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BibLib1.bib
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@article{Kang2017,
abstract = {In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets. In order to enhance crime prediction models, we consider environmental context information, such as broken windows theory and crime prevention through environmental design. In this paper, we propose a feature-level data fusion method with environmental context based on a deep neural network (DNN). Our dataset consists of data collected from various online databases of crime statistics, demographic and meteorological data, and images in Chicago, Illinois. Prior to generating training data, we select crime-related data by conducting statistical analyses. Finally, we train our DNN, which consists of the following four kinds of layers: spatial, temporal, environmental context, and joint feature representation layers. Coupled with crucial data extracted from various domains, our fusion DNN is a product of an efficient decision-making process that statistically analyzes data redundancy. Experimental performance results show that our DNN model is more accurate in predicting crime occurrence than other prediction models. [ABSTRACT FROM AUTHOR]},
author = {Kang, Hang-Bong Hyeon-Woo and Kang, Hang-Bong Hyeon-Woo},
doi = {10.1371/journal.pone.0176244},
editor = {Choo, Kim-Kwang Raymond},
file = {:home/ishanu/Documents/Mendeley Desktop/Kang, Kang/Plos One/Kang, Kang - 2017 - Prediction of crime occurrence from multi-modal data using deep learning.pdf:pdf},
isbn = {1111111111},
issn = {1932-6203},
journal = {Plos One},
mendeley-groups = {reading,crimepred},
month = {apr},
number = {4},
pages = {e0176244},
pmid = {28437486},
publisher = {Public Library of Science},
title = {{Prediction of crime occurrence from multi-modal data using deep learning}},
url = {http://dx.plos.org/10.1371/journal.pone.0176244},
volume = {12},
year = {2017}
}
@TECHREPORT{RePEc:boc:bocoec:744,
title = {Linking Granger Causality and the Pearl Causal Model with Settable Systems},
author = {White, Halbert and Chalak, Karim and Lu, Xun},
year = {2010},
institution = {Boston College Department of Economics},
type = {Boston College Working Papers in Economics},
number = {744},
abstract = {The causal notions embodied in the concept of Granger causality have been argued to belong to a different category than those of Judea Pearl's Causal Model, and so far their relation has remained obscure. Here, we demonstrate that these concepts are in fact closely linked by showing how each relates to straightforward notions of direct causality embodied in settable systems, an extension and refinement of the Pearl Causal Model designed to accommodate optimization, equilibrium, and learning. We then provide straightforward practical methods to test for direct causality using tests for Granger causality.},
keywords = {Causal Models; Conditional Exogeneity; Conditional Independence; Granger Non-causality},
url = {https://EconPapers.repec.org/RePEc:boc:bocoec:744}
}
@book{Goodfellow:2016:DL:3086952,
author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
title = {Deep Learning},
year = {2016},
isbn = {0262035618, 9780262035613},
publisher = {The MIT Press},
}
@book{Pearl:2009:CMR:1642718,
author = {Pearl, Judea},
title = {Causality: Models, Reasoning and Inference},
year = {2009},
isbn = {052189560X, 9780521895606},
edition = {2nd},
publisher = {Cambridge University Press},
address = {New York, NY, USA},
}
[download]
@INPROCEEDINGS{1582183,
author={G. C. Goodwin and J. C. Aguero},
booktitle={Proceedings of the 44th IEEE Conference on Decision and Control},
title={Approximate EM Algorithms for Parameter and State Estimation in Nonlinear Stochastic Models},
year={2005},
volume={},
number={},
pages={368-373},
keywords={Availability;Computer industry;Industrial control;Linear systems;Marine vehicles;Mathematical model;Parameter estimation;State estimation;Stochastic processes;Time measurement},
doi={10.1109/CDC.2005.1582183},
ISSN={0191-2216},
month={Dec},}
@article{Chattopadhyay2018,
abstract = {Identifying meaningful signal buried in noise is a problem of interest arising in diverse scenarios of data-driven modeling. We present here a theoretical framework for exploiting intrinsic geometry in data that resists noise corruption, and might be identifiable under severe obfuscation. Our approach is based on uncovering a valid complete inner product on the space of ergodic stationary finite valued processes, providing the latter with the structure of a Hilbert space on the real field. This rigorous construction, based on non-standard generalizations of the notions of sum and scalar multiplication of finite dimensional probability vectors, allows us to meaningfully talk about "angles" between data streams and data sources, and, make precise the notion of orthogonal stochastic processes. In particular, the relative angles appear to be preserved, and identifiable, under severe noise, and will be developed in future as the underlying principle for robust classification, clustering and unsupervised featurization algorithms.},
archivePrefix = {arXiv},
arxivId = {1801.08256},
author = {Chattopadhyay, Ishanu},
eprint = {1801.08256},
file = {:home/ishanu/Documents/Mendeley Desktop/Chattopadhyay/Unknown/Chattopadhyay - 2018 - A Hilbert Space of Stationary Ergodic Processes.pdf:pdf},
mendeley-groups = {HilbertSpace},
month = {jan},
title = {{A Hilbert Space of Stationary Ergodic Processes}},
url = {http://arxiv.org/abs/1801.08256},
year = {2018}
}
@article{foster2015tradition,
title={Tradition and innovation in scientists' research strategies},
author={Foster, Jacob G and Rzhetsky, Andrey and Evans, James A},
journal={American Sociological Review},
volume={80},
number={5},
pages={875--908},
year={2015},
publisher={SAGE Publications Sage CA: Los Angeles, CA}
}
@article{rzhetsky2015choosing,
title={Choosing experiments to accelerate collective discovery},
author={Rzhetsky, Andrey and Foster, Jacob G and Foster, Ian T and Evans, James A},
journal={Proceedings of the National Academy of Sciences},
volume={112},
number={47},
pages={14569--14574},
year={2015},
publisher={National Acad Sciences}
}
@article{shi2015weaving,
title={Weaving the fabric of science: Dynamic network models of science's unfolding structure},
author={Shi, Feng and Foster, Jacob G and Evans, James A},
journal={Social Networks},
volume={43},
pages={73--85},
year={2015},
publisher={Elsevier}
}
@book{padgett2012emergence,
title={The emergence of organizations and markets},
author={Padgett, John F and Powell, Walter W},
year={2012},
publisher={Princeton University Press}
}
@Article{G01, author = "M. Gribaudo and M. Sereno and A. Horvath and A. Bobbio",
title = "Fluid Stochastic Petri Nets Augmented with Flush-out Arcs: Modelling and Analysis.",
journal = "Discrete Event Dynamic Systems",
pages = "97--117",
month = jan,
year = "2001",
}
@BOOK{HMU01,
author = "J. E. Hopcroft and R. Motwani and J. D. Ullman",
title= "Introduction to Automata Theory, Languages, and Computation, 2nd ed.",
publisher = "Addison-Wesley",
year = 2001 }
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author="S. Jiang and Z. Huang and V. Chandra and R. Kumar",
title="A Polynomial Time Algorithm for Diagnosability of Discrete Event Systems",
journal="IEEE Transactions on Automatic Control",
volume={46},
number={8},
year={2001},
pages={1318--1321},
}
@ARTICLE{JKG01,
author="S. Jiang and R. Kumar and H. Garcia",
title="Diagnosis of Repeated Failures in Discrete Event Systems",
journal=" IEEE Transactions on Robotics and Automation",
volume={19},
number={2},
year={2003},
pages={301-323},
}
@ARTICLE{JKG03,
author="S. Jiang and R. Kumar and H. Garcia",
title="Optimal Sensor Selection for Discrete Event Systems Under Partial Observation",
journal=" IEEE Transactions on Automatic Control",
volume={48},
number={3},
year={2003},
pages={369-381},
}
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publisher = {Elsevier Science Inc.},
address = {New York, NY, USA},
}
@article{LW88b,
author = {F. Lin and W. M. Wonham},
title = {On observability of discrete-event systems},
journal = {Inf. Sci.},
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doi = {http://dx.doi.org/10.1016/0020-0255(88)90001-1},
publisher = {Elsevier Science Inc.},
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}
@Book{MA98,
author = "Moody, J.O. and Antsaklis, P.J.",
title = "Supervisory Control ofDiscrete Event Systems Using Petri Nets",
publisher ="Kluwer Academic",
year = "1998"
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@ARTICLE{RW87,
author={P. J. Ramadge and W. M. Wonham},
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number={1},
year={1987},
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}
@Book{S87,
author="A. Salomaa",
title="Formal Languages",
publisher="Academic Press Professional Inc. San Diego",
year="1987"
}
@ARTICLE{SSLST95,
author={M. Sampath and R. Sengupta and S. Lafortune and K. Sinnamohideen and D. Teneketzis},
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volume={40},
number={9},
year={1995},
pages={1555-1575},
}
@article{W97,
author = "K. Wong",
title = "On the complexity of projections of discrete-event systems",
journal = "IEEE Workshop on Discrete Event Systems",
year = "1998",
pages={201-208},
}
@article{ WW04,
author = "K. Wong and W. Wonham",
title = "The computation of observers in discrete-event systems",
journal = "Discrete Event Dynamic Systems",
volume={14},
number={1},
year = "2004",
pages={55-107},
}
@Book{W01,
author="W. Wonham",
title="Control of Discrete-Event Systems",
publisher="Department of Electrical Engineering, University of Toronto, Canada",
year="2001"
}
@Book{HTC90,
author = "J. V. Leeuwen ",
title = "Handbook of Theoretical Computer Sc.: Formal Models and Semantics",
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volume = "B",
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@Book{BR97,
author = {R.B. Bapat and T.E.S. Raghavan},
title = {Nonnegative matrices and Applications},
publisher = {Cambridge University Press},
address = {},
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}
@Book{CL99,
author = "Cassandras, C.G. and Lafortune, S.",
title = "Introduction to Discrete Event Systems",
publisher = "Kluwer Academic Publishers",
year = "1999"
}
@ARTICLE{CR05,
author={I. Chattopadhyay and A. Ray},
title={A Complex Measure for Linear Grammars},
journal={Demonstratio Mathematica},
volume={38},
number={3},
year={2005},
pages={in press},
}
@ARTICLE{CR06,
author={I. Chattopadhyay and A. Ray},
title = {Renormalized Measure of Regular Languages},
journal={Int. J. Control},
volume={79},
number={9},
year={2006},
pages={1107-1117},
}
@ARTICLE{CR06a,
author={I. Chattopadhyay and A. Ray},
title = {A Language Measure for Partially Observed Discrete Event Systems},
journal={Int. J. Control},
volume={79},
number={9},
year={2006},
pages={1074-1086},
}
@ARTICLE{C-PhD,
author= { I. Chattopadhyay},
title = {Quantitative Control of Probabilistic Discrete Event Systems},
journal={PhD Dissertation, Dept. of Mech. Engg. Pennsylvania State University, {http{://}
etda.libraries.psu.edu {/} theses / approved / WorldWideIndex / ETD-1443}},
year={2006},
}
@ARTICLE{CR07,
author={I. Chattopadhyay and A. Ray},
title = {Language-Measure-Theoretic Optimal Control of Probabilistic Finite-State Systems
},
journal={Int. J. Control},
volume={},
number={},
year={August,2007},
pages={},
}
@ARTICLE{CLL94,
author = {S.L. Chung and S. Lafortune and F. Lin},
title = {Supervisory Control using variable look-ahead policies},
journal = {Discrete Event Dynamic Systems: Theory and Applications},
volume = {4},
number = {3},
year = {July 1994},
pages = {237-268},
}
@book{D81,
author = {E-E. Doberkat},
title = {Stochastic Automata: Stability, Nondeterminism, and Prediction},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
volume = {113},
year = {1981},
isbn = {3-540-10835-1},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
@ARTICLE{G92,
author={V.K. Garg},
title={An Algebraic approach to modeling probabilistic discrete event systems},
journal={Proceedings of 1992 IEEE Conference on Decision and Control},
volume={},
number={},
year={Tucson, AZ, December 1992},
pages={2348-2353},
}
@ARTICLE{G92-2,
author={V.K. Garg},
title={Probabilistic Lnaguages for modeling of \textsc{DED}s},
journal={Proceedings of 1992 IEEE Conference on Information and Sciences},
volume={},
number={},
year={Princeton, NJ, March 1992},
pages={198-203},
}
@ARTICLE{GKM99,
author = {V.K. Garg and R. Kumar and S.I. Marcus},
title = {A probabilistic language formalism for stochastic discrete event systems},
journal = {IEEE Transanctions on Automatic Control},
volume = {44},
number = {2},
year = {1999},
pages = {280-293},
}
@ARTICLE{GRK07,
author = {S. Gupta and A. Ray and E. Keller},
title = {Symbolic time series analysis of ultrasonic data for early detection
of fatigue damage},
journal = {Mechanical Systems and Signal Processing},
year = {2007},
volume = {21},
pages = {866-884},
number = {2},
}
@article{KG2,
author = "R. Kumar and V.K. Garg",
title = "Control of Stochastic Discrete Event Systems Modeled by Probabilistic Languages",
journal = " IEEE Transactions on Automatic Control",
volume = "46",
year = "2001",
url = "citeseer.ist.psu.edu/278517.html"
}
@ARTICLE{LFR05,
author={C.M. Lagoa and J. Fu and A. Ray},
title={Robust Optimal Control of Regular Languages},
journal = {Automatica},
volume = {41},
number = {8},
year = {2005},
pages = {1339-1345},
}
@ARTICLE{LW93,
author = {M. Lawford and W.M. Wonham},
title = {Supervisory Control of probabilistic discrete event systems},
journal = {Proceedings of 36th Midwest Symposium on Circuits and Systems},
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number = {},
year = {1993},
pages = {327-331},
}
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author = {E.T. Lee and L.A. Zadeh},
title = {Note on Fuzzy Languages},
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}
@book{M89,
author = {R. Milner},
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isbn = {0-13-115007-3},
publisher = {Prentice-Hall, Inc.},
address = {Upper Saddle River, NJ, USA},
}
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author = {T. Murata},
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author = "M.K. Molloy",
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