arXiv subjects

Dan Wang

Publications and source records attributed to Dan Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Will the Carbon Border Adjustment Mechanism Impact European Electricity Prices? A GNN-Based Network Analysis

The European Union's Carbon Border Adjustment Mechanism (CBAM) creates a complex challenge for the interconnected European electricity market. Traditional static analyses often miss the cross-border spillover effects that are vital for understanding this policy. This paper addresses this gap by developing a spatio-temporal Graph Neural Network (GNN) framework. It quantifies how CBAM affects electricity prices and carbon intensity (CI) at the same time. We modeled a subgraph of eight European countries. Our results suggest that CBAM is not just a uniform tax. Instead, it acts as a tool that transforms the market and creates structural differences. In our simulated scenarios, we observe that low-carbon countries like France and Switzerland can gain a competitive advantage. This suggests a potential decrease in their domestic electricity prices. Meanwhile, high-carbon countries like Poland face a double burden of rising costs. We identify the primary driver as a fundamental shift in the market's merit order.

cs.LG↗

arXiv2026

All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG

Multilingual Retrieval-Augmented Generation (mRAG) leverages cross-lingual evidence to ground Large Language Models (LLMs) in global knowledge. However, we show that current mRAG systems suffer from a language bias during reranking, systematically favoring English and the query's native language. By introducing an estimated oracle evidence analysis, we quantify a substantial performance gap between existing rerankers and the achievable upper bound. Further analysis reveals a critical distributional mismatch: while optimal predictions require evidence scattered across multiple languages, current systems systematically suppress such ``answer-critical'' documents, thereby limiting downstream generation performance. To bridge this gap, we propose \textit{\textbf{L}anguage-\textbf{A}gnostic \textbf{U}tility-driven \textbf{R}eranker \textbf{A}lignment (LAURA)}, which aligns multilingual evidence ranking with downstream generative utility. Experiments across diverse languages and generation models show that LAURA effectively mitigates language bias and consistently improves mRAG performance.

cs.CL↗

arXiv2026

Generative Channel Knowledge Base With Environmental Information for Joint Source-Channel Coding in Semantic Communications

Semantic knowledge bases are regarded as a promising technology for upcoming 6G communications. However, existing studies mainly focus on source-side semantic modeling while overlooking the structural impact of propagation environments on semantic transmission performance. To address this issue, we propose a generative channel knowledge base (CKB) with environmental information to facilitate joint source-channel coding (JSCC) in semantic communications (SemCom) systems. First, to enable the construction of the CKB, an environment-aware dataset is established by collecting spatial position information, global image features, fine-grained semantic features, and the corresponding channel matrices. A region-of-interest (ROI)-based filtering algorithm is further designed to remove semantic components that are irrelevant to signal propagation. Second, a Transformer-based generative framework is developed to learn the mapping between multidimensional environmental information and channel matrices. A self-attention mechanism is introduced to adaptively fuse heterogeneous features, enabling the construction of a structured CKB. Third, a CKB-driven JSCC SemCom architecture is proposed, where the generated channel knowledge is injected into both of the encoder and decoder to jointly exploit source semantics and channel-environment priors in an end-to-end manner. Experimental results demonstrate that the proposed multidimensional feature fusion method achieves a channel matrix estimation error at the $10^{-3}$ level. Moreover, the CKB-driven JSCC SemCom framework integrated into SemCom systems significantly outperforms existing benchmark schemes in terms of transmission performance.

cs.IT↗

arXiv2026

Key-Embedded Privacy for Decentralized AI in Biomedical Omics

The rapid adoption of data-driven methods in biomedicine has intensified concerns over privacy, governance, and regulation, limiting raw data sharing and hindering the assembly of representative cohorts for clinically relevant AI. This landscape necessitates practical, efficient privacy solutions, as cryptographic defenses often impose heavy overhead and differential privacy can degrade performance, leading to sub-optimal outcomes in real-world settings. Here, we present a lightweight federated learning method, INFL, based on Implicit Neural Representations that addresses these challenges. Our approach integrates plug-and-play, coordinate-conditioned modules into client models, embeds a secret key directly into the architecture, and supports seamless aggregation across heterogeneous sites. Across diverse biomedical omics tasks, including cohort-scale classification in bulk proteomics, regression for perturbation prediction in single-cell transcriptomics, and clustering in spatial transcriptomics and multi-omics with both public and private data, we demonstrate that INFL achieves strong, controllable privacy while maintaining utility, preserving the performance necessary for downstream scientific and clinical applications.

cs.LG↗

arXiv2026

Transformer-based prediction of two-dimensional material electronic properties under elastic strain engineering

Strain engineering provides a powerful route for tuning the electronic properties of two-dimensional (2D) materials, but exploring the full multidimensional strain space with density functional theory (DFT) is computationally prohibitive due to the nonlinear coupling between normal and shear components. In this work, we introduce a Transformer-based, multi-target surrogate model framework that achieves DFT-level bandgap prediction accuracy, reaching a mean absolute error of 0.0103 eV while retaining full interpretability through attention-weight analysis. The learned self-attention map consistently identifies shear strain as the interaction center that influences both bandgap and phonon stability, an insight not readily captured by classical feature-importance metrics. This work establishes attention-based architectures as physically interpretable surrogate models for multi-property prediction, offering a generalizable strategy for accelerating deep elastic strain engineering in materials informatics.

cond-mat.mtrl-sci↗

arXiv2026

Revisiting the Perception-Distortion Trade-off with Spatial-Semantic Guided Super-Resolution

Image super-resolution (SR) aims to reconstruct high resolution images with both high perceptual quality and low distortion, but is fundamentally limited by the perception-distortion trade-off. GAN-based SR methods reduce distortion but still struggle with realistic fine-grained textures, whereas diffusion-based approaches synthesize rich details but often deviate from the input, hallucinating structures and degrading fidelity. This tension raises a key challenge: how to exploit the powerful generative priors of diffusion models without sacrificing fidelity. To address this, we propose SpaSemSR, a spatial-semantic guided diffusion framework with two complementary guidances. First, spatial-grounded textual guidance integrates object-level spatial cues with semantic prompts, aligning textual and visual structures to reduce distortion. Second, semantic-enhanced visual guidance with a multi-encoder design and semantic degradation constraints unifies multimodal semantic priors, improving perceptual realism under severe degradations. These complementary guidances are adaptively fused into the diffusion process via spatial-semantic attention, suppressing distortion and hallucination while retaining the strengths of diffusion models. Extensive experiments on multiple benchmarks show that SpaSemSR achieves a superior perception-distortion balance, producing both realistic and faithful restorations.

cs.CV↗

arXiv2026

Physics-Guided Inverse Design of Optical Waveforms for Nonlinear Electromagnetic Dynamics

Structured optical waveforms are emerging as powerful control fields for the next generation of complex photonic and electromagnetic systems, where the temporal structure of light can determine the ultimate performance of scientific instruments. However, identifying optimal optical drive fields in strongly nonlinear regimes remains challenging because the mapping between optical inputs and system response is high-dimensional and typically accessible only through computationally expensive simulations. Here, we present a physics-guided deep learning framework for the inverse design of optical temporal waveforms. By training a light-weighted surrogate model on simulations, the method enables gradient-based synthesis of optical profiles that compensate nonlinear field distortions in driven particle-field systems. As a representative application, we apply the approach to the generation of electron beams used in advanced photon and particle sources. The learned optical waveform actively suppresses extrinsic emittance growth by more than 52% compared with conventional Gaussian operation and by approximately 9% relative to the theoretical flattop limit in simulation. We further demonstrate experimental feasibility by synthesizing the predicted waveform using a programmable pulse-shaping platform; incorporating the measured optical profile into beamline simulations yields a 31% reduction in the extrinsic emittance contribution. Beyond accelerator applications, this work establishes a general way for physics-guided inverse design of optical control fields, enabling structured light to approach fundamental performance limits in nonlinear photonic and high-frequency electromagnetic systems.

physics.optics↗

arXiv2026

Exploring Multiple Converged States of Network Configurations

Due to the policy-rich BGP, multiple stable forwarding states might exist for the same network topology and configuration, rendering the network convergence non-deterministic. This paper proves that any network with multiple converged states possesses a specific set of critical links which, when flipped (disconnect then reconnect), shifts the network between different stable states. We establish this result under the Stable Path Problem (SPP) framework, and also examine a real-world corner case where SPP doesn't apply. Building on this theoretical foundation, we propose a tentative theoretical verification method for non-determinism with $O(n)$ complexity, where $n$ is the number of edges in a network. Specifically, we separately flip each link in the network and observe whether new converged states emerge. If no new states are discovered, the network is guaranteed to be free of non-determinism. This approach is proved correct when the set of critical links reduces to a single link -- usually the case in the real-world deployments.

cs.NI↗

arXiv2026

PhysConvex: Physics-Informed 3D Dynamic Convex Radiance Fields for Reconstruction and Simulation

Reconstructing and simulating dynamic 3D scenes with both visual realism and physical consistency remains a fundamental challenge. Existing neural representations, such as NeRFs and 3DGS, excel in appearance reconstruction but struggle to capture complex material deformation and dynamics. We propose PhysConvex, a Physics-informed 3D Dynamic Convex Radiance Field that unifies visual rendering and physical simulation. PhysConvex represents deformable radiance fields using physically grounded convex primitives governed by continuum mechanics. We introduce a boundary-driven dynamic convex representation that models deformation through vertex and surface dynamics, capturing spatially adaptive, non-uniform deformation, and evolving boundaries. To efficiently simulate complex geometries and heterogeneous materials, we further develop a reduced-order convex simulation that advects dynamic convex fields using neural skinning eigenmodes as shape- and material-aware deformation bases with time-varying reduced DOFs under Newtonian dynamics. Convex dynamics also offers compact, gap-free volumetric coverage, enhancing both geometric efficiency and simulation fidelity. Experiments demonstrate that PhysConvex achieves high-fidelity reconstruction of geometry, appearance, and physical properties from videos, outperforming existing methods.

cs.CV↗

arXiv2026

Frequency Shaping Control for Oscillation Damping in Weakly-Connected Power Network: A Root Locus Method

Frequency control following a contingency event is of vital concern in power system operations. Leveraging inverter-based resources, it is not hard to shape the center of inertia (COI) frequency nicely. However, under weak grid conditions, it becomes insufficient to solely shape the COI frequency since this aggregate signal fails to reveal the inter-area oscillations. In this manuscript, we advocate for foolproof fine-tuning rules for \emph{frequency shaping control} (FS) based on a systematic analysis of damping ratio and decay rate of inter-area oscillations to simultaneously meet specified metrics for frequency security and oscillatory stability. To this end, building on a modal decomposition, we simplify the oscillation damping problem into a pole-placement task for a set of scalar subsystems, which can be efficiently solved by only investigating the root locus of a scalar subsystem associated with the main mode, while FS inherently guarantees a Nadir-less COI frequency response. Through our proposed root-locus-based oscillatory stability analysis, we derive closed-form expressions for the minimum damping ratio and decay rate among inter-area oscillations in terms of networked system and control parameters under FS. Moreover, we propose useful tuning guidelines for FS which need only simple calculations or visualized tuning to not only shape the COI frequency into a first-order response that converges to a steady-state value within the allowed range but also ensure a satisfactory damping ratio and decay rate of inter-area oscillations following disturbances. As for the common virtual inertia control (VI), although similar oscillatory stability analysis becomes intractable, one can still glean some insights via the root locus method. Numerical simulations validate the proposed tuning for FS as well as the superiority of FS over VI in exponential convergence rate.

eess.SY↗

arXiv2026

Statistical process control via $p$-values

We study statistical process control (SPC) through charting of $p$-values. When in control (IC), any valid sequence $(P_{t})_{t}$ is super-uniform, a requirement that can hold in nonparametric and two-phase designs without parametric modelling of the monitored process. Within this framework, we analyse the Shewhart rule that signals when $P_{t}\leα$. Under super-uniformity alone, and with no assumptions on temporal dependence, we derive universal IC lower bounds for the average run length (ARL) and for the expected time to the $k$th false alarm ($k$-ARL). When conditional super-uniformity holds, these bounds sharpen to the familiar $α^{-1}$ and $kα^{-1}$ rates, giving simple, distribution-free calibration for $p$-value charts. Beyond thresholding, we use merging functions for dependent $p$-values to build EWMA-like schemes that output, at each time $t$, a valid $p$-value for the hypothesis that the process has remained IC up to $t$, enabling smoothing without ad hoc control limits. We also study uniform EWMA processes, giving explicit distribution formulas and left-tail guarantees. Finally, we propose a modular approach to directional and coordinate localisation in multivariate SPC via closed testing, controlling the family-wise error rate at the time of alarm. Numerical examples illustrate the utility and variety of our approach.

stat.ME↗

arXiv2026

Integrated Sensing and Semantic Communication with Adaptive Source-Channel Coding

Semantic communication has emerged as a new paradigm to facilitate the performance of integrated sensing and communication systems in 6G. However, most of the existing works mainly focus on sensing data compression to reduce the subsequent communication overheads, without considering the integrated transmission framework for both the SemCom and sensing tasks. This paper proposes an adaptive source-channel coding and beamforming design framework for integrated sensing and SemCom systems by jointly optimizing the coding rate for SemCom task and the transmit beamforming for both the SemCom and sensing tasks. Specifically, an end-to-end semantic distortion function is approximated by deriving an upper bound composing of source and channel coding induced components, and then a hybrid Cramér-Rao bound (HCRB) is also derived for target position under imperfect time synchronization. To facilitate the joint optimization, a distortion minimization problem is formulated by considering the HCRB threshold, channel uses, and power budget. Subsequently, an alternative optimization algorithm composed of successive convex approximation and fractional programming is proposed to address this problem by decoupling it into two subproblems for coding rate and beamforming designs, respectively. Simulation results demonstrate that our proposed scheme outperforms the conventional deep joint source-channel coding -water filling-zero forcing benchmark.

eess.SP↗

arXiv2026

Surface reconstruction-driven band folding and spin-orbit enhancement at the $α$-antimonene/Au(111) interface

The electronic properties of the two-dimensional (2D) $α$ phase of antimonene are unique, featuring unpinned Dirac cones that can be moved with strain. Here we investigate the structural and electronic properties of an epitaxial 2D $α$-antimonene, grown on Au(111). Using angle-resolved photoemission spectroscopy and density-functional theory, we reveal a strong hybridization at the Sb/Au interface, which imprints a rectangular reconstruction in the Au states, producing a band folding and hybrid bands exhibiting trigonal pockets. Additionally, hybridization displaces part of the Au wavefunction in regions of large electrostatic potential gradient, thereby enhancing spin-orbit splitting. Our work underscores that the pristine electronic properties of $α$-antimonene may be deeply modified by its substrate, and even overwhelmed by the bands of the latter, and also shows that spin-orbit interaction in a heavy metal (Au) can be substantially enhanced by a lighter element (Sb).

cond-mat.mtrl-sci↗

arXiv2025

VQ-VAE Based Digital Semantic Communication with Importance-Aware OFDM Transmission

Semantic communication (SemCom) significantly reduces redundant data and improves transmission efficiency by extracting the latent features of information. However, most of the conventional deep learning-based SemCom systems focus on analog transmission and lack in compatibility with practical digital communications. This paper proposes a vector quantized-variational autoencoder (VQ-VAE) based digital SemCom system that directly transmits the semantic features and incorporates the importance-aware orthogonal frequency division multiplexing (OFDM) transmission to enhance the SemCom performance, where the VQ-VAE generates a discrete codebook shared between the transmitter and receiver. At transmitter, the latent semantic features are firstly extracted by VQ-VAE, and then the shared codebook is adopted to match these features, which are subsequently transformed into a discrete version to adapt the digital transmission. To protect the semantic information, an importance-aware OFDM transmission strategy is proposed to allocate the key features near the OFDM reference signals, where the feature importance is derived from the gradient-based method. At the receiver, the features are rematched with the shared codebook to further correct errors. Finally, experimental results demonstrate that our proposed scheme outperforms the conventional DeepSC and achieves better reconstruction performance under low SNR region.

eess.SP↗

arXiv2025

Understanding Typing-Related Bugs in Solidity Compiler

The correctness of the Solidity compiler is crucial for ensuring the security of smart contracts. However, the implementation complexity of its type system often introduces elusive defects. This paper presents the first systematic empirical study on typing-related bugs in the Solidity compiler. To systematically analyze these bugs, we collected 146 officially confirmed and fixed typing-related bugs from the official GitHub repository of Solidity compiler. For each bug, we conducted an in-depth analysis and classification from four dimensions: symptoms, root causes, exposure conditions, and fix strategies. Through this study, we reveal unique distribution patterns and key characteristics of such bugs, and summarize 12 core findings. We additionally give the implications of our findings, and these implications not only deepen the understanding of inherent weaknesses in the Solidity compiler but also provide new insights for detecting and fixing typing-related bugs in the Solidity compiler.

cs.SE↗

arXiv2025

Quantization in mixed polarization via transverse Poincaré-Birkhoff-Witt theorem

On a prequantizable Kähler manifold $(M, ω, L)$, Chan-Leung-Li constructed a genuine (non-asymptotic) action of a subalgebra of the Berezin-Toeplitz star product on $H^0(M, L^{\otimes k})$ for each level $k$ [14]. We extend their framework to any non-singular polarization $P$ by developing a theory of transverse differential operators associated to $P$: (1) For any pair of locally free $P$-modules $E, E'$, we construct a Poincaré-Birkhoff-Witt isomorphism for the bundle $\widetilde{D}(E, E')$ of transverse differential operators from $E$ to $E'$. When $E, E'$ are trivial rank-$1$ $P$-modules, this recovers the PBW theorem of Laurent-Gengoux-Stiénon-Xu [29] for the Lie pair $(TM_\mathbb{C}, P)$. (2) Using these PBW isomorphisms, we show that the Grothendieck connections on the transeverse jet bundle of $L^{\otimes k}$ give rise to a deformation quantization $(C_M^\infty[[\hbar]], \star)$ together with a sheaf of subalgebras $C_{M, \hbar}^{<\infty}$ that acts on $P$-polarized sections of $L^{\otimes k}$. We obtain a geometric interpretation of $(C_{M, \hbar}^{<\infty}, \star)$ by evaluating at $\hbar = \tfrac{\sqrt{-1}}{k}$, yielding a sheaf $O_k^{(<\infty)}$, and proving that $O_k^{(<\infty)} \cong \widetilde{D}_{L^{\otimes k}}$ as sheaves of filtered algebras, where $\widetilde{D}_{L^{\otimes k}}$ is the sheaf of transverse differential operators on $L^{\otimes k}$. When $P$ is a Kähler polarization, this recovers the result of Chan-Leung-Li [14]. As an application, we study symplectic tori and derive asymptotic expansions for the Toeplitz-type operators in real polarization introduced in [35].

math.SG↗

arXiv2025

Geometric Decentralized Stability Certificate for Power Systems Based on Projecting DW Shells

The development of decentralized stability conditions has gained considerable attention due to the need to analyze multi-agent network systems, such as heterogeneous multi-converter power systems. A recent advance is the application of the small-phase theorem, which extends the passivity theory. However, it requires the transfer function matrix to be sectorial, which may not hold in some frequency range and will result in conservativeness. To address this issue, this paper proposes a geometric decentralized stability condition based on Davis-Wielandt (DW) shell and its projections. Our approach provides a geometric interpretation of the small-gain and small-phase theorems and enables decentralized stability analysis of power systems. It serves as a visualization method to understand the closed-loop interactions and assess the stability of large-scale network systems in a scalable and modular manner.

eess.SY↗

arXiv2025

High-pressure melting and elastic behavior of vanadium and niobium based on ab initio and machine learning molecular dynamics

Under high pressure, the group-VB transition metals vanadium (V) and niobium (Nb) exhibit simple crystal structures but complex physical behaviors, such as anomalous compression-induced softening and heating-induced hardening (CISHIH). Meanwhile, the impact of lattice thermal expansion-induced softening at elevated temperatures on HIH is yet to be investigated. Therefore, this study utilized ab initio (AIMD) and machine learning molecular dynamics (MLMD) to investigate the melting and abnormal mechanical softening-hardening behaviors of V and Nb under high pressure. Simulations reveal that the high-temperature Pnma phase of Nb reported in previous experimental studies is highly susceptible to mechanical instability and reverts to the body-centered cubic (BCC) phase. This discovery prompted a revised determination of the high-pressure melting line of Nb. The melting temperature of Nb significantly exceeds the existing theoretical and experimental estimate compared with that of V. AIMD simulations demonstrate that atomic thermal displacements have a greater influence on the HIH of V and Nb than pure electron temperature effects. In addition, the temperature-dependent anomalous elastic properties of V and Nb were investigated within a pressure range of 0-250 GPa using MLMD. The mechanical properties of V and Nb transitioned from HIH to heating-induced softening, elucidating the competition between thermal-expansion-induced softening and HIH. This study advances fundamental understanding of V and Nb physics, providing crucial theoretical foundations for establishing accurate equations of state and constitutive models for these metals.

cond-mat.mtrl-sci↗