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   https://doi.org/10.3354/meps12711  

   Stern, R; Moore, SK; Trainer, VL; Bill, BD; Fischer, A; Batten, S (November 2018, Marine Ecology Progress Series)
2. On Reporting Durable Patterns in Temporal Proximity Graphs

   https://doi.org/10.1145/3651144  

   Agarwal, Pankaj K; Hu, Xiao; Sintos, Stavros; Yang, Jun (May 2024, Proceedings of the ACM on Management of Data)

   Finding patterns in graphs is a fundamental problem in databases and data mining. In many applications, graphs are temporal and evolve over time, so we are interested in finding durable patterns, such as triangles and paths, which persist over a long time. While there has been work on finding durable simple patterns, existing algorithms do not have provable guarantees and run in strictly super-linear time. The paper leverages the observation that many graphs arising in practice are naturally proximity graphs or can be approximated as such, where nodes are embedded as points in some high-dimensional space, and two nodes are connected by an edge if they are close to each other. We work with an implicit representation of the proximity graph, where nodes are additionally annotated by time intervals, and design near-linear-time algorithms for finding (approximately) durable patterns above a given durability threshold. We also consider an interactive setting where a client experiments with different durability thresholds in a sequence of queries; we show how to compute incremental changes to result patterns efficiently in time near-linear to the size of the changes. 

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3. Spatial and temporal patterns in Arctic mosquito abundance

   https://doi.org/10.1111/een.13198  

   DeSiervo, Melissa_H; Finger‐Higgens, Rebecca_A; Ayres, Matthew_P; Virginia, Ross_A; Culler, Lauren_E (November 2022, Ecological Entomology)

   Abstract Organisms that undergo a shift in ontogeny and habitat type often change their spatial distribution throughout their life cycle, but how this affects population dynamics remains poorly understood.We examined spatial and temporal patterns inAedes nigripesabundance, a widespread univoltine Arctic mosquito species (Diptera: Culicidae), hypothesizing that the spatial distribution of adults would be closely tied to aquatic habitat.We tracked adult densities ofA. nigripesnear Kangerlussuaq, Greenland using emergence traps, CO₂‐baited traps, and sweep‐nets.In back‐to‐back years of sampling (2017 and 2018) we found two‐fold variation in overall abundance.Adults were spatially patchy when first emerging from aquatic habitats but within a week, mean capture rates for host‐seeking adult females were similar across locations, even in places far from larval habitat.Daily variation in mosquito captures was primarily explained by weather, with virtually no mosquito activity when temperatures averaged less than 8°C or wind speeds exceeded 6 m/s. Gravid females (3% of resting adults) were spatially patchy on the landscape, but not always in the same places where most adults emerged.The spatial distribution of adults is quickly uncoupled from the spatial distribution of larvae becauseA. nigripesfemales may disperse far from their natal habitats in search of a blood‐meal and high‐quality oviposition habitat. 8. This research highlights the value of studying ecological processes that act at disparate life stages for understanding the population biology of organisms with complex life cycles. 

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4. Temporal Dependencies and Spatio-Temporal Patterns of Time Series Models

   https://doi.org/10.1609/aaai.v38i21.30396  

   Islam, Khairul (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   The widespread use of Artificial Intelligence (AI) has highlighted the importance of understanding AI model behavior. This understanding is crucial for practical decision-making, assessing model reliability, and ensuring trustworthiness. Interpreting time series forecasting models faces unique challenges compared to image and text data. These challenges arise from the temporal dependencies between time steps and the evolving importance of input features over time. My thesis focuses on addressing these challenges by aiming for more precise explanations of feature interactions, uncovering spatiotemporal patterns, and demonstrating the practical applicability of these interpretability techniques using real-world datasets and state-of-the-art deep learning models. 

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5. Temporal graph patterns by timed automata

   https://doi.org/10.1007/s00778-023-00795-z  

   Aghasadeghi, Amir; Van_den_Bussche, Jan; Stoyanovich, Julia (May 2023, The VLDB Journal)

   Abstract Temporal graphs represent graph evolution over time, and have been receiving considerable research attention. Work on expressing temporal graph patterns or discovering temporal motifs typically assumes relatively simple temporal constraints, such as journeys or, more generally, existential constraints, possibly with finite delays. In this paper we propose to use timed automata to express temporal constraints, leading to a general and powerful notion of temporal basic graph pattern (BGP). The new difficulty is the evaluation of the temporal constraint on a large set of matchings. An important benefit of timed automata is that they support an iterative state assignment, which can be useful for early detection of matches and pruning of non-matches. We introduce algorithms to retrieve all instances of a temporal BGP match in a graph, and present results of an extensive experimental evaluation, demonstrating interesting performance trade-offs. We show that an on-demand algorithm that processes total matchings incrementally over time is preferable when dealing with cyclic patterns on sparse graphs. On acyclic patterns or dense graphs, and when connectivity of partial matchings can be guaranteed, the best performance is achieved by maintaining partial matchings over time and allowing automaton evaluation to be fully incremental. The code and datasets used in our analysis are available athttp://github.com/amirpouya/TABGP. 

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