arXiv subjects

Wei Liang

Publications and source records attributed to Wei Liang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Enhancing Blockchain Cross Chain Interoperability: A Comprehensive Survey

Blockchain technology, introduced in 2008, has revolutionized data storage and transfer across sectors such as finance, healthcare, intelligent transportation, and the metaverse. However, the proliferation of blockchain systems has led to discrepancies in architectures, consensus mechanisms, and data standards, creating data and value silos that hinder the development of an integrated multi chain ecosystem. Blockchain interoperability (a.k.a cross chain interoperability) has thus emerged as a solution to enable seamless data and asset exchange across disparate blockchains. In this survey, we systematically analyze over 150 high impact sources from academic journals, digital libraries, and grey literature to provide an in depth examination of blockchain interoperability. By exploring the existing methods, technologies, and architectures, we offer a classification of interoperability approaches including Atomic Swaps, Sidechains, Light Clients, and so on, which represent the most comprehensive overview to date. Furthermore, we investigate the convergence of academic research with industry practices, underscoring the importance of collaborative efforts in advancing blockchain innovation. Finally, we identify key strategic insights, challenges, and future research trajectories in this field. Our findings aim to support researchers, policymakers, and industry leaders in understanding and harnessing the transformative potential of blockchain interoperability to address current challenges and drive forward a cohesive multi-chain ecosystem.

cs.CR↗

arXiv2025

MetaScenes: Towards Automated Replica Creation for Real-world 3D Scans

Embodied AI (EAI) research requires high-quality, diverse 3D scenes to effectively support skill acquisition, sim-to-real transfer, and generalization. Achieving these quality standards, however, necessitates the precise replication of real-world object diversity. Existing datasets demonstrate that this process heavily relies on artist-driven designs, which demand substantial human effort and present significant scalability challenges. To scalably produce realistic and interactive 3D scenes, we first present MetaScenes, a large-scale, simulatable 3D scene dataset constructed from real-world scans, which includes 15366 objects spanning 831 fine-grained categories. Then, we introduce Scan2Sim, a robust multi-modal alignment model, which enables the automated, high-quality replacement of assets, thereby eliminating the reliance on artist-driven designs for scaling 3D scenes. We further propose two benchmarks to evaluate MetaScenes: a detailed scene synthesis task focused on small item layouts for robotic manipulation and a domain transfer task in vision-and-language navigation (VLN) to validate cross-domain transfer. Results confirm MetaScene's potential to enhance EAI by supporting more generalizable agent learning and sim-to-real applications, introducing new possibilities for EAI research. Project website: https://meta-scenes.github.io/.

cs.CV↗

arXiv2025

EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records

Medical consultation dialogues contain critical clinical information, yet their unstructured nature hinders effective utilization in diagnosis and treatment. Traditional methods, relying on rule-based or shallow machine learning techniques, struggle to capture deep and implicit semantics. Recently, large pre-trained language models and Low-Rank Adaptation (LoRA), a lightweight fine-tuning method, have shown promise for structured information extraction. We propose EMRModel, a novel approach that integrates LoRA-based fine-tuning with code-style prompt design, aiming to efficiently convert medical consultation dialogues into structured electronic medical records (EMRs). Additionally, we construct a high-quality, realistically grounded dataset of medical consultation dialogues with detailed annotations. Furthermore, we introduce a fine-grained evaluation benchmark for medical consultation information extraction and provide a systematic evaluation methodology, advancing the optimization of medical natural language processing (NLP) models. Experimental results show EMRModel achieves an F1 score of 88.1%, improving by49.5% over standard pre-trained models. Compared to traditional LoRA fine-tuning methods, our model shows superior performance, highlighting its effectiveness in structured medical record extraction tasks.

cs.CL↗

arXiv2025

Improved AFSA-Based Beam Training Without CSI for RIS-Assisted ISAC Systems

In this paper, we consider transmit beamforming and reflection patterns design in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems, where the dual-function base station (DFBS) lacks channel state information (CSI). To address the high overhead of cascaded channel estimation, we propose an improved artificial fish swarm algorithm (AFSA) combined with a feedback-based joint active and passive beam training scheme. In this approach, we consider the interference caused by multipath user echo signals on target detection and propose a beamforming design method that balances both communication and sensing performance. Numerical simulations show that the proposed AFSA outperforms other optimization algorithms, particularly in its robustness against echo interference under different communication signal-to-noise ratio (SNR) constraints.

eess.SP↗

arXiv2025

RIS-Aided Integrated Sensing and Communication Waveform Design With Tunable PAPR

Low peak-to-average power ratio (PAPR) transmission is an important and favorable requirement prevalent in radar and communication systems, especially in transmission links integrated with high power amplifiers. Meanwhile, motivated by the advantages of reconfigurable intelligent surface (RIS) in mitigating multi-user interference (MUI) to enhance the communication rate, this paper investigates the design problem of joint waveform and passive beamforming with PAPR constraint for integrated sensing and communication (ISAC) systems, where RIS is deployed for downlink communication. We first construct a trade-off optimization problem for the MUI and beampattern similarity under PAPR constraint. Then, in order to solve this multivariate problem, an iterative optimization algorithm based on alternating direction method of multipliers (ADMM) and manifold optimization is proposed. Finally, the simulation results show that the designed waveforms can well satisfy the PAPR requirement of the ISAC systems and achieve a trade-off between radar and communication performance. Under high signal-to-noise ratio (SNR) conditions, compared to systems without RIS, RIS-aided ISAC systems have a performance improvement of about 50\% in communication rate and at least 1 dB in beampatterning error.

eess.SP↗

arXiv2025

STA-V2A: Video-to-Audio Generation with Semantic and Temporal Alignment

Visual and auditory perception are two crucial ways humans experience the world. Text-to-video generation has made remarkable progress over the past year, but the absence of harmonious audio in generated video limits its broader applications. In this paper, we propose Semantic and Temporal Aligned Video-to-Audio (STA-V2A), an approach that enhances audio generation from videos by extracting both local temporal and global semantic video features and combining these refined video features with text as cross-modal guidance. To address the issue of information redundancy in videos, we propose an onset prediction pretext task for local temporal feature extraction and an attentive pooling module for global semantic feature extraction. To supplement the insufficient semantic information in videos, we propose a Latent Diffusion Model with Text-to-Audio priors initialization and cross-modal guidance. We also introduce Audio-Audio Align, a new metric to assess audio-temporal alignment. Subjective and objective metrics demonstrate that our method surpasses existing Video-to-Audio models in generating audio with better quality, semantic consistency, and temporal alignment. The ablation experiment validated the effectiveness of each module. Audio samples are available at https://y-ren16.github.io/STAV2A.

cs.SD↗

arXiv2025

Video-to-Audio Generation with Hidden Alignment

Generating semantically and temporally aligned audio content in accordance with video input has become a focal point for researchers, particularly following the remarkable breakthrough in text-to-video generation. In this work, we aim to offer insights into the video-to-audio generation paradigm, focusing on three crucial aspects: vision encoders, auxiliary embeddings, and data augmentation techniques. Beginning with a foundational model built on a simple yet surprisingly effective intuition, we explore various vision encoders and auxiliary embeddings through ablation studies. Employing a comprehensive evaluation pipeline that emphasizes generation quality and video-audio synchronization alignment, we demonstrate that our model exhibits state-of-the-art video-to-audio generation capabilities. Furthermore, we provide critical insights into the impact of different data augmentation methods on enhancing the generation framework's overall capacity. We showcase possibilities to advance the challenge of generating synchronized audio from semantic and temporal perspectives. We hope these insights will serve as a stepping stone toward developing more realistic and accurate audio-visual generation models.

cs.SD↗

arXiv2025

FloNa: Floor Plan Guided Embodied Visual Navigation

Humans naturally rely on floor plans to navigate in unfamiliar environments, as they are readily available, reliable, and provide rich geometrical guidance. However, existing visual navigation settings overlook this valuable prior knowledge, leading to limited efficiency and accuracy. To eliminate this gap, we introduce a novel navigation task: Floor Plan Visual Navigation (FloNa), the first attempt to incorporate floor plan into embodied visual navigation. While the floor plan offers significant advantages, two key challenges emerge: (1) handling the spatial inconsistency between the floor plan and the actual scene layout for collision-free navigation, and (2) aligning observed images with the floor plan sketch despite their distinct modalities. To address these challenges, we propose FloDiff, a novel diffusion policy framework incorporating a localization module to facilitate alignment between the current observation and the floor plan. We further collect $20k$ navigation episodes across $117$ scenes in the iGibson simulator to support the training and evaluation. Extensive experiments demonstrate the effectiveness and efficiency of our framework in unfamiliar scenes using floor plan knowledge. Project website: https://gauleejx.github.io/flona/.

cs.RO↗

arXiv2025

Role of $a_0(980)$ in the decays $D^{0} \rightarrow K^{+} K^{-} η$ and $π^{+} π^{-} η$

In present work, we study the reactions $D^{0} \rightarrow K^{+} K^{-} η$ and $D^0 \rightarrow π^{+} π^{-} η$, and find that the $a_0(980)$ state plays a dominant role. At the quark level, the external and internal $W$-emission mechanisms are taken into account, which can hadronize into the final states, and then the $a_0(980)$ state is generated from the final state interaction. Besides, the contributions of other intermediate resonances, such as $ρ(770)$ and $ϕ(1020)$, are also considered. We make a combined fit of the invariant mass spectra measured by the Belle and BESIII Collaborations, where the results are in good agreement with the experiments, and the signal of the $a_0(980)$ shows great significance. Besides, the antisymmetry data for the production of the $a_0(980)^+$ and $a_0(980)^-$ is described well in the combined fit.

hep-ph↗

arXiv2025

A new perspective on brain stimulation interventions: Optimal stochastic tracking control of brain network dynamics

Network control theory (NCT) has recently been utilized in neuroscience to facilitate our understanding of brain stimulation effects. A particularly useful branch of NCT is optimal control, which focuses on applying theoretical and computational principles of control theory to design optimal strategies to achieve specific goals in neural processes. However, most existing research focuses on optimally controlling brain network dynamics from the original state to a target state at a specific time point. In this paper, we present the first investigation of introducing optimal stochastic tracking control strategy to synchronize the dynamics of the brain network to a target dynamics rather than to a target state at a specific time point. We utilized fMRI data from healthy groups, and cases of stroke and post-stroke aphasia. For all participants, we utilized a gradient descent optimization method to estimate the parameters for the brain network dynamic system. We then utilized optimal stochastic tracking control techniques to drive original unhealthy dynamics by controlling a certain number of nodes to synchronize with target healthy dynamics. Results show that the energy associated with optimal stochastic tracking control is negatively correlated with the intrinsic average controllability of the brain network system, while the energy of the optimal state approaching control is significantly related to the target state value. For a 100-dimensional brain network system, controlling the five nodes with the lowest tracking energy can achieve relatively acceptable dynamics control effects. Our results suggest that stochastic tracking control is more aligned with the objective of brain stimulation interventions, and is closely related to the intrinsic characteristics of the brain network system, potentially representing a new direction for future brain network optimal control research.

q-bio.OT↗

arXiv2025

IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction

In this paper, we propose a generalizable deep neural network model for indoor pathloss radio map prediction (termed as IPP-Net). IPP-Net is based on a UNet architecture and learned from both large-scale ray tracing simulation data and a modified 3GPP indoor hotspot model. The performance of IPP-Net is evaluated in the First Indoor Pathloss Radio Map Prediction Challenge in ICASSP 2025. The evaluation results show that IPP-Net achieves a weighted root mean square error of 9.501 dB on three competition tasks and obtains the second overall ranking.

eess.SP↗

arXiv2024

R2G: Reasoning to Ground in 3D Scenes

We propose Reasoning to Ground (R2G), a neural symbolic model that grounds the target objects within 3D scenes in a reasoning manner. In contrast to prior works, R2G explicitly models the 3D scene with a semantic concept-based scene graph; recurrently simulates the attention transferring across object entities; thus makes the process of grounding the target objects with the highest probability interpretable. Specifically, we respectively embed multiple object properties within the graph nodes and spatial relations among entities within the edges, utilizing a predefined semantic vocabulary. To guide attention transferring, we employ learning or prompting-based methods to analyze the referential utterance and convert it into reasoning instructions within the same semantic space. In each reasoning round, R2G either (1) merges current attention distribution with the similarity between the instruction and embedded entity properties or (2) shifts the attention across the scene graph based on the similarity between the instruction and embedded spatial relations. The experiments on Sr3D/Nr3D benchmarks show that R2G achieves a comparable result with the prior works while maintaining improved interpretability, breaking a new path for 3D language grounding.

cs.CV↗

arXiv2024

Roles of $f_{0}(500)$ and $f_{0}(980)$ in the $D_{(s)}^{+}\rightarrowπ^{+}π^{+}π^{-}$ decays

With the recent measurements of the $D_{s}^{+}\rightarrow π^{+}π^{+}π^{-}$ and $D^{+}\rightarrow π^{+}π^{+}π^{-}$ decays by the LHCb Collaboration, we study these two decay processes by considering the final state interaction formalism. Taking into account the external and internal $W$-emission dominant mechanisms at the quark level, our model can naturally explain why the $D_{s}^{+}$ decay only has a contribution from the $f_{0}(980)$ resonance, while the $D^{+}$ decay has contributions from both the $f_{0}(500)$ and $f_{0}(980)$ states. The magnitudes and phases of $π^{+}π^{-}$ for $S$-wave amplitudes in our model are in agreement with the experimental measurements. These results support the interpretations of $f_{0}(500)$ and $f_{0}(980)$ as the $ππ$ and $K\bar{K}$ resonances, respectively, where they are dynamically generated from the $S$-wave pseudoscalar-pseudoscalar meson interactions within the chiral unitary approach.

hep-ph↗

arXiv2024

Security Enhancement of Quantum Communication in Space-Air-Ground Integrated Networks

This paper investigates a transmission scheme for enhancing quantum communication security, aimed at improving the security of space-air-ground integrated networks (SAGIN). Quantum teleportation achieves the transmission of quantum states through quantum channels. In simple terms, an unknown quantum state at one location can be reconstructed on a particle at another location. By combining classical Turbo coding with quantum Shor error-correcting codes, we propose a practical solution that ensures secure information transmission even in the presence of errors in both classical and quantum channels. To provide absolute security under SAGIN, we add a quantum secure direct communication (QSDC) protocol to the current system. Specifically, by accounting for the practical scenario of eavesdropping in quantum channels, the QSDC protocol utilizes virtual entangled pairs to detect the presence of eavesdroppers. Consequently, the overall scheme guarantees both the reliability and absolute security of communication.

quant-ph↗

arXiv2024

An Expeditious Spatial Mean Radiant Temperature Mapping Framework using Visual SLAM and Semantic Segmentation

Ensuring thermal comfort is essential for the well-being and productivity of individuals in built environments. Of the various thermal comfort indicators, the mean radiant temperature (MRT) is very challenging to measure. Most common measurement methodologies are time-consuming and not user-friendly. To address this issue, this paper proposes a novel MRT measurement framework that uses visual simultaneous localization and mapping (SLAM) and semantic segmentation techniques. The proposed approach follows the rule of thumb of the traditional MRT calculation method using surface temperature and view factors. However, it employs visual SLAM and creates a 3D thermal point cloud with enriched surface temperature information. The framework then implements Grounded SAM, a new object detection and segmentation tool to extract features with distinct temperature profiles on building surfaces. The detailed segmentation of thermal features not only reduces potential errors in the calculation of the MRT but also provides an efficient reconstruction of the spatial MRT distribution in the indoor environment. We also validate the calculation results with the reference measurement methodology. This data-driven framework offers faster and more efficient MRT measurements and spatial mapping than conventional methods. It can enable the direct engagement of researchers and practitioners in MRT measurements and contribute to research on thermal comfort and radiant cooling and heating systems.

cs.RO↗

arXiv2024

Resource Allocation Based on Optimal Transport Theory in ISAC-Enabled Multi-UAV Networks

This paper investigates the resource allocation optimization for cooperative communication with non-cooperative localization in integrated sensing and communications (ISAC)-enabled multi-unmanned aerial vehicle (UAV) cooperative networks. Our goal is to maximize the weighted sum of the system's average sum rate and the localization quality of service (QoS) by jointly optimizing cell association, communication power allocation, and sensing power allocation. Since the formulated problem is a mixed-integer nonconvex problem, we propose the alternating iteration algorithm based on optimal transport theory (AIBOT) to solve the optimization problem more effectively. Simulation results demonstrate that the AIBOT can improve the system sum rate by nearly 12% and reduce the localization Cr'amer-Rao bound (CRB) by almost 29% compared to benchmark algorithms.

cs.NI↗

arXiv2024

Beamforming in Secure Integrated Sensing and Communication Systems with Antenna Allocation

In this paper, we consider joint antenna allocation and transmit beamforming design in secure integrated sensing and communication (ISAC) systems. A dual-function base station (DFBS) aims to securely deliver messages to a single-antenna receiver while detecting potential eavesdroppers. To prevent eavesdropping, we incorporate specialized sensing signals, intentionally reducing communication signal power toward suspicious targets to improve sensing. We prioritize minimizing the matching error between the transmitting and required beampatterns for sensing and communication. Our design optimizes antenna allocation and beamforming at the DFBS, meeting minimum secrecy rate and power constraints. We propose solvers based on alternating optimization for the non-convex design problem. Simulations show that the antenna allocation scheme significantly improves safety performance.

cs.NI↗

arXiv2024

A Constant-Approximation Algorithm for Budgeted Sweep Coverage with Mobile Sensors

In this paper, we present the first constant-approximation algorithm for {\em budgeted sweep coverage problem} (BSC). The BSC involves designing routes for a number of mobile sensors (a.k.a. robots) to periodically collect information as much as possible from points of interest (PoIs). To approach this problem, we propose to first examine the {\em multi-orienteering problem} (MOP). The MOP aims to find a set of $m$ vertex-disjoint paths that cover as many vertices as possible while adhering to a budget constraint $B$. We develop a constant-approximation algorithm for MOP and utilize it to achieve a constant-approximation for BSC. Our findings open new possibilities for optimizing mobile sensor deployments and related combinatorial optimization tasks.

cs.DM↗