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Jing Guo

Publications and source records attributed to Jing Guo.

At least 73 records · Page 4Linked to original sources
arXiv2024

Generalized Convolution Quadrature for non smooth sectorial problems

We consider the application of the generalized Convolution Quadrature (gCQ) to approximate the solution of an important class of sectorial problems. The gCQ is a generalization of Lubich's Convolution Quadrature (CQ) that allows for variable steps. The available stability and convergence theory for the gCQ requires non realistic regularity assumptions on the data, which do not hold in many applications of interest, such as the approximation of subdiffusion equations. It is well known that for non smooth enough data the original CQ, with uniform steps, presents an order reduction close to the singularity. We generalize the analysis of the gCQ to data satisfying realistic regularity assumptions and provide sufficient conditions for stability and convergence on arbitrary sequences of time points. We consider the particular case of graded meshes and show how to choose them optimally, according to the behaviour of the data. An important advantage of the gCQ method is that it allows for a fast and memory reduced implementation. We describe how the fast and oblivious gCQ can be implemented and illustrate our theoretical results with several numerical experiments.

math.NA↗
arXiv2024

Analysis and Optimization of Multiple-STAR-RIS Assisted MIMO-NOMA with GSVD Precoding: An Operator-Valued Free Probability Approach

Among the key enabling 6G techniques, multiple-input multiple-output (MIMO) and non-orthogonal multiple-access (NOMA) play an important role in enhancing the spectral efficiency of the wireless communication systems. To further extend the coverage and the capacity, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has recently emerged out as a cost-effective technology. To exploit the benefit of STAR-RIS in the MIMO-NOMA systems, in this paper, we investigate the analysis and optimization of the downlink dual-user MIMO-NOMA systems assisted by multiple STAR-RISs under the generalized singular value decomposition (GSVD) precoding scheme, in which the channel is assumed to be Rician faded with the Weichselberger's correlation structure. To analyze the asymptotic information rate of the users, we apply the operator-valued free probability theory to obtain the Cauchy transform of the generalized singular values (GSVs) of the MIMO-NOMA channel matrices, which can be used to obtain the information rate by Riemann integral. Then, considering the special case when the channels between the BS and the STAR-RISs are deterministic, we obtain the closed-form expression for the asymptotic information rates of the users. Furthermore, a projected gradient ascent method (PGAM) is proposed with the derived closed-form expression to design the STAR-RISs thereby maximizing the sum rate based on the statistical channel state information. The numerical results show the accuracy of the asymptotic expression compared to the Monte Carlo simulations and the superiority of the proposed PGAM algorithm.

eess.SP↗
arXiv2024

Cubic vertices of minimal bicritical graphs

A graph $G$ with four or more vertices is called bicritical if the removal of any pair of distinct vertices of $G$ results in a graph with a perfect matching. A bicritical graph is minimal if the deletion of each edge results in a non-bicritical graph. Recently, Y. Zhang et al. and F. Lin et al. respectively showed that bicritical graphs without removable edges and minimal bricks have at least four cubic vertices. In this note, we show that minimal bicritical graphs also have at least four cubic vertices, so confirming O. Favaron and M. Shi's conjecture in the case of $k=2$ on minimal $k$-factor critical graphs.

math.CO↗
arXiv2024

Superconducting-transition-temperature dependence of superfluid density and conductivity in pressurized cuprate superconductors

What factors fundamentally determine the value of superconducting transition temperature (Tc) in high temperature superconductors has been the subject of intense debate. Following the establishment of an empirical law known as Homes'law, there is a growing consensus in the community that the Tc value of the cuprate superconductors is closely linked to its superfluid density and conductivity. However, all the data supporting this empirical law have been obtained from the ambient-pressure superconductors. In this study, we present the first high-pressure results about the connection of these two quantities with Tc, through the studies on the Bi1.74Pb0.38Sr1.88CuO6+delta and Bi2Sr2CaCu2O8+delta, in which the value of their high-pressure resistivity (the reciprocal of conductivity) is achieved by adopting our newly established method, while the value of superfluid density is extracted using the Homes'law. We highlight that the Tc values are strongly linked the two joint response factors of magnetic field and electric field, i.e. superfluid density and conductivity, respectively, implying that the physics governing the determination of Tc is influenced by the intrinsic electromagnetic fields of the system.

cond-mat.supr-con↗
arXiv2024

Computing optimal partition problems via Lagrange multiplier approach

In this paper, we consider numerical approximations for the optimal partition problem using Lagrange multipliers. By rewriting it into constrained gradient flows, three and four steps numerical schemes based on the Lagrange multiplier approach \cite{ChSh22,ChSh_II22} are proposed to solve the constrained gradient system. Numerical schemes proposed for the constrained gradient flows satisfy the nice properties of orthogonality-preserving, norm-preserving, positivity-preserving and energy dissipating. The proposed schemes are very efficient in which only linear Poisson equations are solved at each time step. Extensive numerical results in 2D and 3D for optimal partition problem are presented to validate the effectiveness and accuracy of the proposed numerical schemes.

math.OC↗
arXiv2024

GTFS2STN: Analyzing GTFS Transit Data by Generating Spatiotemporal Transit Network

The General Transit Feed Specification (GTFS) is an open standard format for recording transit information, utilized by thousands of transit agencies worldwide. This study introduces GTFS2STN, a novel tool that converts static GTFS transit networks into spatiotemporal networks, connecting bus stops across space and time. This transformation enables comprehensive analysis of transit system accessibility. Additionally, we present a web-based application version of the GTFS2STN tool that allows users to generate spatiotemporal networks online and perform basic analyses, including the creation of isochrone maps from a given origin and the calculation of travel time variability between origin-destination pairs over time. Comparative analysis demonstrates that GTFS2STN produces results similar to those of Mapnificent, an existing open-source tool for generating isochrone maps from GTFS inputs. Compared with Mapnificent, GTFS2STN offers enhanced flexibility for researchers and planners to evaluate transit plans, as it allows users to upload and analyze historical or suggested GTFS feeds from any transit agency. This feature facilitates the assessment of accessibility and travel time variability in transit networks over extended periods, making GTFS2STN a valuable tool for the planning and research for the transit systems.

cs.CE↗
arXiv2024

CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection

Recent singing voice synthesis and conversion advancements necessitate robust singing voice deepfake detection (SVDD) models. Current SVDD datasets face challenges due to limited controllability, diversity in deepfake methods, and licensing restrictions. Addressing these gaps, we introduce CtrSVDD, a large-scale, diverse collection of bonafide and deepfake singing vocals. These vocals are synthesized using state-of-the-art methods from publicly accessible singing voice datasets. CtrSVDD includes 47.64 hours of bonafide and 260.34 hours of deepfake singing vocals, spanning 14 deepfake methods and involving 164 singer identities. We also present a baseline system with flexible front-end features, evaluated against a structured train/dev/eval split. The experiments show the importance of feature selection and highlight a need for generalization towards deepfake methods that deviate further from training distribution. The CtrSVDD dataset and baselines are publicly accessible.

eess.AS↗
arXiv2024

Multi-Modal Automatic Prosody Annotation with Contrastive Pretraining of SSWP

In expressive and controllable Text-to-Speech (TTS), explicit prosodic features significantly improve the naturalness and controllability of synthesised speech. However, manual prosody annotation is labor-intensive and inconsistent. To address this issue, a two-stage automatic annotation pipeline is novelly proposed in this paper. In the first stage, we use contrastive pretraining of Speech-Silence and Word-Punctuation (SSWP) pairs to enhance prosodic information in latent representations. In the second stage, we build a multi-modal prosody annotator, comprising pretrained encoders, a text-speech fusing scheme, and a sequence classifier. Experiments on English prosodic boundaries demonstrate that our method achieves state-of-the-art (SOTA) performance with 0.72 and 0.93 f1 score for Prosodic Word and Prosodic Phrase boundary respectively, while bearing remarkable robustness to data scarcity.

eess.AS↗
arXiv2024

Empowering Working Memory for Large Language Model Agents

Large language models (LLMs) have achieved impressive linguistic capabilities. However, a key limitation persists in their lack of human-like memory faculties. LLMs exhibit constrained memory retention across sequential interactions, hindering complex reasoning. This paper explores the potential of applying cognitive psychology's working memory frameworks, to enhance LLM architecture. The limitations of traditional LLM memory designs are analyzed, including their isolation of distinct dialog episodes and lack of persistent memory links. To address this, an innovative model is proposed incorporating a centralized Working Memory Hub and Episodic Buffer access to retain memories across episodes. This architecture aims to provide greater continuity for nuanced contextual reasoning during intricate tasks and collaborative scenarios. While promising, further research is required into optimizing episodic memory encoding, storage, prioritization, retrieval, and security. Overall, this paper provides a strategic blueprint for developing LLM agents with more sophisticated, human-like memory capabilities, highlighting memory mechanisms as a vital frontier in artificial general intelligence.

cs.CL↗
arXiv2024

Experimental investigation of trans-scale displacement responses of wrinkle defects in fiber reinforced composite laminates

Wrinkle defects were found widely exist in the field of industrial products, i.e. wind turbine blades and filament-wound composite pressure vessels. The magnitude of wrinkle wavelength varies from several millimeters to over one hundred millimeters. Locating the wrinkle defects and measuring their responses are very important to the assessment of the structures that containing wrinkle defects. A meso-mechanical modeling is presented based on the homogenization method to obtain the effective stiffness of a graded wrinkle. The finite element simulation predicts the trans-scale response of out-of-plane displacement of wrinkled laminates, where the maximum displacement ranges from nanoscale to millimeter scale. Such trans-scale effect requires different measurement approaches to observe the displacement responses. Here we employed Shearography (Speckle Pattern Shearing Interferometry) and fringe projection profilometry (FPP) method respectively according to the different magnitude of displacement. In FPP method, a displacement extraction algorithm was presented to obtain the out-of-plane displacement. The measurement sensitivity and accuracy of Shearography and FPP are compared, which provides a quantitative reference for industrial non-destructive test.

cs.CV↗
arXiv2024

A Method for Parsing and Vectorization of Semi-structured Data used in Retrieval Augmented Generation

This paper presents a novel method for parsing and vectorizing semi-structured data to enhance the functionality of Retrieval-Augmented Generation (RAG) within Large Language Models (LLMs). We developed a comprehensive pipeline for converting various data formats into .docx, enabling efficient parsing and structured data extraction. The core of our methodology involves the construction of a vector database using Pinecone, which integrates seamlessly with LLMs to provide accurate, context-specific responses, particularly in environmental management and wastewater treatment operations. Through rigorous testing with both English and Chinese texts in diverse document formats, our results demonstrate a marked improvement in the precision and reliability of LLMs outputs. The RAG-enhanced models displayed enhanced ability to generate contextually rich and technically accurate responses, underscoring the potential of vector knowledge bases in significantly boosting the performance of LLMs in specialized domains. This research not only illustrates the effectiveness of our method but also highlights its potential to revolutionize data processing and analysis in environmental sciences, setting a precedent for future advancements in AI-driven applications. Our code is available at https://github.com/linancn/TianGong-AI-Unstructure.git.

cs.DB↗
arXiv2024

A refined convergence estimate for a fourth order finite difference numerical scheme to the Cahn-Hilliard equation

In this article we present a refined convergence analysis for a second order accurate in time, fourth order finite difference numerical scheme for the 3-D Cahn-Hilliard equation, with an improved convergence constant. A modified backward differentiation formula temporal discretization is applied, and a Douglas-Dupont artificial regularization is included to ensure the energy stability. In fact, a standard application of discrete Gronwall inequality leads to a convergence constant dependent on the interface width parameter in an exponential singular form. We aim to obtain an improved estimate, with such a singular dependence only in a polynomial order. A uniform in time functional bounds of the numerical solution, including the higher order Sobolev norms, as well as the associated bounds for the first and second order temporal difference stencil, have to be carefully established. Certain recursive analysis has to be applied in the analysis for the BDF-style temporal stencil. As a result, we are able to apply a spectrum estimate for the linearized Cahn-Hilliard operator, and this technique leads to the refined error estimate. A three-dimensional numerical example of accuracy check is presented as well.

math.NA↗
arXiv2024

OTFS-based Robust MMSE Precoding Design in Over-the-air Computation

Over-the-air computation (AirComp), as a data aggregation method that can improve network efficiency by exploiting the superposition characteristics of wireless channels, has received much attention recently. Meanwhile, the orthogonal time frequency space (OTFS) modulation can provide a strong Doppler resilience and facilitate reliable transmission for high-mobility communications. Hence, in this work, we investigate an OTFS-based AirComp system in the presence of time-frequency dual-selective channels. In particular, we commence from the development of a novel transmission framework for the considered system, where the pilot signal is sent together with data, and the channel estimation is implemented according to the echo from the access point to the sensor, thereby reducing the overhead of channel state information (CSI) feedback. Hereafter, based on the CSI estimated from the previous frame, a robust precoding matrix aiming at minimizing mean square error in the current frame is designed, which takes into account the estimation error from the receiver noise and the outdated CSI. The simulation results demonstrate the effectiveness of the proposed robust precoding scheme by comparing it with the non-robust precoding. The performance gain is more obvious in a high signal-to-noise ratio in case of large channel estimation errors.

cs.IT↗
arXiv2024

Investigating the Physical Properties of Traversable Wormholes in the Modified $f(R,T)$ Gravity

In the paper, we analyze some physical properties of static traversable WH within the framework of $f(R,T)$ modified gravitational theory. Firstly, we explore the validity of the null, weak, dominant and strong energy conditions for wormhole matter for the considered $f(R,T)=R+αR^2+λT$ model. Research shows that it is possible to obtain traversable WH geometry without bring in exotic matter that violates the null energy condition in the $f(R,T)$ theory. The violation of the dominant energy condition in this model may be related to quantum fluctuations or indicates the existence of special matter that violates this EC within the wormhole. Moreover, it is found that in the $f(R,T)=R+αR^2+λT$ model, relative to the GR, the introduction of the geometric term $αR^2$ has no remarkable impact on the wormhole matter components and their properties, while the appearance of the matter-geometry coupling term $λT$ can resolve the question that WH matter violates the null, weak and strong energy condition in GR. Additionally, we investigate dependency of the valid NEC on model parameters and quantify the matter components within the wormhole using the ``volume integral quantifier". Lastly, based on the modified Tolman-Oppenheimer-Volkov equation, we find that the traversable WH in this theory is stable. On the other hand, we use the classical reconstruction technique to derive wormhole solution in $f(R,T)$ theory and discuss the corresponding ECs of matter. It is found that all four ECs (NEC, WEC, SEC and DEC) of matter in the traversable wormholes are valid in this reconstructed $f(R,T)$ model, i.e we provide a wormhole solution without introducing the exotic matter and special matter in $f(R,T)$ theory.

gr-qc↗
arXiv2024

Advanced Unstructured Data Processing for ESG Reports: A Methodology for Structured Transformation and Enhanced Analysis

In the evolving field of corporate sustainability, analyzing unstructured Environmental, Social, and Governance (ESG) reports is a complex challenge due to their varied formats and intricate content. This study introduces an innovative methodology utilizing the "Unstructured Core Library", specifically tailored to address these challenges by transforming ESG reports into structured, analyzable formats. Our approach significantly advances the existing research by offering high-precision text cleaning, adept identification and extraction of text from images, and standardization of tables within these reports. Emphasizing its capability to handle diverse data types, including text, images, and tables, the method adeptly manages the nuances of differing page layouts and report styles across industries. This research marks a substantial contribution to the fields of industrial ecology and corporate sustainability assessment, paving the way for the application of advanced NLP technologies and large language models in the analysis of corporate governance and sustainability. Our code is available at https://github.com/linancn/TianGong-AI-Unstructure.git.

cs.CL↗
arXiv2023

Deconfined quantum critical point lost in pressurized SrCu2(BO3)2

In the field of correlated electron materials, the relation between the resonating spin singlet and antiferromagnetic states has long been an attractive topic for understanding of the interesting macroscopic quantum phenomena, such as the ones emerging from magnetic frustrated materials, antiferromagnets and high-temperature superconductors. SrCu2(BO3)2 is a well-known quantum magnet, and it is theoretically expected to be the candidate of correlated electron material for clarifying the existence of a pressure-induced deconfined quantum critical point (DQCP), featured by a continuous quantum phase transition, between the plaquette-singlet (PS) valence bond solid phase and the antiferromagnetic (AF) phase. However, the real nature of the transition is yet to be identified experimentally due to the technical challenge. Here we show the experimental results for the first time, through the state-of-the-art high-pressure heat capacity measurement, that the PS-AF phase transition of the pressurized SrCu2(BO3)2 at zero field is clearly a first-order one. Our result clarifies the more than two-decade long debates about this key issue, and resonates nicely with the recent quantum entanglement understanding that the theoretically predicted DQCPs in representative lattice models are actually a first-order transition. Intriguingly, we also find that the transition temperatures of the PS and AF phase meet at the same pressure-temperature point, which signifies a bi-critical point as those observed in Fe-based superconductor and heavy-fermion compound, and constitutes the first experimental discovery of the pressure-induced bi-critical point in frustrated magnets. Our results provide fresh information for understanding the evolution among different spin states of correlated electron materials under pressure.

cond-mat.str-el↗
arXiv2023

Outage Performance of Multi-tier UAV Communication with Random Beam Misalignment

By exploiting the degree of freedom on the altitude, unmanned aerial vehicle (UAV) communication can provide ubiquitous communication for future wireless networks. In the case of concurrent transmission of multiple UAVs, the directional beamforming formed by multiple antennas is an effective way to reduce co-channel interference. However, factors such as airflow disturbance or estimation error for UAV communications can cause the occurrence of beam misalignment. In this paper, we investigate the system performance of a multi-tier UAV communication network with the consideration of unstable beam alignment. In particular, we propose a tractable random model to capture the impacts of beam misalignment in the 3D space. Based on this, by utilizing stochastic geometry, an analytical framework for obtaining the outage probability in the downlink of a multi-tier UAV communication network for the closest distance association scheme and the maximum average power association scheme is established. The accuracy of the analysis is verified by Monte-Carlo simulations. The results indicate that in the presence of random beam misalignment, the optimal number of UAV antennas needs to be adjusted to be relatively larger when the density of UAVs increases or the altitude of UAVs becomes higher.

eess.SY↗