scholarly journals Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2156 ◽  
Author(s):  
Guangyuan Zhang ◽  
Xiaoping Rui ◽  
Stefan Poslad ◽  
Xianfeng Song ◽  
Yonglei Fan ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Temporal Resolution ◽  
Large Scale ◽  
Short Term Memory ◽  
Social Geography ◽  
Temporal Analysis ◽  
Economic Research ◽  
Mobile Phone Data ◽  
Short Term ◽  
Fine Grained

Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users’ distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users’ spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people’s mobility derived from the mobile phone users’ density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM.

scholarly journals Commentary on special issue: Syntax and verbal short term memory

2020 ◽  
Vol 40 (4) ◽  
pp. 500-506
Author(s):  
Nick Riches
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Experimental Studies ◽  
Long Term Memory ◽  
Short Term ◽  
Language Abilities ◽  
Term Memory ◽  
Language Impaired ◽  
Fine Grained ◽  
Complex Sentences

Short term memory (STM) and working memory (WM) performance consistently predict language abilities in children with developmental language disorders. However, causality is not fully established. Moreover, evidence from the fine-grained analysis of STM/WM tasks and comprehension of complex sentences, suggests that long term memory (LTM) representations play an important role. Critical assessment of the articles in the special edition focuses on Zebib et al. and Stanford and Delage. Zebib et al. find that sentence repetition by bilingual language-impaired children more strongly reflects WM than overall linguistic ability. This suggests a dependence on WM when linguistic representations are impoverished. However, the process of ranking predictors is problematic. Stanford and Delage find that STM/WM difficulties affect the processing of complex sentences by individuals with Specific Learning Disabilities. Yet, LTM-based explanations focusing on input frequency may also explain this phenomenon. To make progress we need a combination of experimental studies and large-scale longitudinal studies.

scholarly journals Predicting Fine-Grained Traffic Conditions via Spatio-Temporal LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaojuan Wei ◽  
Jinglin Li ◽  
Quan Yuan ◽  
Kaihui Chen ◽  
Ao Zhou ◽  
...  
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Vehicle Speed ◽  
Map Matching ◽  
Short Term ◽  
Fine Grained ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Spatio Temporal ◽  
Road Segments

Predicting traffic conditions for road segments is the prelude of working on intelligent transportation. Many existing methods can be used for short-term or long-term traffic prediction, but they focus more on regions than on road segments. The lack of fine-grained traffic predicting approach hinders the development of ITS. Therefore, MapLSTM, a spatio-temporal long short-term memory network preluded by map-matching, is proposed in this paper to predict fine-grained traffic conditions. MapLSTM first obtains the historical and real-time traffic conditions of road segments via map-matching. Then LSTM is used to predict the conditions of the corresponding road segments in the future. Breaking the single-index forecasting, MapLSTM can predict the vehicle speed, traffic volume, and the travel time in different directions of road segments simultaneously. Experiments confirmed MapLSTM can not only achieve prediction for road segments based a large scale of GPS trajectories effectively but also have higher predicting accuracy than GPR and ConvLSTM. Moreover, we demonstrate that MapLSTM can serve various applications in a lightweight way, such as cognizing driving preferences, learning navigation, and inferring traffic emissions.

scholarly journals A Data-Driven Feature Based Learning Application to Detect Freeway Segment Traffic Status Using Mobile Phone Data

2021 ◽  
Vol 13 (13) ◽  
pp. 7131
Author(s):  
Qiang Liu ◽  
Jianguang Xie ◽  
Fan Ding
Keyword(s):  
Mobile Phone ◽  
Large Scale ◽  
Short Term Memory ◽  
Data Driven ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Main Body ◽  
State Monitoring ◽  
Large Scale Field ◽  
Feature Based

With the finishing of the construction of the main body of a freeway network, adequately monitoring the traffic status of the network has become an urgent need for both travelers and transportation operators. Various methods are proposed to collect traffic information for this purpose. In this article, a data-driven feature-based learning application is implemented to detect segment traffic status using mobile phone data, building on the practical success of deep learning models in other fields. The traffic status estimation is achieved via the application of a three-level long, short-term memory model. Two phone features are extracted from the raw mobile phone data. A large-scale field experiment was conducted using actual data in Jiangsu, China collected over the “National Holiday Golden Week” of 2014. To evaluate the performance, both precision and recall scores are given along with the overall accuracy. The final results of the large-scale experiment indicate that the proposed application performed well and can be an emerging solution for traffic state monitoring when only limited roadside sensing devices are installed.

scholarly journals Two Stage Continuous Gesture Recognition Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 534
Author(s):  
Huogen Wang
Keyword(s):  
Gesture Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Short Term ◽  
Hand Motion ◽  
Spatiotemporal Features ◽  
Spatiotemporal Information ◽  
Video Frames ◽  
Depth Sequences

The paper proposes an effective continuous gesture recognition method, which includes two modules: segmentation and recognition. In the segmentation module, the video frames are divided into gesture frames and transitional frames by using the information of hand motion and appearance, and continuous gesture sequences are segmented into isolated sequences. In the recognition module, our method exploits the spatiotemporal information embedded in RGB and depth sequences. For the RGB modality, our method adopts Convolutional Long Short-Term Memory Networks to learn long-term spatiotemporal features from short-term spatiotemporal features obtained from a 3D convolutional neural network. For the depth modality, our method converts a sequence into Dynamic Images and Motion Dynamic Images through weighted rank pooling and feed them into Convolutional Neural Networks, respectively. Our method has been evaluated on both ChaLearn LAP Large-scale Continuous Gesture Dataset and Montalbano Gesture Dataset and achieved state-of-the-art performance.

Attention Biases Visual Activity in Visual Short-term Memory

2014 ◽  
Vol 26 (7) ◽  
pp. 1377-1389 ◽  
Author(s):  
Bo-Cheng Kuo ◽  
Mark G. Stokes ◽  
Alexandra M. Murray ◽  
Anna Christina Nobre
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Control Mechanisms ◽  
Top Down ◽  
Short Term ◽  
Visual Activity ◽  
Visual Short Term Memory ◽  
Large Scale Network ◽  
Visual Areas ◽  
Scale Network

In the current study, we tested whether representations in visual STM (VSTM) can be biased via top–down attentional modulation of visual activity in retinotopically specific locations. We manipulated attention using retrospective cues presented during the retention interval of a VSTM task. Retrospective cues triggered activity in a large-scale network implicated in attentional control and led to retinotopically specific modulation of activity in early visual areas V1–V4. Importantly, shifts of attention during VSTM maintenance were associated with changes in functional connectivity between pFC and retinotopic regions within V4. Our findings provide new insights into top–down control mechanisms that modulate VSTM representations for flexible and goal-directed maintenance of the most relevant memoranda.

scholarly journals Discovering Regularity Patterns of Mobility Practices Through Mobile Phone Data

2018 ◽  
pp. 173-195
Author(s):  
Paolo Tagliolato ◽  
Fabio Manfredini
Keyword(s):  
Mobile Phone ◽  
Temporal Resolution ◽  
Northern Italy ◽  
Mobile Phone Data ◽  
Urban Region ◽  
Network Data ◽  
Mobile Phone Network

The chapter addresses the issue of analyzing and mapping mobility practices by using different kinds of mobile phone network data that provide geo-located information on mobile phone activity at a high spatial and temporal resolution. The authors present and discuss major findings and drawbacks based on an application carried out on the Milan urban region (Lombardy, Northern Italy) and suggest possible implications for policies.

scholarly journals Polygenic Scores for Cognitive Abilities and their Association with Different Aspects of General Intelligence – a Deep Phenotyping Approach

2020 ◽  
Author(s):  
Erhan Genç ◽  
Caroline Schlüter ◽  
Christoph Fraenz ◽  
Larissa Arning ◽  
Huu Phuc Nguyen ◽  
...  
Keyword(s):  
Cognitive Abilities ◽  
Large Scale ◽  
Short Term Memory ◽  
Life Outcomes ◽  
Short Term ◽  
Polygenic Scores ◽  
Wide Range ◽  
Dna Variants ◽  
Trait Prediction ◽  
Deep Phenotyping

AbstractIntelligence is a highly polygenic trait and GWAS have identified thousands of DNA variants contributing with small effects. Polygenic scores (PGS) can aggregate those effects for trait prediction in independent samples. As large-scale light-phenotyping GWAS operationalized intelligence as performance in rather superficial tests, the question arises which intelligence facets are actually captured. We used deep-phenotyping to investigate the molecular determinantes of individual differences in cognitive ability. We therefore studied the association between PGS of educational attainment (EA-PGS) and intelligence (IQ-PGS) with a wide range of intelligence facets in a sample of 320 healthy adults. EA-PGS and IQ-PGS had the highest incremental R2s for general (3.25%; 1.78%), verbal (2.55%; 2.39%) and numerical intelligence (2.79%; 1.54%) and the weakest for non-verbal intelligence (0.50%; 0.19%) and short-term memory (0.34%; 0.22%). These results indicate that PGS derived from light-phenotyping GWAS do not reflect different facets of intelligence equally well, and thus should not be interpreted as genetic indicators of intelligence per se. The findings refine our understanding of how PGS are related to other traits or life outcomes.

scholarly journals Analyzing the Effects of Temporal Resolution and Classification Confidence for Modeling Land Cover Change with Long Short-Term Memory Networks

2019 ◽  
Vol 11 (23) ◽  
pp. 2784 ◽  
Author(s):  
Alysha van Duynhoven ◽  
Suzana Dragićević
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Temporal Resolution ◽  
Short Term Memory ◽  
Geospatial Data ◽  
Short Term ◽  
Method Performance ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Data Layers

Land cover change (LCC) is typically characterized by infrequent changes over space and time. Data-driven methods such as deep learning (DL) approaches have proven effective in many domains for predictive and classification tasks. When applied to geospatial data, sequential DL methods such as long short-term memory (LSTM) have yielded promising results in remote sensing and GIScience studies. However, the characteristics of geospatial datasets selected for use with these methods have demonstrated important implications on method performance. The number of data layers available, the rate of LCC, and inherent errors resulting from classification procedures are expected to influence model performance. Yet, it is unknown how these can affect compatibility with the LSTM method. As such, the main objective of this study is to explore the capacity of LSTM to forecast patterns that have emerged from LCC dynamics given varying temporal resolutions, persistent land cover classes, and auxiliary data layers pertaining to classification confidence. Stacked LSTM modeling approaches are applied to 17-year MODIS land cover datasets focused on the province of British Columbia, Canada. This geospatial data is reclassified to four major land cover (LC) classes during pre-processing procedures. The evaluation considers the dataset at variable temporal resolutions to demonstrate the significance of geospatial data characteristics on LSTM method performance in several scenarios. Results indicate that LSTM can be utilized for forecasting LCC patterns when there are few limitations on temporal intervals of the datasets provided. Likewise, this study demonstrates improved performance measures when there are classes that do not change. Furthermore, providing classification confidence data as ancillary input also demonstrated improved results when the number of timesteps or temporal resolution is limited. This study contributes to future applications of DL and LSTM methods for forecasting LCC.

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