Growth and characterization of Bi2Se3 thin films by pulsed laser deposition using alloy target

Bi2Se3 thin films were deposited on the (100) oriented Si substrates by pulsed laser deposition technique at different substrate temperatures (room temperature – 400 ºC). The effects of the substrate temperature on the structural and electrical properties of the Bi2Se3 films were studied. The film prepared at room temperature showed a very poor polycrystalline structure with the mainly orthorhombic phase. The crystallinity of the films was improved by heating the substrate during the deposition and the crystal phase of the film changed to the rhombohedral phase as the substrate temperature was higher than 200 ºC. The stoichiometry of the films and the chemical state of Bi and Se elements in the films were studied by fitting the Se 3d and the Bi 4d5/2 peaks of the X-ray photoelectron spectra. The hexagonal structure was seen clearly for the film prepared at the substrate temperature of 400 ºC. The surface roughness of the film increased as the substrate temperature was increased. The electrical resistivity of the film decreased from 1x10-3 to 3 x 10-4 Ω cm as the substrate temperature was increased from room temperature to 400 ºC.Shenyang National Laboratory for Material Science (SYNL), Chin

MPCViT: Searching for MPC-friendly Vision Transformer with Heterogeneous Attention

Secure multi-party computation (MPC) enables computation directly on encrypted data on non-colluding untrusted servers and protects both data and model privacy in deep learning inference. However, existing neural network (NN) architectures, including Vision Transformers (ViTs), are not designed or optimized for MPC protocols and incur significant latency overhead due to the Softmax function in the multi-head attention (MHA). In this paper, we propose an MPC-friendly ViT, dubbed MPCViT, to enable accurate yet efficient ViT inference in MPC. We systematically compare different attention variants in MPC and propose a heterogeneous attention search space, which combines the high-accuracy and MPC-efficient attentions with diverse structure granularities. We further propose a simple yet effective differentiable neural architecture search (NAS) algorithm for fast ViT optimization. MPCViT significantly outperforms prior-art ViT variants in MPC. With the proposed NAS algorithm, our extensive experiments demonstrate that MPCViT achieves 7.9x and 2.8x latency reduction with better accuracy compared to Linformer and MPCFormer on the Tiny-ImageNet dataset, respectively. Further, with proper knowledge distillation (KD), MPCViT even achieves 1.9% better accuracy compared to the baseline ViT with 9.9x latency reduction on the Tiny-ImageNet dataset.Comment: 6 pages, 6 figure

Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10−6) and cell adhesion molecules (corrected p-value = 5.44 × 10−4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.Peer reviewedFinal Published versio

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.Comment: in Chinese language, Accepted by Electric Power Constructio