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Journal articles on the topic 'Sample event data'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Lerner, Jürgen, and Alessandro Lomi. "Reliability of relational event model estimates under sampling: How to fit a relational event model to 360 million dyadic events." Network Science 8, no. 1 (2019): 97–135. http://dx.doi.org/10.1017/nws.2019.57.

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AbstractWe assess the reliability of relational event model (REM) parameters estimated under two sampling schemes: (1) uniform sampling from the observed events and (2) case–control sampling which samples nonevents, or null dyads (“controls”), from a suitably defined risk set. We experimentally determine the variability of estimated parameters as a function of the number of sampled events and controls per event, respectively. Results suggest that REMs can be reliably fitted to networks with more than 12 million nodes connected by more than 360 million dyadic events by analyzing a sample of som

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Foets, Jasper, Carlos E. Wetzel, Núria Martínez-Carreras, Adriaan J. Teuling, Jean-François Iffly, and Laurent Pfister. "Technical note: A time-integrated sediment trap to sample diatoms for hydrological tracing." Hydrology and Earth System Sciences 24, no. 10 (2020): 4709–25. http://dx.doi.org/10.5194/hess-24-4709-2020.

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Abstract. Diatoms, microscopic single-celled algae, are present in almost all habitats containing water (e.g. streams, lakes, soil and rocks). In the terrestrial environment, their diversified species distributions are mainly controlled by physiographical factors and anthropic disturbances which makes them useful tracers in catchment hydrology. In their use as a tracer, diatoms are generally sampled in streams by means of an automated sampling method; as a result, many samples must be collected to cover a whole storm run-off event. As diatom analysis is labour-intensive, a trade-off has to be

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van Gemmeren, Peter, Alaettin Serhan Mete, Jackson Carl Burzynski, et al. "Framework for custom event sample augmentations for ATLAS analysis data." EPJ Web of Conferences 295 (2024): 03016. http://dx.doi.org/10.1051/epjconf/202429503016.

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For HEP event processing, data is typically stored in column-wise synchronized containers, such as most prominently ROOT’s TTree, which have been used for several decades to store by now over 1 exabyte. These containers can combine row-wise association capabilities needed by most HEP event processing frameworks (e.g. Athena for ATLAS) with column-wise storage, which typically results in better compression and more efficient support for many analysis use-cases. One disadvantage is that these containers, TTree in the HEP use-case, require to contain the same attributes for each entry/row (repres

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Chang, Shu-Hui. "A Two-Sample Comparison for Multiple Ordered Event Data." Biometrics 56, no. 1 (2000): 183–89. http://dx.doi.org/10.1111/j.0006-341x.2000.00183.x.

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Turton, David J., Paul D. Boehm, Dean A. Gouveia, Michael Amman, Patrick O'Brien, and Glenn Menard. "Managing The Environmental Data of a Spill Event." International Oil Spill Conference Proceedings 1999, no. 1 (1999): 971–73. http://dx.doi.org/10.7901/2169-3358-1999-1-971.

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ABSTRACT Data is critical to decision making during any spill event. This data can include information on spill movement, weather and other environmental influences, chemical and physical characterization of the released materials, resources at risk, and sampling and analysis information. To assemble this data for on-site decision making, and for post-spill analysis, the authors have developed a Spill Response Database Application (SRDA). SRDA integrates inventory and imaging, database, and geographical information systems (GIS) capabilities. Database capabilities provide for the storage, retr

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Provoost, Pieter, Pooter Daphnis De, Ward Appeltans, et al. "Expanding the Ocean Biogeographic Information System (OBIS) beyond species occurrences." Biodiversity Information Science and Standards 1 (August 22, 2017): e20515. https://doi.org/10.3897/tdwgproceedings.1.20515.

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Data providers in the Ocean Biogeographic Information System (OBIS) network are not just recording species occurrences, they are also recording sampling methodology details and measuring environmental and biotic variables. In order to make OBIS an effective data sharing platform, it needs to be able to store and exchange these data in such a way that they can easily be interpreted by end users, as well as by the tools which will be created to search, analyze and visualize the integrated data. OBIS makes use of Darwin Core Archives (DwC-A) for exchanging data between data providers, regional an

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De, Pooter Daphnis, Ward Appeltans, Nicolas Bailly, et al. "Expanding the Ocean Biogeographic Information System (OBIS) beyond species occurrences." Biodiversity Information Science and Standards 1 (August 11, 2017): e20196. https://doi.org/10.3897/tdwgproceedings.1.20196.

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The Ocean Biogeographic Information System (OBIS) aims to integrate smaller, isolated datasets into a larger, more comprehensive picture of life in our oceans. Therefore, OBIS provides a gateway to many datasets containing information on where and when marine species have been observed. The datasets within OBIS are contributed by a network of hundreds of institutes, projects and individuals, all with the common goal to gain scientific knowledge and to make these data and knowledge easily available to the public. Until recently, OBIS had solely focused on biogeographic data, in the form of pres

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van Gerwen, Nikki, Arjan Blokland, and Arieke J. Rijken. "Assessing the Accuracy of Life Event Calendar Data in an Offender Sample." Justice Quarterly 36, no. 3 (2018): 532–66. http://dx.doi.org/10.1080/07418825.2018.1458143.

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Martinussen, T. "Dynamic path analysis for event time data: large sample properties and inference." Lifetime Data Analysis 16, no. 1 (2009): 85–101. http://dx.doi.org/10.1007/s10985-009-9128-2.

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Crisp, Adam, and Paula Curtis. "Sample size estimation for non-inferiority trials of time-to-event data." Pharmaceutical Statistics 7, no. 4 (2008): 236–44. http://dx.doi.org/10.1002/pst.292.

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Schneider, S., H. Schmidli, and T. Friede. "Blinded sample size re-estimation for recurrent event data with time trends." Statistics in Medicine 32, no. 30 (2013): 5448–57. http://dx.doi.org/10.1002/sim.5977.

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Puckette, Miller. "Event- and sample-based audio processing: A shotgun marriage." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A104. http://dx.doi.org/10.1121/10.0018315.

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Real-time audio computation tends to fall into two very different paradigms. On one hand we deal with continuous streams of audio sample frames having a fixed number of channels and a fixed data type. Our main conceptual tool is the Nyquist theorem. On the other hand, musical scores and sporadic control streams (for instance MIDI or network packets) do not fit comfortably into any fixed data type and need not fall on any fixed sample rate. In this talk I'll describe the choices taken in the design of the Pure Data real-time music and audio programming environment. The design makes it possible

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Muchlinski, David, David Siroky, Jingrui He, and Matthew Kocher. "Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data." Political Analysis 24, no. 1 (2016): 87–103. http://dx.doi.org/10.1093/pan/mpv024.

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The most commonly used statistical models of civil war onset fail to correctly predict most occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of binary data, such as logistic regression, even in their rare event and regularized forms, perform poorly at prediction. We compare the performance of Random Forests with three versions of logistic regression (classic logistic regression, Firth rare events logistic regression, andL1-regularized logistic regression), and find that the algorithmic approach provides significantly more accurate predictions of civil

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Chu, Fei, Hongzhuan Chen, Chunqing Yu, Lihua You, Liang Wang, and Yun Liu. "Quantitative Research on Generalized Linear Modeling of SEU and Test Programs Based on Small Sample Data." Electronics 11, no. 14 (2022): 2242. http://dx.doi.org/10.3390/electronics11142242.

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Complex integrated circuits (ICs) have complex functions and various working modes, which have many factors affecting the performance of a single event effect. The single event effect performance of complex ICs is highly program-dependent and the single event sensitivity of a typical operating mode is generally used to represent the single event performance of the circuits. Traditional evaluation methods fail to consider the cross effects of multiple factors and the comprehensive effects of each factor on the single event soft error cross section. In order to solve this problem, a new quantita

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Lu, Yang, Xiaolei Ma, Yinan Lu, Yuxin Zhou, and Zhili Pei. "A Novel Sample Selection Strategy for Imbalanced Data of Biomedical Event Extraction with Joint Scoring Mechanism." Computational and Mathematical Methods in Medicine 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/7536494.

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Biomedical event extraction is an important and difficult task in bioinformatics. With the rapid growth of biomedical literature, the extraction of complex events from unstructured text has attracted more attention. However, the annotated biomedical corpus is highly imbalanced, which affects the performance of the classification algorithms. In this study, a sample selection algorithm based on sequential pattern is proposed to filter negative samples in the training phase. Considering the joint information between the trigger and argument of multiargument events, we extract triplets of multiarg

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Wen, Chi-Chung, and Yi-Hau Chen. "Sample size determination for semiparametric analysis of current status data." Statistical Methods in Medical Research 28, no. 8 (2018): 2247–57. http://dx.doi.org/10.1177/0962280218761493.

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Semiparametric transformation models, which include the Cox proportional hazards and proportional odds models as special cases, are popular in current practice of survival analysis owing to that, in contrast to parametric models, no assumption on the baseline distribution is required. Although sample size calculations for semiparametric survival analysis with right-censored data are available, no such calculation exits in literature for semiparametric analysis with current status data, where only an examination time and whether the event occurs prior to the examination are observable. We devel

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Cook, Scott J., Jude C. Hays, and Robert J. Franzese. "Fixed effects in rare events data: a penalized maximum likelihood solution." Political Science Research and Methods 8, no. 1 (2018): 92–105. http://dx.doi.org/10.1017/psrm.2018.40.

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AbstractMost agree that models of binary time-series-cross-sectional data in political science often possess unobserved unit-level heterogeneity. Despite this, there is no clear consensus on how best to account for these potential unit effects, with many of the issues confronted seemingly misunderstood. For example, one oft-discussed concern with rare events data is the elimination of no-event units from the sample when estimating fixed effects models. Many argue that this is a reason to eschew fixed effects in favor of pooled or random effects models. We revisit this issue and clarify that th

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Tabaie, Azade, and Allan Fong. "Analysis of General Event Types and Contributing Factors in Patient Safety System Data." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 12, no. 1 (2023): 103–7. http://dx.doi.org/10.1177/2327857923121025.

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Patient safety event reporting systems (PSRS) aim to identify safety hazards by encouraging hospital staff to report adverse events, errors, and potential errors in the healthcare system. PSRS collect different information regarding a safety event such as contributing factors and general event types. Users select contributing factors and general event types of a safety incident from predefined lists; therefore, most frequent general event types and contributing factors are commonly recorded together. Contributing factors are often related to the leading cause of an unsafe condition; therefore,

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Dinart, D., C. Bellera, and V. Rondeau. "Sample size estimation for recurrent event data using multifrailty and multilevel survival models." Revue d'Épidémiologie et de Santé Publique 70 (May 2022): S77. http://dx.doi.org/10.1016/j.respe.2022.03.104.

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Chen, Liddy M., Joseph G. Ibrahim, and Haitao Chu. "Sample Size Determination in Shared Frailty Models for Multivariate Time-to-Event Data." Journal of Biopharmaceutical Statistics 24, no. 4 (2014): 908–23. http://dx.doi.org/10.1080/10543406.2014.901346.

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Gomes, Janice, Fabrice Lucien, Tyler Cooper, et al. "Analytical Considerations in Nanoscale Flow Cytometry of Extracellular Vesicles to Achieve Data Linearity." Thrombosis and Haemostasis 118, no. 09 (2018): 1612–24. http://dx.doi.org/10.1055/s-0038-1668544.

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Background Platelet microparticles (PMPs) and their abundance in the blood are a prognostic biomarker in thrombotic disorders and cancer. Nanoscale flow cytometry (nFC) is ideal for high-throughput analysis of PMPs but these clinical assays have not been developed previously. Objective This article demonstrates that nFC is a suitable technology to enumerate PMPs present in plasma samples in a clinical setting. Materials and Methods nFC was performed using the Apogee A50-Micro instrument. Instrument settings and acquisition parameters were developed with the use of fluorescent beads and plasma

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Nilsson, P., D. Roser, R. Thorwaldsdotter, et al. "SCADA data and the quantification of hazardous events for QMRA." Journal of Water and Health 5, S1 (2007): 99–105. http://dx.doi.org/10.2166/wh.2007.138.

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The objective of this study was to assess the use of on-line monitoring to support the QMRA at water treatment plants studied in the EU MicroRisk project. SCADA data were obtained from three Catchment-to-Tap Systems (CTS) along with system descriptions, diary records, grab sample data and deviation reports. Particular attention was paid to estimating hazardous event frequency, duration and magnitude. Using Shewart and CUSUM we identified ‘change-points’ corresponding to events of between 10 min and &gt; 1 month duration in timeseries data. Our analysis confirmed it is possible to quantify

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Gronsbell, Jessica, Zachary R. McCaw, Timothy Regis, and Lu Tian. "Exact Inference for Random Effects Meta-Analyses for Small, Sparse Data." Stats 8, no. 1 (2025): 5. https://doi.org/10.3390/stats8010005.

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Meta-analysis aggregates information across related studies to provide more reliable statistical inference and has been a vital tool for assessing the safety and efficacy of many high-profile pharmaceutical products. A key challenge in conducting a meta-analysis is that the number of related studies is typically small. Applying classical methods that are asymptotic in the number of studies can compromise the validity of inference, particularly when heterogeneity across studies is present. Moreover, serious adverse events are often rare and can result in one or more studies with no events in at

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Goldman, Anne I., and David W. Hillman. "Exemplary data: Sample size and power in the design of event-time clinical trials." Controlled Clinical Trials 13, no. 4 (1992): 256–71. http://dx.doi.org/10.1016/0197-2456(92)90010-w.

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Meyer, Chistopher, and Rebecca Snyder. "Adapting Museum Workflows to Accommodate Omic-based, Multi-species Occurrences from Environmental Samples." Biodiversity Information Science and Standards 6 (September 19, 2022): e95054. https://doi.org/10.3897/biss.6.95054.

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Natural history museums play a critical role in curating global anthropological, biological and geological diversity. Museums host more than voucher samples for comparative identification purposes. They host a suite of derived products and associated information, which cascade from these records of diversity in time and space. Increasingly, museums capture whole communities through curation of environmental samples and their derived data. Environmental samples are defined as material samples that yield multi-species occurrence records that cannot be sorted into traditional independent lots and

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Rahmawati, Ika Yustina, and Tiara Pandansari. "REAKSI PASAR MODAL DARI DAMPAK PERISTIWA BOM PLAZA SARINAH TERHADAP ABNORMAL RETURN PERUSAHAAN LQ 45 YANG TERDAFTAR DI BEI." Riset Akuntansi dan Keuangan Indonesia 1, no. 2 (2016): 126–33. http://dx.doi.org/10.23917/reaksi.v1i2.2728.

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The purpose of this study was to analyze the reaction of the capital market from the impact of the bombing in Jakarta Sarinah Plaza, which will be indicated by the presence or absence of abnormal return. the sample used is the stock of LQ 45, this study is event study so that the observation period will see the reaction on before, during and after the event. In this study period used was H-5 (before the event), H0 (current events) and H + 5 (after the event). Sources of data obtained from yahoo finance, sahamok.com and IDX. The data used in this research is secondary data, such as the closing

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De, Pooter Daphnis, Ward Appeltans, Nicolas Bailly, et al. "Toward a new data standard for combined marine biological and environmental datasets - expanding OBIS beyond species occurrences." Biodiversity Data Journal 5 (January 9, 2017): e10989. https://doi.org/10.3897/BDJ.5.e10989.

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The Ocean Biogeographic Information System (OBIS) is the world's most comprehensive online, open-access database of marine species distributions. OBIS grows with millions of new species observations every year. Contributions come from a network of hundreds of institutions, projects and individuals with common goals: to build a scientific knowledge base that is open to the public for scientific discovery and exploration and to detect trends and changes that inform society as essential elements in conservation management and sustainable development. Until now, OBIS has focused solely on the coll

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Lu, Yang, Xiaolei Ma, Yinan Lu, and Zhili Pei. "Biomolecular-Level Event Detection: A New Representation of Generating Short Sentence and Sample Selection Strategy." Current Bioinformatics 14, no. 4 (2019): 359–70. http://dx.doi.org/10.2174/1574893614666190204153531.

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Background: Biomolecular-level event extraction is one of the most important branches of information extraction. With the rapid growth of biomedical literature, it is difficult for researchers to manually obtain information of interest, e.g. unknown information of threatening human disease or some biological processes. Therefore, researchers are interested in automatically acquiring information of biomolecular-level events. However, the annotated biomolecular-level event corpus is limited and highly imbalanced, which affects the performance of the classification algorithms and can even lead to

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Barry, Ciarrah, Junxi Liu, Rebecca Richmond, et al. "Exploiting collider bias to apply two-sample summary data Mendelian randomization methods to one-sample individual level data." PLOS Genetics 17, no. 8 (2021): e1009703. http://dx.doi.org/10.1371/journal.pgen.1009703.

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Over the last decade the availability of SNP-trait associations from genome-wide association studies has led to an array of methods for performing Mendelian randomization studies using only summary statistics. A common feature of these methods, besides their intuitive simplicity, is the ability to combine data from several sources, incorporate multiple variants and account for biases due to weak instruments and pleiotropy. With the advent of large and accessible fully-genotyped cohorts such as UK Biobank, there is now increasing interest in understanding how best to apply these well developed

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Asykarulloh, Azam, and Hasan Sultoni. "Covid-19 dan The Stock Reaction Of Islamic Banking In Asia." Musyarakah: Journal of Sharia Economic (MJSE) 4, no. 2 (2024): 111–32. https://doi.org/10.24269/mjse.v4i2.9373.

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The number of COVID-19 cases that hit Asia also put pressure on the economy and turmoil in several stock markets. This study aims to see the reaction of Islamic banking stocks in Asia to the announcement of COVID-19 as a global pandemic by WHO. This research uses an event study method with an observation period of 10 days after and before the event day. Data were obtained through the stock trading website, and 43 banking samples from 11 countries were observed. Data analysis used One-Sample T-Test, Paired Sample T-Test, One Sample Wilcoxon Signed Rank Test, and Paired Sample Wilcoxon Signed Ra

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Li, Lin, and Libin Jia. "Complex Event Information Mining and Processing for Massive Aerospace Big Data." Scalable Computing: Practice and Experience 25, no. 4 (2024): 2540–47. http://dx.doi.org/10.12694/scpe.v25i4.2832.

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This paper intends to analyze the existing problems of remote sensing data from the perspectives of space remote sensing information data capacity and data types. Then, a framework for rapidly analyzing and processing space remote sensing information is constructed. Then, LSTM is used to realize the fault diagnosis of remote sensing data continuity, discrete sample mixing and strong correlation of sample variation. LSTM conducts a multimodal analysis of remote-control commands, which is applied to modeling. The multi-stage LSTM prediction model is established and integrated efficiently to impr

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Ghazizadeh, Ali, Howard L. Fields, and Frederic Ambroggi. "Isolating Event-related Neuronal Responses by Deconvolution." Journal of Neurophysiology 104, no. 3 (2010): 1790–802. http://dx.doi.org/10.1152/jn.00389.2010.

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Although many studies in neuroscience are based on comparing neuronal responses to single, isolated sensory or motor events, multiple events frequently occur in close temporal proximity in freely moving animals. This often obscures the precise temporal correlation between each event and the relevant brain activity. By simulating neuronal responses in multi-event tasks, we show that perievent time histograms (PETHs) greatly distort the underlying true responses. We propose a multi-event deconvolution method that can separate the contribution of each event to the overall neuronal activity. The i

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Wiegand, Boris, Dietrich Klakow, and Jilles Vreeken. "Discovering Interpretable Data-to-Sequence Generators." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4237–44. http://dx.doi.org/10.1609/aaai.v36i4.20343.

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We study the problem of predicting an event sequence given some meta data. In particular, we are interested in learning easily interpretable models that can accurately generate a sequence based on an attribute vector. To this end, we propose to learn a sparse event-flow graph over the training sequences, and statistically robust rules that use meta data to determine which paths to follow. We formalize the problem in terms of the Minimum Description Length (MDL) principle, by which we identify the best model as the one that compresses the data best. As the resulting optimization problem is NP-h

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Krosman, Kazimierz, and Janusz Sosnowski. "Correlating Time Series Signals and Event Logs in Embedded Systems." Sensors 21, no. 21 (2021): 7128. http://dx.doi.org/10.3390/s21217128.

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In many embedded systems, we face the problem of correlating signals characterising device operation (e.g., performance parameters, anomalies) with events describing internal device activities. This leads to the investigation of two types of data: time series, representing signal periodic samples in a background of noise, and sporadic event logs. The correlation process must take into account clock inconsistencies between the data acquisition and monitored devices, which provide time series signals and event logs, respectively. The idea of the presented solution is to classify event logs based

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Lewis, R. M., B. Grundy, and L. A. Kuehn. "Predicting population gene frequency from sample data." Animal Science 78, no. 1 (2004): 03–11. http://dx.doi.org/10.1017/s1357729800053789.

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AbstractWith an increase in the number of candidate genes for important traits in livestock, effective strategies for incorporating such genes into selection programmes are increasingly important. Those strategies in part depend on the frequency of a favoured allele in a population. Since comprehensive genotyping of a population is seldom possible, we investigate the consequences of sampling strategies on the reliability of the gene frequency estimate for a bi-allelic locus. Even within a subpopulation or line, often only a proportion of individuals will be genotype tested. However, through se

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Ariani, Regita, and Aloysius Hari Kristianto. "Analisis Volatilitas Harga Saham dan Return Abnormal: Perbandingan Sebelum dan Sesudah Pemilu." MARGIN ECO 8, no. 1 (2024): 99–110. https://doi.org/10.32764/margin.v8i1.4514.

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This research aims to examine events from one round of elections that occurred by analyzing the average of abnormal returns and stock price volatility before or after the event. In this research, researchers used the event study method. The period used in this research is 14 trading days, each of which is 7 days before and 7 days after the event occurs. The focus of this research is on the IHSG on the Stock Exchange market. Data were analyzed using descriptive statistical techniques and statistical difference tests. Meanwhile, hypothesis testing uses the one sample t test and the pared sample

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Ermilova, A., E. Kovtun, D. Berestnev, and A. Zaytsev. "Hiding Backdoors within Event Sequence Data via Poisoning Attacks." Doklady Mathematics 110, S1 (2024): S288—S298. https://doi.org/10.1134/s1064562424602221.

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Abstract Deep learning’s emerging role in the financial sector’s decision-making introduces risks of adversarial attacks. A specific threat is a poisoning attack that modifies the training sample to develop a backdoor that persists during model usage. However, data cleaning procedures and routine model checks are easy-to-implement actions that prevent the usage of poisoning attacks. The problem is even more challenging for event sequence models, for which it is hard to design an attack due to the discrete nature of the data. We start with a general investigation of the possibility of poisoning

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Lepoint, Gilles, André Heughebaert, and Loïc N. Michel. "Epiphytic bryozoans on Neptune grass – a sample-based data set." ZooKeys 606 (July 21, 2016): 1–10. https://doi.org/10.3897/zookeys.606.8238.

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Background The seagrass Posidonia oceanica L. Delile, commonly known as Neptune grass, is an endemic species of the Mediterranean Sea. It hosts a distinctive and diverse epiphytic community, dominated by various macroalgal and animal organisms. Mediterranean bryozoans have been extensively studied but quantitative data assessing temporal and spatial variability have rarely been documented. In Lepoint et al. (2014a, b) occurrence and abundance data of epiphytic bryozoan communities on leaves of P. oceanica inhabiting Revellata Bay (Corsica, Mediterranean Sea) were reported and trophic ecology o

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Masoomzadeh, Hassan, Penny J. Barton, and Satish C. Singh. "Nonstretch moveout correction of long-offset multichannel seismic data for subbasalt imaging: Example from the North Atlantic." GEOPHYSICS 75, no. 4 (2010): R83—R91. http://dx.doi.org/10.1190/1.3443579.

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We have developed a pragmatic new processing strategy to enhance seismic information obtained from long-offset multichannel seismic data. The conventional processing approach, which treats data on a sample-by-sample basis, is applied at a coarser scale on groups of samples. Using this approach, a reflected event and its vicinity remain unstretched during the normal moveout correction. Isomoveout curves (lines of equal moveout) in the time-velocity panel are employed to apply a constant moveout correction to selected individual events, leading to a nonstretch stack. A zigzag stacking-velocity f

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Blau, Gary, and Glen Miller. "Comparing Two Distinct Miliary Samples on Traumatic Events, Positive Coping Styles and Post Traumatic Growth." Journal of Education and Learning 11, no. 1 (2021): 1. http://dx.doi.org/10.5539/jel.v11n1p1.

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This study collected complete data traumatic event-related information, positive coping styles, and post traumatic growth variables from two different United States (US) military veteran samples: non-combat military veterans (n = 54) and combat military veterans (n = 84). Although both samples represent military veterans, only one sample experienced actual combat, i.e., active fighting in a war against an enemy. All data were collected via online survey. Demographically, both samples were predominately White male, with a four-year college degree being the highest education level frequency. The

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Dean, Cameron. "Heavy flavor machine learning algorithms for fast data processing in sPHENIX." EPJ Web of Conferences 296 (2024): 09006. http://dx.doi.org/10.1051/epjconf/202429609006.

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The sPHENIX experiment at RHIC utilizes the first new heavy ion detector since the switch on of the LHC experiments. It’s optimized for precision jet and heavy flavor physics measurements, and recorded its first collisions in spring 2023. sPHENIX uses a tracking system with streaming readout and barrel calorimetry to reconstruct the collision topology. Event plane detectors, minimum bias detectors and zero-degree calorimeters are used to characterize the event. The streaming readout detectors are capable of recording 10% of the minimum bias rate, in addition to triggered events, in p+p collisi

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Ridho Ramadhan and Sa’diyah El Adawiyah. "Pengaruh Special Event Jakarta Sneakers Day Terhadap Citra PT. Infia Pariwara Nusantara." Jurnal Kajian dan Penelitian Umum 2, no. 1 (2024): 250–64. http://dx.doi.org/10.47861/jkpu-nalanda.v2i1.898.

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Events are one of the public relations activities that can be used to promote a company. In Public Relations studies, events are called special events. An event organizer is a type of business in the field of entertainment. An event organizer is tasked with organizing and designing the course of an event. PT Infia Pariwara Nusantara is a company that specializes in event activities. PT Infia Pariwara Nusantara creates its own events such as Jakarta Sneakers Day, Melodi Alam, and Fantoystic World, and also provides event, campaign and production activity services for brands. The Jakarta Sneaker

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Rumyantseva, Ekaterina, and Kirill Furmanov. "Using out-of-sample Cox–Snell residuals in time-to-event forecasting." Business Informatics 15, no. 1 (2021): 7–18. http://dx.doi.org/10.17323/2587-814x.2021.1.7.18.

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The problem of assessing out-of-sample forecasting performance of event-history models is considered. Time-to-event data are usually incomplete because the event of interest can happen outside the period of observation or not happen at all. In this case, only the shortest possible time is observed and the data are right censored. Traditional accuracy measures like mean absolute or mean squared error cannot be applied directly to censored data, because forecasting errors also remain unobserved. Instead of mean error measures, researchers use rank correlation coefficients: concordance indices by

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Sidorova, Diana N., and Evgeniy N. Pivkin. "Algorithms and methods of data clustering in the analysis of information security event logs." Digital technology security, no. 1 (March 30, 2022): 41–60. http://dx.doi.org/10.17212/2782-2230-2022-1-41-60.

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Security event log files give an idea of the state of the information system and allow you to find anomalies in user behavior and cybersecurity incidents. The existing event logs (application, system, security event logs) and their division into certain types are considered. But automated analysis of security event log data is difficult because it contains a large amount of unstructured data that has been collected from various sources. Therefore, this article presents and describes the problem of analyzing information security event logs. And to solve this problem, new and not particularly st

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Chen, Wei, Qing Li, Jian Bin He, and Tao Jin. "A New Method of Complex Maneuvering Event Detection." Applied Mechanics and Materials 63-64 (June 2011): 313–17. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.313.

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In dealing with such problems as imprecision and poor real-time performance in complex maneuverable events detection. A method based on RFNN (Rough-Fuzzy Neural Network) is proposed. Firstly, the minimal rule sets from data samples are acquired by using the Rough Set Theory; secondly, these rules are used to construct the initial scalar values of neural cells in each layer and their relative parameters in the fuzzy neural network; lastly, parameters of the network are acquired by using BP(back propagation) algorithm. The experimental results indicate that RFNN take advantage of the sample data

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Alromema, Nashwan, Mohd Shafry Rahim, and Ibrahim Albidewi. "Performance Evaluation of Attribute and Tuple Timestamping In Temporal Relational Database." International Journal of Computer Science and Information Security (IJCSIS) 14, no. 09 (2016): 374–83. https://doi.org/10.5281/zenodo.806010.

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Modeling temporal database over relational database using 1NF model is considered the most popular approach. This is because of the easy implementation as well as the modeling and querying power of 1NF model. In this paper, we compare a new approach for representing valid-time temporal database (in terms of structure and performance) to the main models in literature with attribute and tuple timestamping. The measurement of the performance is represented by the processing time to get the required temporal data as well as the size of the whole stored temporal data. A test has been performed by r

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Apriyanto, Jidan, Sri Mulyantini, and Nurmatias Nurmatias. "REAKSI PASAR MODAL INDONESIA TERHADAP PEMILIHAN PRESIDEN AMERIKA SERIKAT TAHUN 2020." JIMFE (Jurnal Ilmiah Manajemen Fakultas Ekonomi) 7, no. 2 (2021): 189–202. http://dx.doi.org/10.34203/jimfe.v7i2.3714.

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ABSTRAKTujuan penelitian ini adalah untuk mengetahui reaksi pasar modal Indonesia ketika terjadi peristiwa politik di Internasional. Perusahaan LQ45 periode Agustus 2020–Januari 2021 dipilih sebagai populasi dan sampel dengan menggunakan metode sample jenuh. Teknik single index model digunakan untuk mencari abnormal return. Periode jendela peristiwa lima belas hari, dan tiga puluh hari periode estimasi dengan teknik analisis data gabungan antara One Sample t-test, One Sample Wilcoxon Signed Rank Test, dan Paired Sample t-test. Hasil analisis menunjukan tidak terdapat imbal hasil tak normal yan

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Raden Ayu Trisnayoni, I Putu Astawa, and I Ketut Sutama. "Sustainable MICE Event Practices: An Explorative Study." International Journal of Travel, Hospitality and Events 1, no. 1 (2022): 74–82. http://dx.doi.org/10.56743/ijothe.v1i1.9.

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 ABSTRACT Purpose: This research aims to provide insight into sustainable event practices implemented by Melali MICE as one of the largest event organizers in Bali, Indonesia. Research methods: The research uses a qualitative approach. The sampling used is purposive. The informant selected as a sample is considered to know the sustainability and organizing of the event. Data collection using in-depth interview methods and participatory observations of the event at Melali MICE. Results and discussion: The cost is high to meet the needs of a green environment and to meet

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Sudarsi, Sudarsi, and Nining Yuniati. "Determinants of decision in event participation perception of event organizer." Jurnal Pariwisata Pesona 7, no. 1 (2022): 132–38. http://dx.doi.org/10.26905/jpp.v7i1.5832.

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The study is aimed to find out the variable effects of accessibility, organizer, accommodation, venue, and price through event participation decisions. The research sample of the population was the event organizer in Yogyakarta and Central Java, however, the data was conducted through a random sampling technique by 100 respondents. There are five variables tested that are thought to have an influence on participants' decisions in participating in the event, namely accessibility (X1), organizer (X2), accommodation (X3), venue (X4), and price perceived (X5). By classical assumption test, it was

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Peng, Yansong, Yueyi Zhang, Peilin Xiao, Xiaoyan Sun, and Feng Wu. "Better and Faster: Adaptive Event Conversion for Event-Based Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2056–64. http://dx.doi.org/10.1609/aaai.v37i2.25298.

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Event cameras are a kind of bio-inspired imaging sensor, which asynchronously collect sparse event streams with many advantages. In this paper, we focus on building better and faster event-based object detectors. To this end, we first propose a computationally efficient event representation Hyper Histogram, which adequately preserves both the polarity and temporal information of events. Then we devise an Adaptive Event Conversion module, which converts events into Hyper Histograms according to event density via an adaptive queue. Moreover, we introduce a novel event-based augmentation method S

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