1,721,026 research outputs found
A unifying look at sequence submodularity
No full textSeveral real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become prohibitive in most cases of practical interest. However, if the objective function is submodular (intuitively, it exhibits a diminishing return property), the optimization problem becomes more manageable. Recently, there has been increasing interest in sequence submodularity in connection with applications such as recommender systems and online ad allocation. However, mostly ad hoc models and solutions have emerged within these applicative contexts. In consequence, the field appears fragmented and lacks coherence. In this paper, we offer a unified view of sequence submodularity and provide a generalized greedy algorithm that enjoys strong theoretical guarantees. We show how our approach naturally captures several application domains, and our algorithm encompasses existing methods, improving over them
Through the lens of sequence submodularity
Full text linkSeveral real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become prohibitive in most cases of practical interest. However, if the objective function is submodular (intuitively, it exhibits a diminishing return property), the optimization problem becomes more manageable. Recently, there has been increasing interest in sequence submodularity in connection with applications such as recommender systems and online ad allocation. However, mostly ad hoc models and solutions have emerged within these applicative contexts. In consequence, the field appears fragmented and lacks coherence. In this paper, we offer a unified view of sequence submodularity and provide a generalized greedy algorithm that enjoys strong theoretical guarantees. We show how our approach naturally captures several application domains, and our algorithm encompasses existing methods, improving over them
Autonomous target search with multiple coordinated UAVS
No full textSearch and tracking is the problem of locating a moving target and following it to its destination. In this work, we consider a scenario in which the target moves across a large geographical area by following a road network and the search is performed by a team of unmanned aerial vehicles (UAVs). We formulate search and tracking as a combinatorial optimization problem and prove that the objective function is submodular. We exploit this property to devise a greedy algorithm. Although this algorithm does not offer strong theoretical guarantees because of the presence of temporal constraints that limit the feasibility of the solutions, it presents remarkably good performance, especially when several UAVs are available for the mission. As the greedy algorithm suffers when resources are scarce, we investigate two alternative optimization techniques: Constraint Programming (CP) and AI planning. Both approaches struggle to cope with large problems, and so we strengthen them by leveraging the greedy algorithm. We use the greedy solution to warm start the CP model and to devise a domain-dependent heuristic for planning. Our extensive experimental evaluation studies the scalability of the different techniques and identifies the conditions under which one approach becomes preferable to the others
Through the lens of sequence submodularity
Full text linkSeveral real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become prohibitive in most cases of practical interest. However, if the objective function is submodular (intuitively, it exhibits a diminishing return property), the optimization problem becomes more manageable. Recently, there has been increasing interest in sequence submodularity in connection with applications such as recommender systems and online ad allocation. However, mostly ad hoc models and solutions have emerged within these applicative contexts. In consequence, the field appears fragmented and lacks coherence. In this paper, we offer a unified view of sequence submodularity and provide a generalized greedy algorithm that enjoys strong theoretical guarantees. We show how our approach naturally captures several application domains, and our algorithm encompasses existing methods, improving over them
A unifying look at sequence submodularity
Full text linkSeveral real-world problems in engineering and applied science require the selection of sequences that maximize a given reward function. Optimizing over sequences as opposed to sets requires exploring an exponentially larger search space and can become prohibitive in most cases of practical interest. However, if the objective function is submodular (intuitively, it exhibits a diminishing return property), the optimization problem becomes more manageable. Recently, there has been increasing interest in sequence submodularity in connection with applications such as recommender systems and online ad allocation. However, mostly ad hoc models and solutions have emerged within these applicative contexts. In consequence, the field appears fragmented and lacks coherence. In this paper, we offer a unified view of sequence submodularity and provide a generalized greedy algorithm that enjoys strong theoretical guarantees. We show how our approach naturally captures several application domains, and our algorithm encompasses existing methods, improving over them. (C) 2021 The Authors. Published by Elsevier B.V
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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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