Authors   Xulei Wu1,2, Bingjie Dang2, Hong Wang5, Xiulong Wu1,*, and Yuchao Yang2,3,4,* 1School of Electronics and Information Engineering, Anhui University, Hefei 230601, China. 2Key Laboratory of Microelectronic Devices and Circuits (MOE), School of Integrated Circuits, Peking University, Beijing 100871, China.

Supporting InformationYee-Fun Lim,* Chee Koon Ng, US Vaitesswar, Kedar Hippalgaonkar*

This supporting information includes supplemental figures, movies, additional results, and the pseudocode of breadth-first-search target generation algorithm.  Corresponding author Email:   yyang60@jhu.edu (Y.Y.)     libome@tsinghua.edu (B.L.)

Proprioception, the ability to perceive one’s own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, we propose a flexible sensor framework that incorporates a novel hybrid modeling strategy, taking advantage of computational mechanics and machine learning. We implement the sensor framework on a large, thin and flexible sensor that transforms sparsely distributed strains into continuous surface shape. Finite element (FE) analysis is utilized to determine sensor design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real-time, robust and high-order surface shape reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated and reported on such a large-scale (A4-paper-size) sensor before.  

Figure S1Experimental system: (A) overall view and (B) generator part.  

This Supporting Information includes:in-depth information on the setup and implementationadditional figures, tables  and equations relevant to the articleinteractive code examples building on the shared data Corresponding author Email:  Roderick.Murray-Smith@glasgow.ac.uk

This Supporting information includes interactive plots, videos, and data captured while performing evaluation and validation experiments for our paper. 

Abstract

This Supporting Information includes:Figure S1, S2, S3Supplementary Video Supplementary Video S1: Locomotion of the mobile robot. Supplementary Video S2: Vortex deforming the liquid-liquid interface. Supplementary Video S3: Locomotion of the mobile robot without electrode attached. Supplementary Video S4: Locomotion of the mobile robot with reversed polarity. Supplementary Video S5: Drawing “SIT” by controlling a floating robot with multiple electrodes.  Corresponding author Email:  vito.cacucciolo@epfl.ch, hshige@shibaura-it.ac.jp

This Supporting information includes:1. Component Selection and Performance of SFA2. Actuator Manufacturing and Preparation of Conductive Ink 3. Average Thickness of Conductive Coating layer on PU Foam4. Time Response of the Actuator in Different Modes 5. Characterization and Experimental Setup6. Measurement and Data Analysis7. Design Specifications of Soft Robotic Applications8. Supporting Video Corresponding author Email:  saravanaprashanth@outlook.com, barbara.mazzolai@iit.it 

This supporting information includes details of the computational model, supplementary figures, interactive 3D CAD model of DraBot and supplementary movies.  Corresponding author Email:  shyni.varghese@duke.edu

This Supporting Information includes the extended description of the superposition state of the asymmetric double-well system in vacuum system and in solution, truth tables for the residue pairs and their corresponding quantum logic gates, and figures for the double well potential energy surfaces and transmission spectra of the residue pairs.Corresponding Authors Email: sysheu@ym.edu.tw and dyyang@pub.iams.sinica.edu.tw   

A document by Yi Jin. Click on the document to view its contents.

Identifying Clostridioides difficile infection (CDI) patients at risk of mortality or recurrence will facilitate prevention, timely treatment and improve clinical outcomes. We aim to establish an open-access web-based prediction system, which estimates CDI patients’ mortality and recurrence outcomes, and explains the machine learning prediction with patients’ characteristics. Prognostic models were developed using four various types of machine learning algorithms and statistical logistics regression model utilizing over 15,000 CDI patients from 41 hospitals in Hong Kong. The boosting-based machine learning algorithm Gradient Boosting Machine (Mortality AUC: 0.7878; Recurrence AUC: 0.7076) outperformed statistical models (Mortality AUC: 0.7573; Recurrence AUC: 0.6927) and other machine learning algorithms. The open-access prediction system for clinicians to assess and interpret the risk factors of CDI patients is now available at https://www.cdiml.care/. In this article, we explain the development of machine learning models and illustrate how to apply hyperparameter tuning with cross-validation to optimize the model accuracy.

Abstract:There is a lack of reliable prognostic biomarkers for hypoxic-ischemic (HI) brain injury in preterm infants. Herein, spectrally detailed wavelet scalograms (WSs), derived from the 1024 Hz sampled electroencephalograms (EEG) of preterm fetal sheep after HI (n ¼ 7), are infused into a high-performance deep convolutional neural network (CNN) pattern classifier to identify high-frequency spike transient biomarkers. The deep WS-CNN pattern classifier identifies EEG spikes with remarkable accuracy of 99.81 ¼ 0.15% (area under curve, AUC ¼ 1.000), cross-validated across 5010 EEG waveforms, during the first 6 h post-HI (42 h total), an important clinical period for diagnosis of HI brain injury. Further, a feature-fusion strategy is introduced to extract the spectrally dominant features of the raw EEG epochs to form robust 3D input matrix sets to be infused into the deep 2D-CNNs for pattern classification. The results show that the proposed WS-CNN approach is less sensitive to the potential morphological variations of spikes across all subjects compared to other deep CNNs and spectral-fuzzy classifiers, allowing the user to flexibly choose an approach depending on their computational requirements. Collectively, the data provide a reliable framework that could help support well-timed diagnosis of at-risk neonates in clinical practice.