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@article{idtrackerai,
title={idtracker.ai: tracking all individuals in small or large collectives of unmarked animals},
author={Romero-Ferrero, Francisco and Bergomi, Mattia G and Hinz, Robert C and Heras, Francisco JH and de Polavieja, Gonzalo G},
journal={Nature Methods},
volume={16},
number={2},
pages={179},
year={2019},
publisher={Nature Publishing Group},
abstract ={Understanding of animal collectives is limited by the ability to track each individual. We describe an algorithm and software that extract all trajectories from video, with high identification accuracy for collectives of up to 100 individuals. idtracker.ai uses two convolutional networks: one that detects when animals touch or cross and another for animal identification. The tool is trained with a protocol that adapts to video conditions and tracking difficulty.},
doi = {https://doi.org/10.1038/s41592-018-0295-5}
}
@article{idtracker,
title={idTracker: tracking individuals in a group by automatic identification of unmarked animals},
author={A. Pérez-Escudero and J. Vicente-Page and R.C. Hinz and S. Arganda and G.G. de Polavieja},
journal={Nature Methods},
volume={11},
number={7},
pages={743},
year={2014},
publisher={Nature Publishing Group},
abstract ={Animals in groups touch each other, move in paths that cross, and interact in complex ways. Current video tracking methods sometimes switch identities of unmarked individuals during these interactions. These errors propagate and result in random assignments after a few minutes unless manually corrected. We present idTracker, a multitracking algorithm that extracts a characteristic fingerprint from each animal in a video recording of a group. It then uses these fingerprints to identify every individual throughout the video. Tracking by identification prevents propagation of errors, and the correct identities can be maintained indefinitely. idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. We tested it on fish (Danio rerio and Oryzias latipes), flies (Drosophila melanogaster), ants (Messor structor) and mice (Mus musculus).},
doi = {https://doi.org/10.1038/nmeth.2994}
}
@article {Aivazian917,
author = {Aivazian, Dikran and Serrano, Ramon L. and Pfeffer, Suzanne},
title = {TIP47 is a key effector for Rab9 localization},
volume = {173},
number = {6},
pages = {917--926},
year = {2006},
doi = {http://dx.doi.org/10.1083/jcb.200510010},
publisher = {Rockefeller University Press},
abstract = {The human genome encodes \~{}70 Rab GTPases that localize to the surfaces of distinct membrane compartments. To investigate the mechanism of Rab localization, chimeras containing heterologous Rab hypervariable domains were generated, and their ability to bind seven Rab effectors was quantified. Two chimeras could bind effectors for two distinctly localized Rabs; a Rab5/9 hybrid bound both Rab5 and Rab9 effectors, and a Rab1/9 hybrid bound to certain Rab1 and Rab9 effectors. These unusual chimeras permitted a test of the importance of effector binding for Rab localization. In both cases, changing the cellular concentration of a key Rab9 effector, which is called tail-interacting protein of 47 kD, moved a fraction of the proteins from their parental Rab localization to that of Rab9. Thus, relative concentrations of certain competing effectors could determine a chimera{\textquoteright}s localization. These data confirm the importance of effector interactions for Rab9 localization, and support a model in which effector proteins rely on Rabs as much as Rabs rely on effectors to achieve their correct steady state localizations.},
issn = {0021-9525},
URL = {http://jcb.rupress.org/content/173/6/917},
eprint = {http://jcb.rupress.org/content/173/6/917.full.pdf},
journal = {The Journal of Cell Biology}
}
@article {Bloss029983,
author = {Bloss, Cinnamon S and Wineinger, Nathan E and Peters, Melissa and Boeldt, Debra L and Ariniello, Lauren and Kim, Ju Young and Sheard, Judy and Komatireddy, Ravi and Barrett, Paddy and Topol, Eric J},
title = {A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors},
year = {2016},
doi = {http://dx.doi.org/10.1101/029983},
publisher = {Cold Spring Harbor Labs Journals},
abstract = {Background. Mobile health and digital medicine technologies are becoming increasingly used by individuals with common, chronic diseases to monitor their health. Numerous devices, sensors, and apps are available to patients and consumers -- some of which have been shown to lead to improved health management and health outcomes. However, no randomized controlled trials have been conducted which examine health care costs, and most have failed to provide study participants with a truly comprehensive monitoring system. Methods. We conducted a prospective randomized controlled trial of adults who had submitted a 2012 health insurance claim associated with hypertension, diabetes, and/or cardiac arrhythmia. The intervention involved receipt of one or more mobile devices that corresponded to their condition(s) and an iPhone with linked tracking applications for a period of 6 months; the control group received a standard disease management program. Moreover, intervention study participants received access to an online health management system which provided participants detailed device tracking information over the course of the study. This was a monitoring system designed by leveraging collaborations with device manufacturers, a connected health leader, health care provider, and employee wellness program -- making it both unique and inclusive. We hypothesized that health resource utilization with respect to health insurance claims may be influenced by the monitoring intervention. We also examined health-self management. Results \& Conclusions. There was little evidence of differences in health care costs or utilization as a result of the intervention. Furthermore, we found evidence that the control and intervention groups were equivalent with respect to most health care utilization outcomes. This result suggests there are not large short-term increases or decreases in health care costs or utilization associated with monitoring chronic health conditions using mobile health or digital medicine technologies. Among secondary outcomes there was some evidence of improvement in health self-management which was characterized by a decrease in the propensity to view health status as due to chance factors in the intervention group. Clinical trial registration ID $\#$ NCT01975428},
URL = {http://biorxiv.org/content/early/2016/01/14/029983},
eprint = {http://biorxiv.org/content/early/2016/01/14/029983.full.pdf},
journal = {bioRxiv}
}
@ARTICLE{lin2017focal,
author={T. {Lin} and P. {Goyal} and R. {Girshick} and K. {He} and P. {Dollár}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Focal Loss for Dense Object Detection},
year={2020},
volume={42},
number={2},
pages={318-327},
doi={https://doi.org/10.1109/TPAMI.2018.2858826}}
@book{warren2001subdivision,
author = {Warren, Joe and Weimer, Henrik},
title = {Subdivision Methods for Geometric Design: A Constructive Approach},
year = {2001},
ISBN = {1558604464},
note = "ISBN: 1558604464",
publisher = {Morgan Kaufmann Publishers Inc.},
address = {San Francisco, CA, USA},
edition = {1st},
abstract = {From the Publisher: The worlds leading animation houses rely increasingly on subdivision methods for creating realistic-looking complex shapes. However, until now there was no one book devoted to this powerful geometric modeling technique. Subdivision Methods for Geometric Design does the job with authority and precision, providing all that is needed to understand how subdivision works its magic, and how to make that magic work. Throughout the book, icons cue readers to visit a companion Web site loaded with interactive exercises, implementations of the books images, and supplementary material. Rich in theory, analysis, and practical information, this book is the complete resource for subdivision methods. Features The result of a collaboration between a leading university researcher and an industry practitioner. The only book devoted exclusively and comprehensively to this important new technology. Provides solid background and theoretical analysis of subdivision as well as a wide variety of specific applications. Addresses algorithms for Bezier and uniform B-Spline curves, Catmull-Clark subdivision for quad meshes, and regularity tests for polyhedral meshes. Via the companion Web site, ( ), provides opportunities for readers to experiment hands-on with implementations in a richly interactive environment. Includes a foreword by Tony DeRose, recipient of the 1999 ACM Computer Graphics Achievement Award for his seminal work in subdivision methods. Author Biography: Joe Warren, Professor of Computer Science at Rice University since 1986, is one of the worlds leading experts on subdivision. Of his nearly 50 computer science papers-published in prestigious forums such as SIGGRAPH, Transactions on Graphics, Computer-Aided Geometric Design, and The Visual Computer-a dozen specifically address subdivision and its applications to computer graphics. Prof. Warren received both his M.S. and Ph.D. in Computer Science at Cornell University. His research interests focus on mathematical methods for representing geometric shape. Henrik Weimer is a research scientist at the DaimlerChrysler Corporate Research Center in Berlin, where he works on knowledge-based support for the design and creation of engineering products. Dr. Weimer obtained his Ph.D. in Computer Science from Rice University.}
}
@article{iwata2015genomic,
title={Genomic prediction of biological shape: elliptic fourier analysis and kernel partial least squares (PLS) regression applied to grain shape prediction in rice (Oryza sativa L.)},
author={Iwata, Hiroyoshi and Ebana, Kaworu and Uga, Yusaku and Hayashi, Takeshi},
journal={PloS One},
volume={10},
number={3},
year={2015},
publisher={Public Library of Science},
doi = {https://doi.org/10.1371/journal.pone.0120610}
}
@article{kuhl1982elliptic,
title={Elliptic Fourier features of a closed contour},
author={Kuhl, Frank P and Giardina, Charles R},
journal={Computer Graphics and Image Processing},
volume={18},
number={3},
pages={236--258},
year={1982},
publisher={Elsevier},
doi = {https://doi.org/10.1016/0146-664X(82)90034-X}
}
@article{hu1962visual,
title={Visual pattern recognition by moment invariants},
author={Hu, Ming-Kuei},
journal={IRE Transactions on Information Theory},
volume={8},
number={2},
pages={179--187},
year={1962},
publisher={IEEE},
doi = {https://doi.org/10.1109/TIT.1962.1057692}
}
@inproceedings{wang2016closed,
title={Closed-loop tracking-by-detection for ROV-based multiple fish tracking},
author={Wang, Gaoang and Hwang, Jenq-Neng and Williams, Kresimir and Cutter, George},
booktitle={2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI)},
pages={7--12},
year={2016},
organization={IEEE},
doi = {https://doi.org/10.1109/CVAUI.2016.014}
}
@article{rasch2016closing,
title={Closing the loop: tracking and perturbing behaviour of individuals in a group in real-time},
author={Rasch, Malte J and Shi, Aobo and Ji, Zilong},
journal={bioRxiv},
pages={071308},
year={2016},
publisher={Cold Spring Harbor Laboratory},
doi = {https://doi.org/10.1101/071308}
}
@article{dell2014automated,
title={Automated image-based tracking and its application in ecology},
author={Dell, Anthony I and Bender, John A and Branson, Kristin and Couzin, Iain D and de Polavieja, Gonzalo G and Noldus, Lucas PJJ and P{\'e}rez-Escudero, Alfonso and Perona, Pietro and Straw, Andrew D and Wikelski, Martin and others},
journal={Trends in Ecology \& Evolution},
volume={29},
number={7},
pages={417--428},
year={2014},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.tree.2014.05.004}
}
@article{fukunaga2015grouptracker,
title={GroupTracker: video tracking system for multiple animals under severe occlusion},
author={Fukunaga, Tsukasa and Kubota, Shoko and Oda, Shoji and Iwasaki, Wataru},
journal={Computational Biology and Chemistry},
volume={57},
pages={39--45},
year={2015},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.compbiolchem.2015.02.006}
}
@article{ohayon2013automated,
title={Automated multi-day tracking of marked mice for the analysis of social behaviour},
author={Ohayon, Shay and Avni, Ofer and Taylor, Adam L and Perona, Pietro and Egnor, SE Roian},
journal={Journal of Neuroscience Methods},
volume={219},
number={1},
pages={10--19},
year={2013},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.jneumeth.2013.05.013}
}
@inproceedings{burgos2012social,
title={Social behavior recognition in continuous video},
author={Burgos-Artizzu, Xavier P and Doll{\'a}r, Piotr and Lin, Dayu and Anderson, David J and Perona, Pietro},
booktitle={2012 IEEE Conference on Computer Vision and Pattern Recognition},
pages={1322--1329},
year={2012},
organization={IEEE},
doi = {https://doi.org/10.1109/CVPR.2012.6247817}
}
@article{sridhar2019tracktor,
title={Tracktor: Image-based automated tracking of animal movement and behaviour},
author={Sridhar, Vivek Hari and Roche, Dominique G and Gingins, Simon},
journal={Methods in Ecology and Evolution},
volume={10},
number={6},
pages={815--820},
year={2019},
publisher={Wiley Online Library},
doi = {https://doi.org/10.1111/2041-210X.13166}
}
@inproceedings{hsu2005real,
title={Real-time multiple tracking using a combined technique},
author={Hsu, Hui-Huang and Shih, Timothy K and Tang, Chia-Tong and Liao, Yi-Chun},
booktitle={19th International Conference on Advanced Information Networking and Applications (AINA'05) Volume 1 (AINA papers)},
volume={1},
pages={111--116},
year={2005},
organization={IEEE},
doi = {https://doi.org/10.1109/AINA.2005.290}
}
@article{Man+18b,
Author = {Kevis-Kokitsi Maninis and Sergi Caelles and Yuhua Chen and Jordi Pont-Tuset and Laura Leal-Taix\'e and Daniel Cremers and Luc {Van Gool}},
Title = {Video Object Segmentation Without Temporal Information},
Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
Year = {2018},
doi = {https://doi.org/10.1109/TPAMI.2018.2838670}
}
@article{munkres1957algorithms,
title={Algorithms for the assignment and transportation problems},
author={Munkres, James},
journal={Journal of the Society for Industrial and Applied Mathematics},
volume={5},
number={1},
pages={32--38},
year={1957},
publisher={SIAM},
doi = {https://doi.org/10.1137/0105003}
}
@article{kuhn1955hungarian,
title={The Hungarian method for the assignment problem},
author={Kuhn, Harold W},
journal={Naval Research Logistics Quarterly},
volume={2},
number={1-2},
pages={83--97},
year={1955},
publisher={Wiley Online Library},
doi = {https://doi.org/10.1002/nav.3800020109}
}
@article{garrido2016generation,
title={Generation of fiducial marker dictionaries using mixed integer linear programming},
author={Garrido-Jurado, Sergio and Mu{\~n}oz-Salinas, Rafael and Madrid-Cuevas, Francisco Jos{\'e} and Medina-Carnicer, Rafael},
journal={Pattern Recognition},
volume={51},
pages={481--491},
year={2016},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.patcog.2015.09.023}
}
@article{pesant2012counting,
title={Counting-based search: Branching heuristics for constraint satisfaction problems},
author={Pesant, Gilles and Quimper, Claude-Guy and Zanarini, Alessandro},
journal={Journal of Artificial Intelligence Research},
volume={43},
pages={173--210},
year={2012},
doi = {https://doi.org/10.1613/jair.3463}
}
@article{little1963algorithm,
title={An algorithm for the traveling salesman problem},
author={Little, John DC and Murty, Katta G and Sweeney, Dura W and Karel, Caroline},
journal={Operations Research},
volume={11},
number={6},
pages={972--989},
year={1963},
publisher={INFORMS},
doi = {https://doi.org/10.1287/opre.11.6.972}
}
@misc{ramshaw2012minimum,
title={On Minimum-Cost Assignments in Unbalanced Bipartite Graphs},
author={Ramshaw, Lyle and Tarjan, Robert E},
editor = {},
publisher={HP Labs, Palo Alto, CA, USA},
year={2012},
note = "{Technical Report}, HPL-2012-40R1, [Online; Accessed 22-Oct-2020]",
url = {https://www.hpl.hp.com/techreports/2012/HPL-2012-40.pdf},
number = "HPL-2012-40R1"
}
@INPROCEEDINGS{ramshaw2012weight,
author={L. {Ramshaw} and R. E. {Tarjan}},
booktitle={2012 IEEE 53rd Annual Symposium on Foundations of Computer Science},
title={A Weight-Scaling Algorithm for Min-Cost Imperfect Matchings in Bipartite Graphs},
year={2012},
volume={},
number={},
pages={581-590},
doi={https://doi.org/10.1109/FOCS.2012.9}}
@misc{zhang1996branch,
title={Branch-and-Bound Search Algorithms and Their Computational Complexity},
author={Zhang Weixiong},
year={1996},
month={May},
note="{Technical Report, ISI/RR-96-443, [Online; Accessed 22-Oct-2020]}",
publisher="University of Southern California/Marina Del Rey Information Sciences Institute",
institution={University of Southern California, Marina Del Rey Information Sciences Institute},
number={ISI/RR-96-443},
url={https://apps.dtic.mil/sti/citations/ADA314598}
}
@misc{pybind11,
author = {Wenzel Jakob and Jason Rhinelander and Dean Moldovan},
year = {2017},
howpublished="\url{https://github.com/pybind/pybind11}",
publisher = {Wenzel Jakob},
note = "[Online; accessed 22-Oct-2020]",
title = {pybind11 -- Seamless operability between C++11 and Python}
}
@misc{thomas2015matching,
author = {Dirk Johannes Thomas},
title = {Matching Problems with Additional Resource Constraints},
note = "{Doctoral Thesis}",
publisher = {Universit{\"a}t Trier},
url = {https://doi.org/10.25353/ubtr-xxxx-7644-a670/},
doi = {10.25353/ubtr-xxxx-7644-a670/},
year = {2016}
}
@article{fredman1987fibonacci,
title={Fibonacci heaps and their uses in improved network optimization algorithms},
author={Fredman, Michael L and Tarjan, Robert Endre},
journal={Journal of the ACM (JACM)},
volume={34},
number={3},
pages={596--615},
year={1987},
publisher={ACM New York, NY, USA},
doi = {https://doi.org/10.1145/28869.28874}
}
@article{bertsekas1981new,
title={A new algorithm for the assignment problem},
author={Bertsekas, Dimitri P},
journal={Mathematical Programming},
volume={21},
number={1},
pages={152--171},
year={1981},
publisher={Springer},
doi = {https://doi.org/10.1007/BF01584237}
}
@article{kalman1960new,
author = {Kalman, R. E.},
title = "{A New Approach to Linear Filtering and Prediction Problems}",
journal = {Journal of Basic Engineering},
volume = {82},
number = {1},
pages = {35-45},
year = {1960},
month = {03},
abstract = "{The classical filtering and prediction problem is re-examined using the Bode-Shannon representation of random processes and the “state-transition” method of analysis of dynamic systems. New results are: (1) The formulation and methods of solution of the problem apply without modification to stationary and nonstationary statistics and to growing-memory and infinite-memory filters. (2) A nonlinear difference (or differential) equation is derived for the covariance matrix of the optimal estimation error. From the solution of this equation the co-efficients of the difference (or differential) equation of the optimal linear filter are obtained without further calculations. (3) The filtering problem is shown to be the dual of the noise-free regulator problem. The new method developed here is applied to two well-known problems, confirming and extending earlier results. The discussion is largely self-contained and proceeds from first principles; basic concepts of the theory of random processes are reviewed in the Appendix.}",
issn = {0021-9223},
doi = {10.1115/1.3662552},
url = {https://doi.org/10.1115/1.3662552},
eprint = {https://asmedigitalcollection.asme.org/fluidsengineering/article-pdf/82/1/35/5518977/35\_1.pdf}
}
@article{stowers2017virtual,
title={Virtual reality for freely moving animals},
author={Stowers, John R and Hofbauer, Maximilian and Bastien, Renaud and Griessner, Johannes and Higgins, Peter and Farooqui, Sarfarazhussain and Fischer, Ruth M and Nowikovsky, Karin and Haubensak, Wulf and Couzin, Iain D and others},
journal={Nature Methods},
volume={14},
number={10},
pages={995},
year={2017},
publisher={Nature Publishing Group},
doi = {https://doi.org/10.1038/nmeth.4399}
}
@article{stowers2014reverse,
title={Reverse engineering animal vision with virtual reality and genetics},
author={Stowers, John R and Fuhrmann, Anton and Hofbauer, Maximilian and Streinzer, Martin and Schmid, Axel and Dickinson, Michael H and Straw, Andrew D},
journal={Computer},
volume={47},
number={7},
pages={38--45},
year={2014},
publisher={IEEE},
doi = {https://doi.org/10.1109/MC.2014.190}
}
@article{perez2017effectiveness,
author = {Luis Perez and
Jason Wang},
title = {The Effectiveness of Data Augmentation in Image Classification using
Deep Learning},
journal = {CoRR},
volume = {abs/1712.04621},
year = {2017},
url = {http://arxiv.org/abs/1712.04621},
archivePrefix = {arXiv},
eprint = {1712.04621},
timestamp = {Mon, 13 Aug 2018 16:48:03 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1712-04621.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{graving2019deepposekit,
title={DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning},
author={Graving, Jacob M and Chae, Daniel and Naik, Hemal and Li, Liang and Koger, Benjamin and Costelloe, Blair R and Couzin, Iain D},
journal={eLife},
volume={8},
pages={e47994},
year={2019},
publisher={eLife Sciences Publications Limited},
doi={https://doi.org/10.7554/eLife.47994}
}
@inproceedings{kingma2014adam,
author = {Diederik P. Kingma and
Jimmy Ba},
editor = {Yoshua Bengio and
Yann LeCun},
title = {Adam: {A} Method for Stochastic Optimization},
booktitle = {3rd International Conference on Learning Representations, {ICLR} 2015,
San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings},
year = {2015},
url = {http://arxiv.org/abs/1412.6980},
timestamp = {Thu, 25 Jul 2019 14:25:37 +0200},
biburl = {https://dblp.org/rec/journals/corr/KingmaB14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
note = "{arXiv:1412.6980}"
}
@misc{clausen1999branch,
title={Branch and bound algorithms-principles and examples},
author={J. Clausen},
publisher={University of Copenhagen},
year={1999},
howpublished="\url{http://www2.imm.dtu.dk/courses/04232/TSPtext.pdf}",
note = "[Online; accessed 22-Oct-2020]"
}
@Inbook{land1960automatic,
author="Land, Ailsa H.
and Doig, Alison G.",
editor="J{\"u}nger, Michael
and Liebling, Thomas M.
and Naddef, Denis
and Nemhauser, George L.
and Pulleyblank, William R.
and Reinelt, Gerhard
and Rinaldi, Giovanni
and Wolsey, Laurence A.",
title="An Automatic Method for Solving Discrete Programming Problems",
bookTitle="50 Years of Integer Programming 1958-2008: From the Early Years to the State-of-the-Art",
year="2010",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="105--132",
abstract="In the late 1950s there was a group of teachers and research assistants at the London School of Economics interested in linear programming and its extensions, in particular Helen Makower, George Morton, Ailsa Land and Alison Doig. We had considered the `Laundry Van Problem' until we discovered that it was known as the Traveling Salesman Problem, and had looked at aircraft timetabling, until quickly realizing that even the planning for the Scottish sector was beyond our capability! Alison Doig (now Harcourt) had studied the paper trim problem for her Masters project in Melbourne before coming to England.",
isbn="978-3-540-68279-0",
doi="10.1007/978-3-540-68279-0_5",
url="https://doi.org/10.1007/978-3-540-68279-0_5"
}
@article{suzuki2003linear,
title={Linear-time connected-component labeling based on sequential local operations},
author={Suzuki, Kenji and Horiba, Isao and Sugie, Noboru},
journal={Computer Vision and Image Understanding},
volume={89},
number={1},
pages={1--23},
year={2003},
publisher={Elsevier},
doi = {https://doi.org/10.1016/S1077-3142(02)00030-9}
}
@inproceedings{4728561,
author={A. AbuBaker and R. Qahwaji and S. Ipson and M. Saleh},
booktitle={2007 IEEE International Conference on Signal Processing and Communications},
title={One Scan Connected Component Labeling Technique},
year={2007},
volume={},
number={},
pages={1283-1286},
doi = {https://doi.org/10.1109/ICSPC.2007.4728561}
}
@inproceedings{chang2003component,
author = {F. Chang and C. Chen},
booktitle = {2013 12th International Conference on Document Analysis and Recognition},
title = {A Component-Labeling Algorithm Using Contour Tracing Technique},
year = {2003},
volume = {3},
issn = {},
pages = {741},
keywords = {null},
doi = {10.1109/ICDAR.2003.1227760},
url = {https://doi.ieeecomputersociety.org/10.1109/ICDAR.2003.1227760},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {August}
}
@article{he2009fast,
title={Fast connected-component labeling},
author={He, Lifeng and Chao, Yuyan and Suzuki, Kenji and Wu, Kesheng},
journal={Pattern recognition},
volume={42},
number={9},
pages={1977--1987},
year={2009},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.patcog.2008.10.013}
}
@InProceedings{pmlr-v9-glorot10a,
title = {Understanding the difficulty of training deep feedforward neural networks},
author = {Xavier Glorot and Yoshua Bengio},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
pages = {249--256},
year = {2010},
editor = {Yee Whye Teh and Mike Titterington},
volume = {9},
series = {Proceedings of Machine Learning Research},
address = {Chia Laguna Resort, Sardinia, Italy},
month = {13--15 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf},
url = {http://proceedings.mlr.press/v9/glorot10a.html},
abstract = {Whereas before 2006 it appears that deep multi-layer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence.}
}
@article{hughey2018challenges,
author = {Hughey, Lacey F. and Hein, Andrew M. and Strandburg-Peshkin, Ariana and Jensen, Frants H. },
title = {Challenges and solutions for studying collective animal behaviour in the wild},
journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
volume = {373},
number = {1746},
pages = {20170005},
year = {2018},
doi = {10.1098/rstb.2017.0005},
URL = {https://royalsocietypublishing.org/doi/abs/10.1098/rstb.2017.0005},
eprint = {https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2017.0005}
,
abstract = { Mobile animal groups provide some of the most compelling examples of self-organization in the natural world. While field observations of songbird flocks wheeling in the sky or anchovy schools fleeing from predators have inspired considerable interest in the mechanics of collective motion, the challenge of simultaneously monitoring multiple animals in the field has historically limited our capacity to study collective behaviour of wild animal groups with precision. However, recent technological advancements now present exciting opportunities to overcome many of these limitations. Here we review existing methods used to collect data on the movements and interactions of multiple animals in a natural setting. We then survey emerging technologies that are poised to revolutionize the study of collective animal behaviour by extending the spatial and temporal scales of inquiry, increasing data volume and quality, and expediting the post-processing of raw data. This article is part of the theme issue ‘Collective movement ecology’. }
}
@article{robie2017machine,
title={Machine vision methods for analyzing social interactions},
author={Robie, Alice A and Seagraves, Kelly M and Egnor, SE Roian and Branson, Kristin},
journal={Journal of Experimental Biology},
volume={220},
number={1},
pages={25--34},
year={2017},
publisher={The Company of Biologists Ltd},
doi = {https://doi.org/10.1242/jeb.142281}
}
@article{francisco2019low,
title={A low-cost, open-source framework for tracking and behavioural analysis of animals in aquatic ecosystems},
author={Francisco, Fritz A and N{\"u}hrenberg, Paul and Jordan, Alex L},
journal={bioRxiv},
pages={571232},
year={2019},
publisher={Cold Spring Harbor Laboratory},
doi = {https://doi.org/10.1101/571232}
}
@inproceedings{williams1978casting,
title={Casting curved shadows on curved surfaces},
author={Williams, Lance},
booktitle={Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques},
pages={270--274},
year={1978},
doi = {https://doi.org/10.1145/800248.807402}
}
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author={S. {Caelles} and K. -. {Maninis} and J. {Pont-Tuset} and L. {Leal-Taixé} and D. {Cremers} and L. {Van Gool}},
booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title={One-Shot Video Object Segmentation},
year={2017},
volume={},
number={},
pages={5320-5329},
doi={https://doi.org/10.1109/CVPR.2017.565}}
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title={Recognizing novel views of three-dimensional objects.},
author={Humphrey, G Keith and Khan, Shakeela C},
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number={2},
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year={1992},
publisher={Canadian Psychological Association},
doi = {https://doi.org/10.1037/h0084320}
}
@inproceedings{liu2009effect,
title={The effect of object features on multiple object tracking and identification},
author={Liu, Tianwei and Chen, Wenfeng and Xuan, Yuming and Fu, Xiaolan},
booktitle={International Conference on Engineering Psychology and Cognitive Ergonomics},
pages={206--212},
year={2009},
organization={Springer},
doi = {https://doi.org/10.1007/978-3-642-02728-4_22}
}
@article{bonter2011applications,
title={Applications of radio frequency identification (RFID) in ornithological research: a review},
author={Bonter, David N and Bridge, Eli S},
journal={Journal of Field Ornithology},
volume={82},
number={1},
pages={1--10},
year={2011},
publisher={Wiley Online Library},
doi = {https://doi.org/10.1111/j.1557-9263.2010.00302.x}
}
@article{mersch2013tracking,
title={Tracking individuals shows spatial fidelity is a key regulator of ant social organization},
author={Mersch, Danielle P and Crespi, Alessandro and Keller, Laurent},
journal={Science},
volume={340},
number={6136},
pages={1090--1093},
year={2013},
publisher={American Association for the Advancement of Science},
doi = {10.1126/science.1234316}
}
@article{crall2015beetag,
title={BEEtag: a low-cost, image-based tracking system for the study of animal behavior and locomotion},
author={Crall, James D and Gravish, Nick and Mountcastle, Andrew M and Combes, Stacey A},
journal={PloS One},
volume={10},
number={9},
year={2015},
publisher={Public Library of Science},
doi = {https://doi.org/10.1371/journal.pone.0136487}
}
@article{fukushima1988neocognitron,
title={Neocognitron: A hierarchical neural network capable of visual pattern recognition},
author={Fukushima, Kunihiko},
journal={Neural Networks},
volume={1},
number={2},
pages={119--130},
year={1988},
publisher={Elsevier},
doi = {https://doi.org/10.1016/0893-6080(88)90014-7}
}
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title={Receptive fields of single neurones in the cat's striate cortex},
author={Hubel, David H and Wiesel, Torsten N},
journal={The Journal of Physiology},
volume={148},
number={3},
pages={574},
year={1959},
publisher={Wiley-Blackwell},
doi = {10.1113/jphysiol.1959.sp006308}
}
@article{hubel1963receptive,
title={Receptive fields of cells in striate cortex of very young, visually inexperienced kittens},
author={Hubel, David H and Wiesel, Torsten N},
journal={Journal of Neurophysiology},
volume={26},
number={6},
pages={994--1002},
year={1963},
doi = {https://doi.org/10.1152/jn.1963.26.6.994}
}
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title={Spatial and chromatic interactions in the lateral geniculate body of the rhesus monkey.},
author={Wiesel, Torsten N and Hubel, David H},
journal={Journal of Neurophysiology},
volume={29},
number={6},
pages={1115--1156},
year={1966},
doi = {https://doi.org/10.1152/jn.1966.29.6.1115}
}
@article{lecun1989backpropagation,
title={Backpropagation applied to handwritten zip code recognition},
author={LeCun, Yann and Boser, Bernhard and Denker, John S and Henderson, Donnie and Howard, Richard E and Hubbard, Wayne and Jackel, Lawrence D},
journal={Neural Computation},
volume={1},
number={4},
pages={541--551},
year={1989},
publisher={MIT Press},
doi = {https://doi.org/10.1162/neco.1989.1.4.541}
}
@article{strandburg2013visual,
title={Visual sensory networks and effective information transfer in animal groups},
author={Strandburg-Peshkin, Ariana and Twomey, Colin R and Bode, Nikolai WF and Kao, Albert B and Katz, Yael and Ioannou, Christos C and Rosenthal, Sara B and Torney, Colin J and Wu, Hai Shan and Levin, Simon A and others},
journal={Current Biology},
volume={23},
number={17},
pages={R709--R711},
year={2013},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.cub.2013.07.059}
}
@article{colavita1974human,
title={Human sensory dominance},
author={Colavita, Francis B},
journal={Perception \& Psychophysics},
volume={16},
number={2},
pages={409--412},
year={1974},
publisher={Springer},
doi = {https://doi.org/10.3758/BF03203962}
}
@article{bilotta2001zebrafish,
title={The zebrafish as a model visual system},
author={Bilotta, Joseph and Saszik, Shannon},
journal={International Journal of Developmental Neuroscience},
volume={19},
number={7},
pages={621--629},
year={2001},
publisher={Elsevier},
doi = {https://doi.org/10.1016/S0736-5748(01)00050-8}
}
@article{brembs2000operant,
title={The operant and the classical in conditioned orientation of \textit{Drosophila melanogaster} at the flight simulator},
author={Brembs, Bj{\"o}rn and Heisenberg, Martin},
journal={Learning \& Memory},
volume={7},
number={2},
pages={104--115},
year={2000},
publisher={Cold Spring Harbor Lab},
doi = {10.1101/lm.7.2.104}
}
@article{bianco2015visuomotor,
title={Visuomotor transformations underlying hunting behavior in zebrafish},
author={Bianco, Isaac H and Engert, Florian},
journal={Current Biology},
volume={25},
number={7},
pages={831--846},
year={2015},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.cub.2015.01.042}
}
@article{bath2014flymad,
title={FlyMAD: rapid thermogenetic control of neuronal activity in freely walking Drosophila},
author={Bath, Daniel E and Stowers, John R and H{\"o}rmann, Dorothea and Poehlmann, Andreas and Dickson, Barry J and Straw, Andrew D},
journal={Nature Methods},
volume={11},
number={7},
pages={756--762},
year={2014},
publisher={Nature Publishing Group},
doi = {https://doi.org/10.1038/nmeth.2973}
}
@article{rodriguez2018toxtrac,
title={ToxTrac: a fast and robust software for tracking organisms},
author={Rodriguez, Alvaro and Zhang, Hanqing and Klaminder, Jonatan and Brodin, Tomas and Andersson, Patrik L and Andersson, Magnus},
journal={Methods in Ecology and Evolution},
volume={9},
number={3},
pages={460--464},
year={2018},
publisher={Wiley Online Library},
doi = {https://doi.org/10.1111/2041-210X.12874}
}
@article{branson2009high,
title={High-throughput ethomics in large groups of Drosophila},
author={Branson, Kristin and Robie, Alice A and Bender, John and Perona, Pietro and Dickinson, Michael H},
journal={Nature Methods},
volume={6},
number={6},
pages={451--457},
year={2009},
publisher={Nature Publishing Group},
doi = {https://doi.org/10.1038/nmeth.1328}
}
@article{pennekamp2015bemovi,
title={BEMOVI, software for extracting behavior and morphology from videos, illustrated with analyses of microbes},
author={Pennekamp, Frank and Schtickzelle, Nicolas and Petchey, Owen L},
journal={Ecology and Evolution},
volume={5},
number={13},
pages={2584--2595},
year={2015},
publisher={Wiley Online Library},
doi = {https://doi.org/10.1002/ece3.1529}
}
@article{hewitt2018novel,
title={A novel automated rodent tracker (ART), demonstrated in a mouse model of amyotrophic lateral sclerosis},
author={Hewitt, Brett M and Yap, Moi Hoon and Hodson-Tole, Emma F and Kennerley, Aneurin J and Sharp, Paul S and Grant, Robyn A},
journal={Journal of neuroscience methods},
volume={300},
pages={147--156},
year={2018},
publisher={Elsevier},
doi = {https://doi.org/10.1016/j.jneumeth.2017.04.006}
}
@article{noldus2001ethovision,
title={EthoVision: a versatile video tracking system for automation of behavioral experiments},
author={Noldus, Lucas PJJ and Spink, Andrew J and Tegelenbosch, Ruud AJ},
journal={Behavior Research Methods, Instruments, \& Computers},
volume={33},
number={3},
pages={398--414},
year={2001},
publisher={Springer},
doi = {https://doi.org/10.3758/BF03195394}
}
@article{risse2017fimtrack,
title={FIMTrack: An open source tracking and locomotion analysis software for small animals},
author={Risse, Benjamin and Berh, Dimitri and Otto, Nils and Kl{\"a}mbt, Christian and Jiang, Xiaoyi},
journal={PLoS Computational Biology},
volume={13},
number={5},
pages={e1005530},
year={2017},
publisher={Public Library of Science},
doi = {https://doi.org/10.1371/journal.pcbi.1005530}
}
@article{rosenthal2015revealing,
title={Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion},
author={Rosenthal, Sara Brin and Twomey, Colin R and Hartnett, Andrew T and Wu, Hai Shan and Couzin, Iain D},
journal={Proceedings of the National Academy of Sciences},
volume={112},
number={15},
pages={4690--4695},
year={2015},
publisher={National Acad Sciences},
doi = {https://doi.org/10.1073/pnas.1420068112}
}
@article{alarcon2018automated,
title={An automated barcode tracking system for behavioural studies in birds},
author={Alarc{\'o}n-Nieto, Gustavo and Graving, Jacob M and Klarevas-Irby, James A and Maldonado-Chaparro, Adriana A and Mueller, Inge and Farine, Damien R},
journal={Methods in Ecology and Evolution},
volume={9},
number={6},
pages={1536--1547},
year={2018},
publisher={Wiley Online Library},
doi = {https://doi.org/10.1111/2041-210X.13005}
}
@article{nagy2013context,
title={Context-dependent hierarchies in pigeons},
author={Nagy, M{\'a}t{\'e} and V{\'a}s{\'a}rhelyi, G{\'a}bor and Pettit, Benjamin and Roberts-Mariani, Isabella and Vicsek, Tam{\'a}s and Biro, Dora},
journal={Proceedings of the National Academy of Sciences},
volume={110},
number={32},
pages={13049--13054},
year={2013},
publisher={National Acad Sciences},
doi={https://doi.org/10.1073/pnas.1305552110}
}
@inproceedings{biggs2018creatures,
title={Creatures great and SMAL: Recovering the shape and motion of animals from video},
author={Biggs, Benjamin and Roddick, Thomas and Fitzgibbon, Andrew and Cipolla, Roberto},
booktitle={Asian Conference on Computer Vision},
pages={3--19},
year={2018},
organization={Springer},
doi={https://doi.org/10.1007/978-3-030-20873-8_1}
}
@article{cavagna2010empirical,
author={Cavagna, Andrea and Cimarelli, Alessio and Giardina, Irene and Parisi, Giorgio and Santagati, Raffaele and Stefanini, Fabio and Tavarone, Raffaele},
title = {From empirical data to inter-individual interactions: unveiling the rules of collective animal behavior},
journal = {Mathematical Models and Methods in Applied Sciences},
volume = {20},
number = {supp01},
pages = {1491-1510},
year = {2010},
doi = {https://doi.org/10.1142/S0218202510004660},
abstract = { Animal groups represent magnificent archetypes of self-organized collective behavior. As such, they have attracted enormous interdisciplinary interest in the last years. From a mechanistic point of view, animal aggregations remind physical systems of particles or spins, where the individual constituents interact locally, giving rise to ordering at the global scale. This analogy has fostered important research, where numerical and theoretical approaches from physics have been applied to models of self-organized motion. In this paper, we discuss how the physics methodology may provide precious conceptual and technical instruments in empirical studies of collective animal behavior. We focus on three-dimensional groups, for which empirical data have been extremely scarce until recently, and describe novel experimental protocols that allow reconstructing aggregations of thousands of individuals. We show how an appropriate statistical analysis of these large-scale data allows inferring important information on the interactions between individuals in a group, a key issue in behavioral studies and a basic ingredient of theoretical models. To this aim, we revisit the approach we recently used on starling flocks, and apply it to a much larger data set, never analyzed before. The results confirm our previous findings and indicate that interactions between birds have a topological rather than metric nature, each individual interacting with a fixed number of neighbors irrespective of their distances. }
}
@article{perez2011collective,
title={Collective animal behavior from Bayesian estimation and probability matching},
author={P{\'e}rez-Escudero, Alfonso and de Polavieja, Gonzalo},
journal={Nature Precedings},
pages={1--1},
year={2011},
publisher={Nature Publishing Group},
doi={https://doi.org/10.1038/npre.2011.5939.2}
}
@article{couzin2009collective,
title={Collective cognition in animal groups},
author={Couzin, Iain D},
journal={Trends in Cognitive Sciences},
volume={13},
number={1},
pages={36--43},
year={2009},
publisher={Elsevier}
}
@article{inada2002order,
title = "Order and Flexibility in the Motion of Fish Schools",
journal = "Journal of Theoretical Biology",
volume = "214",
number = "3",
pages = "371 - 387",
year = "2002",
issn = "0022-5193",
doi = "https://doi.org/10.1006/jtbi.2001.2449",
url = "http://www.sciencedirect.com/science/article/pii/S002251930192449X",
author = "Inada, Yoshinobu and Kawachi, Keiji",
abstract = "The coexistence of order and flexibility in the motion of fish schools was studied by using a simple numerical model and a computer simulation. The numerical model is based on behavioral rules for individuals in the school by considering attraction, repulsion, and parallel-orientation behavior. Each individual follows the same rules and makes school movements. The simulation results show that school order and flexibility are affected by the number of neighbors interacting with an individual in the school and by the randomness of individual motion. Increase in the number of interacting neighbors leads to high order, especially when the number increases from a low value (between one and three). An optimal number of interacting neighbors exists that is relatively low (two or three) for high flexibility, indicating that a fish needs only to pay attention to a few neighbors to realize both order and flexibility. The low randomness of individual motion benefits both order and flexibility. These results indicate that schooling fish have evolved specialized ability for establishing both school order and flexibility."
}
@article{mathis2018deeplabcut,
title={DeepLabCut: markerless pose estimation of user-defined body parts with deep learning},
author={Mathis, Alexander and Mamidanna, Pranav and Cury, Kevin M and Abe, Taiga and Murthy, Venkatesh N and Mathis, Mackenzie Weygandt and Bethge, Matthias},
journal={Nature Neuroscience},
volume={21},
number={9},
pages={1281--1289},
year={2018},
publisher={Nature Publishing Group},
doi={https://doi.org/10.1038/s41593-018-0209-y}
}
@article{pereira2019fast,
title={Fast animal pose estimation using deep neural networks},
author={Pereira, Talmo D and Aldarondo, Diego E and Willmore, Lindsay and Kislin, Mikhail and Wang, Samuel S-H and Murthy, Mala and Shaevitz, Joshua W},
journal={Nature Methods},
volume={16},
number={1},
pages={117--125},
year={2019},
publisher={Nature Publishing Group},
doi={https://doi.org/10.1038/s41592-018-0234-5}
}
@article {Pereira2020.08.31.276246,
author = {Pereira, Talmo D. and Tabris, Nathaniel and Li, Junyu and Ravindranath, Shruthi and Papadoyannis, Eleni S. and Wang, Z. Yan and Turner, David M. and McKenzie-Smith, Grace and Kocher, Sarah D. and Falkner, Annegret L. and Shaevitz, Joshua W. and Murthy, Mala},
title = {SLEAP: Multi-animal pose tracking},
elocation-id = {2020.08.31.276246},
year = {2020},
doi = {10.1101/2020.08.31.276246},
publisher = {Cold Spring Harbor Laboratory},
abstract = {The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation to quantify and model natural animal behavior. This has led to important advances in deep learning-based markerless pose estimation that have been enabled in part by the success of deep learning for computer vision applications. Here we present SLEAP (Social LEAP Estimates Animal Poses), a framework for multi-animal pose tracking via deep learning. This system is capable of simultaneously tracking any number of animals during social interactions and across a variety of experimental conditions. SLEAP implements several complementary approaches for dealing with the problems inherent in moving from single-to multi-animal pose tracking, including configurable neural network architectures, inference techniques, and tracking algorithms, enabling easy specialization and tuning for particular experimental conditions or performance requirements. We report results on multiple datasets of socially interacting animals (flies, bees, and mice) and describe how dataset-specific properties can be leveraged to determine the best configuration of SLEAP models. Using a high accuracy model (\<2.8 px error on 95\% of points), we were able to track two animals from full size 1024 {\texttimes} 1024 pixel frames at up to 320 FPS. The SLEAP framework comes with a sophisticated graphical user interface, multi-platform support, Colab-based GPU-free training and inference, and complete tutorials available, in addition to the datasets, at sleap.ai.Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2020/09/02/2020.08.31.276246},
eprint = {https://www.biorxiv.org/content/early/2020/09/02/2020.08.31.276246.full.pdf},
journal = {bioRxiv}
}
@article {Wild2020.05.06.076943,
author = {Wild, Benjamin and Dormagen, David M and Zachariae, Adrian and Smith, Michael L and Traynor, Kirsten S and Brockmann, Dirk and Couzin, Iain D and Landgraf, Tim},
title = {Social networks predict the life and death of honey bees},
elocation-id = {2020.05.06.076943},
year = {2020},
doi = {10.1101/2020.05.06.076943},
publisher = {Cold Spring Harbor Laboratory},
abstract = {In complex societies, individuals{\textquoteright} roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individual{\textquoteright}s social network that can be obtained without interfering with the colony. This network age accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function.Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2020/09/01/2020.05.06.076943},
eprint = {https://www.biorxiv.org/content/early/2020/09/01/2020.05.06.076943.full.pdf},
journal = {bioRxiv}
}
@article {Gernat1433,
author = {Gernat, Tim and Rao, Vikyath D. and Middendorf, Martin and Dankowicz, Harry and Goldenfeld, Nigel and Robinson, Gene E.},
title = {Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks},
volume = {115},
number = {7},
pages = {1433--1438},
year = {2018},
doi = {10.1073/pnas.1713568115},
publisher = {National Academy of Sciences},
abstract = {Interaction patterns in human communication networks are characterized by intermittency and unpredictable timing (burstiness). Simulated spreading dynamics through such networks are slower than expected. A technology for automated recording of social interactions of individual honeybees, developed by the authors, enables one to study these two phenomena in a nonhuman society. Specifically, by analyzing more than 1.2 million bee social interactions, we demonstrate that burstiness is not a human-specific interaction pattern. We furthermore show that spreading dynamics on bee social networks are faster than expected, confirming earlier theoretical predictions that burstiness and fast spreading can co-occur. We expect that these findings will inform future models of large-scale social organization, spread of disease, and information transmission.Social networks mediate the spread of information and disease. The dynamics of spreading depends, among other factors, on the distribution of times between successive contacts in the network. Heavy-tailed (bursty) time distributions are characteristic of human communication networks, including face-to-face contacts and electronic communication via mobile phone calls, email, and internet communities. Burstiness has been cited as a possible cause for slow spreading in these networks relative to a randomized reference network. However, it is not known whether burstiness is an epiphenomenon of human-specific patterns of communication. Moreover, theory predicts that fast, bursty communication networks should also exist. Here, we present a high-throughput technology for automated monitoring of social interactions of individual honeybees and the analysis of a rich and detailed dataset consisting of more than 1.2 million interactions in five honeybee colonies. We find that bees, like humans, also interact in bursts but that spreading is significantly faster than in a randomized reference network and remains so even after an experimental demographic perturbation. Thus, while burstiness may be an intrinsic property of social interactions, it does not always inhibit spreading in real-world communication networks. We anticipate that these results will inform future models of large-scale social organization and information and disease transmission, and may impact health management of threatened honeybee populations.},
issn = {0027-8424},
URL = {https://www.pnas.org/content/115/7/1433},
eprint = {https://www.pnas.org/content/115/7/1433.full.pdf},
journal = {Proceedings of the National Academy of Sciences}
}
@article{DENNIS20081939,
title = {Appearance Matters: Artificial Marking Alters Aggression and Stress},
journal = {Poultry Science},