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A methodology with a distributed algorithm for large-scale trajectory distribution prediction

INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2019年第4期33卷 833-854页

作者： Guo, QiuLei Karimi, Hassan A. Univ Pittsburgh Sch Comp & Informat Geoinformat Lab Pittsburgh PA 15260 USA

In this paper, we propose a method for predicting the distributions of people's trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people's spatial and temporal mobility patterns, the complexity of a city level's road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements;(2) different methods to estimate corresponding flow trajectory distributions in the road network;(3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method.

关键词： Trajectory distribution big data spatial-temporal prediction distributed algorithm

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AN ASYNCHRONOUS distributed algorithm FOR REAL-TIME PAYMENTS-PROCESSING IN A PARTIALLY-CONNECTED NETWORK OF BANKS

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INFORMATION & MANAGEMENT 1994年第4期26卷 199-217页

作者： LEE, T GHOSH, S BROWN UNIV DIV ENGNPROVIDENCERI 02912

The payment-processing system in the banking industry consists of deposits, withdrawals, and transfers of monies through the use of cash, checks, magnetic tapes, and electronic transactions. Of these, nearly all of the check processing through the United States Federal Reserve System, most of the check processing in the private networks, and a part of the electronic transactions are realized through the utilization of the principles of batch-mode processing. This is a conservative and secure means of transaction processing wherein operations, initiated by users, are stored within the system for a certain length of time, typically a few hours to a day or a week, and completed during off-hours i.e.;when the bank is closed to users. Batch-mode processing suffers from many limitations, the principal ones being that: (i) users are denied real-time access to their money, and that (ii) a user's banking privileges cannot be extended - a facility that is increasingly being demanded by users. A centralized banking algorithm, similar to the Swiss Interbank Clearing System (SIC), is inadequate for the United States of America with nearly 12,700 financial institutions and which is extremely vulnerable to a natural calamity or an act of terrorism. This paper proposes a new, distributed architecture for payments processing within a network of major banks as an alternative to the Federal Reserve System. This approach distributes the processing operations to multiple, concurrent, cooperating geographically distributed computers i.e., at many sites, to achieve real-time transaction processing. In essence, a user's most recent account balance and the banking privileges of withdrawal, deposit and, transfer are available to a user, transparently, anywhere i.e., at any of the major banks constituting the network. The accuracy of every transaction is guaranteed and, as a result, (i) banks are not exposed to the risk of fraudulent or bad credits, and (ii) users are not denied complete acce

关键词： BANKING PAYMENTS-PROCESSING BROAD-BAND-ISDN COMMUNICATION NETWORKS SIMULATION LOOSELY-COUPLED PARALLEL PROCESSORS distributed algorithm MODELING NETWORK OF WORKSTATIONS FINANCIAL INSTITUTIONS

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Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2020年第3期27卷 376-385页

作者： Duan, Rui Boland, Mary Regina Liu, Zixuan Liu, Yue Chang, Howard H. Xu, Hua Chu, Haitao Schmid, Christopher H. Forrest, Christopher B. Holmes, John H. Schuemie, Martijn J. Berlin, Jesse A. Moore, Jason H. Chen, Yong Univ Penn Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Harvard Univ Dept Stat Cambridge MA 02138 USA Emory Univ Dept Biostat & Bioinformat Atlanta GA 30322 USA Univ Texas Hlth Sci Ctr Houston Sch Biomed Informat Houston TX 77030 USA Univ Minnesota Sch Publ Hlth Div Biostat Minneapolis MN 55455 USA Brown Univ Dept Biostat Providence RI 02912 USA Childrens Hosp Philadelphia Div Gen Pediat Philadelphia PA 19104 USA Janssen Res & Dev LLC Titusville NJ USA

Objectives: We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites. Materials and Methods: ODAL effectively utilizes the information from the local site (where the patient-level data are accessible) and incorporates the first-order (ODAL1) and second-order (ODAL2) gradients of the likelihood function from other sites to construct an estimator without requiring iterative communication across sites or transferring patient-level data. We evaluated ODAL via extensive simulation studies and an application to a dataset from the University of Pennsylvania Health System. The estimation accuracy was evaluated by comparing it with the estimator based on the combined individual participant data or pooled data (ie, gold standard). Results: Our simulation studies revealed that the relative estimation bias of ODAL1 compared with the pooled estimates was < 3%, and the ratio of standard errors was <1.25 for all scenarios. ODAL2 achieved higher accuracy (with relative bias <0.1% and ratio of standard errors <1.05). In real data analysis, we investigated the associations of 100 medications with fetal loss during pregnancy. We found that ODAL1 provided estimates with relative bias <10% for 85% of medications, and ODAL2 has relative bias <10% for 99% of medications. For communication cost, ODAL1 requires transferring p numbers from each site to the local site and ODAL2 requires transferring (p x p + p) numbers from each site to the local site, where p is the number of parameters in the regression model. Conclusions: This study demonstrates that ODAL is privacy-preserving and communication-efficient with small bias and high statistical efficiency.

关键词： distributed algorithm electronic health record logistic regression learning health system

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Challenges, Designs, and Performances of a distributed algorithm for Minimum-Latency of Data-Aggregation in Multi-Channel WSNs

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2019年第1期16卷 192-205页

作者： Ngoc-Tu Nguyen Liu, Bing-Hong Chu, Shao-, I Weng, Hao-Zhe Univ Minnesota Twin Cities Dept Comp Sci & Engn Minneapolis MN 55455 USA Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung 80778 Taiwan Natl Kaohsiung Univ Appl Sci Kaohsiung 80778 Taiwan

In wireless sensor networks (WSNs), the sensed data by sensors need to be gathered, so that one very important application is periodical data collection. There is much effort which aimed at the data collection scheduling algorithm development to minimize the latency. Most of previous works investigating the minimum latency of data collection issue have an ideal assumption that the network is a centralized system, in which the entire network is completely synchronized with full knowledge of components. In addition, most of existing works often assume that any (or no) data in the network are allowed to be aggregated into one packet and the network models are often treated as tree structures. However, in practical, WSNs are more likely to be distributed systems, since each sensor's knowledge is disjointed to each other, and a fixed number of data are allowed to be aggregated into one packet. This is a formidable motivation for us to investigate the problem of minimum latency for the data aggregation without data collision in the distributed WSNs when the sensors are considered to be assigned the channels and the data are compressed with a flexible aggregation ratio, termed the minimum-latency collision-avoidance multiple-dataaggregation scheduling with multi-channel (MLCAMDAS-MC) problem. A new distributed algorithm, termed the distributed collision-avoidance scheduling (DCAS) algorithm, is proposed to address the MLCAMDAS-MC. Finally, we provide the theoretical analyses of DCAS and conduct extensive simulations to demonstrate the performance of DCAS.

关键词： Data aggregation algorithm design and analysis distributed algorithm collision free wireless sensor networks (WSNs)

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Learning from local to global: An efficient distributed algorithm for modeling time-to-event data

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2020年第7期27卷 1028-1036页

作者： Duan, Rui Luo, Chongliang Schuemie, Martijn J. Tong, Jiayi Liang, C. Jason Chang, Howard H. Boland, Mary Regina Bian, Jiang Xu, Hua Holmes, John H. Forrest, Christopher B. Morton, Sally C. Berlin, Jesse A. Moore, Jason H. Mahoney, Kevin B. Chen, Yong Univ Penn Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Janssen Res & Dev LLC Titusville NJ USA NIAID Biostat Res Branch Bethesda MD 20892 USA Emory Univ Dept Biostat & Bioinformat Atlanta GA 30322 USA Univ Florida Coll Med Dept Hlth Outcomes & Biomed Informat Gainesville FL USA Univ Florida Canc Informat & eHlth Core Hlth Canc Ctr Gainesville FL USA Univ Texas Hlth Sci Ctr Houston Sch Biomed Informat Houston TX 77030 USA Childrens Hosp Philadelphia Appl Clin Res Ctr Philadelphia PA 19104 USA Virginia Polytech Inst & State Univ Dept Stat Blacksburg VA 24061 USA Johnson & Johnson Titusville NJ USA Univ Penn Penn Hlth Syst Perelman Sch Med Philadelphia PA 19104 USA

Objective: We developed and evaluated a privacy-preserving One-shot distributed algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites. Materials and Methods: Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study and (2) a real-world use case study using 4 datasets from the Observational Health Data Sciences and Informatics network. Results: On the one hand, our simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (ie, the pooled estimator). The relative bias was <0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 20% when the event rate is 1%. In the Observational Health Data Sciences and Informatics network application, the ODAC estimates have a relative bias <5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC. Conclusions: ODAC is a privacy-preserving and noniterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it

关键词： Cox proportional hazards model data integration distributed algorithm electronic health record meta-analysis

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Privacy-Preserving distributed algorithm for Sparse Vector Sum

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BULLETIN OF THE BELGIAN MATHEMATICAL SOCIETY-SIMON STEVIN 2010年第2期17卷 235-242页

作者： Zhong, Sheng SUNY Buffalo Dept Comp Sci & Engn Amherst NY 14260 USA

We study the following problem: B-0 and B-1 each has a sparse input vector V-0 and V-1;for each j we need to decide whether B-0[j] + B-1[j] > t. We give a privacy-preserving algorithm, in which B-0 and B-1 do not need to reveal any information about their input vectors to each other, except the output of algorithm. Our algorithm is highly efficient.

关键词： Cryptography distributed algorithm Sparse Vector

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Optimal Design of Microstrip Bandpass Filter in Ultra-wideband (UWB) Wireless Communication for Ship Based on distributed algorithm

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JOURNAL OF COASTAL RESEARCH 2018年第Spe83期000卷 585-589页

作者： Zhan, Aiyun Hu, Yong Yu, Meng East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China

The traditional methods designed for microstrip bandpass filter in ultra-wideband (UWB) wireless communication for ship have some problems in performance. On the basis of the distributed algorithm, an optimal design method of microstrip bandpass filter in UWB wireless communication for ship is proposed in this paper. The low pass-filter is in series with high pass filter used to complete the design of the microstrip bandpass filter in UWB wireless communication for ship. Alternately loaded form of grounding impedance of trapezoid and triangle is used to realize low pass filteris adopted, while the high pass filter is optimized by distortion and miniaturization. The clock cycle in the filter is controlled by the distributed algorithm. The experimental results show that the optimization design of this method is correct and the expected goal is realized.

关键词： distributed algorithm ultra-wideband (UWB) microstrip bandpass filter

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Advancing Interpretable Regression Analysis for Binary Data: A Novel distributed algorithm Approach

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STATISTICS IN MEDICINE 2024年第29期43卷 5573-5582页

作者： Tong, Jiayi Li, Lu Reps, Jenna Marie Lorman, Vitaly Jing, Naimin Edmondson, Mackenzie Lou, Xiwei Jhaveri, Ravi Kelleher, Kelly J. Pajor, Nathan M. Forrest, Christopher B. Bian, Jiang Chu, Haitao Chen, Yong Univ Penn Ctr Hlth AI & Synth Evidence CHASE Dept Biostat Epidemiol & Informat Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Johns Hopkins Bloomberg Sch Publ Hlth Dept Biostat Baltimore MD 21205 USA Univ Penn Sch Arts & Sci Grad Grp Appl Math & Computat Sci Philadelphia PA 19104 USA Janssen Res & Dev Titusville NJ USA Observat Hlth Data Sci & Informat OHDSI New York NY USA Erasmus Univ Med Ctr Rotterdam Netherlands Childrens Hosp Philadelphia Ctr Appl Clin Res Philadelphia PA 19104 USA Merck & Co Inc Biostat & Res Decis Sci Rahway NJ USA Univ Florida Coll Med Hlth Outcomes & Biomed Informat Gainesville FL USA Ann & Robert H Lurie Childrens Hosp Chicago Div Infect Dis Chicago IL USA Nationwide Childrens Hosp Ctr Child Hlth Equ & Outcomes Res Abigail Wexner Res Inst Columbus OH USA Cincinnati Childrens Hosp Med Ctr James M Anderson Ctr Hlth Syst Excellence Div Pulm Med Cincinnati OH USA Cincinnati Childrens Hosp Med Ctr James M Anderson Ctr Hlth Syst Excellence Div Biomed Informat Cincinnati OH USA Univ Cincinnati Coll Med Cincinnati OH USA Pfizer Inc Stat Res & Data Sci Ctr New York NY USA Univ Penn Perelman Sch Med Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Penn Inst Biomed Informat IBI Philadelphia PA 19104 USA Leonard Davis Inst Hlth Econ Philadelphia PA 19104 USA Penn Med Ctr Evidence based Practice CEP Philadelphia PA 19104 USA

Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two-step meta-analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one-shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP-B. We evaluate the performance of our method through both simulation studies and real-world case analyses of postacute sequelae of SARS-CoV-2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta-analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication-efficient and privacy-preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two-step meta-analysis methods. Overall, ODAP-B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator.

关键词： binary data distributed algorithm modified Poisson regression relative risk

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A distributed algorithm for Computing a Common Fixed Point of a Family of Paracontractions

A Distributed Algorithm for Computing a Common Fixed Point o...

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10th IFAC Symposium on Nonlinear Control Systems (NOLCOS)

作者： Fullmer, Daniel Wang, Lili Morse, A. Stephen Dept Elect Engn New Haven CT 06520 USA

A distributed algorithm is described for finding a common fixed point of a family of m > 1 nonlinear maps M-i : IRn -> IRn assuming that each map is a paracontraction and that at least one such common fixed point exists. The common fixed point is simultaneously computed by m agents assuming each agent i knows only M-i, the current estimates of the fixed point generated by its neighbors, and nothing more. Each agent recursively updates its estimate of a fixed point by utilizing the current estimates generated by each of its neighbors. Neighbor relations are characterized by a time-varying directed graph N(t) whose Vertices correspond to agents and whose arcs depict neighbor relations. It is shown that for any family of paracontractions M(i,)i epsilon {1,2, ... , m} which has at least one common fixed point, and any sequence of strongly connected neighbor graphs N(t), t = 1,2, ... , the algorithm causes all agent estimates to converge to a common fixed point. (C) 2016, IFAC (International Federation Control) Hosting By Elsevier Ltd. All rights reserverd.

关键词： distributed algorithm paracontraction nonlinear

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A distributed algorithm to Achieve Transparent Coexistence for a Secondary Multi-Hop MIMO Network

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2016年第9期15卷 6063-6077页

作者： Yuan, Xu Qin, Xiaoqi Tian, Feng Shi, Yi Hou, Y. Thomas Lou, Wenjing Midkiff, Scott F. Kompella, Sastry Virginia Polytech Inst & State Univ Blacksburg VA 24061 USA Nanjing Univ Posts & Telecommun Nanjing 210003 Jiangsu Peoples R China Intelligent Automat Inc Rockville MD 20855 USA US Naval Res Lab Washington DC 20375 USA

The transparent coexistence (TC) paradigm allows simultaneous activation of the secondary users with the primary users as long as their interference to the primary users can be properly canceled. This paradigm has the potential to offer much more efficient spectrum sharing than the traditional interweave paradigm. In this paper, we design a distributed algorithm to achieve this paradigm for a secondary multi-hop network. For interference cancelation (IC), we employ MIMO at secondary nodes. We present a distributed iterative algorithm to maximize each secondary session's throughput while meeting all IC requirements under TC. By maintaining two local sets for each node, we can keep track of the node's IC responsibility. Although no explicit node ordering is maintained in our distributed algorithm, we prove that our distributed data structure at each node (with the use of two local sets) can be mapped to an explicit global node ordering for IC among all nodes in the network. This guarantees that each active node's degree-of-freedoms allocated for IC is feasible at the physical layer. Our algorithm is iterative in nature and all steps can be accomplished based on local information exchange among the neighboring nodes. We present the simulation results to show that the performance of our distributed algorithm is highly competitive when compared with an upper bound solution from the corresponding centralized problem.

关键词： Primary network secondary network spectrum sharing coexistence distributed algorithm multi-hop network MIMO interference cancelation

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