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Quantum mechanical phenomena are revolutionizing classical engineering fields such as signal processing or cryptography. When randomness plays an important role, like in cryptography where random bit sequences guarantee certain levels of security, quantum mechanical phenomena allow new ways of generating random bit sequences. Such sequences have a lot of applications in the communication sector, e.g., regarding data transmission, simulation, sensors or radars, and beyond. They can be generated deterministically (e.g., by using polynomials, resulting in pseudo-random sequences) or in a non-deterministic way (e.g., by using physical noise sources like external devices or sensors, resulting in random sequences). Important characteristics of such binary sequences can be modelled by gap processes in conjunction with the probability theory. Recently, all-optical approaches have attracted a lot of research interest. In this work, an adaptation of the quantum key distribution setup is utilized for generating randomised bit sequences. The simulation results show that all-optically generated sequences very well resemble the theoretically ideal probability density characteristic. Additionally, an experimental optical setup is developed that confirms the simulation results. Furthermore, m-sequences show very promising results as well as Gold sequences. Additionally, the level of burstiness, i.e., the distribution of ones and zeros throughout the sequence, is studied for the different sequences. The results enable the finding that generator polynomials with concentrated non-zero coefficients lead to more bursty bit sequences. [ABSTRACT FROM AUTHOR]

Most linguistic studies of political speaking in the field of critical discourse analysis tend to focus on speeches delivered by prominent politicians either in a domestic party-political setting or in the international arena. Less attention has been afforded to speeches by civil rights activists and campaigners for other progressive causes. To fill this gap, the present paper focuses on political speaking occurring outside of the party-political setting. The data comprises 120 American activist speeches from the years 1808–2016. The analysis focuses on the construction of ingroups and outgroups, and whether the use of personal pronouns is affected by the type of audience. The frequency trends bring forth new information about the referential complexity of pronouns within individual speeches. [ABSTRACT FROM AUTHOR]

Hippocampal pyramidal neurons exhibit diverse spike patterns and gene expression profiles. However, their relationships with single neurons are not fully understood. In this study, we designed an electrophysiology-based experimental procedure to identify gene expression profiles using RNA sequencing of single hippocampal pyramidal neurons whose spike patterns were recorded in living mice. This technique involves a sequence of experiments consisting of in vivo juxtacellular recording and labeling, brain slicing, cell collection, and transcriptome analysis. We demonstrated that the expression levels of a subset of genes in individual hippocampal pyramidal neurons were significantly correlated with their spike burstiness, submillisecond-level spike rise times or spike rates, directly measured by in vivo electrophysiological recordings. Because this methodological approach can be applied across a wide range of brain regions, it is expected to contribute to studies on various neuronal heterogeneities to understand how physiological spike patterns are associated with gene expression profiles. [ABSTRACT FROM AUTHOR]

There is an increasing interest in determining if there exist observable patterns or structures within the digits of irrational numbers. We extend this search by investigating the interval in position between two consecutive occurrences of the same digit, a kind of waiting time statistics. We characterise these by the burstiness measure which distinguishes if the inter-event times are periodic, bursty, or Poisson processes. Furthermore, the complexity–entropy plane was used to determine if the intervals are stochastic or chaotic. We analyse sequences of the first 1 million digits of the numbers π , e, 2 , and ϕ. We find that the intervals between single, double, and triple digits are Poisson processes with a burstiness measure in the range − 0.05 ≤ B ≤ 0.05 for the four numbers studied. This result is supported by a complexity–entropy plane analysis, which shows that the time intervals have the same characteristics as Gaussian noise. The four irrational numbers have identical degrees of complexity and burstiness in their inter-event analysis. [ABSTRACT FROM AUTHOR]

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. Medical image analysis and Nature appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade.

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. Medical image analysis and Nature appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade. [ABSTRACT FROM AUTHOR]

Posttraumatic Growth (PTG), characterized by newfound meaning, perspective, and purpose for trauma survivors, remains enigmatic in its nature. This state is thought to arise from the dynamic interplay of biopsychosocial factors; however, the nature of this interplay is unclear. This study aimed to investigate the intricate relationship between PTG and facial affect dynamics, shedding light on the complex interplay of biopsychosocial factors that underpin this transformative process. We conducted a comprehensive investigation involving 19 wildfire survivors who provided daily self-reported PTG ratings alongside smartphone videos analyzed using Automated Facial Affect Recognition (AFAR) software. Our findings revealed compelling evidence of selforganization within facial affect, as indicated by notably high mean R2 and shape parameter values (i.e., nonlinear indices indicative of structural integrity and flexibility). Further regression analyses unveiled a significant interaction between the degree of facial affect "burstiness" and coping self-efficacy (CSE) on PTG. This interaction suggested that PTG development was a nuanced process intricately linked to the coherence of emotion patterns exhibited by individuals. These insights illuminate the multifaceted dynamics at play in the emergence of PTG and contribute to a broader understanding of its biopsychosocial foundations. [ABSTRACT FROM AUTHOR]

During everyday interactions, mothers and infants achieve behavioral synchrony at multiple levels. The ebb-and-flow of mother-infant physical proximity may be a central type of synchrony that establishes a common ground for infant-mother interaction. However, the role of proximity in language exchanges is relatively unstudied, perhaps because structured tasks—the common setup for observing infant-caregiver interactions—establish proximity by design. We videorecorded 100 mothers (U.S. Hispanic N = 50, U.S. Non-Hispanic N = 50) and their 13- to 23-month-old infants during natural activity at home (1-to-2 h per dyad), transcribed mother and infant speech, and coded proximity continuously (i.e., infants and mother within arms reach). In both samples, dyads entered proximity in a bursty temporal pattern, with bouts of proximity interspersed with bouts of physical distance. As hypothesized, Non-Hispanic and Hispanic mothers produced more words and a greater variety of words when within arms reach than out of arms reach. Similarly, infants produced more utterances that contained words when close to mother than when not. However, infants babbled equally often regardless of proximity, generating abundant opportunities to play with sounds. Physical proximity expands opportunities for language exchanges and infants’ communicative word use, although babies accumulate massive practice babbling even when caregivers are not proximal.

The temporal dynamics of interference in wireless networks affects their performance but has only been studied for some cases. This article addresses this gap by analyzing high-interference events, called pikes, concluding that they arrive in bursts in many cases. Specifically, we show that in Poisson networks with random access and multipath fading, the pike interarrival time increases with the interference correlation, irrespective of the source of correlation and burstiness of pikes. To demonstrate the applicability of this theory, we conduct a measurement campaign with an automotive user in different commercial 4G cellular networks. The experimental results indicate that interference pikes are bursty in the real world as well.

There is an increasing interest in determining if there exist observable patterns or structures within the digits of irrational numbers. We extend this search by investigating the interval in position between two consecutive occurrences of the same digit, a kind of waiting time statistics. We characterise these by the burstiness measure which distinguishes if the inter-event times are periodic, bursty, or Poisson processes. Furthermore, the complexity–entropy plane was used to determine if the intervals are stochastic or chaotic. We analyse sequences of the first 1 million digits of the numbers π, e, 2, and ϕ. We find that the intervals between single, double, and triple digits are Poisson processes with a burstiness measure in the range −0.05≤B≤0.05 for the four numbers studied. This result is supported by a complexity–entropy plane analysis, which shows that the time intervals have the same characteristics as Gaussian noise. The four irrational numbers have identical degrees of complexity and burstiness in their inter-event analysis.

Abstract Social networks have been shown to enhance player experience in online games and to be important for the players, who often build complex communities. In online and mobile games, the behavior of players is bursty as they tend to play intensively at first for a short time and then quit playing altogether. Such players are known as churners. In the literature, several attempts have been made at predicting player churn in online and mobile games using behavioral features from the games’ player logs as input in supervised machine learning models. Previous research shows that information from social networks provides alternative and significant information when predicting churn, and yet the importance of networks has not been fully researched in mobile gaming. In this research, we study player churn in a mobile free-to-play game with one-versus-one matches. We build two types of networks based on how two players are matched. We train churn prediction models with features extracted from the networks to evaluate their predictive performance in terms of churn. Furthermore, we predict churn using the players’ behavioral features during their first day of game playing. According to our results, the network features greatly increase the predictive performance of the models, indicating that they carry alternative information about intention to churn. In addition, the first-day features are quite predictive, which means that first day activity is sufficient to predict churn of players quite accurately, validating the bursty behavior. Our research gives an indication of which aspects of game playing are associated with churn and allow us to study influence and social factors in mobile games.

Quantification of rainfall intermittency via. interevent time distribution, series of continuous wet spells (burst size) and variability in interevent times between rainfall events is essential for planning and management of water resources and hydrologic extremes. However, their structure, quantification and association with long-term climatology are less explored. In this paper, a complex system-based measure – burstiness – is used to quantify the variability of interevent times across six meteorologically homogenous zones of India. It is observed that burstiness is related to the burst size as well as long-term rainfall climatology. The existence of unimodal and bimodal structure in burstiness distribution reveals the uniqueness and differences in the rainfall patterns. The differences in sensitivity of rainfall to burstiness highlight the role of interplay between climate-landscape and reveals the importance of the intermittent structure of rainfall. The study provides an approach to model intermittency by preserving the temporal structure of the interevent time distribution. [ABSTRACT FROM AUTHOR]

To develop smart home technology designed to analyze the activity of residents based on the logs of installed sensors, an activity model tailored to individuals must be constructed from less privacy-invasive sensors to avoid interference in daily life. Unsupervised machine learning techniques are desirable to automatically construct such models without costly data annotation, but their application has not yet been sufficiently successful. In this study, we show that an activity model can be effectively estimated without activity labels via the Dirichlet multinomial mixture (DMM) model. The DMM model assumes that sensor signals are generated according to a Dirichlet multinomial distribution conditioned on a single unobservable activity and can capture the burstiness of sensors, in which even sensors that rarely fire may fire repeatedly after being triggered. We demonstrate the burstiness phenomenon in real data using passive infrared ray motion sensors. For such data, the assumptions of the DMM model are more suitable than the assumptions employed in models used in previous studies. Moreover, we extend the DMM model so that each activity depends on the preceding activity to capture the Markov dependency of activities, and a Gibbs sampler used in the model estimation algorithm is also presented. An empirical study using publicly available data collected in real-life settings shows that the DMM models can discover activities more correctly than the other models and expected to be used as a primitive activity extraction tool in activity analysis. [ABSTRACT FROM AUTHOR]

Experience‐dependent change pervades early human development. Though trajectories of developmental change have been well charted in many domains, the episode‐to‐episode schedules of experiences on which they are hypothesized to depend have not. Here, we took up this issue in a domain known to be governed in part by early experiences: music. Using a corpus of longform audio recordings, we parameterized the daily schedules of music encountered by 35 infants ages 6–12 months. We discovered that everyday music episodes, as well as the interstices between episodes, typically persisted less than a minute, with most daily schedules also including some very extended episodes and interstices. We also discovered that infants encountered music episodes in a bursty rhythm, rather than a periodic or random rhythm, over the day. These findings join a suite of recent discoveries from everyday vision, motor, and language that expand our imaginations beyond artificial learning schedules and enable theorists to model the history‐dependence of developmental process in ways that respect everyday sensory histories. Future theories about how infants build knowledge across multiple episodes can now be parameterized using these insights from infants' everyday lives. [ABSTRACT FROM AUTHOR]

Autoscaling methods are used for cloud-hosted applications to dynamically scale the allocated resources for guaranteeing Quality-of-Service (QoS). The public-facing application serves dynamic workloads, which contain bursts and pose challenges for autoscaling methods to ensure application performance. Existing State-of-the-art autoscaling methods are burst-oblivious to determine and provision the appropriate resources. For dynamic workloads, it is hard to detect and handle bursts online for maintaining application performance. In this article, we propose a novel burst-aware autoscaling method which detects burst in dynamic workloads using workload forecasting, resource prediction, and scaling decision making while minimizing response time service-level objectives (SLO) violations. We evaluated our approach through a trace-driven simulation, using multiple synthetic and realistic bursty workloads for containerized microservices, improving performance when comparing against existing state-of-the-art autoscaling methods. Such experiments show an increase of $\times $ × 1.09 in total processed requests, a reduction of $\times $ × 5.17 for SLO violations, and an increase of $\times $ × 0.767 cost as compared to the baseline method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Autoscaling methods are employed to ensure the scalability of cloud-hosted applications. The public-facing applications are prone to receive sudden workload bursts, and the existing autoscaling methods do not handle the bursty workloads gracefully. It is challenging to detect the burst online from the incoming dynamic workload traffic, and then identifying appropriate resources to address the burst without overprovisioning is even harder. In this paper, we address this challenge by investigating the appropriate method for online burst detection and then proposed a novel predictive autoscaling method to use burst detection for satisfying specific response time requirements. We compared the proposed method with multiple state-of-the-art baseline autoscaling methods under multiple realistic and synthetic bursty workloads for a benchmark application. Our experimental results show a 60.8% average decrease in response time violations as compared to the baseline method.

Determining the aperiodicity of large earthquake recurrences is key to forecast future rupture behavior. Aperiodicity is classically expressed as the coefficient of variation of recurrence intervals, though the recent trend to express it as burstiness is more intuitive and avoids minor inaccuracies. Due to the underestimation of burstiness in records with a low number of recurrence intervals, the paradigm is to obtain long paleoseismic records with many events. Here, we present a suite of synthetic paleoseismic records designed around the Weibull and inverse Gaussian distributions that demonstrate that age uncertainty relative to the mean recurrence interval causes overestimation of burstiness. The effects of overestimation and underestimation interact and give complex results for accurate estimates of aperiodicity. Furthermore, we show that the way recurrence intervals are sampled from a paleoseismic record can have strong influences on the resulting statistic and its implication for probabilistic seismic hazard assessment. Comparing values of burstiness between paleoseismic records should therefore be done with caution. Plain Language Summary: To forecast future earthquake activity, paleoseismologists aim to have many events in a single sedimentary record to estimate the periodicity of an earthquake sequence with as little uncertainty as possible. This focus on the number of events is not wrong, but event age uncertainty is another—often neglected and not yet described—source of uncertainty that can interfere in estimating periodicity correctly. In this study, we show in what way and by how much event age uncertainty affects the uncertainty in periodicity. We create a model of many different artificial earthquake sequences. For our model setup, we choose: (1) two types of patterns; (2) six degrees of periodicity; (3) 10 different levels of event age uncertainty; and (4) a wide range of number of events (from 4 to 101 events). Then we create 50,000 earthquake sequences for each unique combination within this spectrum and analyze the variability in periodicity. We find that low number of events underestimates periodicity and high age uncertainty overestimates periodicity. Having many events in a record is more important, if the earthquake sequence is not periodic. Having accurately dated events is more important, if the earthquake sequence is periodic. Key Points: Low number of recurrence intervals in paleoseismic records underestimates aperiodicityHigh age uncertainty relative to the mean recurrence interval in paleoseismic records overestimates aperiodicityFor calculating coefficient of variation and burstiness it matters how recurrence intervals are sampled from records [ABSTRACT FROM AUTHOR]

The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and psychologists have limited ready-to-use methods and training for quantifying structures and patterns in behavioral time series. In this paper, we will introduce four techniques to interpret and analyze high-density multi-modal behavior data, namely, to: (1) visualize the raw time series, (2) describe the overall distributional structure of temporal events (Burstiness calculation), (3) characterize the non-linear dynamics over multiple timescales with Chromatic and Anisotropic Cross-Recurrence Quantification Analysis (CRQA), (4) and quantify the directional relations among a set of interdependent multimodal behavioral variables with Granger Causality. Each technique is introduced in a module with conceptual background, sample data drawn from empirical studies and ready-to-use Matlab scripts. The code modules showcase each technique’s application with detailed documentation to allow more advanced users to adapt them to their own datasets. Additionally, to make our modules more accessible to beginner programmers, we provide a “Programming Basics” module that introduces common functions for working with behavioral timeseries data in Matlab. Together, the materials provide a practical introduction to a range of analyses that psychologists can use to discover temporal structure in high-density behavioral data.

Virtual machine placement is one of the main sub-problems in the virtual machine consolidation process which faces different challenges. Burst-aware placement plays a key role in improving energy efficiency and reducing the SLA violations in cloud computing systems and hence requires special attention and investigation. Therefore, in this study, we will present burst-aware algorithms in order to decrease the resource wastage and reduce SLA violations. By presenting these algorithms, we aim to minimize the negative effects of workload bursts on the process of making decisions about the placement of virtual machines. We use random and real-world datasets and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding energy efficiency and performance, compared to the benchmark methods. [ABSTRACT FROM AUTHOR]

Studying research fronts enables researchers to understand how their academic fields emerged, how they are currently developing and their changes over time. While topic modelling tools help discover themes in documents, they employ a "bag-of-words" approach and require researchers to manually label categories, specify the number of topics a priori, and make assumptions about word distributions in documents. This paper proposes an alternative approach based on entity linking, which links word strings to entities from a knowledge base, to help solve issues associated with "bag-of-words" approaches by automatically identifying topics based on entity mentions. To study topic trends and popularity, we use four indicators—Mann–Kendall's test, Sen's slope analysis, z-score values and Kleinberg's burst detection algorithm. The combination of these indicators helps us understand which topics are particularly active ("hot" topics), which are decreasing ("cold" topics or past "bursty" topics) and which are maturely developed. We apply the approach and indicators to the fields of Information Science and Accounting. [ABSTRACT FROM AUTHOR]

• We propose a novel model called the Multinomial Scaled Dirichlet (MSD) for modeling count data. • We derive a new family of distributions that are approximations to MSD distributions to handle high-dimensional and sparse data that we call (EMSD). • We develop a minimum message length (MML) criterion for determining of the number of components in EMSD mixture model. • We evaluate the performance of both approaches through a set of extensive empirical experiments on challenging real-world applications. • Results revealed that both MSD and EMSD capture the burstiness phenomenon successfully and correctly, and EMSD is many times faster than MSD. The multinomial distribution and the Dirichlet Compound Multinomial (DCM) are widely accepted to model count data. However, recent research showed that the Dirichlet is not the best choice as a prior to multinomial. We propose a novel model called the Multinomial Scaled Dirichlet (MSD) distribution that is the composition of the scaled Dirichlet distribution and the multinomial. Moreover, to improve the computation efficiency in high-dimensional spaces, we propose to approximate the MSD as a member of the exponential family. The performance evaluation of the proposed models is conducted through a set of extensive empirical experiments on challenging applications, namely, text classification, facial expression recognition, and texture images clustering. The results show that the proposed model, and its approximation, strive to achieve higher accuracy compared to the state-of-the-art generative models for count data clustering, while the approximation EMSD is many times faster than the corresponding MSD. [ABSTRACT FROM AUTHOR]

During everyday interactions, mothers and infants achieve behavioral synchrony at multiple levels. The ebb-and-flow of mother-infant physical proximity may be a central type of synchrony that establishes a common ground for infant-mother interaction. However, the role of proximity in language exchanges is relatively unstudied, perhaps because structured tasks—the common setup for observing infant-caregiver interactions—establish proximity by design. We videorecorded 100 mothers (U.S. Hispanic N = 50, U.S. Non-Hispanic N = 50) and their 13- to 23-month-old infants during natural activity at home (1-to-2 h per dyad), transcribed mother and infant speech, and coded proximity continuously (i.e., infants and mother within arms reach). In both samples, dyads entered proximity in a bursty temporal pattern, with bouts of proximity interspersed with bouts of physical distance. As hypothesized, Non-Hispanic and Hispanic mothers produced more words and a greater variety of words when within arms reach than out of arms reach. Similarly, infants produced more utterances that contained words when close to mother than when not. However, infants babbled equally often regardless of proximity, generating abundant opportunities to play with sounds. Physical proximity expands opportunities for language exchanges and infants' communicative word use, although babies accumulate massive practice babbling even when caregivers are not proximal. [ABSTRACT FROM AUTHOR]

Pada makalah ini dibahas analisis kinerja leaky bucket terintegrasi pada jaringan ATM dengan kombinasi control adaptif, metode marking dan metode prioritas. Juga ditinjau kinerja leaky bucket hasil dimensioning. Sebagai ukuran kinerja digunakan peluang sel hilang di jaringan dan throughput jaringan. Trafik masukan diasumsikan model 2-phase MMBP. Penelitian serupa yang telah dilakukan oleh X. Wu menggunakan model trafik masuk batch traffic dengan prores kedatangan Poisson dan panjang batch terdistribusi geometris. Hasil simulasi menunjukkan peluang sel hilang di jaringan makin berkurang dengan ukuran token pool K yang makin kecil; berkurangnya laju pembangkitan token mengakibatkan bertambahnya throughput jaringan; panjang burst rata-rata trafik masukan yang semakin besar akan memperbesar hilangnya sel di jaringan; dan kemudian, kinerja leaky bucket makin meningkat dengan digabungkannya mekanisme pengontrol kongesti lainnya seperti pengontrolan adaptif, marking, dan prioritas. Untuk leaky bucket hasil dimensioning, simulasi antara lain menunjukkan bahwa jika laju kedatangan sel conforming (sesuai dengan kontrak trafik), maka peluang sel hilang di jaringan makin kecil jika token pool makin kecil. Untuk (M+K) yang sama, leaky bucket dengan buffer menghasilkan peluang sel hilang di jaringan lebih kecil dibandingkan dengan tanpa buffer. Leaky bucket dengan buffer M besar dan pembangkitan token N besar menghasilkan peluang sel hilang di jaringan terkecil. Jika laju kedatangan sel makin jauh dari conforming, leaky bucket dengan N besar menghasilkan throughtput jaringan yang lebih besar. Performance Analysis of Leaky Buckets Integrated in ATM Networks This paper is an analysis of the performance of leaky buckets, which are integrated within ATM networks combined with adaptive control, marking and prioritizing. Also discussed is the performance of leaky bucket dimensioning. This performance is evaluated through network cell loss probabilities and network throughput. The arrival process is a 2-phase MMBP. Similar research has been done by Wu [10,11] using incoming traffic model batch traffic with a Poisson arrival process and a geometrically distributed batch length. The result of the simulation shows that the network cell loss probability is reduced as the token pool size K becomes smaller and smaller, the decrease of the token generation rate causes an increase in the network throughput; the network cell loss probability is higher in the case of a larger mean burst length; and the performance of the leaky bucket can be increased by incorporating other control mechanisms such as adaptive control, marking and prioritizing. For the leaky bucket dimensioning, the results of the simulation show, among other things, that if the incoming cell rate is conforming (appropriate to traffic contract) and the token pool is reduced, then the probability of network cell loss is also reduced. If (M + K) are the same, the buffered leaky bucket results in a network cell loss probability is smaller than for an unbuffered leaky bucket. The leaky bucket with big buffer M and big token generation N result in the smallest network cell loss probability. If the incoming cell rate is far from conforming, the leaky bucket with big N results in a bigger network throughput.

A common approach to analyze a covariate-sample count matrix, an element of which represents how many times a covariate appears in a sample, is to factorize it under the Poisson likelihood. We show its limitation in capturing the tendency for a covariate present in a sample to both repeat itself and excite related ones. To address this limitation, we construct negative binomial factor analysis (NBFA) to factorize the matrix under the negative binomial likelihood, and relate it to a Dirichlet-multinomial distribution based mixed-membership model. To support countably infinite factors, we propose the hierarchical gamma-negative binomial process. By exploiting newly proved connections between discrete distributions, we construct two blocked and a collapsed Gibbs sampler that all adaptively truncate their number of factors, and demonstrate that the blocked Gibbs sampler developed under a compound Poisson representation converges fast and has low computational complexity. Example results show that NBFA has a distinct mechanism in adjusting its number of inferred factors according to the sample lengths, and provides clear advantages in parsimonious representation, predictive power, and computational complexity over previously proposed discrete latent variable models, which either completely ignore burstiness, or model only the burstiness of the covariates but not that of the factors. [ABSTRACT FROM AUTHOR]

The gigantic growth of the internet communication technology has illustrated its value and benefits to private businesses, government organizations, worldwide professionals, academic institutes and individuals over the past few years. The size and range of computing devices connected to the internet, substantially increased because of IPv6 and offers the potential to establish a much more powerful internet compared to the IPv4. IPV6 developed by the IETF to deal with a shortage of IP addresses under IPv4. New features of IPv6 enhance packet processing speeds over routers, switches and end systems. These improved features will have different traffic characteristics than IPv4. The internet traffic which was earlier assumed as Poisson is now shown to have fractal characteristics as; heavy tailedness, self-similarity and long range dependency. Internet traffic showing above characteristics are found to have burstiness at multiple timescales. This behavior impacts network performance and degrades it substantially. It also increases complexity for network design and create difficulties to maintain desired QoS. IPv4 traffic has been well established as self-similar traffic. Nowadays, IPv6 forming a larger share of the internet traffic and it is pivotal to asses IPv6 with regards to fractal behavior. This will enable network designers to do necessary changes in the existing network to reconcile with IPv6. In this paper we compared IPv4 and IPv6 with respect to fractal behavioral characteristics. It is found that IPv6 shows higher degree of heavy tailedness, higher values of Hurst parameter values, higher fractal dimension values i.e. it is more self-similar, greater autocorrelation achieved even at larger lag and thus showing more burstiness.

Abstract: Recent studies of naturalistic face‐to‐face communication have demonstrated coordination patterns such as the temporal matching of verbal and non‐verbal behavior, which provides evidence for the proposal that verbal and non‐verbal communicative control derives from one system. In this study, we argue that the observed relationship between verbal and non‐verbal behaviors depends on the level of analysis. In a reanalysis of a corpus of naturalistic multimodal communication (Louwerse, Dale, Bard, & Jeuniaux, ), we focus on measuring the temporal patterns of specific communicative behaviors in terms of their burstiness. We examined burstiness estimates across different roles of the speaker and different communicative modalities. We observed more burstiness for verbal versus non‐verbal channels, and for more versus less informative language subchannels. Using this new method for analyzing temporal patterns in communicative behaviors, we show that there is a complex relationship between verbal and non‐verbal channels. We propose a “temporal heterogeneity” hypothesis to explain how the language system adapts to the demands of dialog. [ABSTRACT FROM AUTHOR]

Well-established automatic analyses of texts mainly consider frequencies of linguistic units, e.g. letters, words, and bigrams. In a recent, alternative approach, medium and large-scale text structures were used in opposition to the belief that text structure is dominated by the language features. In this paper, we introduce a generalized similarity measure to compare texts which accounts for both the network structure of texts and the role of individual words in the networks. The similarity measure is used for authorship attribution of three collections of books, each composed of 8 authors and 10 books per author. High accuracy rates were obtained with typical values between 90 % and 98 . 75 % , much higher than with the traditional term frequency-inverse document frequency (tf-idf) approach for the same collections. These accuracies are also higher than those obtained solely with the topology of networks. We conclude that the different properties of specific words on the macroscopic scale structure of a whole text are as relevant as their frequency of appearance; conversely, considering the identity of nodes brings further knowledge about a piece of text represented as a network. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

GitHub is a social collaborative coding platform on which software developers not only collaborate on codes but also share knowledge through blogs using GitHub Pages. In this article, we analyze the blogging behavior of software developers on GitHub Pages. The results show that both the commit number and the inter-event time of two consecutive blogging actions follow heavy-tailed distribution. We further observe a significant variety of activity among individual developers, and a strongly positive correlation between the activity and the power-law exponent of the inter-event time distribution. We also find a difference between the user behaviors of GitHub Pages and other online systems which is driven by the diversity of users and length of contents. In addition, our result shows an obvious difference between the majority of developers and elite developers in their burstiness property. [ABSTRACT FROM AUTHOR]

Duration can directly reflect the collective reading behaviors of library user book holding. In this paper, by introducing the burstiness and memory coefficients, we empirically investigate the collective book holding behavior of three university libraries. The statistical results show that there are similar properties among the students with different backgrounds, presenting the burstiness 〈 B 〉 = − 0.2 and memory 〈 M 〉 = 0.5 for three datasets, which indicates that memory and random effects coexist in student book holding durations. In addition, we analyze the behavior patterns without duplicate durations by merging a series of books borrowed and returned at the same time. The results show the average burstiness B increases to −0.16 and memory M drops to 0.16 for three datasets, which indicates that both duplicate behavior and student's preference affect the memory effect. Furthermore, we present a model which assumes student's next book holding duration follows the previous one with probability p , and with probability 1 − p , the student would hold the book independently. The experimental results show that the presented model can reproduce the burstiness and memory effect of student book holding durations when p = 0.5 for empirical datasets and p = 0.2 for de-duplicate datasets, which indicate that the student's preferential holding behavior occurs with the probability p . This work helps in deeply understanding the regularity of duration-based human behaviors. [ABSTRACT FROM AUTHOR]

Objective To better understand the mechanisms that lead to the sudden and unexpected occurrence of seizures, with the neuronal correlate being abnormally synchronous discharges that disrupt neuronal function. Methods To address this problem, we recorded single neuron activity in epilepsy patients during the transition to seizures to uncover specific changes of neuronal firing patterns. We focused particularly on neurons repeatedly firing discrete groups of high-frequency action potentials (so called bursters) that have been associated with ictogenesis. We analyzed a total of 459 single neurons and used the mean autocorrelation time as a quantitative measure of burstiness. To unravel the intricate roles of excitation and inhibition, we also examined differential contributions from putative principal cells and interneurons. Results During interictal recordings, burstiness was significantly higher in the seizure onset hemisphere, an effect found only for principal cells, but not for interneurons, and which disappeared before seizures. Conclusion These findings deviate from conventional views of ictogenesis that propose slowly-increasing aggregates of bursting neurons which give rise to seizures once they reach a critical mass. Significance Instead our results are in line with recent hypotheses that bursting may represent a protective mechanism by preventing direct transmission of postsynaptic high-frequency oscillations. [ABSTRACT FROM AUTHOR]

Object retrieval is still an open question. A promising approach is based on the matching of visual phrases. However, this routine is often corrupted by visual phrase burstiness, i.e., the repetitive occurrence of some certain visual phrases. Burstiness leads to over-counting the co-occurring visual patterns between two images, thus would deteriorate the accuracy of image similarity measurement. On the other hand, existing methods are incapable of capturing the complete geometric variation between images. In this paper, we propose a novel strategy to address the two problems. Firstly, we propose a unified framework for matching geometry-constrained visual phrases. This framework provides a possibility of combing the optimal geometry constraints to improve the validity of matched visual phrases. Secondly, we propose to address the problem of visual phrase burstiness from a probabilistic view. This approach effectively filters out the bursty visual phrases through explicitly modelling their distribution. Experiments on five benchmark datasets demonstrate that our method outperforms other approaches consistently and significantly. [ABSTRACT FROM AUTHOR]

The continual launches of new online social media that meet the most varied people's needs are resulting in a simultaneous adoption of different social platforms. As a consequence people are pushed to handle their identity across multiple platforms. However, due the to specialization of the services, people's identity and behavior are often partial, incomplete and scattered in different 'places'. To overcome this identity fragmentation and to give an all-around picture of people's online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how and when users post their favorite contents, about their centrality on different social media and about the choice of their username. Specifically we gathered the posting activities and social sites usage from Alternion, a social media aggregator. The analysis of social media usage shows that Alternion data reflect the novel trend of today's users of branching out into different social platforms. However the novelty is the multidimensional and longitudinal nature of the dataset. Having at our disposal users' degree in five different social networks, we performed a rank correlation analysis on users' degree centrality and we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals' importance changes from medium to medium. The longitudinal nature of the dataset has been exploited to investigate the posting activity. We find a slightly positive correlation on how often users publish on different social media and we confirm the burstiness of the posting activities extending it to multidimensional time-series. Finally we show that users tend to use similar usernames to keep their identifiability across social sites. [ABSTRACT FROM AUTHOR]

Recently, methods from the statistical physics of complex systems have been applied successfully to identify universal features in the long-range correlations (LRCs) of written texts. However, in real texts, these universal features are being intermingled with language-specific influences. This paper aims at the characterization and further understanding of the interplay between universal and language-specific effects on the LRCs in texts. To this end, we apply the language-sensitive mapping of written texts to word-length series (wls) and analyse large parallel (of same content) corpora from 10 languages classified to four families (Romanic, Germanic, Greek and Uralic). The autocorrelation functions of the wls reveal tiny but persistent LRCs decaying at large scales following a power-law with a language-independent exponent 0.60-0.65. The impact of language is displayed in the amplitude of correlations where a relative standard deviation 40% among the analyzed languages is observed. The classification to language families seems to play a significant role since, the Finnish and Germanic languages exhibit more correlations than the Greek and Roman families. To reveal the origins of the LRCs, we focus on the long words and perform burst and correlation analysis in their positions along the corpora. We find that the universal features are linked more to the correlations of the inter-long word distances while the language-specific aspects are related more to their distributions. [ABSTRACT FROM AUTHOR]

As the largest microblog service in China, Sina Weibo has attracted numerous automated applications (known as bots) due to its popularity and open architecture. We classify the active users from Sina Weibo into human, bot-based and hybrid groups based solely on the study of temporal features of their posting behavior. The anomalous burstiness parameter and time-interval entropy value are exploited to characterize automation. We also reveal different behavior patterns among the three types of users regarding their reposting ratio, daily rhythm and active days. Our findings may help Sina Weibo manage a better community and should be considered for dynamic models of microblog behaviors. [ABSTRACT FROM AUTHOR]

We study the variation of word frequencies in Russian literary texts. Our findings indicate that the standard deviation of a word’s frequency across texts depends on its average frequency according to a power law with exponent 1 2 < α < 1 , which shows that the rarer words have a relatively larger degree of frequency volatility (that is, higher “burstiness”). A latent factor model has been estimated to investigate the structure of the word frequency distribution. The findings suggest that the dependence of a word’s frequency volatility on its average frequency can be explained by the asymmetry in the distribution of latent factors. [ABSTRACT FROM AUTHOR]

The gigantic growth of the internet communication technology has illustrated its value and benefits to private businesses, government organizations, worldwide professionals, academic institutes and individuals over the past few years. The size and range of computing devices connected to the internet, substantially increased because of IPv6 and offers the potential to establish a much more powerful internet compared to the IPv4. IPv6 developed by the IETF to deal with a shortage of IP addresses under IPv4. New features of IPv6 enhance packet processing speeds over routers, switches and end systems. These improved features will have different traffic characteristics than IPv4. The internet traffic which was earlier assumed as Poisson is now shown to have fractal characteristics as; heavy tailedness, self-similarity and long range dependency. Internet traffic showing above characteristics are found to have burstiness at multiple timescales. This behavior impacts network performance and degrades it substantially. It also increases complexity for network design and create difficulties to maintain desired QoS. IPv4 traffic has been well established as self-similar traffic. Nowadays, IPv6 forming a larger share of the internet traffic and it is pivotal to asses IPv6 with regards to fractal behavior. This will enable network designers to do necessary changes in the existing network to reconcile with IPv6. In this paper we compared IPv4 and IPv6 with respect to fractal behavioral characteristics. It is found that IPv6 shows higher degree of heavy tailedness, higher values of Hurst parameter values, higher fractal dimension values i.e. it is more self-similar, greater autocorrelation achieved even at larger lag and thus showing more burstiness. [ABSTRACT FROM AUTHOR]

Background Neuronal discharge patterns can be described by three principle patterns, namely, regular, irregular, and bursty. New method Available discrimination metrics, including global ISI metrics (e.g., coefficient of variation (CV), asymmetric index), local variables (CV2, CV for a sequence of two ISIs; IR, difference of log of two adjacent ISIs; LVr, local variation with refractory period), Fano factor (FF) and Allan factor (AF), and three new burst metrics, ‘burst percentage’ (BP), ‘burst tendency’ (BT) and ‘burst entropy’ (BE), were extensively tested on representative simulated spike trains. Upon verifying that individual metrics could not by themselves reliably classify the diverse simulation patterns, a novel tri-component classification algorithm was developed. Inadequate metrics were rejected and the remaining selected metrics were grouped and weighted using multiple metric optimization to form three proxy metrics: ‘regularity’ (combining local variables), ‘burstiness’ (combining BP, BT and BE), and ‘corruption’ (combining FF and AF). Results The accuracy of the proxy metrics was verified on a large set of neuronal spike trains extracellularly recorded from multiple regions of the brain in unsedated normal and dystonic rats. Cross-validation of the tri-component classifier against meticulous subjective classification of these data demonstrated an agreement of 95.9%, with a high discriminatory power of 2.6. Comparison with existing methods The tri-component classifier was demonstrated to well outperform individual metrics on all aspects of pattern and corruption discrimination. Conclusions The tri-component classifier provides a novel, reliable algorithm to differentiate highly diverse neuronal discharge patterns and discriminate natural or erroneous corruption in the signal. [ABSTRACT FROM AUTHOR]

This paper studies the correlation of memory accesses in high-performance computer systems from a time dependence perspective, and concludes that correlations in memory access-arrival times are inconsistent, either with little correlation or with evident and abundant correlations. Thus neither independent identically distributed or self-similar is appropriate to characterize all memory activities. For memory workload with evident correlations, we present both pictorial and statistical evidence that memory accesses have self-similar like behavior. In addition, we implement a memory access series generator in which the inputs are the measured properties of the available trace data. Experimental results show that this model can accurately emulate the complex access arrival behaviors in both workloads with little and strong correlations, particularly for the burstiness characteristics in the memory workloads. [ABSTRACT FROM AUTHOR]

Center of pressure (COP) signals have been widely used to investigate various aspects of human balance during quiet standing. Here, we compute a set of measures — including burstiness, memory and local variation — to quantify temporal patterns in COP zero-crossings. Specifically, we investigate the effect of stance type (bipedal and unipedal) on the proposed measures. Data were obtained from a group of 20 health and young subjects. The results suggest that these measures are able to detect differences in zero-crossing patterns between bipedal and unipedal stance. We also perform a test–retest reliability analysis for each measure and a pairwise correlation analysis for combinations of measures. Finally, we discuss some potential implications of our results for the study of human balance. • Statistical physics and complex systems tools are used to characterize temporal inhomogeneity in postural sway signals. • Differences between double leg and single leg conditions can be detected using burstiness, memory and local variation. • Zero-crossing patterns may provide information about the dynamics of human balance. [ABSTRACT FROM AUTHOR]

The Internet has become for many the most important medium for staying informed about current news events. Some events cause heightened interest on a topic, which in turn yields a higher frequency of the search queries related to it. These queries are going through a 'query burst'. In this paper we examine the behavior of search engine users during a query burst, compared to before and after the burst. We are interested in how this behavior changes, and how it affects other stake-holders in web search.We analyze one year of web-search and news-search logs, looking at query bursts from multiple perspectives. First, we adopt the perspective of search engine users, describing changes in their effort and interest while searching. Second, we adopt the perspective of news providers by comparing web search and news search query bursts. Third, we look at the burst from the perspective of content providers.We study the conditions under which content providers can 'ride' a wave of increased interest on a topic, and obtain a share of the user's increased attention. We do so by identifying the class of queries that can be considered as an opportunity for content providers that are 'late-comers' for a query, in the sense of not being among the first to write about its topic. We also present a simple model for predicting the click share content providers could obtain if they decide to provide content about these queries. [ABSTRACT FROM AUTHOR]

We review some recent endeavors and add some new results to characterize and understand underlying mechanisms in Wikipedia (WP), the paradigmatic example of collaborative value production. We analyzed the statistics of editorial activity in different languages and observed typical circadian and weekly patterns, which enabled us to estimate the geographical origins of contributions to WPs in languages spoken in several time zones. Using a recently introduced measure we showed that the editorial activities have intrinsic dependencies in the burstiness of events. A comparison of the English and Simple English WPs revealed important aspects of language complexity and showed how peer cooperation solved the task of enhancing readability. One of our focus issues was characterizing the conflicts or edit wars in WPs, which helped us to automatically filter out controversial pages. When studying the temporal evolution of the controversiality of such pages we identified typical patterns and classified conflicts accordingly. Our quantitative analysis provides the basis of modeling conflicts and their resolution in collaborative environments and contribute to the understanding of this issue, which becomes increasingly important with the development of information communication technology. [ABSTRACT FROM AUTHOR]

The increasing demand for high-quality streaming video delivered to mobile clients necessitates efficient bandwidth utilization and allocation at not only the wireless channel but also the wired backhaul of broadband cellular networks. In this context, we propose techniques for increasing the link utilization and enhancing the quality-of-experience (QoE) for end users while multiplexing video streams over a wired link. For increasing the link utilization, we present a generic multi-tiered bandwidth estimation and scheduling scheme that can guarantee lower bounds on loss for flows at lower tiers. This scheme can be used for supporting heterogeneous loss classes, providing differentiated losses for different layers of video streams, or providing per-flow guarantees using lower aggregate bandwidth than schemes proposed in the literature. For enhancing the end-user QoE, we present a scheme for minimizing correlated losses and improving the smoothness of video quality by minimizing the maximum loss suffered by any logical unit of a stream and also the variability in loss across the length of the stream. In extensive simulations performed using video sources encoded in various formats, our multi-tiered scheme could lower the estimated bandwidth and improve statistical multiplexing gains by up to 25% with two and three classes and over 30% in the context of providing per-flow guarantees and differentiated loss for different layers. Our loss-minimization approach could lower the maximum loss by a factor of five and the loss variance by more than an order of magnitude. [ABSTRACT FROM PUBLISHER]

With the growing number of document sets accessible online, tracking their evolution over time (story tracking) became an increasingly interesting problem. In this paper we propose a story tracking method based on the dynamics of keyword-association graphs. We create a graph representation of the story evolution that we call story graphs, and investigate how graph structure can be used for detecting and discovering new developments in the story. First we investigate the possibly interesting graph properties for development detection. We continue by investigating how graph structure can be linked to the sentences representing developments. For this we create an evaluation framework which bridges the gap between temporal text mining patterns and sentences. We apply this framework to evaluate our method against other temporal text mining methods. Our experiments show that story graphs perform at similar levels overall, but provide distinctive advantages in some settings. [ABSTRACT FROM AUTHOR]

The complexity of human interactions with social and natural phenomena is mirrored in the way we describe our experiences through natural language. In order to retain and convey such a high dimensional information, the statistical properties of our linguistic output has to be highly correlated in time. An example are the robust observations, still largely not understood, of correlations on arbitrary long scales in literary texts. In this paper we explain how long-range correlations flow from highly structured linguistic levels down to the building blocks of a text (words, letters, etc.). By combining calculations and data analysis we show that correlations take form of a bursty sequence of events once we approach the semantically relevant topics of the text. The mechanisms we identify are fairly general and can be equally applied to other hierarchical settings. [ABSTRACT FROM AUTHOR]