528 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
#nowplaying-rs
No full text<p>The nowplaying-rs dataset features context- and content features of listening events. It contains 11.6 million music listening events of 139K users and 346K tracks collected from Twitter. The dataset comes with a rich set of item content features and user context features, as well as timestamps of the listening events. Moreover, some of the user context features imply the cultural origin of the users, and some others - like hashtags - give clues to the emotional state of a user underlying a listening event.</p>
<p>The dataset contains three files:</p>
<ul>
<li>user_track_hashtag_timestamp.csv contains basic information about each listening event. For each listening event, we provide an id, the user_id, track_id, hashtag, created_at </li>
<li>context_content_features.csv: contains all context and content features. For each listening event, we provide the id of the event, user_id, track_id, artist_id, content features regarding the track mentioned in the event (instrumentalness, liveness, speechiness, danceability, valence, loudness, tempo, acousticness, energy, mode, key) and context features regarding the listening event (coordinates (as geoJSON), place (as geoJSON), geo (as geoJSON), tweet_language, created_at, user_lang, time_zone, entities contained in the tweet).</li>
<li>sentiment_values.csv contains sentiment information for hashtags. It contains the hashtag itself and the sentiment values gathered via four different sentiment dictionaries: AFINN, Opinion Lexicon, Sentistrength Lexicon and vader. For each of these dictionaries we list the minimum, maximum, sum and average of all sentiments of the tokens of the hashtag (if available, else we list empty values). However, as most hashtags only consist of a single token, these values are equal in most cases. Please note that the lexica are rather diverse and therefore, are able to resolve very different terms against a score. Hence, the resulting csv is rather sparse. The file contains the following comma-separated values: <hashtag, vader_min, vader_max, vader_sum,vader_avg, afinn_min, afinn_max, afinn_sum, afinn_avg, ol_min, ol_max, ol_sum, ol_avg, ss_min, ss_max, ss_sum, ss_avg >, where we abbreviate all scores gathered over the Opinion Lexicon with the prefix 'ol'. Similarly, 'ss' stands for SentiStrength. </li>
</ul>
<p>Please note that user_track_hashtag_timestamp.csv and context_content_features.csv partly provide the same features. We deliberately chose to do so to be able to provide useable files that do not have to be matched and joined with each other to perform e.g., simple recommendation tasks.</p>
<p>Please also find the training and test-splits for the dataset in this repo. Also, Asmita provides prototypical implementations of a context-aware recommender system based on the dataset at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM.</p>
<p><br>
If you make use of this dataset, please cite the following paper where we describe and experiment with the dataset:</p>
<p>@inproceedings{smc18,<br>
title = {#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems},<br>
author = {Asmita Poddar and Eva Zangerle and Yi-Hsuan Yang},<br>
url = {http://mac.citi.sinica.edu.tw/~yang/pub/poddar18smc.pdf},<br>
year = {2018},<br>
date = {2018-07-04},<br>
booktitle = {Proceedings of the 15th Sound & Music Computing Conference},<br>
address = {Limassol, Cyprus},<br>
note = {code at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM},<br>
tppubtype = {inproceedings}<br>
}</p>
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
A Low SWaP-C Radar Altimeter Transceiver Design for Small Satellites
No full textThis paper discusses the design details of a high resolution, low "Size, Weight, Power and Cost" (SWaP-C) radar altimeter (RA) system. Operating frequency of the radar is chosen within the Ka-band to achieve the desired size and weight requirements, that are highly demanded for the small satellite missions in a cost-efficient way. We propose a system design such that, an intended radar altimeter can be built by using the Commercial off the Shelf (COTS) components. The simulation results show that the proposed RA has high potentiality for realization.Accepted author manuscriptMicrowave Sensing, Signals & SystemsAtmospheric Remote SensingMathematical Geodesy and Positionin
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