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All Technical Committee Conferences (Searched in: Recent 10 Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP, MSS | 2025-03-13 16:45 |
Okinawa | Miyakojima City Central Community Center | Time series segmentation using recurrence plot and particle swarm optimization methods Ayaka Nakazawa, Fuya Kumagai, Takatoshi Inaba, Takafumi Matsuura (NIT), Yutaka Shimada (Saitama Univ.), Takayuki Kimura (NIT)  MSS2024-85 NLP2024-126 |
Recently, methods for identifying and segmenting significant patterns in large-scale data have gained attention. One suc... [more] | MSS2024-85 NLP2024-126 pp.90-95 |
| NLP, MSS | 2025-03-14 15:35 |
Okinawa | Miyakojima City Central Community Center | A basis transformation of time series signals using classical multi-dimensional scaling and its application to network inference Mayu Ohira, Yutaka Shimada (Saitama Univ.) MSS2024-107 NLP2024-148 |
Real-world systems can be described as coupled dynamical systems, which sometimes exhibit complex behavior due to the i... [more] |
MSS2024-107 NLP2024-148 pp.203-207 |
| 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 |
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