A Large Scale Crowd Density Classification Using Spatio-Temporal Local Binary Pattern

2017 ◽  
Author(s):  
Sonu Lamba ◽  
Neeta Nain
Keyword(s):  
Large Scale ◽  
Local Binary Pattern ◽  
Crowd Density ◽  
Spatio Temporal

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK

Science ◽  
2021 ◽  
pp. eabf2946
Author(s):  
Louis du Plessis ◽  
John T. McCrone ◽  
Alexander E. Zarebski ◽  
Verity Hill ◽  
Christopher Ruis ◽  
...  
Keyword(s):  
Large Scale ◽  
Phylogenetic Analyses ◽  
Transmission Dynamics ◽  
Genetic Lineage ◽  
Size Dependent ◽  
Spatio Temporal ◽  
The Uk ◽  
Lineage Structure ◽  
And Control ◽  
Lineage Diversity

The UK’s COVID-19 epidemic during early 2020 was one of world’s largest and unusually well represented by virus genomic sampling. Here we reveal the fine-scale genetic lineage structure of this epidemic through analysis of 50,887 SARS-CoV-2 genomes, including 26,181 from the UK sampled throughout the country’s first wave of infection. Using large-scale phylogenetic analyses, combined with epidemiological and travel data, we quantify the size, spatio-temporal origins and persistence of genetically-distinct UK transmission lineages. Rapid fluctuations in virus importation rates resulted in >1000 lineages; those introduced prior to national lockdown tended to be larger and more dispersed. Lineage importation and regional lineage diversity declined after lockdown, while lineage elimination was size-dependent. We discuss the implications of our genetic perspective on transmission dynamics for COVID-19 epidemiology and control.

scholarly journals Hybrid Graph Neural Networks for Crowd Counting

2020 ◽  
Vol 34 (07) ◽  
pp. 11693-11700 ◽  
Author(s):  
Ao Luo ◽  
Fan Yang ◽  
Xin Li ◽  
Dong Nie ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Network Architecture ◽  
Message Passing ◽  
Large Scale ◽  
State Of The Art ◽  
Density Variation ◽  
Feature Maps ◽  
Crowd Counting ◽  
Multi Scale ◽  
Crowd Density ◽  
Graph Neural Networks

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges: (i) multi-scale relations capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can capture and distill richer relations between nodes to obtain more powerful representations, providing robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art algorithms by a large margin.

scholarly journals Historical drought patterns over Canada and their teleconnections with large-scale climate signals

2018 ◽  
Vol 22 (6) ◽  
pp. 3105-3124 ◽  
Author(s):  
Zilefac Elvis Asong ◽  
Howard Simon Wheater ◽  
Barrie Bonsal ◽  
Saman Razavi ◽  
Sopan Kurkute
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Kendall Test ◽  
Wavelet Coherence ◽  
Climate Indices ◽  
Data Sets ◽  
Canadian Prairies ◽  
Agriculture Industry ◽  
Spatio Temporal

Abstract. Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems, and health. However, nationwide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950–2013. The Mann–Kendall test, rotated empirical orthogonal function, continuous wavelet transform, and wavelet coherence analyses are used, respectively, to investigate the trend, spatio-temporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified – the Canadian Prairies and northern central Canada. The analyses also revealed the presence of a dominant periodicity of between 8 and 32 months in the Prairie region and between 8 and 40 months in the northern central region. These cycles of low-frequency variability are found to be associated principally with the Pacific–North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, their duration, and how often they occur.

CSMC

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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