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Award type：Award from Japanese society, conference, symposium, etc. 

Award type：Award from international society, conference, symposium, etc. 

Award type：Award from Japanese society, conference, symposium, etc. 

Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

Award type：Award from Japanese society, conference, symposium, etc. 

Award type：Award from Japanese society, conference, symposium, etc. 

Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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Award type：Award from international society, conference, symposium, etc.  Country：Japan

Award type：Award from international society, conference, symposium, etc.  Country：Japan

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Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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Award type：Award from Japanese society, conference, symposium, etc.  Country：Japan

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>Integrating theoretical models into machine learning models holds immense potential for constructing efficient, robust, and interpretable models. Here, we propose a hybrid architecture that hierarchically integrates a biological pursuit model into deep reinforcement learning. This approach enables seamless acceleration-mode switching and geometrically reasonable action selection, demonstrating our hierarchical predator agents realized efficient navigation in a predator-prey environment. Interestingly, our results have commonalities with group hunting behaviors observed in nature, suggesting the potential application of our model as a tool for providing new insights into biology.</p>

DOI： 10.11517/pjsai.jsai2024.0_3i5os27b04

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>Ultimate is a sport where two teams of seven players compete to score points by passing a disc and catching it in the end zone. Unlike other sports, Ultimate has the unique characteristic that players holding the disc cannot move, emphasizing the importance of creating space to receive passes. While there is ample data and research on space in sports like soccer and basketball, such information is lacking for Ultimate. This study aims to focus on the widely adopted 3-vs-3 format in training by many teams and evaluate the space during offensive plays. The data used in this study was obtained through filming with a drone camera, and position data was acquired through angle correction and coordinate transformations. The model employed in this study is based on a soccer's pitch control model, which indicates the probability of controlling the ball at various points on the pitch. This model is adapted to suit the rules of Ultimate, where players cannot move while holding the disc. By combining weights that take into account position and distance with the values of the pitch control, space evaluation metrics are calculated, and the relationship between scoring and the space evaluation metric immediately before a pass was analyzed.</p>

DOI： 10.11517/pjsai.jsai2024.0_3xin221

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>Most data analysis in soccer requires tracking data of players and ball and event data, and currently well-funded professional leagues are collecting a lot of data manually through companies. Previous research has used machine learning models to perform specific event detection from professional broadcast video, but amateur video is difficult to be analyzed due to the existence of various recording conditions. The purpose of this study is to perform specific event detection from amateur full-pitch video and to examine the validity of front-line defensive evaluation based on the detection results. In this study, two methods were used to detect events from video: the first is a deep learning-based e2e-spot method that uses video frames as input and predictive labels as output; the second is a rule-based method that uses ball velocity from ball tracking data and detects the start of a pass based on changes in velocity. We performed the fine-tuning of e2e-spot on amateur full-pitch video, was able to detect events more accurately than the rule-based method. We examined how accurately the estimated events can be used to evaluate front line defenses in soccer.</p>

DOI： 10.11517/pjsai.jsai2024.0_3xin247

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>Recent advancements in large foundation models have spanned various domains, including natural language processing, autonomous driving, and multivariate time series forecasting. Meanwhile, applied sports analysis has become widespread, with a particular focus on constructing quantitative evaluation methods for players and teams through the modeling of match situations. Despite this, a soccer foundation model capable of performing various tasks within a single model remains unexplored. This study explores a potential soccer foundation model by applying a multivariate time series prediction architecture for forecasting soccer trajectory data. We propose using log data from soccer simulation leagues for training, taking into account 1) the small scale of real trajectory data and 2) the effectiveness of synthetic data in constructing foundation models as indicated by previous research. Furthermore, we evaluate the effectiveness of the embedding representations by qualitatively comparing their similarities with actual soccer trajectories, confirming their applicability in downstream tasks.</p>

DOI： 10.11517/pjsai.jsai2024.0_3i5os27b02

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>In a basketball game, the players compete against each other in a five-on-five match. In particular, it is important for players with different playstyles to cooperate and score efficiently during possessions, which take place many times in one game. In a previous study, the compatibility of players was examined using clustering results based on each player's statistics, called stats, but the findings obtained were considered to be limited due to the method of selecting features that included both offense and defense. This study focuses only on offense and aims to examine more specifically the impact of player combinations on scoring efficiency. In this study, two different methods are used to capture the playstyles of players on offense: one is a newly proposed method that clusters the tendency of shots based on the Wasserstein distance, the distance between distributions, which considers the set of shots of each player as a probability distribution using shooting features created from tracking data. The other is a method for clustering players' roles in the offense, which is a modification of the existing method. By creating and interpreting a machine learning model that predicts stats representing scoring efficiency from information on lineups based on these two clusterings, new insights into the compatibility of players were obtained.</p>

DOI： 10.11517/pjsai.jsai2024.0_1d4gs1004

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>When modeling real-world biological multi-agents with reinforcement learning, there is a domain gap between the source real-world data and the target reinforcement learning environment. Therefore, the target dynamics are adapted to the unknown source dynamics. In this study, we propose a reinforcement learning method that uses information obtained by adapting source action to target action in a supervised manner as a method for domain adaptation in multi-agent reinforcement learning from real-world demonstrations. In limited situations such as 2vs1 chase-escape, 2vs2 and 4vs8 in soccer, we show that the agent learned to imitate the demonstrations and obtain rewards compared to the baseline.</p>

DOI： 10.11517/pjsai.jsai2024.0_1e5gs504

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>In applied sports analytics, especially in goal-oriented sports such as soccer, the necessity to process continuous data involving multiple agents poses a significant challenge for comprehensive analysis. Existing metrics, which often rely on static and simple inputs such as the ball's coordinates and nearby statistical data, fail to account for the broader match context. Recently, there has been growing interest in Large Language Models (LLMs), recognized for their potential not only in natural language processing but also in reinforcement learning and multi-agent trajectory forecasting. However, their application in soccer analytics is still in its infancy. This study introduces an innovative approach that employs language models to predict sequences tokenizing both the observable states and selected actions of players on the field. This method aims to model the probabilities associated with players' decision-making and state transitions. Furthermore, this research pioneers a framework to calculate and evaluate the state-action value (Q-value) of each player and team, leveraging the model's outputs within a reinforcement learning context. This marks the first attempt to establish such a quantitative evaluation framework in this domain.</p>

DOI： 10.11517/pjsai.jsai2024.0_2f4gs505

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>With the development of measurement technology, people in various fields analyze multi-agent spatio-temporal data. However, since the analysis is limited to what occurred and there is a lack of information in the data, there are challenges in utilizing the results of data analysis for future decision-making and in finding solutions to data uncertainty. Hence, counterfactual and information completion play an essential role, but only some examples of applying these methods to the data exist. This paper proposes methods to evaluate behavior by considering counterfactuals and to complement information for the data. In particular, we do this for open soccer data that includes information about a more significant number of agents. The former proposes a method to evaluate team defenses by considering counterfactuals with a mathematical model that is easy for people to understand. The latter proposes a deep generative model that can complement information about players' velocities lacking in such data while considering features related to agents' interactions. We expect this research will enable more people to analyze and support players and coaches in decision-making.</p>

DOI： 10.11517/pjsai.jsai2024.0_3i5os27b03

CiNii Research

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Lead author,　Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press Ltd.  

<p>Understanding the principles of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. The rules regarding the real-world biological multi-agent behaviors such as those in team sports are often largely unknown due to their inherently higher-order interactions, cognition, and body dynamics. Estimation of the rules from data, i.e., via data-driven approaches such as machine learning, provides an effective way to analyze such behaviors. Although most data-driven models have non-linear structures and high predictive performances, it is sometimes hard to interpret them. This survey focuses on data-driven analysis for quantitative understanding of behaviors in invasion team sports such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways. The first approach involves the visualization of learned representations and the extraction of mathematical structures behind the behaviors. The second approach can be used to test hypotheses by simulating and controlling future and counterfactual behaviors. Lastly, the potential practical applications of extracted rules, features, and generated behaviors are discussed. These approaches can contribute to a better understanding of multi-agent behaviors in the real world.</p>

DOI： 10.20965/jrm.2021.p0505

CiNii Research

Language：English   Publishing type：Research paper (scientific journal)  

Understanding biological network dynamics is a fundamental issue in various scientific and engineering fields. Network theory is capable of revealing the relationship between elements and their propagation; however, for complex collective motions, the network properties often transiently and complexly change. A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties. Here we apply a data-driven spectral analysis called graph dynamic mode decomposition, which obtains the dynamical properties for collective motion classification. Using a ballgame as an example, we classified the strategic collective motions in different global behaviours and discovered that, in addition to the physical properties, the contextual node information was critical for classification. Furthermore, we discovered the label-specific stronger spectra in the relationship among the nearest agents, providing physical and semantic interpretations. Our approach contributes to the understanding of principles of biological complex network dynamics from the perspective of nonlinear dynamical systems.

DOI： 10.1038/s41598-020-58064-w

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Language：English   Publishing type：Research paper (scientific journal)  

Living organisms dynamically and flexibly operate a great number of components. As one of such redundant control mechanisms, low-dimensional coordinative structures among multiple components have been investigated. However, structures extracted from the conventional statistical dimensionality reduction methods do not reflect dynamical properties in principle. Here we regard coordinative structures in biological periodic systems with unknown and redundant dynamics as a nonlinear limit-cycle oscillation, and apply a data-driven operator-theoretic spectral analysis, which obtains dynamical properties of coordinative structures such as frequency and phase from the estimated eigenvalues and eigenfunctions of a composition operator. Using segmental angle series during human walking as an example, we first extracted the coordinative structures based on dynamics; e.g. the speed-independent coordinative structures in the harmonics of gait frequency. Second, we discovered the speed-dependent time-evolving behaviours of the phase by estimating the eigenfunctions via our approach on the conventional low-dimensional structures. We also verified our approach using the double pendulum and walking model simulation data. Our results of locomotion analysis suggest that our approach can be useful to analyse biological periodic phenomena from the perspective of nonlinear dynamical systems.

DOI： 10.1038/s41598-019-53187-1

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Language：English   Publishing type：Research paper (scientific journal)  

Understanding nonlinear dynamical systems (NLDSs) is challenging in a variety of engineering and scientific fields. Dynamic mode decomposition (DMD), which is a numerical algorithm for the spectral analysis of Koopman operators, has been attracting attention as a way of obtaining global modal descriptions of NLDSs without requiring explicit prior knowledge. However, since existing DMD algorithms are in principle formulated based on the concatenation of scalar observables, it is not directly applicable to data with dependent structures among observables, which take, for example, the form of a sequence of graphs. In this paper, we formulate Koopman spectral analysis for NLDSs with structures among observables and propose an estimation algorithm for this problem. This method can extract and visualize the underlying low-dimensional global dynamics of NLDSs with structures among observables from data, which can be useful in understanding the underlying dynamics of such NLDSs. To this end, we first formulate the problem of estimating spectra of the Koopman operator defined in vector-valued reproducing kernel Hilbert spaces, and then develop an estimation procedure for this problem by reformulating tensor-based DMD. As a special case of our method, we propose the method named as Graph DMD, which is a numerical algorithm for Koopman spectral analysis of graph dynamical systems, using a sequence of adjacency matrices. We investigate the empirical performance of our method by using synthetic and real-world data.

DOI： 10.1016/j.neunet.2019.04.020

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s10489-024-05996-9

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Walter de Gruyter GmbH  

Abstract

Studies to understand the shooting preferences of basketball players relied exclusively on data on shot location, which did not lead to concrete understandings because they contained no information on how they moved to that location. Therefore, this study tried to cluster the players’ shooting tendencies using the tracking data of the players’ movements during the game. To do this, we first created hand-crafted shot features that included information on the pre-shot movement. Using those features, the dissimilarity of shooting tendencies between players was computed by considering the shot set of each player as a probability distribution and calculating the Wasserstein distance between them. The clustering based on their dissimilarity resulted in more clusters than in previous studies and allowed for specific shooting styles to be defined. Clustering using Gower distance as a dissimilarity measure for shot features, including a categorical feature, extracted clusters of shots that are useful for understanding players’ more detailed shooting tendencies. These results prove that it is not only the shot location but also how the player moved before the shot that is important to capture the player’s shooting preferences.

DOI： 10.2478/ijcss-2025-0003

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Public Library of Science (PLoS)  

When playing on an sloped golf course, the ball often misses the hole to the front-lower side, which is also referred to as the “amateur side.” This can be attributed to the difficulty in perceiving the green slope and choosing the appropriate launch direction and ball speed, especially for amateurs. The study had three main objectives. The first was to examine whether a front-down bias toward the hole could be experimentally observed in golfers’ putting. Second, to measure golfers’ aims and movements to determine why golfers, especially amateurs, putt poorly on steep slopes. Third, to investigate using a miniature platform how golfers recognized the steepness of the slopes and the relationship between their aim and their slope recognition. Twelve professionals and twelve intermediate amateurs were asked to perform in the 1- and 3-degree conditions (left-to-right line) at a distance of 3 m from the hole. Participants wore shutter goggles to inhibit visual feedback of outcomes. The aim, address, ball launch direction (analyzed as angles), and kinematics of the putter head and ball were measured. The results of this study experimentally confirmed the amateur side and indicated that the aim, address, and launch directions of the amateurs were smaller than those of the professionals. As for reproducing the slope task, we found that the amateurs underestimated the steepness on both conditions when compared to professionals. Additionally, golfers’ aim and slope recognition were weakly correlated. These suggest that amateurs, compared to professionals, were unable to choose the optimal speed of the putter head and the launch direction of the ball in relation to the slope. Furthermore, amateurs’ recognition was worse than that of professionals, which may affect their aim.

DOI： 10.1371/journal.pone.0314820

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1007/s11042-024-18881-x

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Other Link： https://link.springer.com/article/10.1007/s11042-024-18881-x/fulltext.html

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s11042-024-20360-2

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.5220/0013066300003828

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Other Link： https://dblp.uni-trier.de/db/conf/icsports/icsports2024.html#KurodaU0K24

Language：English   Publishing type：Research paper (scientific journal)  

Pedestrian action prediction is crucial for many applications such as autonomous driving. However, state-of-the-art methods lack the explainability needed for trustworthy predictions. In this paper, a novel framework called MulCPred is proposed that explains its predictions based on multi-modal concepts represented by training samples. Previous concept-based methods have limitations, including the following: (1) they cannot be directly applied to multi-modal cases; (2) they lack the locality needed to attend to details in the inputs; (3) they are susceptible to mode collapse. These limitations are tackled accordingly through the following approaches: (1) a linear aggregator to integrate the activation results of the concepts into predictions, which associates concepts of different modalities and provides ante hoc explanations of the relevance between the concepts and the predictions; (2) a channel-wise recalibration module that attends to local spatiotemporal regions, which enables the concepts with locality; (3) a feature regularization loss that encourages the concepts to learn diverse patterns. MulCPred is evaluated on multiple datasets and tasks. Both qualitative and quantitative results demonstrate that MulCPred is promising in improving the explainability of pedestrian action prediction without obvious performance degradation. Moreover, by removing unrecognizable concepts, MulCPred shows improved cross-dataset prediction performance, suggesting its potential for further generalization.

DOI： 10.3390/s24206742

PubMed

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s10994-024-06608-w

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1007/s40747-024-01466-4

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

DOI： 10.1145/3689061.3689077

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Other Link： https://dblp.uni-trier.de/db/conf/mmsports/mmsports2024.html#TanakaS024

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Walter de Gruyter GmbH  

Abstract

Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including in team sports. In this study, a multi-agent statistically discriminative sub-trajectory mining (MA-Stat-DSM) method is proposed that takes a set of binary-labelled agent trajectory matrices as input and incorporates Hausdorff distance to identify sub-matrices that statistically significantly discriminate between the two groups of labelled trajectory matrices. Utilizing 2015/16 SportVU NBA tracking data, agent trajectory matrices representing attacks consisting of the trajectories of five agents (the ball, shooter, last passer, shooter defender, and last passer defender), were truncated to correspond to the time interval following the receipt of the ball by the last passer, and labelled as effective or ineffective based on a definition of attack effectiveness that we devise in the current study. After identifying appropriate parameters for MA-Stat-DSM by iteratively applying it to all matches involving the two top- and two bottom-placed teams from the 2015/16 NBA season, the method was then applied to selected matches and could identify and visualize the portions of plays, e.g., involving passing, on-, and/or off-the-ball movements, which were most relevant in rendering attacks effective or ineffective.

DOI： 10.1515/jqas-2023-0039

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Other Link： https://www.degruyter.com/document/doi/10.1515/jqas-2023-0039/pdf

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

In professional basketball, the accurate prediction of scoring opportunities
based on strategic decision-making is crucial for spatial and player
evaluations. However, traditional models often face challenges in accounting
for the complexities of off-ball movements, which are essential for
comprehensive performance evaluations. In this study, we propose two
mathematical models to predict off-ball scoring opportunities in basketball,
considering pass-to-score and dribble-to-score sequences: the Ball Movement for
Off-ball Scoring (BMOS) and the Ball Intercept and Movement for Off-ball
Scoring (BIMOS) models. The BMOS model adapts principles from the Off-Ball
Scoring Opportunities (OBSO) model, originally designed for soccer, to
basketball, whereas the BIMOS model also incorporates the likelihood of
interception during ball movements. We evaluated these models using player
tracking data from 630 NBA games in the 2015-2016 regular season, demonstrating
that the BIMOS model outperforms the BMOS model in terms of team scoring
prediction accuracy, while also highlighting its potential for further
development. Overall, the BIMOS model provides valuable insights for tactical
analysis and player evaluation in basketball.

arXiv

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Other Link： http://arxiv.org/pdf/2406.08749v2

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Group coordination is defined as interactions with other members to implement a task that is difficult to do alone or to achieve higher performance than an individual. Meanwhile, the adjustment process in coordination is not uniquely determined because each individual has many degrees of freedom. It is more difficult to explain and model complex and dynamic coordination, such as nonverbal behavior of three or more members than pair or verbal interaction. Hence, we previously introduced a coordinated drawing task and conducted the behavioral experiment. The triads operated reels to change the tensions of threads connected to a pen, shared three heterogeneous roles (pulling, relaxing, and adjusting), and moved the pen to draw an equilateral triangle. The results indicated that the adjusting role was related to high task performance by helping resiliently without disturbing the pen’s smooth movement while avoiding great pen deviation. However, this experiment alone cannot explain details of the adjustment process of tension. To supplement these findings, this study formulated the three roles using equations of motion. The multi-agent simulation results showed that the adjusting role might use the degree of pen deviation reflected by the others’ motor information, such as the operating procedures and forces, to change the tension and draw at least three sides. Although it is necessary to consider that we used the experimental task, our study contributes to the fundamental understanding of resilient adjustment in coordination by sharing heterogeneous roles as the first step.

DOI： 10.1007/s00354-024-00277-y

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Other Link： https://link.springer.com/article/10.1007/s00354-024-00277-y/fulltext.html

Language：English   Publishing type：Research paper (scientific journal)  

Cognitive scientists believe that adaptable intelligent agents like humans perform spatial reasoning tasks by learned causal mental simulation. The problem of learning these simulations is called predictive world modeling. We present the first framework for a learning open-vocabulary predictive world model (OV-PWM) from sensor observations. The model is implemented through a hierarchical variational autoencoder (HVAE) capable of predicting diverse and accurate fully observed environments from accumulated partial observations. We show that the OV-PWM can model high-dimensional embedding maps of latent compositional embeddings representing sets of overlapping semantics inferable by sufficient similarity inference. The OV-PWM simplifies the prior two-stage closed-set PWM approach to the single-stage end-to-end learning method. CARLA simulator experiments show that the OV-PWM can learn compact latent representations and generate diverse and accurate worlds with fine details like road markings, achieving 69 mIoU over six query semantics on an urban evaluation sequence. We propose the OV-PWM as a versatile continual learning paradigm for providing spatio-semantic memory and learned internal simulation capabilities to future general-purpose mobile robots.

DOI： 10.3390/s24144735

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press Ltd.  

Studying collective behavior in opposing multi-agent teams is crucial across game theory, robotics, and sports analytics. In sports, especially football, team tactics involve intricate strategic spatial and action behaviors displayed as event sequences during possession. Understanding and analyzing these tactics is essential for successful training, strategic planning, and on-field success. While traditional approaches, such as notational and statistical analyses, offer valuable insights into team tactics, they often lack a comprehensive consideration of contextual information, thereby limiting the holistic evaluation of teams’ performances. To bridge this gap and capture the nuanced intricacies of team tactics, we employed advanced methodologies. The sequential pattern mining algorithm PrefixSpan was utilized to extract tactical patterns from possession sequences, enabling a deeper understanding of how teams strategize and adapt during play. Additionally, the neural marked spatio temporal point process (NMSTPP) model was leveraged to model and predict team behaviors, facilitating a fair comparison among teams. The evaluation of team possessions was further enhanced through the innovative holistic possession utilization score metrics, providing a more nuanced assessment of performance. In our experimental exploration, we identified and classified five distinct team tactics, validated the efficacy of the NMSTPP model when integrating StatsBomb 360 data, and conducted a comprehensive analysis of English Premier League teams during the 2022/2023 season. The results were visualized using radar plots and scatter plots with mean shift clustering. Lastly, the potential applications to RoboCup were discussed.

DOI： 10.20965/jrm.2024.p0603

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1007/978-3-031-61932-8_18

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Other Link： https://dblp.uni-trier.de/db/conf/hci/hci2024-56.html#IchikawaF24

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/CVPRW63382.2024.00327

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Other Link： https://dblp.uni-trier.de/db/conf/cvpr/cvprw2024.html#YeungI022

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/CVPRW63382.2024.00340

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Other Link： https://dblp.uni-trier.de/db/conf/cvpr/cvprw2024.html#ScottUDUBKKOK022

Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/CVPRW63382.2024.00336

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Other Link： https://dblp.uni-trier.de/db/conf/cvpr/cvprw2024.html#SuzukiTT022

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Cold Spring Harbor Laboratory  

ABSTRACT

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed as an advanced hunting strategy requiring large brains that involve high level cognition. However, recent findings that collaborative hunting have also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes using computational multi-agent simulation based on deep reinforcement learning. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between observations and actions via distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our results of computational ecology emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

DOI： 10.1101/2022.10.10.511517

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

The application of visual tracking to the performance analysis of sports players in dynamic competitions is vital for effective coaching. In doubles matches, coordinated positioning is crucial for maintaining control of the court and minimizing opponents’ scoring opportunities. The analysis of such teamwork plays a vital role in understanding the dynamics of the game. However, previous studies have primarily focused on analyzing and assessing singles players without considering occlusion in broadcast videos. These studies have relied on discrete representations, which involve the analysis and representation of specific actions (e.g., strokes) or events that occur during the game while overlooking the meaningful spatial distribution. In this work, we present the first annotated drone dataset from top and back views in badminton doubles and propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance. We present an efficient framework of deep neural networks that enables the calculation of full probability surfaces. This framework utilizes the embedding of a Gaussian mixture map of players’ positions and employs graph convolution on their poses. In the experiment, we verify our approach by comparing various baselines and discovering the correlations between the score and control area. Additionally, we propose a practical application for assessing optimal positioning to provide instructions during a game. Our approach offers both visual and quantitative evaluations of players’ movements, thereby providing valuable insights into doubles teamwork. The dataset and related project code is available at https://github.com/Ning-D/Drone_BD_ControlArea

DOI： 10.1007/s11042-023-16362-1

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Other Link： https://link.springer.com/article/10.1007/s11042-023-16362-1/fulltext.html

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2024.3400589

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.2478/ijcss-2024-0002

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/SMC54092.2024.10831469

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Other Link： https://dblp.uni-trier.de/db/conf/smc/smc2024.html#WangHFTMMS24

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

In this study, we analyzed golfers' swing movement to extract differences in proficiency and individual characteristics using two-dimensional video data from a single camera. We conducted an experiment with 27 golfers who had a wide range of skill levels, using a 7-iron; we acquired video data with a camera on the sagittal plane. For data extraction, we used pose estimation (using HRNet) and object detection (using DeepLabCut) methods to extract human-joint and club-head data. We examined the relationship between proficiency and individual characteristics vis-à-vis forward tilt angle and club trajectory. The results showed that the stability and reproducibility of the forward tilt angle are characteristics of proficiency. Highly skilled golfers showed low variability and high reproducibility between trials in forward tilt angle. However, we found that club trajectory may not be a characteristic of proficiency but rather an individual characteristic. Club trajectory was divided roughly into clockwise rotation and counterclockwise rotation. Thus, the analysis based on video data from a single markerless camera enabled the extraction of the differences in proficiency and individual characteristics of golf swing. This suggests the usefulness of our system for simply evaluating golf swings and applying it to motor learning and coaching situations.

DOI： 10.3389/fspor.2023.1272038

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

This study aims to clarify why the aiming method in golf putting in risky situations differs based on skill level. This study set up a difficult challenge (steep slopes and fast ball rolling greens), which required even professional golfers to change their aim. A total of 12 tour professionals and 12 intermediate amateurs were asked to perform a steep-slope task with no visual feedback of outcomes (no FB) followed by a task with visual feedback (with FB). The aim of the task was for the ball to enter the hole in one shot. Additionally, the participants were told that if the ball did not enter the hole, it was to at least stop as close to it as possible. The participant's aim (as an angle) and the kinematics of the putter head and ball were measured. The results indicated that professionals' highest ball trajectory points were significantly higher than that of amateurs, especially with FB. Additionally, professionals had higher ball-launch angles (the direction of the ball when the line connecting the ball and the center of the hole is 0 degrees) and lower peak putter head velocities than amateurs. Furthermore, the aim angle, indicating the golfer's decision-making, was higher for professionals under both conditions. However, even with FB, the amateurs' aim angles were lower and the difference between trials was smaller than that of professionals. Therefore, this study confirmed that the professionals made more drastic changes to their aim to find low-risk routes than the amateurs and that the amateurs’ ability to adjust their aim was lower than that of professionals. The results suggest that the reason for the amateurs' inability to find low-risk routes lies in their decision-making. The professionals found better routes; however, there were individual differences in their routes.

DOI： 10.3389/fspor.2023.1131390

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Walter de Gruyter GmbH  

Abstract

Recent measurement technologies enable us to analyze baseball at higher levels of complexity. There are, however, still many unclear points around pitching strategy. There are two elements that make it difficult to measure the effect of a pitching strategy. First, most public datasets do not include location data where the catcher demands a ball, which is essential information to obtain the battery’s intent. Second, there are many confounders associated with pitching/batting results when evaluating pitching strategy. We here clarify the effect of pitching attempts to a specific location, e.g., inside or outside. We employ a causal inference framework called stratified analysis using a propensity score to evaluate the effects while removing the effect of confounding factors. We use a pitch-by-pitch dataset of Japanese professional baseball games held in 2014–2019, which includes location data where the catcher demands a ball. The results reveal that an outside pitching attempt is more effective than an inside one to minimize allowed run average. In addition, the stratified analysis shows that the outside pitching attempt is effective regardless of the magnitude of the estimated batter’s ability, and the proportion of pitched inside for pitcher/batter. Our analysis provides practical insights into selecting a pitching strategy to minimize allowed runs.

DOI： 10.1515/jqas-2021-0060

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Other Link： https://www.degruyter.com/document/doi/10.1515/jqas-2021-0060/pdf

Language：English   Publishing type：Research paper (international conference proceedings)  

Language：English   Publishing type：Research paper (scientific journal)  

Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

While forecasting football match results has long been a popular topic, a practical model for football participants, such as coaches and players, has not been considered in great detail. In this study, we propose a generalized and interpretable machine learning model framework that only requires coaches' decisions and player quality features for forecasting. By further allowing the model to embed historical match statistics, features that consist of significant information, during the training process the model was practical and achieved both high performance and interpretability. Using five years of data (over 1,700 matches) from the English Premier League, our results show that our model was able to achieve high performance with an F1-score of 0.47, compared to the baseline betting odds prediction, which had an F1-score of 0.39. Moreover, our framework allows football teams to adapt for tactical decision-making, strength and weakness identification, formation and player selection, and transfer target validation. The framework in this study would have proven the feasibility of building a practical match result forecast framework and may serve to inspire future studies.

DOI： 10.1371/journal.pone.0284318

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Language：English   Publishing type：Research paper (scientific journal)  

Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enable our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception.

DOI： 10.1109/LRA.2023.3291924

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/GCCE59613.2023.10315532

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Other Link： https://dblp.uni-trier.de/db/conf/gcce/gcce2023.html#TanakaSTF23

Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3606038.3616171

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Other Link： https://dblp.uni-trier.de/db/conf/mmsports/mmsports2023.html#TanakaST023

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2023.3336425

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/MOST57249.2023.00024

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Other Link： https://dblp.uni-trier.de/db/conf/most/most2023.html#KarlssonC0OT23

Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2023.3269287

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/GCCE59613.2023.10315410

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Other Link： https://dblp.uni-trier.de/db/conf/gcce/gcce2023.html#KobayashiUTF23

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：一般社団法人 人工知能学会  

DOI： 10.11517/jsaifpai.121.0_18

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)  

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/CVPRW56347.2022.00401

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Other Link： https://dblp.uni-trier.de/db/conf/cvpr/cvpr2022w.html#ScottUOKF022

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1109/ACCESS.2022.3175314

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1137/21m1399907

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Language：English   Publishing type：Research paper (international conference proceedings)  

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)  

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>Evaluating the performance of players in dynamic competition plays a vital role in effective sports coaching. However, the evaluation of players in racket sports has been still difficult in a quantitative manner, because it is derived from the integration of complex tactical and technical (i.e., whole-body movement) performances. In this paper, we propose a new evaluation method for racket sports based on deep reinforcement learning, which can analyze the player's motion in more detail than the results (i.e., scores). Our method uses historical data including players' tactical and technical performance information to learn the next score probability as Q function, which is used to value players’ actions. We verified our approach by comparing various models and present the effectiveness of our method through use cases that analyze the performance of the top badminton players in world-class events.</p>

DOI： 10.11517/pjsai.jsai2022.0_1s1is304

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>Cooperative hunting is a widespread form of cooperation in nature, and it is known that the level of organization of this predation varies among species. However, how cooperative forms of predation have evolved and been maintained is not well understood. In this study, we addressed this issue using a multi-agent simulation based on deep reinforcement learning. We examined changes in behavioral strategies when changing factors that have been suggested to be associated with predation forms by previous observations in nature, and found that the highest level of organization with role division among individuals was emerged under the combined conditions of two factors: difficulty of prey capture, and food (reward) sharing. These results suggest that sophisticated predation forms, which have been thought to require high cognition, can evolve from relatively simple cognitive and learning mechanisms, and emphasize the close link between the predation form and the environment where the organism lives.</p>

DOI： 10.11517/pjsai.jsai2022.0_3g4os15b03

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>Bats recognize their surrounding environment by processing the echoes of ultrasonic waves emitted by themselves. Many species of bats live in groups, and many individuals emerge together from roosts. In this study, we used high-sensitivity video cameras to measure the flight trajectories of bats emerging from the cave in three dimensions, and investigated their flight trails. As a result, we found there were three behavioral patterns during emerging: exiting the cave, returning to the cave, and some other action. In addition, we applied the Granger causality method (Fujii et al., NeurlPS'21) to analyze the swarm behavior mechanism of emerging bats. The results showed that forward individuals flew in such a way that they were "repulsed" from or "approached" the other individuals. This suggests that bats, which use sound to understand their environment, are also influenced by backward individuals, which cannot be captured visually, suggesting that bats have a unique swarming mechanism that differs from model animals for group behavior, mainly visual animals.</p>

DOI： 10.11517/pjsai.jsai2022.0_3g4os15b02

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>Soccer is a game in which many players and the ball interact in complex ways. Regarding the quantitative evaluation of soccer attackers, there have been many and few studies on the player with and without the ball, respectively. However, it is still difficult to evaluate an attacking player without the ball and intention to receive it, and to reveal how movement contributes to the creation of scoring opportunities compared to typical (or predicted) movements. In this paper, we evaluate players who create off-ball scoring opportunities by comparing the reference movements generated by trajectory prediction with actual movements. In the proposed method, first, the trajectory is predicted using a graph variational recurrent neural network that can accurately model the relationship between players and predict the long-term trajectory. Next, based on the difference in the existing off-ball evaluation index between the actual data and the predicted trajectory, we evaluate how the actual movement contributes to scoring opportunity compared to the predicted movement as a reference. In the verification, we show that the evaluation of the proposed method is intuitive, using the relationship with the scores with all 18 teams in the Japanese professional soccer league and the example of one game.</p>

DOI： 10.11517/pjsai.jsai2022.0_3g4os15b05

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure of an batter's hitting contribution. However, this measure ignores the game situation, such as the runners on base, which coaches and batters are known to consider when employing multiple hitting strategies. Yet, the effectiveness of these strategies is unknown. This is probably because (1) we cannot obtain the batter's strategy and (2) it is difficult to estimate the effect of the strategies. Here, we propose a new method for estimating the effect using counterfactual batting simulation. To this end, we propose a deep learning model that transforms batting ability when batting strategy is changed. This method can estimate the effects of various strategies, which has been traditionally difficult with actual game data. We found that, when the switching cost of batting strategies can be ignored, the use of different strategies increased runs. When the switching cost is considered, the conditions for increasing runs are limited. Our validation results suggest that our simulation could clarify the effect of using multiple batting strategies.</p>

DOI： 10.11517/pjsai.jsai2022.0_3g4os15b04

CiNii Research

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

Because of the challenges associated with measuring human perception and strategy, the process of human performance from perception to motion to results is not fully understood. Therefore, this study clarifies the phase at which errors occur and how differences in skill level manifest in a motor task requiring an accurate environmental perception and fine movement control. We assigned a golf putting task and comprehensively examined various errors committed in five phases of execution. Twelve tour professionals and twelve intermediate amateur golfers performed the putting task on two surface conditions: flat and a 0.4-degree incline. The participants were instructed to describe the topographical characteristics of the green before starting the trials on each surface (environmental perception phase). Before each attempt, the participants used the reflective markers to indicate their aim point from which the ball would be launched (decision-making phase). We measured the clubface angle and impact velocity to highlight the pre-motion and motion errors (pre-motion and motion phase). In addition, mistakes in the final ball position were analyzed as result errors (post-performance phase). Our results showed that more than half of the amateurs committed visual–somatosensory errors in the perception phase. Moreover, their aiming angles in the decision-making phase differed significantly from the professionals, with no significant differences between slope conditions. In addition, alignment errors, as reported in previous studies, occurred in the pre-motion phase regardless of skill level (i.e., increased in the 0.4-degree condition). In the motion phase, the intermediate-level amateurs could not adjust their clubhead velocity control to the appropriate level, and the clubhead velocity and clubface angle control were less reproducible than those of the professionals. To understand the amateur result errors in those who misperceived the slopes, we checked the individual results focusing on the final ball position. We found that most of these participants had poor performance, especially in the 0.4-degree condition. Our results suggest that the amateurs’ pre-motion and strategy errors depended on their visual–somatosensory errors.

DOI： 10.3389/fpsyg.2021.697914

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Public Library of Science (PLoS)  

Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team’s matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team’s and opposition teams’ perspectives using SPP, and compare these with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts.

DOI： 10.1371/journal.pone.0256329

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

<title>Abstract</title>Skilled interception behavior often relies on accurate predictions of external objects because of a large delay in our sensorimotor systems. To deal with the sensorimotor delay, the brain predicts future states of the target based on the current state available, but it is still debated whether internal representations acquired from prior experience are used as well. Here we estimated the predictive manner by analyzing the response behavior of a pursuer to a sudden directional change of the evasive target, providing strong evidence that prediction of target motion by the pursuer was incompatible with a linear extrapolation based solely on the current state of the target. Moreover, using neural network models, we validated that nonlinear extrapolation as estimated was computationally feasible and useful even against unknown opponents. These results support the use of internal representations in predicting target motion, suggesting the usefulness and versatility of predicting external object motion through internal representations.

DOI： 10.1007/s00422-021-00891-9

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Other Link： https://link.springer.com/article/10.1007/s00422-021-00891-9/fulltext.html

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Cognitive Science Society  

DOI： 10.11225/cs.2020.069

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Sociality is the tendency to spontaneously interact with others to establish and maintain relationships. Some approaches, including questionnaires, tests, controlled experiments, and qualitative field research, cannot capture complex social interactions, such as in children during nursery activities, because of problems with ecological validity and the labor cost of analysis. Here, we introduced a new methodology for the quantitative analysis of spontaneous social movement and investigated children's group behavior using position data. We periodically visited a nursery and recorded videos of eurhythmics, in which children move in tune with music, in different classes. The results revealed that children in the six-year-old class approached others in a short period of time (within one second) and established group behavior like that in a game of tag. It can be interpreted that such social behavior may include actions related to the cognition of anticipating others' behaviors in a complex situation. Although only a small amount of data could be acquired, this study suggests one of the characteristics of social behaviors in the classroom considering an ecological approach.

DOI： 10.1371/journal.pone.0246041

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DOI： 10.1109/GCCE50665.2020.9291841

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Other Link： https://dblp.uni-trier.de/db/conf/gcce/gcce2020.html#TeranishiFT20

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Team sports activities are effective for improving the negative symptoms and cognitive functions in patients with schizophrenia. However, the interpersonal coordination during the sports and visual cognition of patients with schizophrenia who have team sports habits are unknown. The main objectives of this study were to test two hypotheses: first, patients with schizophrenia perform the skill requiring ball passing and receiving worse than healthy controls; and second, the patients will be impaired in these functionings in accordance with the previous studies regarding schizophrenia in general. Twelve patients with schizophrenia and 15 healthy controls, who had habits in football, participated in this study. The participants performed three conventional cognitive tests and a 3-vs-1 ball possession task to evaluate their interpersonal coordination. The results showed that in the 3-vs-1 possession task, the displacement in the pass angle for the patients was significantly smaller than that for the control. The recall in the complex figure test, the performance in the trail making test, and that in the five-choice reaction task for the patients were worse than those for the control. Moreover, we found the significant partial correlations in the patients between the extradimensional shift error and the pass angle as well as between the time in the trail making test and the displacement in the pass angle, whereas there was no significant correlation in the control group. This study clarified the impaired interpersonal coordination during team sports and the visual cognition of patients with schizophrenia who have team sports habits.

DOI： 10.1371/journal.pone.0241863

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DOI： 10.1145/3397536.3422210

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Other Link： https://dblp.uni-trier.de/db/conf/gis/gis2020.html#KandaTFT20

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japanese Cognitive Science Society  

<p>In this paper, we propose a research approach that is used in cognitive science to investigate more complex coordination mechanisms between three or more people. That is, we propose an approach that uses position data to quantitatively analyze group behavior and to link these characteristics with the cognition of anticipating others' behaviors. It is important for coordination to anticipate others' behaviors and to adapt one's own body movement to others based on anticipation. We highlight previous studies on problem solving and learning in cognitive science which have investigated interaction processes from verbal protocols during task implementation and indicated the importance of understanding others' perspectives. Additionally, recent cognitive models of estimating others' intentions and anticipating others' behaviors during interactions using non-verbal information such as eye movement, posture, and gesture, have been investigated. Considering these previous studies, we focus on group behavior and propose to apply the new approach mentioned above to discuss a mechanism of more complex coordination. We also refer to some studies of biological group behaviors in biology, artificial life, and sports science, and demonstrate a potential issue that such papers did not focus on the cognition related to coordinative group behaviors. This paper illustrates an example of discussing interactions with others, to which the new approach is applied. Our previous study here analyzed children's group behavior during nursery activities using position data and linked these characteristics with the cognitive development of anticipating others' behaviors based on spontaneous sociality. However, it is difficult to investigate some details of group behavior due to the limitation of field measurement, for example, the accuracy of a child's anticipation and whether a child moved based upon anticipation. In future work, it is important to analyze controlled group behavior and to indicate accuracy of individuals' anticipation from movement data to solve these problems.</p>

DOI： 10.11225/cs.2020.026

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

DOI： 10.3389/fpsyg.2020.00513

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DOI： https://doi.org/10.3389/fpsyg.2020.00513

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DOI： 10.5220/0009144604760483

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DOI： https://doi.org/10.1038/s41598-020-58064-w

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Publishing type：Research paper (scientific journal)   Publisher：The Japanese Society for Artificial Intelligence  

<p>This paper proposes an efficient similarity search method for massive collections of trajectories. This method enables fast similarity searches by leveraging locality sensitive hashing and tries. To achieve memory efficiency, we propose two novel techniques of node reduction and a space-efficient representation for tries. We experimentally test our method on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that our method performs superiorly with respect to search time and memory efficiency.</p>

DOI： 10.11517/pjsai.JSAI2020.0_2C4OS7a01

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：日本バスケットボール学会  

DOI： 10.34396/jsbs.6.0_1

CiNii Research

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SciTePress  

Accurate extraction of foregrounds in videos is one of the challenging problems in computer vision. In this study, we propose dynamic mode decomposition via dictionary learning (dl-DMD), which is applied to extract moving objects by separating the sequence of video frames into foreground and background information with a dictionary learned using block patches on the video frames. Dynamic mode decomposition (DMD) decomposes spatiotemporal data into spatial modes, each of whose temporal behavior is characterized by a single frequency and growth/decay rate and is applicable to split a video into foregrounds and the background when applying it to a video. And, in dl-DMD, DMD is applied on coefficient matrices estimated over a learned dictionary, which enables accurate estimation of dynamical information in videos. Due to this scheme, dl-DMD can analyze the dynamics of respective regions in a video based on estimated amplitudes and temporal evolution over patches. The results on synthetic data exhibit that dl-DMD outperforms the standard DMD and compressed DMD (cDMD) based methods. Also, the results of an empirical performance evaluation in the case of foreground extraction from videos using publicly available dataset demonstrates the effectiveness of the proposed dl-DMD algorithm and achieves a performance that is comparable to that of the state-of-the-art techniques in foreground extraction tasks.

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We explored how practice and actual putting strokes differed between professionals and high-level golf amateurs, and how practice strokes reflected subtle differences in putting distances. We analysed swing amplitude, impact velocity, and acceleration profile of the club-head. The acceleration profiles showed that the motor control pattern of the practice stroke differed from that of the actual stroke. To clarify the effects of different putting distances on the practice stroke and to analyse how much the actual stroke could be explained by the practice stroke, we conducted individual regression analyses. The practice strokes of all participants could be divided into three strategies and five types by the coefficient of determination and the slope. This implies that the purpose of the practice stroke varied among golfers. Most golfers used the individual velocity criteria in their practice strokes, which resulted in different putting distances based on their criteria. Unexpectedly, we found no significant difference in skill level between professionals and high-level amateurs. The results of this study imply that the practice stroke does not duplicate the actual stroke, even for professional golfers with excellent skills. However, most high-level golfers adopted distance-dependent control strategies for slightly different putting distances.

DOI： 10.1080/17461391.2019.1595159

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The present study utilized induced acceleration analysis to clarify the contributions of muscular and gravitational torques to the kinematics of vertical pointing movements performed by the upper limb. The study included eight healthy men with a mean age of 25 years. The experiment was divided into three blocks with ten trials in each, comprising five upward and five downward, randomly executed movements. The movements were recorded by a motion capture system and were subsequently analyzed. During the deceleration phase of the upward movement and the acceleration phase of the downward movement, the angular acceleration induced by gravitational torque contributed more to the generation of net induced angular acceleration than the angular acceleration induced by muscular torque. In addition, the difference between the net induced angular acceleration profiles during the upward and downward movements was mainly attributable to the difference between the respective angular acceleration profiles induced by muscular torque. These findings suggest that the central nervous system considers the gravitational effect on the upper limb in a phase-specific manner and accordingly generates a torque-derived kinematic difference with respect to the movement direction.

DOI： 10.1016/j.heliyon.2019.e02012

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：日本バスケットボール学会  

DOI： 10.34396/jsbs.5.0_17

CiNii Research

Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Japan Society for Basketball Studies  

DOI： 10.34396/jsbs.5.0_1

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Language：Japanese   Publisher：トレーニング科学研究会  

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Event date： 2023.7

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Event date： 2023.3

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Event date： 2023.3

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Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Event date： 2023.1

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.12

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Event date： 2022.11

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2022.11

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Event date： 2022.10

Language：English   Presentation type：Oral presentation (invited, special)  

Country：France  

Event date： 2022.9

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2022.9

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Event date： 2022.9

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2022.9

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2022.9

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Event date： 2022.9

Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Event date： 2022.7

Language：English   Presentation type：Oral presentation (general)  

Event date： 2022.7

Language：English   Presentation type：Oral presentation (general)  

Event date： 2022.7

Language：English   Presentation type：Oral presentation (general)  

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Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.6

Language：English   Presentation type：Oral presentation (general)  

Event date： 2022.5

Language：English   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Country：China  

Event date： 2021.11

Language：Japanese   Presentation type：Poster presentation  

Event date： 2021.11

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021.9

Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

Event date： 2021.6

Language：English   Presentation type：Oral presentation (general)  

Event date： 2021.6

Language：English   Presentation type：Oral presentation (general)  

Event date： 2021.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2019.9

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

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Event date： 2019.9

Language：Japanese   Presentation type：Poster presentation  

Country：Japan  

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Event date： 2019.8

Language：English   Presentation type：Oral presentation (invited, special)  

Country：Korea, Republic of  

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Event date： 2019.8

Language：English   Presentation type：Oral presentation (invited, special)  

Country：Korea, Republic of  

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Event date： 2019.6

Language：English   Presentation type：Oral presentation (general)  

Country：Japan  

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Event date： 2018.11

Language：Japanese   Presentation type：Poster presentation  

Country：Japan  

Event date： 2018.3

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：大阪大学   Country：Japan  

Event date： 2017.12

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2017.11

Language：English   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2017.9

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2017.5

Language：Japanese   Presentation type：Oral presentation (general)  

Country：Japan  

Event date： 2017.2

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2016.11

Language：English  

Country：United States  

Event date： 2016.10

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2016.9

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2016.8

Language：Japanese  

Country：Japan  

Event date： 2016.8

Language：Japanese  

Country：Japan  

Event date： 2016.5

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Country：Japan  

Event date： 2016.3

Language：Japanese   Presentation type：Oral presentation (invited, special)  

Venue：立命館大学   Country：Japan  

Event date： 2016.3

Language：English   Presentation type：Symposium, workshop panel (nominated)  

Country：Japan  

Event date： 2015.10

Language：English  

Country：Japan  

Event date： 2015.9

Language：Japanese   Presentation type：Symposium, workshop panel (nominated)  

Country：Japan  

Event date： 2015.6

Language：English   Presentation type：Oral presentation (general)  

Country：Spain  

Event date： 2014.8

Language：English   Presentation type：Oral presentation (general)  

Country：United States  

Presentation type：Oral presentation (general)  

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Language：English   Presentation type：Oral presentation (general)  

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Presentation type：Oral presentation (general)  

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Presentation type：Oral presentation (invited, special)  

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Presentation type：Oral presentation (invited, special)  

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Language：English   Presentation type：Oral presentation (general)  

Language：English   Presentation type：Oral presentation (general)  

<p>Analysis of multi-agent trajectories is one of the fundamental issues for understanding real-world biological movements. For trajectory analysis, combining with labels (e.g. scored or not in ballgames) can obtain insights rather than only from trajectories. However, the previous deep-learning based method used only single agent trajectory in animals and cannot be directly applied to multi-agent ballgame trajectories. In this paper, we propose a comparative analysis method to analyze multi-agent trajectories in basketball. We adopt a neural network approach using multi-agent motion characteristics (e.g., distances between agents and objects) as the input and based on an attention mechanism to automatically detect segments in trajectories that are characteristic of one group. It enables us to understand the difference between groups by highlighting segmented trajectories and which variables correlate with the labels. We verified our approach by comparing various baselines and demonstrated the effectiveness of our method through use cases that analyze the attacking plays in the NBA league data.</p>

DOI： 10.11517/pjsai.jsai2023.0_3u1is304

CiNii Research

Language：English   Presentation type：Oral presentation (general)  

<p>Humans often interact with others to achieve a group goal. Many studies on cognitive science, neuroscience, and sports science have investigated the coordination mechanisms and suggested the importance of role-sharing and adjustment using others' motor information. However, the adjustment process in the nonverbal behavior of a triad is not fully understood due to complex and dynamic interactions. We previously introduced a coordinated drawing task and conducted the behavioral experiment. The triads operated reels to change thread tensions, shared three heterogeneous roles (pulling, relaxing, and adjusting), and moved a pen connected to the three threads to draw an equilateral triangle. The results indicated that the adjusting role was related to high task performance, who helped resiliently without disturbing the pen's smooth movement while avoiding great pen deviation. To supplement these findings, this study formulated the three roles using equations of motion. The multi-agent simulation results showed that the adjusting role might use the degree of pen deviation reflected by others' motor information, such as the operating procedures and forces, and change the tension to draw at least three sides. The contribution of this study is to enhance the fundamental understanding of resilient adjustment required in team sports and haul seines.</p>

DOI： 10.11517/pjsai.jsai2023.0_2u5is503

CiNii Research

Language：Japanese   Presentation type：Oral presentation (invited, special)  

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Language：Japanese   Presentation type：Oral presentation (invited, special)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (invited, special)  

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Language：Japanese   Presentation type：Oral presentation (invited, special)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Poster presentation  

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Language：English   Presentation type：Oral presentation (invited, special)  

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Language：English   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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