arXiv subjects

Xin Zhu

Publications and source records attributed to Xin Zhu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Critical sensing with a single bosonic mode without boson-boson interactions

Critical phenomena of quantum systems are useful for enhancement of quantum sensing. However, experimental realizations of criticality enhancement have been confined to very few systems, owing to the stringent requirements, including the thermodynamical or scaling limit, and fine control of interacting quantum susystems or particles. We here propose a simple critical quantum sensing scheme that requires neither of these conditions. The critical system is realized with a single parametrically-driven bosonic mode involving many non-interacting bosons. We calculate the quantum Fisher information, and perform a simulation, which confirms the criticality-enabled enhancement. We further detail the response of one of the quadratures to the variation of the control parameter. The numerical results reveal that its inverted variance exhibits a diverging behavior at the critical point. Based on the presently available control techniques of parametric driving, we expect our scheme can be realized in different systems, e.g., ion traps and superconducting circuits.

quant-ph↗

arXiv2024

Towards Reliable Advertising Image Generation Using Human Feedback

In the e-commerce realm, compelling advertising images are pivotal for attracting customer attention. While generative models automate image generation, they often produce substandard images that may mislead customers and require significant labor costs to inspect. This paper delves into increasing the rate of available generated images. We first introduce a multi-modal Reliable Feedback Network (RFNet) to automatically inspect the generated images. Combining the RFNet into a recurrent process, Recurrent Generation, results in a higher number of available advertising images. To further enhance production efficiency, we fine-tune diffusion models with an innovative Consistent Condition regularization utilizing the feedback from RFNet (RFFT). This results in a remarkable increase in the available rate of generated images, reducing the number of attempts in Recurrent Generation, and providing a highly efficient production process without sacrificing visual appeal. We also construct a Reliable Feedback 1 Million (RF1M) dataset which comprises over one million generated advertising images annotated by human, which helps to train RFNet to accurately assess the availability of generated images and faithfully reflect the human feedback. Generally speaking, our approach offers a reliable solution for advertising image generation.

cs.CV↗

arXiv2024

Quantum Geometric Tensor and Critical Metrology in the Anisotropic Dicke Model

We investigate the quantum phase transition in the anisotropic Dicke model through an examination of the quantum geometric tensor of the ground state. In this analysis, two distinct classical limits exhibit their unique anisotropic characteristics. The classical spin limit demonstrates a preference for the rotating-wave coupling, whereas the classical oscillator limit exhibits symmetry in the coupling strength of the bias. The anisotropic features of the classical spin limit persist at finite scales. Furthermore, we observe that the interplay among the anisotropic ratio, spin length, and frequency ratio can collectively enhance the critical behaviors. This critical enhancement without trade-off between these factors provides a flexible method for quantum precision measurement.

quant-ph↗

arXiv2024

Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets

In this paper, we propose a set of transform-based neural network layers as an alternative to the $3\times3$ Conv2D layers in Convolutional Neural Networks (CNNs). The proposed layers can be implemented based on orthogonal transforms such as the Discrete Cosine Transform (DCT), Hadamard transform (HT), and biorthogonal Block Wavelet Transform (BWT). Furthermore, by taking advantage of the convolution theorems, convolutional filtering operations are performed in the transform domain using element-wise multiplications. Trainable soft-thresholding layers, that remove noise in the transform domain, bring nonlinearity to the transform domain layers. Compared to the Conv2D layer, which is spatial-agnostic and channel-specific, the proposed layers are location-specific and channel-specific. Moreover, these proposed layers reduce the number of parameters and multiplications significantly while improving the accuracy results of regular ResNets on the ImageNet-1K classification task. Furthermore, they can be inserted with a batch normalization layer before the global average pooling layer in the conventional ResNets as an additional layer to improve classification accuracy.

cs.CV↗

arXiv2024

CBNet: A Plug-and-Play Network for Segmentation-Based Scene Text Detection

Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion. However, the segmentation process only considers each pixel independently, and the expansion process is difficult to achieve a favorable accuracy-speed trade-off. In this paper, we propose a Context-aware and Boundary-guided Network (CBN) to tackle these problems. In CBN, a basic text detector is firstly used to predict initial segmentation results. Then, we propose a context-aware module to enhance text kernel feature representations, which considers both global and local contexts. Finally, we introduce a boundary-guided module to expand enhanced text kernels adaptively with only the pixels on the contours, which not only obtains accurate text boundaries but also keeps high speed, especially on high-resolution output maps. In particular, with a lightweight backbone, the basic detector equipped with our proposed CBN achieves state-of-the-art results on several popular benchmarks, and our proposed CBN can be plugged into several segmentation-based methods. Code is available at https://github.com/XiiZhao/cbn.pytorch.

cs.CV↗

arXiv2024

A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer

In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing. It implements the regular convolutional layers in the Hadamard transform domain. The idea is based on the HT convolution theorem which states that the dyadic convolution between two vectors is equivalent to the element-wise multiplication of their HT representation. Computing the HT is simply the application of a Hadamard gate to each qubit individually, so the HT computations of our proposed layer can be implemented on a quantum computer. Compared to the regular Conv2D layer, the proposed HT-perceptron layer is computationally more efficient. Compared to a CNN with the same number of trainable parameters and 99.26\% test accuracy, our HT network reaches 99.31\% test accuracy with 57.1\% MACs reduced in the MNIST dataset; and in our ImageNet-1K experiments, our HT-based ResNet-50 exceeds the accuracy of the baseline ResNet-50 by 0.59\% center-crop top-1 accuracy using 11.5\% fewer parameters with 12.6\% fewer MACs.

cs.CV↗

arXiv2024

Relation-Aware Diffusion Model for Controllable Poster Layout Generation

Poster layout is a crucial aspect of poster design. Prior methods primarily focus on the correlation between visual content and graphic elements. However, a pleasant layout should also consider the relationship between visual and textual contents and the relationship between elements. In this study, we introduce a relation-aware diffusion model for poster layout generation that incorporates these two relationships in the generation process. Firstly, we devise a visual-textual relation-aware module that aligns the visual and textual representations across modalities, thereby enhancing the layout's efficacy in conveying textual information. Subsequently, we propose a geometry relation-aware module that learns the geometry relationship between elements by comprehensively considering contextual information. Additionally, the proposed method can generate diverse layouts based on user constraints. To advance research in this field, we have constructed a poster layout dataset named CGL-Dataset V2. Our proposed method outperforms state-of-the-art methods on CGL-Dataset V2. The data and code will be available at https://github.com/liuan0803/RADM.

cs.CV↗

arXiv2023

Quantum metric and metrology with parametrically-driven Tavis-Cummings models

We study the quantum metric in a driven Tavis-Cummings model, comprised of multiple qubits interacting with a quantized photonic field. The parametrical driving of the photonic field breaks the system's U(1) symmetry down to a ${\rm Z}_2$ symmetry, whose spontaneous breaking initiates a superradiant phase transition. We analytically solved the eigenenergies and eigenstates, and numerically simulated the system behaviors near the critical point. The critical behaviors near the superradiant phase transition are characterized by the quantum metric, defined in terms of the response of the quantum state to variation of the control parameter. In addition, a quantum metrological protocol based on the critical behaviors of the quantum metric near the superradiant phase transition is proposed, which enables greatly the achievable measurement precision.

quant-ph↗

arXiv2023

Domain Generalization with Fourier Transform and Soft Thresholding

Domain generalization aims to train models on multiple source domains so that they can generalize well to unseen target domains. Among many domain generalization methods, Fourier-transform-based domain generalization methods have gained popularity primarily because they exploit the power of Fourier transformation to capture essential patterns and regularities in the data, making the model more robust to domain shifts. The mainstream Fourier-transform-based domain generalization swaps the Fourier amplitude spectrum while preserving the phase spectrum between the source and the target images. However, it neglects background interference in the amplitude spectrum. To overcome this limitation, we introduce a soft-thresholding function in the Fourier domain. We apply this newly designed algorithm to retinal fundus image segmentation, which is important for diagnosing ocular diseases but the neural network's performance can degrade across different sources due to domain shifts. The proposed technique basically enhances fundus image augmentation by eliminating small values in the Fourier domain and providing better generalization. The innovative nature of the soft thresholding fused with Fourier-transform-based domain generalization improves neural network models' performance by reducing the target images' background interference significantly. Experiments on public data validate our approach's effectiveness over conventional and state-of-the-art methods with superior segmentation metrics.

eess.IV↗

arXiv2023

A novel asymmetrical autoencoder with a sparsifying discrete cosine Stockwell transform layer for gearbox sensor data compression

The lack of an efficient compression model remains a challenge for the wireless transmission of gearbox data in non-contact gear fault diagnosis problems. In this paper, we present a signal-adaptive asymmetrical autoencoder with a transform domain layer to compress sensor signals. First, a new discrete cosine Stockwell transform (DCST) layer is introduced to replace linear layers in a multi-layer autoencoder. A trainable filter is implemented in the DCST domain by utilizing the multiplication property of the convolution. A trainable hard-thresholding layer is applied to reduce redundant data in the DCST layer to make the feature map sparse. In comparison to the linear layer, the DCST layer reduces the number of trainable parameters and improves the accuracy of data reconstruction. Second, training the autoencoder with a sparsifying DCST layer only requires a small number of datasets. The proposed method is superior to other autoencoder-based methods on the University of Connecticut (UoC) and Southeast University (SEU) gearbox datasets, as the average quality score is improved by 2.00% at the lowest and 32.35% at the highest with a limited number of training samples

cs.LG↗

arXiv2023

Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer

Electroencephalogram (EEG) data compression is necessary for wireless recording applications to reduce the amount of data that needs to be transmitted. In this paper, an asymmetrical sparse autoencoder with a discrete cosine transform (DCT) layer is proposed to compress EEG signals. The encoder module of the autoencoder has a combination of a fully connected linear layer and the DCT layer to reduce redundant data using hard-thresholding nonlinearity. Furthermore, the DCT layer includes trainable hard-thresholding parameters and scaling layers to give emphasis or de-emphasis on individual DCT coefficients. Finally, the one-by-one convolutional layer generates the latent space. The sparsity penalty-based cost function is employed to keep the feature map as sparse as possible in the latent space. The latent space data is transmitted to the receiver. The decoder module of the autoencoder is designed using the inverse DCT and two fully connected linear layers to improve the accuracy of data reconstruction. In comparison to other state-of-the-art methods, the proposed method significantly improves the average quality score in various data compression experiments.

eess.SP↗

arXiv2023

Mutual Query Network for Multi-Modal Product Image Segmentation

Product image segmentation is vital in e-commerce. Most existing methods extract the product image foreground only based on the visual modality, making it difficult to distinguish irrelevant products. As product titles contain abundant appearance information and provide complementary cues for product image segmentation, we propose a mutual query network to segment products based on both visual and linguistic modalities. First, we design a language query vision module to obtain the response of language description in image areas, thus aligning the visual and linguistic representations across modalities. Then, a vision query language module utilizes the correlation between visual and linguistic modalities to filter the product title and effectively suppress the content irrelevant to the vision in the title. To promote the research in this field, we also construct a Multi-Modal Product Segmentation dataset (MMPS), which contains 30,000 images and corresponding titles. The proposed method significantly outperforms the state-of-the-art methods on MMPS.

cs.CV↗

arXiv2023

Entanglement-interference complementarity and experimental demonstration in a superconducting circuit

Quantum entanglement between an interfering particle and a detector for acquiring the which-path information plays a central role for enforcing Bohr's complementarity principle. However, the quantitative relation between this entanglement and the fringe visibility remains untouched upon for an initial mixed state. Here we find an equality for quantifying this relation. Our equality characterizes how well the interference pattern can be preserved when an interfering particle, initially carrying a definite amount of coherence, is entangled, to a certain degree, with a which-path detector. This equality provides a connection between entanglement and interference in the unified framework of coherence, revealing the quantitative entanglement-interference complementarity. We experimentally demonstrate this relation with a superconducting circuit, where a resonator serves as a which-path detector for an interfering qubit. The measured fringe visibility of the qubit's Ramsey signal and the qubit-resonator entanglement exhibit a complementary relation, in well agreement with the theoretical prediction.

quant-ph↗

arXiv2023

Criticality-Enhanced Quantum Sensing in the Anisotropic Quantum Rabi Model

Quantum systems that undergo quantum phase transitions exhibit divergent susceptibility and can be exploited as probes to estimate physical parameters. We generalize the dynamic framework for criticality-enhanced quantum sensing by the quantum Rabi model (QRM) to its anisotropic counterpart and derive the correspondingly analytical expressions for the quantum Fisher information (QFI). We find that the contributions of the rotating-wave and counterrotating-wave interaction terms are symmetric at the limit of the infinite ratio of qubit frequency to field frequency, with the QFI reaching a maximum for the isotropic quantum Rabi model. At finite frequency scaling, we analytically derive the inverted variance of higher-order correction and find that it is more affected by the rotating-wave coupling than by the counterrotating-wave coupling.

quant-ph↗

arXiv2023

Real-time Wireless ECG-derived Respiration Rate Estimation Using an Autoencoder with a DCT Layer

In this paper, we present a wireless ECG-derived Respiration Rate (RR) estimation using an autoencoder with a DCT Layer. The wireless wearable system records the ECG data of the subject and the respiration rate is determined from the variations in the baseline level of the ECG data. A straightforward Fourier analysis of the ECG data obtained using the wireless wearable system may lead to incorrect results due to uneven breathing. To improve the estimation precision, we propose a neural network that uses a novel Discrete Cosine Transform (DCT) layer to denoise and decorrelates the data. The DCT layer has trainable weights and soft-thresholds in the transform domain. In our dataset, we improve the Mean Squared Error (MSE) and Mean Absolute Error (MAE) of the Fourier analysis-based approach using our novel neural network with the DCT layer.

eess.SP↗

arXiv2022

Critical quantum sensing based on the Jaynes-Cummings model with a squeezing drive

Quantum sensing improves the accuracy of measurements of relevant parameters by exploiting the unique properties of quantum systems. The divergent susceptibility of physical systems near a critical point for quantum phase transition enables criticality-enhanced quantum sensing. The quantum Rabi model (QRM), composed of a single qubit coupled to a single bosonic field, represents a good candidate for realizing such critical enhancement for its simplicity, but it is experimentally challenging to achieve the ultrastrong qubit-field coupling required to realize the critical phenomena. In this work, we explore an alternative to construct the analog of the QRM for the sensing, exploiting the criticality appearing in the Jaynes-Cummings (JC) model whose bosonic field is parametrically driven, not necessitating the ultrastrong coupling condition thus to some extent relaxing the requirement for the practical implementation.

quant-ph↗

arXiv2022

DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer

In this paper, we propose a novel Discrete Cosine Transform (DCT)-based neural network layer which we call DCT-perceptron to replace the $3\times3$ Conv2D layers in the Residual neural Network (ResNet). Convolutional filtering operations are performed in the DCT domain using element-wise multiplications by taking advantage of the Fourier and DCT Convolution theorems. A trainable soft-thresholding layer is used as the nonlinearity in the DCT perceptron. Compared to ResNet's Conv2D layer which is spatial-agnostic and channel-specific, the proposed layer is location-specific and channel-specific. The DCT-perceptron layer reduces the number of parameters and multiplications significantly while maintaining comparable accuracy results of regular ResNets in CIFAR-10 and ImageNet-1K. Moreover, the DCT-perceptron layer can be inserted with a batch normalization layer before the global average pooling layer in the conventional ResNets as an additional layer to improve classification accuracy.

cs.CV↗

arXiv2022

Further understanding the nature of $a_0(1710)$ in the $D^+_s \to π^0 K^+ K^0_S$ decay

Based on our previous work about the role of $a_0(1710)$ in the $D_s^+\toπ^+K_S^0K_S^0$ decay [Phy. Rev. D 105, 116010 (2022)], we perform a further theoretical study of $a_0(1710)^+$ in the process $D^+_s \to π^0 a_0(1710)^+ \to π^0 K^+ K^0_S$. In addition to $a_0(1710)$, the contributions of $K^*$ and $a_0(980)$ are also taken into account. Firstly, we consider the contributions from the tree diagrams of $K^{*+} \to K^+π^0$ and $\bar{K}^{*0} \to π^0 \bar{K}^0$. Secondly, we describe the final state interaction of $K\bar{K}$ in the chiral unitary approach to study the contribution of $a_0(980)$, while the $a_0(1710)$ state is dynamically generated from the $K^*\bar{K}^*$ interaction, and then decays into $K^+\bar{K}^0$. Since the final $K^+ K_S^0$ state is in pure isospin $I=1$, the $D_s^+\toπ^0K^+K_s^0$ decay is an ideal process to study the $a_0(1710)^+$ and $a_0(980)^+$ resonances. Based on our theoretical calculations, it is found that the recent experimental measurements on the $K^+K^0_S$, $π^0K^+$, and $π^0 K_S^0$ invariant mass distributions can be well reproduced, which supports the molecular $K^*\bar{K}^*$ nature of the scalar $a_0(1710)$ resonance.

hep-ph↗