arXiv subjects

Lei Wang

Publications and source records attributed to Lei Wang.

At least 19 recordsLinked to original sources

arXiv2026

Minimizing Bottleneck Overload Risk in Online Edge Task Placement under Workload Uncertainty

In edge computing, tasks must be placed on servers of finite capacity before workloads are realized, often with only workload means and variances known. An assignment balanced on expected load can still leave a server with more fluctuations than its residual capacity can absorb. Per-server risk constraints can enforce a prescribed overload tolerance but do not distinguish the assignments that meet it by how close the riskiest server comes to the limit. We show that, for independent tasks, placement preserves a period's total variance and total residual capacity, and decides only how fluctuation, measured by standard deviation, is paired with residual capacity across servers. The risk ratio, the ratio of fluctuation to residual capacity, parameterizes a server's worst-case overload probability over all aggregate workload distributions with given mean and variance. We minimize the largest risk ratio, attained at the risk bottleneck. Risk-Bottleneck Placement (RBP) places tasks in descending order of standard deviation, each on the server with the smallest post-assignment risk ratio, and refines the assignment with relocations and swaps within a finite per-period online budget. On small instances solved exactly by enumerating every assignment, minimizing the maximum expected utilization leaves the largest risk ratio 25.66-29.19% above its exact minimum on average, even with risk-minimizing tie-breaking, and the corresponding gap for RBP is 1.77-2.64%. In trace-derived multi-period simulations at two workload pressures, RBP reduces the worst-server overload probability by 26.0% and 18.0% relative to the best baseline and remains the lowest at every tested pressure and scale.

cs.DC↗

arXiv2026

Latent Spectral Prompting for Multimodal Hyperspectral Object Tracking

Hyperspectral videos provide rich spectral signatures for robust tracking under challenging conditions such as camouflage and low illumination. However, limited hyperspectral annotations and the large RGB-hyperspectral domain gap hinder the direct use of modern temporally pretrained RGB trackers. We propose SProTrack, a multimodal framework that transfers temporal knowledge from pretrained RGB trackers to the hyperspectral domain through latent spectral prompting. The key idea is to decouple temporal modeling from spectral adaptation, allowing temporal dynamics to be inherited from RGB foundation models while hyperspectral information is learned from unlabeled data in a self-supervised manner. Unlike band-selection methods that discard most spectral information or prompt-based approaches that rely on local-search crops, SProTrack learns a global, scene-level spectral representation from the full hyperspectral frame using an autoencoder. This latent representation is used to guide spectral prompt extraction while a pretrained RGB tracker preserves strong temporal modeling on RGB-compatible inputs. By encoding spectral-spatial structure into compact latent prompts, SProTrack bridges the RGB-hyperspectral modality gap without extensive retraining or dense supervision. Experiments on HOT2020 and HOT2023 demonstrate that SProTrack achieves the best overall performance, with strong results across all spectral domains.

cs.CV↗

arXiv2026

MedHEB: Benchmarking Medical Embeddings Across Heterogeneous Clinical Evidence

Clinical evidence is inherently relational: medical images are interpreted with prior studies, cross-modal examinations, reports, and localized findings, and retrieving relevant evidence is therefore central to clinical interpretation. Recent foundation models increasingly encode these heterogeneous sources into shared embedding spaces, yet existing embedding evaluation often focuses on isolated matches, leaving unclear whether current models retrieve relevant evidence across changes in modality, appearance, and granularity. We introduce MedHEB, a unified benchmark with 107 retrieval tasks from 54 datasets, including both in-distribution and held-out-source evaluation, across three axes: evidence form (2D images, 3D volumes, text), pairing type (image--image, image--text, text--text, and modality-bridging visual matching), and relevance granularity (global, question-conditioned, region-level). Under a single ranking protocol, MedHEB varies clinically relevant evidence definitions to test whether embedding proximity remains meaningful across heterogeneous evidence, including modality-bridging visual retrieval and region-level text-to-region retrieval. Across representative models, performance is uneven across retrieval families: models strong on conventional image--text or text--text retrieval are less reliable on modality-bridging and region-level tasks. Current embedding spaces capture some clinical relationships well but do not yet provide balanced retrieval across heterogeneous evidence. We further provide MedEmb, an MLLM-based reference embedding model trained on the MedHEB ID split, as a reproducible baseline for unified 2D--3D--text medical retrieval. We release MedHEB with preprocessing scripts, evaluation code, access manifests, and MedEmb to support research on clinically meaningful medical embeddings.

cs.CV↗

arXiv2026

FiDeR: Denoising Astronomical Images at the Observational Resolution

We present Finite-iteration Deconvolution--Reconvolution Denoising (FiDeR), a method for reducing noise in astronomical images while retaining approximately the input point-spread function (PSF). We first apply a finite number of Richardson--Lucy (RL) iterations to the nonnegative source-plus-background image. We then measure the response of a calibration point source processed with the same settings and derive a reconvolution kernel matched to the observational PSF. The calibration includes the background-to-source flux ratio. We compare FiDeR with six denoisers using simulated point-source and mixed stellar--galaxy fields, and observations from the James Webb Space Telescope Near-Infrared Camera (NIRCam) and Mid-Infrared Instrument (MIRI). In the stellar tests, bright-source widths remain close to the input widths; faint sources show small residual offsets. For the mixed field, FiDeR reaches peak signal-to-noise ratios of 46.701 and 44.850 dB at backgrounds of 10 and 20 counts per pixel, with wing errors of 0.657 and 0.858 counts. It also gives small aperture-flux and shape errors, although block-matching and three-dimensional filtering (BM3D) gives higher structural similarity. The NIRCam data show lower core and combined-region errors, while the three-exposure MIRI test gives benefits mainly in the stellar aperture and photometry. These results support substantial noise reduction with little change in the measured PSF within the tested regimes. The denoised images can provide inputs for further reconstruction and measurement; their uncertainties must account for residual noise correlations.

astro-ph.IM↗

arXiv2026

Aperture Photometric Accuracy of Point-spread-function-deconvolved Astronomical Images

This paper introduces an improved Richardson--Lucy (RL) framework utilizing negative backfilling (NB) and the novel filtered image deconvolution and noise backfilling (FiDNoBa) algorithm. By integrating a low-pass prefiltering stage---specifically employing a Kalman filter---with the reintegration of filtered-out noise components after RL iterations, we effectively suppress noise artifacts while maintaining rigorous flux integrity. We extensively evaluate our Kalman + RL + NB approach using both numerical simulations and real observational data. Simulations across three regimes demonstrate superior photometric accuracy, reducing the relative mean deviation (RMD) to below 1\% for isolated sources, maintaining high fidelity (RMD $\sim$0.37\%) for blended Sérsic galaxies, and achieving $\sim$5\% RMD in crowded stellar fields while significantly increasing source recovery rates compared to traditional point-spread function photometry. Crucially, we validate the practical efficacy of FiDNoBa on real space-based (JWST/NIRCam) and ground-based (Very Large Telescope Survey Telescope OmegaCAM) images. The results confirm that our method reliably handles complex instrumental signatures, such as the extended wings of the STPSF, mitigating the severe ringing artifacts typical of traditional RL deconvolution while ensuring highly stable aperture photometry in practical survey processing.

astro-ph.IM↗

arXiv2026

FrontierMat: Benchmarking Language Models at the Frontier of Materials R&D

To accelerate materials discovery, language models must be able to address problems at the research frontier. Yet textbook- and examination-derived benchmarks primarily assess established knowledge, leaving performance on frontier research problems largely untested. We introduce FrontierMat, a benchmark of 330 problems drawn from frontier academic and industrial research in fields including aerospace, semiconductors, embodied intelligence and nuclear fusion. Every problem is validated by leading experimentalists and industry experts in its field. Each problem presents a real study and asks the model to infer a withheld outcome or draw a conclusion from selected experimental evidence. Problems are annotated by functional domain, material system and the scale of the governing mechanism; 130 require cross-scale reasoning. The strongest general-purpose model scores only 53.7%, and on matched problems, cross-scale problems score 8.7 points below single-scale ones. Half of the problems are solved by at most one model, and models fail chiefly by endorsing what is plausible rather than what was observed. A domain-adaptation study with meta-weighted continued pretraining and scale-modular self-distillation raises an open-weight model from 36.7% to 55.4%, matching the strongest proprietary model, with improvements on public materials benchmarks and no decline in aggregate performance on general benchmarks. FrontierMat provides a testbed for measuring progress toward language models that support frontier materials R&D.

cs.AI↗

arXiv2026

Robust Principal Component Analysis via $\ell_0$ Alternating Minimization

Robust principal component analysis concerns the recovery of a low-rank matrix $Z$ and a sparse matrix $S$ from their observed sum $D = Z + S$. In this paper, we study this recovery problem under an $\ell_0$ sparsity penalty and a rank constraint on the low-rank component. By exploiting the entrywise separable structure of the sparse subproblem, we obtain an exact closed-form solution via hard thresholding with box clipping, avoiding the shrinkage bias of $\ell_1$-based methods. The low-rank subproblem is solved by truncated SVD. We prove block-coordinate convergence of the resulting alternating minimization scheme, establish uniqueness of block minimizers under non-degeneracy and singular value gap conditions, establish local linear convergence under a contraction condition on the Fréchet derivative of the truncated SVD operator restricted to the sparse support, and provide a second-order optimality analysis via the Riemannian Hessian. Experiments on synthetic data, images, and video show that the proposed algorithm outperforms the existing convex and nonconvex algorithms, most notably in support recovery and in the accuracy-efficiency trade-off.

math.OC↗

arXiv2026

Multiple-relaxation-time total enthalpy lattice Boltzmann model for liquid-vapor phase change

In this paper, a multiple-relaxation-time (MRT) total enthalpy lattice Boltzmann (LB) model is proposed for liquid-vapor phase change. The pseudopotential LB method is employed to describe the liquid-vapor multiphase dynamics, while the thermal field is solved using an MRT LB formulation with total enthalpy as the evolution variable, thereby incorporating sensible and latent heat into a unified framework. A latent heat expression based on the equation of state (EOS) is further derived to provide a thermodynamic reference for its specification. Multiscale analysis demonstrates that the proposed model recovers the target macroscopic energy equation without introducing the nonlinear thermodynamic source term commonly required in temperature-based formulations, thereby avoiding the explicit evaluation of $\nabla\cdot\mathbf{u}$. The model is validated through free droplet evaporation, droplet evaporation on a heated surface, and bubble growth and departure during boiling, showing good agreement with published results. Simulations with different prescribed latent heats are further conducted in all three cases, demonstrating that the latent heat can be independently specified according to the physical conditions considered without altering the equation of state.

physics.flu-dyn↗

arXiv2026

Hy-Nomas: Towards Free-form Interactive World Generation

Interactive world generation requires models to follow successive commands while preserving relevant history and action consequences. We present Hy-Nomas, a video world model with an 8B diffusion backbone that supports free-form text instructions and continuous camera control across multiple interaction turns. It generates variable-length segments conditioned on current inputs and cached history. Our central design aligns structured video captions with action-aware causal attention: descriptive context remains accessible across segments, while action instructions are restricted to their intended temporal windows. A vision-language Prompt Enhancer (PE) translates user requests and multimodal interaction histories into structured conditions for the next segment. A multi-stage curriculum progressively introduces causal generation, text interaction, camera control, and training on model-generated histories, followed by diffusion distillation. In human evaluation, Hy-Nomas shows superior performance over existing world models. We release model weights, code and benchmarks, together with a lightweight adaptation workflow, to support reproducible research on interactive world generation.

cs.CV↗

arXiv2026

Electroweak phase transition triggered by a long-lived scalar and LHC searches

We investigate a long-lived scalar $S$ that triggers a strongly first-order electroweak phase transition (SFOEWPT) in the singlet scalar extension of standard model (SM). A sufficient small mixing angle between $S$ and the SM-like Higgs boson $h$ naturally leads to a long lifetime for $S$. We focus on the mass range $m_S>m_h/2$, where the dominant production channel at the LHC is the Higgs-mediated process $gg\to h^*\to SS$ with an off-shell $h$. By analyzing the finite-temperature effective potential, we identify the parameter space realizing an SFOEWPT and calculate the associated gravitational-wave spectra, finding that part of the parameter space can be probed by LISA. We further study the LHC sensitivity to the long-lived $S$ using the ATLAS detector and proposed far detectors, including ANUBIS, CODEX-b, FACET, FASER2, MoEDAL-MAPP1 and MAPP2, and MATHUSLA. The combined sensitivity of the main detector and far detectors, particularly ANUBIS and MATHUSLA, allows a large fraction of the SFOEWPT parameter space to be probed for $10^{-10}\leq \sinθ\leq 10^{-5}$, with $m_S$ up to $250~\mathrm{GeV}$. For $\sinθ\leq 10^{-11}$, however, the SFOEWPT parameter space becomes inaccessible to both the main detector and the far-detector searches.

hep-ph↗

arXiv2026

Yb/Sr optical clock comparison at 2.2E-18 uncertainty over a 1350-km fiber link

Redefining the International system of Units (SI) second requires independent frequency-ratio measurements between clocks based on different laboratories, with an overall uncertainty below 5E-18, to validate individual clock evaluations through consistency checks. Reaching this uncertainty over long distances has remained challenging. So far, comparisons at this level have been achieved only a few times, between co-located clocks, and their results disagree markedly. Previous high-precision comparisons have therefore been limited to local or kilometre-scale links. Here we compare a ytterbium optical lattice clock in Wuhan with a strontium optical lattice clock in Shanghai over a phase-stabilized fiber link spanning 1350 km, and determine the Yb/Sr frequency ratio to be 1.207 507 039 343 337 716 0(26), with a Bayesian statistical uncertainty of 1.14E-18 and a total uncertainty of 2.18E-18. To our knowledge, this is the first intercity long distance frequency-ratio measurement, whose total uncertainty reaches the level required by the redefinition roadmap. The measured ratio exhibits a significant correlation with Earth tides and its response agree with the model within the statistical uncertainty. At intercity scale, this comparison demonstrates the capability of an optical-clock network for frequency comparison and Earths gravitational-potential sensing, a key step toward a wide-area optical-clock network.

physics.optics↗

arXiv2026

Optimal Steady-State Regulation of Nonlinear Systems: A Feedback Optimization Approach

This paper studies the optimal steady-state regulation problem for a class of nonlinear systems. The regulated output is required to track a trajectory that minimizes a steady-state cost depending on both the output and the exo-system. To characterize the desired steady-state behavior, we introduce a generalized regulator equation and develop a two-layer framework. The optimization layer generates the optimal reference online through a steady-state generator combined with gradient-feedback optimization dynamics, while the control layer employs backstepping to stabilize the plant and achieve the desired optimal steady-state regulation. Under suitable immersion and minimum-phase assumptions, global asymptotic stability is established.

eess.SY↗

arXiv2026

Weaponizing the Exit: Input Privacy Leakage via Exit Routing Manipulation in Multi-Exit Networks

Multi-exit networks improve inference efficiency by allowing inputs to terminate early at intermediate exits. We identify a privacy vulnerability in cloud deployments of these networks: a malicious Cloud Service Provider (CSP) can infer sensitive properties of individual user inputs solely from inference latency, without accessing inputs, intermediate states, or model outputs. The attack exploits the CSP's control over the model supply chain. Prior to deployment, the CSP supplies a specially crafted multi-exit network manipulated so that inputs carrying a target sensitive property are routed to a designated exit with high probability, while other inputs exit elsewhere. At serving time, the CSP infers the exit index from per-query latency, thereby determining whether each input possesses the target property without accessing inputs, outputs, or internal model states. We propose a training framework combining selective label smoothing and entropy-covariance optimization to control confidence distributions across exits and reduce premature termination. We conduct experiments on four datasets across ten target properties. The results demonstrate strong attack effectiveness and stealthiness. The attack achieves an average F1 score of 0.73, compared to 0.25 for the strongest baseline, while main task accuracy drops by less than 1.3\% and inference speedup degradation remains below 0.1 in most settings. Our code is available at https://github.com/kazefjj/Weaponizing_the_Exit.

cs.CR↗

arXiv2026

Robust Anomalous Hall Effect in Epitaxial Cubic D0$_3$-Fe$_3$Ge Nanoplates Grown by Chemical Vapor Deposition

The iron-based ferromagnetic materials have emerged as a promising system for spintronics due to their high Curie temperatures and significant anomalous Hall effect (AHE) driven by Berry curvature. While there have been investigations into the epitaxial growth of non-layered iron-based ferromagnetic materials, the realization of high-quality single crystal thin films continues to be a formidable obstacle. Meanwhile, the issues such as grain boundary scattering further hinder their characterization and application. Here, we report the first chemical vapor deposition (CVD) synthesis of single-crystalline cubic D0$_3$-Fe$_3$Ge nanoplates. This compound represents a substantial departure from the conventional hexagonal phases typically observed in the Fe-Ge system. Structural characterizations confirm the cubic lattice and stoichiometric purity of the crystals. Structural analysis identifies a strong epitaxial effect, resulting in a giant compressive strain of ~4.16% at the interface that relaxes gradually toward the surface. The second-harmonic generation (SHG) signals might further reveal that such interfacial strain disrupted the inherent centro-symmetry of the cubic phase. SQUID magnetometry shows robust room-temperature ferromagnetism and clear magnetic anisotropy. Magneto-transport measurements reveal a robust AHE that remains stable at temperatures far exceeding room temperature. The scaling observed between the anomalous Hall resistance and longitudinal resistance strongly suggests that this effect is dominated by the intrinsic Berry curvature mechanism. Furthermore, density functional theory (DFT) calculations reveal that the huge epitaxial strain induces band reconstructions and creates dense spin-orbit coupling gaps near the Fermi level, effectively doubling the anomalous Hall conductivity to match our experimental observations.

cond-mat.mtrl-sci↗

arXiv2026

RealComm: Benchmarking and Adapting Audio Deepfake Detection over Real Communication Channels

Acoustic capture, device processing, and transmission coding in real calls jointly alter speech signals and may affect the reliability of audio deepfake detection. To systematically evaluate these effects, we introduce RealComm, a dataset pairing digital speech with recordings of the same utterances transmitted through real mobile calls. The acquisition design covers acoustic and wired injection, handset and speakerphone operation, and caller-side denoising, with bona fide and synthetic speech recorded under matched conditions. The dataset includes RealCommBench for evaluation and RealCommTrain for adaptation. We further develop RealComm-Aug, a staged waveform augmentation pipeline for simulating acquisition and transmission effects, and investigate its combination with real-call training. Experiments with existing detectors reveal substantial degradation after call transmission, even for models with strong digital-domain performance; acoustic injection is particularly challenging. Paired quality and spectral analyses show that quality degradation is associated with poorer detection across recording conditions for the evaluated detectors, while high-frequency attenuation alone does not explain detection difficulty. Simulated augmentation and real-call training provide complementary benefits, with their combination achieving the lowest call-domain error for each of three evaluated detectors. For Teffic-Audio, the equal error rate decreases from 37.94% to 22.37%. The demo and benchmark access information are provided through the project repository.

eess.AS↗

arXiv2026

Beyond Finite-Sum Optimization: A Primal-Dual Method for Decentralized Finite-Max Optimization

Many decentralized systems are judged by their worst-performing agents rather than their average performance. We study finite-max optimization over a connected undirected network, where each agent knows only its own smooth, possibly nonconvex objective. Quadratic regularization yields a smooth finite-sum representation with a scalar dual variable and locally computable clipped weights. We develop an alternating primal-dual gradient tracking (APDGT) method, using neighbor communication without any global or centralized projections. Under smoothness and level-boundedness assumptions, a localized Lyapunov analysis with vanishing regularization establishes bounded primal iterates and Clarke stationarity of every accumulation point of their network average. The averaged sequence contains an $\varepsilon$-stationary point, measured by near-active gradients, within $O (\varepsilon^{-3})$ iterations. Synthetic tests and decentralized logistic regression on Fashion-MNIST illustrate convergence behavior and potential improvements in worst-agent performance.

math.OC↗

arXiv2026

First-Principles Effective Mass in the Three-Dimensional Uniform Electron Gas

The quasiparticle effective mass $m^*$ of the three-dimensional uniform electron gas (UEG) is a fundamental Fermi-liquid parameter whose value and density dependence have remained controversial for decades. Using renormalized perturbation theory with explicit counterterms, we determine $m^*$ in the metallic regime ($r_s \le 6$) from first principles by two complementary routes -- the self-energy and the forward-scattering four-point vertex via the $p$-wave spin-symmetric Landau parameter $F_1^s$ -- that agree within uncertainties at each density through sixth renormalized order. The resulting $m^*/m$ remains close to unity throughout the metallic regime, with a shallow non-monotonic density dependence -- a minimum near $r_s\approx 1$ followed by a gentle upturn -- reflecting the interplay of exchange and dynamical screening in the self-energy, and disfavoring strong monotonic suppression. This finding supports a physical picture for the metallic UEG in which dominant charge correlations are concentrated in nearly forward scattering and generate only a weak $F_1^s$ component.

cond-mat.str-el↗

arXiv2026

MGE-PO: Entropy-Aware Credit Assignment for Mathematical Reasoning

GRPO assigns the same group-relative advantage to equally rewarded responses within a question, leaving their generation uncertainty unused. We introduce MGE-PO, centered on four-quadrant (4Q) advantage shaping that combines verified correctness with rollout generation entropy. Correctness determines the sign of an additional correction, while entropy controls its magnitude: lower-entropy successes receive larger bonuses and higher-entropy failures receive larger penalties. An online entropy-based question curriculum supports this credit rule using cached rollout measurements. At the final checkpoint after eight training epochs on Qwen3-8B-Base, adding 4Q across three policy optimization settings yields absolute gains of 3.23-3.69% in six-benchmark macro avg@32 and 4.24-11.06% in macro pass@32. On Qwen2.5-7B-Base, the complete configuration raises four-benchmark macro avg@32 from 41.5% to 45.4%.

cs.CL↗