102,343 research outputs found
Evaluation of Azadirachta indica leaf extract for hypoglycaemic activity in rats
No full textWater soluble fractions separated from the crude leaf extract of Azadirachta indica A. Juss. lowered hyperglycaemia in streptozotocin diabetes. Systematic fractionation of the concentrates led to the isolation of flavonol glycosides, quercetin-3-O-β-D-glucoside, myricetin-3-O-rutinoside, quercetin-3-O-rutinoside, kaempferol-3-O-rutinoside, kaempferol-3-O-β-D-glucoside and quercetin-3-O-α-L-rhamnoside
Better bounds on the adaptivity gap of influence maximization under full-adoption feedback
No full textIn the influence maximization (IM) problem, we are given a social network and a budget k, and we look for a set of k nodes in the network, called seeds, that maximize the expected number of nodes that are reached by an influence cascade generated by the seeds, according to some stochastic model for influence diffusion. Extensive studies have been done on the IM problem, since this definition by Kempe et al. [26]. However, most of the work focuses on the non-adaptive version of the problem where all the k seed nodes must be selected before the cascade starts. In this paper we study the adaptive IM, where the nodes are selected sequentially one by one, and the decision on the i-th seed can be based on the observed cascade produced by the first i−1 seeds. We focus on the full-adoption feedback in which we can observe the entire cascade of each previously selected seed under the independent cascade model where each edge is associated with an independent probability of diffusing influence. Previous works showed that there are constant upper bounds on the adaptivity gap, which compares the performance of an adaptive algorithm against a non-adaptive one, but the analyses used to prove these bounds only work for specific graph classes such as in-arborescences, out-arborescences, and one-directional bipartite graphs. Our main result is the first sub-linear upper bound that holds for any graph. Specifically, we show that the adaptivity gap is upper-bounded by n3+1, where n is the number of nodes in the graph. Moreover, we improve over the known upper bound for in-arborescences from 2e/(e−1)≈3.16 to 2e2/(e2−1)≈2.31. Then, we consider (β,γ)-bounded-activation graphs, where all nodes but β influence in expectation at most γ∈[0,1) neighbors each; for this class of influence graphs we show that the adaptivity gap is at most [Formula presented]. Finally, we study α-bounded-degree graphs, that is the class of undirected graphs in which the sum of node degrees higher than two is at most α, and show that the adaptivity gap is upper-bounded by α+O(1); we also show that in 0-bounded-degree graphs, i.e. undirected graphs in which each connected component is a path or a cycle, the adaptivity gap is at most 3e3/(e3−1)≈3.16. To prove our bounds, we introduce new techniques to relate adaptive policies with non-adaptive ones that might be of their own interest
Better Bounds on the Adaptivity Gap of Influence Maximization under Full-adoption Feedback
No full textIn the influence maximization (IM) problem, we are given a social network and a budget k, and we look for a set of k nodes in the network, called seeds, that maximize the expected number of nodes that are reached by an influence cascade generated by the seeds, according to some stochastic model for influence diffusion. Extensive studies have been done on the IM problem, since his definition by Kempe, Kleinberg, and Tardos (2003). However, most of the work focuses on the nonadaptive version of the problem where all the k seed nodes must be selected before that the cascade starts. In this paperwe study the adaptive IM, where the nodes are selected sequentially one by one, and the decision on the i-th seed can be based on the observed cascade produced by the first i - 1 seeds. We focus on the full-adoption feedback in which we can observe the entire cascade of each previously selected seed and on the independent cascade model where each edge is associated with an independent probability of diffusing influence. Previous works showed that there are constant upper bounds on the adaptivity gap, which compares the performance of an adaptive algorithm against a non-adaptive one, but the analyses used to prove these bounds only works for specific graph classes such as in-arborescences, out-arborescences, and one-directional bipartite graphs. Our main result is the first sub-linear upper bound that holds for any graph. Specifically, we show that the adaptivity gap is upper-bounded by 3 √n+1, where = is the number of nodes in the graph. Moreover we improve over the known upper bound for in-arborescences from 2e/(e-1) ≈ 3.16 to 2e2/(e2-1) ≈ 2.31. Finally, we study α-bounded graphs, a class of undirected graphs in which the sum of node degrees higher than two is at most α, and show that the adaptivity gap is upper-bounded by √α +O(1). Moreover, we show that in 0-bounded graphs, i.e. undirected graphs in which each connected component is a path or a cycle, the adaptivity gap is at most 3e3/e3-1) ≈ 3.16. To prove our bounds, we introduce new techniques to relate adaptive policies with non-adaptive ones that might be of their own interest
Better bounds on the adaptivity gap of influence maximization under full-adoption feedback
No full textIn the influence maximization (IM) problem, we are given a social network and a budget k, and we look for a set of k nodes in the network, called seeds, that maximize the expected number of nodes that are reached by an influence cascade generated by the seeds, according to some stochastic model for influence diffusion. Extensive studies have been done on the IM problem, since his definition by Kempe, Kleinberg, and Tardos (2003). However, most of the work focuses on the non-adaptive version of the problem where all the k seed nodes must be selected before that the cascade starts. In this paper we study the adaptive IM, where the nodes are selected sequentially one by one, and the decision on the i-th seed can be based on the observed cascade produced by the first i-1 seeds. We focus on the full-adoption feedback in which we can observe the entire cascade of each previously selected seed and on the independent cascade model where each edge is associated with an independent probability of diffusing influence.
Previous works showed that there are constant upper bounds on the adaptivity gap, which compares the performance of an adaptive algorithm against a non-adaptive one, but the analyses used to prove these bounds only works for specific graph classes such as in-arborescences, out-arborescences, and one-directional bipartite graphs. Our main result is the first sub-linear upper bound that holds for any graph. Specifically, we show that the adaptivity gap is upper-bounded by ∛n+1, where n is the number of nodes in the graph. Moreover we improve over the known upper bound for in-arborescences from 2e/(e-1)≈3.16 to 2e²/(e²-1)≈2.31. Finally, we study α-bounded graphs, a class of undirected graphs in which the sum of node degrees higher than two is at most α, and show that the adaptivity gap is upper-bounded by √α+O(1). Moreover, we show that in 0-bounded graphs, i.e. undirected graphs in which each connected component is a path or a cycle, the adaptivity gap is at most 3e³/(e³-1)≈3.16.
To prove our bounds, we introduce new techniques to relate adaptive policies with non-adaptive ones that might be of their own interest
A construction of SKT manifolds using toric geometry
No full textWe produce infinite families of SKT manifolds by using methods of toric geometry like the J-construction. These SKT manifolds are total spaces of certain principal G-bundles over smooth projective toric varieties, where G is an even dimensional compact connected Lie group
Improved approximation factor for adaptive influence maximization via simple greedy strategies
No full textIn the adaptive influence maximization problem, we are given a social network and a budget k, and we iteratively select k nodes, called seeds, in order to maximize the expected number of nodes that are reached by an influence cascade that they generate according to a stochastic model for influence diffusion. The decision on the next seed to select is based on the observed cascade of previously selected seeds. We focus on the myopic feedback model, in which we can only observe which neighbors of previously selected seeds have been influenced and on the independent cascade model, where each edge is associated with an independent probability of diffusing influence. While adaptive policies are strictly stronger than non-adaptive ones, in which all the seeds are selected beforehand, the latter are much easier to design and implement and they provide good approximation factors if the adaptivity gap, the ratio between the adaptive and the non-adaptive optima, is small. Previous works showed that the adaptivity gap is at most 4, and that simple adaptive or non-adaptive greedy algorithms guarantee an approximation of 1/4 (1 - 1/e) ≈ 0.158 for the adaptive optimum. This is the best approximation factor known so far for the adaptive influence maximization problem with myopic feedback. In this paper, we directly analyze the approximation factor of the non-adaptive greedy algorithm, without passing through the adaptivity gap, and show an improved bound of 1/2(1 - 1/e) ≈ 0.316. Therefore, the adaptivity gap is at most 2e/e-1 ≈ 3.164. To prove these bounds, we introduce a new approach to relate the greedy non-adaptive algorithm to the adaptive optimum. The new approach does not rely on multi-linear extensions or random walks on optimal decision trees, which are commonly used techniques in the field. We believe that it is of independent interest and may be used to analyze other adaptive optimization problems. Finally, we also analyze the adaptive greedy algorithm, and show that guarantees an improved approximation factor of 1 - 1/√e ≈ 0.393
Comparison of acute elastic recoil between the SAPIEN-XT and SAPIEN valves in transfemoral-transcatheter aortic valve replacement
No full textArticle first published online: 13 November 2014Abstract not availableAatish Garg, Akhil Parashar, Shikhar Agarwal, Olcay Aksoy, Muhammad Hammadah, Kanhaiya Lal Poddar, Rishi Puri, Lars G. Svensson, Amar Krishnaswamy, E. Murat Tuzcu and Samir R. Kapadi
-Connections on principal bundles over complete -varieties
No full textLet be a complete variety over an algebraically closed field of
characteristic zero, equipped with an action of an algebraic group . Let
be a reductive group. We study the notion of -connection on a principal
-bundle. We give necessary and sufficient criteria for the existence of
-connections extending the Atiyah-Weil type criterion for holomorphic
connections obtained by Azad and Biswas. We also establish a relationship
between the existence of -connection and equivariant structure on a
principal -bundle, under the assumption that is semisimple and simply
connected. These results have been obtained by Biswas et al. when the
underlying variety is smooth.Comment: 23 page
Bibliographie Hilarion G. Petzold 1958 – 2009 mit Anhang als Einführung
No full textDieses Archiv enthält die Gesamtbibliographie der Werke des Autors nebst einiger Texte „Über H. G. Petzold“ im Schlussteil der Bibliographie sowie einen Anhang mit einer Einführung in die Architektur des Werkes in seinem wissenslogischen Aufbau als Ausarbeitung seines „Tree of Science Modells“ (2007).This archive contains the complete bibliography of the author and some texts about H. G. Petzold, moreover an epilogue with an introduction to the architecture of the works in its epistemological structure and composition and as an elaborations of Petzold’s „Tree of Science Modell (2007).https://www.fpi-publikation.de/polyloge/01-2009-petzold-h-g-gesamtbibliographie-h-g-petzold-1958-2009-updating-november2009/peerReviewedpublishedVersio
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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