Burst Detection Algorithm Research Articles

Diverse beta burst waveform motifs characterize movement-related cortical dynamics.

Classical analyses of induced, frequency-specific neural activity typically average band-limited power over trials. More recently, it has become widely appreciated that in individual trials, beta band activity occurs as transient bursts rather than amplitude-modulated oscillations. Most studies of beta bursts treat them as unitary, and having a stereotyped waveform. However, we show there is a wide diversity of burst shapes. Using a biophysical model of burst generation, we demonstrate that waveform variability is predicted by variability in the synaptic drives that generate beta bursts. We then use a novel, adaptive burst detection algorithm to identify bursts from human MEG sensor data recorded during a joystick-based reaching task, and apply principal component analysis to burst waveforms to define a set of dimensions, or motifs, that best explain waveform variance. Finally, we show that bursts with a particular range of waveform motifs, ones not fully accounted for by the biophysical model, differentially contribute to movement-related beta dynamics. Sensorimotor beta bursts are therefore not homogeneous events and likely reflect distinct computational processes.

IRIS burst properties in active regions

Context. Interface Region Imaging Spectrograph (IRIS) bursts are localised features thought to be driven by magnetic reconnection. Although these events are well-studied, it remains unknown whether their properties vary as their host active regions (ARs) evolve. Aims. In this article, we aim to understand whether the measurable properties (e.g. frequency, area, location, spectral characteristics) of IRIS bursts are consistent during the evolution of their host ARs. Methods. We study 42 dense 400-step rasters sampled by IRIS. These rasters each covered one of seven ARs, with each AR being sampled at least four times over a minimum of 48 h. An automated IRIS burst detection algorithm is used to identify IRIS burst profiles in this work. Data from the Solar Dynamics Observatory’s Helioseismic and Magnetic Imager are also used to provide context about the co-spatial line-of-sight magnetic field. Results. Of the rasters studied, 36 (86%) were found to contain IRIS burst profiles. Five ARs (11850, 11909, 11916, 12104, and 12139) contained IRIS burst profiles in each raster that sampled them whilst one AR (11871) was found to contain no IRIS burst profiles at any time. A total of 4019 IRIS burst profiles belonging to 752 connected objects, which we define as parent IRIS bursts, were studied. IRIS burst profiles were only detected within compact regions in each raster, with these regions appearing to increase in size as the host ARs aged. No systematic changes in the frequency of IRIS burst profiles or the spectral characteristic of IRIS burst profiles through time were found for these ARs. Finally, 93% of parent IRIS bursts with areas between 1 arcsec2 and 4 arcsec2 were observed to occur co-spatial to bi-poles in the photosphere. Conclusions. IRIS bursts are small-scale brightenings which have remarkably consistent spectral and spatial properties throughout the evolution of ARs. These events predominantly form within the cores of larger and more complex ARs, with the regions containing these events appearing to increase in size as the host region itself evolves.

Mapping 70 Years of advancements in management research on sustainability

Sustainability provides a research paradigm that, when embedded into management language, theory, and method, provides a systems approach to drawing interconnections between business activities and grand societal challenges. With mounting evidence detailing the salience of sustainable business transformations, the gap between conventional management research and practice is stark. With little formal discussion on how this gap can be bridged, this study examines the application of sustainability in top business and management literature and details opportunities for synergizing these research agendas to inform future action-oriented research. Examining 46,856 publications across 27 top management journals, this comparative analysis finds that 800 articles (1.71%) speak specifically to sustainability. Adopting the Poisson burst detection algorithm and interdisciplinary mapping techniques, we trace the evolution and positionality of sustainability discourse in management literature. Though nascent, sustainability management distinguishes itself through a lens of pragmatism versus empirics, as well as through discernible theoretical and methodological approaches. We also find that sustainability discourse is growing at a faster rate, with higher aggregate citation counts and with prominence in select sub-disciplines relative to its conventional counterparts. We conclude with a research agenda - for grand societal challenges to become actionable, a new language is required, engaging a range of disciplines, theories, and methodologies in the form of sustainability management. • Sustainability management literature is better positioned to address grand societal challenges. • This paper examines how sustainability is discussed across 27 top management journals. • Though a small subset, sustainability management literature is increasing and highly impactful. • Key linguistic, theoretical, methodological, and ontological differences are discussed. • Opportunities for future research on the utility of sustainability management are proposed.

An infodemiological framework for tracking the spread of SARS-CoV-2 using integrated public data.

The outbreak of the SARS-CoV-2 novel coronavirus has caused a health crisis of immeasurable magnitude. Signals from heterogeneous public data sources could serve as early predictors for infection waves of the pandemic, particularly in its early phases, when infection data was scarce. In this article, we characterize temporal pandemic indicators by leveraging an integrated set of public data and apply them to a Prophet model to predict COVID-19 trends. An effective natural language processing pipeline was first built to extract time-series signals of specific articles from a news corpus. Bursts of these temporal signals were further identified with Kleinberg's burst detection algorithm. Across different US states, correlations for Google Trends of COVID-19 related terms, COVID-19 news volume, and publicly available wastewater SARS-CoV-2 measurements with weekly COVID-19 case numbers were generally high with lags ranging from 0 to 3 weeks, indicating them as strong predictors of viral spread. Incorporating time-series signals of these effective predictors significantly improved the performance of the Prophet model, which was able to predict the COVID-19 case numbers between one and two weeks with average mean absolute error rates of 0.38 and 0.46 respectively across different states

Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal

In recent years, distributed unique word (DUW) has been widely used in satellite single carrier TDMA signals, such as very small aperture terminal (VSAT) satellite systems. Different from the centralized structure of traditional unique word, DUW is uniformly dispersed in a burst signal, where the traditional unique word detection methods are not applicable anymore. For this, we propose a robust burst detection algorithm based on DUW. Firstly, we allocated the sliding detection windows with the same structures as DUW in order to effectively detect it. Secondly, we adopt the method of time delay conjugate multiplication to eliminate the influence of frequency offset on detection performance. Due to the uniform dispersion of DUW, it naturally has two different kinds of time delays, namely the delay within the group and the delay between the two groups. So, we divide the traditional dual correlation formula into two parts to calculate them separately and obtain a dual correlation detection algorithm, which is suitable for DUW. Simulation and experimental results demonstrate that when the distribution structure of DUW changes, detection probability of the proposed algorithm fluctuates little, and its variance is 1.56×10−5, which is 99.83% lower than the existing DUW detection algorithms. In addition, its signal to noise ratio (SNR) threshold is about 1 dB lower than the existing algorithms under the same circumstance of the missed detection probability.

An Analysis of Spikes in Atmospheric Imaging Assembly (AIA) Data

The Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) returns high-resolution images of the solar atmosphere in seven extreme ultraviolet (EUV) wavelength channels. The images are processed on the ground to remove intensity spikes arising from energetic particles hitting the instrument, and the despiked images are provided to the community. In this article, a three-hour series of images from the 171 Å channel obtained on 28 February 2017 was studied to investigate how often the despiking algorithm gave false positives caused by compact brightenings in the solar atmosphere. The latter were identified through spikes appearing in the same detector pixel for three consecutive frames. 1096 examples were found from the 900 image frames. These “three-spikes” were assigned to 126 dynamic solar features, and it is estimated that the three-spike method identifies 19% of the total number of features affected by despiking. For any ten-minute sequence of AIA 171 Å images there are around 37 solar features that have their intensity modified by despiking. The features are found in active regions, quiet Sun, and coronal holes and, in relation to solar surface area, there is a greater proportion within coronal holes. In 96% of the cases, the despiked structure is a compact brightening with a size of two arcsec or less, and the remaining 4% have narrow, elongated structures. By applying an EUV burst detection algorithm, we found that 96% of the events could be classified as EUV bursts. None of the spike events are rendered invisible by the AIA processing pipeline, but the total intensity over an event’s lifetime can be reduced by up to 67%. Users are recommended to always restore the original intensities in AIA data when studying short-lived or rapidly evolving features that exhibit fine-scale structure.

TKES: A Novel System for Extracting Trendy Keywords from Online News Sites

As the Smart city trend especially artificial intelligence, data science, and the internet of things has attracted lots of attention, many researchers have created various smart applications for improving people’s life quality. As it is very essential to automatically collect and exploit information in the era of industry 4.0, a variety of models have been proposed for storage problem solving and efficient data mining. In this paper, we present our proposed system, Trendy Keyword Extraction System (TKES), which is designed for extracting trendy keywords from text streams. The system also supports storing, analyzing, and visualizing documents coming from text streams. The system first automatically collects daily articles, then it ranks the importance of keywords by calculating keywords’ frequency of existence in order to find trendy keywords by using the Burst Detection Algorithm which is proposed in this paper based on the idea of Kleinberg. This method is used for detecting bursts. A burst is defined as a period of time when a keyword is continuously and unusually popular over the text stream and the identification of bursts is known as burst detection procedure. The results from user requests could be displayed visually. Furthermore, we create a method in order to find a trendy keyword set which is defined as a set of keywords that belong to the same burst. This work also describes the datasets used for our experiments, processing speed tests of our two proposed algorithms.

Analysis on the research progress of library and information science since the new century

PurposeLibrary science and information science, two subdisciplines of library and information science (LIS), are developed independently but interconnectedly. In this information age, LIS is in a special period of transformation and development, which has caused some changes in both library science and information science. By accurately capturing these changes and analyzing them, the authors can effectively map the development of LIS in the new century, thus providing a reference for the evolution and development of the field. The purposes of this paper are to explore the mainstream research fields and frontiers of library science and information science, respectively, since the new century, and to make a comparative analysis of the two subdisciplines.Design/methodology/approachBy using CiteSpace to visualize LIS journals, this study draws knowledge maps of the two subdisciplines of LIS through the co-occurrence descriptors network. Using burst detection algorithm, this study detects words of high frequency variation by investigating the time frequency distribution.FindingsThe results show that the research focus of library science has experienced a change from traditional to digital library while information science has moved from information to data focus. This study also finds the similarities and differences between mainstream areas of library science and information science.Originality/valueThis study focuses on the evolution of library science and information science, and explores their mainstream research fields and frontiers in the 21st century. These findings will promote the transformation and development of LIS as well as provide research directions for scholars in the field.

Global scientific output trend for Akkermansia muciniphila research: a bibliometric and scientometric analysis

BackgroundAkkermansia muciniphila is an anaerobic bacterium residing in the healthy intestinal tract of host and its quantity has a negative correlation with various host diseases. This study for the first time provides a holistic bibliometric aspect of Akkermansi muciniphila research in the literature and shows the hot topic terms of these articles in any period of time.MethodsScopus database was selected to retrieve documents relevant to Akkermansia muciniphila in any language up to 2019. The bibliometric profile of Akkermansia muciniphila articles including subject area, year distribution, citations, institutions, journals, authors, and countries was systematically characterized and the collaboration networks of authors and countries as well as the burst detection algorithm of the words in the titles, abstracts and keywords were visualized.ResultsThere is a progressive growing trend in research on Akkermansia from 2004 to 2019 with a total of 566 articles during this period. Out of 353 original articles, there are 194 animal studies (155 studies on mice) and 112 human studies. Also, 65 various diseases were investigated in these studies. The most focused conditions are obesity (71 articles) and type2 diabetes (39 articles). The United States is the leading country on Akkermansia publications (n = 132), followed by China (n = 95). Frontiers in Microbiology is the most dominant journal with 23 Akkermansia publications. In addition, “cancer” is the hot topic of recent Akkermansia research.ConclusionAkkermansia research is of progressive interest during the last decade and the studies on this subject move towards its relationship with cancer and its promising effect on health.

Power-law energy distributions of small-scale impulsive events on the active Sun: results from IRIS

ABSTRACT Numerous studies have analysed inferred power-law distributions between frequency and energy of impulsive events in the outer solar atmosphere in an attempt to understand the predominant energy supply mechanism in the corona. Here, we apply a burst detection algorithm to high-resolution imaging data obtained by the Interface Region Imaging Spectrograph to further investigate the derived power-law index, γ, of bright impulsive events in the transition region. Applying the algorithm with a constant minimum event lifetime (of either 60 s or 110 s) indicated that the target under investigation, such as Plage and Sunspot, has an influence on the observed power-law index. For regions dominated by sunspots, we always find γ < 2; however, for data sets where the target is a plage region, we often find that γ > 2 in the energy range (∼1023, ∼1026) erg. Applying the algorithm with a minimum event lifetime of three time-steps indicated that cadence was another important factor, with the highest cadence data sets returning γ > 2 values. The estimated total radiative power obtained for the observed energy distributions is typically 10–25 per cent of what would be required to sustain the corona indicating that impulsive events in this energy range are not sufficient to solve coronal heating. If we were to extend the power-law distribution down to an energy of 1021 erg, and assume parity between radiative energy release and the deposition of thermal energy, then such bursts could provide 25–50 per cent of the required energy to account for the coronal heating problem.

Integrating Unified Medical Language System and Kleinberg's Burst Detection Algorithm into Research Topics of Medications for Post-Traumatic Stress Disorder.

BackgroundThe treatment of post-traumatic stress disorder (PTSD) has long been a challenge because the symptoms of PTSD are multifaceted. PTSD is primarily treated with psychotherapy and medication, or a combination of psychotherapy and medication. The present study was designed to analyze the literature on medications for PTSD and explore high-frequency common drugs and low-frequency burst drugs by burst detection algorithm combined with Unified Medical Language System (UMLS) and provide references for developing new drugs for PTSD.MethodsPublications related to medications for PTSD from 2010 to 2019 were identified through PubMed, Web of Science Core Collection, and BIOSIS Previews. SemRep and SemRep semantic result processing system were performed to extract the set of drug concepts with therapeutic relationship according to the semantic relationship of UMLS. Kleinberg’s burst detection algorithm was applied to calculate the burst weight index of drug concepts by a Java-based program. These concepts were sorted according to the frequency and the burst weight index.ResultsFour hundred and fifty-nine treatment-related drug concepts were extracted. The drug with the highest burst weight index was “Psilocybine”, a hallucinogen, which was more likely to be a hotspot for the pharmacotherapy of PTSD. The highest frequency concept was “prazosin”, which was more likely to be the focus of research in the medications for PTSD.ConclusionThe present study assessed the medication-related literature on PTSD treatment, providing a framework of burst words detection-based method, a baseline of information for future research and the new attempt for the discovery of textual knowledge. The bibliometric analysis based on the burst detection algorithm combined with UMLS has shown certain feasibility in amplifying the microscopic changes of a specific research direction in a field, it can also be used in other aspects of disease and to explore the trends of various disciplines.

Temporal microstructure of dyadic social behavior during relationship formation in mice.

Socially competent animals must learn to modify their behavior in response to their social partner in a contextually appropriate manner. Dominant-subordinate relationships are a particularly salient social context for mice. Here we observe and analyze the microstructure of social and non-social behaviors as 21 pairs of outbred CD-1 male mice (Mus Musculus) establish dominant-subordinate relationships during daily 20-minute interactions for five consecutive days in a neutral environment. Firstly, using a Kleinberg burst detection algorithm, we demonstrate aggressive and subordinate interactions occur in bursting patterns followed by quiescent periods rather than being uniformly distributed across social interactions. Secondly, we identify three phases of dominant-subordinate relationship development (pre-, middle-, and post-resolution) by utilizing two statistical methods to identify stability in aggressive and subordinate behavior across these bursts. Thirdly, using First Order Markov Chains we find that dominant and subordinate mice show distinct behavioral transitions, especially between tail rattling and other aggressive/subordinate behaviors. Further, dominant animals engaged in more digging and allogrooming behavior and were more likely to transition from sniffing their partner’s body to head, whereas subordinates were more likely to transition from head sniffing to side-by-side contact. Lastly, we utilized a novel method (Forward Spike Time Tiling Coefficient) to assess how individuals respond to the behaviors of their partner. We found that subordinates decrease their tail rattling and aggressive behavior in response to aggressive but not subordinate behavior exhibited by dominants and that tail rattling in particular may function to deescalate aggressive behavior in pairs. Our findings demonstrate that CD-1 male mice rapidly establish dominance relationships and modify their social and non-social behaviors according to their current social status. The methods that we detail also provide useful tools for other researchers wishing to evaluate the temporal dynamics of rodent social behavior.

Application of entity linking to identify research fronts and trends

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.

Overview of trends in global epigenetic research (2009–2017)

Epigenetics, one of the most rapidly intensifying fields of biological research, explores inheritable gene expression not governed by alterations in the DNA sequence. This article analysed the literature on epigenetics published between 2009 and 2017 using qualitative and visualisation techniques. The data were drawn from Clarivate Analytics’ Web of Science Core Collection in January 2018. CiteSpace V software was used to establish an intellectual overview, based on 13,295 scientific articles and review papers. Document co-citations were analysed and a variety of graphics was created. The aim was to define the scope of the field and identify its constituent specialities with automatic procedures based on the keywords and titles in the citing articles in the clusters identified. Ten subspecialities were identified. Field core papers were defined from the co-citation findings, Kleinberg’s burst detection algorithm and the structural variation theory metrics calculated for all the articles selected. The findings can be used to acquire an in-depth view of the patterns and trends in place in the domain and identify innovative and potentially transformational studies.

The impact of competition strength and density on performance: The technological competition networks in the wind energy industry

Technological competition plays an important role in technological development and innovation. However, technological competition networks have long been ignored in research. Based on the social network theory, we proposed that the innovation of an organization is greatly affected by the technological competition networks that it is embedded in. We introduced International Patent Classifications (IPCs) information to construct technological competition networks of organizations, and explored how competition network characteristics influence the organizational competitive capability and innovation performance.Based on the wind energy patent data between 2005 and 2014, we synthetically investigated the technological development and competition networks in the industry. Our findings show that (1) the evolutionary trends of patents and technology elements growth exhibit similar patterns (i.e., increasing before 2011 and decreasing after 2011); (2) the burst detection algorithm detects that 140 technological elements and 132 organizations experienced a sudden growth in production; (3) wind energy organizations intensively compete with each other, and in each year, only 10% of organizations can improve their technological competitive positions in the following year; (4) competition density in a competition network increases the technological competitive capability and innovation performance of firms. Additionally, competition strength has an inverted-U shaped relationship with both the aforementioned outcomes.

Measuring Dynamic Correlations of Words in Written Texts with an Autocorrelation Function

In this study, we regard written texts as time series data and try to investigate dynamic correlations of word occurrences by utilizing an autocorrelation function (ACF). After defining appropriate formula for the ACF that is suitable for expressing the dynamic correlations of words, we use the formula to calculate ACFs for frequent words in 12 books. The ACFs obtained can be classified into two groups: One group of ACFs shows dynamic correlations, with these ACFs well described by a modified Kohlrausch-Williams-Watts (KWW) function; the other group of ACFs shows no correlations, with these ACFs fitted by a simple stepdown function. A word having the former ACF is called a Type-I word and a word with the latter ACF is called a Type-II word. It is also shown that the ACFs of Type-II words can be derived theoretically by assuming that the stochastic process governing word occurrence is a homogeneous Poisson point process. Based on the fitting of the ACFs by KWW and stepdown functions, we propose a measure of word importance which expresses the extent to which a word is important in a particular text. The validity of the measure is confirmed by using the Kleinburg’s burst detection algorithm.

Piecing together the puzzle: Improving event content coverage for real-time sub-event detection using adaptive microblog crawling

In an age when people are predisposed to report real-world events through their social media accounts, many researchers value the benefits of mining user generated content from social media. Compared with the traditional news media, social media services, such as Twitter, can provide more complete and timely information about the real-world events. However events are often like a puzzle and in order to solve the puzzle/understand the event, we must identify all the sub-events or pieces. Existing Twitter event monitoring systems for sub-event detection and summarization currently typically analyse events based on partial data as conventional data collection methodologies are unable to collect comprehensive event data. This results in existing systems often being unable to report sub-events in real-time and often in completely missing sub-events or pieces in the broader event puzzle. This paper proposes a Sub-event detection by real-TIme Microblog monitoring (STRIM) framework that leverages the temporal feature of an expanded set of news-worthy event content. In order to more comprehensively and accurately identify sub-events this framework first proposes the use of adaptive microblog crawling. Our adaptive microblog crawler is capable of increasing the coverage of events while minimizing the amount of non-relevant content. We then propose a stream division methodology that can be accomplished in real time so that the temporal features of the expanded event streams can be analysed by a burst detection algorithm. In the final steps of the framework, the content features are extracted from each divided stream and recombined to provide a final summarization of the sub-events. The proposed framework is evaluated against traditional event detection using event recall and event precision metrics. Results show that improving the quality and coverage of event contents contribute to better event detection by identifying additional valid sub-events. The novel combination of our proposed adaptive crawler and our stream division/recombination technique provides significant gains in event recall (44.44%) and event precision (9.57%). The addition of these sub-events or pieces, allows us to get closer to solving the event puzzle.

Analytical evaluation of emerging scientific trends in business intelligence through the utilisation of burst detection algorithm

Business intelligence has become mainstream in recent scientific research trends. The purpose of this research is to study the emerging and fading themes of the business intelligence domain through an analytical overview of keywords, titles and abstracts. Among scientometrics methods for representing the emergent and disappearing trends, the 'burst detection' algorithm has been chosen and applied to the current dataset of high-ranked international papers which can help scholars and practitioners to understand a better overview of business intelligence field by visualising the changes in a recent time period. For this purpose, the data related to business intelligence has been gathered from Web of Science (WoS) core collection dataset between the years 1980-2014 and the burst detection algorithm has been applied on the 'abstract', 'title' and 'keywords' of the dataset which has shown interesting informative results for the future researchers to concentrate on.

Analytical evaluation of emerging scientific trends in business intelligence through the utilisation of burst detection algorithm

Analytical evaluation of emerging scientific trends in business intelligence through the utilisation of burst detection algorithm

Commentary: Burst Firing in a Motion-Sensitive Neural Pathway Correlates with Expansion Properties of Looming Objects That Evoke Avoidance Behaviors.

Commentary: Burst Firing in a Motion-Sensitive Neural Pathway Correlates with Expansion Properties of Looming Objects That Evoke Avoidance Behaviors.