arXiv subjects

Ping Zhang

Publications and source records attributed to Ping Zhang.

At least 253 records · Page 14Linked to original sources

arXiv2024

Rate Splitting Multiple Access-Enabled Adaptive Panoramic Video Semantic Transmission

In this paper, we propose an adaptive panoramic video semantic transmission (APVST) framework enabled by rate splitting multiple access (RSMA). The APVST framework consists of a semantic transmitter and receiver, utilizing a deep joint source-channel coding structure to adaptively extract and encode semantic features from panoramic frames. To achieve higher spectral efficiency and conserve bandwidth, APVST employs an entropy model and a dimension-adaptive module to control the transmission rate. Additionally, we take weighted-to-spherically-uniform peak signal-to-noise ratio (WS-PSNR) and weighted-to-spherically-uniform structural similarity (WS-SSIM) as distortion evaluation metrics for panoramic videos and design a weighted self-attention module for APVST. This module integrates weights and feature maps to enhance the quality of the immersive experience. Considering the overlap in the field of view when users watch panoramic videos, we further utilize RSMA to split the required panoramic video semantic streams into common and private messages for transmission. We propose an RSMA-enabled semantic stream transmission scheme and formulate a joint problem of latency and immersive experience quality by optimizing the allocation ratios of power, common rate, and channel bandwidth, aiming to maximize the quality of service (QoS) scores for users. To address the above problem, we propose a deep reinforcement learning algorithm based on proximal policy optimization (PPO) with high efficiency to handle dynamically changing environments. Simulation results demonstrate that our proposed APVST framework saves up to 20% and 50% of channel bandwidth compared to other semantic and traditional video transmission schemes, respectively. Moreover, our study confirms the efficiency of RSMA in panoramic video transmission, achieving performance gains of 13% and 20% compared to NOMA and OFDMA.

eess.IV↗

arXiv2024

Association between a Failed Prominence Eruption and the Drainage of Mass from Another Prominence

Sympathetic eruptions of solar prominences have been studied for decades, however, it is usually difficult to identify their causal links. Here we present two failed prominence eruptions on 26 October 2022 and explore their connections. Using stereoscopic observations, the south prominence (PRO-S) erupts with untwisting motions, flare ribbons occur underneath, and new connections are formed during the eruption. The north prominence (PRO-N) rises up along with PRO-S, and its upper part disappears due to catastrophic mass draining along an elongated structure after PRO-S failed eruption. We suggest that the eruption of PRO-S initiates due to a kink instability, further rises up, and fails to erupt due to reconnection with surrounding fields. The elongated structure connecting PRO-N overlies PRO-S, which causes the rising up of PRO-N along with PRO-S and mass drainage after PRO-S eruption. This study suggests that a prominence may end its life through mass drainage forced by an eruption underneath.

astro-ph.SR↗

arXiv2024

Knowledge Base Enabled Semantic Communication: A Generative Perspective

Semantic communication is widely touted as a key technology for propelling the sixth-generation (6G) wireless networks. However, providing effective semantic representation is quite challenging in practice. To address this issue, this article takes a crack at exploiting semantic knowledge base (KB) to usher in a new era of generative semantic communication. Via semantic KB, source messages can be characterized in low-dimensional subspaces without compromising their desired meanings, thus significantly enhancing the communication efficiency. The fundamental principle of semantic KB is first introduced, and a generative semantic communication architecture is developed by presenting three sub-KBs, namely source, task, and channel KBs. Then, the detailed construction approaches for each sub-KB are described, followed by their utilization in terms of semantic coding and transmission. A case study is also provided to showcase the superiority of generative semantic communication over conventional syntactic communication and classical semantic communication. In a nutshell, this article establishes a scientific foundation for the exciting uncharted frontier of generative semantic communication.

cs.IT↗

arXiv2024

Federated Contrastive Learning for Personalized Semantic Communication

In this letter, we design a federated contrastive learning (FedCL) framework aimed at supporting personalized semantic communication. Our FedCL enables collaborative training of local semantic encoders across multiple clients and a global semantic decoder owned by the base station. This framework supports heterogeneous semantic encoders since it does not require client-side model aggregation. Furthermore, to tackle the semantic imbalance issue arising from heterogeneous datasets across distributed clients, we employ contrastive learning to train a semantic centroid generator (SCG). This generator obtains representative global semantic centroids that exhibit intra-semantic compactness and inter-semantic separability. Consequently, it provides superior supervision for learning discriminative local semantic features. Additionally, we conduct theoretical analysis to quantify the convergence performance of FedCL. Simulation results verify the superiority of the proposed FedCL framework compared to other distributed learning benchmarks in terms of task performance and robustness under different numbers of clients and channel conditions, especially in low signal-to-noise ratio and highly heterogeneous data scenarios.

eess.SP↗

arXiv2024

Deep Generative Modeling Reshapes Compression and Transmission: From Efficiency to Resiliency

Information theory and machine learning are inextricably linked and have even been referred to as "two sides of the same coin". One particularly elegant connection is the essential equivalence between probabilistic generative modeling and data compression or transmission. In this article, we reveal the dual-functionality of deep generative models that reshapes both data compression for efficiency and transmission error concealment for resiliency. We present how the contextual predictive capabilities of powerful generative models can be well positioned to be strong compressors and estimators. In this sense, we advocate for viewing the deep generative modeling problem through the lens of end-to-end communications, and evaluate the compression and error restoration capabilities of foundation generative models. We show that the kernel of many large generative models is powerful predictor that can capture complex relationships among semantic latent variables, and the communication viewpoints provide novel insights into semantic feature tokenization, contextual learning, and usage of deep generative models. In summary, our article highlights the essential connections of generative AI to source and channel coding techniques, and motivates researchers to make further explorations in this emerging topic.

cs.IT↗

arXiv2024

Global stability of large Fourier mode for 3-D anisotropic Navier-Stokes equations in cylindrical domain

In this paper, we first establish the global existence and stability of solutions to 3-D classical Navier-Stokes equations $(NS)$ in an infinite cylindrical domain with large Fourier mode initial data. Then we extend similar result for 3-D anisotropic Navier-Stokes equations $(ANS).$ We remark that due to the loss of vertical viscosity in $(ANS),$ the construction of the energy functionals for $(ANS)$ is much more subtle than that of $(NS).$ Compared with our previous paper for $(NS)$, we improve the polynomial decay in $k$ for the Fourier coefficients of the solution to be exponential decay in $k$ here.

math.AP↗

arXiv2024

Three-gap superconductivity with $T_{c}$ above 80 K in hydrogenated 2D monolayer LiBC

Although the metalization of semiconductor bulk LiBC has been experimentally achieved, various flaws, including the strong lattice distortion, the uncontrollability of phase transition under pressure, usually appear. In this work, based on the first-principles calculations, we propose a new way of hydrogenation to realize metalization. Using the fully anisotropic Migdal-Eliashberg theory, we investigate the superconducting behaviors in the stable monolayers LiBCH and LiCBH, in which C and B atoms are hydrogenated, respectively. Our findings indicate that the monolayers possess the high $T_{c}$ of 82.0 and 82.5 K, respectively, along with the interesting three-gap superconducting natures. The Fermi sheets showing the obvious three-region distribution characteristics and the abnormally strong electron-phonon coupling (EPC) are responsible for the high-$T_{c}$ three-gap superconductivity. Furthermore, the $T_{c}$ can be dramatically boosted up to 120.0 K under 3.5 \% tensile strain. To a great extent, the high $T_{c}$ is beyond the liquid nitrogen temperature ($77$ K), which is beneficial for the applications in future experiments. This study not only explores the superconducting properties of the monolayers LiBCH and LiCBH, but also offers practical insights into the search for high-$T_{c}$ superconductors.

cond-mat.supr-con↗

arXiv2024

Holographic MIMO Systems, Their Channel Estimation and Performance

Holographic multiple-input multiple-output (MIMO) systems constitute a promising technology in support of next-generation wireless communications, thus paving the way for a smart programmable radio environment. However, despite its significant potential, further fundamental issues remain to be addressed, such as the acquisition of accurate channel information. Indeed, the conventional angular-domain channel representation is no longer adequate for characterizing the sparsity inherent in holographic MIMO channels. To fill this knowledge gap, in this article, we conceive a decomposition and reconstruction (DeRe)-based framework for facilitating the estimation of sparse channels in holographic MIMOs. In particular, the channel parameters involved in the steering vector, namely the azimuth and elevation angles plus the distance (AED), are decomposed for independently constructing their own covariance matrices. Then, the acquisition of each parameter can be formulated as a compressive sensing (CS) problem by harnessing the covariance matrix associated with each individual parameter. We demonstrate that our solution exhibits an improved performance and imposes a reduced pilot overhead, despite its reduced complexity. Finally, promising open research topics are highlighted to bridge the gap between the theory and the practical employment of holographic MIMO schemes.

cs.IT↗

arXiv2024

Optimizing Generative AI Networking: A Dual Perspective with Multi-Agent Systems and Mixture of Experts

In the continued development of next-generation networking and artificial intelligence content generation (AIGC) services, the integration of multi-agent systems (MAS) and the mixture of experts (MoE) frameworks is becoming increasingly important. Motivated by this, this article studies the contrasting and converging of MAS and MoE in AIGC-enabled networking. First, we discuss the architectural designs, operational procedures, and inherent advantages of using MAS and MoE in generative AI to explore its functionality and applications fully. Next, we review the applications of MAS and MoE frameworks in content generation and resource allocation, emphasizing their impact on networking operations. Subsequently, we propose a novel multi-agent-enabled MoE-proximal policy optimization (MoE-PPO) framework for 3D object generation and data transfer scenarios. The framework uses MAS for dynamic task coordination of each network service provider agent and MoE for expert-driven execution of respective tasks, thereby improving overall system efficiency and adaptability. The simulation results demonstrate the effectiveness of our proposed framework and significantly improve the performance indicators under different network conditions. Finally, we outline potential future research directions.

cs.NI↗

arXiv2024

Inquire, Interact, and Integrate: A Proactive Agent Collaborative Framework for Zero-Shot Multimodal Medical Reasoning

The adoption of large language models (LLMs) in healthcare has attracted significant research interest. However, their performance in healthcare remains under-investigated and potentially limited, due to i) they lack rich domain-specific knowledge and medical reasoning skills; and ii) most state-of-the-art LLMs are unimodal, text-only models that cannot directly process multimodal inputs. To this end, we propose a multimodal medical collaborative reasoning framework \textbf{MultiMedRes}, which incorporates a learner agent to proactively gain essential information from domain-specific expert models, to solve medical multimodal reasoning problems. Our method includes three steps: i) \textbf{Inquire}: The learner agent first decomposes given complex medical reasoning problems into multiple domain-specific sub-problems; ii) \textbf{Interact}: The agent then interacts with domain-specific expert models by repeating the ``ask-answer'' process to progressively obtain different domain-specific knowledge; iii) \textbf{Integrate}: The agent finally integrates all the acquired domain-specific knowledge to accurately address the medical reasoning problem. We validate the effectiveness of our method on the task of difference visual question answering for X-ray images. The experiments demonstrate that our zero-shot prediction achieves state-of-the-art performance, and even outperforms the fully supervised methods. Besides, our approach can be incorporated into various LLMs and multimodal LLMs to significantly boost their performance.

cs.AI↗

arXiv2024

Integrated and DC-powered superconducting microcomb

Frequency combs, specialized laser sources emitting multiple equidistant frequency lines, have revolutionized science and technology with unprecedented precision and versatility. Recently, integrated frequency combs are emerging as scalable solutions for on-chip photonics. Here, we demonstrate a fully integrated superconducting microcomb that is easy to manufacture, simple to operate, and consumes ultra-low power. Our turnkey apparatus comprises a basic nonlinear superconducting device, a Josephson junction, directly coupled to a superconducting microstrip resonator. We showcase coherent comb generation through self-started mode-locking. Therefore, comb emission is initiated solely by activating a DC bias source, with power consumption as low as tens of picowatts. The resulting comb spectrum resides in the microwave domain and spans multiple octaves. The linewidths of all comb lines can be narrowed down to 1 Hz through a unique coherent injection-locking technique. Our work represents a critical step towards fully integrated microwave photonics and offers the potential for integrated quantum processors.

cond-mat.supr-con↗

arXiv2024

Non-stationary Domain Generalization: Theory and Algorithm

Although recent advances in machine learning have shown its success to learn from independent and identically distributed (IID) data, it is vulnerable to out-of-distribution (OOD) data in an open world. Domain generalization (DG) deals with such an issue and it aims to learn a model from multiple source domains that can be generalized to unseen target domains. Existing studies on DG have largely focused on stationary settings with homogeneous source domains. However, in many applications, domains may evolve along a specific direction (e.g., time, space). Without accounting for such non-stationary patterns, models trained with existing methods may fail to generalize on OOD data. In this paper, we study domain generalization in non-stationary environment. We first examine the impact of environmental non-stationarity on model performance and establish the theoretical upper bounds for the model error at target domains. Then, we propose a novel algorithm based on adaptive invariant representation learning, which leverages the non-stationary pattern to train a model that attains good performance on target domains. Experiments on both synthetic and real data validate the proposed algorithm.

cs.LG↗

arXiv2024

MSene: A new large family of two-dimensional transition metal sulfide with MXene structure

In this work, we theoretically report a new large family of two-dimensional (2D) transition metal sulfides $M$$_{2}$S with MXene structure in 2H and 1T phases, which we name as MSene. Twenty-four out of fifty-eight MSenes are proved to be stable. Notably, this family includes twelve superconducting (SC) materials, seven SC topological metals (SCTMs), four charge density wave (CDW) materials, and five magnetic materials including one ferromagnetic (FM) and four antiferromagnetic (AFM) materials. For example, 2H-Mo$_{2}$S is a SCTM which exhibits SC critical temperature ($T_{c}$) of 10.2 K and nontrivial topological properties; 1T-Hf$_{2}$S is a CDW material with the CDW originating from electron-phonon coupling. The CDW can be suppressed by compressive strain, leading to the emergence of superconductivity; 2H-Cr$_{2}$S and 1T-Mn$_{2}$S show FM and AFM properties, respectively. Thus, the new large family we predicted shows rich physical properties and significantly expands the repertoire of 2D materials. It serves as a novel platform for investigating the competition or coexistence of multiple orders such as SC, CDW, FM, AFM and topological orders in 2D materials.

cond-mat.mtrl-sci↗

arXiv2024

Predictive Modeling with Temporal Graphical Representation on Electronic Health Records

Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships between historical visits and medical events, and the inherent structural information within these elements. Existing patient representation methods can be roughly categorized into sequential representation and graphical representation. The sequential representation methods focus only on the temporal relationships among longitudinal visits. On the other hand, the graphical representation approaches, while adept at extracting the graph-structured relationships between various medical events, fall short in effectively integrate temporal information. To capture both types of information, we model a patient's EHR as a novel temporal heterogeneous graph. This graph includes historical visits nodes and medical events nodes. It propagates structured information from medical event nodes to visit nodes and utilizes time-aware visit nodes to capture changes in the patient's health status. Furthermore, we introduce a novel temporal graph transformer (TRANS) that integrates temporal edge features, global positional encoding, and local structural encoding into heterogeneous graph convolution, capturing both temporal and structural information. We validate the effectiveness of TRANS through extensive experiments on three real-world datasets. The results show that our proposed approach achieves state-of-the-art performance.

cs.LG↗

arXiv2024

MambaJSCC: Deep Joint Source-Channel Coding with Visual State Space Model

Lightweight and efficient deep joint source-channel coding (JSCC) is a key technology for semantic communications. In this paper, we design a novel JSCC scheme named MambaJSCC, which utilizes a visual state space model with channel adaptation (VSSM-CA) block as its backbone for transmitting images over wireless channels. The VSSM-CA block utilizes VSSM to integrate two-dimensional images with the state space, enabling feature extraction and encoding processes to operate with linear complexity. It also incorporates channel state information (CSI) via a newly proposed CSI embedding method. This method deploys a shared CSI encoding module within both the encoder and decoder to encode and inject the CSI into each VSSM-CA block, improving the adaptability of a single model to varying channel conditions. Experimental results show that MambaJSCC not only outperforms Swin Transformer based JSCC (SwinJSCC) but also significantly reduces parameter size, computational overhead, and inference delay (ID). For example, with employing an equal number of the VSSM-CA blocks and the Swin Transformer blocks, MambaJSCC achieves a 0.48 dB gain in peak-signal-to-noise ratio (PSNR) over SwinJSCC while requiring only 53.3% multiply-accumulate operations, 53.8% of the parameters, and 44.9% of ID.

cs.IT↗

arXiv2024

WDMoE: Wireless Distributed Large Language Models with Mixture of Experts

Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, but how wireless communications can support LLMs has not been extensively studied. In this paper, we propose a wireless distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE, deploying LLMs collaboratively across edge servers of base station (BS) and mobile devices in the wireless communications system. Specifically, we decompose the MoE layer in LLMs by deploying the gating network and the preceding neural network layer at BS, while distributing the expert networks across the devices. This arrangement leverages the parallel capabilities of expert networks on distributed devices. Moreover, to overcome the instability of wireless communications, we design an expert selection policy by taking into account both the performance of the model and the end-to-end latency, which includes both transmission delay and inference delay. Evaluations conducted across various LLMs and multiple datasets demonstrate that WDMoE not only outperforms existing models, such as Llama 2 with 70 billion parameters, but also significantly reduces end-to-end latency.

cs.IT↗

arXiv2024

Spectral and Imaging Observations of a C2.3 White-Light Flare from the Advanced Space-Based Solar Observatory (ASO-S) and the Chinese H$α$ Solar Explorer (CHASE)

Solar white-light flares are characterized by an enhancement in the optical continuum, which are usually large flares (say X- and M-class flares). Here we report a small C2.3 white-light flare (SOL2022-12-20T04:10) observed by the \emph{Advanced Space-based Solar Observatory} and the \emph{Chinese H$α$ Solar Explorer}. This flare exhibits an increase of $\approx$6.4\% in the photospheric Fe \textsc{i} line at 6569.2\,Å and {$\approx$3.2\%} in the nearby continuum. The continuum at 3600\,Å also shows an enhancement of $\approx$4.7\%. The white-light brightening kernels are mainly located at the flare ribbons and co-spatial with nonthermal hard X-ray sources, which implies that the enhanced white-light emissions are related to nonthermal electron-beam heating. At the brightening kernels, the Fe \textsc{i} line displays an absorption profile that has a good Gaussian shape, with a redshift up to $\approx$1.7 km s$^{-1}$, while the H$α$ line shows an emission profile though having a central reversal. The H$α$ line profile also shows a red or blue asymmetry caused by plasma flows with a velocity of several to tens of km s$^{-1}$. It is interesting to find that the H$α$ asymmetry is opposite at the conjugate footpoints. It is also found that the CHASE continuum increase seems to be related to the change of photospheric magnetic field. Our study provides comprehensive characteristics of a small white-light flare that help understand the energy release process of white-light flares.

astro-ph.SR↗

arXiv2024

Origin of Ferroelectricity and Superconductivity with Nontrivial Electronic Topology in Fluorinated Nb2N

Two-dimensional (2D) intrinsic superconductors with nontrivial topological band and vertical ferroelectricity exhibit fascinating characteristics to achieving electrostatic control of quantum phases. While, only a few such 2D materials have been theoretically predicted. In this work, based on first principles calculations, we explore the superconductivity and ferroelectric properties in fluorinated 2D Nb2N. In the stable Nb2NF2, H3-Nb2NF2 breaks the spatial inversion symmetry, exhibiting vertical ferroelectric. More interestingly, it not only possesses intrinsic superconductivity with superconducting transition temperatures (Tc) of 10 K, but also exhibits nontrivial band topology. While, H1-Nb2NF2 shows topological band and superconductivity with Tc of 32 K, surpassing most of 2D conventional topological superconductors' candidates. Our research has enriched 2D superconducting materials with nontrivial band topology and ferroelectric properties, and provided a theoretical basis for the preparation of devices switching between superconducting and ferroelectric states with external electric field.

cond-mat.supr-con↗