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Journal articles on the topic 'Burstiness'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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1

Cruz, Rene L. "Service Burstiness and Dynamic Burstiness Measures: A Framework." Journal of High Speed Networks 1, no. 2 (1992): 105–27. http://dx.doi.org/10.3233/jhs-1992-1201.

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2

Hartleb, Detlef, Andreas Ahrens, and Jelena Zascerinska. "EXPLORING THE IMPACT OF BURSTINESS ON THE SERVICE PROCESS AT THE CASH REGISTER." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 3 (June 16, 2021): 104–9. http://dx.doi.org/10.17770/etr2021vol3.6563.

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The service process is the key phase in any queue system applied to business and industry operations. The service process in shops is defined as the payment process at the cash register. The service process consists of two elements or sub-processes: the waiting in the queue to the cash register as well as the payment processing (scanning the goods, giving receipts to customers, etc). Analysis of burstiness as the indicator of the service process has been well-established. Against this background on burstiness as the indicator of the service process, burstiness is also defined as a factor that influences the service process. However, burstiness as a factor in the service process has not attracted a lot of research attention. The aim of this paper is to analyse burstiness as a factor in the service process underpinning the elaboration of scenarios of the service process for the queue management purposes. The present work mostly employs theoretical methods: scientific literature analysis, synthesis, modelling, comparison, and systematization. The theoretical research results in the outline of the conceptual framework for exploring the impact of burstiness on the service process. The key concepts have been identified, namely binary customer behaviour, buyers’ burstiness, bottlenecks’ emergence at the server, and queue management. The logical chain of the development has been emphasized: binary customer behaviour → buyers’ burstiness → bottlenecks’ emergence at the check-out station or cash register (server) → queue management. The presented logical chain allows finding out that buyers’ burstiness leads to the queue appearance in the service process. In turn, queue appearance requires queue management measures. Hence, buyers’ burstiness influences on the decisions in regard to queue management within the service process. Further on, two functions of buyers’ burstiness are defined: the indicators of the service process, and the factor that influences the service process. This bi-modal role of buyers’ burstiness in the service process highlights the complex nature of the queue management. Five scenarios of the service process will allow using a combination of queue management measures in each scenario or even between scenarios. The findings of the comparative study propose the structure of the service process as the unity of the waiting in the queue to the cash register and the payment processing at the cash register, i.e. scanning of the goods and the payment. The present research has some limitations. Further research tends to validate the model of five scenarios of the service process for the queue management purposes. Comparative studies on buyers’ burstiness in the service process will be continued, too.
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3

DeSimone, Antonio. "Generating burstiness in networks." ACM SIGCOMM Computer Communication Review 21, no. 1 (January 2, 1991): 24–31. http://dx.doi.org/10.1145/116030.116032.

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4

Nguyen, Ngoc Huy, and Myung Kyun Kim. "Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks." Energies 13, no. 23 (December 4, 2020): 6430. http://dx.doi.org/10.3390/en13236430.

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Although mature industrial wireless sensor network applications increasingly require low-power operations, deterministic communications, and end-to-end reliability, it is very difficult to achieve these goals because of link burstiness and interference. In this paper, we propose a novel link quality estimation mechanism named the burstiness distribution metric, which uses the distribution of burstiness in the links to deal with variations in wireless link quality. First, we estimated the quality of the link at the receiver node by counting the number of consecutive packets lost in each link. Based on that, we created a burstiness distribution list and estimated the number of transmissions. Our simulation in the Cooja simulator from Contiki-NG showed that our proposal can be used in scheduling as an input metric to calculate the number of transmissions in order to achieve a reliability target in industrial wireless sensor networks.
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5

D'Auria, Bernardo, and Sidney I. Resnick. "Data network models of burstiness." Advances in Applied Probability 38, no. 02 (June 2006): 373–404. http://dx.doi.org/10.1017/s0001867800001014.

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We review characteristics of data traffic which we term stylized facts: burstiness, long-range dependence, heavy tails, bursty behavior determined by high-bandwidth users, and dependence determined by users without high transmission rates. We propose an infinite-source Poisson input model which supplies traffic in adjacent time slots. We study properties of the model as slot width decreases and traffic intensity increases. This model has the ability to account for many of the stylized facts.
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6

Neuts, Marcel F. "The burstiness of point processes∗." Communications in Statistics. Stochastic Models 9, no. 3 (January 1993): 445–66. http://dx.doi.org/10.1080/15326349308807275.

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7

Woodward, M. E. "Burstiness of interrupted Bernoulli process." Electronics Letters 30, no. 18 (September 1, 1994): 1466–67. http://dx.doi.org/10.1049/el:19941042.

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8

D'Auria, Bernardo, and Sidney I. Resnick. "Data network models of burstiness." Advances in Applied Probability 38, no. 2 (June 2006): 373–404. http://dx.doi.org/10.1239/aap/1151337076.

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We review characteristics of data traffic which we term stylized facts: burstiness, long-range dependence, heavy tails, bursty behavior determined by high-bandwidth users, and dependence determined by users without high transmission rates. We propose an infinite-source Poisson input model which supplies traffic in adjacent time slots. We study properties of the model as slot width decreases and traffic intensity increases. This model has the ability to account for many of the stylized facts.
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9

Guillemin, Fabrice, Jacqueline Boyer, and Alain Dupuis. "Burstiness in broadband integrated networks." Performance Evaluation 15, no. 3 (September 1992): 163–76. http://dx.doi.org/10.1016/0166-5316(92)90032-c.

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10

Oudah, Hussein, Bogdan Ghita, Taimur Bakhshi, Abdulrahman Alruban, and David J. Walker. "Using Burstiness for Network Applications Classification." Journal of Computer Networks and Communications 2019 (August 20, 2019): 1–10. http://dx.doi.org/10.1155/2019/5758437.

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Network traffic classification is a vital task for service operators, network engineers, and security specialists to manage network traffic, design networks, and detect threats. Identifying the type/name of applications that generate traffic is a challenging task as encrypting traffic becomes the norm for Internet communication. Therefore, relying on conventional techniques such as deep packet inspection (DPI) or port numbers is not efficient anymore. This paper proposes a novel flow statistical-based set of features that may be used for classifying applications by leveraging machine learning algorithms to yield high accuracy in identifying the type of applications that generate the traffic. The proposed features compute different timings between packets and flows. This work utilises tcptrace to extract features based on traffic burstiness and periods of inactivity (idle time) for the analysed traffic, followed by the C5.0 algorithm for determining the applications that generated it. The evaluation tests performed on a set of real, uncontrolled traffic, indicated that the method has an accuracy of 79% in identifying the correct network application.
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11

Starobinski, D., and M. Sidi. "Stochastically bounded burstiness for communication networks." IEEE Transactions on Information Theory 46, no. 1 (2000): 206–12. http://dx.doi.org/10.1109/18.817518.

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12

Lappas, Theodoros, Marcos R. Vieira, Dimitrios Gunopulos, and Vassilis J. Tsotras. "On the spatiotemporal burstiness of terms." Proceedings of the VLDB Endowment 5, no. 9 (May 2012): 836–47. http://dx.doi.org/10.14778/2311906.2311911.

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13

Goh, K. I., and A. L. Barabási. "Burstiness and memory in complex systems." EPL (Europhysics Letters) 81, no. 4 (January 17, 2008): 48002. http://dx.doi.org/10.1209/0295-5075/81/48002.

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14

Solé-Pareta, Josep, and Jordi Domingo-Pascual. "Burstiness characterization of ATM cell streams." Computer Networks and ISDN Systems 26, no. 11 (August 1994): 1351–63. http://dx.doi.org/10.1016/0169-7552(94)90002-7.

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15

Ji, Zhihang, Fan Wang, Xiang Gao, Lijuan Xu, and Xiaopeng Hu. "SSNet: Learning Mid-Level Image Representation Using Salient Superpixel Network." Applied Sciences 10, no. 1 (December 23, 2019): 140. http://dx.doi.org/10.3390/app10010140.

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In the standard bag-of-visual-words (BoVW) model, the burstiness problem of features and the ignorance of high-order information often weakens the discriminative power of image representation. To tackle them, we present a novel framework, named the Salient Superpixel Network, to learn the mid-level image representation. For reducing the impact of burstiness occurred in the background region, we use the salient regions instead of the whole image to extract local features, and a fast saliency detection algorithm based on the Gestalt grouping principle is proposed to generate image saliency maps. In order to introduce the high-order information, we propose a weighted second-order pooling (WSOP) method, which is capable of exploiting the high-order information and further alleviating the impact of burstiness in the foreground region. Then, we conduct experiments on six image classification benchmark datasets, and the results demonstrate the effectiveness of the proposed framework with either the handcrafted or the off-the-shelf CNN features.
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16

Wang, Mao, Lili Zhao, Yuewei Ming, En Zhu, and Jianping Yin. "Boosting landmark retrieval baseline with burstiness detection." IET Computer Vision 12, no. 3 (December 18, 2017): 312–21. http://dx.doi.org/10.1049/iet-cvi.2016.0504.

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17

Valaee, S. "A recursive estimator of worst-case burstiness." IEEE/ACM Transactions on Networking 9, no. 2 (April 2001): 211–22. http://dx.doi.org/10.1109/90.917077.

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18

Kang Seok Seo and Byeong Gi Lee. "Measurement-based admission control using maximum burstiness." IEEE Communications Letters 6, no. 9 (September 2002): 403–5. http://dx.doi.org/10.1109/lcomm.2002.803472.

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19

Boyer, Jacqueline, Alain Dupuis, and Fabrice Guillemin. "Burstiness and resource management in ATM networks." International Journal of Digital & Analog Communication Systems 6, no. 4 (1993): 173–81. http://dx.doi.org/10.1002/dac.4510060403.

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20

Mohammed, Munatel, and Abdelkrim Haqiq. "Dynamic resource allocation for service in mobile cloud computing with Markov modulated arrivals." International Journal of Modeling, Simulation, and Scientific Computing 12, no. 05 (June 21, 2021): 2150038. http://dx.doi.org/10.1142/s1793962321500380.

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Mobile Cloud Computing (MCC) is a modern architecture that brings together cloud computing, mobile computing and wireless networks to assist mobile devices in storage, computing and communication. A cloud environment is developed to support a wide range of users that request the cloud resources in a dynamic environment with possible constraints. Burstiness in users service requests causes drastic and unpredictable increases in the resource requests that have a crucial impact on policies of resource allocation. How can the cloud system efficiently handle such burstiness when the cloud resources are limited? This problem becomes a hot issue in the MCC research area. In this paper, we develop a system model for the resource allocation based on the Semi-Markovian Decision Process (SMDP), able of dynamically assigning the mobile service requests to a set of cloud resources, to optimize the usage of cloud resources and maximize the total long-term expected system reward when the arrival process is a finite-state Markov-Modulated Poisson Process (MMPP). Numerical results show that our proposed model performs much better than the Greedy algorithm in terms of achieving higher system rewards and lower service requests blocking probabilities, especially when the burstiness degree is high, and the cloud resources are limited.
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21

Gernat, Tim, Vikyath D. Rao, Martin Middendorf, Harry Dankowicz, Nigel Goldenfeld, and Gene E. Robinson. "Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks." Proceedings of the National Academy of Sciences 115, no. 7 (January 29, 2018): 1433–38. http://dx.doi.org/10.1073/pnas.1713568115.

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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.
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22

XIE, Yang, and Koji EGUCHI. "Multimedia Topic Models Considering Burstiness of Local Features." IEICE Transactions on Information and Systems E97.D, no. 4 (2014): 714–20. http://dx.doi.org/10.1587/transinf.e97.d.714.

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23

Bingmer, Markus, Julia Schiemann, Jochen Roeper, and Gaby Schneider. "Measuring burstiness and regularity in oscillatory spike trains." Journal of Neuroscience Methods 201, no. 2 (October 2011): 426–37. http://dx.doi.org/10.1016/j.jneumeth.2011.08.013.

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24

Subramanian, V. G., and B. Hajek. "Broad-band fading channels: signal burstiness and capacity." IEEE Transactions on Information Theory 48, no. 4 (April 2002): 809–27. http://dx.doi.org/10.1109/18.992762.

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25

Cholvi, Vicent, Juan Echagüe, and Jean-Yves Le Boudec. "Worst case burstiness increase due to FIFO multiplexing." Performance Evaluation 49, no. 1-4 (September 2002): 491–506. http://dx.doi.org/10.1016/s0166-5316(02)00116-5.

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26

Akbarpour, Mohammad, and Matthew O. Jackson. "Diffusion in networks and the virtue of burstiness." Proceedings of the National Academy of Sciences 115, no. 30 (July 9, 2018): E6996—E7004. http://dx.doi.org/10.1073/pnas.1722089115.

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Whether an idea, information, or infection diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. People are not always available to interact with others, and people differ in the timing of when they are active. Some people are active for long periods and then inactive for long periods, while others switch more frequently from being active to inactive and back. We show that maximizing diffusion in classic contagion processes requires heterogeneous activity patterns across agents. In particular, maximizing diffusion comes from mixing two extreme types of people: those who are stationary for long periods of time, changing from active to inactive or back only infrequently, and others who alternate frequently between being active and inactive.
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27

Casale, Giuliano, Ningfang Mi, and Evgenia Smirni. "Model-Driven System Capacity Planning under Workload Burstiness." IEEE Transactions on Computers 59, no. 1 (January 2010): 66–80. http://dx.doi.org/10.1109/tc.2009.135.

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28

Alizai, Muhammad Hamad, Olaf Landsiedel, and Klaus Wehrle. "Exploiting the Burstiness of Intermediate-Quality Wireless Links." International Journal of Distributed Sensor Networks 8, no. 3 (March 13, 2012): 826702. http://dx.doi.org/10.1155/2012/826702.

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29

Jo, Hang-Hyun, Márton Karsai, János Kertész, and Kimmo Kaski. "Circadian pattern and burstiness in mobile phone communication." New Journal of Physics 14, no. 1 (January 25, 2012): 013055. http://dx.doi.org/10.1088/1367-2630/14/1/013055.

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30

Casale, Giuliano, Amir Kalbasi, Diwakar Krishnamurthy, and Jerry Rolia. "BURN: Enabling Workload Burstiness in Customized Service Benchmarks." IEEE Transactions on Software Engineering 38, no. 4 (July 2012): 778–93. http://dx.doi.org/10.1109/tse.2011.58.

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31

Golomb, David, Cuiyong Yue, and Yoel Yaari. "Contribution of Persistent Na+ Current and M-Type K+ Current to Somatic Bursting in CA1 Pyramidal Cells: Combined Experimental and Modeling Study." Journal of Neurophysiology 96, no. 4 (October 2006): 1912–26. http://dx.doi.org/10.1152/jn.00205.2006.

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The intrinsic firing modes of adult CA1 pyramidal cells vary along a continuum of “burstiness” from regular firing to rhythmic bursting, depending on the ionic composition of the extracellular milieu. Burstiness is low in neurons exposed to a normal extracellular Ca2+ concentration ([Ca2+]o), but is markedly enhanced by lowering [Ca2+]o, although not by blocking Ca2+ and Ca2+-activated K+ currents. We show, using intracellular recordings, that burstiness in low [Ca2+]o persists even after truncating the apical dendrites, suggesting that bursts are generated by an interplay of membrane currents at or near the soma. To study the mechanisms of bursting, we have constructed a conductance-based, one-compartment model of CA1 pyramidal neurons. In this neuron model, reduced [Ca2+]o is simulated by negatively shifting the activation curve of the persistent Na+ current ( INaP) as indicated by recent experimental results. The neuron model accounts, with different parameter sets, for the diversity of firing patterns observed experimentally in both zero and normal [Ca2+]o. Increasing INaP in the neuron model induces bursting and increases the number of spikes within a burst but is neither necessary nor sufficient for bursting. We show, using fast-slow analysis and bifurcation theory, that the M-type K+ current ( IM) allows bursting by shifting neuronal behavior between a silent and a tonically active state provided the kinetics of the spike generating currents are sufficiently, although not extremely, fast. We suggest that bursting in CA1 pyramidal cells can be explained by a single compartment “square bursting” mechanism with one slow variable, the activation of IM.
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32

Yaron, Opher, and Moshe Sidi. "Generalized Processor Sharing Networks with Exponentially Bounded Burstiness Arrivals." Journal of High Speed Networks 3, no. 4 (1994): 375–87. http://dx.doi.org/10.3233/jhs-1994-3404.

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33

Jean-Marie, Alain, Yvan Calas, and Tigist Alemu. "On the compromise between burstiness and frequency of events." Performance Evaluation 62, no. 1-4 (October 2005): 382–99. http://dx.doi.org/10.1016/j.peva.2005.07.020.

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34

Casale, Giuliano, Ningfang Mi, and Evgenia Smirni. "Bound analysis of closed queueing networks with workload burstiness." ACM SIGMETRICS Performance Evaluation Review 36, no. 1 (June 12, 2008): 13–24. http://dx.doi.org/10.1145/1384529.1375460.

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35

Wang, Zheng, and Jon Crowcroft. "Analysis of burstiness and jitter in real-time communications." ACM SIGCOMM Computer Communication Review 23, no. 4 (October 1993): 13–19. http://dx.doi.org/10.1145/167954.166239.

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36

Mancastroppa, Marco, Alessandro Vezzani, Miguel A. Muñoz, and Raffaella Burioni. "Burstiness in activity-driven networks and the epidemic threshold." Journal of Statistical Mechanics: Theory and Experiment 2019, no. 5 (May 24, 2019): 053502. http://dx.doi.org/10.1088/1742-5468/ab16c4.

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37

Au, Tat-Ming, and Hassan Mehrpour. "Analysis of leaky bucket mechanism based on burstiness characterization." International Journal of Communication Systems 8, no. 6 (November 1995): 347–58. http://dx.doi.org/10.1002/dac.4500080602.

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38

Li, Yantao, Daniel Graham, Gang Zhou, Xin Qi, Shaojiang Deng, and Di Xiao. "Discrete-time Markov Model for Wireless Link Burstiness Simulations." Wireless Personal Communications 72, no. 2 (March 7, 2013): 987–1004. http://dx.doi.org/10.1007/s11277-013-1051-x.

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39

Rahmani, Somayeh, Vahid Khajehvand, and Mohsen Torabian. "Burstiness-aware virtual machine placement in cloud computing systems." Journal of Supercomputing 76, no. 1 (October 22, 2019): 362–87. http://dx.doi.org/10.1007/s11227-019-03037-8.

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40

Xu, Zuobing, and Ram Akella. "Improving probabilistic information retrieval by modeling burstiness of words." Information Processing & Management 46, no. 2 (March 2010): 143–58. http://dx.doi.org/10.1016/j.ipm.2009.12.004.

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41

Liu, Sen, Jiawei Huang, Wenchao Jiang, and Jianxin Wang. "Reducing traffic burstiness for MPTCP in data center networks." Journal of Network and Computer Applications 192 (October 2021): 103169. http://dx.doi.org/10.1016/j.jnca.2021.103169.

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42

Zhao, Jumin, Hao Tian, and Deng-ao Li. "Channel Prediction Based on BP Neural Network for Backscatter Communication Networks." Sensors 20, no. 1 (January 5, 2020): 300. http://dx.doi.org/10.3390/s20010300.

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Backscatter communication networks are receiving a lot of attention thanks to the application of ultra-low power sensors. Because of the large amount of sensor data, increasing network throughput becomes a key issue, so rate adaption based on channel quality is a novel direction. Most existing methods share common drawbacks; that is, spatial and frequency diversity cannot be considered at the same time or channel probe is expensive. In this paper, we propose a channel prediction scheme for backscatter networks. The scheme consists of two parts: the monitoring module, which uses the data of the acceleration sensor to monitor the movement of the node itself, and uses the link burstiness metric β to monitor the burstiness caused by the environmental change, thereby determining that new data of channel quality are needed. The prediction module predicts the channel quality at the next moment using a prediction algorithm based on BP (back propagation) neural network. We implemented the scheme on readers. The experimental results show that the accuracy of channel prediction is high and the network goodput is improved.
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43

Mukherjee, P., and E. Kaplan. "Dynamics of neurons in the cat lateral geniculate nucleus: in vivo electrophysiology and computational modeling." Journal of Neurophysiology 74, no. 3 (September 1, 1995): 1222–43. http://dx.doi.org/10.1152/jn.1995.74.3.1222.

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1. We investigated the time domain transformation that thalamocortical relay cells of the cat lateral geniculate nucleus (LGN) perform on their retinal input, and used computational modeling to explore the biophysical properties that determine the dynamics of the LGN relay cells in vivo. 2. We recorded simultaneously the input (S potentials) and output (action potentials) of 50 cat LGN relay cells stimulated by drifting sinusoidal gratings of varying temporal frequency. The temporal modulation transfer functions (TMTFs) of the neurons were derived from these data. The burstiness of the LGN spike trains was also assessed using objective criteria. 3. We found that the form of the TMTF was quite variable among cells, ranging from low-pass to strongly band-pass. The optimal temporal frequency of band-pass neurons was between 2 and 8 Hz. In addition, the TMTF of some cells was nonstationary: their temporal tuning changed with time. 4. The temporal tuning of a cell was directly related to the degree of burstiness of its spike train. Tonically firing relay cells had low-pass TMTFs, whereas the most bursty neurons exhibited the most sharply band-pass transfer functions. This was also true for single cells that altered their temporal tuning: a shift to more band-pass tuning was associated with increased burstiness of the spike train, and vice versa. 5. We constructed a computer simulation of the LGN relay cell. The model was a simplified five-channel version of the thalamocortical neuron model of McCormick and Huguenard. It incorporated the quantitative kinetics of the Ca2+ T channel, as well as the Hodgkin-Huxley Na+ and K+ channels, as the only active membrane currents. To simulate the in vivo dynamics of the relay cell, the input to the model consisted of trains of synaptic potentials, recorded as S potentials in our physiological experiments. 6. When the resting membrane potential of the model neuron was relatively depolarized, the model's TMTF was low-pass, with no bursting evident in the simulated spike train. At hyperpolarized resting membrane potentials, however, the modeled TMTF was band-pass, with frequent burst discharges. Thus the biophysical model reproduced not only the range of dynamics seen in real LGN relay cells, but also the dependence of the overall dynamics on the burstiness of the spike train. However, neither of these phenomena could be simulated without the T channel. Thus the simulations demonstrated that the T-type Ca2+ channel was necessary and sufficient to explain the LGN dynamics observed in physiological experiments.(ABSTRACT TRUNCATED AT 400 WORDS)
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44

Kotozaki, Shota. "IDENTIFYING LOCAL BURSTINESS IN A SEQUENCE OF BATCHED GEOREFERENCED DOCUMENTS." International Journal of Electronic Commerce Studies 6, no. 2 (December 2015): 269–88. http://dx.doi.org/10.7903/ijecs.1347.

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45

Zhang, Sheng, Zhuzhong Qian, Zhaoyi Luo, Jie Wu, and Sanglu Lu. "Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds." IEEE Transactions on Parallel and Distributed Systems 27, no. 4 (April 1, 2016): 964–77. http://dx.doi.org/10.1109/tpds.2015.2425403.

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46

Xue, Cheng, Daniel Kaping, Sonia Baloni Ray, B. Suresh Krishna, and Stefan Treue. "Spatial Attention Reduces Burstiness in Macaque Visual Cortical Area MST." Cerebral Cortex 27, no. 1 (November 22, 2016): 83–91. http://dx.doi.org/10.1093/cercor/bhw326.

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47

Rosenfeld, Simon. "Origins of Stochasticity and Burstiness in High-Dimensional Biochemical Networks." EURASIP Journal on Bioinformatics and Systems Biology 2009 (2009): 1–14. http://dx.doi.org/10.1155/2009/362309.

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48

Johnson, Mary A., Danielle Liu, and Surya Narayana. "Burstiness descriptors for markov renewal processes and markovian arrival processes." Communications in Statistics. Stochastic Models 13, no. 3 (January 1997): 619–46. http://dx.doi.org/10.1080/15326349708807442.

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49

Konstantopoulos, T., and V. Anantharam. "Optimal flow control schemes that regulate the burstiness of traffic." IEEE/ACM Transactions on Networking 3, no. 4 (1995): 423–32. http://dx.doi.org/10.1109/90.413216.

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50

Yan, Dengcheng, Zhen Shao, Yiwen Zhang, and Bin Qi. "BurstBiRank: Co-Ranking Developers and Projects in GitHub with Complex Network Structures and Bursty Interactions." Complexity 2020 (December 16, 2020): 1–12. http://dx.doi.org/10.1155/2020/7264396.

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Abstract:
With the wide adoption of social collaborative coding, more and more developers participate and collaborate on platforms such as GitHub through rich social and technical relationships, forming a large-scale complex technical system. Like the functionalities of critical nodes in other complex systems, influential developers and projects usually play an important role in driving this technical system to more optimized states with higher efficiency for software development, which makes it a meaningful research direction on identifying influential developers and projects in social collaborative coding platforms. However, traditional ranking methods seldom take into account the continuous interactions and the driving forces of human dynamics. In this paper, we combine the bursty interactions and the bipartite network structure between developers and projects and propose the BurstBiRank model. Firstly, the burstiness between each pair of developers and projects is calculated. Secondly, a weighted developer-project bipartite network is constructed using the burstiness as weight. Finally, an iterative score diffusion process is applied to this bipartite network and a final ranking score is obtained at the stationary state. The real-world case study on GitHub demonstrates the effectiveness of our proposed BurstBiRank and the outperformance of traditional ranking methods.
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