lead to differentially rotating remnants. In this paper we numerically construct models of differentially rotating neutron stars in general relativity and determine their maximum allowed mass. We model the stars adopting a polytropic equation of state and tabulate maximum allowed masses as a function of differential rotation and stiffness of the equation of state. We also provide a crude argument that yields a qualitative estimate of the effect of stiffness and differential rotation on the maximum allowed mass.Comment: 6 pages, to appear in Ap

Mapping functional traits: comparing abundance and presence-absence estimates at large spatial scales

Efforts to quantify the composition of biological communities increasingly focus on functional traits. The composition of communities in terms of traits can be summarized in several ways. Ecologists are beginning to map the geographic distribution of trait-based metrics from various sources of data, but the maps have not been tested against independent data. Using data for birds of the Western Hemisphere, we test for the first time the most commonly used method for mapping community trait composition – overlaying range maps, which assumes that the local abundance of a given species is unrelated to the traits in question – and three new methods that as well as the range maps include varying degrees of information about interspecific and geographic variation in abundance. For each method, and for four traits (body mass, generation length, migratory behaviour, diet) we calculated community-weighted mean of trait values, functional richness and functional divergence. The maps based on species ranges and limited abundance data were compared with independent data on community species composition from the American Christmas Bird Count (CBC) scheme coupled with data on traits. The correspondence with observed community composition at the CBC sites was mostly positive (62/73 correlations) but varied widely depending on the metric of community composition and method used (R2: 5.6×10−7 to 0.82, with a median of 0.12). Importantly, the commonly-used range-overlap method resulted in the best fit (21/22 correlations positive; R2: 0.004 to 0.8, with a median of 0.33). Given the paucity of data on the local abundance of species, overlaying range maps appears to be the best available method for estimating patterns of community composition, but the poor fit for some metrics suggests that local abundance data are urgently needed to allow more accurate estimates of the composition of communities

On the Maximum Mass of Differentially Rotating Neutron Stars

We construct relativistic equilibrium models of differentially rotating neutron stars and show that they can support significantly more mass than their nonrotating or uniformly rotating counterparts. We dynamically evolve such ``hypermassive'' models in full general relativity and show that there do exist configurations which are dynamically stable against radial collapse and bar formation. Our results suggest that the remnant of binary neutron star coalescence may be temporarily stabilized by differential rotation, leading to delayed collapse and a delayed gravitational wave burst.Comment: 4 pages, 2 figures, uses emulateapj.sty; to appear in ApJ Letter

Radiation of Angular Momentum by Neutrinos from Merged Binary Neutron Stars

We study neutrino emission from the remnant of an inspiraling binary neutron star following coalescence. The mass of the merged remnant is likely to exceed the stability limit of a cold, rotating neutron star. However, the angular momentum of the remnant may also approach or even exceed the Kerr limit, J/M^2 = 1, so that total collapse may not be possible unless some angular momentum is dissipated. We find that neutrino emission is very inefficient in decreasing the angular momentum of these merged objects and may even lead to a small increase in J/M^2. We illustrate these findings with a post-Newtonian, ellipsoidal model calculation. Simple arguments suggest that the remnant may form a bar mode instability on a timescale similar to or shorter than the neutrino emission timescale, in which case the evolution of the remnant will be dominated by the emission of gravitational waves.Comment: 12 pages AASTeX, 2 figures, to appear in Ap

Selection Of A Novel Aptamer Against Vitronectin Using Capillary Electrophoresis And Next Generation Sequencing

Breast cancer (BC) results in ≃40,000 deaths each year in the United States and even among survivors treatment of the disease may have devastating consequences, including increased risk for heart disease and cognitive impairment resulting from the toxic effects of chemotherapy. Aptamer-mediated drug delivery can contribute to improved treatment outcomes through the selective delivery of chemotherapy to BC cells, provided suitable cancer-specific antigens can be identified. We report here the use of capillary electrophoresis in conjunction with next generation sequencing to develop the first vitronectin (VN) binding aptamer (VBA-01; Kd 405 nmol/l, the first aptamer to vitronectin (VN; Kd = 405 nmol/l), a protein that plays an important role in wound healing and that is present at elevated levels in BC tissue and in the blood of BC patients relative to the corresponding nonmalignant tissues. We used VBA-01 to develop DVBA-01, a dimeric aptamer complex, and conjugated doxorubicin (Dox) to DVBA-01 (7:1 ratio) using pH-sensitive, covalent linkages. Dox conjugation enhanced the thermal stability of the complex (60.2 versus 46.5°C) and did not decrease affinity for the VN target. The resulting DVBA-01-Dox complex displayed increased cytotoxicity to MDA-MB-231 BC cells that were cultured on plasticware coated with VN (1.8 × 10⁻⁶mol/l) relative to uncoated plates (2.4 × 10⁻⁶ mol/l), or plates coated with the related protein fibronectin (2.1 × 10⁻⁶ mol/l). The VBA-01 aptamer was evaluated for binding to human BC tissue using immunohistochemistry and displayed tissue specific binding and apparent association with BC cells. In contrast, a monoclonal antibody that preferentially binds to multimeric VN primarily stained extracellular matrix and vessel walls of BC tissue. Our results indicate a strong potential for using VN-targeting aptamers to improve drug delivery to treat BC

InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure

Volumetric models have become a popular representation for 3D scenes in recent years. One breakthrough leading to their popularity was KinectFusion, which focuses on 3D reconstruction using RGB-D sensors. However, monocular SLAM has since also been tackled with very similar approaches. Representing the reconstruction volumetrically as a TSDF leads to most of the simplicity and efficiency that can be achieved with GPU implementations of these systems. However, this representation is memory-intensive and limits applicability to small-scale reconstructions. Several avenues have been explored to overcome this. With the aim of summarizing them and providing for a fast, flexible 3D reconstruction pipeline, we propose a new, unifying framework called InfiniTAM. The idea is that steps like camera tracking, scene representation and integration of new data can easily be replaced and adapted to the user's needs. This report describes the technical implementation details of InfiniTAM v3, the third version of our InfiniTAM system. We have added various new features, as well as making numerous enhancements to the low-level code that significantly improve our camera tracking performance. The new features that we expect to be of most interest are (i) a robust camera tracking module; (ii) an implementation of Glocker et al.'s keyframe-based random ferns camera relocaliser; (iii) a novel approach to globally-consistent TSDF-based reconstruction, based on dividing the scene into rigid submaps and optimising the relative poses between them; and (iv) an implementation of Keller et al.'s surfel-based reconstruction approach.Comment: This article largely supersedes arxiv:1410.0925 (it describes version 3 of the InfiniTAM framework

Intelligent opinion mining and sentiment analysis using artificial neural networks

The article formulates a rigorously developed concept of opinion mining and sentiment analysis using hybrid neural networks. This conceptual method for processing natural-language text enables a variety of analyses of the subjective content of texts. It is a methodology based on hybrid neural networks for detecting subjective content and potential opinions, as well as a method which allows us to classify different opinion type and sentiment score classes. Moreover, a general processing scheme, using neural networks, for sentiment and opinion analysis has been presented. Furthermore, a methodology which allows us to determine sentiment regression has been devised. The paper proposes a method for classification of the text being examined based on the amount of positive, neutral or negative opinion it contains. The research presented here offers the possibility of motivating and inspiring further development of the methods that have been elaborated in this paper.Stuart, KDC.; Majewski, M. (2015). Intelligent opinion mining and sentiment analysis using artificial neural networks. Lecture Notes in Computer Science. 9492:103-110. doi:10.1007/978-3-319-26561-2_13S1031109492Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)Chen, H., Zimbra, D.: AI and opinion mining. IEEE Intell. Syst. 25(3), 74–80 (2010)Majewski, M., Zurada, J.M.: Sentence recognition using artificial neural networks. Knowl. Based Syst. 21(7), 629–635 (2008)Kacalak, W., Stuart, K.D., Majewski, M.: Intelligent natural language processing. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 584–587. Springer, Heidelberg (2006)Kacalak, W., Stuart, K., Majewski, M.: Selected problems of intelligent handwriting recognition. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. Advances in Soft Computing, vol. 41, pp. 298–305. Springer, Cancun (2007)Stuart, K.D., Majewski, M.: Selected problems of knowledge discovery using artificial neural networks. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007, Part III. LNCS, vol. 4493, pp. 1049–1057. Springer, Heidelberg (2007)Stuart, K., Majewski, M.: A new method for intelligent knowledge discovery. In: Castillo, O., Melin, P., Ross, O.M., Cruz, R.S., Pedrycz, W., Kacprzyk, J. (eds.) IFSA 2007. Advances in Soft Computing, vol. 42, pp. 721–729. Springer, Heidelberg (2007)Stuart, K.D., Majewski, M.: Artificial creativity in linguistics using evolvable fuzzy neural networks. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 437–442. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M.: Evolvable neuro-fuzzy system for artificial creativity in linguistics. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 46–53. Springer, Heidelberg (2008)Stuart, K.D., Majewski, M., Trelis, A.B.: Selected problems of intelligent corpus analysis through probabilistic neural networks. In: Zhang, L., Lu, B.-L., Kwok, J. (eds.) ISNN 2010, Part II. LNCS, vol. 6064, pp. 268–275. Springer, Heidelberg (2010)Stuart, K.D., Majewski, M., Trelis, A.B.: Intelligent semantic-based system for corpus analysis through hybrid probabilistic neural networks. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011, Part I. LNCS, vol. 6675, pp. 83–92. Springer, Heidelberg (2011)Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109–118 (1990)Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991