There is an increased use of Internet-of-Things and wearable sensing devices in the urban marathon to ensure an effective response to unforeseen medical needs. However, the massive amount of real-time, heterogeneous movement and psychological data of runners impose great challenges on prompt medical incident analysis and intervention. Conventional approaches compile such data into one dashboard visualization to facilitate rapid data absorption but fail to support joint decision-making and operations in medical encounters. In this paper, we present MaraVis, a real-time urban marathon visualization and coordinated intervention system. It first visually summarizes real-time marathon data to facilitate the detection and exploration of possible anomalous events. Then, it calculates an optimal camera route with an arrangement of shots to guide offline effort to catch these events in time with a smooth view transition. We conduct a within-subjects study with two baseline systems to assess the efficacy of MaraVis.
As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and efficiency of HFL over local training, clues for inspecting the behaviors of the participating clients and the federated model are usually lacking due to the privacy-preserving nature of HFL. Consequently, the users can only conduct a shallow-level analysis of potential abnormal behaviors and have limited means to assess the contributions of individual clients and implement the necessary intervention. Visualization techniques have been introduced to facilitate the HFL process inspection, usually by providing model metrics and evaluation results as a dashboard representation. Although the existing visualization methods allow a simple examination of the HFL model performance, they cannot support the intensive exploration of the HFL process. In this study, strictly following the HFL privacy-preserving protocol, we design an exploratory visual analytics system for the HFL process termed HFLens, which supports comparative visual interpretation at the overview, communication round, and client instance levels. Specifically, the proposed system facilitates the investigation of the overall process involving all clients, the correlation analysis of clients' information in one or different communication round(s), the identification of potential anomalies, and the contribution assessment of each HFL client. Two case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps in understanding and diagnosing the HFL process better.
Quantum Key Distribution (QKD) is a technology that allows secure key exchange between two distant users. A widespread adoption of QKD requires the development of simple, low-cost, and stable systems. However, implementation of the current QKD requires a complex self-alignment process during the initial stage and an additional hardware to compensate the environmental disturbances. In this study, we have presented the implementation of a simple QKD with the help of a stable transmitter-receiver scheme, which simplifies the self-alignment and is robust enough to withstand environmental disturbances. In case of the stability test, the implementation system is able to remain stable for 48 hours and exhibits an average quantum bit error rate of less than 1% without any feedback control. The scheme is also tested over a fiber spool, obtaining a stable and secure finite key rate of 7.32k bits per second over a fiber spool extending up to 75 km.The demonstrated long-term stability and obtained secure key rate prove that our method of implementation is a promising alternative for practical QKD systems, in particular, for Cubesat platform and satellite applications.Quantum key distribution (QKD) [1], as validated by quantum mechanics, is capable of providing everlasting security that does not rely on any future hardware advances. Since its first invention by Bennet and Brassard in 1984 [2], QKD has been attracting a lot of interest, and its feasibility has been experimentally verified in optical fiber [3][4][5][6], free space [7][8][9], and underwater channels [10,11]. The use of field-test QKD networks has been reported in China [12], Japan [13] for various applications. More recently, the first quantum science satellite Micius was launched, which paved the way for satellite-to-ground QKD [14,15] and satellite-relayed intercontinental communication [16].Recent researches have focused on developing simpler, lower-cost, and more robust QKD implementations for widespread usage. This has led to the development of self-compensated modulators designed for different photonic degrees of freedom [17][18][19] as well as several simpler methods of implementation of QKD [20,21]. Moreover, over the years, integrated photonics based on different platforms has also been introduced for stable and miniaturized systems [22][23][24][25]. Furthermore, a detailed study of various novel technologies has been done with an aim to: simplify the consumption of temporal synchronization [26], directly
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Figure 1: ShuttleVis includes (A) a dataset loader and data description; (B) overview of car-hailing reimbursement records across different departments and descriptions of the departure and arrival time; (C) directional clustering configuration view to help analysts identify appropriate travel directions;(D) map view to visualize identified directional and regional clustering results, and comparative ranking view that includes (E1) a ranking of shuttle bus stops in terms of (E2) metrics in consecutive regional clusters along one travel direction, (E3) timetables of selected shuttle routes, and (E4) radar chart showing attribute distributions of selected routes.
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