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### Metal selective co-ordinative self-assembly of π -donors

JAIN, ANKIT, VENKATA RAO, K, GOSWAMI, ANKITA, GEORGE, SUBI
Journal of Chemical Sciences, 2011, Vol.123(6), pp.773-781 [Peer Reviewed Journal]
Springer Science & Business Media B.V.
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Title: Metal selective co-ordinative self-assembly of π -donors
Author: JAIN, ANKIT; VENKATA RAO, K; GOSWAMI, ANKITA; GEORGE, SUBI
Subject: Self-assembly ; -donor molecules ; nanostructures ; metal ion sensing
Description: Metal selective co-ordinative nanostructures were constructed by the supramolecular co-assembly of pyridine appended TTF ( TTF-Py ) and pyrene ( PYR-Py ) derivatives in appropriate solvent composition mixtures with metal ions. Microscopic analyses show that TTF-Py shows distinctive morphology with either of these metal ions, forming I-D tapes with 1:1 Pb 2 +  complex and 2-D sheets with 1:2 Zn 2 +  complex. A rationale has been provided from molecular level packing for such hierarchical changes. In case of Cu 2 +  , we have observed an anomalous binding of metal ion to the core sulphur groups causing redox changes in the TTF core. PYR-Py on the other hand is shown to be a fluorescent sensor for Pb 2 +  , Zn 2 +  , Hg 2 +  and Ag  +  . With different fluorescent response for metal complexes, we essentially obtained similar 1-D assemblies suggesting similar binding modes for all of them. Supramolecular approach through which morphology of an electron donor moiety can be engineered by metal ions can be a new tool in nanoelectronics. Graphical Abstract Metal selective self-assembly of pyridine appended donors moieties of TTF and Pyrene, to various nanostructures is presented. The supramolecular morphology is greatly dependent on the metal ion and a direct correlation of binding mode with the nanostructures is observed.
Is part of: Journal of Chemical Sciences, 2011, Vol.123(6), pp.773-781
Identifier: 0974-3626 (ISSN); 0973-7103 (E-ISSN); 10.1007/s12039-011-0163-7 (DOI)
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### Effect of color reconnection and rope formation on resonance production in p$-$p collisions in Pythia 8

Goswami, Ankita, Nayak, Ranjit, Nandi, Basanta Kumar, Dash, Sadhana
Cornell University
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Title: Effect of color reconnection and rope formation on resonance production in p$-$p collisions in Pythia 8
Author: Goswami, Ankita; Nayak, Ranjit; Nandi, Basanta Kumar; Dash, Sadhana
Subject: High Energy Physics - Phenomenology
Description: The resonance production in proton$-$proton collisions at $\sqrt{s}$ = 7 TeV and 13 TeV have been investigated using Pythia 8 event generator within the framework of microscopic processes like color reconnection and rope hadronization. Specifically, the observable effects of different modes of color...
Identifier: 1911.00559 (ARXIV ID)
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### Effect of color reconnection and rope formation on resonance production in p$$-$$p collisions in Pythia 8

Goswami, Ankita, Nayak, Ranjit, Nandi, Basanta, Dash, Sadhana
arXiv.org, Nov 1, 2019
© ProQuest LLC All rights reserved, Engineering Database, Publicly Available Content Database, ProQuest Engineering Collection, ProQuest Technology Collection, ProQuest SciTech Collection, Materials Science & Engineering Database, ProQuest Central (new), ProQuest Central Korea, SciTech Premium Collection, Technology Collection, ProQuest Central Essentials, ProQuest One Academic, Engineering Collection (ProQuest)
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Title: Effect of color reconnection and rope formation on resonance production in p$$-$$p collisions in Pythia 8
Author: Goswami, Ankita; Nayak, Ranjit; Nandi, Basanta; Dash, Sadhana
Subject: Quarks ; Collisions ; Partons ; Baryons ; Quarks ; Rope ; Resonance ; Protons ; Color ; Quantum Chromodynamics
Description: The resonance production in proton$$-$$proton collisions at $$\sqrt{s}$$ = 7 TeV and 13 TeV have been investigated using Pythia 8 event generator within the framework of microscopic processes like color reconnection and rope hadronization. Specifically, the observable effects of different modes of color reconnections on the ratio of yields of mesonic and baryonic resonances with respect to their stable counterpart have been explored as a function of event activity. A suppression in the ratio is observed as a function of number of multi-partonic interactions for mesonic resonances. The $$\mathrm{\phi/K}$$ and $$\mathrm{\phi/\pi}$$ ratios show an enhancement for high multiplicity events due to enhanced production of strange quarks via the microscopic process of rope hadronization in the partonic phase. The mechanism of the hadronization of color ropes together with the QCD-based color reconnection of partons predicted an enhancement in the ratio for baryonic resonances to non-resonance baryons...
Is part of: arXiv.org, Nov 1, 2019
Identifier: 2331-8422 (E-ISSN)
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### Benchmarks for Graph Embedding Evaluation

Goyal, Palash, Huang, Di, Goswami, Ankita, Canedo, Arquimedes, Ferrara, Emilio
arXiv.org, Aug 26, 2019
© ProQuest LLC All rights reserved, Engineering Database, Publicly Available Content Database, ProQuest Engineering Collection, ProQuest Technology Collection, ProQuest SciTech Collection, Materials Science & Engineering Database, ProQuest Central (new), ProQuest Central Korea, SciTech Premium Collection, Technology Collection, ProQuest Central Essentials, ProQuest One Academic, Engineering Collection (ProQuest)
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Title: Benchmarks for Graph Embedding Evaluation
Author: Goyal, Palash; Huang, Di; Goswami, Ankita; Canedo, Arquimedes; Ferrara, Emilio
Contributor: Ferrara, Emilio (pacrepositoryorg)
Subject: Graphs ; Properties (Attributes) ; Graphical Representations ; Embedding ; Benchmarks ; Social and Information Networks ; Machine Learning
Description: Graph embedding is the task of representing nodes of a graph in a low-dimensional space and its applications for graph tasks have gained significant traction in academia and industry. The primary difference among the many recently proposed graph embedding methods is the way they preserve the inherent properties of the graphs. However, in practice, comparing these methods is very challenging. The majority of methods report performance boosts on few selected real graphs. Therefore, it is difficult to generalize these performance improvements to other types of graphs. Given a graph, it is currently impossible to quantify the advantages of one approach over another. In this work, we introduce a principled framework to compare graph embedding methods. Our goal is threefold: (i) provide a unifying framework for comparing the performance of various graph embedding methods, (ii) establish a benchmark with real-world graphs that exhibit different structural properties, and (iii) provide users with...
Is part of: arXiv.org, Aug 26, 2019
Identifier: 2331-8422 (E-ISSN)
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### Benchmarks for Graph Embedding Evaluation

Goyal, Palash, Huang, Di, Goswami, Ankita, Chhetri, Sujit Rokka, Canedo, Arquimedes, Ferrara, Emilio
Cornell University
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Title: Benchmarks for Graph Embedding Evaluation
Author: Goyal, Palash; Huang, Di; Goswami, Ankita; Chhetri, Sujit Rokka; Canedo, Arquimedes; Ferrara, Emilio
Subject: Computer Science - Social And Information Networks ; Computer Science - Machine Learning
Description: Graph embedding is the task of representing nodes of a graph in a low-dimensional space and its applications for graph tasks have gained significant traction in academia and industry. The primary difference among the many recently proposed graph embedding methods is the way they preserve the inherent properties of the graphs. However, in practice, comparing these methods is very challenging. The majority of methods report performance boosts on few selected real graphs. Therefore, it is difficult to generalize these performance improvements to other types of graphs. Given a graph, it is currently impossible to quantify the advantages of one approach over another. In this work, we introduce a principled framework to compare graph embedding methods. Our goal is threefold: (i) provide a unifying framework for comparing the performance of various graph embedding methods, (ii) establish a benchmark with real-world graphs that exhibit different structural properties, and (iii) provide users with a tool to identify the best graph embedding method for their data. This paper evaluates 4 of the most influential graph embedding methods and 4 traditional link prediction methods against a corpus of 100 real-world networks with varying properties. We organize the 100 networks in terms of their properties to get a better understanding of the embedding performance of these popular methods. We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance. We rank the state-of-the-art embedding approaches using the GFS-score and show that it can be used to understand and evaluate novel embedding approaches. We envision that the proposed framework (https://www.github.com/palash1992/GEM-Benchmark) will serve the community as a benchmarking platform to test and compare the performance of future graph embedding techniques.
Identifier: 1908.06543 (ARXIV ID)
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