Circular polarizers based on the metasurface suffer from a trade-off between the structural complexity and the polarization extinction ratio (ER). Herein, we present a single-layer chiral metasurface with strong circular dichroism. The structure turns a circularly polarized incident beam into a linearly polarized beam, achieving a high circular polarization ER. The operating wavelength of the proposed metasurface is tunable by changing the geometric parameters. The metasurface’s localized surface plasmon resonances between structures ensure strong chiral optical effects. We further experimentally demonstrate the circular dichroism of the fabricated metasurface.

精确的、与波长相关的相位调制在许多领域中是必不可少的, 比如超分辨成像、全彩色全息、微纳加工以及光通讯。这一要求很难通过单一的传统光学元件实现, 一般需要使用多个光学元件组合完成。本文提出一种可以实现波长选择性波前整形的超表面设计方法。具体来说, 本文设计了一种超表面, 它能够对785 nm波长的光做涡旋相位调制, 同时不影响590 nm波长的光保持原有相位分布。本文通过干涉仪以及对应点扩散函数的测量来验证不同波长下的波前分布。与已提出的空间复用方式以及色散工程的方法相比, 我们提出的设计方法更加直接, 优化难度小, 适用于需要波长选择性编码的光学系统。本文所提平面光学器件对于需要波长选择性编码的光学系统具有重要应用意义。

The sedentary lifestyle of modern people is becoming more and more common, and a lack of physical activity is a risk factor for many diseases. Therefore, this project introduces a mobile application that works on iOS and Android, PoseBuddy, through computer vision, provides interactive experiences, personalized workout plans, and competition through connections with others. It allows users to exercise anytime and anywhere with fragmented time without the limitation of venues. PoseBuddy forms an interactive mode that obtains users’ pose data through the mobile device’s front camera, inputting to a high-accuracy human pose estimation model provided by TensorFlow, MoveNet. Afterwards, it provides real-time audio feedback during exercise after being verified by the accuracy validation algorithm. The real-time inputs from the camera feed can be captured and processed asynchronously by the system, allowing users to know in real-time if they are carrying out the workout correctly. Additionally, PoseBuddy is also a sports platform that allows users to post their experiences after exercising and making friends.

Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures. A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC. Extensive experiments are conducted on three benchmark datasets. The results have demonstrated that the new model can realistically capture the complex data structures and further improve the performance for HMLC comparing with the state-of-the-art methods. To facilitate future research, our code is publicly available.

In hearing-loss community, sign language is a primary tool to communicate with people while there is communication gap between hearing-loss people with normal hearing people. Continuous sign language recognition, which can bridge the communication gap, is a challenging task because of the weakly supervised ordered annotations where no frame-level label is provided. To overcome this problem, connectionist temporal classification (CTC) is the most widely used method. However, CTC learning could perform bad if extracted features are not good. For better feature extraction, this work presents the novel self-attention-based fully-inception (SAFI) networks for vision-based end-to-end continuous sign language recognition. Considering the length of sign words differs from each other, we introduce fully inception network with different receptive field to extract dynamic clip-level features. To further boost the performance, the fully inception network with an auxiliary classifier is trained with aggregation cross entropy (ACE) loss. Then the self-attention networks as global sequential feature extractor is used to model the clip-level features with CTC. The proposed model is optimized by jointly training with ACE on clip-level feature learning and CTC on global sequential feature learning in an end-to-end fashion. The best method in the baselines achieves 35.6% WER on validation set and 34.5% WER on test set. It employs a better decoding algorithm for pseudo label to do the EM-like optimization to fine tune CNN module. In contrast, our approach focuses on the better feature extraction for end-to-end learning. To alleviate the overfitting on the limited dataset, we employ temporal elastic deformation to triple the real-world dataset RWTH-PHOENIX-Weather 2014. Experimental results on the real-world dataset RWTH-PHOENIX-Weather 2014 demonstrate the effectiveness of our approach which achieves 31.7% WER on validation set and 31.3% WER on test set.

Class imbalance is a challenging problem in many real-world applications such as fraudulent transactions detection in finance and diagnosis of rare diseases in medicine, which has attracted more and more attention in the community of machine learning and data mining. The main issue is how to capture the fundamental characteristics of the imbalanced data distribution. In particular, whether the hidden pattern can be truly mined from minority class is still a largely unanswered question after all it contains limited instances. The existing methods provide only a partial understanding of this issue and result in the biased and inaccurate classifiers. To overcome this issue, we propose a novel imbalanced classification framework with two stages. The first stage aims to accurately determine the class distributions by a supervised class distribution learning method under the Wasserstein auto-encoder framework. The second stage makes use of the generative adversarial networks to simultaneously generate instances according to the learnt class distributions and mine the discriminative structure among classes to train the final classifier. This proposed framework focuses on Supervised Class Distribution Learning for Generative Adversarial Networks-based imbalanced classification (SCDL-GAN). By comparing with the state-of-the-art methods, the experimental results demonstrate that SCDL-GAN consistently benefits the imbalanced classification task in terms of several widely-used evaluation metrics on five benchmark datasets.

In imbalanced data classification, it is hard to learn the hidden pattern from the minority class due to its insufficient information. To solve this problem, a popular type of sampling methods is proposed based on Active Learning framework, but they still suffer from two key issues: how to keep the structure of the original data and avoid imbalance during the learning process. In this paper, we proposed a novel Active Learning framework (COAL) to select and generate informative instances. To keep the structure and enhance the diversity of the original data, we make use of Clustering-based uncertainty sampling to find informative instances. Meanwhile, to avoid imbalance in the active learning process, we make use of Oversampling method to balance the quantities between classes. Extensive experiments have been conducted by using real world datasets with a large range of imbalance ratio (from 2.78 to 66.67). The experimental results show that the proposed COAL outperforms state-of-the-art methods in terms of several well-known evaluation metrics.