arXiv subjects

Xin Wang

Publications and source records attributed to Xin Wang.

At least 1,207 records · Page 67Linked to original sources

arXiv2022

Unconventional Quantum Electrodynamics with Hofstadter-Ladder Waveguide

We propose a novel quantum electrodynamics (QED) platform where quantum emitters interact with a Hofstadter-ladder waveguide. We demonstrate several intriguing phenomena stemming from the exotic dispersion relation and vacuum mode properties led by the effective spin-orbit coupling, which have no analog in other QED setups. First, by assuming emitter's frequency to be resonant with the lower band, we find that the spontaneous emission is chiral with most photonic field decaying unidirectionally. Both numerical and analytical results indicate that the Hofstadter-ladder waveguide can be engineered as a well-performed chiral quantum bus. Second, the dynamics of emitters of giant atom form is explored by considering their frequencies below the lower band. Due to quantum interference, we find that both the emitter-waveguide interaction and the amplitudes of bound states are periodically modulated by giant emitter's size. The periodical length depends on the positions of energy minima points induced by the spin-orbit coupling. Last, we consider the interaction between two giant emitters mediated by bound states, and find that their dipole-dipole interaction vanishes (is enhanced) when maximum destructive (constructive) interference happens.

quant-ph↗

arXiv2022

Power and limitations of single-qubit native quantum neural networks

Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. While the applications of QNN have been widely investigated, its theoretical foundation remains less understood. In this paper, we formulate a theoretical framework for the expressive ability of data re-uploading quantum neural networks that consist of interleaved encoding circuit blocks and trainable circuit blocks. First, we prove that single-qubit quantum neural networks can approximate any univariate function by mapping the model to a partial Fourier series. We in particular establish the exact correlations between the parameters of the trainable gates and the Fourier coefficients, resolving an open problem on the universal approximation property of QNN. Second, we discuss the limitations of single-qubit native QNNs on approximating multivariate functions by analyzing the frequency spectrum and the flexibility of Fourier coefficients. We further demonstrate the expressivity and limitations of single-qubit native QNNs via numerical experiments. We believe these results would improve our understanding of QNNs and provide a helpful guideline for designing powerful QNNs for machine learning tasks.

quant-ph↗

arXiv2022

Early results from GLASS-JWST. IV: Spatially resolved metallicity in a low-mass $z\sim3$ galaxy with NIRISS

We report the first gas-phase metallicity map of a distant galaxy measured with the James Webb Space Telescope (JWST). We use the NIRISS slitless spectroscopy acquired by the GLASS Early Release Science program to spatially resolve the rest-frame optical nebular emission lines in a gravitationally lensed galaxy at $z=3.06$ behind the Abell 2744 galaxy cluster. This galaxy (dubbed GLASS-Zgrad1) has stellar mass $\sim10^{8.6} M_\odot$, instantaneous star formation rate $\sim8.6$ $M_\odot$/yr (both corrected for lensing magnification), and global metallicity one-fourth solar. From its emission line maps ([O III], H$β$, H$γ$, [Ne III], and [O II]) we derive its spatial distribution of gas-phase metallicity using a well-established forward-modeling Bayesian inference method. The exquisite resolution and sensitivity of JWST/NIRISS, combined with lensing magnification, enable us to resolve this $z\sim3$ dwarf galaxy in $\gtrsim$50 resolution elements with sufficient signal, an analysis hitherto not possible. We find that the radial metallicity gradient of GLASS-Zgrad1 is strongly inverted (i.e. positive): $Δ\log({\rm O/H})/Δr$ = $0.165\pm0.023$ $\mathrm{dex~kpc^{-1}}$. This inverted gradient may be due to tidal torques induced by a massive nearby ($\sim$15 kpc projected) galaxy, which can cause inflows of metal-poor gas into the central regions of GLASS-Zgrad1. These first results showcase the power of JWST wide-field slitless spectroscopic modes to resolve the mass assembly and chemical enrichment of low-mass galaxies in and beyond the peak epoch of cosmic star formation ($z\gtrsim2$). Reaching masses $\lesssim 10^9~M_\odot$ at these redshifts is especially valuable to constrain the effects of galactic feedback and environment, and is possible only with JWST's new capabilities.

astro-ph.GA↗

arXiv2022

The VoicePrivacy 2022 Challenge Evaluation Plan

For new participants - Executive summary: (1) The task is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content, paralinguistic attributes, intelligibility and naturalness. (2) Training, development and evaluation datasets are provided in addition to 3 different baseline anonymization systems, evaluation scripts, and metrics. Participants apply their developed anonymization systems, run evaluation scripts and submit objective evaluation results and anonymized speech data to the organizers. (3) Results will be presented at a workshop held in conjunction with INTERSPEECH 2022 to which all participants are invited to present their challenge systems and to submit additional workshop papers. For readers familiar with the VoicePrivacy Challenge - Changes w.r.t. 2020: (1) A stronger, semi-informed attack model in the form of an automatic speaker verification (ASV) system trained on anonymized (per-utterance) speech data. (2) Complementary metrics comprising the equal error rate (EER) as a privacy metric, the word error rate (WER) as a primary utility metric, and the pitch correlation and gain of voice distinctiveness as secondary utility metrics. (3) A new ranking policy based upon a set of minimum target privacy requirements.

eess.AS↗

arXiv2022

Early results from GLASS-JWST. II: NIRCam extra-galactic imaging and photometric catalog

We present the reduced images and multi-wavelength catalog of the first JWST NIRCam extra-galactic observations from the GLASS Early Release Science Program, obtained as coordinated parallels of the NIRISS observations of the Abell 2744 cluster. Images in seven bands (F090W, F115W, F150W, F200W, F277W, F356W, F444W) have been reduced using an augmented version of the official JWST pipeline; we discuss the procedures adopted to remove or mitigate defects in the raw images. We obtain a multi--band catalog by means of forced aperture photometry on PSF-matched images at the position of F444W-detected sources. The catalog is intended to enable early scientific investigations, and it is optimized for faint galaxies; it contains 6368 sources, with limiting magnitude 29.7 at 5$σ$ in F444W. We release both images and catalog in order to allow the community to familiarize with the JWST NIRCam data and evaluate their merit and limitations given the current level of knowledge of the instrument.

astro-ph.GA↗

arXiv2022

Early results from GLASS-JWST. XI: Stellar masses and mass-to-light ratio of z>7 galaxies

We exploit James Webb Space Telescope (JWST) NIRCam observations from the GLASS-JWST-Early Release Science program to investigate galaxy stellar masses at z>7. We first show that JWST observations reduce the uncertainties on the stellar mass by a factor of at least 5-10, when compared with the highest quality data sets available to date. We then study the UV mass-to-light ratio, finding that galaxies exhibit a two orders of magnitude range of M/L_UV values for a given luminosity, indicative of a broad variety of physical conditions and star formation histories. As a consequence, previous estimates of the cosmic star stellar mass density - based on an average correlation between UV luminosity and stellar mass - can be biased by as much as a factor of ~6. Our first exploration demonstrates that JWST represents a new era in our understanding of stellar masses at z>7, and therefore of the growth of galaxies prior to cosmic reionization.

astro-ph.GA↗

arXiv2022

The VoicePrivacy 2020 Challenge: Results and findings

This paper presents the results and analyses stemming from the first VoicePrivacy 2020 Challenge which focuses on developing anonymization solutions for speech technology. We provide a systematic overview of the challenge design with an analysis of submitted systems and evaluation results. In particular, we describe the voice anonymization task and datasets used for system development and evaluation. Also, we present different attack models and the associated objective and subjective evaluation metrics. We introduce two anonymization baselines and provide a summary description of the anonymization systems developed by the challenge participants. We report objective and subjective evaluation results for baseline and submitted systems. In addition, we present experimental results for alternative privacy metrics and attack models developed as a part of the post-evaluation analysis. Finally, we summarize our insights and observations that will influence the design of the next VoicePrivacy challenge edition and some directions for future voice anonymization research.

cs.CL↗

arXiv2022

On the scaling and anisotropy of two subranges in the inertial range of solar wind turbulence

Intermittency and anisotropy are two important aspects of plasma turbulence, which the solar wind provides a natural laboratory to investigate. However, their forms and nature are still under debate, making it difficult to achieve a consensus in the theoretical interpretation. Here, we perform higher-order statistics for the observations in the fast solar wind at 1.48 au obtained by Ulysses and in the slow solar wind at 0.17 au obtained by Parker Solar Probe (PSP). We find that two subranges clearly exist in the inertial range and they present distinct features with regard to the intermittency and anisotropy. The subrange 1 with smaller scale has a multifractal scaling with the second index $ξ(2) \sim 2/3$ and the subrange 2 with larger scale is also multifractal but with $ξ(2) \sim 1/2$. The break between two subranges locates at the same spatial scale for both Ulysses and PSP observations. Subrange 1 is multifractal in the direction perpendicular to the local magnetic field with $ξ_{\perp}(2) \sim 2/3$ and seems to be monoscaling in the parallel direction with $ξ_{\parallel}(2) \sim 1$. Subrange 2 is multifractal in both parallel and perpendicular directions with $ξ_{\perp}(2) \sim 1/2$ and $ξ_{\parallel}(2) \sim 2/3$. Both subrange 1 and subrange 2 present power and wavevector anisotropies. The distinct features of two subranges suggest that a transition from weak to strong turbulence may occur and the spatial scale of the break may not evolve with the solar wind expansion. These new results update our knowledge of the inertial range and provide strong observational constraints on the understanding of intermittency and anisotropy in solar wind turbulence.

astro-ph.SR↗

arXiv2022

Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI Systems

The present paper considers the model-based and data-driven control of unknown linear time-invariant discrete-time systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which also offers a way of co-designing the ETS matrix and the controller. To further alleviate the sampling burden of ETS due to its continuous/periodic detection, a self-triggering scheme (STS) is developed. Leveraging pre-collected input-state data, an algorithm for predicting the next transmission instant is given, while achieving system stability. Finally, numerical simulations showcase the efficacy of ETS and STS in reducing data transmissions as well as of the proposed co-design methods.

eess.SY↗

arXiv2022

Early results from GLASS-JWST. III: Galaxy candidates at z$\sim$9-15

We present the results of a first search for galaxy candidates at z$\sim$9--15 on deep seven-bands NIRCam imaging acquired as part of the GLASS-JWST Early Release Science Program on a flanking field of the Frontier Fields cluster A2744. Candidates are selected via two different renditions of the Lyman-break technique, isolating objects at z$\sim$9-11, and z$\sim$9-15, respectively, supplemented by photometric redshifts obtained with two independent codes. We find five color-selected candidates at z$>$9, plus one additional candidate with photometric redshift z$_{phot}\geq$9. In particular, we identify two bright candidates at $M_{UV}\simeq -21$ that are unambiguously placed at $z\simeq 10.6$ and $z\simeq 12.2$, respectively. The total number of galaxies discovered at $z>9$ is in line with the predictions of a non-evolving LF. The two bright ones at $z>10$ are unexpected given the survey volume, although cosmic variance and small number statistics limits general conclusions. This first search demonstrates the unique power of JWST to discover galaxies at the high redshift frontier. The candidates are ideal targets for spectroscopic follow-up in Cycle$-2$.

astro-ph.GA↗

arXiv2022

3D Matting: A Soft Segmentation Method Applied in Computed Tomography

Three-dimensional (3D) images, such as CT, MRI, and PET, are common in medical imaging applications and important in clinical diagnosis. Semantic ambiguity is a typical feature of many medical image labels. It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation. In 2D medical images, using soft masks instead of binary masks generated by image matting to characterize lesions can provide rich semantic information, describe the structural characteristics of lesions more comprehensively, and thus benefit the subsequent diagnoses and analyses. In this work, we introduce image matting into the 3D scenes to describe the lesions in 3D medical images. The study of image matting in 3D modality is limited, and there is no high-quality annotated dataset related to 3D matting, therefore slowing down the development of data-driven deep-learning-based methods. To address this issue, we constructed the first 3D medical matting dataset and convincingly verified the validity of the dataset through quality control and downstream experiments in lung nodules classification. We then adapt the four selected state-of-the-art 2D image matting algorithms to 3D scenes and further customize the methods for CT images. Also, we propose the first end-to-end deep 3D matting network and implement a solid 3D medical image matting benchmark, which will be released to encourage further research.

eess.IV↗

arXiv2022

Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation

Real human conversation data are complicated, heterogeneous, and noisy, from which building open-domain dialogue systems remains a challenging task. In fact, such dialogue data still contains a wealth of information and knowledge, however, they are not fully explored. In this paper, we show existing open-domain dialogue generation methods that memorize context-response paired data with autoregressive or encode-decode language models underutilize the training data. Different from current approaches, using external knowledge, we explore a retrieval-generation training framework that can take advantage of the heterogeneous and noisy training data by considering them as "evidence". In particular, we use BERTScore for retrieval, which gives better qualities of the evidence and generation. Experiments over publicly available datasets demonstrate that our method can help models generate better responses, even such training data are usually impressed as low-quality data. Such performance gain is comparable with those improved by enlarging the training set, even better. We also found that the model performance has a positive correlation with the relevance of the retrieved evidence. Moreover, our method performed well on zero-shot experiments, which indicates that our method can be more robust to real-world data.

cs.CL↗

arXiv2022

Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans

Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in \textit{Caenorhabditis elegans}. The constant motion and deformation of the nematode nervous system, however, impose a great challenge for consistent identification of densely packed neurons in a behaving animal. Here, we propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving \textit{C. elegans}. First, potential neuronal regions from a stack of fluorescence images are detected by a deep learning algorithm. Second, 2-dimensional neuronal regions are fused into 3-dimensional neuron entities. Third, by exploiting the neuronal density distribution surrounding a neuron and relative positional information between neurons, a multi-class artificial neural network transforms engineered neuronal feature vectors into digital neuronal identities. With a small number of training samples, our bottom-up approach is able to process each volume - $1024 \times 1024 \times 18$ in voxels - in less than 1 second and achieves an accuracy of $91\%$ in neuronal detection and above $80\%$ in neuronal tracking over a long video recording. Our work represents a step towards rapid and fully automated algorithms for decoding whole brain activity underlying naturalistic behaviors.

q-bio.QM↗

arXiv2022

Coupling two charge qubits via a superconducting resonator operating in the resonant and dispersive regimes

A key challenge for semiconductor quantum-dot charge qubits is the realization of long-range qubit coupling and performing high-fidelity gates based on it. Here, we describe a new type of charge qubit formed by an electron confined in a triple-quantum-dot system, enabling single and two-qubit gates working in the dipolar and quadrupolar detuning sweet spots. We further present the form for the long-range dipolar coupling between the charge qubit and the superconducting resonator. Based on the hybrid system composed of the qubits and the resonator, we present two types of entangling gates: the dynamical iSWAP gate and holonomic entangling gate, which are operating in the dispersive and resonant regimes, respectively. We find that the fidelity for the iSWAP gate can reach fidelity higher than 99\% for the noise level typical in experiments. Meanwhile, the fidelity for the holonomic gate can surpass 98\% if the anharmonicity in the resonator is large enough. Our proposal offers an alternative useful way to build up high-fidelity quantum computation for charge qubits in semiconductor quantum dot.

quant-ph↗

arXiv2022

A Novel Energy Efficiency Metric for Next Generation Wireless Communication Networks

As a core performance metric for green communications, the conventional energy efficiency definition has successfully resolved many issues in the energy efficient wireless network design. In the past several generations of wireless communication networks, the traditional energy efficiency measure plays an important role to guide many energy saving techniques for slow varying traffic profiles. However, for the next generation wireless networks, the traditional energy efficiency fails to capture the traffic and capacity variations of wireless networks in temporal or spatial domains, which is shown to be quite popular, especially with ultra-scale multiple antennas and space-air-ground integrated network. In this paper, we present a novel energy efficiency metric named integrated relative energy efficiency (IREE), which is able to jointly measure the traffic profiles and the network capacities from the energy efficiency perspective. On top of that, the IREE based green trade-offs have been investigated and compared with the conventional energy efficient design. Moreover, we apply the IREE based green trade-offs to evaluate several candidate technologies for 6G networks, including reconfigurable intelligent surfaces and space-air-ground integrated network. Through some analytical and numerical results, we show that the proposed IREE metric is able to capture the wireless traffic and capacity mismatch property, which is significantly different from the conventional energy efficiency metric. Since the IREE oriented design or deployment strategy is able to consider the network capacity improvement and the wireless traffic matching simultaneously, it can be regarded as a useful guidance for future energy efficient network design.

cs.NI↗

arXiv2022

MIntRec: A New Dataset for Multimodal Intent Recognition

Multimodal intent recognition is a significant task for understanding human language in real-world multimodal scenes. Most existing intent recognition methods have limitations in leveraging the multimodal information due to the restrictions of the benchmark datasets with only text information. This paper introduces a novel dataset for multimodal intent recognition (MIntRec) to address this issue. It formulates coarse-grained and fine-grained intent taxonomies based on the data collected from the TV series Superstore. The dataset consists of 2,224 high-quality samples with text, video, and audio modalities and has multimodal annotations among twenty intent categories. Furthermore, we provide annotated bounding boxes of speakers in each video segment and achieve an automatic process for speaker annotation. MIntRec is helpful for researchers to mine relationships between different modalities to enhance the capability of intent recognition. We extract features from each modality and model cross-modal interactions by adapting three powerful multimodal fusion methods to build baselines. Extensive experiments show that employing the non-verbal modalities achieves substantial improvements compared with the text-only modality, demonstrating the effectiveness of using multimodal information for intent recognition. The gap between the best-performing methods and humans indicates the challenge and importance of this task for the community. The full dataset and codes are available for use at https://github.com/thuiar/MIntRec.

cs.AI↗

arXiv2022

Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis

Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. Recent works have been devoted to leveraging text summarization and have achieved promising results. However, these summarization-based methods did not take full advantage of the summary including ignoring the inherent interactions between the summary and document. As a result, they limited the representation to express major points in the document, which is highly indicative of the key sentiment. In this paper, we study how to effectively generate a discriminative representation with explicit subject patterns and sentiment contexts for DSA. A Hierarchical Interaction Networks (HIN) is proposed to explore bidirectional interactions between the summary and document at multiple granularities and learn subject-oriented document representations for sentiment classification. Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation. We extensively evaluate our proposed models on three public datasets. The experimental results consistently demonstrate the effectiveness of our proposed models and show that HIN-SR outperforms various state-of-the-art methods.

cs.CL↗

arXiv2022

Domain Shift-oriented Machine Anomalous Sound Detection Model Based on Self-Supervised Learning

Thanks to the development of deep learning, research on machine anomalous sound detection based on self-supervised learning has made remarkable achievements. However, there are differences in the acoustic characteristics of the test set and the training set under different operating conditions of the same machine (domain shifts). It is challenging for the existing detection methods to learn the domain shifts features stably with low computation overhead. To address these problems, we propose a domain shift-oriented machine anomalous sound detection model based on self-supervised learning (TranSelf-DyGCN) in this paper. Firstly, we design a time-frequency domain feature modeling network to capture global and local spatial and time-domain features, thus improving the stability of machine anomalous sound detection stability under domain shifts. Then, we adopt a Dynamic Graph Convolutional Network (DyGCN) to model the inter-dependence relationship between domain shifts features, enabling the model to perceive domain shifts features efficiently. Finally, we use a Domain Adaptive Network (DAN) to compensate for the performance decrease caused by domain shifts, making the model adapt to anomalous sound better in the self-supervised environment. The performance of the suggested model is validated on DCASE 2020 task 2 and DCASE 2022 task 2.

cs.SD↗