SAYAMA, Hiroki

写真a

Affiliation

Faculty of Commerce, School of Commerce

Job title

Professor(without tenure)

Homepage URL

http://bingweb.binghamton.edu/~sayama/

Profile

Please see below for his full CV:
http://bingweb.binghamton.edu/~sayama/cv.pdf

Education 【 display / non-display

  • 1994.04
    -
    1999.03

    University of Tokyo   Graduate School of Science   Department of Information Science  

  • 1990.04
    -
    1994.03

    University of Tokyo   Faculty of Science   Department of Information Science  

Degree 【 display / non-display

  • The University of Tokyo   Doctor of Science

  • The University of Tokyo   Master of Science

  • The University of Tokyo   Bachelor of Science

Research Experience 【 display / non-display

  • 2018.04
    -
    Now

    Waseda University   Faculty of Commerce   Professor

  • 2017.09
    -
    Now

    Binghamton University, State University of New York   Professor

  • 2012.01
    -
    2017.08

    Binghamton University, State University of New York   Associate Professor

  • 2006.01
    -
    2011.12

    Binghamton University, State University of New York   Assistant Professor

  • 2004.04
    -
    2005.12

    University of Electro-Communications   Department of Human Communication   Associate Professor

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Professional Memberships 【 display / non-display

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    American Association for the Advancement of Science

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    IEEE Systems, Man, & Cybernetics Society

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    IEEE Computational Intelligence Society

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    IEEE

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    Network Science Society

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Research Areas 【 display / non-display

  • Computational science

  • Mathematical informatics

  • Intelligent informatics

  • Web informatics and service informatics

Research Interests 【 display / non-display

  • systems science

  • adaptive networks

  • social networks

  • social systems

  • interactive systems

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Papers 【 display / non-display

  • Detecting Dynamic States of Temporal Networks Using Connection Series Tensors

    Shun Cao, Hiroki Sayama, Pietro De Lellis

    Complexity   abs/2007.12756  2020.12

     View Summary

    Many temporal networks exhibit multiple system states, such as weekday and
    weekend patterns in social contact networks. The detection of such distinct
    states in temporal network data has recently been explored as it helps reveal
    underlying dynamical processes. A commonly used method is network aggregation
    over a time window, which aggregates a subsequence of multiple network
    snapshots into one static network. This method, however, necessarily discards
    temporal dynamics within the time window. Here we develop a new method for
    detecting dynamic states in temporal networks using information regarding the
    timeline of contacts between each pair of nodes. We apply a similarity measure
    informed by the techniques of processing time series and community detection to
    sequentially discompose a given temporal network into multiple dynamic states
    (including repeated ones). Experiments with empirical temporal network data
    demonstrated that our method outperformed the conventional approach using
    simple network aggregation in revealing interpretable system states. In
    addition, our method allows users to analyze hierarchical temporal structures
    and to uncover dynamic state at different spatial/temporal resolutions.

    DOI

  • Mathematically Modeling Anhedonia in Schizophrenia: A Stochastic Dynamical Systems Approach

    Gregory P Strauss, Farnaz Zamani Esfahlani, Eric Granholm, Jason Holden, Katherine Frost Visser, Lisa A Bartolomeo, Hiroki Sayama

    Schizophrenia Bulletin   43   S99 - S99  2020.09  [Refereed]  [International journal]

     View Summary

    OBJECTIVE: Anhedonia, traditionally defined as a diminished capacity for pleasure, is a core symptom of schizophrenia (SZ). However, modern empirical evidence indicates that hedonic capacity may be intact in SZ and anhedonia may be better conceptualized as an abnormality in the temporal dynamics of emotion. METHOD: To test this theory, the current study used ecological momentary assessment (EMA) to examine whether abnormalities in one aspect of the temporal dynamics of emotion, sustained reward responsiveness, were associated with anhedonia. Two experiments were conducted in outpatients diagnosed with SZ (n = 28; n = 102) and healthy controls (n = 28; n = 71) who completed EMA reports of emotional experience at multiple time points in the day over the course of several days. Markov chain analyses were applied to the EMA data to evaluate stochastic dynamic changes in emotional states to determine processes underlying failures in sustained reward responsiveness. RESULTS: In both studies, Markov models indicated that SZ had deficits in the ability to sustain positive emotion over time, which resulted from failures in augmentation (ie, the ability to maintain or increase the intensity of positive emotion from time t to t+1) and diminution (ie, when emotions at time t+1 are opposite in valence from emotions at time t, resulting in a decrease in the intensity of positive emotion over time). Furthermore, in both studies, augmentation deficits were associated with anhedonia. CONCLUSIONS: These computational findings clarify how abnormalities in the temporal dynamics of emotion contribute to anhedonia.

    DOI PubMed

  • Simulating Systems Thinking under Bounded Rationality

    Mark W. Sellers, Hiroki Sayama, Andreas D. Pape

    Complexity   2020   3469263 - 12  2020.09

    DOI

  • The Role of Criticality of Gene Regulatory Networks in Morphogenesis

    Hyobin Kim, Hiroki Sayama

    IEEE Transactions on Cognitive and Developmental Systems   12 ( 3 ) 390 - 400  2020.09

    DOI

  • Network Analysis Indicates That Avolition Is the Most Central Domain for the Successful Treatment of Negative Symptoms: Evidence From the Roluperidone Randomized Clinical Trial

    Gregory P Strauss, Farnaz Zamani Esfahlani, Hiroki Sayama, Brian Kirkpatrick, Mark G Opler, Jay B Saoud, Michael Davidson, Remy Luthringer

    Schizophrenia Bulletin   46 ( 4 ) 964 - 970  2020.07  [International journal]

     View Summary

    A recent conceptual development in schizophrenia is to view its manifestations as interactive networks rather than individual symptoms. Negative symptoms, which are associated with poor functional outcome and reduced rates of recovery, represent a critical need in schizophrenia therapeutics. MIN101 (roluperidone), a compound in development, demonstrated efficacy in the treatment of negative symptoms in schizophrenia. However, it is unclear how the drug achieved its effect from a network perspective. The current study evaluated the efficacy of roluperidone from a network perspective. In this randomized clinical trial, participants with schizophrenia and moderate to severe negative symptoms were randomly assigned to roluperidone 32 mg (n = 78), 64 mg (n = 83), or placebo (N = 83). Macroscopic network properties were evaluated to determine whether roluperidone altered the overall density of the interconnections among symptoms. Microscopic properties were evaluated to examine which individual symptoms were most influential (ie, interconnected) on other symptoms in the network and are responsible for successful treatment effects. Participants receiving roluperidone did not differ from those randomized to placebo on macroscopic properties. However, microscopic properties (degree and closeness centrality) indicated that avolition was highly central in patients receiving placebo and that roluperidone reduced this level of centrality. These findings suggest that decoupling the influence of motivational processes from other negative symptom domains is essential for producing global improvements. The search for pathophysiological mechanisms and targeted treatment development should be focused on avolition, with the expectation of improvement in the entire constellation of negative symptoms if avolition is effectively treated.

    DOI PubMed

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Books and Other Publications 【 display / non-display

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Misc 【 display / non-display

  • Visualizing Collective Idea Generation and Innovation Processes in Social Networks

    Yiding Cao, Yingjun Dong, Minjun Kim, Neil G. MacLaren, Sriniwas Pandey, Shelley D. Dionne, Francis J. Yammarino, Hiroki Sayama

       2021.10

     View Summary

    Collective idea generation and innovation processes are complex and dynamic,
    involving a large amount of qualitative narrative information that is difficult
    to monitor, analyze and visualize using traditional methods. In this study, we
    developed three new visualization methods for collective idea generation and
    innovation processes and applied them to data from online collaboration
    experiments. The first visualization is the Idea Cloud, which helps monitor
    collective idea posting activity and intuitively tracks idea clustering and
    transition. The second visualization is the Idea Geography, which helps
    understand how the idea space and its utility landscape are structured and how
    collaboration was performed in that space. The third visualization is the Idea
    Network, which connects idea dynamics with the social structure of the people
    who generated them, displaying how social influence among neighbors may have
    affected collaborative activities and where innovative ideas arose and spread
    in the social network.

  • How Lévy flights triggered by presence of defectors affect evolution of cooperation in spatial games

    Genki Ichinose, Daiki Miyagawa, Erika Chiba, Hiroki Sayama

       2021.05

     View Summary

    Cooperation among individuals has been key to sustaining societies. However,
    natural selection favors defection over cooperation. Cooperation can be favored
    when the mobility of individuals allows cooperators to form a cluster (or
    group). Mobility patterns of animals sometimes follow a L\'evy flight. A L\'evy
    flight is a kind of random walk but it is composed of many small movements with
    a few big movements. Here, we developed an agent-based model in a square
    lattice where agents perform L\'evy flights depending on the fraction of
    neighboring defectors. For comparison, we also tested normal-type movements
    implemented by a uniform distribution. We focus on how the sensitivity to
    defectors when performing L\'evy flights promotes the evolution of cooperation.
    Results of evolutionary simulations showed that L\'evy flights outperformed
    normal movements for cooperation in all sensitivities. In L\'evy flights,
    cooperation was most promoted when the sensitivity to defectors was moderate.
    Finally, as the population density became larger, higher sensitivity was more
    beneficial for cooperation to evolve.

  • Utterance Clustering Using Stereo Audio Channels

    Yingjun Dong, Neil G. MacLaren, Yiding Cao, Francis J. Yammarino, Shelley D. Dionne, Michael D. Mumford, Shane Connelly, Hiroki Sayama, Gregory A. Ruark

       2020.09

     View Summary

    Utterance clustering is one of the actively researched topics in audio signal
    processing and machine learning. This study aims to improve the performance of
    utterance clustering by processing multichannel (stereo) audio signals.
    Processed audio signals were generated by combining left- and right-channel
    audio signals in a few different ways and then extracted embedded features
    (also called d-vectors) from those processed audio signals. This study applied
    the Gaussian mixture model for supervised utterance clustering. In the training
    phase, a parameter sharing Gaussian mixture model was conducted to train the
    model for each speaker. In the testing phase, the speaker with the maximum
    likelihood was selected as the detected speaker. Results of experiments with
    real audio recordings of multi-person discussion sessions showed that the
    proposed method that used multichannel audio signals achieved significantly
    better performance than a conventional method with mono audio signals in more
    complicated conditions.

  • Reduced mobility of infected agents suppresses but lengthens disease in biased random walk

    Genki Ichinose, Yoshiki Satotani, Hiroki Sayama, Takashi Nagatani

       2018.07

     View Summary

    Various theoretical models have been proposed to understand the basic nature
    of epidemics. Recent studies focus on the effects of mobility to epidemic
    process. However, uncorrelated random walk is typically assumed as the type of
    movement. In our daily life, the movement of people sometimes tends to be
    limited to a certain direction, which can be described by biased random walk.
    Here, we developed an agent-based model of susceptible-infected-recovered (SIR)
    epidemic process in a 2D continuous space where agents tend to move in a
    certain direction in addition to random movement. Moreover, we mainly focus on
    the effect of the reduced mobility of infected agents. Our model assumes that,
    when people are infected, their movement activity is greatly reduced because
    they are physically weakened by the disease. By conducting extensive
    simulations, we found that when the movement of infected people is limited, the
    final epidemic size becomes small. However, that crucially depended on the
    movement type of agents. Furthermore, the reduced mobility of infected agents
    lengthened the duration of the epidemic because the infection progressed
    slowly.

  • NiCE Teacher Workshop: Engaging K-12 Teachers in the Development of Curricular Materials That Utilize Complex Networks Concepts

    Emma K. Towlson, Lori Sheetz, Ralucca Gera, Jon Roginski, Catherine Cramer, Stephen Uzzo, Hiroki Sayama

       2018.05

     View Summary

    Our educational systems must prepare students for an increasingly
    interconnected future, and teachers require equipping with modern tools, such
    as network science, to achieve this. We held a Networks in Classroom Education
    (NiCE) workshop for a group of 21 K-12 teachers with various disciplinary
    backgrounds. The explicit aim of this was to introduce them to concepts in
    network science, show them how these concepts can be utilized in the classroom,
    and empower them to develop resources, in the form of lesson plans, for
    themselves and the wider community. Here we detail the nature of the workshop
    and present its outcomes - including an innovative set of publicly available
    lesson plans. We discuss the future for successful integration of network
    science in K-12 education, and the importance of inspiring and enabling our
    teachers.

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Awards 【 display / non-display

  • ISAL Outstanding Paper of the Decade (2003-2013) Award

    2018   International Society for Artificial Life   Swarm Chemistry

    Winner: SAYAMA Hiroki

  • Best Poster Award

    2017   The 2017 International School and Conference on Network Science (NetSci 2017)  

    Winner: SAYAMA Hiroki

  • First Place, 的magining Science category

    2017   Binghamton University   Visualized for the Blind

    Winner: SAYAMA Hiroki

  • ISAL Exceptional Service Award

    2016   International Society for Artificial Life  

    Winner: SAYAMA Hiroki

  • Lois B. DeFleur Faculty Prize for Academic Achievement

    2016   Binghamton University  

    Winner: SAYAMA Hiroki

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Research Projects 【 display / non-display

  • マルチエージェントシステムが示す局所的・大域的挙動の理論的解析

  • 空間的分布を持ち局所的に交配する個体群における自発的パターン生成とその進化過程の研究

  • 自己複製し進化する人工システムの構築

  • Theoretical Analysis of Local and Global Behaviors of Multi-Agent Systems

  • Emergence and Evolution of Patterns in Spatially Distributed Locally Interacting Populations

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Specific Research 【 display / non-display

 

Syllabus 【 display / non-display