Measuring Topological Field Theories: Lattice Models and Field-Theoretic Description

Recent years have witnessed a surge of interest in performing measurements within topological phases of matter, e.g., symmetry-protected topological (SPT) phases and topological orders. Notably, measurements of certain SPT states have been known to be related to Kramers-Wannier duality and Jordan-Wigner transformations, giving rise to long-range entangled states and invertible phases, such as the Kitaev chain. Moreover, measurements of topologically ordered states correspond to charge condensations. In this work, we present a field-theoretic framework for describing measurements within topological field theories. We employ various lattice models as examples to illustrate the outcomes of measuring local symmetry operators within topological phases, demonstrating their agreement with the predictions from field-theoretic descriptions. We demonstrate that these measurements can lead to SPT, spontaneous symmetry-breaking, and topologically ordered phases. Specifically, when there is emergent symmetry after measurement, the remaining symmetry and emergent symmetry will have a mixed anomaly, which leads to long-ranged entanglement.Comment: 43 page

Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Zero-shot referring image segmentation is a challenging task because it aims to find an instance segmentation mask based on the given referring descriptions, without training on this type of paired data. Current zero-shot methods mainly focus on using pre-trained discriminative models (e.g., CLIP). However, we have observed that generative models (e.g., Stable Diffusion) have potentially understood the relationships between various visual elements and text descriptions, which are rarely investigated in this task. In this work, we introduce a novel Referring Diffusional segmentor (Ref-Diff) for this task, which leverages the fine-grained multi-modal information from generative models. We demonstrate that without a proposal generator, a generative model alone can achieve comparable performance to existing SOTA weakly-supervised models. When we combine both generative and discriminative models, our Ref-Diff outperforms these competing methods by a significant margin. This indicates that generative models are also beneficial for this task and can complement discriminative models for better referring segmentation. Our code is publicly available at https://github.com/kodenii/Ref-Diff

Computational Screening of New Perovskite Materials Using Transfer Learning and Deep Learning

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us to predict the formation energy with sufficient precision of a large number of materials of which only the structural information is available. The enlarged training set is then used to train a convolutional neural network model (the screening model) with the generic Magpie elemental features with high prediction power. Extensive experiments demonstrate the superior performance of our transfer learning model and screening model compared to the baseline models. We then applied the screening model to filter out promising new perovskite materials out of 21,316 hypothetical perovskite structures with a large portion of them confirmed by existing literature

BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability

Traditional sentiment construction in finance relies heavily on the dictionary-based approach, with a few exceptions using simple machine learning techniques such as Naive Bayes classifier. While the current literature has not yet invoked the rapid advancement in the natural language processing, we construct in this research a textual-based sentiment index using a novel model BERT recently developed by Google, especially for three actively trading individual stocks in Hong Kong market with hot discussion on Weibo.com. On the one hand, we demonstrate a significant enhancement of applying BERT in sentiment analysis when compared with existing models. On the other hand, by combining with the other two existing methods commonly used on building the sentiment index in the financial literature, i.e., option-implied and market-implied approaches, we propose a more general and comprehensive framework for financial sentiment analysis, and further provide convincing outcomes for the predictability of individual stock return for the above three stocks using LSTM (with a feature of a nonlinear mapping), in contrast to the dominating econometric methods in sentiment influence analysis that are all of a nature of linear regression.Comment: 10 pages, 1 figure, 5 tables, submitted to NeurIPS 2019, under revie

Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments showed that our method achieves better performance than conventional regression algorithms such as support vector machines and Random Forest. It is also better than CNN models using only the OFM features, the Magpie features, or the basic one-hot encodings. This demonstrates the advantages of CNN and feature fusion for materials property prediction. Finally, we visualized the two-dimensional OFM descriptors and analyzed the features extracted by the CNN to obtain greater understanding of the CNN-OFM model

Research on synchronverter-based regenerative braking energy feedback system of urban rail transit

Generally running with frequent braking over short distances, the urban rail transit train generates great quantities of regenerative braking energy (RBE). The RBE feedback system can effectively recycle RBE and give it back to the AC grid. However, the lack of damp and inertia of generators makes conventional PWM RBE feedback system more sensitive to power fluctuations. To address this issue, a synchronverter-based RBE feedback system of urban rail transit is designed in this paper. First, the structure of the feedback system is presented. Then, the synchronverter-based control strategy with greater flexibility and higher stability is fully discussed. Furthermore, the parameter design of the system is analyzed in detail. Finally, simulation results and experimental results are provided to show the good dynamic performance of the system. Using this synchronverter-based approach, the system supplies traction power to the traction network when the train accelerates and gives the RBE back to the AC grid when the train brakes, in light of the variation of the DC bus voltage. Moreover, the system can be self-synchronized with the AC grid and make corresponding power management on the basis of changes in the voltage amplitude as well as the frequency of the grid. In this sense, the RBE feedback system becomes more flexible, effective and robust