scholarly journals DeepDPM: Dynamic Population Mapping via Deep Neural Network

2019 ◽  
Vol 33 ◽  
pp. 1294-1301 ◽  
Author(s):  
Zefang Zong ◽  
Jie Feng ◽  
Kechun Liu ◽  
Hongzhi Shi ◽  
Yong Li
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Real Life ◽  
External Information ◽  
Full Time ◽  
Population Distributions ◽  
Coarse Data ◽  
Dynamic Population ◽  
Population Mapping ◽  
Resolution Data

Dynamic high resolution data on human population distribution is of great importance for a wide spectrum of activities and real-life applications, but is too difficult and expensive to obtain directly. Therefore, generating fine-scaled population distributions from coarse population data is of great significance. However, there are three major challenges: 1) the complexity in spatial relations between high and low resolution population; 2) the dependence of population distributions on other external information; 3) the difficulty in retrieving temporal distribution patterns. In this paper, we first propose the idea to generate dynamic population distributions in full-time series, then we design dynamic population mapping via deep neural network(DeepDPM), a model that describes both spatial and temporal patterns using coarse data and point of interest information. In DeepDPM, we utilize super-resolution convolutional neural network(SRCNN) based model to directly map coarse data into higher resolution data, and a timeembedded long short-term memory model to effectively capture the periodicity nature to smooth the finer-scaled results from the previous static SRCNN model. We perform extensive experiments on a real-life mobile dataset collected from Shanghai. Our results demonstrate that DeepDPM outperforms previous state-of-the-art methods and a suite of frequent data-mining approaches. Moreover, DeepDPM breaks through the limitation from previous works in time dimension so that dynamic predictions in all-day time slots can be obtained.

scholarly journals HyperNews: Simultaneous News Recommendation and Active-Time Prediction via a Double-Task Deep Neural Network

2020 ◽  
Author(s):  
Rui Liu ◽  
Huilin Peng ◽  
Yong Chen ◽  
Dell Zhang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Real Life ◽  
Online News ◽  
News Article ◽  
Active Time ◽  
Performance Improvements ◽  
Time Prediction ◽  
Novel Approach ◽  
News Recommendation

Personalized news recommendation can help users stay on top of the current affairs without being overwhelmed by the endless torrents of online news. However, the freshness or timeliness of news has been largely ignored by current news recommendation systems. In this paper, we propose a novel approach dubbed HyperNews which explicitly models the effect of timeliness on news recommendation. Furthermore, we introduce an auxiliary task of predicting the so-called "active-time" that users spend on each news article. Our key finding is that it is beneficial to address the problem of news recommendation together with the related problem of active-time prediction in a multi-task learning framework. Specifically, we train a double-task deep neural network (with a built-in timeliness module) to carry out news recommendation and active-time prediction simultaneously. To the best of our knowledge, such a "kill-two-birds-with-one-stone" solution has seldom been tried in the field of news recommendation before. Our extensive experiments on real-life news datasets have not only confirmed the mutual reinforcement of news recommendation and active-time prediction but also demonstrated significant performance improvements over state-of-the-art news recommendation techniques.

scholarly journals A Study on the Application and the Advancement of Deep Neural Network Algorithm

2022 ◽  
Vol 2146 (1) ◽  
pp. 012001
Author(s):  
Jiulin Song ◽  
Yansheng Chen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Real Life ◽  
Research Area ◽  
Network Algorithms ◽  
Network Algorithm ◽  
New Type ◽  
Neural Network Algorithm ◽  
Global And Local

Abstract Deep neural network is a new type of learning algorithm, which has both global and local aspects and performs well in pattern recognition and computational speed. In recent years, deep neural network algorithm has been widely used in scientific research and real life, but its complexity, parallelism and other characteristics lead it to be a very challenging and innovative research area. This study briefly introduces the basic principles and theoretical knowledge of deep neural network algorithms, and mainly discusses their applications and Advancement of feature extraction in the field.

scholarly journals Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network

2019 ◽  
Vol 11 (15) ◽  
pp. 1741 ◽  
Author(s):  
Yeonjin Lee ◽  
Daehyeon Han ◽  
Myoung-Hwan Ahn ◽  
Jungho Im ◽  
Su Jeong Lee
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
High Resolution ◽  
Random Forest ◽  
Correlation Coefficient ◽  
Deep Neural Network ◽  
Precipitable Water ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Resolution Data

Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches—random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)—are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 μ m , respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed.

The economic analysis of rationality in the tradition of Harvey Leibenstein

2020 ◽  
pp. 21-38
Author(s):  
Ivan V. Rozmainsky ◽  
Yulia I. Pashentseva
Keyword(s):  
Economic Analysis ◽  
Real Life ◽  
Informal Institutions ◽  
External Information ◽  
Urban Residents ◽  
The Future ◽  
Information Constraints ◽  
High Degree ◽  
Market Capitalism

The paper is devoted to the economic analysis of rationality in the tradition of Harvey Leibenstein: the authors perceive rationality as “calculatedness” when making decisions, while the degree of this “calculatedness” is interpreted as a variable. Thus, this approach does not correspond to the generally accepted neoclassical interpretation of rationality, according to which rationality is both full and constant. The authors believe that such a neoclassical approach makes too stringent requirements for the abilities of people. In real life, people do not behave like calculating machines. The paper discusses various factors limiting the degree of rationality of individuals. One group of factors is associated with external information constraints such as the complexity and extensiveness of information, as well as the uncertainty of the future. Another group of factors is related to informal institutions. In particular, the paper states that the system of planned socialism contributes to less rationality than the system of market capitalism. Thus, in the post-socialist countries, including contemporary Russia, one should not expect a high degree of rationality of the behavior of economic entities. The paper mentions, in particular, the factors of rationality caused by informal institutions, such as the propensity to calculate, the propensity to be independent when making decisions and the propensity to set goals. The authors also believe that people who live on their own are usually more rational than people who share a common household with someone else. This assumption is verified econometrically based on data on young urban residents collected by the authors. It turned out that the behavior of people included in this database, in general, corresponds to what the authors believed.

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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