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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 46  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NLP, MSS 2023-03-16
10:00
Nagasaki
(Primary: On-site, Secondary: Online)
Detecting causality for marked point processes
Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) MSS2022-80 NLP2022-125
In this report, by modifying conventional causality detection method for nonlinear dynamical systems, we propose a causa... [more] MSS2022-80 NLP2022-125
pp.89-94
NLP, MSS 2023-03-17
13:50
Nagasaki
(Primary: On-site, Secondary: Online)
An analysis on backbone structure of word co-occurrence networks
Shoudai Tazawa, Yutaka Shimada (Saitama Univ.) MSS2022-102 NLP2022-147
Complex networks observed in the real world contain strongly connected vertex pairs. A set of such vertex pairs and thei... [more] MSS2022-102 NLP2022-147
pp.186-191
IN, CCS
(Joint)
2022-08-05
10:30
Hokkaido Hokkaido University(Centennial Hall)
(Primary: On-site, Secondary: Online)
Detecting causality for spike trains based on reconstructing dynamical system from inter-spike intervals
Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) CCS2022-36
In this report, by modifying a nonlinear method of detecting causality, we propose a method of detecting causality for p... [more] CCS2022-36
pp.48-53
NLP, MICT, MBE, NC
(Joint) [detail]
2022-01-22
16:25
Online Online Reconstructing dynamical system from marked point processes and its application to real world data
Kazuya Sawada, Nina Sviridova (TUS), Yutaka Shimada (Saitama Univ.), Tour Ikeguchi (TUS) NLP2021-116 MICT2021-91 MBE2021-77
In this report, we applied the method of reconstructing dynamical system for the marked point process
to the human pho... [more]
NLP2021-116 MICT2021-91 MBE2021-77
pp.209-212
NLP 2021-12-18
14:15
Oita J:COM Horuto Hall OITA A study on estimating appropriate parameters of state space reconstruction
Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2021-64
In this report, we investigated appropriate parameter values for reconstructing a dynamical system using delay-coordinat... [more] NLP2021-64
pp.96-99
NC, NLP
(Joint)
2021-01-21
10:05
Online Online Extraction of Property for Nonlinear Time Series by Changing Density of Recurrence Plots
Shiki Kanamaru, Nina Sviridova (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2020-41
A recurrence plot is one of the most effective nonlinear time series analysis methods for qualitatively understanding a ... [more] NLP2020-41
pp.7-12
NLP 2020-09-10
10:30
Online Online On Investigation of Invariant Characteristics of Deterministic Nonlinear Dynamical Systems and Marked Point Processes
Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2020-22
In recent years, various kinds of time series data have been observed. One of the kinds is a marked point process. For e... [more] NLP2020-22
pp.1-6
CCS, NLP 2020-06-05
13:25
Online Online Analysis of influence of network structure on information diffusion
Luyan XU, Kazuya Sawada (TUS), Yutaka Shimada (SU), Tohru Ikeguchi (TUS) NLP2020-15 CCS2020-5
In this paper, we propose an information diffusion model with eigenvector centrality. Using the proposed model, we inves... [more] NLP2020-15 CCS2020-5
pp.23-26
CCS, NLP 2020-06-05
13:50
Online Online Analysis of Language Network for New Testament
Kihei Magishi, Tomoko Matsumoto (TUS), Yutaka Shimada (SU), Tohru Ikeguchi (TUS) NLP2020-16 CCS2020-6
 [more] NLP2020-16 CCS2020-6
pp.27-32
MSS, NLP
(Joint)
2020-03-10
09:55
Aichi  
(Cancelled but technical report was issued)
A method foe estimating gene regulatory networks based on multiple source of information about regulatory relationships
Haga Cham, Yutaka Shimada, Takaomi Shigehara (Saitama Univ.) NLP2019-124
Gene regulatory networks play an important role in the expression and activities of genes, where specific proteins and g... [more] NLP2019-124
pp.65-70
MSS, NLP
(Joint)
2020-03-10
10:20
Aichi  
(Cancelled but technical report was issued)
Detecting unobserved nodes in networks of dynamical systems estimated only from time series data.
Yuuki Sakakibara, Yutaka Shimada, Takaomi Shigehara (Saitama Univ.) NLP2019-125
Real complex phenomena can be described as coupled dynamical systems, where many dynamical systems are coupled with each... [more] NLP2019-125
pp.71-76
MSS, NLP
(Joint)
2020-03-10
10:45
Aichi  
(Cancelled but technical report was issued)
Analysis of language network for Japanese and English translations of the New Testament
Kihei Magishi, Tomoko Matsumoto (TUS), Yutaka Shimada (SU), Tohru Ikeguchi (TUS) NLP2019-126
This paper investigated characteristics of Japanese and English language networks.
We used the New Testament as a sampl... [more]
NLP2019-126
pp.77-82
MSS, NLP
(Joint)
2020-03-10
11:10
Aichi  
(Cancelled but technical report was issued)
Information Diffusion Model with Network Centrality and Community Structure
Luyan Xu, Kazuya Sawada (TUS), Yutaka Shimada (SU), Tohru Ikeguchi (TUS) NLP2019-127
In this report, we proposed two new information diffusion models based on a conventional information diffusion model. Th... [more] NLP2019-127
pp.83-88
MSS, NLP
(Joint)
2020-03-10
13:30
Aichi  
(Cancelled but technical report was issued)
Influence of Resolution of Time Series Data on Causality Detection
Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2019-128
In this report, we investigated the influence of resolution of time series data
on causality detection by Convergent C... [more]
NLP2019-128
pp.89-94
NLP 2019-05-10
13:50
Oita J:COM HoltoHALL OITA Structure estimation of a neural network using Inter-spike-interval
Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Ikeguchi Tohru (TUS) NLP2019-3
In this paper, we apply the causal estimation method of Convergent Cross Mapping to a mathematical model of neural netwo... [more] NLP2019-3
pp.13-18
NLP 2019-05-10
14:55
Oita J:COM HoltoHALL OITA Feature extraction of nonlinear time series signal by threshold variation of recurrence plot
Shiki Kanamaru (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2019-5
In this report, we propose a feature extraction method of nonlinear time series by threshold variation of the recurrence... [more] NLP2019-5
pp.23-28
NLP 2019-05-10
16:50
Oita J:COM HoltoHALL OITA Visibility Graph for marked point process and its application to analyzing structural features of musical composition
Fujia Mao (TUS), Yutaka Shimada (SU), Tohru Ikeguchi (TUS) NLP2019-9
Visibility Graph (VG) is a method of time series analysis.By using VG, time series data can be tansformed to a network t... [more] NLP2019-9
pp.47-52
NLP, NC
(Joint)
2019-01-23
11:00
Hokkaido The Centennial Hall, Hokkaido Univ. Performance Evaluation of Chaotic Random Numbers Using Integer Logistic Map by NIST Test
Shiki Kanamaru (TUS), Yutaka Shimada (Saitama Univ.), Kantaro Fujiwara (UT), Tohru Ikeguchi (TUS) NLP2018-100
In this report, we generated pseudorandom numbers using chaotic dynamics and investigated its performance as pseudorando... [more] NLP2018-100
pp.23-28
NLP, NC
(Joint)
2019-01-23
11:20
Hokkaido The Centennial Hall, Hokkaido Univ. An analysis on chaotic marked point process using constrained random shuffled surrogate data
Kohei Yamamoto (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2018-101
Marked point process data refer to a time series of discrete events
with additional information.
For example, se... [more]
NLP2018-101
pp.29-34
NLP, NC
(Joint)
2019-01-23
11:40
Hokkaido The Centennial Hall, Hokkaido Univ. Performance analysis of a modified Chaos MIMO system with efficient use of bit information
Ryo Yamazaki (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) NLP2018-102
The multiple-input multiple-output (MIMO) is one of the wireless communication methods that use multiple transmit antenn... [more] NLP2018-102
pp.35-40
 Results 1 - 20 of 46  /  [Next]  
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