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Footprints of the Dance: An Early Seventeenth-Century Dance Master's Notebook, written by Jennifer Neville

Judith Rock
2019 Journal of Jesuit Studies  
In her newest book, musicologist and dance historian Jennifer Neville has made a substantial contribution to our knowledge of early seventeenthcentury dance and its social and political importance in the  ...  Neville presents it to us in depth, with photographs of seventy-seven pages of the manuscript's specifically dance-related material.  ... 
doi:10.1163/22141332-00601012-13 fatcat:g24jtfhdnncf3movuj7ktykgaq

Deep Lifetime Clustering [article]

S Chandra Mouli, Leonardo Teixeira, Jennifer Neville, Bruno Ribeiro
2019 arXiv   pre-print
The goal of lifetime clustering is to develop an inductive model that maps subjects into K clusters according to their underlying (unobserved) lifetime distribution. We introduce a neural-network based lifetime clustering model that can find cluster assignments by directly maximizing the divergence between the empirical lifetime distributions of the clusters. Accordingly, we define a novel clustering loss function over the lifetime distributions (of entire clusters) based on a tight upper bound
more » ... of the two-sample Kuiper test p-value. The resultant model is robust to the modeling issues associated with the unobservability of termination signals, and does not assume proportional hazards. Our results in real and synthetic datasets show significantly better lifetime clusters (as evaluated by C-index, Brier Score, Logrank score and adjusted Rand index) as compared to competing approaches.
arXiv:1910.00547v2 fatcat:euggux4vgbfivmepehug4k7opy

Cluster-Based Social Reinforcement Learning [article]

Mahak Goindani, Jennifer Neville
2020 arXiv   pre-print
Social Reinforcement Learning methods, which model agents in large networks, are useful for fake news mitigation, personalized teaching/healthcare, and viral marketing, but it is challenging to incorporate inter-agent dependencies into the models effectively due to network size and sparse interaction data. Previous social RL approaches either ignore agents dependencies or model them in a computationally intensive manner. In this work, we incorporate agent dependencies efficiently in a compact
more » ... del by clustering users (based on their payoff and contribution to the goal) and combine this with a method to easily derive personalized agent-level policies from cluster-level policies. We also propose a dynamic clustering approach that captures changing user behavior. Experiments on real-world datasets illustrate that our proposed approach learns more accurate policy estimates and converges more quickly, compared to several baselines that do not use agent correlations or only use static clusters.
arXiv:2003.00627v2 fatcat:cs44ippmebbmdoqrjmldpsxnei

When I say diversity

Neville Chiavaroli, Julia Blitz, Jennifer Cleland
2020 Medical Education  
Diversity has become an important term in medical education, impacting on curriculum design, selection policies, and school culture. For some, it may have acquired the status of a 'god term', an essential concept which influences and guides educational practice, yet may also be used as a 'rhetorical absolute', as has been suggested for the term 'competence' in medical education. This positioning recognises the significance of diversity as a concept. However, it may also do as much disservice as
more » ... would resistance or scepticism, as it does not encourage the necessary 'unpacking' of the complexity and evolution of diversity as a key educational idea.
doi:10.1111/medu.14299 pmid:32725636 fatcat:qia6e4of4feiljrupqpiiudwqy

Bibliography [chapter]

Megan Cavell, Jennifer Neville
2020 Riddles at work in the early medieval tradition  
Elias, Megan Cavell and Jennifer Neville -9781526133724 Downloaded from at 05/07/2020 12:02:24AM via free access  ... 
doi:10.7765/9781526133724.00033 fatcat:5nztgwkgxnguvok6gztzbspe5i

Index [chapter]

Megan Cavell, Jennifer Neville
2020 Riddles at work in the early medieval tradition  
Neville -9781526133724 Downloaded from at 05/06/2020 11:34:23PM via free access  ...  , 110, 193, 274n.22 tree 89n.9, 265-7, 271-2, 282-3 trope 8, 21-3, 25-9, 35, 37n.6, 48, 164, 196, 231-2, 235, 237-8, 241, 271 Vafþrúðnismál 150, 159n. 79, 115, 178, 198, 202 Megan Cavell and Jennifer  ... 
doi:10.7765/9781526133724.00034 fatcat:t4gnw7zqsffg3nh2lj3ttdcrii

Space-Efficient Sampling from Social Activity Streams [article]

Nesreen K. Ahmed, Jennifer Neville, Ramana Kompella
2012 arXiv   pre-print
In order to efficiently study the characteristics of network domains and support development of network systems (e.g. algorithms, protocols that operate on networks), it is often necessary to sample a representative subgraph from a large complex network. Although recent subgraph sampling methods have been shown to work well, they focus on sampling from memory-resident graphs and assume that the sampling algorithm can access the entire graph in order to decide which nodes/edges to select. Many
more » ... rge-scale network datasets, however, are too large and/or dynamic to be processed using main memory (e.g., email, tweets, wall posts). In this work, we formulate the problem of sampling from large graph streams. We propose a streaming graph sampling algorithm that dynamically maintains a representative sample in a reservoir based setting. We evaluate the efficacy of our proposed methods empirically using several real-world data sets. Across all datasets, we found that our method produce samples that preserve better the original graph distributions.
arXiv:1206.4952v1 fatcat:7j6s3ovddzgjtez72x25dediyi

Contents [chapter]

Megan Cavell, Jennifer Neville
2020 Riddles at work in the early medieval tradition  
Neville 288 Part II: Ideas Introduction to Part II -Megan Cavell and Jennifer Neville 109 6 Warriors and their battle gear: conceptual blending in Anhaga (R.5) and Waepnum Awyrged (R.20) -Karin  ...  Koppinen 265 16 Bibliography 291 Index 317 Megan Cavell and Jennifer Neville -9781526133724 Downloaded from at 05/07/2020 12:09:13AM via free access  ... 
doi:10.7765/9781526133724.00002 fatcat:eutwgpggnbhc3lmwdhmaz62n6u

Representations and Ensemble Methods for Dynamic Relational Classification [article]

Ryan A. Rossi, Jennifer Neville
2011 arXiv   pre-print
Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose a framework for discovering temporal representations of relational data to increase the accuracy of statistical relational learning algorithms. The temporal relational
more » ... ions serve as a basis for classification, ensembles, and pattern mining in evolving domains. The framework includes (1) selecting the time-varying relational components (links, attributes, nodes), (2) selecting the temporal granularity, (3) predicting the temporal influence of each time-varying relational component, and (4) choosing the weighted relational classifier. Additionally, we propose temporal ensemble methods that exploit the temporal-dimension of relational data. These ensembles outperform traditional and more sophisticated relational ensembles while avoiding the issue of learning the most optimal representation. Finally, the space of temporal-relational models are evaluated using a sample of classifiers. In all cases, the proposed temporal-relational classifiers outperform competing models that ignore the temporal information. The results demonstrate the capability and necessity of the temporal-relational representations for classification, ensembles, and for mining temporal datasets.
arXiv:1111.5312v1 fatcat:ayyvsybdzfhqbdecobsz2uhoii

Lightweight Compositional Embeddings for Incremental Streaming Recommendation [article]

Mengyue Hang, Tobias Schnabel, Longqi Yang, Jennifer Neville
2022 arXiv   pre-print
Most work in graph-based recommender systems considers a static setting where all information about test nodes (i.e., users and items) is available upfront at training time. However, this static setting makes little sense for many real-world applications where data comes in continuously as a stream of new edges and nodes, and one has to update model predictions incrementally to reflect the latest state. To fully capitalize on the newly available data in the stream, recent graph-based
more » ... ion models would need to be repeatedly retrained, which is infeasible in practice. In this paper, we study the graph-based streaming recommendation setting and propose a compositional recommendation model – Lightweight Compositional Embedding (LCE) – that supports incremental updates under low computational cost. Instead of learning explicit embeddings for the full set of nodes, LCE learns explicit embeddings for only a subset of nodes and represents the other nodes implicitly, through a composition function based on their interactions in the graph. This provides an effective, yet efficient, means to leverage streaming graph data when one node type (e.g., items) is more amenable to static representation. We conduct an extensive empirical study to compare LCE to a set of competitive baselines on three large-scale user-item recommendation datasets with interactions under a streaming setting. The results demonstrate the superior performance of LCE, showing that it achieves nearly skyline performance with significantly fewer parameters than alternative graph-based models.
arXiv:2202.02427v1 fatcat:6uznu3lfmzhutcdyaw3vdhagsi

Front matter [chapter]

Megan Cavell, Jennifer Neville
2020 Riddles at work in the early medieval tradition  
Riddles at work in the early medieval tradition: Words, ideas, interactions Megan Cavell and Jennifer Neville (eds) Published by Manchester University Press Altrincham Street, Manchester M1 7JA, UK  ...  Neville -9781526133724 Downloaded from at 05/06/2020 10:57:53PM via free access Marilina Cesario and Hugh Magennis (eds) 19.  ... 
doi:10.7765/9781526133724.00001 fatcat:464fbb3c45aehantf4wt2mi4cu

Network Sampling: From Static to Streaming Graphs [article]

Nesreen K. Ahmed and Jennifer Neville and Ramana Kompella
2012 arXiv   pre-print
., different classification methods [Rossi and Neville 2012] ), and study complex network processes (e.g., information diffusion [Bakshy et al. 2012] ).  ...  analyzing a single input network and research has considered how to further split the input network into training and testing networks for evaluation [Körner and Wrobel 2006; Macskassy and Provost 2007; Neville  ... 
arXiv:1211.3412v1 fatcat:4k3vrxwe65h3nisl323d27qeby

Anomaly Detection in Dynamic Networks of Varying Size [article]

Timothy La Fond, Jennifer Neville, Brian Gallagher
2014 arXiv   pre-print
Dynamic networks, also called network streams, are an important data representation that applies to many real-world domains. Many sets of network data such as e-mail networks, social networks, or internet traffic networks are best represented by a dynamic network due to the temporal component of the data. One important application in the domain of dynamic network analysis is anomaly detection. Here the task is to identify points in time where the network exhibits behavior radically different
more » ... m a typical time, either due to some event (like the failure of machines in a computer network) or a shift in the network properties. This problem is made more difficult by the fluid nature of what is considered "normal" network behavior. The volume of traffic on a network, for example, can change over the course of a month or even vary based on the time of the day without being considered unusual. Anomaly detection tests using traditional network statistics have difficulty in these scenarios due to their Density Dependence: as the volume of edges changes the value of the statistics changes as well making it difficult to determine if the change in signal is due to the traffic volume or due to some fundamental shift in the behavior of the network. To more accurately detect anomalies in dynamic networks, we introduce the concept of Density-Consistent network statistics. On synthetically generated graphs anomaly detectors using these statistics show a a 20-400% improvement in the recall when distinguishing graphs drawn from different distributions. When applied to several real datasets Density-Consistent statistics recover multiple network events which standard statistics failed to find.
arXiv:1411.3749v1 fatcat:q4os7zrxkfhxhfagx4vkhxaaiu

Role-Dynamics: Fast Mining of Large Dynamic Networks [article]

Ryan Rossi, Brian Gallagher, Jennifer Neville, Keith Henderson
2012 arXiv   pre-print
To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different
more » ... nities. Our novel approach learns the appropriate structural role dynamics for any arbitrary network and tracks the changes over time. In particular, we uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network. We identify interesting node and network patterns such as stationary and non-stationary roles, spikes/steps in role-memberships (perhaps indicating anomalies), increasing/decreasing role trends, among many others. Our results indicate that the nodes in each of these networks have distinct connectivity patterns that are non-stationary and evolve considerably over time. Overall, the experiments demonstrate the effectiveness of our approach for fast mining and tracking of the dynamics in large networks. Furthermore, the dynamic structural representation provides a basis for building more sophisticated models and tools that are fast for exploring large dynamic networks.
arXiv:1203.2200v1 fatcat:3rgnptfxrfd6hhzayvsqgbs77e

Transforming Graph Representations for Statistical Relational Learning [article]

Ryan A. Rossi, Luke K. McDowell, David W. Aha, Jennifer Neville
2012 arXiv   pre-print
, 2008; Rossi & Neville, 2010) .  ...  al., 2003b) , RPT (Neville et al., 2003a) Construction Link Aggregations (Kahanda & Neville, 2009) MLN Structure Learning (Kok & Domingos, 2009 , 2010 Graph Features (Lichtenwalter et al., 2010  ... 
arXiv:1204.0033v1 fatcat:32zwr7gadfbo5kvsdh57w3gtci
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