Leveraging Orbital Information and Atomic Feature in Deep Learning Model

Predicting material properties base on micro structure of materials has long been a challenging problem. Recently many deep learning methods have been developed for material property prediction. In this study, we propose a crystal representation learning framework, Orbital CrystalNet, OCrystalNet, which consists of two parts: atomic descriptor generation and graph representation learning. In OCrystalNet, we first incorporate orbital field matrix (OFM) and atomic features to construct OFM-feature atomic descriptor, and then the atomic descriptor is used as atom embedding in the atom-bond message passing module which takes advantage of the topological structure of crystal graphs to learn crystal representation. To demonstrate the capabilities of OCrystalNet we performed a number of prediction tasks on Material Project dataset and JARVIS dataset and compared our model with other baselines and state of art methods. To further present the effectiveness of OCrystalNet, we conducted ablation study and case study of our model. The results show that our model have various advantages over other state of art models

SAFS: A Deep Feature Selection Approach for Precision Medicine

In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prioritizing risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach is to use deep learning to identify significant risk factors affecting left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk. The results show that our feature learning and representation approach leads to better results in comparison with others

SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud

Poles and building edges are frequently observable objects on urban roads, conveying reliable hints for various computer vision tasks. To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud. To train our model without the time consuming and tedious data labeling process, we first generate synthetic primitives for the basic appearance of target lines, and build an iterative line auto-labeling process to gradually refine line labels on real LiDAR scans. Our segmentation model can extract lines under arbitrary scale perturbations, and we use shared EdgeConv encoder layers to train the two segmentation and descriptor heads jointly. Base on the model, we can build a highly-available global registration module for point cloud registration, in conditions without initial transformation hints. Experiments have demonstrated that our line-based registration method is highly competitive to state-of-the-art point-based approaches. Our code is available at https://github.com/zxrzju/SuperLine3D.git.Comment: 17 pages, ECCV 2022 Accepte

Photonic Floquet skin-topological effect

Non-Hermitian skin effect and photonic topological edge states are of great interest in non-Hermitian physics and optics. However, the interplay between them is largly unexplored. Here, we propose and demonstrate experimentally the non-Hermitian skin effect that constructed from the nonreciprocal flow of Floquet topological edge states, which can be dubbed 'Floquet skin-topological effect'. We first show the non-Hermitian skin effect can be induced by pure loss when the one-dimensional (1D) system is periodically driven. Next, based on a two-dimensional (2D) Floquet topological photonic lattice with structured loss, we investigate the interaction between the non-Hermiticity and the topological edge states. We observe that all the one-way edge states are imposed onto specific corners, featuring both the non-Hermitian skin effect and topological edge states. Furthermore, a topological switch for the skin-topological effect is presented by utilizing the gap-closing mechanism. Our experiment paves the way of realizing non-Hermitian topological effects in nonlinear and quantum regimes

Radio-Frequency Interference Estimation for Multiple Random Noise Sources

As more compact designs and more assembled function modules are utilized in modern electronic devices, radio-frequency interference (RFI) source reconstruction is becoming more challenging because different noise sources may contribute simultaneously. This article presents a novel methodology to reconstruct multiple random noise sources on a real-world product, including several double-data-rate (DDR) memory modules and a high-speed connector. The DDR modules located beneath a heatsink cause random noise-like signals, which renders phase measurements challenging. An approach based on the tuned-receiver mode of a vector network analyzer is developed to measure the field phase from the random DDR signals, which can be further modeled with a Huygens\u27 box using the measured field magnitude and phase. Moreover, the connector can be modeled using an equivalent magnetic dipole. Furthermore, the total RFI power from the DDR memory modules and the high-speed connector, which generate uncorrelated RFI noise, is found to equal the summation of the individual power values obtained by an root mean square detector, which can be mathematically corroborated. Using the proposed method, the reconstructed source model can predict RFI values close to measurement results with less than 5 dB deviation

LiSum: Open Source Software License Summarization with Multi-Task Learning

Open source software (OSS) licenses regulate the conditions under which users can reuse, modify, and distribute the software legally. However, there exist various OSS licenses in the community, written in a formal language, which are typically long and complicated to understand. In this paper, we conducted a 661-participants online survey to investigate the perspectives and practices of developers towards OSS licenses. The user study revealed an indeed need for an automated tool to facilitate license understanding. Motivated by the user study and the fast growth of licenses in the community, we propose the first study towards automated license summarization. Specifically, we released the first high quality text summarization dataset and designed two tasks, i.e., license text summarization (LTS), aiming at generating a relatively short summary for an arbitrary license, and license term classification (LTC), focusing on the attitude inference towards a predefined set of key license terms (e.g., Distribute). Aiming at the two tasks, we present LiSum, a multi-task learning method to help developers overcome the obstacles of understanding OSS licenses. Comprehensive experiments demonstrated that the proposed jointly training objective boosted the performance on both tasks, surpassing state-of-the-art baselines with gains of at least 5 points w.r.t. F1 scores of four summarization metrics and achieving 95.13% micro average F1 score for classification simultaneously. We released all the datasets, the replication package, and the questionnaires for the community

A Planar Low-Profile Meander Antenna Design for Wireless Terminal Achieving Low RF Interference and High Isolation in Multi-Antenna Systems

In this article, a meander line internal antenna used for wireless terminal is proposed. The current of this antenna is mostly distributed on the antenna radiator itself, rather than on the main board of the wireless device. As a result, the chance of having radiofrequency (RF) interference issues, which usually result in receiver desensitization in wireless radios, can be significantly reduced. The antenna has good radiation performance in the vertical polarization with a low physical profile, compared with the existing antenna designs for typical wireless terminals. The antenna has efficiency similar to the monopole antenna with much less reference/ground plane dependence, achieving lower RF interference, which is demonstrated by the noise coupling measurements in a predefined digital clock - antenna configuration. Furthermore, the mutual coupling (i.e., isolation) between two such antennas is studied and the envelope correlation coefficient between the two antennas is found to be low. A router assembled with the two proposed antennas is tested, and the total isotropic sensitivity is found lower compared with monopole antennas, due to the characteristics of low RF interference and high isolation of the proposed antenna