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16 results on '"Ss P"'

1. Enhancing Security Control Production With Generative AI

Author

Ling, Chen, Ghashami, Mina, Gao, Vianne, Torkamani, Ali, Vaulin, Ruslan, Mangam, Nivedita, Jain, Bhavya, Diwan, Farhan, SS, Malini, Cheng, Mingrui, Kumar, Shreya Tarur, and Candelario, Felix

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Security controls are mechanisms or policies designed for cloud based services to reduce risk, protect information, and ensure compliance with security regulations. The development of security controls is traditionally a labor-intensive and time-consuming process. This paper explores the use of Generative AI to accelerate the generation of security controls. We specifically focus on generating Gherkin codes which are the domain-specific language used to define the behavior of security controls in a structured and understandable format. By leveraging large language models and in-context learning, we propose a structured framework that reduces the time required for developing security controls from 2-3 days to less than one minute. Our approach integrates detailed task descriptions, step-by-step instructions, and retrieval-augmented generation to enhance the accuracy and efficiency of the generated Gherkin code. Initial evaluations on AWS cloud services demonstrate promising results, indicating that GenAI can effectively streamline the security control development process, thus providing a robust and dynamic safeguard for cloud-based infrastructures.

Published
2024

2. Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation

Author

SS, Sidharth, AR, Keerthana, R, Gokul, and KP, Anas

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Accurate approximation of complex nonlinear functions is a fundamental challenge across many scientific and engineering domains. Traditional neural network architectures, such as Multi-Layer Perceptrons (MLPs), often struggle to efficiently capture intricate patterns and irregularities present in high-dimensional functions. This paper presents the Chebyshev Kolmogorov-Arnold Network (Chebyshev KAN), a new neural network architecture inspired by the Kolmogorov-Arnold representation theorem, incorporating the powerful approximation capabilities of Chebyshev polynomials. By utilizing learnable functions parametrized by Chebyshev polynomials on the network's edges, Chebyshev KANs enhance flexibility, efficiency, and interpretability in function approximation tasks. We demonstrate the efficacy of Chebyshev KANs through experiments on digit classification, synthetic function approximation, and fractal function generation, highlighting their superiority over traditional MLPs in terms of parameter efficiency and interpretability. Our comprehensive evaluation, including ablation studies, confirms the potential of Chebyshev KANs to address longstanding challenges in nonlinear function approximation, paving the way for further advancements in various scientific and engineering applications.

Published
2024

3. Data harvesting vs data farming: A study of the importance of variation vs sample size in deep learning-based auto-segmentation for breast cancer patients

Author

Buhl, ES, Maae, E, Matthiessen, LW, Nielsen, MH, Maraldo, M, Møller, M, Elleberg, S, Al-Rawi, SAJ, Offersen, BV, and Korreman, SS

Subjects
Physics - Medical Physics
Abstract

The aim of this study was to investigate the difference in output, when training a model in three different scenarios: a large clinical delineated data set (with 700/78 patients for training/testing, from the Danish Breast Cancer Group (DBCG) RT Nation Study), a clinical but curated dataset (with 328/36 patients for training/testing, from the DBCG RT Nation Study) and a smaller, but dedicated data set created by delineation experts (with 123/14 patients for training/testing, consensus delineations created by delineation experts). The model performance was estimated based on the performance metrics dice similarity coefficient (DSC), Hausdorff 95th percentile (HD95) and mean surface distance (MSD). Models were tested in test sets from their own cohort, and afterwards also compared in the dedicated data test set. The difference between model output was finally estimated by measuring the mean width and cranial caudal length of the model output for the models. When testing the model output between the clinical models and the dedicated models in their own test set, the two clinical models had a poorer performance, than the dedicated models, but not all metrics showed statistically significance. When testing the models in the dedicated data, the dedicated model showed a slightly better performance, along with fewer segmentation outliers. As a way of taking advantage of the strength from both types of data set, it could be an option to use a large clinical data set as a baseline model, and then finetune with smaller sized cohorts with dedicated delineations., Comment: 4 pages, 2 figures, 2 tables, submitted to the ICCR 2024

Published
2024

4. OCTraN: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios

Author

Ganesh, Aditya Nalgunda, Badrinath, Dhruval Pobbathi, Kumar, Harshith Mohan, SS, Priya, and Narayan, Surabhi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Modern approaches for vision-centric environment perception for autonomous navigation make extensive use of self-supervised monocular depth estimation algorithms that output disparity maps. However, when this disparity map is projected onto 3D space, the errors in disparity are magnified, resulting in a depth estimation error that increases quadratically as the distance from the camera increases. Though Light Detection and Ranging (LiDAR) can solve this issue, it is expensive and not feasible for many applications. To address the challenge of accurate ranging with low-cost sensors, we propose, OCTraN, a transformer architecture that uses iterative-attention to convert 2D image features into 3D occupancy features and makes use of convolution and transpose convolution to efficiently operate on spatial information. We also develop a self-supervised training pipeline to generalize the model to any scene by eliminating the need for LiDAR ground truth by substituting it with pseudo-ground truth labels obtained from boosted monocular depth estimation., Comment: This work was accepted as a spotlight presentation at the Transformers for Vision Workshop @CVPR 2023

Published
2023

5. Controlled coalescence-induced droplet jumping on flexible superhydrophobic substrates

Author

Pal, Gopal Chandra, SS, Siddharth, Agarwal, Manish, and Sharma, Chander Shekhar

Subjects
Physics - Fluid Dynamics, Physics - Applied Physics
Abstract

Sessile droplets coalescing on superhydrophobic substrates spontaneously jump from the surface. In this process, the excess surface energy available at the initiation of coalescence overcomes the minimal surface adhesion and manifests as sufficient kinetic energy to propel the droplets away from the substrate. Here, we show that the coalescence induced droplet jumping velocity is significantly curtailed if the superhydrophobic substrate is flexible in nature. Through detailed experimental measurements and numerical simulations, we demonstrate that the droplet jumping velocity and jumping height can be reduced by as much as 40 % and 64%, respectively, by synergistically tuning the substrate stiffness and substrate frequency. We show that this hitherto unexplored aspect of droplet coalescence jumping can be gainfully exploited in water harvesting from dew and fog harvesting. Additionally, through an exemplar butterfly wing substrate, we demonstrate that this effect is likely to manifest on many natural superhydrophobic substrates due to their inherent flexibility.

Published
2023

6. Communication Lower-Bounds for Distributed-Memory Computations for Mass Spectrometry based Omics Data

Author

Saeed, Fahad, Haseeb, Muhammad, and Iyengar, SS

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Quantitative Biology - Genomics, Quantitative Biology - Molecular Networks
Abstract

Mass spectrometry (MS) based omics data analysis require significant time and resources. To date, few parallel algorithms have been proposed for deducing peptides from mass spectrometry-based data. However, these parallel algorithms were designed, and developed when the amount of data that needed to be processed was smaller in scale. In this paper, we prove that the communication bound that is reached by the \emph{existing} parallel algorithms is $\Omega(mn+2r\frac{q}{p})$, where $m$ and $n$ are the dimensions of the theoretical database matrix, $q$ and $r$ are dimensions of spectra, and $p$ is the number of processors. We further prove that communication-optimal strategy with fast-memory $\sqrt{M} = mn + \frac{2qr}{p}$ can achieve $\Omega({\frac{2mnq}{p}})$ but is not achieved by any existing parallel proteomics algorithms till date. To validate our claim, we performed a meta-analysis of published parallel algorithms, and their performance results. We show that sub-optimal speedups with increasing number of processors is a direct consequence of not achieving the communication lower-bounds. We further validate our claim by performing experiments which demonstrate the communication bounds that are proved in this paper. Consequently, we assert that next-generation of \emph{provable}, and demonstrated superior parallel algorithms are urgently needed for MS based large systems-biology studies especially for meta-proteomics, proteogenomic, microbiome, and proteomics for non-model organisms. Our hope is that this paper will excite the parallel computing community to further investigate parallel algorithms for highly influential MS based omics problems., Comment: 20 pages, 6 figures, preprint

Published
2020

7. Spatially Resolved Water Emission from Gravitationally Lensed Dusty Star Forming Galaxies at z $\sim$ 3

Author

Jarugula, Sreevani, Vieira, Joaquin D., Spilker, Justin S., Apostolovski, Yordanka, Aravena, Manuel, Bethermin, Matthieu, de Breuck, Carlos, Chen, Chian-Chou, Cunningham, Daniel J. M., Dong, Chenxing, Greve, Thomas, Hayward, Christopher C., Hezaveh, Yashar, Litke, Katrina C., Mangian, Amelia C, Narayanan, Desika, Phadke, Kedar, Reuter, Cassie A., Van der Werf, Paul, and ß, Axel Wei

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Water ($\rm H_{2}O$), one of the most ubiquitous molecules in the universe, has bright millimeter-wave emission lines easily observed at high-redshift with the current generation of instruments. The low excitation transition of $\rm H_{2}O$, p$-$$\rm H_{2}O$(202 $-$ 111) ($\nu_{rest}$ = 987.927 GHz) is known to trace the far-infrared (FIR) radiation field independent of the presence of active galactic nuclei (AGN) over many orders-of-magnitude in FIR luminosity (L$_{\rm FIR}$). This indicates that this transition arises mainly due to star formation. In this paper, we present spatially ($\sim$0.5 arcsec corresponding to $\sim$1 kiloparsec) and spectrally resolved ($\sim$100 kms$^{-1}$) observations of p$-$$\rm H_{2}O$(202 $-$ 111) in a sample of four strong gravitationally lensed high-redshift galaxies with the Atacama Large Millimeter/submillimeter Array (ALMA). In addition to increasing the sample of luminous ($ > $ $10^{12}$L$_{\odot}$) galaxies observed with $\rm H_{2}O$, this paper examines the L$_{\rm H_{2}O}$/L$_{\rm FIR}$ relation on resolved scales for the first time at high-redshift. We find that L$_{\rm H_{2}O}$ is correlated with L$_{\rm FIR}$ on both global and resolved kiloparsec scales within the galaxy in starbursts and AGN with average L$_{\rm H_{2}O}$/L$_{\rm FIR}$ =$2.76^{+2.15}_{-1.21}\times10^{-5}$. We find that the scatter in the observed L$_{\rm H_{2}O}$/L$_{\rm FIR}$ relation does not obviously correlate with the effective temperature of the dust spectral energy distribution (SED) or the molecular gas surface density. This is a first step in developing p$-$$\rm H_{2}O$(202 $-$ 111) as a resolved star formation rate (SFR) calibrator.

Published
2019
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8. Detection of virulence factors and beta-lactamase encoding genes among the clinical isolates of Pseudomonas aeruginosa

Author

MKK, Fazlul, A, Najnin, Y, Farzana, MA, Rashid, S, Deepthi, C, Srikumar, Rashid, SS, and MHM, Nazmul

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

Background: Pseudomonas aeruginosa has emerged as a significant opportunistic bacterial pathogen that causes nosocomial infections in healthcare settings resulting in treatment failure throughout the world. This study was carried out to compare the relatedness between virulence characteristics and \b{eta}-lactamase encoding genes producing Pseudomonas aeruginosa. Methods: A total of 120 P. aeruginosa isolates were obtained from both paediatric and adult patients of Selayang Hospital, Kuala Lumpur, Malaysia. Phenotypic methods were used to detect various virulence factors (Phospholipase, Hemolysin, Gelatinase, DNAse, and Biofilm). All the isolates were evaluated for production of extended spectrum beta-lactamase (ESBL) as well as metallo \b{eta}-lactamase (MBL) by Double-disk synergy test (DDST) and E-test while AmpC \b{eta}-lactamase production was detected by disk antagonism test. Results: In this study, 120 Pseudomonas aeruginosa isolates (20 each from blood, wounds, respiratory secretions, stools, urine, and sputum samples) were studied. Among Pseudomonas aeruginosa isolates, the distribution of virulence factors was positive for hemolysin (48.33%), DNAse (43.33%), phospholipase (40.83%), gelatinase (31.66%) production and biofilm formation (34%) respectively. The prevalence of multiple \b{eta}-lactamase in P. aeruginosa showed 19.16% ESBL, 7.5% MBL and 10.83% AmpC production respectively. Conclusion: A regular surveillance is required to reduce public healt, Comment: International Journal of Pharmaceutical Research, 2019

Published
2019
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9. Constraining the p-mode--g-mode tidal instability with GW170817

Author

The LIGO Scientific Collaboration, The Virgo Collaboration, Abbott, B. P., Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adams, T., Addesso, P., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, B., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Allen, B., Allen, G., Allocca, A., Aloy, M. A., Altin, P. A., Amato, A., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Arun, K. G., Ascenzi, S., Ashton, G., Ast, M., Aston, S. M., Astone, P., Atallah, D. V., Aubin, F., Aufmuth, P., Aulbert, C., AultONeal, K., Austin, C., Avila-Alvarez, A., Babak, S., Bacon, P., Badaracco, F., Bader, M. K. M., Bae, S., Baker, P. T., Baldaccini, F., Ballardin, G., Ballmer, S. W., Banagiri, S., Barayoga, J. C., Barclay, S. E., Barish, B. C., Barker, D., Barkett, K., Barnum, S., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Bartos, I., Bassiri, R., Basti, A., Batch, J. C., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Beer, C., Bejger, M., Belahcene, I., Bell, A. S., Beniwal, D., Bensch, M., Berger, B. K., Bergmann, G., Bernuzzi, S., Bero, J. J., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bilenko, I. A., Bilgili, S. A., Billingsley, G., Billman, C. R., Birch, J., Birney, R., Birnholtz, O., Biscans, S., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bloemen, S., Bock, O., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Bohe, A., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Booth, C. D., Bork, R., Boschi, V., Bose, S., Bossie, K., Bossilkov, V., Bosveld, J., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branchesi, M., Brau, J. E., Briant, T., Brighenti, F., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brown, D. D., Brunett, S., Buchanan, C. C., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, D., Buy, C., Byer, R. L., Cabero, M., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Canizares, P., Cannon, K. C., Cao, H., Cao, J., Capano, C. D., Capocasa, E., Carbognani, F., Caride, S., Carney, M. F., Diaz, J. Casanueva, Casentini, C., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cepeda, C. B., Cerdá-Durán, P., Cerretani, G., Cesarini, E., Chaibi, O., Chamberlin, S. J., Chan, M., Chao, S., Charlton, P., Chase, E., Chassande-Mottin, E., Chatterjee, D., Chatziioannou, Katerina, Cheeseboro, B. D., Chen, H. Y., Chen, X., Chen, Y., Cheng, H. -P., Chia, H. Y., Chincarini, A., Chiummo, A., Chmiel, T., Cho, H. S., Cho, M., Chow, J. H., Christensen, N., Chu, Q., Chua, A. J. K., Chua, S., Chung, K. W., Chung, S., Ciani, G., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirelli, C. E., Cirone, A., Clara, F., Clark, J. A., Clearwater, P., Cleva, F., Cocchieri, C., Coccia, E., Cohadon, P. -F., Cohen, D., Colla, A., Collette, C. G., Collins, C., Cominsky, L. R., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corley, K. R., Cornish, N., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Couvares, P., Covas, P. B., Cowan, E. E., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Cripe, J., Crowder, S. G., Cullen, T. J., Cumming, A., Cunningham, L., Cuoco, E., Canton, T. Dal, Dálya, G., Danilishin, S. L., DÁntonio, S., Danzmann, K., Dasgupta, A., Costa, C. F. Da Silva, Dattilo, V., Dave, I., Davier, M., Davis, D., Daw, E. J., Day, B., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Pozzo, W., Demos, N., Denker, T., Dent, T., De Pietri, R., Derby, J., Dergachev, V., De Rosa, R., De Rossi, C., DeSalvo, R., de Varona, O., Dhurandhar, S., D\'{\ı, M. C., Di Fiore, az} L., Di Giovanni, M., Di Girolamo, T., Di Lieto, A., Ding, B., Di Pace, S., Di Palma, I., Di Renzo, F., Dmitriev, A., Doctor, Z., Dolique, V., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Álvarez, M. Dovale, Downes, T. P., Drago, M., Dreissigacker, C., Driggers, J. C., Du, Z., Dupej, P., Dwyer, S. E., Easter, P. J., Edo, T. B., Edwards, M. C., Effler, A., Eggenstein, H. -B., Ehrens, P., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Essick, R. C., Estelles, H., Estevez, D., Etienne, Z. B., Etzel, T., Evans, M., Evans, T. M., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favata, M., Fays, M., Fee, C., Fehrmann, H., Feicht, J., Fejer, M. M., Feng, F., Fernandez-Galiana, A., Ferrante, I., Ferreira, E. C., Ferrini, F., Fidecaro, F., Fiori, I., Fiorucci, D., Fishbach, M., Fisher, R. P., Fishner, J. M., Fitz-Axen, M., Flaminio, R., Fletcher, M., Fong, H., Font, J. A., Forsyth, P. W. F., Forsyth, S. S., Fournier, J. -D., Frasca, S., Frasconi, F., Frei, Z., Freise, A., Frey, R., Frey, V., Fritschel, P., Frolov, V. V., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gammaitoni, L., Ganija, M. R., Gaonkar, S. G., Garcia, A., Garc\'{\ı, C., Garufi, a-Quirós} F., Gateley, B., Gaudio, S., Gaur, G., Gayathri, V., Gemme, G., Genin, E., Gennai, A., George, D., George, J., Gergely, L., Germain, V., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, S., Giacomazzo, B., Giaime, J. A., Giardina, K. D., Gill, K., Giordano, G., Glover, L., Goetz, E., Goetz, R., Goncharov, B., González, G., Castro, J. M. Gonzalez, Gopakumar, A., Gorodetsky, M. L., Gossan, S. E., Gosselin, M., Gouaty, R., Grado, A., Graef, C., Granata, M., Grant, A., Gras, S., Gray, C., Greco, G., Green, A. C., Green, R., Gretarsson, E. M., Groot, P., Grote, H., Grunewald, S., Gruning, P., Guidi, G. M., Gulati, H. K., Guo, X., Gupta, A., Gupta, M. K., Gushwa, K. E., Gustafson, E. K., Gustafson, R., Halim, O., Hall, B. R., Hall, E. D., Hamilton, E. Z., Hamilton, H. F., Hammond, G., Haney, M., Hanke, M. M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanson, J., Hardwick, T., Harms, J., Harry, G. M., Harry, I. W., Hart, M. J., Haster, C. -J., Haughian, K., Healy, J., Heidmann, A., Heintze, M. C., Heitmann, H., Hello, P., Hemming, G., Hendry, M., Heng, I. S., Hennig, J., Heptonstall, A. W., Hernandez, F. J., Heurs, M., Hild, S., Hinderer, T., Hoak, D., Hochheim, S., Hofman, D., Holland, N. A., Holt, K., Holz, D. E., Hopkins, P., Horst, C., Hough, J., Houston, E. A., Howell, E. J., Hreibi, A., Huerta, E. A., Huet, D., Hughey, B., Hulko, M., Husa, S., Huttner, S. H., Huynh-Dinh, T., Iess, A., Indik, N., Ingram, C., Inta, R., Intini, G., Isa, H. N., Isac, J. -M., Isi, M., Iyer, B. R., Izumi, K., Jacqmin, T., Jani, K., Jaranowski, P., Johnson, D. S., Johnson, W. W., Jones, D. I., Jones, R., Jonker, R. J. G., Ju, L., Junker, J., Kalaghatgi, C. V., Kalogera, V., Kamai, B., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Karki, S., Karvinen, K. S., Kasprzack, M., Katolik, M., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaufer, S., Kawabe, K., Keerthana, N. V., Kéfélian, F., Keitel, D., Kemball, A. J., Kennedy, R., Key, J. S., Khalili, F. Y., Khamesra, B., Khan, H., Khan, I., Khan, S., Khan, Z., Khazanov, E. A., Kijbunchoo, N., Kim, Chunglee, Kim, J. C., Kim, K., Kim, W., Kim, W. S., Kim, Y. -M., King, E. J., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klimenko, S., Knowles, T. D., Koch, P., Koehlenbeck, S. M., Koley, S., Kondrashov, V., Kontos, A., Korobko, M., Korth, W. Z., Kowalska, I., Kozak, D. B., Krämer, C., Kringel, V., Krishnan, B., Królak, A., Kuehn, G., Kumar, P., Kumar, R., Kumar, S., Kuo, L., Kutynia, A., Kwang, S., Lackey, B. D., Lai, K. H., Landry, M., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lee, C. H., Lee, H. K., Lee, H. M., Lee, H. W., Lee, K., Lehmann, J., Lenon, A., Leonardi, M., Leroy, N., Letendre, N., Levin, Y., Li, J., Li, T. G. F., Li, X., Linker, S. D., Littenberg, T. B., Liu, J., Liu, X., Lo, R. K. L., Lockerbie, N. A., London, L. T., Longo, A., Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lough, J. D., Lovelace, G., Lück, H., Lumaca, D., Lundgren, A. P., Lynch, R., Ma, Y., Macas, R., Macfoy, S., Machenschalk, B., MacInnis, M., Macleod, D. M., Hernandez, I. Maga\ na, na-Sandoval, F. Maga\, Zertuche, L. Maga\ na, Magee, R. M., Majorana, E., Maksimovic, I., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Marchesoni, F., Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Martelli, F., Martellini, L., Martin, I. W., Martin, R. M., Martynov, D. V., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Matichard, F., Matone, L., Mavalvala, N., Mazumder, N., McCann, J. J., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McGuire, S. C., McIver, J., McManus, D. J., McRae, T., McWilliams, S. T., Meacher, D., Meadors, G. D., Mehmet, M., Meidam, J., Mejuto-Villa, E., Melatos, A., Mendell, G., Mendoza-Gandara, D., Mercer, R. A., Mereni, L., Merilh, E. L., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Metzdorff, R., Meyers, P. M., Miao, H., Michel, C., Middleton, H., Mikhailov, E. E., Milano, L., Miller, A. L., Miller, A., Miller, B. B., Miller, J., Millhouse, M., Mills, J., Milovich-Goff, M. C., Minazzoli, O., Minenkov, Y., Ming, J., Mishra, C., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Moffa, D., Mogushi, K., Mohan, M., Mohapatra, S. R. P., Montani, M., Moore, C. J., Moraru, D., Moreno, G., Morisaki, S., Mours, B., Mow-Lowry, C. M., Mueller, G., Muir, A. W., Mukherjee, Arunava, Mukherjee, D., Mukherjee, S., Mukund, N., Mullavey, A., Munch, J., niz, E. A. Mu\, Muratore, M., Murray, P. G., Nagar, A., Napier, K., Nardecchia, I., Naticchioni, L., Nayak, R. K., Neilson, J., Nelemans, G., Nelson, T. J. N., Nery, M., Neunzert, A., Nevin, L., Newport, J. M., Ng, K. Y., Ng, S., Nguyen, P., Nguyen, T. T., Nichols, D., Nielsen, A. B., Nissanke, S., Nitz, A., Nocera, F., Nolting, D., North, C., Nuttall, L. K., Obergaulinger, M., Oberling, J., OBrien, B. D., ODea, G. D., Ogin, G. H., Oh, J. J., Oh, S. H., Ohme, F., Ohta, H., Okada, M. A., Oliver, M., Oppermann, P., Oram, Richard J., OŔeilly, B., Ormiston, R., Ortega, L. F., OŚhaughnessy, R., Ossokine, S., Ottaway, D. J., Overmier, H., Owen, B. J., Pace, A. E., Pagano, G., Page, J., Page, M. A., Pai, A., Pai, S. A., Palamos, J. R., Palashov, O., Palomba, C., Pal-Singh, A., Pan, Howard, Pan, Huang-Wei, Pang, B., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Paoletti, F., Paoli, A., Papa, M. A., Parida, A., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patil, M., Patricelli, B., Pearlstone, B. L., Pedersen, C., Pedraza, M., Pedurand, R., Pekowsky, L., Pele, A., Penn, S., Perez, C. J., Perreca, A., Perri, L. M., Pfeiffer, H. P., Phelps, M., Phukon, K. S., Piccinni, O. J., Pichot, M., Piergiovanni, F., Pierro, V., Pillant, G., Pinard, L., Pinto, I. M., Pirello, M., Pitkin, M., Poggiani, R., Popolizio, P., Porter, E. K., Possenti, L., Post, A., Powell, J., Prasad, J., Pratt, J. W. W., Pratten, G., Predoi, V., Prestegard, T., Principe, M., Privitera, S., Prodi, G. A., Prokhorov, L. G., Puncken, O., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Quetschke, V., Quintero, E. A., Quitzow-James, R., Rabeling, D. S., Radkins, H., Raffai, P., Raja, S., Rajan, C., Rajbhandari, B., Rakhmanov, M., Ramirez, K. E., Ramos-Buades, A., Rana, Javed, Rapagnani, P., Raymond, V., Razzano, M., Read, J., Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Ren, W., Ricci, F., Ricker, P. M., Riles, K., Rizzo, M., Robertson, N. A., Robie, R., Robinet, F., Robson, T., Rocchi, A., Rolland, L., Rollins, J. G., Roma, V. J., Romano, R., Romel, C. L., Romie, J. H., Rosińska, D., Ross, M. P., Rowan, S., Rüdiger, A., Ruggi, P., Rutins, G., Ryan, K., Sachdev, S., Sadecki, T., Sakellariadou, M., Salconi, L., Saleem, M., Salemi, F., Samajdar, A., Sammut, L., Sampson, L. M., Sanchez, E. J., Sanchez, L. E., Sanchis-Gual, N., Sandberg, V., Sanders, J. R., Sarin, N., Sassolas, B., Saulson, P. R., Sauter, O., Savage, R. L., Sawadsky, A., Schale, P., Scheel, M., Scheuer, J., Schmidt, P., Schnabel, R., Schofield, R. M. S., Schönbeck, A., Schreiber, E., Schuette, D., Schulte, B. W., Schutz, B. F., Schwalbe, S. G., Scott, J., Scott, S. M., Seidel, E., Sellers, D., Sengupta, A. S., Sentenac, D., Sequino, V., Sergeev, A., Setyawati, Y., Shaddock, D. A., Shaffer, T. J., Shah, A. A., Shahriar, M. S., Shaner, M. B., Shao, L., Shapiro, B., Shawhan, P., Shen, H., Shoemaker, D. H., Shoemaker, D. M., Siellez, K., Siemens, X., Sieniawska, M., Sigg, D., Silva, A. D., Singer, L. P., Singh, A., Singhal, A., Sintes, A. M., Slagmolen, B. J. J., Slaven-Blair, T. J., Smith, B., Smith, J. R., Smith, R. J. E., Somala, S., Son, E. J., Sorazu, B., Sorrentino, F., Souradeep, T., Spencer, A. P., Srivastava, A. K., Staats, K., Steinke, M., Steinlechner, J., Steinlechner, S., Steinmeyer, D., Steltner, B., Stevenson, S. P., Stocks, D., Stone, R., Stops, D. J., Strain, K. A., Stratta, G., Strigin, S. E., Strunk, A., Sturani, R., Stuver, A. L., Summerscales, T. Z., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Swinkels, B. L., Szczepańczyk, M. J., Tacca, M., Tait, S. C., Talbot, C., Talukder, D., Tanner, D. B., Tápai, M., Taracchini, A., Tasson, J. D., Taylor, J. A., Taylor, R., Tewari, S. V., Theeg, T., Thies, F., Thomas, E. G., Thomas, M., Thomas, P., Thorne, K. A., Thrane, E., Tiwari, S., Tiwari, V., Tokmakov, K. V., Toland, K., Tonelli, M., Tornasi, Z., Torres-Forné, A., Torrie, C. I., Töyrä, D., Travasso, F., Traylor, G., Trinastic, J., Tringali, M. C., Trozzo, L., Tsang, K. W., Tse, M., Tso, R., Tsuna, D., Tsukada, L., Tuyenbayev, D., Ueno, K., Ugolini, D., Urban, A. L., Usman, S. A., Vahlbruch, H., Vajente, G., Valdes, G., van Bakel, N., van Beuzekom, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Schaaf, L., van Heijningen, J. V., van Veggel, A. A., Vardaro, M., Varma, V., Vass, S., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venkateswara, K., Venugopalan, G., Verkindt, D., Vetrano, F., Viceré, A., Viets, A. D., Vinciguerra, S., Vine, D. J., Vinet, J. -Y., Vitale, S., Vo, T., Vocca, H., Vorvick, C., Vyatchanin, S. P., Wade, A. R., Wade, L. E., Wade, M., Walet, R., Walker, M., Wallace, L., Walsh, S., Wang, G., Wang, H., Wang, J. Z., Wang, W. H., Wang, Y. F., Ward, R. L., Warner, J., Was, M., Watchi, J., Weaver, B., Wei, L. -W., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Wessel, E. K., We{ß, P., Westerweck, els} J., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wilken, D., Williams, D., Williams, R. D., Williamson, A. R., Willis, J. L., Willke, B., Wimmer, M. H., Winkler, W., Wipf, C. C., Wittel, H., Woan, G., Woehler, J., Wofford, J. K., Wong, W. K., Worden, J., Wright, J. L., Wu, D. S., Wysocki, D. M., Xiao, S., Yam, W., Yamamoto, H., Yancey, C. C., Yang, L., Yap, M. J., Yazback, M., Yu, Hang, Yu, Haocun, Yvert, M., Zadrożny, A., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, M., Zhang, T., Zhang, Y. -H., Zhao, C., Zhou, M., Zhou, Z., Zhu, S. J., Zhu, X. J., Zimmerman, A. B., Zucker, M. E., Zweizig, J., and Weinberg, Nevin N.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We analyze the impact of a proposed tidal instability coupling $p$-modes and $g$-modes within neutron stars on GW170817. This non-resonant instability transfers energy from the orbit of the binary to internal modes of the stars, accelerating the gravitational-wave driven inspiral. We model the impact of this instability on the phasing of the gravitational wave signal using three parameters per star: an overall amplitude, a saturation frequency, and a spectral index. Incorporating these additional parameters, we compute the Bayes Factor ($\ln B^{pg}_{!pg}$) comparing our $p$-$g$ model to a standard one. We find that the observed signal is consistent with waveform models that neglect $p$-$g$ effects, with $\ln B^{pg}_{!pg} = 0.03^{+0.70}_{-0.58}$ (maximum a posteriori and 90% credible region). By injecting simulated signals that do not include $p$-$g$ effects and recovering them with the $p$-$g$ model, we show that there is a $\simeq 50\%$ probability of obtaining similar $\ln B^{pg}_{!pg}$ even when $p$-$g$ effects are absent. We find that the $p$-$g$ amplitude for 1.4 $M_\odot$ neutron stars is constrained to $\lesssim \text{few}\times10^{-7}$, with maxima a posteriori near $\sim 10^{-7}$ and $p$-$g$ saturation frequency $\sim 70\, \mathrm{Hz}$. This suggests that there are less than a few hundred excited modes, assuming they all saturate by wave breaking. For comparison, theoretical upper bounds suggest a $p$-$g$ amplitude $\lesssim 10^{-6}$ and $\lesssim 10^{3}$ modes saturating by wave breaking. Thus, the measured constraints only rule out extreme values of the $p$-$g$ parameters. They also imply that the instability dissipates $\lesssim 10^{51}\, \mathrm{ergs}$ over the entire inspiral, i.e., less than a few percent of the energy radiated as gravitational waves., Comment: 7 pages, 2 figures

Published
2018
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10. Measurement of the relative yields of $\psi(2S)$ to $\psi(1S)$ mesons produced at forward and backward rapidity in $p$$+$$p$, $p$$+$Al, $p$$+$Au, and $^{3}$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author

Adare, A., Aidala, C., Ajitanand, N. N., Akiba, Y., Alfred, M., Andrieux, V., Aoki, K., Apadula, N., Asano, H., Ayuso, C., Azmoun, B., Babintsev, V., Bai, M., Bandara, N. S., Bannier, B., Barish, K. N., Bathe, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belmont, R., Berdnikov, A., Berdnikov, Y., Blau, D. S., Boer, M., Bok, J. S., Bownes, E. K., Boyle, K., Brooks, M. L., Bryslawskyj, J., Bumazhnov, V., Butler, C., Campbell, S., CanoaRoman, C., Cervantes, R., Chen, C. -H., Chi, C. Y., Chiu, M., Choi, I. J., Choi, J. B., Chujo, T., Citron, Z., Connors, M., Cronin, N., Csanád, M., Csörgő, T., Danley, T. W., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dion, A., Diss, P. B., Dixit, D., Do, J. H., Drees, A., Drees, K. A., Dumancic, M., Durham, J. M., Durum, A., Dusing, J. P., Elder, T., Enokizono, A., En'yo, H., Esumi, S., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, M., Finger, Jr., M., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukuda, Y., Gal, C., Gallus, P., Garg, P., Ge, H., Giordano, F., Glenn, A., Goto, Y., Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guragain, H., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Han, S. Y., Hanks, J., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., He, X., Hemmick, T. K., Hill, J. C., Hill, K., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Huang, S., Imai, K., Inaba, M., Iordanova, A., Isenhower, D., Ito, Y., Ivanishchev, D., Jacak, B. V., Jezghani, M., Ji, Z, Jia, J., Jiang, X., Johnson, B. M., Jorjadze, V., Jouan, D., Jumper, D. S., Kanda, S., Kang, J. H., Kapukchyan, D., Karthas, E., Kawall, D., Kazantsev, A. V., Key, J. A., Khachatryan, V., Khanzadeev, A., Kim, C., Kim, D. J., Kim, E. -J., Kim, G. W., Kim, M., Kimball, M. L., Kimelman, B., Kincses, D., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Komkov, B., Kotler, J. R., Kotov, D., Kudo, S., Kurita, K., Kurosawa, M., Kwon, Y., Lacey, R., Lajoie, J. G., Lallow, E. O., Lebedev, A., Lee, S., Lee, S. H., Leitch, M. J., Leung, Y. H., Lewis, N. A., Li, X., Lim, S. H., Liu, L. D., Liu, M. X., Loggins, V-R, Loggins, V. -R., Lovasz, K., Lynch, D., Majoros, T., Makdisi, Y. I., Makek, M., Malaev, M., Manion, A., Manko, V. I., Mannel, E., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Meles, A., Mendez, A. R., Mendoza, M., Mignerey, A. C., Mihalik, D. E. M., Milov, A., Mishra, D. K., Mitchell, J. T., Mitsuka, G., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morrison, D. P., Morrow, S. I. M., Moukhanova, T. V., Murakami, T., Murata, J., Mwai, A., Nagai, K., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakano, K., Nattrass, C., Netrakanti, P. K., Niida, T., Nishimura, S., Nouicer, R., Novák, T., Novitzky, N., Novotny, R., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Koop, J. D. Orjuela, Osborn, J. D., Oskarsson, A., Ottino, G. J., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, J. S., Park, S., Pate, S. F., Patel, M., Peng, J. -C., Peng, W., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pinson, R., Pisani, R. P., Press, C. J., Pun, A., Purschke, M. L., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Rolnick, S. D., Rosati, M., Rowan, Z., Rubin, J. G., Runchey, J., Safonov, A. S., Sahlmueller, B., Saito, N., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, K., Sato, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seidl, R., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shioya, T., Shukla, P., Sickles, A., Silva, C. L., Silva, J. A., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stepanov, M., Stien, H., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Syed, SS, Sziklai, J., Takeda, A, Taketani, A., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarnai, G., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Velkovska, J., Virius, M., Vrba, V., Vukman, N., Wang, X. R., Wang, Z., Watanabe, Y., Watanabe, Y. S., Wei, F., White, A. S., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xu, C., Xu, Q., Xue, L., Yalcin, S., Yamaguchi, Y. L., Yamamoto, H., Yanovich, A., Yoo, J. H., Yoon, I., Yu, H., Yushmanov, I. E., Zajc, W. A., Zelenski, A., Zhou, S., and Zou, L.

Subjects
Nuclear Experiment
Abstract

The PHENIX Collaboration has measured the ratio of the yields of $\psi(2S)$ to $\psi(1S)$ mesons produced in $p$$+$$p$, $p$$+$Al, $p$$+$Au, and $^{3}$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV over the forward and backward rapidity intervals $1.2<|y|<2.2$. We find that the ratio in $p$$+$$p$ collisions is consistent with measurements at other collision energies. In collisions with nuclei, we find that in the forward ($p$-going or $^{3}$He-going) direction, the relative yield of $\psi(2S)$ mesons to $\psi(1S)$ mesons is consistent with the value measured in \pp collisions. However, in the backward (nucleus-going) direction, the $\psi(2S)$ is preferentially suppressed by a factor of $\sim$2. This suppression is attributed in some models to breakup of the weakly-bound $\psi(2S)$ through final state interactions with comoving particles, which have a higher density in the nucleus-going direction. These breakup effects may compete with color screening in a deconfined quark-gluon plasma to produce sequential suppression of excited quarkonia states., Comment: 364 authors, 10 pages, 5 figures, 2 tables, 2014,15 data. v2 is version accepted by Phys. Rev. C. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Published
2016
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11. The Detailed Science Case for the Maunakea Spectroscopic Explorer: the Composition and Dynamics of the Faint Universe

Author

McConnachie, Alan, Babusiaux, Carine, Balogh, Michael, Driver, Simon, Côté, Pat, Courtois, Helene, Davies, Luke, Ferrarese, Laura, Gallagher, Sarah, Ibata, Rodrigo, Martin, Nicolas, Robotham, Aaron, Venn, Kim, Villaver, Eva, Bovy, Jo, Boselli, Alessandro, Colless, Matthew, Comparat, Johan, Denny, Kelly, Duc, Pierre-Alain, Ellison, Sara, de Grijs, Richard, Fernandez-Lorenzo, Mirian, Freeman, Ken, Guhathakurta, Raja, Hall, Patrick, Hopkins, Andrew, Hudson, Mike, Johnson, Andrew, Kaiser, Nick, Koda, Jun, Konstantopoulos, Iraklis, Koshy, George, Lee, Khee-Gan, Nusser, Adi, Pancoast, Anna, Peng, Eric, Peroux, Celine, Petitjean, Patrick, Pichon, Christophe, Poggianti, Bianca, Schmid, Carlo, Shastri, Prajval, Shen, Yue, Willot, Chris, Croom, Scott, Lallement, Rosine, Schimd, Carlo, Smith, Dan, Walker, Matthew, Willis, Jon, Colless, Alessandro Bosselli Matthew, Goswami, Aruna, Jarvis, Matt, Jullo, Eric, Kneib, Jean-Paul, Konstantopoloulous, Iraklis, Newman, Jeff, Richard, Johan, Sutaria, Firoza, Taylor, Edwar, van Waerbeke, Ludovic, Battaglia, Giuseppina, Hall, Pat, Haywood, Misha, Sakari, Charli, Seibert, Arnaud, Thirupathi, Sivarani, Wang, Yuting, Wang, Yiping, Babas, Ferdinand, Bauman, Steve, Caffau, Elisabetta, Laychak, Mary Beth, Crampton, David, Devost, Daniel, Flagey, Nicolas, Han, Zhanwen, Higgs, Clare, Hill, Vanessa, Ho, Kevin, Isani, Sidik, Mignot, Shan, Murowinski, Rick, Pandey, Gajendra, Salmon, Derrick, Siebert, Arnaud, Simons, Doug, Starkenburg, Else, Szeto, Kei, Tully, Brent, Vermeulen, Tom, Withington, Kanoa, Arimoto, Nobuo, Asplund, Martin, Aussel, Herve, Bannister, Michele, Bhatt, Harish, Bhargavi, SS, Blakeslee, John, Bland-Hawthorn, Joss, Bullock, James, Burgarella, Denis, Chang, Tzu-Ching, Cole, Andrew, Cooke, Jeff, Cooper, Andrew, Di Matteo, Paola, Favole, Ginevra, Flores, Hector, Gaensler, Bryan, Garnavich, Peter, Gilbert, Karoline, Gonzalez-Delgado, Rosa, Guhathakurta, Puragra, Hasinger, Guenther, Herwig, Falk, Hwang, Narae, Jablonka, Pascale, Jarvis, Matthew, Kamath, Umanath, Kewley, Lisa, Borgne, Damien Le, Lewis, Geraint, Lupton, Robert, Martell, Sarah, Mateo, Mario, Mena, Olga, Nataf, David, Newman, Jeffrey, Pérez, Enrique, Prada, Francisco, Puech, Mathieu, Recio-Blanco, Alejandra, Robin, Annie, Saunders, Will, Smith, Daniel, Stalin, C. S., Tao, Charling, Thanjuvur, Karun, Tresse, Laurence, van Waerbeke, Ludo, Wang, Jian-Min, Yong, David, Zhao, Gongbo, Boisse, Patrick, Bolton, James, Bonifacio, Piercarlo, Bouchy, Francois, Cowie, Len, Cunha, Katia, Deleuil, Magali, de Mooij, Ernst, Dufour, Patrick, Foucaud, Sebastien, Glazebrook, Karl, Hutchings, John, Kobayashi, Chiaki, Kudritzki, Rolf-Peter, Li, Yang-Shyang, Lin, Lihwai, Lin, Yen-Ting, Makler, Martin, Narita, Norio, Park, Changbom, Ransom, Ryan, Ravindranath, Swara, Reddy, Bacham Eswar, Sawicki, Marcin, Simard, Luc, Srianand, Raghunathan, Storchi-Bergmann, Thaisa, Umetsu, Keiichi, Wang, Ting-Gui, Woo, Jong-Hak, and Wu, Xue-Bing

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

MSE is an 11.25m aperture observatory with a 1.5 square degree field of view that will be fully dedicated to multi-object spectroscopy. More than 3200 fibres will feed spectrographs operating at low (R ~ 2000 - 3500) and moderate (R ~ 6000) spectral resolution, and approximately 1000 fibers will feed spectrographs operating at high (R ~ 40000) resolution. MSE is designed to enable transformational science in areas as diverse as tomographic mapping of the interstellar and intergalactic media; the in-situ chemical tagging of thick disk and halo stars; connecting galaxies to their large scale structure; measuring the mass functions of cold dark matter sub-halos in galaxy and cluster-scale hosts; reverberation mapping of supermassive black holes in quasars; next generation cosmological surveys using redshift space distortions and peculiar velocities. MSE is an essential follow-up facility to current and next generations of multi-wavelength imaging surveys, including LSST, Gaia, Euclid, WFIRST, PLATO, and the SKA, and is designed to complement and go beyond the science goals of other planned and current spectroscopic capabilities like VISTA/4MOST, WHT/WEAVE, AAT/HERMES and Subaru/PFS. It is an ideal feeder facility for E-ELT, TMT and GMT, and provides the missing link between wide field imaging and small field precision astronomy. MSE is optimized for high throughput, high signal-to-noise observations of the faintest sources in the Universe with high quality calibration and stability being ensured through the dedicated operational mode of the observatory. (abridged), Comment: 210 pages, 91 figures. Exposure draft. Appendices to the Detailed Science Case can be found at http://mse.cfht.hawaii.edu/docs/

Published
2016

12. Improving circuit miniaturization and its efficiency using Rough Set Theory

Author

Rawat, Sarvesh SS, Mor, Dheeraj Dilip, Kumar, Anugrah, Roy, Sanjiban Shekar, and kumar, Rohit

Subjects
Computer Science - Learning, Computer Science - Artificial Intelligence
Abstract

High-speed, accuracy, meticulousness and quick response are notion of the vital necessities for modern digital world. An efficient electronic circuit unswervingly affects the maneuver of the whole system. Different tools are required to unravel different types of engineering tribulations. Improving the efficiency, accuracy and low power consumption in an electronic circuit is always been a bottle neck problem. So the need of circuit miniaturization is always there. It saves a lot of time and power that is wasted in switching of gates, the wiring-crises is reduced, cross-sectional area of chip is reduced, the number of transistors that can implemented in chip is multiplied many folds. Therefore to trounce with this problem we have proposed an Artificial intelligence (AI) based approach that make use of Rough Set Theory for its implementation. Theory of rough set has been proposed by Z Pawlak in the year 1982. Rough set theory is a new mathematical tool which deals with uncertainty and vagueness. Decisions can be generated using rough set theory by reducing the unwanted and superfluous data. We have condensed the number of gates without upsetting the productivity of the given circuit. This paper proposes an approach with the help of rough set theory which basically lessens the number of gates in the circuit, based on decision rules., Comment: The International Conference on Machine Intelligence Research and Advancement,ICMIRA-2013

Published
2013

13. Phishing Detection by determining reliability factor using rough set theory

Author

Kumar, Anugrah, Roy, Sanjiban Shekar, Rawat, Sarvesh SS, and Saxena, Sanklan

Subjects
Computer Science - Artificial Intelligence
Abstract

Phishing is a common online weapon, used against users, by Phishers for acquiring a confidential information through deception. Since the inception of internet, nearly everything, ranging from money transaction to sharing information, is done online in most parts of the world. This has also given rise to malicious activities such as Phishing. Detecting Phishing is an intricate process due to complexity, ambiguity and copious amount of possibilities of factors responsible for phishing . Rough sets can be a powerful tool, when working on such kind of Applications containing vague or imprecise data. This paper proposes an approach towards Phishing Detection Using Rough Set Theory. The Thirteen basic factors, directly responsible towards Phishing, are grouped into four Strata. Reliability Factor is determined on the basis of the outcome of these strata, using Rough Set Theory . Reliability Factor determines the possibility of a suspected site to be Valid or Fake. Using Rough set Theory most and the least influential factors towards Phishing are also determined., Comment: The International Conference on Machine Intelligence Research and Advancement, ICMIRA-2013

Published
2013

14. Dynamical mass of a star cluster in M83: a test of fibre-fed multi-object spectroscopy

Author

Moll, SL, de Grijs, R, Anders, P, Crowther, PA, Larsen, SS, Smith, LJ, and Zwart, SF Portegies

Subjects
Astrophysics
Abstract

(Abridged) Aims: We obtained VLT/FLAMES+UVES high-resolution, fibre-fed spectroscopy (FFS) of five young massive clusters in M83 (NGC 5236). This forms the basis of a pilot study testing the feasibility of using FFS to measure the velocity dispersions of several clusters simultaneously, in order to determine their dynamical masses; Methods: We adopted two methods for determining the velocity dispersion of the star clusters: cross-correlating the cluster spectrum with the template spectra and minimising a chi^2 value between the cluster spectrum and the broadened template spectra. Cluster 805 in M83 was chosen as a control to test the reliability of the method, through a comparison with the results obtained from a standard echelle VLT/UVES spectrum obtained by Larsen & Richtler; Results: We find no dependence of the velocity dispersions measured for a cluster on the choice of red giant versus red supergiant templates, nor on the method adopted. We measure a velocity dispersion of sigma_los = 10.2+/-1.1 km/s for cluster 805 from our FFS. Our FLAMES+UVES velocity dispersion measurement gives M_vir = (6.6+/-1.7)e5 M_sun, consistent with previous results. This is a factor of ~3 greater than the cluster's photometric mass, indicating a lack of virial equilibrium. However, based on its effective star formation efficiency, the cluster is likely to virialise, and may survive for a Hubble time, in the absence of external disruptive forces; Conclusions: We find that reliable velocity dispersions can be determined from FFS. The advantages of observing several clusters simultaneously outweighs the difficulty of accurate galaxy background subtraction, providing that the targets are chosen to provide sufficient S/N ratios, and are much brighter than the galaxy background., Comment: 10 pages, 4 figures, accepted by A&A

Published
2008
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15. Test problems for relativistic magnetohydrodynamics

Author

SS, Komissarov

Subjects
Astrophysics
Abstract

Some of the parameters of the test problems for numerical relativistic MHD given in Table 1 in Komissarov(1999) are incorrect. Here we give the correct values.

Published
2002

16. Large Gauge Transformations and Magnetic Vortices in Axial Gauge QCD_{3+1}

Author

Grie\{ss}hammer, H. W.

Subjects
High Energy Physics - Phenomenology
Abstract

It is shown that in the modified axial gauge version of canonically quantized QCD$_{3+1}$ on a torus only nongeneric gauge field configurations allow for large gauge transformations. For the other configurations, the gauge is fixed completely. Such configurations carry nonzero total magnetic abelian fluxes, correspond to magnetic vortices parallel to the coordinate axes and are incorporated using both singular gauge fields and a change of boundary conditions., Comment: LaTeX, 14 pages, to be published in the proceedings of the "Workshop on Nonperturbative Approaches to QCD" at the ECT, Trento; no special macros

Published
1995

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