Spatio-temporal uncertainty and cortical-hippocampal interactions: fMRI study

Nisha Dalal; Virginia Flanagin; Stefan Glasauer

doi:10.1167/15.12.991

CSMC

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3494959 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-22

Author(s):

Hai Wang ◽

Baoshen Guo ◽

Shuai Wang ◽

Tian He ◽

Desheng Zhang

Keyword(s):

Large Scale ◽

Mobile Network ◽

Signal Propagation ◽

Spectrum Management ◽

External Information ◽

Temporal Uncertainty ◽

Large Area ◽

Fine Grained ◽

Map Construction ◽

Spatio Temporal

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.

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End-user Preference for and Understanding of Hurricane Forecast Graphs

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181321651142 ◽

2021 ◽

Vol 65 (1) ◽

pp. 606-610

Author(s):

Barbara Millet ◽

Sharanya J. Majumdar ◽

Alberto Cairo ◽

Carolina Diaz ◽

Qinyu Ding ◽

...

Keyword(s):

Tropical Cyclone ◽

Risk Communication ◽

Prior Experience ◽

User Preference ◽

Laboratory Setting ◽

End User ◽

Temporal Uncertainty ◽

Spatio Temporal ◽

The Impact ◽

National Hurricane Center

Hurricane forecast graphics have the challenging task of communicating information about spatio-temporal uncertainty. This study assesses the impact of graph literacy and graph format on user preference and understanding. In a laboratory setting, we compared user responses to official National Hurricane Center advisory maps and alternative visualizations. Results indicate that prior experience with a visualization drives preference and that graph literacy, visualization format, and tropical cyclone characteristics, in combination, influence interpretations of hurricane forecast track. The findings from this study are expected to inform redesign efforts of hurricane risk communication products.

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