arXiv subjects

Ke Wang

Publications and source records attributed to Ke Wang.

At least 73 records · Page 4Linked to original sources

arXiv2026

Mitigating Phase Errors to Improve Signal Quality in RIS-Assisted Satellite Communications

This research presents an advanced framework designed to enhance the received power in satellite-to-Earth communications by utilizing Reconfigurable Intelligent Surfaces (RIS) and focuses on mitigating phase errors arising from hardware imperfections associated with RIS systems. A comprehensive analysis of the phase errors arising from these imperfections is conducted, leading to the development of a robust analytical model that quantitatively incorporates these errors into the assessment of received power evaluations. Subsequently, we propose a methodology to selectively exclude RIS elements that are prone to errors, thereby improving the phase alignment of the received signal and enhancing overall system efficiency. While this strategy leads to a marginal decrease in received power, Bayesian Optimization (BO) is employed to optimize the RIS configuration, maintaining the desired power levels and ensuring signal integrity. The research also delves into the complexities introduced by shadowing effects combined with phase errors. To address these compounded challenges, a decision-making framework utilizing targeted BO is introduced to dynamically optimize RIS configurations, thereby enhancing system robustness and performance under adverse operational conditions. Numerical simulations validate the framework's efficacy in adaptively managing RIS elements, ensuring robust signal integrity and improved reception despite variations stemming from environmental and hardware factors. By addressing critical challenges in RIS-augmented satellite communications, this work highlights the transformative potential of adaptive optimization strategies in advancing the reliability and efficiency of next-generation wireless networks.

eess.SP↗

arXiv2026

Performance Evaluation of High Power Microwave Systems Against UAVs A Probabilistic Antenna Propagation Framework with Sensitivity Analysis

We present an uncertainty-aware probabilistic framework for high-power microwave (HPM) counter-UAV performance under stochastic target motion, beam-pointing uncertainty, atmospheric propagation, and uncertain target susceptibility. It couples stochastic UAV kinematics, a jitter-to-gain model, free-space spreading, gaseous absorption, and rain attenuation, and a logistic energy--response model to derive closed-form statistics of received pulse energy and per-pulse and cumulative effectiveness probabilities. Slant-range variability arises from integrated acceleration noise. Received pulse energy is treated as a target-level exposure metric rather than the exact energy absorbed by an internal component. Closed-form moments and a log-normal approximation yield the mean per-pulse probability through Gaussian--Hermite quadrature and a dwell-time expression under an independent-pulse assumption. Analytical predictions closely match Monte Carlo results under matched assumptions. For a vulnerable-target threshold of $E_{\mathrm{th}}=10^{-2}\,\mathrm{J}$, the model predicts $\bar{P}_{\mathrm{kill}}\gtrsim0.4$ per pulse and $P_{\mathrm{kill,tot}}>99\%$ within about $0.1\,\mathrm{s}$ at kilohertz PRF. For a hardened target with $E_{\mathrm{th}}=10^{-1}\,\mathrm{J}$, it predicts $\bar{P}_{\mathrm{kill}}\approx2.2\times10^{-4}$ ($\approx0.02\%$) and $P_{\mathrm{kill,tot}}\approx20\%$ after $1\,\mathrm{s}$ at $1\,\mathrm{kHz}$ under the i.i.d. pulse assumption. Elasticity analysis identifies slant range as dominant ($S_{\bar{R}}\approx-2$), followed by aperture diameter and transmit power; pointing jitter and atmospheric variability are less influential in the evaluated regimes. Within its assumptions, the framework supports system sizing, trade-off analysis, and risk-aware mission planning.

eess.SP↗

arXiv2026

nuTruck: Benchmarking Autonomous Driving Planning for Distributed Electric-drive Trucks

The dominance of traditional rule-based methods in autonomous driving has gradually been replaced by learning-based approaches. While learning-based planners have achieved considerable success in passenger vehicles, their performance on heavy-duty trucks, particularly modern distributed electric-drive trucks (DETs), remains largely unexplored. To facilitate research and application of learning-based planners in DETs, this letter presents the first high-fidelity benchmark, called nuTruck, designed to support large-scale neural network training and closed-loop evaluation. Given the complex dynamics and high rollover susceptibility of DETs, we first incorporate a highly accurate nonlinear truck dynamical model into the simulation, which enables independent driving and steering of all wheels and captures dynamic load transfer caused by acceleration, deceleration, and cornering, thereby allowing quantitative assessment of rollover risk in closed-loop simulation. Second, we adapt several rule-based and learning-based planners as baselines for DETs and evaluate their performance in closed-loop simulation. Finally, using real-world driving scenarios from the nuPlan dataset, we conduct extensive closed-loop evaluations, analyzing not only conventional collision-free planning performance, but also the dynamical safety of the planned trajectories. The proposed nuTruck benchmark is expected to serve as a new standard for fair and realistic evaluation of autonomous driving planners on DETs.

cs.RO↗

arXiv2026

Particle decay and energy conservation in the Kerr-Newman black hole

In this paper, we study the decay of a particle in Kerr--Newman spacetime. Both theoretical analysis and numerical simulations show that when a particle splits in Kerr--Newman spacetime, its mass is inevitably reduced, and this mass deficit is transformed into kinetic energy in the center-of-mass frame. We denote the parent particle as $O$ and the daughter particles as $A$ and $B$. We also find that as the charge parameter $Q$ increases, the specific angular momenta of the parent particle $O$ and the daughter particle $B$ become smaller, the masses of the two daughter particles become larger, the absolute values of their specific energies become smaller, the relative Lorentz factors among the three particles become smaller, and their four-velocities also become smaller. These trends are opposite to those observed when increasing the cosmological constant in Kerr--de Sitter spacetime.

gr-qc↗

arXiv2026

Gravitationally Lensed View of DSFG-1 in PLCK G165.7+67.0: Strong Dust Emission and Spatially Resolved Stellar Population Analysis with JWST and SMA

We present a detailed stellar population analysis of the strongly lensed dusty star-forming galaxy (DSFG) PLCK G165.7+67.0 DSFG-1 at $z = 2.236$, combining JWST NIRCam imaging with new Submillimeter Array (SMA) observations. This source is multiply imaged into two lensed components: image 1a, with a moderate magnification factor of $μ\sim 5$, and image 1bc, with an extreme magnification factor of $μ\sim 40$. The new SMA observations detect significant dust continuum emission at 225GHz and 273GHz, with combined flux densities of $S_{\rm cont}=(1.19\pm0.38)$ mJy in image 1a and $S_{\rm cont}=(10.02\pm0.85)$ mJy in image 1bc, indicating active star formation at sub-kpc scale. Based on the integrated SED modeling, DSFG-1 exhibits a lensing amplification-corrected stellar mass of $M_{\star} = (1.2 \pm 0.4) \times 10^{10} M_{\odot}$, and a star-formation rate (SFR) of $(103 \pm 14) M_{\odot}\,\mathrm{yr^{-1}}$, similar to previous $Hα$-based results, placing it four times above the star-forming main sequence at this redshift. Its location on the size-mass plane and its morphological properties suggest that the system occupies a transitional phase between star-forming late-type galaxies and compact early-type systems. Together with its elevated star-formation activity, this is consistent with a rapidly evolving galaxy observed during Cosmic Noon. We further investigate the spatially resolved stellar population properties, and found significant spatial variations in stellar age and dust attenuation. These results point to a non-uniform star-formation history and highlight the complex interplay between dust geometry, stellar growth, and gravitational lensing, consistent with a merger scenario.

astro-ph.GA↗

arXiv2026

Advancing Astrophysics with the SKA II

Advancing Astrophysics with the SKA II (AASKAII), written by our science community, outlines the transformative scientific advances that will be enabled by the SKA telescopes. In the decade since the publication of the previous edition, telescope designs have matured, construction has commenced, and the SKA Organisation has evolved into the SKA Observatory (SKAO). At the same time, observations from SKA precursor and pathfinder telescopes have provided new insights into longstanding scientific challenges while revealing entirely new phenomena. Published in advance of the first science verification campaign for the SKA Observatory, this volume looks ahead to the coming decades of discovery and innovation in radio astronomy. AASKAII spans the broad range of scientific research enabled by the SKA telescopes, SKA-Mid and SKA-Low. The contributions are organised into six thematic categories according to their scientific focus. The opening section presents overview chapters from the SKA Science Working Groups, around which our community is organised. Each overview provides the broader context that connects the contributions in this volume to the key scientific questions being pursued by their respective communities.

astro-ph.IM↗

arXiv2026

SAMBA: A Scatter-Guided Masked Bidirectional Mamba Foundation Model for SAR Target Recognition

Synthetic aperture radar automatic target recognition (SAR ATR) is critical for Earth observation and defense, but its practical deployment is constrained by scarce annotated training data. Self-supervised pre-training alleviates this label bottleneck, yet prevailing Transformer architectures incur prohibitive quadratic computational complexity, and conventional universal masking neglects the unique electromagnetic scattering properties intrinsic to SAR imagery. To address these limitations, we propose SAMBA (Scattering-Guided Bidirectional Mamba), an efficient self-supervised pre-training foundation model for SAR target interpretation. Our framework features three core innovations: (i) a linear-complexity Mamba encoder with a mid-sequence class token to mitigate computational bottlenecks; (ii) a three-level hierarchical Scattering-Guided Masked Autoencoder (SG-MAE) masking strategy guided by SAR physical priors, aligning the pretext task with SAR's intrinsic imaging mechanism; (iii) a lightweight SpatialMix feature interaction module to enhance cross-region feature fusion. We also design a two-stage cross-domain pre-training pipeline to optimize the overall pre-training process. Extensive evaluations demonstrate that SAMBA consistently delivers superior performance across all pre-training configurations, with substantially fewer parameters than both CNN and Transformer baselines. Compared with the default masking strategy in standard MAE, the proposed SG-MAE strategy further boosts the model's few-shot transfer capability. Benchmarking on seven downstream datasets covering classification and detection tasks shows SAMBA achieves state-of-the-art (SOTA) performance on most metrics, fully validating its robust generalizability across diverse SAR interpretation tasks. Source code and pre-trained weights are publicly available at https://github.com/mynswkk/SAMBA.

cs.CV↗

arXiv2026

The Milky Way Atlas for Linear Filaments III: Giant filaments and magnetic fields as evidence of a bubbly Galactic disk

Linear filamentary structures are fundamental constituents of the interstellar medium and play a central role in star formation. Their relative orientation with respect to the ambient magnetic field (B-field) provides key constraints on filament formation mechanisms. We investigate the relative orientation between Milky Way linear filaments (MWLFs) and the plane-of-sky B-field using polarization observations from the Atacama Cosmology Telescope (ACT) DR6, complemented by Planck data. Filament orientations are compared with the local B-field and the Galactic plane, while projection effects and statistical significance are quantified using Monte Carlo simulations of vector pairs in three-dimensions. We find no strong preferential alignment between MWLFs and the ambient B-field. Although the B-field is preferentially aligned with the Galactic plane with relative angles $θ_{\rm BG} \sim0-25°$, filament orientations exhibit a bimodal distribution, being either parallel or perpendicular to the plane ($θ_{\rm FG} \sim0-15°$ and $\sim75-90°$). Filaments located far from the Galactic midplane ($|z|>90$ pc) preferentially show perpendicular alignment with both the plane and the B-field, whereas those near the midplane exhibit a bimodal orientation. These results indicate that large-scale B-fields do not dominate the formation of MWLFs and instead favor a super-Alfvénic regime in which magnetic forces are dynamically subdominant, as expected for filaments associated with supernova-driven shells. Overall, our findings suggest that a face-on view of the Milky Way would resemble nearby disk galaxies such as M74, as observed in JWST images, with its disk structured by a network of supernova-driven bubbles (i.e., a bubbly disk).

astro-ph.GA↗

arXiv2026

OASIF: An Efficient Obfuscation-Aware Self-Improving Framework for LLM-Based Assembly Code Instruction Following and Comprehension

Large Language Models (LLMs) have recently shown promise in automated binary analysis, yet they remain brittle under commercial-grade obfuscation. We present OASIF, an Obfuscation-Aware Self-evolving Instruction-Following framework for obfuscated assembly comprehension. OASIF couples a token-efficient assembly encoder with a lightweight projector to expose long obfuscated code to a pretrained code LLM under a bounded context budget and follows a three-phase training: (i) feature-space alignment, (ii) supervised instruction fine-tuning, and (iii) online self-evolving reinforcement learning with hybrid rewards, enabling continual adaptation with minimal manual verification. On VMISA-Bench, a challenging out-of-distribution suite featuring three commercial VM-based obfuscators, OASIF consistently improves open-source backbones; Qwen2.5-Coder-Instruct-14B attains Success Rate gains of +15.9, +5.8, and +16.9 percentage points (pp) on Code Virtualizer, Themida (v3.0.7), and VMProtect (v3.5), respectively, and improves the OASIF-Bench average by +9.8. OASIF further delivers stable gains across seven standard BCSD benchmarks while preserving general and domain-relevant capabilities on HumanEval, VulBench, and HumanEval-Decompile.

cs.SE↗

arXiv2026

Exploring $KΞ^$ and $K^Ξ$ molecular states and the triangle singularity in the $K^- p \to K Ξ(1530)$ reaction

We investigate the $K^- p \to K Ξ(1530)$ reaction within an effective Lagrangian approach, exploring possible $K Ξ^*$ and $K^* Ξ$ hadronic molecular states and the role of the triangle singularity (TS). The $Λ(2050)3/2^-$ is interpreted as a $K Ξ^*$ molecule, whereas a $K^* Ξ$ molecule with $I(J^P)=0(3/2^-)$ and mass about 2150~MeV denoted as $Λ(2150)$ can generate a TS through triangle-loop diagrams with intermediate $K^*$, $Ξ$, and $π$. The peak structure observed in the cross section near $\sqrt{s}=2.25$ GeV is analyzed in terms of both the $Σ(2250)$ resonance production and the TS mechanism associated with $Λ(2150)$. We find that the TS induces pronounced spin effects in the final state $Ξ^*$, which can be probed through measurements of its spin density matrix elements. In particular, significant variations of the spin observables in the $\sqrt{s}=2.2$--$2.3$ GeV region serve as a distinct TS signature absent in a pure resonance scenario. Furthermore, for the three-body reaction $K^- p \to K^+ π^- Ξ^0$, we demonstrate that $Ξ^*$ spin observables can be reliably extracted from the $π$ angular distribution in the $Ξπ$ rest frame by applying an appropriate kinematic cut on the $Ξπ$ invariant mass to suppress background contributions. These predictions can be tested in future high-precision measurements at J-PARC, providing crucial insights into the nature of the TS and the possible existence of the $K^* Ξ$ molecular state.

hep-ph↗

arXiv2026

Sparsity-adaptive concentration inequalities for random polynomials

We prove concentration inequalities for polynomials of independent, sparse $α$-sub-exponential random variables. Specifically, we consider $X_i=δ_iξ_i$, where the Bernoulli selectors $δ_i$ are independent with parameters $p_i$, and the variables $ξ_i$ are independent $α$-sub-exponential random variables (not necessarily centered). For any polynomial $f:\mathbb R^n\to\mathbb R $ of degree at most $D$ and any $0<α\le 1 $, we establish an $L_r$-moment bound for $f(X)-\mathbb E f(X)$ in terms of partition norms of sparsity-weighted expected derivative tensors. The weights count distinct coordinates rather than multiplicities and therefore distinguish diagonal, partially diagonal, and off-diagonal contributions. This captures the sparse scaling in both collective fluctuation regimes and extreme-coordinate regimes. When all sparsity parameters are equal to one, our result recovers the polynomial concentration inequality of Götze, Sambale, and Sinulis. In degree two, it recovers sparse Hanson-Wright bounds. As applications, we derive deviation inequalities for the distance between a sparse simple random tensor and a fixed subspace, and obtain lower bounds for the smallest singular value of matrices whose columns are independent sparse simple random tensors.

math.PR↗

arXiv2026

Breaking the Evaluation Paradox: Evaluating High-Entropy Search with Computationally Irreducible Constraints

Evaluating the exhaustive search capabilities of large language models (LLMs) is plagued by a fundamental paradox: verifying completeness requires complete ground truth, yet high-entropy enumeration tasks make such ground truth impossible for humans to create. This causes benchmarks to systematically penalize models for outperforming their human annotators. Despite rapid progress in web-search and deep research agents -- which now issue hundreds of queries, traverse diverse sites, and synthesize long reports -- evaluation still largely relies on partially annotated answer sets, LLM-based judges, or single-answer questions that avoid genuinely exhaustive search scenarios. We break this paradox by shifting the evaluation paradigm from simulating a messy reality to constructing computationally pure challenges. We introduce VERITAS (Verifiable Traversal Assessment for Search), a framework built on the principle of computationally irreducible constraints. By introducing novel, non-optimizable constraints, we create verifiable, sparse-answer search tasks that are computationally equivalent to exhaustive enumeration. These constraints are easy to verify but impossible for LLMs or search engines to optimize, forcing agents to genuinely traverse the entire search space. VERITAS can automatically generate a virtually infinite number of test cases with perfect ground truth and precise difficulty control, with marginal instance cost dominated by hash computations. This provides not only a robust benchmark for evaluating systematic exploration under uncertainty but also a scalable method for generating training data to improve these crucial, yet underdeveloped, capabilities.

cs.IR↗

arXiv2026

Implicit Likelihood Inference of the Neutrino Mass Hierarchy from Cosmological Data

In this paper, we turn to the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline to perform a multi-round ILI of the neutrino mass hierarchy from cosmological data, including $TT$, $TE$, $EE$ power spectra of Planck 2018 and distance ratios of DESI DR2. More precisely, we first embed the CMB power spectra simulator $\mathtt{CLASS}$ into the LtU-ILI pipeline. And then, opting for Sequential Neural Likelihood Estimation (SNLE), we sequentially train neural networks using $6$ rounds of $10000$ simulations to target a ``black box'' likelihood of our forward model with one additional neutrino mass hierarchy parameter $\tildeΔ$ and six base cosmological parameters. We find $\tildeΔ=0.12^{+0.21}_{-0.23}~(68\%{\rm CL})$.

astro-ph.CO↗

arXiv2026

A Geometric Pathway for Tuning Ferroelectric Properties via Polar State Reconfiguration

We report the discovery of a geometric pathway for tuning ferroelectric properties through thermally driven reconfiguration between coexisting polar states in Li-substituted NaNbO3. Using first-principles density functional theory calculation and 7Li solid-state nuclear magnetic resonance spectroscopy measurement, we reveal that Li substitution creates two distinct polar configurations whose transformation under annealing enhances the Curie temperature and induces piezoelectric hardening. Our findings establish a geometrically-driven polar state reconfiguration mechanism, providing a general design principle for ferroics whereby macroscopic functional properties can be engineered via lattice geometry.

cond-mat.mtrl-sci↗

arXiv2026

A Spatio-Temporal Expert Prefetching Framework for Efficient MoE-based LLM Inference

Mixture-of-Experts (MoE) based large language models (LLMs), such as Qwen and DeepSeek, have recently emerged as an effective approach to improving model capacity without proportionally increasing computational cost. By replacing the conventional feed-forward network in dense LLMs with a set of experts and activating only a subset of them for each input token, MoE models significantly increase the total number of parameters while keeping the per-token computation relatively manageable. However, this dynamic and irregular expert activation pattern also introduces substantial expert loading overhead during inference, since the required experts must be fetched on demand according to token-dependent routing results. As a result, expert loading latency becomes a major source of performance and energy inefficiency. To this end, we first perform a comprehensive analysis of expert selection behavior in various MoE-based LLMs and applications, including language understanding and code generation. Our analysis reveals that, within each application domain, expert requests exhibit strong correlation across both adjacent MoE layers and consecutive decoding tokens, making future expert activations predictable. Based on this insight, we propose ST-MoE, a spatio-temporal expert prefetching framework that proactively stages experts ahead of use to overlap expert loading with ongoing computation. ST-MoE combines a lightweight runtime prediction mechanism that preserves the original routing behavior with a reconfigurable hardware design that efficiently supports dynamic expert prefetching. The combined effect of the prediction mechanism with the supporting hardware significantly improves MoE inference performance and energy efficiency while preserving model inference accuracy.

cs.AR↗

arXiv2026

Molecular Beam Epitaxy of Mn2In2Se5 van der Waals Layers Using Mn Intercalation

The weak van der Waals (vdW) force in layered chalcogenide materials has enabled the growth of ternary chalcogenide layers using unconventional approaches. Here, we report the molecular beam epitaxy (MBE) growth of Mn2In2Se5, a spin glass material with high level of magnetic frustration, through the heterointegration of MnSe on In2Se3. Directly depositing α-MnSe on the vdW In2Se3 layers results in Mn intercalation, transforming the In2Se3 layer into Mn2In2Se5. Large growth windows, including substrate temperatures from 250-450 °C and Se:Mn flux ratio of 1.1-3.1, have been identified for the intercalation process. With an optimized MnSe deposition time, smooth, single-crystalline, and (0001)-oriented Mn2In2Se5 layers with a root-mean-square (RMS) roughness of 1.5 nm can be synthesized. Further extending the MnSe deposition time results in the growth of uniform rock-salt structured α-MnSe (111) layers with a thickness of up to 8 nm and a narrow full-width-at-half-maximum (FWHM) of 0.35° in MnSe (222) XRD rocking curves. This report presents a unique approach for the growths of uniform and single-crystalline Mn2In2Se5 vdW layers using MBE, and potentially opens a pathway for synthesis of ternary vdW chalcogenides by intercalation of new atomic species in binary vdW chalcogenides.

cond-mat.mtrl-sci↗

arXiv2026

PriFT: Prior-Support Guided Supervised Fine-Tuning

Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement learning (RL), but it can show weaker generalization than RL. A key limitation is its off-policy objective: SFT fits fixed demonstrations token by token, including targets poorly aligned with the model's pretrained distribution, which can lead to overfitting. A recent line of work addresses this issue by assigning larger training weights to tokens better aligned with the current model's predictive distribution, with the intuition that fitting these tokens are less distortive to the model's pretrained knowledge and representations. However, computing the token weights from the model that is currently fine-tuned entangles token weights with the optimization trajectory, inducing a self-reinforcing dynamics as the distribution rapidly departs from the pretrained model. To address this, we propose PriFT (Prior-support guided Fine-Tuning), which derives token weights from a frozen pretrained reference to obtain a stable reweighting signal unaffected by fine-tuning. This signal estimates prior support: the extent to which each target token is supported by the pretrained distribution. Across multiple existing token-reweighting rules, replacing the reweighting signal from the online model to pretrained model consistently improves performance. We introduce two instantiations: PriFT-prob uses pretrained token probability, while PriFT-mass selects tokens by cumulative probability mass under the pretrained distribution. Extensive experiments on mathematical reasoning, code generation, and medical question answering show that PriFT achieves state-of-the-art results among SFT baselines and provides a better initialization for subsequent RL training.

cs.CL↗

arXiv2026

Hotspot Images from Magnetic Reconnection Processes in the plunging Region of a Kerr Black Hole

Employing the hotspot imaging technique, this work investigates the plasma motion trajectories prior to and following the Comisso-Asenjo mechanism within the plunging region. After a concise overview of the magnetic reconnection process in the plunging region of a Kerr black hole, we present the hotspot model and the associated imaging methodology. Through numerical simulations, we separately examine the hotspot images in the plunging region under three conditions: no magnetic reconnection, with magnetic reconnection, and when the escape condition fails. These outcomes are also contrasted with hotspot images in the circular orbit zone. Our findings reveal that for hotspot images without magnetic reconnection, when the plasma follows plunging orbits, the flare strength gradually declines; conversely, for circular orbits, the flare strength remains approximately constant. Additionally, we observe that the signal indicative of energy extraction is less conspicuous in the plunging region compared to the circular orbit region.

gr-qc↗