arXiv subjects

Shuai Yu

Publications and source records attributed to Shuai Yu.

22 records · Page 2Linked to original sources

arXiv2019

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency

Machine learning and optimization algorithms have been widely applied in the design and optimization for photonic devices. In this article, we briefly review recent progress of this field of research and show some data-driven applications (e.g. spectrum prediction, inverse design and performance optimization) for novel graphene metamaterials (GMs). The structure of the GMs is well-designed to achieve the wideband plasmon induced transparency effect, which is regarded as optimization object and can be theoretically demonstrated by using transfer matrix method. Some classical machine learning algorithms, including k nearest neighbour, decision tree, random forest and artificial neural networks, are utilized to equivalently substitute the numerical simulation in the forward spectrum prediction and complete the inverse design for the GMs. The calculated results demonstrate that all the algorithms are effective and the random forest has advantages in terms of accuracy and training speed. Moreover, the single-objective and multi-objective optimization algorithms are used to achieve steep transmission characteristics by synthetically taking many performance metrics into consideration. The maximum difference between the transmission peaks and dips in the optimized transmission spectrum can reach 0.97. In comparison to previous works, we provide a guidance for intelligent design of photonic devices and advanced materials based on machine learning and evolutionary algorithms.

physics.optics↗

arXiv2019

NAIRS: A Neural Attentive Interpretable Recommendation System

In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS. A self-attention network, as a key component of the system, is designed to assign attention weights to interacted items of a user. This attention mechanism can distinguish the importance of the various interacted items in contributing to a user profile. Based on the user profiles obtained by the self-attention network, NAIRS offers personalized high-quality recommendation. Moreover, it develops visual cues to interpret recommendations. This demo application with the implementation of NAIRS enables users to interact with a recommendation system, and it persistently collects training data to improve the system. The demonstration and experimental results show the effectiveness of NAIRS.

cs.IR↗

arXiv2018

In vivo ultrasound-switchable fluorescence imaging

Ultrasound-switchable fluorescence (USF) imaging was recently developed to overcome the limitation of the poor spatial resolution of the conventional fluorescence imaging in centimeter-deep tissue. However, in vivo USF imaging has not been achieved so far because of the lack of stable near-infrared contrast agents in a biological environment and the lack of data about their biodistributions. In this study, for the first time, we achieved in vivo USF imaging successfully in mice with high resolution. USF imaging in porcine heart tissue and mouse breast tumor via local injections were studied and demonstrated. In vivo and ex vivo USF imaging of the mouse spleen via intravenous injections was also successfully achieved. The results showed that the USF contrast agent adopted in this study was very stable in a biological environment, and it was mainly accumulated into the spleen of the mice. All the results of USF imaging were validated by a commercial micro-CT.

physics.bio-ph↗

arXiv2018

GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding

In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering.

cs.SI↗