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Xi Chen

Publications and source records attributed to Xi Chen.

At least 847 records · Page 47Linked to original sources
arXiv2021

Confining photonic nanojet in a microwell on microsphere lens for highly efficient light focusing, signal amplification and quantitative detection

Dielectric microspheres or microbeads can squeeze light into the subwavelength scale via photonic nanojet (PNJ) focusing. This enables strong light-matter interactions within its focus and induces extraordinary effects such as enhancements in light emission, signal collection and various other applications. However, critical challenges exist on how to efficiently and precisely loading the studied objects into the desired location of the PNJ focusing, and subsequently measure the field and signal of interests precisely and reliably. Such technique is currently missing. We present for the first time a unique microwell-decorated microsphere lens (u-well lens), with a semi-open microwell sample compartment directly fabricated on top of the microsphere lens. The u-well lens confines PNJ in a semi-open microwell and allows passive trapping of individual micro-object into the PNJ focusing area with high efficiency and spatial accuracy. We demonstrate that individual fluorescent microsphere of different sizes can be readily introduced to the designated location with loading efficiency >70% and generates reproducible enhanced fluorescence signals with standard deviation better than that can be quantitatively measured. A comprehensive analysis on the optical properties of the u-well lens reveals the synergistic effect of field enhancement and collection efficiency on the optical enhancement. We finally employ this special microsphere lens for fluorescent-bead-based biotin concentration analysis. The results suggest a greatly enhanced sensitivity and highly improved detection limit, opening the door for its application in highly sensitive and quantitative detection.

physics.optics↗
arXiv2021

Adversarial Robustness of Probabilistic Network Embedding for Link Prediction

In today's networked society, many real-world problems can be formalized as predicting links in networks, such as Facebook friendship suggestions, e-commerce recommendations, and the prediction of scientific collaborations in citation networks. Increasingly often, link prediction problem is tackled by means of network embedding methods, owing to their state-of-the-art performance. However, these methods lack transparency when compared to simpler baselines, and as a result their robustness against adversarial attacks is a possible point of concern: could one or a few small adversarial modifications to the network have a large impact on the link prediction performance when using a network embedding model? Prior research has already investigated adversarial robustness for network embedding models, focused on classification at the node and graph level. Robustness with respect to the link prediction downstream task, on the other hand, has been explored much less. This paper contributes to filling this gap, by studying adversarial robustness of Conditional Network Embedding (CNE), a state-of-the-art probabilistic network embedding model, for link prediction. More specifically, given CNE and a network, we measure the sensitivity of the link predictions of the model to small adversarial perturbations of the network, namely changes of the link status of a node pair. Thus, our approach allows one to identify the links and non-links in the network that are most vulnerable to such perturbations, for further investigation by an analyst. We analyze the characteristics of the most and least sensitive perturbations, and empirically confirm that our approach not only succeeds in identifying the most vulnerable links and non-links, but also that it does so in a time-efficient manner thanks to an effective approximation.

cs.SI↗
arXiv2021

Online Covariance Matrix Estimation in Stochastic Gradient Descent

The stochastic gradient descent (SGD) algorithm is widely used for parameter estimation, especially for huge data sets and online learning. While this recursive algorithm is popular for computation and memory efficiency, quantifying variability and randomness of the solutions has been rarely studied. This paper aims at conducting statistical inference of SGD-based estimates in an online setting. In particular, we propose a fully online estimator for the covariance matrix of averaged SGD iterates (ASGD) only using the iterates from SGD. We formally establish our online estimator's consistency and show that the convergence rate is comparable to offline counterparts. Based on the classic asymptotic normality results of ASGD, we construct asymptotically valid confidence intervals for model parameters. Upon receiving new observations, we can quickly update the covariance matrix estimate and the confidence intervals. This approach fits in an online setting and takes full advantage of SGD: efficiency in computation and memory.

stat.ML↗
arXiv2021

PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction

Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples.

cs.CL↗
arXiv2021

Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding

Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embedding-based models, and the conventional reasoning and lexical matching based systems. The former compute the similarity of entities via their cross-KG embeddings, but they usually rely on an ideal supervised learning setting for good performance and lack appropriate reasoning to avoid logically wrong mappings; while the latter address the reasoning issue but are poor at utilizing the KG graph structures and the entity contexts. In this study, we aim at combining the above two solutions and thus propose an iterative framework named PRASE which is based on probabilistic reasoning and semantic embedding. It learns the KG embeddings via entity mappings from a probabilistic reasoning system named PARIS, and feeds the resultant entity mappings and embeddings back into PARIS for augmentation. The PRASE framework is compatible with different embedding-based models, and our experiments on multiple datasets have demonstrated its state-of-the-art performance.

cs.CL↗
arXiv2021

The Semiclassical Resolvent on Conic Manifolds and Application to Schrödinger Equations

In this article, we shall construct the resolvent of Laplacian at high energies near the spectrum on non-product conic manifolds with a single cone tip. Microlocally, the resolvent kernel is the sum of b-pseudodifferential operators, scattering pseudodifferential operators and intersecting Legendrian distributions. As an application, we shall establish Strichartz estimates for Schrödinger equations on non-compact manifolds with multiple non-product conic singularities.

math.AP↗
arXiv2021

Non-parametric estimation of Stochastic Differential Equations from stationary time-series

We study efficiency of non-parametric estimation of diffusions (stochastic differential equations driven by Brownian motion) from long stationary trajectories. First, we introduce estimators based on conditional expectation which is motivated by the definition of drift and diffusion coefficients. These estimators involve time- and space-discretization parameters for computing expected values from discretely-sampled stationary data. Next, we analyze consistency and mean squared error of these estimators depending on computational parameters. We derive relationships between the number of observational points, time- and space-discretization parameters in order to achieve the optimal speed of convergence and minimize computational complexity. We illustrate our approach with numerical simulations.

math.PR↗
arXiv2021

OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding

Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities. Current methods have explored and utilized the graph structure, the entity names and attributes, but ignore the ontology (or ontological schema) which contains critical meta information such as classes and their membership relationships with entities. In this paper, we propose an ontology-guided entity alignment method named OntoEA, where both KGs and their ontologies are jointly embedded, and the class hierarchy and the class disjointness are utilized to avoid false mappings. Extensive experiments on seven public and industrial benchmarks have demonstrated the state-of-the-art performance of OntoEA and the effectiveness of the ontologies.

cs.CL↗
arXiv2021

Multiple Charge density waves and lattice superstructures in thin-layer TmTe2 and TmTe3

We have grown thulium tellurides (TmTe2, TmTe3) thin layers (less than four layers) on graphene/SiC (0001) by molecular beam epitaxy. The charge density waves (CDWs) and lattice superstructures (LSs) are investigated by scanning tunneling microscopy. Clear CDW patterns in real space are observed on surface of metallic TmTe3. Two CDWs are with wave vectors 0.29c* and 0.31a* respectively. LSs with various periods are unveiled on the surface of TmTe2 and TmTe3. The electronic structures of these films are semiconducting. These results show that superstructures in rare earth tellurides can have two origins, CDWs or LSs.

cond-mat.mtrl-sci↗
arXiv2021

Balancing orbital effects and onsite Coulomb repulsion through Na modulations in NaxVO2

Vanadium oxides have been highly attractive for over half a century since the discovery of the metal insulator transition near room temperatures. Here NaxVO2 is studied through a systematic comparison with other layered sodium metal oxides with early 3d transition metals, first disclosing a unified evolution pattern of Na density waves through in situ XRD analysis. Combining ab-initio simulations and theoretical modelings, a sodium-modulated Peierls-like transition mechanism is then proposed for the bonding formation of metal ion dimers. More importantly, the unique trimer structure in NaxVO2 is shown to be very sensitive to the onsite Coulomb repulsion value, suggesting a delicate balance between strong electronic correlations and orbital effects that can be precisely modulated by both Na compositions and atomic stackings. This unveils a unique opportunity to design strongly correlated materials with tailored electronic transitions through electrochemical modulations and crystallographic designs, to elegantly balance various competition effects. We think the understanding will also help further elucidate complicated electronic behaviors in other vanadium oxide systems.

cond-mat.mtrl-sci↗
arXiv2021

Accent Recognition with Hybrid Phonetic Features

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, the frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with the language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the Accented English Speech Recognition Challenge (AESRC) 2020 dataset. The results demonstrate that our approach can obtain a 6.57% relative improvement on the validation set. We also get a 7.28% relative improvement on the final test set for this competition, showing the merits of the proposed method.

eess.AS↗
arXiv2021

Composite active drag control in turbulent channel flows

A composite drag control (CDC) combining the opposition (OC) and spanwise opposed wall-jet forcing (SOJF) methods is studied in a turbulent channel flow via direct numerical simulation of the incompressible Navier-Stokes equations. A maximum drag reduction of about 33% is obtained for CDC -- much higher than that produced by either individual method (namely, 19% for SOJF and 23% for OC). Due to the small power input required for both OC and SOJF methods, a significant net power saving (about 32%) is achieved via CDC. Flow analysis shows that CDC can take advantage of both OC and SOJF methods to better suppress drag producing, near-wall turbulent structures -- vortices and streaks. In particular, due to the presence of the large-scale coherent swirls generated by SOJF, it is more effective than OC in suppressing the random turbulence. Moreover, due to the OC's role in suppressing random small-scale turbulence, CDC requires weaker large-scale coherent swirls than those using SOJF only -- hence decreasing the drag contribution associated with large-scale swirls. In summary, our results suggest prospects of employing composite control strategy for effective skin friction drag reduction, particularly at very high Reynolds numbers.

physics.flu-dyn↗
arXiv2021

A low-communication-overhead parallel method for the 3D incompressible Navier-Stokes equations

This paper presents a low-communication-overhead parallel method for solving the 3D incompressible Navier-Stokes equations. A fully-explicit projection method with second-order space-time accuracy is adopted. Combined with fast Fourier transforms, the parallel diagonal dominant (PDD) algorithm for the tridiagonal system is employed to solve the pressure Poisson equation, differing from its recent applications to compact scheme derivatives computation (Abide et al. 2017) and alternating-direction-implicit method (Moon et al. 2020). The number of all-to-all communications is decreased to only two, in a 2D pencil-like domain decomposition. The resulting MPI/OpenMP hybrid parallel code shows excellent strong scalability up to $10^4$ cores and small wall-clock time per timestep. Numerical simulations of turbulent channel flow at different friction Reynolds numbers ($Re_τ$ = 550, 1000, 2000) have been conducted and the statistics are in good agreement with the reference data. The proposed method allows massively simulation of wall turbulence at high Reynolds numbers as well as many other incompressible flows.

physics.flu-dyn↗
arXiv2021

Interventional Aspect-Based Sentiment Analysis

Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when encountering confounder such as non-target aspects. In this paper, we take a causal view to addressing this issue. We propose a simple yet effective method, namely, Sentiment Adjustment (SENTA), by applying a backdoor adjustment to disentangle those confounding factors. Experimental results on the Aspect Robustness Test Set (ARTS) dataset demonstrate that our approach improves the performance while maintaining accuracy in the original test set.

cs.CL↗
arXiv2021

Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management

For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL.To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs. In this paper, we propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot. Based on inverse adversarial reinforcement learning, our designed reward model can provide more accurate and explainable reward signals for state-action pairs.Extensive evaluations show that our approach can be applied to a wide range of reinforcement learning-based dialog systems and significantly improves both the performance and the speed of convergence.

cs.CL↗
arXiv2021

The Sobolev Inequalities on Real Hyperbolic Spaces and Eigenvalue Bounds for Schrödinger Operators with Complex Potentials

In this paper, we prove the uniform estimates for the resolvent $(Δ- α)^{-1}$ as a map from $L^q$ to $L^{q'}$ on real hyperbolic space $\mathbb{H}^n$ where $α\in \mathbb{C}\setminus [(n - 1)^2/4, \infty)$ and $2n/(n + 2) \leq q < 2$. In contrast with analogous results on Euclidean space $\mathbb{R}^n$, the exponent $q$ here can be arbitrarily close to $2$. This striking improvement is due to two non-Euclidean features of hyperbolic space: the Kunze-Stein phenomenon and the exponential decay of the spectral measure. In addition, we apply this result to the study of eigenvalue bounds of the Schrödinger operator with a complex potential. The improved Sobolev inequality results in a better long range eigenvalue bound on $\mathbb{H}^n$ than that on $\mathbb{R}^n$.

math.AP↗
arXiv2021

Structural phase transition of two-dimensional monolayer SnTe from artificial neural network

As machine learning becomes increasingly important in engineering and science, it is inevitable that machine learning techniques will be applied to the investigation of materials, and in particular the structural phase transitions common in ferroelectric materials. Here, we build and train an artificial neural network to accurately predict the energy change associated with atom displacements and use the trained artificial neural network in Monte-Carlo simulations on ferroelectric materials to investigate their phase transitions. We apply this approach to two-dimensional monolayer SnTe and show that it can indeed be used to simulate the phase transitions and predict the transition temperature. The artificial neural network, when viewed as a universal mathematical structure, can be readily transferred to the investigation of other ferroelectric materials when training data generated with ab initio methods are available.

cond-mat.mtrl-sci↗