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Finding Periodic Discrete Events in Noisy Streams

Abhirup Ghosh, Christopher Lucas, Rik Sarkar
2017 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17  
Periodic phenomena are ubiquitous, but detecting and predicting periodic events can be di cult in noisy environments.  ...  In experiments on real and simulated data we nd that it outperforms existing methods in accuracy and can track changes in periodicity and other characteristics in dynamic event streams.  ...  INTRODUCTION We will study discrete event streams that exhibit approximate periodicity. ese sequences contain noise events interleaved with the periodic events, together with variations in the times that  ... 
doi:10.1145/3132847.3132981 dblp:conf/cikm/GhoshLS17 fatcat:q7y7io3eajgehc7fcto7misk5q

Finding the maximum-a-posteriori behaviour of agents in an agent-based model [article]

Daniel Tang
2020 arXiv   pre-print
In this paper we consider the problem of finding the most probable set of events that could have led to a set of partial, noisy observations of some dynamical system.  ...  In particular, we consider the case of a dynamical system that is a (possibly stochastic) time-stepping agent-based model with a discrete state space, the (possibly noisy) observations are the number of  ...  In a continuous-time model agent behaviour consists of discrete events that can happen at any time expressed as a real number. In this paper we will consider discrete-time models.  ... 
arXiv:2005.02096v1 fatcat:2oq7l3mdlrgmbftul4u6fuq4la

A Novel System Anomaly Prediction System Based on Belief Markov Model and Ensemble Classification

Xiaozhen Zhou, Shanping Li, Zhen Ye
2013 Mathematical Problems in Engineering  
Evidential Markov chain method is able to deal with noisy data but is not suitable in complex data stream scenario.  ...  The Belief Markov chain that we propose has extended Evidential Markov chain and can cope with noisy data stream.  ...  Evidential Markov model [13] has made big improvement by being capable of coping with noisy data. Following is an example of explicit boundary problem in discrete-time Markov chain.  ... 
doi:10.1155/2013/179390 fatcat:jmkuivjqwvb6lhu54mmsj2ciyy

Mining correlated bursty topic patterns from coordinated text streams

Xuanhui Wang, ChengXiang Zhai, Xiao Hu, Richard Sproat
2007 Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07  
streams in the same period.  ...  For example, when a major event happens, all the news articles published by different agencies in different languages tend to cover the same event for a certain period, exhibiting a correlated bursty topic  ...  In particular, the discovery of topics in one stream should pay attention to which period other streams suggest to be promising for finding a correlated bursty topic pattern.  ... 
doi:10.1145/1281192.1281276 dblp:conf/kdd/WangZHS07 fatcat:gvcou6rofveu3miryrnsjnjlsq

Minimal spiking neuron for solving multi-label classification tasks [article]

Jakub Fil, Dominique Chu
2020 arXiv   pre-print
We find that over a wide range of parameters the GNM can learn at least as well as the MST.  ...  In addition to randomly generated but fixed patterns, we expose the neuron to a stream of noisy background activity.  ...  Following common practice in SNN, we assume that the GNM is updated in discrete time. However, we also find that a continuous time version of the GNM can classify well.  ... 
arXiv:2003.02902v1 fatcat:scl6qcze3rh55igo6whopgqg3i

Monitoring data stream reliability in smart city environments

Daniel Kuemper, Thorben Iggena, Marten Fischer, Ralf Toenjes, Elke Pulvermueller
2016 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT)  
Reliable information processing is an indispensable task in Smart City environments.  ...  Like in this work they use similar data sources in order to find "witnesses" for a given sensor reading.  ...  (Sum) Cultural-Events Event Location Periodic 1set/day Capacity Max. Visitors Agg.(Sum) TomTom Traffic Flow Road Segment Periodic 1/30min Current Speed km/h Agg.  ... 
doi:10.1109/wf-iot.2016.7845441 dblp:conf/wf-iot/KuemperIFTP16 fatcat:4tpx2p2tc5h4xpjc7m4loqsai4

Abnormality detection in noisy biosignals

Emine Merve Kaya, Mounya Elhilali
2013 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
In this work, we propose a mechanism to find instances of potential interest in time series for further analysis. Adaptive Kalman filters are employed in parallel among different feature axes.  ...  Lung sounds recorded in noisy conditions are used as an example application, with spectro-temporal feature extraction to capture the complex variabilities in sound.  ...  After finding the standard of the incoming sound feature within the initial buffer, a separate Kalman is allocated to each stream.  ... 
doi:10.1109/embc.2013.6610409 pmid:24110596 pmcid:PMC5983885 dblp:conf/embc/KayaE13 fatcat:6uvuuipsvbee3ggxmev6qiq22i

Dictionary Learning with Accumulator Neurons [article]

Gavin Parpart, Carlos Gonzalez, Terrence C. Stewart, Edward Kim, Jocelyn Rego, Andrew O'Brien, Steven Nesbit, Garrett T. Kenyon, Yijing Watkins
2022 arXiv   pre-print
video from event based DVS cameras.  ...  On a classification task that requires identification of the suite from a deck of cards being rapidly flipped through as viewed by a DVS camera, we find essentially no degradation in performance as the  ...  We gratefully acknowledge support from the Advanced Scientific Computing Research (ASCR) program office in the Department of Energy's (DOE) Office of Science, award no.77902.  ... 
arXiv:2205.15386v1 fatcat:issiovcjxvbnbkxomxatjonf7m

Quantum Fluctuations of Light: A Modern Perspective on Wave/Particle Duality [article]

H. J. Carmichael
2001 arXiv   pre-print
Photon antibunching advocating the discrete (particles), is contrasted with amplitude squeezing which speaks of the continuous (waves).  ...  We review studies of the fluctuations of light made accessible by the invention of the laser and the strong interactions realized in cavity QED experiments.  ...  It, however, leads us away from the stream of light particles and towards the view that light is indeed a noisy wave; not, on the other hand, exactly the wave BKS had in mind.  ... 
arXiv:quant-ph/0104073v1 fatcat:nwnpzgs7kvfl7mvkecugtdmire

Transient analysis of fluctuations of electrical conductivity as tracer in the streambed

C. Schmidt, A. Musolff, N. Trauth, M. Vieweg, J. H. Fleckenstein
2012 Hydrology and Earth System Sciences Discussions  
The water fluxes vary over time driven by short-term flood events or seasonal variations in stream flow and groundwater level.  ...  Variations of electrical conductivity (EC) are used as a natural tracer to detect transient travel times and flow velocities in an in-stream gravel bar.  ...  The mean EC amplitude in the stream during one period is 21 µS cm −1 . The EC signal in the stream bed lags the stream signal at both the USS and DSS location.  ... 
doi:10.5194/hessd-9-6345-2012 fatcat:hqhlcq4rnrg6xmjcwcuifrsjlm

Predicting Ramp Events with a Stream-Based HMM Framework [chapter]

Carlos Abreu Ferreira, João Gama, Vítor Santos Costa, Vladimiro Miranda, Audun Botterud
2012 Lecture Notes in Computer Science  
In this paper we introduce the SHREA framework, a stream-based model that continuously learns a discrete HMM model from wind power and wind speed measurements.  ...  To forecast ramp events we use recent wind speed forecasts and the Viterbi algorithm, that incrementally finds the most probable ramp event to occur.  ...  In this paper we present SHREA a novel stream-based framework that predicts ramping events in short term wind power forecasting.  ... 
doi:10.1007/978-3-642-33492-4_19 fatcat:tmmkl4swp5g3dabbi2aqyismcq

Diversify your workflow! - An inconvenient suggestion to analyze spike data from intracranial recordings [article]

Sukru Okkesim, Shavika Rastogi, Olaf Christ, Peter Hubka, Nicole Rosskothen-Kuhl, Ulrich Hofmann
2021 bioRxiv   pre-print
We suggest to increase reliability in findings by only accepting and further processing events accepted by more than one processing pipeline.  ...  pipelines already in its initial step.  ...  Spike detection is the process of detecting interesting events in the stream of raw and noisy signals, which may then in a following step be assigned as tentative action potentials related to actual neurons  ... 
doi:10.1101/2021.03.10.434718 fatcat:v3lhmwgbhjfs5eeofre2272jae

Minimal Spiking Neuron for Solving Multilabel Classification Tasks

Jakub Fil, Dominique Chu
2020 Neural Computation  
We find that over a wide range of parameters, the GNM can learn at least as well as the MST does.  ...  In addition to randomly generated but fixed patterns, we expose the neuron to a stream of noisy background activity.  ...  It is updated in discrete time.  ... 
doi:10.1162/neco_a_01290 pmid:32433898 fatcat:drx7ymi7k5h63io7wdzanlczby

Learning Automaton Based On-Line Discovery and Tracking of Spatio-temporal Event Patterns [chapter]

Anis Yazidi, Ole-Christoffer Granmo, Min Lin, Xifeng Wen, B. John Oommen, Martin Gerdes, Frank Reichert
2010 Lecture Notes in Computer Science  
Discovering and tracking of spatio-temporal patterns in noisy sequences of events is a difficult task that has become increasingly pertinent due to recent advances in ubiquitous computing, such as communitybased  ...  In this paper, we introduce a new scheme for discovering and tracking noisy spatio-temporal event patterns, with the purpose of suppressing reoccurring patterns, while discerning novel events.  ...  Rather, it is the learning scheme we propose 4 for determining whether a given spatio-temporal event pattern can be found in a stream of events, in an on-line manner, and under noisy conditions.  ... 
doi:10.1007/978-3-642-15246-7_31 fatcat:sw7kvcukivdjtdmpbdc5elmc3m

Analyzing feature trajectories for event detection

Qi He, Kuiyu Chang, Ee-Peng Lim
2007 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07  
algorithm to detect both aperiodic and periodic events.  ...  In this paper, we 1) first applied spectral analysis to categorize features for different event characteristics: important and less-reported, periodic and aperiodic; 2) modeled aperiodic features with  ...  We also designed an unsupervised greedy algorithm to detect both aperiodic and periodic events, which was successful in detecting real events as shown in the evaluation on a real news stream.  ... 
doi:10.1145/1277741.1277779 dblp:conf/sigir/HeCL07 fatcat:xupzzprtp5cgzbcuzitc3564fi
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