2,751,959 Hits in 5.0 sec

Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events [article]

Sharaj Panwar, Paul Rad, Tzyy-Ping Jung, Yufei Huang
2020 arXiv   pre-print
We further extended this network to a class-conditioned variant that also includes a classification branch to perform event-related classification.  ...  We propose a novel Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to synthesize EEG data.  ...  Based on this novel WGAN model, we also proposed a classconditioned WGAN model that performs event classification in addition to sample generation.  ... 
arXiv:1911.04379v2 fatcat:kyn7pr7nbvgyhl7a2kvouknb7y

Generative models of simultaneously heavy-tailed distributions of inter-event times on nodes and edges [article]

E. Fonseca dos Reis, A. Li, N. Masuda
2020 arXiv   pre-print
In the present study, we propose a generative model and its variants to explain this phenomenon.  ...  The model produces distributions of inter-event times for both individual nodes and edges that resemble heavy-tailed distributions across some scales.  ...  (b) Sequence of events generated by a power-law distribution of IET on each edge of the star network and the sequence of events on node 1 in (a).  ... 
arXiv:2008.13034v1 fatcat:ld7onyxq3bhwphhvqkfvewu6pi

Predicting event response in a nested catchment with generalized linear models and a distributed watershed model

T. Graeff, E. Zehe, T. Blume, T. Francke, B. Schröder
2012 Hydrological Processes  
coefficient with the general linear model.  ...  estimates of a distributed conceptual model, (iii) the comparison of the dynamics of observed soil moisture and simulated saturation deficit of the hydrological model and (iv) the analysis of the relationship  ...  As a result, the sample size of events is too small and the distribution too skewed to allow a split sampling for model validation.  ... 
doi:10.1002/hyp.8463 fatcat:bb4itgzxafczljmvx6k4reg6xm

Using climate change models to assess the probability of weather extremes events: a local scale study based on the generalized extreme value distribution

Mariana Fontolan, Ana Carolina Freitas Xavier, Heloisa Ramos Pereira, Gabriel Constantino Blain
2019 Bragantia  
Within a control run , correction factors based on the GEV parameters have been proposed to approach the distributions generated from the models to those built from the weather station of Campinas.  ...  This study used the generalized extreme value distribution (GEV) to evaluate the ability of two nested models (Eta-HadGEM2-ES and Eta-MIROC5) to assess the probability of daily extremes of air temperature  ...  Generalized Extreme Value distribution and goodness-of-fit tests The GEV is a parametric distribution in which the cumulative probability of a particular event x is given by its three parameters: location  ... 
doi:10.1590/1678-4499.2018144 fatcat:qwjjwx7dvnau3pb4qd2bj4ncui

Open Event Extraction from Online Text using a Generative Adversarial Network

Rui Wang, Deyu ZHOU, Yulan He
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
AEM models an event with a Dirichlet prior and uses a generator network to capture the patterns underlying latent events.  ...  However, these approaches typically assume that all words in a document are generated from a single event.  ...  It treats an event as a latent variable and models the generation of an event as a joint distribution of its individual event elements.  ... 
doi:10.18653/v1/d19-1027 dblp:conf/emnlp/WangZH19 fatcat:amds5zskpbfubavmml6evfgy3m

Open Event Extraction from Online Text using a Generative Adversarial Network [article]

Rui Wang and Deyu Zhou and Yulan He
2019 arXiv   pre-print
AEM models an event with a Dirichlet prior and uses a generator network to capture the patterns underlying latent events.  ...  However, these approaches typically assume that all words in a document are generated from a single event.  ...  It treats an event as a latent variable and models the generation of an event as a joint distribution of its individual event elements.  ... 
arXiv:1908.09246v1 fatcat:gn6g6pmarvgprmhjt27alekqgq

The Angantyr Model for Heavy-Ion Physics in PYTHIA8 †‡

Harsh Shah
2019 Proceedings (MDPI)  
We have formulated a new model for collisions with nuclei, called Angantyr, which is now included in PYTHIA8 event generator.  ...  In this manner we are providing an event generator to be used to simulate events from pp to AA with the same underlying physics approach.  ...  Figure 1 . 1 b) Centrality-dependent η distribution, pPb, √ s NN = 5 TeV. (a) ∑ E Pb ⊥ distribution of generated pPb events at √ s NN = 5 TeV.  ... 
doi:10.3390/proceedings2019010018 fatcat:kiz7tjitfnaz7mlqscvjaxhwhi

Modelling of Hydrological Drought Events in the Upper Tana Basin of Kenya

Jones F. Agwata, Wellington N. Wamicha, Christopher M. Ondieki
2014 IOSR Journal of Mechanical and Civil Engineering  
The frequency distributions fitted to the two drought events The distributions of best fit for the drought events were identified using the Z value obtained from the average L-moment statistics of a particular  ...  Results showed that the frequency distribution of best fit for duration and severity was the Generalized Normal while the Pearson Type III distribution was the distribution of worst fit for both duration  ...  distribution models to hydrological drought events.  ... 
doi:10.9790/1684-11134148 fatcat:f46z6zzoxjhrnbgw4bmuqktof4

Exploring theory space with Monte Carlo reweighting

James S. Gainer, Joseph Lykken, Konstantin T. Matchev, Stephen Mrenna, Myeonghun Park
2014 Journal of High Energy Physics  
We describe how the reweighting of events may allow this challenge to be met, as fully simulated Monte Carlo samples generated for arbitrary benchmark models can be effectively re-used.  ...  This makes experimental attempts to constrain the parameter space of motivated new physics models with a high degree of generality quite challenging.  ...  This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s)  ... 
doi:10.1007/jhep10(2014)078 fatcat:wwlupylya5brtbh6kof5hpswzi

GEAM: A General and Event-Related Aspects Model for Twitter Event Detection [chapter]

Yue You, Guangyan Huang, Jian Cao, Enhong Chen, Jing He, Yanchun Zhang, Liang Hu
2013 Lecture Notes in Computer Science  
In this paper, we propose a General and Eventrelated Aspects Model (GEAM), a new topic model for event detection from Twitter that associates General topics and Event-related Aspects with events.  ...  We then introduce a collapsed Gibbs sampling algorithm to estimate the word distributions of General topics and Event-related Aspects in GEAM.  ...  GEAM models each tweet as a mixture of Event-related Aspects and General topics, then generates each word from the Event-related Aspects or General topics word distribution.  ... 
doi:10.1007/978-3-642-41154-0_24 fatcat:o2brfzugafb65kuu3wr7qtb4ra

Automatic Generation of DistAlgo Programs from Event-B Models [chapter]

Alexis Grall
2020 Lecture Notes in Computer Science  
Our objective is to develop an automatic transformation of distributed algorithm Event-B [2] models into DistAlgo [7] programs.  ...  The Event-B language combines refinement techniques and state based modelling and is adapted to the verification of distributed systems [3, 12] .  ...  Related Works Code generation from Event-B models has been a subject of interest in the B community.  ... 
doi:10.1007/978-3-030-48077-6_34 fatcat:a4mbawsb6vevzasaf4echw4izm

An approach for the determination of precipitation input for worst-case flood modelling

Guido Felder, Rolf Weingartner
2016 Hydrological Sciences Journal  
A Monte Carlo approach allows for the generation of a wide range of different spatio-temporal distributions of an extreme precipitation event that can be tested with a rainfall-runoff model that generates  ...  a hydrograph for each of these distributions.  ...  Any one of the generated spatiotemporal distributions is used to iteratively force a simple model, as presented in the model design section.  ... 
doi:10.1080/02626667.2016.1151980 fatcat:f6qaskr77zcoxdzw4cbrqllyry

A new generalization of Weibull distribution with application to a breast cancer data set

Abdus S. Wahed, The Minh Luong, Jong-Hyeon Jeong
2009 Statistics in Medicine  
In this article, we propose a new generalization of the Weibull distribution, which incorporates the exponentiated Weibull distribution introduced by Mudholkar and Srivastava [1] as a special case.  ...  In terms of statistical significance of the treatment effect and model adequacy, all generalized models lead to similar conclusions, suggesting that the beta-Weibull family is a reasonable candidate for  ...  This model can also be viewed as a generalization of the model of Mudholkar et al. [6] .  ... 
doi:10.1002/sim.3598 pmid:19424958 pmcid:PMC3057135 fatcat:g4evfw5abnhzbdqonfqzcf54iu

Stochastic generation of hourly rainstorm events

Shiang-Jen Wu, Yeou-Koung Tung, Jinn-Chuang Yang
2006 Stochastic environmental research and risk assessment (Print)  
This paper presents a Monte-Carlo based stochastic hourly rainfall generation model considering correlated non-normal random rainstorm characteristics, as well as dependence of various rainstorm patterns  ...  Occurrence of rainstorm events can be characterized by the number of events, storm duration, rainfall depth, inter-event time and temporal variation of rainfall within a rainstorm event.  ...  Modeling number of rainstorm events To generate rainstorm sequences over a period of several years, the distribution properties for annual number of rainstorm events must be specified in advance.  ... 
doi:10.1007/s00477-006-0056-3 fatcat:ivpyqg333vggphfi2llii63lne

Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation

John Blue, Braden Kronheim, Michelle Kuchera, Raghuram Ramanujan, S. Campana, G.A. Stewart, C. Biscarat, C.I. Rovelli, S. Roiser, B. Hegner
2021 EPJ Web of Conferences  
Our model takes only a fraction of the time necessary for conventional detector simulation methods, running on a CPU in less than a millisecond per event.  ...  We demonstrate that the model produces accurate conditional reconstructed jet transverse momentum (pT) distributions over a wide range of pT for the input parton jet.  ...  Nishita Dessai for their valuable suggestions on identifying the pre-hadronization parton-level event and Dr. Sezen Sekmen for numerous discussions about fast detector simulation.  ... 
doi:10.1051/epjconf/202125103055 fatcat:m3bftt4yt5g6zet2bgpyy4vcqi
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