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1. Dawnside Auroral Polarization Streams

   https://doi.org/10.1029/2019JA027742  

   Liu, Jiang; Lyons, L. R.; Wang, Chih‐Ping; Hairston, M. R.; Zhang, Yongliang; Zou, Ying (August 2020, Journal of Geophysical Research: Space Physics)

   null (Ed.)
2. Streams on FIRE: Populations of Detectable Stellar Streams in the Milky Way and FIRE

   https://doi.org/10.3847/1538-4357/acc582  

   Shipp, Nora; Panithanpaisal, Nondh; Necib, Lina; Sanderson, Robyn; Erkal, Denis; Li, Ting S.; Santistevan, Isaiah B.; Wetzel, Andrew; Cullinane, Lara R.; Ji, Alexander P.; et al (May 2023, The Astrophysical Journal)

   Abstract We present the first detailed comparison of populations of dwarf galaxy stellar streams in cosmological simulations and the Milky Way. In particular, we compare streams identified around 13 Milky Way analogs in the FIRE-2 simulations to streams observed by the Southern Stellar Stream Spectroscopic Survey ( S 5 ). For an accurate comparison, we produce mock Dark Energy Survey (DES) observations of the FIRE streams and estimate the detectability of their tidal tails and progenitors. The number and stellar mass distributions of detectable stellar streams is consistent between observations and simulations. However, there are discrepancies in the distributions of pericenters and apocenters, with the detectable FIRE streams, on average, forming at larger pericenters (out to >110 kpc) and surviving only at larger apocenters (≳40 kpc) than those observed in the Milky Way. We find that the population of high-stellar-mass dwarf galaxy streams in the Milky Way is incomplete. Interestingly, a large fraction of the FIRE streams would only be detected as intact satellites in DES-like observations, since their tidal tails have too low surface brightness to be detectable. We thus predict a population of yet-undetected tidal tails around Milky Way satellites, as well as a population of fully undetected low-surface-brightness stellar streams, and estimate their detectability with the Rubin Observatory. Finally, we discuss the causes and implications of the discrepancies between the stream populations in FIRE and the Milky Way, and explore future avenues for tests of satellite disruption in cosmological simulations. 

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3. Anomaly Detection in Catalog Streams

   https://doi.org/10.1109/TBDATA.2022.3161925  

   Yang, Chen; Du, Zhihui; Meng, Xiaofeng; Zhang, Xukang; Hao, Xinli; Bader, David A (March 2022, IEEE Transactions on Big Data)

   etecting valuable anomalies with high accuracy and low latency from large amounts of streaming data is a challenge. This article focuses on a special kind of stream, the catalog stream, which has a high-level structure to analyze the stream effectively. We first formulate the anomaly detection in catalog streams as a constrained optimization problem based on a catalog stream matrix. Then, a novel filtering-identifying based anomaly detection algorithm (FIAD) is proposed, which includes two complementary strategies, true event identifying and false alarm filtering. Different kinds of attention windows are developed to provide corresponding data for various algorithm components. The identifying strategy includes true events in a much smaller candidate set. Meanwhile, the filtering strategy significantly removes false positives. A scalable catalog stream processing framework CSPF is designed to support the proposed method efficiently. Extensive experiments are conducted on the catalog stream data sets from an astronomy observation. The experimental results show that the proposed method can achieve a false-positive rate as low as 0.04%, reduces the false alarms by 98.6% compared with the existing methods, and the latency to handle each catalog is 2.1 seconds. Furthermore, a total of 36 transient candidates are detected from one observation season. 

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4. Correlation Clustering in Data Streams

   Ahn, Kook Jin; Cormode, Graham; Guha, Sudipto; McGregor, Andrew; Wirth, Anthony (January 2021, Algorithmica)
5. Counting Simplices in Hypergraph Streams

   Chakrabarti, Amit; Haris, Themistoklis (October 2022, Leibniz international proceedings in informatics)

   We consider the problem of space-efficiently estimating the number of simplices in a hypergraph stream. This is the most natural hypergraph generalization of the highly-studied problem of estimating the number of triangles in a graph stream. Our input is a k-uniform hypergraph H with n vertices and m hyperedges, each hyperedge being a k-sized subset of vertices. A k-simplex in H is a subhypergraph on k+1 vertices X such that all k+1 possible hyperedges among X exist in H. The goal is to process the hyperedges of H, which arrive in an arbitrary order as a data stream, and compute a good estimate of T_k(H), the number of k-simplices in H. We design a suite of algorithms for this problem. As with triangle-counting in graphs (which is the special case k = 2), sublinear space is achievable but only under a promise of the form T_k(H) ≥ T. Under such a promise, our algorithms use at most four passes and together imply a space bound of O(ε^{-2} log δ^{-1} polylog n ⋅ min{(m^{1+1/k})/T, m/(T^{2/(k+1)})}) for each fixed k ≥ 3, in order to guarantee an estimate within (1±ε)T_k(H) with probability ≥ 1-δ. We also give a simpler 1-pass algorithm that achieves O(ε^{-2} log δ^{-1} log n⋅ (m/T) (Δ_E + Δ_V^{1-1/k})) space, where Δ_E (respectively, Δ_V) denotes the maximum number of k-simplices that share a hyperedge (respectively, a vertex), which generalizes a previous result for the k = 2 case. We complement these algorithmic results with space lower bounds of the form Ω(ε^{-2}), Ω(m^{1+1/k}/T), Ω(m/T^{1-1/k}) and Ω(mΔ_V^{1/k}/T) for multi-pass algorithms and Ω(mΔ_E/T) for 1-pass algorithms, which show that some of the dependencies on parameters in our upper bounds are nearly tight. Our techniques extend and generalize several different ideas previously developed for triangle counting in graphs, using appropriate innovations to handle the more complicated combinatorics of hypergraphs. 

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