arXiv subjects

Peng Shi

Publications and source records attributed to Peng Shi.

At least 109 records · Page 6Linked to original sources

arXiv2020

Bloch-type photonic skyrmions in optical chiral multilayers

Magnetic skyrmions are topological quasiparticles in magnetic field. Until recently, as one of their photonic counterparts, Néel-type photonic skyrmion is discovered in surface plasmon polaritons. The deep-subwavelength features of the photonic skyrmions suggest their potentials in quantum technologies and data storage. So far, the Bloch-type photonic skyrmion has yet to be demonstrated in this brand new research field. Here, by exploiting the quantum spin Hall effect of a plasmonic optical vortex in multilayered structure, we predict the existence of photonic twisted-Néel- and Bloch-type skyrmions in chiral materials. Their chirality-dependent features can be considered as additional degrees-of-freedom for future chiral sensing, information processing and storage technologies. In particular, our findings enlarge the family of photonic skyrmions and reveal a remarkable resemblance of the feature of chiral materials in two seemingly distant fields: photonic skyrmions and magnetic skyrmions.

physics.optics↗

arXiv2020

Modeling Multivariate Time Series with Copula-linked Univariate D-vines

This paper proposes a novel multivariate time series model named Copula-linked univariate D-vines (CuDvine), which enables the simultaneous copula-based modeling of both temporal and cross-sectional dependence for multivariate time series. To construct CuDvine, we first build a semiparametric univariate D-vine time series model (uDvine) based on a D-vine. The uDvine generalizes the existing first-order copula-based Markov chain models to Markov chains of an arbitrary-order. Building upon uDvine, we construct CuDvine by linking multiple uDvines via a parametric copula. As a simple and tractable model, CuDvine provides flexible models for marginal behavior and temporal dependence of time series, and can also incorporate sophisticated cross-sectional dependence such as time-varying and spatio-temporal dependence for high-dimensional applications. Robust and computationally efficient procedures, including a sequential model selection method and a two-stage MLE, are proposed for model estimation and inference, and their statistical properties are investigated. Numerical experiments are conducted to demonstrate the flexibility of CuDvine, and to examine the performance of the sequential model selection procedure and the two-stage MLE. Real data applications on the Australian electricity price data demonstrate the superior performance of CuDvine to traditional multivariate time series models.

stat.ME↗

arXiv2020

Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL

Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any input. However, in the real scenario, users can input any text that may not be able to be answered by a SQL query. In this work, we propose TriageSQL, the first cross-domain text-to-SQL question intention classification benchmark that requires models to distinguish four types of unanswerable questions from answerable questions. The baseline RoBERTa model achieves a 60% F1 score on the test set, demonstrating the need for further improvement on this task. Our dataset is available at https://github.com/chatc/TriageSQL.

cs.CL↗

arXiv2020

What is Shear Wave

This study shows that the traditional definition of shear wave breaks the shear stress reciprocity. By analyzing the displacement field for shear wave and the motion equation of material element, it is found that the displacement field is related to local rigid body rotation, and the local rigid body rotation cannot be balanced by the stress state assumed in classical continuum mechanics. It is also found that the displacement field caused by shear deformation, which is neither divergence nor rotation, is replaced with that caused by local rigid body rotation during the derivation of wave equation. This indicates that the definition of shear wave is beyond the basic assumption of continuum mechanics. The study modified the constitutive relation and elastic tensor based on that the traditionally defined shear wave is objective. The motion equation corresponding to shear wave is also derived. It is concluded from the definition of traditional shear wave that the local rigid body rotation should contribute stress and the shear stress reciprocity is not prerequisite for nonpolar continuum.

physics.class-ph↗

arXiv2020

Transverse spin dynamics of light: the generalized spin-momentum locking for structured guided modes

Quantum spin-Hall effect, a manifestation of topological properties that govern the behavior of surface states, was studied intensively in condensed matter physics resulting in the discovery of topological insulators. The quantum spin-Hall effect of light was introduced for surface plane-waves which intrinsically carry transverse optical spin, leading to many intriguing phenomena and applications in unidirectional waveguiding, metrology and quantum technologies. In addition to spin, optical waves can exhibit complex topological properties of vectorial electromagnetic fields, associated with orbital angular momentum or nonuniform intensity variations. Here, by considering both spin and angular momentum, we demonstrate a generalized spin-momentum relationship that governs vectorial properties of guided electromagnetic waves, extending optical quantum spin-Hall effect to a two-dimensional vector field of structured guided wave. The effect results in the appearance of the out-of-plane transverse optical spins, which vary progressively from the 'up' state to the 'down' state around the energy flow, and their variation is uniquely locked to the energy propagation direction. The related spin-momentum locking in a chiral spin swirl is demonstrated with four kinds of surface structured waves and proven both theoretically and experimentally. The results provide understanding of the spin dynamics in electromagnetic guided waves and show great importance in spin optics, topological photonics and optical spin-based devices and techniques.

physics.optics↗

arXiv2020

Periodic event-triggered output regulation for linear multi-agent systems

This study considers the problem of periodic event-triggered (PET) cooperative output regulation for a class of linear multi-agent systems. The advantage of the PET output regulation is that the data transmission and triggered condition are only needed to be monitored at discrete sampling instants. It is assumed that only a small number of agents can have access to the system matrix and states of the leader. Meanwhile, the PET mechanism is considered not only in the communication between various agents, but also in the sensor-to-controller and controller-to-actuator transmission channels for each agent. The above problem set-up will bring some challenges to the controller design and stability analysis. Based on a novel PET distributed observer, a PET dynamic output feedback control method is developed for each follower. Compared with the existing works, our method can naturally exclude the Zeno behavior, and the inter-event time becomes multiples of the sampling period. Furthermore, for every follower, the minimum inter-event time can be determined \textit{a prior}, and computed directly without the knowledge of the leader information. An example is given to verify and illustrate the effectiveness of the new design scheme.

eess.SY↗

arXiv2020

Mapping the near-field spin angular momenta in the structured surface plasmon polaritons field

Optical spin angular momenta in a confined electromagnetic field exhibit remarkable difference with their free space counterparts, in particular, the optical transverse spin that is locked with the energy propagating direction lays the foundation for many intriguing physical effects such as unidirectional transportation, quantum spin Hall effect, photonic Skyrmion, etc. In order to investigate the underlying physics behind the spin-orbit interactions as well as to develop the optical spin-based applications, it is crucial to uncover the spin texture in a confined field, yet it faces challenge due to their chiral and near-field vectorial features. Here, we propose a scanning imaging technique which can map the near-field distributions of the optical spin angular momenta with an achiral dielectric nanosphere. The spin angular momentum component normal to the interface can be uncovered experimentally by employing the proposed scanning imaging technique and the three-dimensional spin vector can be reconstructed theoretically with the experimental results. The experiment is demonstrated on the example of surface plasmon polaritons excited by various vector vortex beams under a tight-focusing configuration, where the spin-orbit interaction emerges clearly. The proposed method, which can be utilized to reconstruct the photonic Skyrmion and other photonic topological structures, is straightforward and of high precision, and hence it is expected to be valuable for the study of near-field spin optics and topological photonics.

physics.optics↗

arXiv2019

Cross-Lingual Relevance Transfer for Document Retrieval

Recent work has shown the surprising ability of multi-lingual BERT to serve as a zero-shot cross-lingual transfer model for a number of language processing tasks. We combine this finding with a similarly-recently proposal on sentence-level relevance modeling for document retrieval to demonstrate the ability of multi-lingual BERT to transfer models of relevance across languages. Experiments on test collections in five different languages from diverse language families (Chinese, Arabic, French, Hindi, and Bengali) show that models trained with English data improve ranking quality, without any special processing, both for (non-English) mono-lingual retrieval as well as cross-lingual retrieval.

cs.IR↗

arXiv2019

Aligning Cross-Lingual Entities with Multi-Aspect Information

Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a source language with their counterparts in target languages. In this work, we investigate embedding-based approaches to encode entities from multilingual KGs into the same vector space, where equivalent entities are close to each other. Specifically, we apply graph convolutional networks (GCNs) to combine multi-aspect information of entities, including topological connections, relations, and attributes of entities, to learn entity embeddings. To exploit the literal descriptions of entities expressed in different languages, we propose two uses of a pretrained multilingual BERT model to bridge cross-lingual gaps. We further propose two strategies to integrate GCN-based and BERT-based modules to boost performance. Extensive experiments on two benchmark datasets demonstrate that our method significantly outperforms existing systems.

cs.CL↗

arXiv2019

Regression for Copula-linked Compound Distributions with Applications in Modeling Aggregate Insurance Claims

In actuarial research, a task of particular interest and importance is to predict the loss cost for individual risks so that informative decisions are made in various insurance operations such as underwriting, ratemaking, and capital management. The loss cost is typically viewed to follow a compound distribution where the summation of the severity variables is stopped by the frequency variable. A challenging issue in modeling such outcome is to accommodate the potential dependence between the number of claims and the size of each individual claim. In this article, we introduce a novel regression framework for compound distributions that uses a copula to accommodate the association between the frequency and the severity variables, and thus allows for arbitrary dependence between the two components. We further show that the new model is very flexible and is easily modified to account for incomplete data due to censoring or truncation. The flexibility of the proposed model is illustrated using both simulated and real data sets. In the analysis of granular claims data from property insurance, we find substantive negative relationship between the number and the size of insurance claims. In addition, we demonstrate that ignoring the frequency-severity association could lead to biased decision-making in insurance operations.

stat.AP↗

arXiv2019

Simple Attention-Based Representation Learning for Ranking Short Social Media Posts

This paper explores the problem of ranking short social media posts with respect to user queries using neural networks. Instead of starting with a complex architecture, we proceed from the bottom up and examine the effectiveness of a simple, word-level Siamese architecture augmented with attention-based mechanisms for capturing semantic "soft" matches between query and post tokens. Extensive experiments on datasets from the TREC Microblog Tracks show that our simple models not only achieve better effectiveness than existing approaches that are far more complex or exploit a more diverse set of relevance signals, but are also much faster. Implementations of our samCNN (Simple Attention-based Matching CNN) models are shared with the community to support future work.

cs.CL↗

arXiv2019

Simple BERT Models for Relation Extraction and Semantic Role Labeling

We present simple BERT-based models for relation extraction and semantic role labeling. In recent years, state-of-the-art performance has been achieved using neural models by incorporating lexical and syntactic features such as part-of-speech tags and dependency trees. In this paper, extensive experiments on datasets for these two tasks show that without using any external features, a simple BERT-based model can achieve state-of-the-art performance. To our knowledge, we are the first to successfully apply BERT in this manner. Our models provide strong baselines for future research.

cs.CL↗

arXiv2019

A new perspective from a Dirichlet model for forecasting outstanding liabilities of nonlife insurers

Forecasting the outstanding claim liabilities to set adequate reserves is critical for a nonlife insurer's solvency. Chain-Ladder and Bornhuetter-Ferguson are two prominent actuarial approaches used for this task. The selection between the two approaches is often ad hoc due to different underlying assumptions. We introduce a Dirichlet model that provides a common statistical framework for the two approaches, with some appealing properties. Depending on the type of information available, the model inference naturally leads to either Chain-Ladder or Bornhuetter-Ferguson prediction. Using claims data on Worker's compensation insurance from several US insurers, we discuss both frequentist and Bayesian inference.

stat.AP↗

arXiv2019

Matching Entities Across Different Knowledge Graphs with Graph Embeddings

This paper explores the problem of matching entities across different knowledge graphs. Given a query entity in one knowledge graph, we wish to find the corresponding real-world entity in another knowledge graph. We formalize this problem and present two large-scale datasets for this task based on exiting cross-ontology links between DBpedia and Wikidata, focused on several hundred thousand ambiguous entities. Using a classification-based approach, we find that a simple multi-layered perceptron based on representations derived from RDF2Vec graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only small amounts of training data. The contributions of our work are datasets for examining this problem and strong baselines on which future work can be based.

cs.CL↗

arXiv2019

Implementation of Frequency-Severity Association in BMS Ratemaking

A Bonus-Malus System (BMS) in insurance is a premium adjustment mechanism widely used in a posteriori ratemaking process to set the premium for the next contract period based on a policyholder's claim history. The current practice in BMS implementation relies on the assumption of independence between claim frequency and severity, despite the fact that a series of recent studies report evidence of a significant frequency-severity relationship, particularly in automobile insurance. To address this discrepancy, we propose a copula-based correlated random effects model to accommodate the dependence between claim frequency and severity, and further illustrate how to incorporate such dependence into the current BMS. We derive analytical solutions to the optimal relativities under the proposed framework and provide numerical experiments based on real data analysis to assess the effect of frequency-severity dependence in BMS ratemaking.

stat.AP↗

arXiv2019

Electrical control of spin mixing conductance in a Y$_3$Fe$_5$O$_{12}$/Platinum bilayer

We report a tunable spin mixing conductance, up to $\pm 22\%$, in a Y${}_{3}$Fe${}_{5}$O${}_{12}$/Platinum (YIG/Pt) bilayer.This control is achieved by applying a gate voltage with an ionic gate technique, which exhibits a gate-dependent ferromagnetic resonance line width. Furthermore, we observed a gate-dependent spin pumping and spin Hall angle in the Pt layer, which is also tunable up to $\pm$ 13.6\%. This work experimentally demonstrates spin current control through spin pumping and a gate voltage in a YIG/Pt bilayer, demonstrating the crucial role of the interfacial charge density for the spin transport properties in magnetic insulator/heavy metal bilayers.

cond-mat.mes-hall↗

arXiv2018

Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks

We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward decomposition of the problem into entity detection, entity linking, relation prediction, and evidence combination, we explore simple yet strong baselines. On the popular SimpleQuestions dataset, we find that basic LSTMs and GRUs plus a few heuristics yield accuracies that approach the state of the art, and techniques that do not use neural networks also perform reasonably well. These results show that gains from sophisticated deep learning techniques proposed in the literature are quite modest and that some previous models exhibit unnecessary complexity.

cs.CL↗

arXiv2018

IcoRating: A Deep-Learning System for Scam ICO Identification

Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP, NEO) have been rapidly gaining ground in use, value, and understanding among the public, bringing astonishing profits to investors. Unlike other money and banking systems, most digital tokens do not require central authorities. Being decentralized poses significant challenges for credit rating. Most ICOs are currently not subject to government regulations, which makes a reliable credit rating system for ICO projects necessary and urgent. In this paper, we introduce IcoRating, the first learning--based cryptocurrency rating system. We exploit natural-language processing techniques to analyze various aspects of 2,251 digital currencies to date, such as white paper content, founding teams, Github repositories, websites, etc. Supervised learning models are used to correlate the life span and the price change of cryptocurrencies with these features. For the best setting, the proposed system is able to identify scam ICO projects with 0.83 precision. We hope this work will help investors identify scam ICOs and attract more efforts in automatically evaluating and analyzing ICO projects.

cs.CL↗