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Qiong Wu

Publications and source records attributed to Qiong Wu.

At least 109 records · Page 6Linked to original sources

Origin of the density wave instability in trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$ revealed by optical and ultrafast spectroscopy

In the intricate phase diagram of unconventional superconductors characterized by intertwined electronic orders and superconductivity, a key step in understanding the superconducting mechanism is to investigate the parent compounds from which superconductivity emerges through doping or pressure. In this study, we employed optical spectroscopy and ultrafast reflectivity measurements to examine the density wave instability in the trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$, which displays pressure-induced superconductivity up to 30 K. Our optical spectroscopy measurements reveal that La$_{4}$Ni$_{3}$O$_{10}$ behaves as a metal with a high plasma frequency. Upon cooling, we observed a distinct formation of a density wave energy gap in both optical conductivity and pump-probe measurements. The gap feature is more pronounced compared to the bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$. Through a comparison of the experimentally determined plasma frequency with first-principles calculations, we classify La$_{4}$Ni$_{3}$O$_{10}$ as a moderately electron-correlated material, resembling the parent compound of iron-based superconductors but exhibiting weaker correlation than the bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$. The enhanced gap feature and weaker electronic correlation in La$_{4}$Ni$_{3}$O$_{10}$ may explain its lower superconductivity transition temperature under high pressure. These findings significantly advance our comprehension of the density wave and superconductivity mechanisms in the trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$.

cond-mat.str-el↗

Routing Experts: Learning to Route Dynamic Experts in Multi-modal Large Language Models

Recently, mixture of experts (MoE) has become a popular paradigm for achieving the trade-off between modal capacity and efficiency of multi-modal large language models (MLLMs). Different from previous efforts, we are dedicated to exploring the dynamic expert path in an already exist MLLM and show that a standard MLLM can be also a mixture of experts. To approach this target, we propose a novel dynamic expert scheme for MLLMs, termed Routing Experts (RoE), which can achieve example-dependent optimal path routing without obvious structure tweaks. Meanwhile, a new regularization of structure sparsity is also introduced to enforce MLLMs to learn more short-cut inference, ensuring the efficiency. In addition, we also realize the first attempt of aligning the training and inference schemes of MLLMs in terms of network routing. To validate RoE, we apply it to a set of latest MLLMs, including LLaVA-1.5, LLaVA-HR and VILA, and conduct extensive experiments on a bunch of VL benchmarks. The experiment results not only show the great advantages of our RoE in improving MLLMs' efficiency, but also yield obvious advantages than MoE-LLaVA in both performance and speed, e.g., an average performance gain of 3.3% on 5 benchmarks while being faster.

cs.MM↗

Enhancing Robustness and Security in ISAC Network Design: Leveraging Transmissive Reconfigurable Intelligent Surface with RSMA

In this paper, we propose a novel transmissive reconfigurable intelligent surface transceiver-enhanced robust and secure integrated sensing and communication network. A time-division sensing communication mechanism is designed for the scenario, which enables communication and sensing to share wireless resources. To address the interference management problem and hinder eavesdropping, we implement rate-splitting multiple access (RSMA), where the common stream is designed as a useful signal and an artificial noise, while taking into account the imperfect channel state information and modeling the channel for the illegal users in a fine-grained manner as well as giving an upper bound on the error. We introduce the secrecy outage probability and construct an optimization problem with secrecy sum-rate as the objective functions to optimize the common stream beamforming matrix, the private stream beamforming matrix and the timeslot duration variable. Due to the coupling of the optimization variables and the infinity of the error set, the proposed problem is a nonconvex optimization problem that cannot be solved directly. In order to address the above challenges, the block coordinate descent-based second-order cone programming algorithm is used to decouple the optimization variables and solving the problem. Specifically, the problem is decoupled into two subproblems concerning the common stream beamforming matrix, the private stream beamforming matrix, and the timeslot duration variable, which are solved by alternating optimization until convergence is reached. To solve the problem, S-procedure, Bernstein's inequality and successive convex approximation are employed to deal with the objective function and non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in improving the secrecy energy efficiency and the Cramér-Rao boundary.

cs.IT↗

What Kind of Visual Tokens Do We Need? Training-free Visual Token Pruning for Multi-modal Large Language Models from the Perspective of Graph

Recent Multimodal Large Language Models(MLLMs) often use a large number of visual tokens to compensate their visual shortcoming, leading to excessive computation and obvious visual redundancy. In this paper, we investigate what kind of visual tokens are needed for MLLMs, and reveal that both foreground and background tokens are critical for MLLMs given the varying difficulties of examples. Based on this observation, we propose a graph-based method towards training-free visual token pruning, termed G-Prune.In particular, G-Prune regards visual tokens as nodes, and construct their connections based on their semantic similarities. Afterwards, the information flow is propagated via weighted links, and the most important tokens after iterations are kept for MLLMs, which can be front or background.To validate G-Prune, we apply it to a recent MLLM called LLaVA-NeXT, and conduct extensive experiments on a set of benchmarks.The experiment results show that G-Prune can greatly reduce computation overhead while retaining high performance on both coarse- and fine-grained tasks. For instance, G-Prune can reduce 63.57\% FLOPs of LLaVA-NeXT on VQA2.0 and TextVQA with only 0.95\% and 2.34\% accuracy drops, respectively.

cs.CV↗

Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings

Cross-View Geo-Localization tackles the challenge of image geo-localization in GNSS-denied environments, including disaster response scenarios, urban canyons, and dense forests, by matching street-view query images with geo-tagged aerial-view reference images. However, current research often relies on benchmarks and methods that assume center-aligned settings or account for only limited decentrality, which we define as the offset of the query image relative to the reference image center. Such assumptions fail to reflect real-world scenarios, where reference databases are typically pre-established without the possibility of ensuring perfect alignment for each query image. Moreover, decentrality is a critical factor warranting deeper investigation, as larger decentrality can substantially improve localization efficiency but comes at the cost of declines in localization accuracy. To address this limitation, we introduce DReSS (Decentrality Related Street-view and Satellite-view dataset), a novel dataset designed to evaluate cross-view geo-localization with a large geographic scope and diverse landscapes, emphasizing the decentrality issue. Meanwhile, we propose AuxGeo (Auxiliary Enhanced Geo-Localization) to further study the decentrality issue, which leverages a multi-metric optimization strategy with two novel modules: the Bird's-eye view Intermediary Module (BIM) and the Position Constraint Module (PCM). These modules improve the localization accuracy despite the decentrality problem. Extensive experiments demonstrate that AuxGeo outperforms previous methods on our proposed DReSS dataset, mitigating the issue of large decentrality, and also achieves state-of-the-art performance on existing public datasets such as CVUSA, CVACT, and VIGOR.

cs.CV↗

Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models

Recent progress in Multimodal Large Language Models(MLLMs) often use large image tokens to compensate the visual shortcoming of MLLMs, which not only exhibits obvious redundancy but also greatly exacerbates the already high computation. Token pruning is an effective solution for speeding up MLLMs, but when and how to drop tokens still remains a challenge. In this paper, we propose a novel and training-free approach for the effective visual token pruning of MLLMs, termed FitPrune, which can quickly produce a complete pruning recipe for MLLMs according to a pre-defined budget. Specifically, FitPrune considers token pruning as a statistical problem of MLLM and its objective is to find out an optimal pruning scheme that can minimize the divergence of the attention distributions before and after pruning. In practice, FitPrune can be quickly accomplished based on the attention statistics from a small batch of inference data, avoiding the expensive trials of MLLMs. According to the pruning recipe, an MLLM can directly remove the redundant visual tokens of different examples during inference. To validate FitPrune, we apply it to a set of recent MLLMs, including LLaVA-1.5, LLaVA-HR and LLaVA-NEXT, and conduct extensive experiments on a set of benchmarks. The experimental results show that our FitPrune can not only reduce the computational complexity to a large extent, while retaining high performance, e.g., -54.9% FLOPs for LLaVA-NEXT with only 0.5% accuracy drop. Notably, the pruning recipe can be obtained in about 5 minutes. Our code is available at https://github.com/ywh187/FitPrune.

cs.CV↗

Cross-View Image Set Geo-Localization

Cross-view geo-localization (CVGL) has been widely applied in fields such as robotic navigation and augmented reality. Existing approaches primarily use single images or fixed-view image sequences as queries, which limits perspective diversity. In contrast, when humans determine their location visually, they typically move around to gather multiple perspectives. This behavior suggests that integrating diverse visual cues can improve geo-localization reliability. Therefore, we propose a novel task: Cross-View Image Set Geo-Localization (Set-CVGL), which gathers multiple images with diverse perspectives as a query set for localization. To support this task, we introduce SetVL-480K, a benchmark comprising 480,000 ground images captured worldwide and their corresponding satellite images, with each satellite image corresponds to an average of 40 ground images from varied perspectives and locations. Furthermore, we propose FlexGeo, a flexible method designed for Set-CVGL that can also adapt to single-image and image-sequence inputs. FlexGeo includes two key modules: the Similarity-guided Feature Fuser (SFF), which adaptively fuses image features without prior content dependency, and the Individual-level Attributes Learner (IAL), leveraging geo-attributes of each image for comprehensive scene perception. FlexGeo consistently outperforms existing methods on SetVL-480K and two public datasets, SeqGeo and KITTI-CVL, achieving a localization accuracy improvement of over 22% on SetVL-480K.

cs.CV↗

Beamforming Design and Multi-User Scheduling in Transmissive RIS Enabled Distributed Cooperative ISAC Networks with RSMA

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered distributed cooperative integrated sensing and communication (ISAC) network to enhance coverage as well as to enhance wireless environment understanding. Based on the network requirements, the users are categorized into cooperative users (CUEs) and destination users (DUEs), and the CUEs utilize their own resources to serve the DUEs. To realize cooperation, we implement rate-splitting multiple access (RSMA) at the base station (BS), where the common stream is decoded and reencoded at the CUEs and forwarded to the DUEs, while the private stream satisfies the CUEs' own communication requirements. We construct an optimization problem with maximum minimum radar mutual information (RMI) as the objective function to optimize the BS beamforming matrix, the CUE beamforming matrices, the common stream rate vectors, and the user scheduling vectors. Due to the coupling of the optimization variables and non-convex operation, the proposed problem is a non-convex optimization problem that cannot be solved directly. To address the above challenges, we adopt a consensus alternating direction method of multipliers (ADMM) framework to decouple the optimization variables and solve it. Specifically, the problem is decoupled into multiple subproblems and solved by iterative optimization independently until overall convergence is achieved. Finally, numerical results validate the superiority of the proposed scheme in terms of improving communication sum-rate and RMI, and greatly reduce the algorithm complexity.

cs.IT↗

V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario

This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized. To get rid of the reliance on a central controller and reduce computation time, a distributed solution to this problem implemented among CAVs through Vehicles-to-Everything (V2X) communication is proposed. Unlike existing method, our method can distribute the computational task among CAVs and carry out parallel solving through V2X communication. Then, a multi-vehicles model predictive control (MPC) problem aimed at maximizing system stability and minimizing control input is formulated based on the solution of the first problem subject to strict safety constants and input limits. Due to these complex constraints, this problem becomes high-dimensional, centralized, and non-convex. To solve it in a short time, a decomposition and convex reformulation method, namely distributed cooperative iterative model predictive control (DCIMPC), is proposed. This method leverages the communication capability of CAVs to decompose the problem, making full use of the computational resources on vehicles to achieve fast solutions and distributed control. The two above problems with their corresponding solving methods form the systemic framework of the V2X assisted distributed computing and control. Simulations have been conducted to evaluate the framework's convergence, safety, and solving speed. Additionally, extra experiments are conducted to validate the performance of DCIMPC. The results show that our method can greatly improve computation speed without sacrificing system performance.

eess.SY↗

Field-free superconducting diode effect and magnetochiral anisotropy in FeTe0.7Se0.3 junctions with the inherent asymmetric barrier

Nonreciprocal electrical transport, characterized by an asymmetric relationship between current and voltage, plays a crucial role in modern electronic industries. Recent studies have extended this phenomenon to superconductors, introducing the concept of the superconducting diode effect (SDE). The SDE is characterized by unequal critical supercurrents along opposite directions. Due to the requirement on broken inversion symmetry, the SDE is commonly accompanied by electrical magnetochiral anisotropy (eMCA) in the resistive state. Achieving a magnetic field-free SDE with field tunability is pivotal for advancements in superconductor devices. Conventionally, the field-free SDE has been achieved in Josephson junctions by intentionally intercalating an asymmetric barrier layer. Alternatively, internal magnetism was employed. Both approaches pose challenges in the selection of superconductors and fabrication processes, thereby impeding the development of SDE. Here, we present a field-free SDE in FeTe0.7Se0.3 (FTS) junction with eMCA, a phenomenon absent in FTS single nanosheets. The field-free property is associated with the presence of a gradient oxide layer on the upper surface of each FTS nanosheet, while the eMCA is linked to spin-splitting arising from the absence of inversion symmetry. Both the SDE and eMCA respond to magnetic fields with distinct temperature dependencies. This work presents a versatile and straightforward strategy for advancing superconducting electronics.

cond-mat.supr-con↗

A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning

Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using physical separation for each user data. However, with the development of complex application scenarios such as the Internet of Things (IoT) and Smart Earth, the conventional resource allocation schemes can no longer effectively support these growing computational and communication demands. Therefore, joint resource optimization may be the key solution to the scaling problem. This paper simultaneously addresses the multifaceted challenges of computation and communication, with the growing multiple resource demands. We systematically review the joint allocation strategies for different resources (computation, data, communication, and network topology) in FEL, and summarize the advantages in improving system efficiency, reducing latency, enhancing resource utilization and enhancing robustness. In addition, we present the potential ability of joint optimization to enhance privacy preservation by reducing communication requirements, indirectly. This work not only provides theoretical support for resource management in federated learning (FL) systems, but also provides ideas for potential optimal deployment in multiple real-world scenarios. By thoroughly discussing the current challenges and future research directions, it also provides some important insights into multi-resource optimization in complex application environments.

cs.LG↗

Muon Spin Relaxation Study of Spin Dynamics on a Kitaev honeycomb material H$_3$LiIr$_2$O$_6$

The vacancy effect in quantum spin liquid (QSL) has been extensively studied. A finite density of random vacancies in the Kitaev model can lead to a pileup of low-energy density of states (DOS), which is generally experimentally determined by a scaling behavior of thermodynamic or magnetization quantities. Here, we report detailed muon spin relaxation ($μ$SR) results of H$_3$LiIr$_2$O$_6$, a Kitaev QSL candidate with vacancies. The absence of magnetic order is confirmed down to 80 mK, and the spin fluctuations are found to be persistent at low temperatures. Intriguingly, the time-field scaling law of longitudinal-field (LF)-$μ$SR polarization is observed down to 0.1 K. This indicates a dynamical scaling, whose critical exponent 0.46 is excellently consistent with the scaling behavior of specific heat and magnetization data. All the observations point to the finite DOS with the form $N(E) \sim E^{-0.5}$ , which is expected for the Kitaev QSL in the presence of vacancies. Our μSR study provides a dynamical fingerprint of the power-law low-energy DOS, and introduces a crucial new insight into the vacancy effect in QSL.

cond-mat.str-el↗

WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks

Wireless networks are increasingly facing challenges due to their expanding scale and complexity. These challenges underscore the need for advanced AI-driven strategies, particularly in the upcoming 6G networks. In this article, we introduce WirelessAgent, a novel approach leveraging large language models (LLMs) to develop AI agents capable of managing complex tasks in wireless networks. It can effectively improve network performance through advanced reasoning, multimodal data processing, and autonomous decision making. Thereafter, we demonstrate the practical applicability and benefits of WirelessAgent for network slicing management. The experimental results show that WirelessAgent is capable of accurately understanding user intent, effectively allocating slice resources, and consistently maintaining optimal performance.

cs.NI↗

Multistage Robust Average Randomized Spectral Risk Optimization

In this paper, we revisit the multistage spectral risk minimization models proposed by Philpott et al.~\cite{PdF13} and Guigues and Römisch \cite{GuR12} but with some new focuses. We consider a situation where the decision maker's (DM's) risk preferences may be state-dependent or even inconsistent at some states, and consequently there is not a single deterministic spectral risk measure (SRM) which can be used to represent the DM's preferences at each stage. We adopt the recently introduced average randomized SRM (ARSRM) (in \cite{li2022randomization}) to describe the DM's overall risk preference at each stage. To solve the resulting multistage ARSRM (MARSRM) problem, we apply the well-known stochastic dual dynamic programming (SDDP) method which generates a sequence of lower and upper bounds in an iterative manner. Under some moderate conditions, we prove that the optimal solution can be found in a finite number of iterations. The MARSRM model generalizes the one-stage ARSRM and simplifies the existing multistage state-dependent preference robust model \cite{liu2021multistage}, while also encompassing the mainstream multistage risk-neutral and risk-averse optimization models \cite{GuR12,PdF13}. In the absence of complete information on the probability distribution of the DM's random preferences, we propose to use distributionally robust ARSRM (DR-ARSRM) to describe the DM's preferences at each stage. We detail computational schemes for solving both MARSRM and DR-MARSRM. Finally, we examine the performance of MARSRM and DR-MARSRM by applying them to an asset allocation problem with transaction costs and compare them with standard risk neutral and risk averse multistage linear stochastic programming (MLSP) models.

math.OC↗

Channel Characterization of IRS-assisted Resonant Beam Communication Systems

To meet the growing demand for data traffic, spectrum-rich optical wireless communication (OWC) has emerged as a key technological driver for the development of 6G. The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking. However, its transmission through the air is susceptible to losses caused by obstructions. In this paper, we propose an intelligent reflecting surface (IRS) assisted RBC system with the optical frequency doubling method, where the resonant beam in frequency-fundamental and frequency-doubled is transmitted through both direct line-of-sight (LoS) and IRS-assisted channels to maintain steady-state oscillation and enable communication without echo-interference, respectively. Then, we establish the channel model based on Fresnel diffraction theory under the near-field optical propagation to analyze the transmission loss and frequency-doubled power analytically. Furthermore, communication power can be maximized by dynamically controlling the beam-splitting ratio between the two channels according to the loss levels encountered over air. Numerical results validate that the IRS-assisted channel can compensate for the losses in the obstructed LoS channel and misaligned receivers, ensuring that communication performance reaches an optimal value with dynamic ratio adjustments.

eess.SY↗

Age of Information Analysis for Multi-Priority Queue and NOMA Enabled C-V2X in IoV

As development Internet-of-Vehicles (IoV) technology and demand for Intelligent Transportation Systems (ITS) increase, there is a growing need for real-time data and communication by vehicle users. Traditional request-based methods face challenges such as latency and bandwidth limitations. Mode 4 in Connected Vehicle-to-Everything (C-V2X) addresses latency and overhead issues through autonomous resource selection. However, Semi-Persistent Scheduling (SPS) based on distributed sensing may lead to increased collision. Non-Orthogonal Multiple Access (NOMA) can alleviate the problem of reduced packet reception probability due to collisions. Moreover, the concept of Age of Information (AoI) is introduced as a comprehensive metric reflecting reliability and latency performance, analyzing the impact of NOMA on C-V2X communication system. AoI indicates the time a message spends in both local waiting and transmission processes. In C-V2X, waiting process can be extended to queuing process, influenced by packet generation rate and Resource Reservation Interval (RRI). The transmission process is mainly affected by transmission delay and success rate. In C-V2X, a smaller selection window (SW) limits the number of available resources for vehicles, resulting in higher collision rates with increased number of vehicles. SW is generally equal to RRI, which not only affects AoI in queuing process but also AoI in the transmission process. Therefore, this paper proposes an AoI estimation method based on multi-priority data type queues and considers the influence of NOMA on the AoI generated in both processes in C-V2X system under different RRI conditions. This work aims to gain a better performance of C-V2X system comparing with some known algorithms.

cs.NI↗

CAT: Interpretable Concept-based Taylor Additive Models

As an emerging interpretable technique, Generalized Additive Models (GAMs) adopt neural networks to individually learn non-linear functions for each feature, which are then combined through a linear model for final predictions. Although GAMs can explain deep neural networks (DNNs) at the feature level, they require large numbers of model parameters and are prone to overfitting, making them hard to train and scale. Additionally, in real-world datasets with many features, the interpretability of feature-based explanations diminishes for humans. To tackle these issues, recent research has shifted towards concept-based interpretable methods. These approaches try to integrate concept learning as an intermediate step before making predictions, explaining the predictions in terms of human-understandable concepts. However, these methods require domain experts to extensively label concepts with relevant names and their ground-truth values. In response, we propose CAT, a novel interpretable Concept-bAsed Taylor additive model to simply this process. CAT does not have to require domain experts to annotate concepts and their ground-truth values. Instead, it only requires users to simply categorize input features into broad groups, which can be easily accomplished through a quick metadata review. Specifically, CAT first embeds each group of input features into one-dimensional high-level concept representation, and then feeds the concept representations into a new white-box Taylor Neural Network (TaylorNet). The TaylorNet aims to learn the non-linear relationship between the inputs and outputs using polynomials. Evaluation results across multiple benchmarks demonstrate that CAT can outperform or compete with the baselines while reducing the need of extensive model parameters. Importantly, it can explain model predictions through high-level concepts that human can understand.

cs.LG↗