NTT Communication Science Laboratories Open House 2018
Thursday, May 31st 12:00-17:30 Friday, June 1st 9:30-16:00
This year the NTT Communication Science Laboratories Open House 2018 will be held on May 31st (Thu) and June 1st (Fri) at the NTT Keihanna Building in Kyoto. We will present our latest research findings and accomplishments in the field of information and human sciences through various lectures and exhibitions. We look forward to seeing you!



  • Thursday, May 31st, 12:00 - 17:30
  • Friday, June 1st, 9:30 - 16:00


Admission Charge

  • No Admission Charge
  • No Pre-Registration Required

Photos of Open House 2017


Head's Talk 5/31(Thu)

  • 13:20 - 13:50
    Shifting to new dimensions
    ∼Further initiatives to deepen Communication Science∼

    Takeshi Yamada, Vice President, Head of NTT Communication Science Laboratories

Invited Talk 5/31(Thu)

  • 14:00 - 15:00
    AI, ethics and social impact
    Hiroshi Nakagawa, Group director, Artificial Intelligence in Society Research Group, RIKEN Center for Advanced Intelligence Project

Research Talks


  • 15:30 - 16:10
    The real worth of quantum computers with elementary operations
    ∼Analysis of computational power of gate-based quantum computers∼

    Yasuhiro Takahashi, Media Information Laboratory


  • 11:00 - 11:40
    Role of haptics in improving wellbeing
    ∼Science and design of touch can enhance human flourishing∼

    Junji Watanabe, Human Information Science Laboratory
  • 13:00 - 13:40
    Beyond combinatorial explosion
    ∼Enumeration and optimization with Binary Decision Diagrams∼

    Masaaki Nishino, Innovative Communication Laboratory
  • 13:50 - 14:30
    Speech production and perception share common brain pathways
    ∼Investigation of the mechanisms of speech communication by speech conversion and brain imaging∼

    Sadao Hiroya, Human Information Science Laboratory

Exhibition Program

Science of Machine Learning

  • Finding similar voice recordings in big data
    Graph index-based audio similarity search
  • Learning feature combinations from multiple tasks
    MOFM: low-rank regression for learning common factors
  • Where do they come from? Where are they going?
    Data assimilation and navigation learning for crowd
  • Datafying cities
    Event analysis by environmental sensing and machine learning
  • Memory efficient deep learning for mobile devices
    Quantized neural networks for model compression
  • Optics makes machine learning much faster
    Photonic reservoir computing for high-speed machine learning
  • Interpreting deep learning from network structure
    Detecting communities in trained layered neural networks

Science of Communication and Computation

  • Can I borrow your quantum memory?
    High-speed quantum computations with uninitialized qubits
  • Designing fault-tolerant networks
    Maximizing network reliability via binary decision diagrams
  • Can computer translate considering context?
    Context understanding tests for neural machine translation
  • Early vocabulary development in late talkers
    Collecting and analyzing data from pediatric medical fields
  • Chatting with robots broadens your knowledge
    Integration of chat and QA based on two-robot coordination
  • Anytime, anywhere, we can speak like a native!
    Speech rhythm conversion by mobile application
  • Sharing enthusiasm between remote sites
    Applause coding for bi-lateral immersive sharing

Science of Media Information

  • Illumination-based color saturation control
    Spectral operation using color enhancement factors
  • Pay attention to the speaker you want to listen to
    Computational selective hearing based on deep learning
  • Solving two-choice questions makes AI clever
    Deep pairwise comparison model for ASR hypothesis selection
  • Estimating objects' visuals only from audio
    Cross-media scene analysis
  • Cast shadows add dimensions
    Projection mapping giving depth illusion to real objects
  • Converting impression and intelligibility of speech
    Speech attribute conversion using deep generative models
  • Creating favorite images with selective decisions
    Hierarchical image analysis and synthesis with DTLC-GAN

Science of Human

  • Predicting attention to the ears by the eyes
    Auditory spatial attention revealed as pupillary response
  • Measuring, understanding, and empowering wellbeing
    Cross-disciplinary research toward “eudaimonic wellbeing”
  • Understanding human hearing with AI
    Analyzing auditory neural mechanisms with machine learning
  • How do excellent batters hit the ball?
    Cognitive processes revealed by body movements in batting
  • How do excellent batters look at the ball?
    Cognitive processes revealed by eye movements in batting
  • The sooner you decide, the better you can localize
    Visual motion processing in the perception and action
  • Let’s FEEL shape and action by a force
    Can we receive environmental information by Buru-Navi4 ?
  • Feeling bumps on a flat sheet
    Magnetic haptic printing technology

Websites of Open House