arXiv subjects

Hui Liu

Publications and source records attributed to Hui Liu.

At least 469 records · Page 26Linked to original sources

arXiv2021

Observation of the acceleration of light in a tapered optical fiber

One of the most fascinating aspects of quantum fields in curved spacetime is the Unruh effect. The direct experimental detection of Unruh temperature has remained an elusive challenge up to now. Gradient optical waveguides manipulating the dispersion of photons are assumed to realize the great acceleration of effective particles, leading to a high effective Unruh temperature. However, experimentally achieving this optical waveguide has not yet been reported. In this work, we exploit a tapered fiber to simulate the accelerated motion of effective particles and obtain an effective Unruh temperature. When light propagating in a tapered fiber is affected by the external high refractive index medium, a leaky phenomenon akin to bremsstrahlung will be observed, and the pattern of leaky radiation is dependent on the acceleration of photons. During the experiments, different accelerations corresponding to different Unruh temperatures are achieved by controlling the shape of the tapered waveguide.

physics.optics↗

arXiv2021

Enhanced Directional Quantum Emission by Tunable Topological Doubly-Resonant Cavities

How to utilize topological microcavities to control quantum emission is one of the ongoing research topics in the optical community. In this work, we investigate the emission of quantum emitters in doubly-resonant topological Tamm microcavity, which can simultaneously achieve dual resonances at two arbitrary wavelengths according to the needs of practical application. To achieve the enhancement of quantum emission in such cavities, we have exploited the tunable doubly-resonant modes, in which one of resonant modes corresponds to the pump laser wavelength and the other one is located at the emission wavelength of quantum emitters. Both theoretical and experimental results demonstrate that the pump excitation and emission efficiencies of quantum emitters are greatly enhanced. The main physical mechanism can be explained by the doubly-resonant cavity temporal coupled-mode theory. Furthermore, we observe the faster emission rate and the higher efficiency of unidirectional quantum emission, which have promising applications in optical detection, sensing, filtering, and light-emitting devices.

physics.optics↗

arXiv2021

A Hybrid Approach to Fine-grained Automated Fault Localization

Fault localization is to identify faulty source code. It could be done on various granularities, e.g., classes, methods, and statements. Most of the automated fault localization (AFL) approaches are coarse-grained because it is challenging to accurately locate fine-grained faulty software elements, e.g., statements. SBFL, based on dynamic execution of test cases only, is simple, intuitive, and generic (working on various granularities). However, its accuracy deserves significant improvement. To this end, in this paper, we propose a hybrid fine-grained AFL approach based on both dynamic spectrums and static statement types. The rationale of the approach is that some types of statements are significantly more/less error-prone than others, and thus statement types could be exploited for fault localization. On a crop of faulty programs, we compute the error-proneness for each type of statements, and assign priorities to special statement types that are steadily more/less error-prone than others. For a given faulty program under test, we first leverage traditional spectrum-based fault localization algorithm to identify all suspicious statements and to compute their suspicious scores. For each of the resulting suspicious statements, we retrieve its statement type as well as the special priority associated with the type. The final suspicious score is the product of the SBFL suspicious score and the priority assigned to the statement type. A significant advantage of the approach is that it is simple and intuitive, making it efficient and easy to interpret/implement. We evaluate the proposed approach on widely used benchmark Defects4J. The evaluation results suggest that the proposed approach outperforms widely used SBFL, reducing the absolute waste effort (AWE) by 9.3% on average.

cs.SE↗

arXiv2021

Thermal properties of light mesons from holography

The thermal properties of light mesons, including the temperature dependence of their masses (both screening and pole masses) and thermal widths, are studied in a two-flavor ($N_f=2$) soft-wall AdS/QCD model. By solving the spatial correlation functions, we extract the screening masses ($m_{\rm{scr}}$) from their poles. The screening masses of pseudo-scalar ($π$) and axial-vector ($a_1$) mesons increase almost monotonously with the increase of temperature. The screening masses of scalar ($σ$) and vector ($ρ$) mesons decrease at low temperature and increase at high temperature. The pole masses ($m_{\rm{pole}}$) and the thermal widths ($Γ$) are extracted from the temporal correlation functions and the corresponding spectral functions. The results indicate that the pole masses have local minima at low temperature and increase at high temperature. The thermal widths increase rapidly above the chiral crossover temperature $T_{cp}$, indicating the dissociations of mesons at high temperature. Furthermore, the degeneration of the chiral partners ($π$ and $σ$, $ρ$ and $a_1$) above $T_{cp}$ is observed from the screening and pole masses, revealing the chiral symmetry restoration at the hadronic spectrum level. Finally, we numerically verify that the spectral functions in the temporal regime are strongly related to the quasi-normal modes with complex frequencies $ω_0=m_{\rm{pole}}-i Γ/2$.

hep-ph↗

arXiv2021

Generation and Tunability of Supermodes in Tamm Plasmon Topological Superlattices

In this study, we propose and experimentally demonstrate a novel kind of Tamm plasmon topological superlattice (TTS) by engineering Tamm photonic crystals (TPCs) belonging to a different class of topology. Utilizing specifically designed double-layer metasurfaces etching on planar multilayered photonic structures, the TPC that supports the Tamm plasmon photonic bandgap is realized in the visible regime. Through the coupling of topological interface states existing between different TPCs, hybrid topological interface states of Tamm plasmon, called supermodes, are obtained that can be fully described by a tight-binding model. Meanwhile, we can achieve a tunable bandwidth of supermodes via varying the etching depth difference between double-layer metasurfaces. We show that the bandwidth decreases with the increase of etching depth difference, resulting in a nearly flat dispersion of supermodes with strong localization regardless of excitation angles. All the results are experimentally verified by measuring angular-resolved reflectance spectra. The TTS and supermodes proposed here open a new pathway for the manipulation of Tamm plasmons, based on which various promising applications such as integrated photonic devices, optical sensing, and enhancing light-matter interactions can be realized.

physics.optics↗

arXiv2021

Tunability of Spin-Dependent Secondary Topological Interface States Induced in an Optical Complex Superlattice

The past decade has witnessed a booming development of topological photonics, which revolutionizes the methodology for controlling the behavior of light. A gigantic achievement is to engineer robust confined modes localized at interfaces between topologically distinct regions, where the optical context can trigger exotic topological phenomena exclusive to photons. Here, we provide an experimentally flexible approach to engineering topologically induced interface states in the visible regime via a unique design of complex superlattice formed by connecting two component superlattices of distinguished topological phases. Assisted by the intrinsic pseudospin degree due to the splitting between TM and TE polarized modes, we attain a precise manipulation of the spin-dependent topological interface states that can manifest themselves straightforwardly through transmission spectra. More specifically, since these topological localized modes stem from the hybridization of artificial photonic orbitals that are of topological origin as well, they are deemed as a novel topological effect and thus named as the secondary topological interface states. Our work develops an innovative and productive strategy to tune topologically protected localized modes, based on which various applications such as selective local enhancement can be exploited.

physics.optics↗

arXiv2021

Probing rotated Weyl physics on nonlinear lithium niobate-on-insulator chips

Topological photonics, featured by stable topological edge states resistant to perturbations, has been utilized to design robust integrated devices. Here, we present a study exploring the intriguing topological rotated Weyl physics in a 3D parameter space based on quaternary waveguide arrays on lithium niobate-on-insulator (LNOI) chips. Unlike previous works that focus on the Fermi arc surface states of a single Weyl structure, we can experimentally construct arbitrary interfaces between two Weyl structures whose orientations can be freely rotated in the synthetic parameter space. This intriguing system was difficult to realize in usual 3D Weyl semimetals due to lattice mismatch. We found whether the interface can host gapless topological interface states (TISs) or not, is determined by the relative rotational directions of the two Weyl structures. In the experiment, we have probed the local characteristics of the TISs through linear optical transmission and nonlinear second harmonic generation. Our study introduces a novel path to explore topological photonics on LNOI chips and various applications in integrated nonlinear and quantum optics.

physics.optics↗

arXiv2021

AutoLoss: Automated Loss Function Search in Recommendations

Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could lead to suboptimal recommendation quality and training efficiency. Some recent efforts rely on exhaustively or manually searched weights to fuse a group of candidate loss functions, which is exceptionally costly in computation and time. They also neglect the various convergence behaviors of different data examples. In this work, we propose an AutoLoss framework that can automatically and adaptively search for the appropriate loss function from a set of candidates. To be specific, we develop a novel controller network, which can dynamically adjust the loss probabilities in a differentiable manner. Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors. Such design improves the model's generalizability and transferability between deep recommender systems and datasets. We evaluate the proposed framework on two benchmark datasets. The results show that AutoLoss outperforms representative baselines. Further experiments have been conducted to deepen our understandings of AutoLoss, including its transferability, components and training efficiency.

cs.IR↗

arXiv2021

Maximum Entropy Subspace Clustering Network

Deep subspace clustering networks have attracted much attention in subspace clustering, in which an auto-encoder non-linearly maps the input data into a latent space, and a fully connected layer named self-expressiveness module is introduced to learn the affinity matrix via a typical regularization term (e.g., sparse or low-rank). However, the adopted regularization terms ignore the connectivity within each subspace, limiting their clustering performance. In addition, the adopted framework suffers from the coupling issue between the auto-encoder module and the self-expressiveness module, making the network training non-trivial. To tackle these two issues, we propose a novel deep subspace clustering method named Maximum Entropy Subspace Clustering Network (MESC-Net). Specifically, MESC-Net maximizes the entropy of the affinity matrix to promote the connectivity within each subspace, in which its elements corresponding to the same subspace are uniformly and densely distributed. Furthermore, we design a novel framework to explicitly decouple the auto-encoder module and the self-expressiveness module. We also theoretically prove that the learned affinity matrix satisfies the block-diagonal property under the independent subspaces. Extensive quantitative and qualitative results on commonly used benchmark datasets validate MESC-Net significantly outperforms state-of-the-art methods.

cs.CV↗

arXiv2021

Quantum Key Recovery Attack on SIMON Block Cipher

The quantum security of lightweight block ciphers is receiving more and more attention. However, the existing quantum attacks on lightweight block ciphers mainly focused on the quantum exhaustive search, while the quantum dedicated attacks combined with classical cryptanalysis methods haven't been well studied. In this paper, we study quantum key recovery attack on SIMON block cipher using Quantum Amplitude Amplification algorithm in Q1 model. At first, we reanalyze the quantum circuit complexity of quantum master key exhaustive search on SIMON block cipher. The Clifford gates count is estimated more accurately and the T gate count is reduced. We also reduce the T-depth and Full-depth due to some minor modifications to the circuit. Then, based on the differential cryptanalysis on SIMON32, SIMON48 and SIMON64 given by Biryukov et al. in FSE 2014, we give quantum round key recovery attacks on these SIMON variants and analyze quantum circuit complexity separately. We take the quantum attack on 19-round SIMON32/64 for an example and design the quantum circuit of the key recovery process. The two phases of this attack could be regarded as two QAA instances separately, and the first QAA instance consists of four sub-QAA instances. We conclude that the encryption complexity and circuit complexity of quantum dedicated attacks on 19-round SIMON32/64, 19-round SIMON 48 and 26-round SIMON64/128 are both lower than those of the quantum exhaustive search on these variants separately. Our work firstly studies the quantum dedicated attack on SIMON block cipher from the perspective of quantum circuit complexity, which is a more fine-grained analysis of quantum dedicated attacks' complexity.

quant-ph↗

arXiv2021

Supermetal-insulator transition in a non-Hermitian network model

We study a non-Hermitian and non-unitary version of the two-dimensional Chalker-Coddington network model with balanced gain and loss. This model belongs to the class D^dagger with particle-hole symmetry^dagger and hosts both the non-Hermitian skin effect as well as exceptional points. By calculating its two-terminal transmission, we find a novel contact effect induced by the skin effect, which results in a non-quantized transmission for chiral edge states. In addition, the model exhibits an insulator to 'supermetal' transition, across which the transmission changes from exponentially decaying with system size to exponentially growing with system size. In the clean system, the critical point separating insulator from supermetal is characterized by a non-Hermitian Dirac point that produces a quantized critical transmission of 4, instead of the value of 1 expected in Hermitian systems. This change in critical transmission is a consequence of the balanced gain and loss. When adding disorder to the system, we find a critical exponent for the divergence of the localization length ν\approx 1, which is the same as that characterizing the universality class of two-dimensional Hermitian systems in class D. Our work provides a novel way of exploring the localization behavior of non-Hermitian systems, by using network models, which in the past proved versatile tools to describe Hermitian physics.

cond-mat.mes-hall↗

arXiv2021

Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots

Persona can function as the prior knowledge for maintaining the consistency of dialogue systems. Most of previous studies adopted the self persona in dialogue whose response was about to be selected from a set of candidates or directly generated, but few have noticed the role of partner in dialogue. This paper makes an attempt to thoroughly explore the impact of utilizing personas that describe either self or partner speakers on the task of response selection in retrieval-based chatbots. Four persona fusion strategies are designed, which assume personas interact with contexts or responses in different ways. These strategies are implemented into three representative models for response selection, which are based on the Hierarchical Recurrent Encoder (HRE), Interactive Matching Network (IMN) and Bidirectional Encoder Representations from Transformers (BERT) respectively. Empirical studies on the Persona-Chat dataset show that the partner personas neglected in previous studies can improve the accuracy of response selection in the IMN- and BERT-based models. Besides, our BERT-based model implemented with the context-response-aware persona fusion strategy outperforms previous methods by margins larger than 2.7% on original personas and 4.6% on revised personas in terms of hits@1 (top-1 accuracy), achieving a new state-of-the-art performance on the Persona-Chat dataset.

cs.CL↗

arXiv2021

Rethinking of Radar's Role: A Camera-Radar Dataset and Systematic Annotator via Coordinate Alignment

Radar has long been a common sensor on autonomous vehicles for obstacle ranging and speed estimation. However, as a robust sensor to all-weather conditions, radar's capability has not been well-exploited, compared with camera or LiDAR. Instead of just serving as a supplementary sensor, radar's rich information hidden in the radio frequencies can potentially provide useful clues to achieve more complicated tasks, like object classification and detection. In this paper, we propose a new dataset, named CRUW, with a systematic annotator and performance evaluation system to address the radar object detection (ROD) task, which aims to classify and localize the objects in 3D purely from radar's radio frequency (RF) images. To the best of our knowledge, CRUW is the first public large-scale dataset with a systematic annotation and evaluation system, which involves camera RGB images and radar RF images, collected in various driving scenarios.

cs.CV↗

arXiv2021

DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g., e-commerce and news feed sites). However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos). Developing an optimal advertising algorithm in recommendations faces immense challenges because interpolating ads improperly or too frequently may decrease user experience, while interpolating fewer ads will reduce the advertising revenue. Thus, in this paper, we propose a novel advertising strategy for the rec/ads trade-off. To be specific, we develop an RL-based framework that can continuously update its advertising strategies and maximize reward in the long run. Given a recommendation list, we design a novel Deep Q-network architecture that can determine three internally related tasks jointly, i.e., (i) whether to interpolate an ad or not in the recommendation list, and if yes, (ii) the optimal ad and (iii) the optimal location to interpolate. The experimental results based on real-world data demonstrate the effectiveness of the proposed framework.

cs.IR↗

arXiv2021

The Impact of COVID-19 on Urban Energy Consumption of the Commercial Tourism City

In 2020, the COVID-19 pandemic spreads all over the world. In order to alleviate the spread of the epidemic, various blockade policies have been implemented in many areas. In order to formulate a better epidemic prevention policy for urban energy consumption of the commercial tourism cities, this paper first analyses the energy characteristics of Macao during the epidemic period from two aspects, based on the energy consumption data of Macao. On this basis, the power consumption characteristics of commercial tourism cities during the epidemic were analyzed. Then, this paper provides analysis of the characteristics of the energy consumption in different fields of commercial tourism cities from the aspects of hotel, transportation, tourism culture and public utilities. Finally, a detailed analysis of the energy consumption characteristics of commercial tourism cities represented by Macao during the epidemic period is provided, by comparing with some typical countries.

physics.soc-ph↗

arXiv2021

Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning

Exploiting label hierarchies has become a promising approach to tackling the zero-shot multi-label text classification (ZS-MTC) problem. Conventional methods aim to learn a matching model between text and labels, using a graph encoder to incorporate label hierarchies to obtain effective label representations \cite{rios2018few}. More recently, pretrained models like BERT \cite{devlin2018bert} have been used to convert classification tasks into a textual entailment task \cite{yin-etal-2019-benchmarking}. This approach is naturally suitable for the ZS-MTC task. However, pretrained models are underexplored in the existing work because they do not generate individual vector representations for text or labels, making it unintuitive to combine them with conventional graph encoding methods. In this paper, we explore to improve pretrained models with label hierarchies on the ZS-MTC task. We propose a Reinforced Label Hierarchy Reasoning (RLHR) approach to encourage interdependence among labels in the hierarchies during training. Meanwhile, to overcome the weakness of flat predictions, we design a rollback algorithm that can remove logical errors from predictions during inference. Experimental results on three real-life datasets show that our approach achieves better performance and outperforms previous non-pretrained methods on the ZS-MTC task.

cs.CL↗

arXiv2021

Network model for higher-order topological phases

We introduce a two-dimensional network model that realizes a higher-order topological phase (HOTP). We find that in the HOTP the bulk and boundaries of the system are gapped, and a total of 16 corner states are protected by the combination of a four-fold rotation, a phase-rotation, and a particle-hole symmetry. In addition, the model exhibits a strong topological phase at a point of maximal coupling. This behavior is in opposition to conventional network models, which are gapless at this point. By introducing the appropriate topological invariants, we show how a point group symmetry can protect a topological phase in a network. Our work provides the basis for the realization of HOTP in alternative experimental platforms implementing the network model.

cond-mat.mes-hall↗

arXiv2021

Self-supervised Symmetric Nonnegative Matrix Factorization

Symmetric nonnegative matrix factorization (SNMF) has demonstrated to be a powerful method for data clustering. However, SNMF is mathematically formulated as a non-convex optimization problem, making it sensitive to the initialization of variables. Inspired by ensemble clustering that aims to seek a better clustering result from a set of clustering results, we propose self-supervised SNMF (S$^3$NMF), which is capable of boosting clustering performance progressively by taking advantage of the sensitivity to initialization characteristic of SNMF, without relying on any additional information. Specifically, we first perform SNMF repeatedly with a random nonnegative matrix for initialization each time, leading to multiple decomposed matrices. Then, we rank the quality of the resulting matrices with adaptively learned weights, from which a new similarity matrix that is expected to be more discriminative is reconstructed for SNMF again. These two steps are iterated until the stopping criterion/maximum number of iterations is achieved. We mathematically formulate S$^3$NMF as a constraint optimization problem, and provide an alternative optimization algorithm to solve it with the theoretical convergence guaranteed. Extensive experimental results on $10$ commonly used benchmark datasets demonstrate the significant advantage of our S$^3$NMF over $12$ state-of-the-art methods in terms of $5$ quantitative metrics. The source code is publicly available at https://github.com/jyh-learning/SSSNMF.

cs.LG↗