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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. 

Federated Learning (FL) is a promising framework for multiple clients to learn a joint model without directly sharing the data. In addition to high utility of the joint model, rigorous privacy protection of the data and communication efficiency are important design goals. Many existing efforts achieve rigorous privacy by ensuring differential privacy for intermediate model parameters, however, they assume a uniform privacy parameter for all the clients. In practice, different clients may have different privacy requirements due to varying policies or preferences. In this paper, we focus on explicitly modeling and leveraging the heterogeneous privacy requirements of different clients and study how to optimize utility for the joint model while minimizing communication cost. As differentially private perturbations affect the model utility, a natural idea is to make better use of information submitted by the clients with higher privacy budgets (referred to as "public" clients, and the opposite as "private" clients). The challenge is how to use such information without biasing the joint model. We propose P rojected F ederated A veraging (PFA), which extracts the top singular subspace of the model updates submitted by "public" clients and utilizes them to project the model updates of "private" clients before aggregating them. We then propose communication-efficient PFA+, which allows "private" clients to upload projected model updates instead of original ones. Our experiments verify the utility boost of both algorithms compared to the baseline methods, whereby PFA+ achieves over 99% uplink communication reduction for "private" clients. 

Abstract The border between Georgia and South Carolina has a moderate amount of seismicity typical of the Piedmont Province of the eastern United States and greater than most other intraplate regions. Historical records suggest on average a Mw 4.5 earthquake every 50 yr in the region of the J. Strom Thurmond Reservoir, which is located on the border between Georgia and South Carolina. The Mw 4.1 earthquake on 15 February 2014 near Edgefield, South Carolina, was one of the largest events in this region recorded by nearby modern seismometers, providing an opportunity to study its source properties and aftershock productivity. Using the waveforms of the Mw 4.1 mainshock and the only cataloged Mw 3.0 aftershock as templates, we apply a matched‐filter technique to search for additional events between 8 and 22 February 2014. The resulting six new detections are further employed as new templates to scan for more events. Repeating the waveform‐matching method with new templates yields 13 additional events, for a total of 19 previously unidentified events with magnitude 0.06 and larger. The low number of events suggests that this sequence is deficient in aftershock production, as compared with expected aftershock productivities for other mainshocks of similar magnitudes. Hypocentral depths of the Mw 4.1 mainshock and Mw 3.0 aftershock are estimated by examining the differential time between a depth phase called sPL and P‐wave arrivals, as well as by modeling the depth phase of body waves at shorter periods. The best‐fitting depths for both events are around 3–4 km. The obtained stress drops for the Mw 4.1 mainshock and Mw 3.0 aftershock are 3.75 and 4.44 MPa, respectively. The corresponding updated moment magnitude for the aftershock is 2.91.