arXiv subjects

Xiaoyi Liu

Publications and source records attributed to Xiaoyi Liu.

52 records · Page 3Linked to original sources

arXiv2024

Application of Multimodal Fusion Deep Learning Model in Disease Recognition

This paper introduces an innovative multi-modal fusion deep learning approach to overcome the drawbacks of traditional single-modal recognition techniques. These drawbacks include incomplete information and limited diagnostic accuracy. During the feature extraction stage, cutting-edge deep learning models including convolutional neural networks (CNN), recurrent neural networks (RNN), and transformers are applied to distill advanced features from image-based, temporal, and structured data sources. The fusion strategy component seeks to determine the optimal fusion mode tailored to the specific disease recognition task. In the experimental section, a comparison is made between the performance of the proposed multi-mode fusion model and existing single-mode recognition methods. The findings demonstrate significant advantages of the multimodal fusion model across multiple evaluation metrics.

cs.CV↗

arXiv2024

Null states and time evolution in a toy model of black hole dynamics

Spacetime wormholes can provide non-perturbative contributions to the gravitational path integral that make the actual number of states $e^S$ in a gravitational system much smaller than the number of states $e^{S_{\mathrm{p}}}$ predicted by perturbative semiclassical effective field theory. The effects on the physics of the system are naturally profound in contexts in which the perturbative description actively involves $N = O(e^S)$ of the possible $e^{S_{\mathrm{p}}}$ perturbative states; e.g., in late stages of black hole evaporation. Such contexts are typically associated with the existence of non-trivial quantum extremal surfaces. However, by forcing a simple topological gravity model to evolve in time, we find that such effects can also have large impact for $N\ll e^S$ (in which case no quantum extremal surfaces can arise). In particular, even for small $N$, the insertion of generic operators into the path integral can cause the non-perturbative time evolution to differ dramatically from perturbative expectations. On the other hand, this discrepancy is small for the special case where the inserted operators are non-trivial only in a subspace of dimension $D \ll e^S$. We thus study this latter case in detail. We also discuss potential implications for more realistic gravitational systems.

hep-th↗

arXiv2024

New Well-Posed Boundary Conditions for Semi-Classical Euclidean Gravity

We consider four-dimensional Euclidean gravity in a finite cavity. Dirichlet conditions do not yield a well-posed elliptic system, and Anderson has suggested boundary conditions that do. Here we point out that there exists a one-parameter family of boundary conditions, parameterized by a constant $p$, where a suitably Weyl rescaled boundary metric is fixed, and all give a well-posed elliptic system. Anderson and Dirichlet boundary conditions can be seen as the limits $p \to 0$ and $\infty$ of these. Focussing on static Euclidean solutions, we derive a thermodynamic first law. Restricting to a spherical spatial boundary, the infillings are flat space or the Schwarzschild solution, and have similar thermodynamics to the Dirichlet case. We consider smooth Euclidean fluctuations about the flat space saddle; for $p > 1/6$ the spectrum of the Lichnerowicz operator is stable -- its eigenvalues have positive real part. Thus we may regard large $p$ as a regularization of the ill-posed Dirichlet boundary conditions. However for $p < 1/6$ there are unstable modes, even in the spherically symmetric and static sector. We then turn to Lorentzian signature. For $p < 1/6$ we may understand this spherical Euclidean instability as being paired with a Lorentzian instability associated with the dynamics of the boundary itself. However, a mystery emerges when we consider perturbations that break spherical symmetry. Here we find a plethora of dynamically unstable modes even for $p > 1/6$, contrasting starkly with the Euclidean stability we found. Thus we seemingly obtain a system with stable thermodynamics, but unstable dynamics, calling into question the standard assumption of smoothness that we have implemented when discussing the Euclidean theory.

hep-th↗

arXiv2023

Semantic Synchronization for Enhanced Reliability in Communication Systems

As a new communication paradigm, semantic communication has received widespread attention in communication fields. However, since the decoding of semantic signals relies on contextual knowledge, misalignment between the starting position of the semantic signal and the AI-based semantic decoder would prevent source signal recovery and reconstruction. To achieve more precise semantic communication, this study proposes an image-based semantic synchronization method leveraging intrinsic semantic features of image content. Specifically, a shared synchronized image (SyncImg) is encoded into a synchronization vector header at the transmitter and sent to the receiver. The receiver adopts a sliding window semantic decoder combined with classification and template matching methods to locate the synchronization point. Experimental results demonstrate that compared with traditional methods, the proposed method achieves a lower miss detected ratio (MDR) and root-mean-square error (RMSE) under low signal-to-noise ratios, realizing accurate synchronization of semantic signals across different devices.

eess.SP↗

arXiv2023

Stability of saddles and choices of contour in the Euclidean path integral for linearized gravity: Dependence on the DeWitt Parameter

Due to the conformal factor problem, the definition of the Euclidean gravitational path integral requires a non-trivial choice of contour. The present work examines a generalization of a recently proposed rule-of-thumb \cite{Marolf:2022ntb} for selecting this contour at quadratic order about a saddle. The original proposal depended on the choice of an indefinite-signature metric on the space of perturbations, which was taken to be a DeWitt metric with parameter $α=-1$. This choice was made to match previous results, but was otherwise admittedly {\it ad hoc}. To begin to investigate the physics associated with the choice of such a metric, we now explore contours defined using analogous prescriptions for $α\neq -1$. We study such contours for Euclidean gravity linearized about AdS-Schwarzschild black holes in reflecting cavities with thermal (canonical ensemble) boundary conditions, and we compare path-integral stability of the associated saddles with thermodynamic stability of the classical spacetimes. While the contour generally depends on the choice of DeWitt parameter $α$, the precise agreement between these two notions of stability found at $α=-1$ continues to hold over the finite interval $(-2,-2/d)$, where $d$ is the dimension of the bulk spacetime. This agreement manifestly fails for $α> -2/d$ when the DeWitt metric becomes positive definite. However, we also find dramatic failures for $α< -2$ that correlate with breakdowns of the de Donder-like gauge condition defined by $α$, and at which the relevant fluctuation operator fails to be diagonalizable. This provides criteria that may be useful in predicting metrics on the space of perturbations that give physically-useful contours in more general settings. Along the way, we also identify an interesting error in \cite{Marolf:2022ntb}, though we show this error to be harmless.

hep-th↗

arXiv2023

Semantic Communications System with Model Division Multiple Access and Controllable Coding Rate for Point Cloud

Point cloud, as a 3D representation, is widely used in autonomous driving, virtual reality (VR), and augmented reality (AR). However, traditional communication systems think that the point cloud's semantic information is irrelevant to communication, which hinders the efficient transmission of point clouds in the era of artificial intelligence (AI). This paper proposes a point cloud based semantic communication system (PCSC), which uses AI-based encoding techniques to extract the semantic information of the point cloud and joint source-channel coding (JSCC) technology to overcome the distortion caused by noise channels and solve the "cliff effect" in traditional communication. In addition, the system realizes the controllable coding rate without fine-tuning the network. The method analyzes the coded semantic vector's importance and discards semantically-unimportant information, thereby improving the transmission efficiency. Besides, PCSC and the recently proposed non-orthogonal model division multiple access (MDMA) technology are combined to design a point cloud MDMA transmission system (M-PCSC) for multi-user transmission. Relevant experimental results show that the proposed method outperforms the traditional method 10dB in the same channel bandwidth ratio under the PSNR D1 and PSNR D2 metrics. In terms of transmission, the proposed method can effectively solve the "cliff effect" in the traditional methods.

cs.MM↗

arXiv2023

High speed free-space optical communication using standard fiber communication component without optical amplification

Free-space optical communication (FSO) can achieve fast, secure and license-free communication without need for physical cables, making it a cost-effective, energy-efficient and flexible solution when the fiber connection is unavailable. To establish FSO connection on-demand, it is essential to build portable FSO devices with compact structure and light weight. Here, we develop a miniaturized FSO system and realize 9.16 Gbps FSO between two nodes that is 1 km apart, using a commercial single-mode-fiber-coupled optical transceiver module without optical amplification. Using our 4-stage acquisition, pointing and tracking (APT) systems, the tracking error is within 3 μrad and results an average link loss of 13.7 dB, which is the key for this high-bandwidth FSO demonstration without optical amplification. Our FSO link has been tested up to 4 km, with link loss of 18 dB that is limited by the foggy weather during the test. Longer FSO distances can be expected with better weather condition and optical amplification. With single FSO device weight of only 9.5 kg, this result arouses massive applications of field-deployable high-speed wireless communication.

eess.SP↗

arXiv2023

Interpretable System Identification and Long-term Prediction on Time-Series Data

Time-series prediction has drawn considerable attention during the past decades fueled by the emerging advances of deep learning methods. However, most neural network based methods lack interpretability and fail in extracting the hidden mechanism of the targeted physical system. To overcome these shortcomings, an interpretable sparse system identification method without any prior knowledge is proposed in this study. This method adopts the Fourier transform to reduces the irrelevant items in the dictionary matrix, instead of indiscriminate usage of polynomial functions in most system identification methods. It shows an interpretable system representation and greatly reduces computing cost. With the adoption of $l_1$ norm in regularizing the parameter matrix, a sparse description of the system model can be achieved. Moreover, Three data sets including the water conservancy data, global temperature data and financial data are used to test the performance of the proposed method. Although no prior knowledge was known about the physical background, experimental results show that our method can achieve long-term prediction regardless of the noise and incompleteness in the original data more accurately than the widely-used baseline data-driven methods. This study may provide some insight into time-series prediction investigations, and suggests that an white-box system identification method may extract the easily overlooked yet inherent periodical features and may beat neural-network based black-box methods on long-term prediction tasks.

cs.LG↗

arXiv2022

A Scheme for Deterministic N-photon State Generation Using Lithium Niobate on Insulator Device

Large-photon-number quantum state is a fundamental but non-resolved request for practical quantum information applications. Here we propose an N-photon state generation scheme that is feasible and scalable, using lithium niobate on insulator circuits. Such scheme is based on the integration of a common building block called photon-number doubling unit (PDU), for deterministic single-photon parametric down-conversion and up-conversion. The PDU relies on 10^7-optical-quality-factor resonator and mW-level on-chip power, which is within the current fabrication and experiment limits. N-photon state generation schemes, with cluster and GHZ state as examples, are shown for different quantum tasks.

quant-ph↗

arXiv2022

NxtPost: User to Post Recommendations in Facebook Groups

In this paper, we present NxtPost, a deployed user-to-post content-based sequential recommender system for Facebook Groups. Inspired by recent advances in NLP, we have adapted a Transformer-based model to the domain of sequential recommendation. We explore causal masked multi-head attention that optimizes both short and long-term user interests. From a user's past activities validated by defined safety process, NxtPost seeks to learn a representation for the user's dynamic content preference and to predict the next post user may be interested in. In contrast to previous Transformer-based methods, we do not assume that the recommendable posts have a fixed corpus. Accordingly, we use an external item/token embedding to extend a sequence-based approach to a large vocabulary. We achieve 49% abs. improvement in offline evaluation. As a result of NxtPost deployment, 0.6% more users are meeting new people, engaging with the community, sharing knowledge and getting support. The paper shares our experience in developing a personalized sequential recommender system, lessons deploying the model for cold start users, how to deal with freshness, and tuning strategies to reach higher efficiency in online A/B experiments.

cs.LG↗

arXiv2019

Examining the weak cosmic censorship conjecture by gedanken experiments for Kerr-Sen black holes

In this paper, we investigate the weak cosmic censorship conjecture for the Kerr-Sen black holes by considering the new version of the gedanken experiments proposed recently by Sorce and Wald. After deriving the first two order perturbation inequalities in the low energy limit of heterotic string theory based on the Iyer-Wald formalism and applying it into the Kerr-Sen black hole, we find that the Kerr-Sen black hole can not be overspun or overcharged by the charged matter collision after taking into account the second-order perturbation inequality, although they can be destroyed by the scene only considering the first-order perturbation inequality. Therefore, the weak cosmic censorship conjecture is preserved in the Kerr-Sen black hole at this level.

hep-th↗

arXiv2019

Strong cosmic censorship for the Dirac field in the higher dimensional Reissner-Nordstrom--de Sitter black hole

We investigate the strong cosmic censorship for the Dirac field in the higher dimensional Reissner-Norstrom--de Sitter black hole. To achieve this, we first use the conformal transformation trick to massage the Dirac equation to a pair of coupled equations in a meticulously chosen orthonormal basis and derive the criterion on the quasinormal modes for the violation of the strong cosmic censorship, which turns out to be independent of the spacetime dimension. Then we apply the Crank-Nicolson method to evolve our Dirac equation in the double null coordinates and extract the low-lying quasinormal modes from the evolution data by the Prony method. It is shown for the spacetime dimension $D=4, 5, 6$ under consideration that although the strong cosmic censorship is violated by the perturbation from the neutral Dirac field in the near-extremal black hole, the strong cosmic censorship can be restored when the charge of the Dirac field is increased beyond a critical value. The closer to the extremal limit the black hole is, the larger the critical charge of the Dirac field is.

hep-th↗

arXiv2019

Data Extraction from Charts via Single Deep Neural Network

Automatic data extraction from charts is challenging for two reasons: there exist many relations among objects in a chart, which is not a common consideration in general computer vision problems; and different types of charts may not be processed by the same model. To address these problems, we propose a framework of a single deep neural network, which consists of object detection, text recognition and object matching modules. The framework handles both bar and pie charts, and it may also be extended to other types of charts by slight revisions and by augmenting the training data. Our model performs successfully on 79.4% of test simulated bar charts and 88.0% of test simulated pie charts, while for charts outside of the training domain it degrades for 57.5% and 62.3%, respectively.

cs.CV↗

arXiv2018

Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering

Visual question answering (VQA) is of significant interest due to its potential to be a strong test of image understanding systems and to probe the connection between language and vision. Despite much recent progress, general VQA is far from a solved problem. In this paper, we focus on the VQA multiple-choice task, and provide some good practices for designing an effective VQA model that can capture language-vision interactions and perform joint reasoning. We explore mechanisms of incorporating part-of-speech (POS) tag guided attention, convolutional n-grams, triplet attention interactions between the image, question and candidate answer, and structured learning for triplets based on image-question pairs. We evaluate our models on two popular datasets: Visual7W and VQA Real Multiple Choice. Our final model achieves the state-of-the-art performance of 68.2% on Visual7W, and a very competitive performance of 69.6% on the test-standard split of VQA Real Multiple Choice.

cs.CV↗

arXiv2017

Downlink Non-Orthogonal Multiple Access with Limited Feedback

In this paper, we analyze downlink non-orthogonal multiple access (NOMA) networks with limited feedback. Our goal is to derive appropriate transmission rates for rate adaptation and minimize outage probability of minimum rate for the constant-rate data service, based on distributed channel feedback information from receivers. We propose an efficient quantizer with variable-length encoding that approaches the best performance of the case where perfect channel state information is available everywhere. We prove that in the typical application with two receivers, the losses in the minimum rate and outage probability decay at least exponentially with the minimum feedback rate. We analyze the diversity gain and provide a sufficient condition for the quantizer to achieve the maximum diversity order. For NOMA with $K$ receivers where $K > 2$, we solve the minimum rate maximization problem within an accuracy of $ε$ in time complexity of $O\left(K\log\frac{1}ε\right)$, then, we apply the previously proposed quantizers for $K = 2$ to the case of $K > 2$. Numerical simulations are presented to demonstrate the efficiency of our proposed quantizers and the accuracy of the analytical results.

cs.IT↗

arXiv2015

Opening the band gap of graphene through silicon doping for improved performance of graphene/GaAs heterojunction solar cells

Graphene has attracted increasing interests due to its remarkable properties, however, the zero band gap of monolayer graphene might limit its further electronic and optoelectronic applications. Herein, we have successfully synthesized monolayer silicon-doped graphene (SiG) in large area by chemical vapor deposition method. Raman spectroscopy and X-ray photoelectron spectroscopy measurements evidence silicon atoms are doped into graphene lattice with the doping level of 3.4 at%. The electrical measurement based on field effect transistor indicates that the band gap of graphene has been opened by silicon doping, which is around 0. 28 eV supported by the first-principle calculations, and the ultraviolet photoelectron spectroscopy demonstrates the work function of SiG is 0.13 eV larger than that of graphene. Moreover, the SiG/GaAs heterostructure solar cells show an improved power conversion efficiency of 33.7% in average than that of graphene/GaAs solar cells, which are attributed to the increased barrier height and improved interface quality. Our results suggest silicon doping can effectively engineer the band gap of monolayer graphene and SiG has great potential in optoelectronic device applications.

cond-mat.mtrl-sci↗