scholarly journals The rhythms of the night: increase in online night activity and emotional resilience during the spring 2020 Covid-19 lockdown

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Maria Castaldo ◽  
Tommaso Venturini ◽  
Paolo Frasca ◽  
Floriana Gargiulo
Keyword(s):  
Large Scale ◽  
Activity Patterns ◽  
Daily Rhythms ◽  
Online Activity ◽  
Emotional Resilience ◽  
Fine Grained ◽  
Exogenous Shocks ◽  
Content Consumption ◽  
Youtube Videos ◽  
Human Activity Patterns

Abstract Context The lockdown orders established in multiple countries in response to the Covid-19 pandemic are arguably one of the most widespread and deepest shock experienced by societies in recent years. Studying their impact trough the lens of social media offers an unprecedented opportunity to understand the susceptibility and the resilience of human activity patterns to large-scale exogenous shocks. Firstly, we investigate the changes that this upheaval has caused in online activity in terms of time spent online, themes and emotion shared on the platforms, and rhythms of content consumption. Secondly, we examine the resilience of certain platform characteristics, such as the daily rhythms of emotion expression. Data Two independent datasets about the French cyberspace: a fine-grained temporal record of almost 100 thousand YouTube videos and a collection of 8 million Tweets between February 17 and April 14, 2020. Findings In both datasets we observe a reshaping of the circadian rhythms with an increase of night activity during the lockdown. The analysis of the videos and tweets published during lockdown shows a general decrease in emotional contents and a shift from themes like work and money to themes like death and safety. However, the daily patterns of emotions remain mostly unchanged, thereby suggesting that emotional cycles are resilient to exogenous shocks.

scholarly journals Human Activity Patterns in Big Data for Healthcare Applications

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1101-1103
Keyword(s):  
Big Data ◽  
Human Development ◽  
Activity Patterns ◽  
Normal Model ◽  
Healthcare Applications ◽  
Fine Grained ◽  
Huge Data ◽  
Mining Works ◽  
Human Activity Patterns ◽  
Ultimate Objective

Nowadays, there is a consistently growing migration of people to urban domains. Therapeutic administrations organizations are a champion among the most testing viewpoints that is massively impacted by the colossal surge of people to downtown territories. In this manner, urban zones far and wide are placing enthusiastically in cutting edge change with a ultimate objective to give progressively profitable organic network to people. In such change, a large number homes are being equipped with canny contraptions (for example splendid meters, sensors, etc.) which produce massive volumes of fine-grained and indexical data that can be penniless down to help sharp city organizations. In this paper, we propose a model that utilizations splendid home huge data as techniques for picking up and finding human development structures for restorative administrations applications. We propose the use of normal model mining, bunch examination and gauge to measure and dismember essentialness use changes begun by occupants' direct. Since people's penchants are generally recognized by standard timetables, finding these calendars empowers us to see strange activities that may exhibit people's inconveniences in taking oversee to themselves, for instance, not arranging sustenance or not using shower/shower. Our places of business the need to analyze transient imperativeness use structures at the machine level, which is clearly related to human activities. The data from sharp meters is recursively mined in the quantum/data cut of 24 hours, and the results are kept up across over dynamic mining works out. [1,2,3,4,5]

scholarly journals Strategic resources and smallholder performance at the bottom of the pyramid

2019 ◽  
Vol 22 (3) ◽  
pp. 365-380 ◽  
Author(s):  
Matthias Olthaar ◽  
Wilfred Dolfsma ◽  
Clemens Lutz ◽  
Florian Noseleit
Keyword(s):  
Large Scale ◽  
Business Environment ◽  
Agricultural Economics ◽  
Local Market ◽  
Bottom Of The Pyramid ◽  
Fine Grained ◽  
Strategic Resources ◽  
Relative Contribution ◽  
The Relationship ◽  
Resource Based Theory

In a competitive business environment at the Bottom of the Pyramid smallholders supplying global value chains may be thought to be at the whims of downstream large-scale players and local market forces, leaving no room for strategic entrepreneurial behavior. In such a context we test the relationship between the use of strategic resources and firm performance. We adopt the Resource Based Theory and show that seemingly homogenous smallholders deploy resources differently and, consequently, some do outperform others. We argue that the ‘resource-based theory’ results in a more fine-grained understanding of smallholder performance than approaches generally applied in agricultural economics. We develop a mixed-method approach that allows one to pinpoint relevant, industry-specific resources, and allows for empirical identification of the relative contribution of each resource to competitive advantage. The results show that proper use of quality labor, storage facilities, time of selling, and availability of animals are key capabilities.

scholarly journals Inverse Infiltration Modeling of Dike Covers Made of Dredged Material Using PEST and AMALGAM

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 41
Author(s):  
Tim Jurisch ◽  
Stefan Cantré ◽  
Fokke Saathoff
Keyword(s):  
Large Scale ◽  
Measurement Data ◽  
Fine Grained ◽  
Dredged Materials ◽  
Measurement Results ◽  
Large Scale Field ◽  
Eigenvalue Ratio ◽  
Ill Posed ◽  
Materials Used ◽  
Installation Technology

A variety of studies recently proved the applicability of different dried, fine-grained dredged materials as replacement material for erosion-resistant sea dike covers. In Rostock, Germany, a large-scale field experiment was conducted, in which different dredged materials were tested with regard to installation technology, stability, turf development, infiltration, and erosion resistance. The infiltration experiments to study the development of a seepage line in the dike body showed unexpected measurement results. Due to the high complexity of the problem, standard geo-hydraulic models proved to be unable to analyze these results. Therefore, different methods of inverse infiltration modeling were applied, such as the parameter estimation tool (PEST) and the AMALGAM algorithm. In the paper, the two approaches are compared and discussed. A sensitivity analysis proved the presumption of a non-linear model behavior for the infiltration problem and the Eigenvalue ratio indicates that the dike infiltration is an ill-posed problem. Although this complicates the inverse modeling (e.g., termination in local minima), parameter sets close to an optimum were found with both the PEST and the AMALGAM algorithms. Together with the field measurement data, this information supports the rating of the effective material properties of the applied dredged materials used as dike cover material.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

scholarly journals Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3065
Author(s):  
Libo Wang ◽  
Rui Li ◽  
Dongzhi Wang ◽  
Chenxi Duan ◽  
Teng Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Research Field ◽  
Learning Approaches ◽  
Fine Grained ◽  
Urban Scene ◽  
Fine Resolution ◽  
With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

scholarly journals A Multiscale Survival Process for Modeling Human Activity Patterns

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0151473 ◽  
Author(s):  
Tianyang Zhang ◽  
Peng Cui ◽  
Chaoming Song ◽  
Wenwu Zhu ◽  
Shiqiang Yang
Keyword(s):  
Human Activity ◽  
Activity Patterns ◽  
Human Activity Patterns

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.

A Large-scale Analysis of Hundreds of In-memory Key-value Cache Clusters at Twitter

2021 ◽  
Vol 17 (3) ◽  
pp. 1-35
Author(s):  
Juncheng Yang ◽  
Yao Yue ◽  
K. V. Rashmi
Keyword(s):  
Large Scale ◽  
Production Systems ◽  
Wide Spectrum ◽  
Use Cases ◽  
Scale Analysis ◽  
Business Logic ◽  
Traffic Pattern ◽  
Fine Grained ◽  
Memory Cache ◽  
Large Scale Analysis

Modern web services use in-memory caching extensively to increase throughput and reduce latency. There have been several workload analyses of production systems that have fueled research in improving the effectiveness of in-memory caching systems. However, the coverage is still sparse considering the wide spectrum of industrial cache use cases. In this work, we significantly further the understanding of real-world cache workloads by collecting production traces from 153 in-memory cache clusters at Twitter, sifting through over 80 TB of data, and sometimes interpreting the workloads in the context of the business logic behind them. We perform a comprehensive analysis to characterize cache workloads based on traffic pattern, time-to-live (TTL), popularity distribution, and size distribution. A fine-grained view of different workloads uncover the diversity of use cases: many are far more write-heavy or more skewed than previously shown and some display unique temporal patterns. We also observe that TTL is an important and sometimes defining parameter of cache working sets. Our simulations show that ideal replacement strategy in production caches can be surprising, for example, FIFO works the best for a large number of workloads.

Disparity and Dynamics of Social Distancing Behaviors in Japan: An Investigation of mobile phone mobility data (Preprint)

2021 ◽  
Author(s):  
Zeyu Lyu ◽  
Hiroki Takikawa
Keyword(s):  
Large Scale ◽  
Age Groups ◽  
Mitigation Strategies ◽  
Social Distancing ◽  
Demographic Groups ◽  
Fine Grained ◽  
Mobility Data ◽  
The Social ◽  
The Government ◽  
Over Time

BACKGROUND The availability of large-scale and fine-grained aggregated mobility data has allowed researchers to observe the dynamic of social distancing behaviors at high spatial and temporal resolutions. Despite the increasing attentions paid to this research agenda, limited studies have focused on the demographic factors related to mobility and the dynamics of social distancing behaviors has not been fully investigated. OBJECTIVE This study aims to assist in the design and implementation of public health policies by exploring the social distancing behaviors among various demographic groups over time. METHODS We combined several data sources, including mobile tracking data and geographical statistics, to estimate visiting population of entertainment venues across demographic groups, which can be considered as the proxy of social distancing behaviors. Then, we employed time series analyze methods to investigate how voluntary and policy-induced social distancing behaviors shift over time across demographic groups. RESULTS Our findings demonstrate distinct patterns of social distancing behaviors and their dynamics across age groups. The population in the entertainment venues comprised mainly of individuals aged 20–40 years, while according to the dynamics of the mobility index and the policy-induced behavior, among the age groups, the extent of reduction of the frequency of visiting entertainment venues during the pandemic was generally the highest among younger individuals. Also, our results indicate the importance of implementing the social distancing policy promptly to limit the spread of the COVID-19 infection. However, it should be noticed that although the policy intervention during the second wave in Japan appeared to increase the awareness of the severity of the pandemic and concerns regarding COVID-19, its direct impact has been largely decreased could only last for a short time. CONCLUSIONS At the time we wrote this paper, in Japan, the number of daily confirmed cases was continuously increasing. Thus, this study provides a timely reference for decision makers about the current situation of policy-induced compliance behaviors. On the one hand, age-dependent disparity requires target mitigation strategies to increase the intention of elderly individuals to adopt mobility restriction behaviors. On the other hand, considering the decreasing impact of self-restriction recommendations, the government should employ policy interventions that limit the resurgence of cases, especially by imposing stronger, stricter social distancing interventions, as they are necessary to promote social distancing behaviors and mitigate the transmission of COVID-19. CLINICALTRIAL None

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