Secure tumor classification by shallow neural network using homomorphic encryption

Disclosure of patients genetic information in the process of applying machine learning techniques for tumor classification hinders the privacy of personal information. Homomorphic Encryption (HE), which supports operations between encrypted data, can be used as one of the tools to perform such computation without information leakage, but it brings great challenges for directly applying general machine learning algorithms due to the limitations of operations supported by HE. In particular, non-polynomial activation functions, including softmax functions, are difficult to implement with HE and require a suitable approximation method to minimize the loss of accuracy. In the secure genome analysis competition called iDASH 2020, it is presented as a competition task that a multi-label tumor classification method that predicts the class of samples based on genetic information using HE. We develop a secure multi-label tumor classification method using HE to ensure privacy during all the computations of the model inference process. Our solution is based on a 1-layer neural network with the softmax activation function model and uses the approximate HE scheme. We present an approximation method that enables softmax activation in the model using HE and a technique for efficiently encoding data to reduce computational costs. In addition, we propose a HE-friendly data filtering method to reduce the size of large-scale genetic data. We aim to analyze the dataset from The Cancer Genome Atlas (TCGA) dataset, which consists of 3,622 samples from 11 types of cancers, genetic features from 25,128 genes. Our preprocessing method reduces the number of genes to 4,096 or less and achieves a microAUC value of 0.9882 (85% accuracy) with a 1-layer shallow neural network. Using our model, we successfully compute the tumor classification inference steps on the encrypted test data in 3.75 minutes. As a result of exceptionally high microAUC values, our solution was awarded co-first place in iDASH 2020 Track 1: Secure multi-label Tumor classification using Homomorphic Encryption. Our solution is the first result of implementing a neural network model with softmax activation using HE. Also, HE optimization methods presented in this work enable machine learning implementation using HE or other challenging HE applications.This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2020-0-00840, Development and Library Implementation of Fully Homomorphic ML Algorithms supporting Neural Network Learning over Encrypted Data)

Ginseng intake and Alzheimer disease-specific cognition in older adults according to apolipoprotein ε4 allele status

BackgroundThe probable association among ginseng intake, Alzheimer’s disease (AD)-specific cognition, and apolipoprotein ε4 (APOE4) remains poorly investigated. Hence, we examined the association between ginseng intake and AD-specific cognition in older adults under the moderating effect of APOE4 status.MethodsThis study enrolled 160 adults aged 65–90 years without dementia. All participants underwent comprehensive dietary and clinical assessments including ginseng intake, AD-related cognition (i.e., delayed episodic memory, as the earliest cognitive change in AD), and non-memory cognition for comparative purposes.ResultsGinseng intake was associated with higher delayed episodic memory, but not non-memory cognition, compared to no ginseng intake. The interaction between ginseng intake and APOE4 status had a significant effect on delayed episodic memory. Subgroup analyses showed that ginseng intake was associated with higher delayed episodic memory in the APOE4-negative but not the APOE4-positive subgroup. The benefits of ginseng intake on delayed episodic memory were prominent in the high duration (≥5 years) and midlife onset (<65 years) groups.ConclusionOur study of older adults with no dementia suggests that ginseng intake (with high duration and midlife onset) had a beneficial effect on AD-specific cognitive decline, i.e., the delayed episodic memory. In addition, APOE4 status moderates the association between ginseng intake status and AD-specific cognitive decline

Simulating Complex Hair with Robust Collision Handling

We present a new framework for simulating dynamic movements of complex hairstyles. The proposed framework, which treats hair as a collection of wisps, includes new approaches to simulating dynamic wisp movements and handling wisp-body collisions and wisp-wisp interactions. For the simulation of wisps, we introduce a new hair dynamics model, a hybrid of the rigid multi-body serial chain and mass-spring models, to formulate the simulation system using an implicit integration method. Consequently, the simulator can impose collision/contact constraints systematically, allowing it to handle wisp-body collisions efficiently without the need for backtracking or subtimestepping. In addition, the simulator handles wisp-wisp collisions based on impulses while taking into account viscous damping and cohesive forces. Experimental results show that the proposed technique can stably simulate hair with intricate geometries while robustly handling wisp-body collisions and wisp-wisp interactions

Research Problems for Creating Digital Actors

An interesting challenge for the computer graphics community is to use computer graphics technology to simulate digital actors that seem so real that people cannot tell whether they are animated or real. Our group is engaged in an ongoing project to develop and integrate the techniques required for creating digital actors. In particular, our research has been focused on components such as facial animation, hair animation, clothing animation, and body animation, which are crucial to the successful realization of digital actors. This article summarizes the results of our research on those topics, reviews other approaches that have been taken in digital actor research, and outlines the challenges that must be overcome in this area

Highly-Sensitive Plasmonic Nano-Ring Transistor for Monolithic Terahertz Active Antenna

We report a highly-sensitive plasmonic nano-ring transistor for monolithic terahertz (THz) active antenna. By designing an ultimate asymmetric transistor on a metal-gate structure, more enhanced (180 times) channel charge asymmetry has been obtained in comparison with a bar-type asymmetric transistor of our previous work. In addition, by exploiting ring-type transistor itself as a monolithic circular active antenna, which is designed for a 0.12-THz resonance frequency, we experimentally demonstrated the highly-enhanced responsivity (Rv) > 1 kV/W (x 5) and reduced noise-equivalent power (NEP) < 10 pW/Hz0.5 (x 1/10)

Thiometallate precursors for the synthesis of supported Pt and PtNi nanoparticle electrocatalysts: Size-focusing by S capping

Herein, we report for the first time the successful preparation of thiometallate-based precursors for use in a bottom-up synthetic process of supported Pt and PtNi nanoparticle catalyst. This precursor enabled the monodisperse synthesis of supported Pt nanoparticles and the in situ formation of S, which were caught directly in a collection system by the nanoparticle synthetic processes consisting of impregnation and thermal processes. S is proven to act as a capping agent in generating highly stable nanoparticles with the size ranging from 2 nm to 3 nm and further favors the formation of monodispersed particles by solid-state digestive ripening. The proposed synthetic methodology can be applied to high-quality PtNi alloy nanoparticle systems. The current route is readily scalable, and multi-gram quantities can be prepared. The prepared carbon-supported Pt and PtNi nanoparticles were characterized as electrocatalysts for the oxygen reduction reaction and exhibited superior performance and durability to commercial Pt/C