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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]

Recent studies of naturalistic face-to-face communication havedemonstrated temporal coordination patterns such as thesynchronization of verbal and non-verbal behavior, which providesevidence for the proposal that verbal and non-verbalcommunicative control derives from one system. In this study, weargue that the observed relationship between verbal and non-verbalbehaviors depends on the level of analysis. In a re-analysis of acorpus of naturalistic multimodal communication (Louwerse et al.,2012), we focus on measuring the temporal patterns of specificcommunicative behaviors in terms of their burstiness. Weexamined burstiness estimates across different roles of the speakerand different communicative channels. We observed moreburstiness for verbal versus non-verbal channels, and for moreversus less informative language sub-channels. These findingsdemonstrate a new method for analyzing temporal patterns incommunicative behaviors, and they suggest a more complexrelationship between verbal and non-verbal channels thansuggested by prior studies.

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that NA and TS were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Yagishita, Go, Okamoto, Arimura, Ikegaya and Sasaki.)

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.