1,721,017 research outputs found
Trees with an On-Line Degree Ramsey Number of Four
Full text linkOn-line Ramsey theory studies a graph-building game between two players. The player called Builder builds edges one at a time, and the player called Painter paints each new edge red or blue after it is built. The graph constructed is called the background graph. Builder's goal is to cause the background graph to contain a monochromatic copy of a given goal graph, and Painter's goal is to prevent this. In the S[subscript k]-game variant of the typical game, the background graph is constrained to have maximum degree no greater than k. The on-line degree Ramsey number [˚over R][subscript Δ](G) of a graph G is the minimum k such that Builder wins an S[subscript k]-game in which G is the goal graph. Butterfield et al. previously determined all graphs G satisfying [˚ over R][subscript Δ](G)≤3. We provide a complete classification of trees T satisfying [˚ over R][subscript Δ](T)=4.National Science Foundation (U.S.) (Grant DMS-0754106)United States. National Security Agency (Grant H98230-06-1-0013
Acyclic Subgraphs of Planar Digraphs
Full text linkAn acyclic set in a digraph is a set of vertices that induces an acyclic subgraph. In 2011, Harutyunyan conjectured that every planar digraph on n vertices without directed 2-cycles possesses an acyclic set of size at least 3n=5. We prove this conjecture for digraphs where every directed cycle has length at least 8. More generally, if g is the length of the shortest directed cycle, we show that there exists an acyclic set of size at least (1 - 3/g)n
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
AI in climate change adaptation : applications to remote sensing and climate science
Full text linkLe changement climatique représente l’un des défis les plus pressants de notre société, entraînant une augmentation des phénomènes météorologiques extrêmes et des altérations fondamentales des écosystèmes terrestres. Face à l’accélération de ces impacts, il existe un besoin urgent de capacités avancées de surveillance et de modélisation pour guider les efforts d’adaptation et d’atténuation. L’apprentissage profond s’est imposé comme un outil puissant pour analyser les données environnementales complexes et améliorer notre compréhension des systèmes terrestres. Dans cette thèse, nous explorons comment l’apprentissage profond peut être appliqué efficacement pour répondre aux défis critiques en télédétection et en sciences du climat. Dans le domaine des sciences du climat, cette thèse présente de nouveaux jeux de données et un cadre d’apprentissage profond sous contraintes strictes pour l’analyse des données climatiques, garantissant la préservation des principes physiques tels que la conservation de la masse et de l’énergie. Nous développons d’abord un jeu de données complet pour le transfert radiatif atmosphérique - la composante la plus coûteuse en calcul des modèles climatiques - en évaluant les modèles à l’état de l’art sur ce benchmark. De plus, pour résoudre le problème du coût computationnel des simulations climatiques à haute résolution, nous introduisons des méthodes de réduction d’échelle sous contraintes. Nous présentons diverses couches de contraintes - additives, multiplicatives et basées sur softmax - qui peuvent être intégrées à toute architecture tout en assurant la conservation de l’énergie entre les prédictions à basse et haute résolution. Appliquées au jeu de données ERA5, nos méthodes atteignent une précision supérieure tout en maintenant la cohérence physique. Pour les applications de télédétection, nous développons un cadre efficace pour la segmentation sémantique des arbres utilisant des séries temporelles d’images aériennes. Notre approche combine un module de traitement léger pour l’analyse des données temporelles avec une fonction de perte hiérarchique qui exploite les relations taxonomiques dans les étiquettes des espèces d’arbres. À travers des expériences approfondies, nous démontrons que notre méthode surpasse les approches existantes de l’état de l’art tout en maintenant des performances robustes à travers les hiérarchies taxonomiques.Climate change presents one of society’s most pressing challenges, driving increases in extreme weather events and fundamental alterations to Earth’s ecosystems. As these impacts accelerate, there is an urgent need for advanced monitoring and modeling capabilities to guide adaptation and mitigation efforts. Deep learning has emerged as a powerful tool for analyzing complex environmental data and improving our understanding of Earth systems. In this thesis, we explore how deep learning can be effectively applied to address critical challenges in remote sensing and climate science. In the climate science domain, this thesis introduces novel datasets and a framework for hard-constrained deep learning in climate data analysis, ensuring that physical principles like mass and energy conservation are preserved. We first develop and open-source a comprehensive dataset for atmospheric radia- tive transfer, evaluating state-of-the-art models on this benchmark. Additionally, to address the computational expense of high-resolution climate model simulations, we introduce constrained downscaling methods. We present various constraint layers - additive, multiplicative, and softmax-based - that can be integrated into any architecture while enforcing energy conservation between low and high-resolution predictions. When applied to the ERA5 dataset, our methods achieve superior accuracy while maintaining physical consistency. For remote sensing applications, we develop an efficient framework for tree semantic segmentation using aerial imagery time series. Our approach combines a lightweight processor module for temporal data analysis with a hierarchical loss function that leverages taxonomic relationships in tree species labels. Through extensive experiments, we demonstrate that our method outperforms existing state-of-the-art approaches while maintaining robust performance across taxonomic hierarchies
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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