18,460 research outputs found
Letter from K. M. Mehta to John Sloan, June 5, 1951
No full text1 leaf (single sided)Letter from K. M. Mehta to John Sloan, June 5, 195
Letter from K. M. Mehta to John Sloan, June 5, 1951
No full text1 leaf (single sided)Letter from K. M. Mehta to John Sloan, June 5, 195
Replication Data for: Chemokine Binding to PSGL-1 is Controlled by O-Glycosylation and Tyrosine Sulfation
No full textThis dataset is for the paper titled "Chemokine binding to PSGL-1 is controlled by O-glycosylation and tyrosine sulfation" by Goth, C. K. and Mehta, A. Y. et al
C. C. Mehta
No full textOn the life and works of Chandravadan Chimanlal Mehta, b. 1901, Gujarati author
Depth First Search in the Semi-streaming Model
No full textDepth first search (DFS) tree is a fundamental data structure for solving various graph problems. The classical algorithm for building a DFS tree requires O(m+n) time for a given undirected graph G having n vertices and m edges. In the streaming model, an algorithm is allowed several passes (preferably single) over the input graph having a restriction on the size of local space used.
Now, a DFS tree of a graph can be trivially computed using a single pass if O(m) space is allowed. In the semi-streaming model allowing O(n) space, it can be computed in O(n) passes over the input stream, where each pass adds one vertex to the DFS tree. However, it remains an open problem to compute a DFS tree using o(n) passes using o(m) space even in any relaxed streaming environment.
We present the first semi-streaming algorithms that compute a DFS tree of an undirected graph in o(n) passes using o(m) space. We first describe an extremely simple algorithm that requires at most ceil[n/k] passes to compute a DFS tree using O(nk) space, where k is any positive integer. For example using k=sqrt{n}, we can compute a DFS tree in sqrt{n} passes using O(n sqrt{n}) space. We then improve this algorithm by using more involved techniques to reduce the number of passes to ceil[h/k] under similar space constraints, where h is the height of the computed DFS tree. In particular, this algorithm improves the bounds for the case where the computed DFS tree is shallow (having o(n) height). Moreover, this algorithm is presented in form of a framework that allows the flexibility of using any algorithm to maintain a DFS tree of a stored sparser subgraph as a black box, which may be of an independent interest. Both these algorithms essentially demonstrate the existence of a trade-off between the space and number of passes required for computing a DFS tree. Furthermore, we evaluate these algorithms experimentally which reveals their exceptional performance in practice. For both random and real graphs, they require merely a few passes even when allowed just O(n) space
Las cenizas de cáscaras de arroz utilizadas para reducir la temperatura del hormigón en masa, de alta resistencia
No full textEl Prof. P. K. Mehta ha puesto de manifiesto que las cenizas de las cascaras del arroz tienen las propiedades de una sílice muy reactiva
Gene expression signatures of postnatal depression
No full text<p>This dataset consists of RNA sequencing gene expression data for 137 women with postnatal depression, which are also published in 2021 in the following publication: Mehta, D., Grewen, K., Pearson, B. <em>et al.</em> Genome-wide gene expression changes in postpartum depression point towards an altered immune landscape. <em>Transl Psychiatry</em> 11, 155 (2021). </p>
<p>This dataset was collected by the University of North Carolina, USA, through a University of Queensland grant, obtained by Dr Divya Mehta while employed at UQ. </p>
TIEGCM-ROM
No full textDeposited here are the readme and archived folder containing a formatted data Matlab file and an accompanying Matlab script.The '.mat' contains the variables to compute the full and reduced rank dynamic and input matrices for a reduced order representation of the Thermosphere-Ionosphere-Electrodynamics Global Circulation Model (TIEGCM). The description of the model is provided in 'A quasi-physical dynamic reduced order model for thermospheric mass density via Hermitian Space Dynamic Mode Decomposition' in Space Weather (currently under review).DOD: Air Force Office of Scientific ResearchMehta, Piyush M. (2018). TIEGCM-ROM. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/D6GM4G
NANOTECHNOLOGY: RISK, ETHICS AND LAW
No full textList of figures, tables and boxes -- List of contributors -- Preface and acknowledgements -- List of acronyms and abbreviations -- 1. Introduction: the challenge of nanotechnologies / Geoffrey Hunt and Michael D. Mehta -- Pt. One. Introducing nanotechnology -- 2. Nanotechnology: from 'wow' to 'yuck'? / Kristen Kulinowski -- 3. Nanotechnology: from Feynman to funding / K. Eric Drexler -- 4. Microsystems and nanoscience for biomedical applications: a view to the future / Linda M. Pilarski, Michael D. Mehta, Timothy Caulfield, Karan V.I.S. Kaler and Christopher J. Backhouse -- 5. Nanotechnoscience and complex systems: the case for nanology / Geoffrey Hunt -- Pt. Two. Regional developments -- 6. Nanotechnologies and society in Japan / Matsuda Masami, Geoffrey Hunt and Obayashi Masayuki -- 7. Nanotechnologies and society in the USA / Kirsty Mills -- 8. Nanotechnologies and society in Europe / Geoffrey Hunt -- 9. Nanotechnologies and society in Canada / Linda Goldenberg -- Pt. Three. Benefits and risks -- 10. From biotechnology to nanotechnology: what can we learn from earlier technologies? / Michael D. Mehta -- 11. Getting nanotechnology right the first time / John Balbus, Richard Denison, Karen Florini and Scott Walsh -- 12. Risk management and regulation in an emerging technology / Roland Clift -- 13. Nanotechnology and nanoparticle toxicity: a case for precaution / C. Vyvyan Howard and December S.K. Ikah -- 14. The future of nanotechnology in food science and nutrition: can science predict its safety? / Árpád Pusztai and Susan Bardocz -- Pt. Four. Ethics and public understanding -- 15. The global ethics of nanotechnology / Geoffrey Hunt -- 16. Going public: risk, trust and public understanding of nanotechnologies / Julie Barnett, Anna Carr and Roland Clift -- 17. Dwarfing the social? Nanotechnology lessons from the biotechnology front / Edna F. Einsiedel and Linda Goldenberg -- Pt. Five. Law and regulation -- 18. Nanotechnologies and the law of patents: a collision course / Siva Vaidhyanathan -- 19. Nanotechnologles and civil liability / Alan Hannah and Geoffrey Hunt -- 20. Nanotechnologies and the ethical conduct of research involving human subjects / Lorraine Sheremeta -- 21. Nanotechnologies and corporate criminal liability / Celia Wells and Juanita Elias -- Pt. Six. Conclusion -- 22. What makes nanotechnologies special? / Michael D. Mehta and Geoffrey Hunt -- Appendix: measurement scales and glossary -- Inde
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