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Finding Periodic Discrete Events in Noisy Streams
2017
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM '17
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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]
2020
arXiv
pre-print
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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
2013
Mathematical Problems in Engineering
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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
2007
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07
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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]
2020
arXiv
pre-print
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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
2016
2016 IEEE 3rd World Forum on Internet of Things (WF-IoT)
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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
2013
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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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]
2022
arXiv
pre-print
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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]
2001
arXiv
pre-print
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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
2012
Hydrology and Earth System Sciences Discussions
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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]
2012
Lecture Notes in Computer Science
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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]
2021
bioRxiv
pre-print
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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
2020
Neural Computation
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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]
2010
Lecture Notes in Computer Science
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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
2007
Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07
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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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