arXiv subjects

Shuo Zhou

Publications and source records attributed to Shuo Zhou.

42 records · Page 3Linked to original sources

arXiv2019

Phase evolution and thermal stability of high Curie temperature BiScO$_3$-PbTiO$_3$-Pb(Cd${1/3}$Nb$_{2/3}$)O$_3$ ceramics near MPB

Piezoelectric and ferroelectric ceramics with a high Curie temperature (Tc) have attracted a growing attention owning to their applications under severe environments. In this work, phase structure, dielectric, ferroelectric and piezoelectric properties of (0.975-x)BiScO3-xPbTiO3-0.025Pb(Cd1/3Nb2/3)O3 ceramics (x = 0.58-0.64) were studied. A composition-induced structural transformation occurs from rhombohedral phase to tetragonal phase through an intermediate monoclinic phase with the increasing PT concentration. The relationship between structure and electrical properties of the system were discussed. The BS-xPT-PCN system near the morphotropic phase boundary (x = 0.62) exhibits excellent piezoelectric and ferroelectric performances with d33 = 508 pC/N, kp = 56%, and Pr = 40 uC/cm2. The high-temperature piezoelectricity of the sample with MPB (x = 0.62) was characterized by an in situ XRD. The excellent thermal stability of the crystal structure and the piezoelectric property indicate that the BS-xPT-PCN system is a promising candidate for high temperature piezoelectric applications.

cond-mat.mtrl-sci↗

arXiv2019

Domain Independent SVM for Transfer Learning in Brain Decoding

Brain imaging data are important in brain sciences yet expensive to obtain, with big volume (i.e., large p) but small sample size (i.e., small n). To tackle this problem, transfer learning is a promising direction that leverages source data to improve performance on related, target data. Most transfer learning methods focus on minimizing data distribution mismatch. However, a big challenge in brain imaging is the large domain discrepancies in cognitive experiment designs and subject-specific structures and functions. A recent transfer learning approach minimizes domain dependence to learn common features across domains, via the Hilbert-Schmidt Independence Criterion (HSIC). Inspired by this method, we propose a new Domain Independent Support Vector Machine (DI-SVM) for transfer learning in brain condition decoding. Specifically, DI-SVM simultaneously minimizes the SVM empirical risk and the dependence on domain information via a simplified HSIC. We use public data to construct 13 transfer learning tasks in brain decoding, including three interesting multi-source transfer tasks. Experiments show that DI-SVM's superior performance over eight competing methods on these tasks, particularly an improvement of more than 24% on multi-source transfer tasks.

cs.LG↗

arXiv2018

Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI

While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional structure, high dimensionality, and small number of samples available. Recent sparse multilinear regression methods based on tensor are emerging as promising solutions for fMRI, yet existing works rely on unfolding/folding operations and a tensor rank relaxation with limited tightness. The newly proposed tensor singular value decomposition (t-SVD) sheds light on new directions. In this work, we study t-SVD for sparse multilinear regression and propose a Sparse tubal-regularized multilinear regression (Sturm) method for fMRI. Specifically, the Sturm model performs multilinear regression with two regularization terms: a tubal tensor nuclear norm based on t-SVD and a standard L1 norm. We further derive the algorithm under the alternating direction method of multipliers framework. We perform experiments on four classification problems, including both resting-state fMRI for disease diagnosis and task-based fMRI for neural decoding. The results show the superior performance of Sturm in classifying fMRI using just a small number of voxels.

cs.CV↗

arXiv2018

Dimensionality-Driven Learning with Noisy Labels

Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs). We propose a new perspective for understanding DNN generalization for such datasets, by investigating the dimensionality of the deep representation subspace of training samples. We show that from a dimensionality perspective, DNNs exhibit quite distinctive learning styles when trained with clean labels versus when trained with a proportion of noisy labels. Based on this finding, we develop a new dimensionality-driven learning strategy, which monitors the dimensionality of subspaces during training and adapts the loss function accordingly. We empirically demonstrate that our approach is highly tolerant to significant proportions of noisy labels, and can effectively learn low-dimensional local subspaces that capture the data distribution.

cs.CV↗

arXiv2017

Providing Effective Real-time Feedback in Simulation-based Surgical Training

Virtual reality simulation is becoming popular as a training platform in surgical education. However, one important aspect of simulation-based surgical training that has not received much attention is the provision of automated real-time performance feedback to support the learning process. Performance feedback is actionable advice that improves novice behaviour. In simulation, automated feedback is typically extracted from prediction models trained using data mining techniques. Existing techniques suffer from either low effectiveness or low efficiency resulting in their inability to be used in real-time. In this paper, we propose a random forest based method that finds a balance between effectiveness and efficiency. Experimental results in a temporal bone surgery simulation show that the proposed method is able to extract highly effective feedback at a high level of efficiency.

cs.AI↗

arXiv2017

Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training

Simulation-based training (SBT) is gaining popularity as a low-cost and convenient training technique in a vast range of applications. However, for a SBT platform to be fully utilized as an effective training tool, it is essential that feedback on performance is provided automatically in real-time during training. It is the aim of this paper to develop an efficient and effective feedback generation method for the provision of real-time feedback in SBT. Existing methods either have low effectiveness in improving novice skills or suffer from low efficiency, resulting in their inability to be used in real-time. In this paper, we propose a neural network based method to generate feedback using the adversarial technique. The proposed method utilizes a bounded adversarial update to minimize a L1 regularized loss via back-propagation. We empirically show that the proposed method can be used to generate simple, yet effective feedback. Also, it was observed to have high effectiveness and efficiency when compared to existing methods, thus making it a promising option for real-time feedback generation in SBT.

cs.LG↗