scholarly journals Estimating Latent People Flow without Tracking Individuals

2018 ◽  
Author(s):  
Yusuke Tanaka ◽  
Tomoharu Iwata ◽  
Takeshi Kurashima ◽  
Hiroyuki Toda ◽  
Naonori Ueda
Keyword(s):  
New York ◽  
New York City ◽  
Real World ◽  
Diffusion Processes ◽  
Temporal Dynamics ◽  
Location Information ◽  
Aggregated Data ◽  
Proposed Model ◽  
Real World Datasets ◽  
Flow Conservation

Analyzing people flows is important for better navigation and location-based advertising. Since the location information of people is often aggregated for protecting privacy, it is not straightforward to estimate transition populations between locations from aggregated data. Here, aggregated data are incoming and outgoing people counts at each location; they do not contain tracking information of individuals. This paper proposes a probabilistic model for estimating unobserved transition populations between locations from only aggregated data. With the proposed model, temporal dynamics of people flows are assumed to be probabilistic diffusion processes over a network, where nodes are locations and edges are paths between locations. By maximizing the likelihood with flow conservation constraints that incorporate travel duration distributions between locations, our model can robustly estimate transition populations between locations. The statistically significant improvement of our model is demonstrated using real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City.

Abstract T P390: Baseline Stroke Knowledge of 4th-6th Grade Children in New York City Public Schools Has No Appreciable Trend from 2005-2014

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
James M Noble ◽  
Cailey Simmons ◽  
Mindy F Hecht ◽  
Olajide Williams
Keyword(s):  
New York ◽  
New York City ◽  
Public Schools ◽  
Real World ◽  
York City ◽  
Hip Hop ◽  
Expected Value ◽  
Stroke Symptom ◽  
Stroke Diagnosis ◽  
Stroke Knowledge

Background and Purpose: To examine whether the baseline stroke knowledge of children in schools participating in our Hip Hop Stroke program has changed since its inception in late 2005. Methods: We gathered baseline stroke knowledge surveys from 2,839 students enrolled in the Hip Hop Stroke program from November 2005 through April 2014 with median annual enrollment of 344 (range 55 to 582). All students were enrolled in New York City public schools, in 4th through 6th grade. Students who left ≥3 questions blank were discarded; other blank answers were treated as missing. Data were analyzed using binomial, Chi-Square and regression analysis (SPSS v22.0). Results: Overall there was no consistent trend in baseline stroke knowledge by academic year. Overall, 28.4% of students recognized stroke occurred in the brain (expected value 25% [p<0.001], range from 13.8-61.2% for any given year). With stroke diagnosis provided, 85.5% of 1436 students knew to call 911, whereas only 59.6% of 1243 students knew to call 911 when given a hypothetical real-world stroke symptom scenario without stroke diagnosis included, p<0.001. For a composite assessment of knowledge including 4 stroke symptoms (blurred vision, facial droop, sudden headache, slurred speech), 1 distractor (chest pain), and urgent action plan (call 911), asked consistently since 2006, overall students scored a mean 2.86 (95% CI: 2.80-2.92; possible range 0-6, expected value 2.75), with annual scores ranging from 2.54-3.56. Conclusion: Stroke knowledge among elementary school students remains low and has not appreciably changed during the last 9 years. The use of hypothetical real-world stroke symptom scenarios may more accurately reflect intent to call 911 for stroke than the use of questions in which stroke diagnosis is given.

scholarly journals State Variable Effects in Graphical Event Models

2020 ◽  
Author(s):  
Debarun Bhattacharjya ◽  
Dharmashankar Subramanian ◽  
Tian Gao
Keyword(s):  
Real World ◽  
Continuous Time ◽  
Point Processes ◽  
Temporal Dynamics ◽  
Search Algorithm ◽  
State Variables ◽  
Discrete State ◽  
State Variable ◽  
Event Models ◽  
Real World Datasets

Many real-world domains involve co-evolving relationships between events, such as meals and exercise, and time-varying random variables, such as a patient's blood glucose levels. In this paper, we propose a general framework for modeling joint temporal dynamics involving continuous time transitions of discrete state variables and irregular arrivals of events over the timeline. We show how conditional Markov processes (as represented by continuous time Bayesian networks) and multivariate point processes (as represented by graphical event models) are among various processes that are covered by the framework. We introduce and compare two simple and interpretable yet practical joint models within the framework with relevant baselines on simulated and real-world datasets, using a graph search algorithm for learning. The experiments highlight the importance of jointly modeling event arrivals and state variable transitions to better fit joint temporal datasets, and the framework opens up possibilities for models involving even more complex dynamics whenever suitable.

scholarly journals Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

2020 ◽  
Vol 34 (04) ◽  
pp. 5956-5963
Author(s):  
Xianfeng Tang ◽  
Huaxiu Yao ◽  
Yiwei Sun ◽  
Charu Aggarwal ◽  
Prasenjit Mitra ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Missing Values ◽  
Temporal Dynamics ◽  
Multivariate Time Series ◽  
Temporal Distribution ◽  
Joint Modeling ◽  
Adversarial Training ◽  
Global Patterns ◽  
Real World Datasets

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problem. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework øurs, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of øurs for MTS forecasting with missing values and its robustness under various missing ratios.

Expert Detection and Recommendation Model With User-Generated Tags in Collaborative Tagging Systems

2020 ◽  
Vol 31 (4) ◽  
pp. 24-45
Author(s):  
Mengmeng Shen ◽  
Jun Wang ◽  
Ou Liu ◽  
Haiying Wang
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Real World ◽  
Agglomerative Hierarchical Clustering ◽  
Collaborative Tagging ◽  
Excellent Performance ◽  
Recommendation Algorithm ◽  
Proposed Model ◽  
Target User ◽  
Real World Datasets

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

scholarly journals Do Stay at Home Orders and Cloth Face Coverings Control COVID-19 in New York City? Results from a SIER Model Based on Real World Data

2020 ◽  
Author(s):  
Jian Li ◽  
Yuming Wang ◽  
Jing Wu ◽  
Jing-Wen Ai ◽  
Hao-Cheng Zhang ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Real World ◽  
York City ◽  
Phase 1 ◽  
Real World Data ◽  
World Data ◽  
Social Distancing ◽  
Slowing Down ◽  
Predicted Values

Abstract Public health interventions have been implemented to contain the outbreak of COVID-19 in New York City. However, the assessment of those interventions, e.g. social distancing, cloth face covering based on the real-world data from filed study is lacking. The SEIR compartmental model was used to evaluate the social distancing and cloth face covering effect on the daily culminative laboratory confirmed cases in NYC, and COVID-19 transmissibility. The latter was measured by Rt reproduction numbers in three phases which were based on two interventions in implemented in the timeline. The transmissibility decreased from phase 1 to phase 3. The Initial, R0 was 4.60 in Phase 1 without any intervention. After social distancing, the Rt value was reduced by 68%, while after the mask recommendation, it was further reduced by ~60%. Interventions resulted in significant reduction of confirmed case numbers, relative to predicted values based on SEIR model without intervention. Our findings highlight the effectiveness of social distancing and cloth face coverings in slowing down the spread of SARS-CoV-2 in NYC.

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

Jack Kirby

2018 ◽  
pp. 191-205
Author(s):  
Andrew Alan Smith
Keyword(s):  
New York ◽  
Twentieth Century ◽  
New York City ◽  
Working Class ◽  
Real World ◽  
York City ◽  
Background Information ◽  
East Side ◽  
Alter Ego

Ben “The Thing” Grimm of the Fantastic Four is portrayed as a working-class “guy,” despite the vast amount of money at his disposal as a principal in Fantastic Four, Inc. However, his origins go back further than his first appearance in 1961, to the childhood of his co-creator and original artist, Jack Kirby. Kirby, a working-class Jew from the slums of Lower East Side New York City in the early part of the twentieth century, patterned Grimm after himself. Even after both Kirby and cocreator Stan Lee left Fantastic Four, successive writers and artists would include new pieces of background information about the character cementing the direct correlation between the fictional Thing and his real-world creator and alter ego, Jack Kirby.

scholarly journals A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability

Algorithms ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 17 ◽  
Author(s):  
Emmanuel Pintelas ◽  
Ioannis E. Livieris ◽  
Panagiotis Pintelas
Keyword(s):  
Machine Learning ◽  
Real World ◽  
High Performance ◽  
Black Box ◽  
Box Model ◽  
Proposed Model ◽  
Wide Range ◽  
Critical Issues ◽  
Key Factor ◽  
Real World Datasets

Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decisions on a wide range of applications. Nevertheless, we still seek to understand and explain how these models work and make decisions. Explainability and interpretability in machine learning is a significant issue, since in most of real-world problems it is considered essential to understand and explain the model’s prediction mechanism in order to trust it and make decisions on critical issues. In this study, we developed a Grey-Box model based on semi-supervised methodology utilizing a self-training framework. The main objective of this work is the development of a both interpretable and accurate machine learning model, although this is a complex and challenging task. The proposed model was evaluated on a variety of real world datasets from the crucial application domains of education, finance and medicine. Our results demonstrate the efficiency of the proposed model performing comparable to a Black-Box and considerably outperforming single White-Box models, while at the same time remains as interpretable as a White-Box model.

scholarly journals A Generative Model for Dynamic Networks with Applications

2019 ◽  
Vol 33 ◽  
pp. 7842-7849
Author(s):  
Shubham Gupta ◽  
Gaurav Sharma ◽  
Ambedkar Dukkipati
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Real World ◽  
Link Prediction ◽  
Temporal Dynamics ◽  
Dynamic Networks ◽  
Collaboration Networks ◽  
Proposed Model ◽  
Latent Space ◽  
Over Time

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.

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