A duality between pairs of split decompositions for a Q-polynomial distance-regular graph

AbstractLet Γ denote a Q-polynomial distance-regular graph with diameter D≥3 and standard module V. Recently, Ito and Terwilliger introduced four direct sum decompositions of V; we call these the (μ,ν)-split decompositions of V, where μ,ν∈{↓,↑}. In this paper we show that the (↓,↓)-split decomposition and the (↑,↑)-split decomposition are dual with respect to the standard Hermitian form on V. We also show that the (↓,↑)-split decomposition and the (↑,↓)-split decomposition are dual with respect to the standard Hermitian form on V

Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms

This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training.Comment: 2019 IEEE/IFIP International Conference on Dependable Systems and Networks Supplementa

Self-Supervised Motion Retargeting with Safety Guarantee

In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos. While it requires paired data consisting of human poses and their corresponding robot configurations, it significantly alleviates the necessity of time-consuming data-collection via novel paired data generating processes. Our self-supervised learning procedure consists of two steps: automatically generating paired data to bootstrap the motion retargeting, and learning a projection-invariant mapping to handle the different expressivity of humans and humanoid robots. Furthermore, our method guarantees that the generated robot pose is collision-free and satisfies position limits by utilizing nonparametric regression in the shared latent space. We demonstrate that our method can generate expressive robotic motions from both the CMU motion capture database and YouTube videos

Drosophila Graf regulates mushroom body β-axon extension and olfactory long-term memory

Abstract Loss-of-function mutations in the human oligophrenin-1 (OPHN1) gene cause intellectual disability, a prevailing neurodevelopmental condition. However, the role OPHN1 plays during neuronal development is not well understood. We investigated the role of the Drosophila OPHN1 ortholog Graf in the development of the mushroom body (MB), a key brain structure for learning and memory in insects. We show that loss of Graf causes abnormal crossing of the MB β lobe over the brain midline during metamorphosis. This defect in Graf mutants is rescued by MB-specific expression of Graf and OPHN1. Furthermore, MB α/β neuron-specific RNA interference experiments and mosaic analyses indicate that Graf acts via a cell-autonomous mechanism. Consistent with the negative regulation of epidermal growth factor receptor (EGFR)-mitogen-activated protein kinase (MAPK) signaling by Graf, activation of this pathway is required for the β-lobe midline-crossing phenotype of Graf mutants. Finally, Graf mutants have impaired olfactory long-term memory. Our findings reveal a role for Graf in MB axon development and suggest potential neurodevelopmental functions of human OPHN1.This work was supported by grants from the National Research Foundation of Korea (2017M3C7A1025368 and 2019R1A2C2089437)