arXiv subjects

Wei Zhong

Publications and source records attributed to Wei Zhong.

At least 19 recordsLinked to original sources

arXiv2026

Risk Occupancy: A New and Efficient Paradigm through Vehicle-Road-Cloud Collaboration

This paper proposes a novel 4D risk occupancy (RiskOcc) perception paradigm under the Vehicle-Road-Cloud integrated architecture, which unifies object detection and local mapping into a single representation spanning four dimensions: road-surface x and y coordinates, risk, and time. Distinct from conventional grid occupancy and risk field methods, this paradigm adopts an anchor-node-based perception approach with a concise yet reliable risk quantification scheme, enabling flexible and accurate capture of static and dynamic object occupancy states at current and future time steps, with per-occupancy-unit risk quantification. Compared with 3D-Occ, the proposed RiskOcc requires only one layer of data volume and achieves higher perceptual computational efficiency. Visualizations of risk occupancy perception results are presented based on the DAIR-V2X dataset, and a path planning method is developed to verify the usability of the risk occupancy map. Experimental results show that at an initial braking speed of 8 m/s, the model improves safety redundancy by 12.5\% and reduces average deceleration by 5.41\%, enhancing both safety and comfort. In summary, this study introduces a novel perception paradigm for intelligent connected vehicle (ICV), providing a compact and interpretable risk representation for cooperative perception and downstream planning in ICV systems.

cs.RO↗

arXiv2026

Spatiotemporal Vortex Rings Induced by Spatiotemporal Coupling

Vortices and vortex rings are topological structures that arise in various physical systems. However, the generation of spatiotemporal vortices (STVs) and vortex rings (STVRs) has so far relied on complex, often active wavefront modulation. We theoretically and experimentally demonstrate that spatiotemporal coupling can drive unstructured wave packets to form vortices upon scattering from simple obstacles. The resulting STVs and STVRs possess controllable topological charges and excellent propagation stability. These findings reveal a fundamental mechanism for spatiotemporal singularity formation and provide a universal route to structured-wave generation.

physics.class-ph↗

arXiv2026

Biweighted Poisson Subsampling for Convoluted Rank Regression with Massive Data

Optimal subsampling efficiently selects the most informative data points, enabling accurate statistical inference while significantly reducing computational burden for massive datasets. However, the existing relevant methods can not directly be applied to pairwise loss problems, particularly for convoluted rank regression (CRR), due to the double summation structure in objective function. To this end, we first propose a new BIweighted Poisson Subsampling (BIPS) framework for such problems through designing a proper weight for a pair of observations instead of for a single observation for objective function. Two concrete inverse probability weighting strategies are considered. Secondly, we focus on the CRR models, under which the BIPS estimator (BIPS-CRR) is formulated. We establish consistency and asymptotic normality for BIPS-CRR, derive its optimal Poisson subsampling probabilities under the L-optimality criterion, and provide a practical algorithm to facilitate implementation. Thirdly, we develop a distributed estimator for CRR that incorporates BIPS as a pilot subsampling strategy. This estimation is globally efficient and is robust to both randomly and non-randomly distributed datasets in distributed computing environments. Extensive simulations and a real-world application demonstrate the excellent finite-sample performance of proposed methodology. Additionally, our BIPS can be readily extended to other U-statistics optimization problems and pairwise learning tasks.

math.ST↗

arXiv2026

Toward Heisenberg-Limited Interferometry with Dual Squeezers

The canonical Mach-Zehnder interferometer fed with a coherent state and a squeezed-vacuum state of equal intensities is theoretically predicted to achieve Heisenberg scaling in phase sensitivity. However, this ultimate performance is unattainable using direct photon-number-difference detection due to a divergence arising precisely at the optimal equal-intensity regime. In this work, we introduce a dual-squeezing approach that overcomes this fundamental limitation. Our scheme employs an additional single-mode squeezer before detection, forming a paired configuration with the input squeezer used to generate the squeezed-vacuum state. We analytically demonstrate that the resulting dual-squeezing Mach-Zehnder interferometer enables Heisenberg-limited phase sensitivity with di rect photon-number-difference detection, while remaining robust against detection noise. Our work provides a feasible and robust route toward quantum-limited interferometric phase measurements

quant-ph↗

arXiv2026

Crossover Scaling of Binder Cumulant and its application in Non-reciprocal Sandpiles

In this letter, we unveil a robust, pre-asymptotic scaling regime for the Binder cumulant $U_L$, a central finite-size scaling tool, demonstrating $U_L\sim N^{-1} |t|^{-dν}$ (disordered phase) and $\frac{2}{3}-U_L\sim N^{-1} |t|^{-dν}$ (ordered phase), with $t$ being the reduced control parameter, and $N$, $d$, $ν$ represent the total number of sites, the dimensionality, and correlation length exponent, respectively. Leveraging this result, we resolve a fundamental question on the stability of universality classes under the breaking of microscopic reciprocity. For the conserved Manna sandpile, we show that reciprocal biases preserve its universality class, merely shifting the critical point. In striking contrast, any non-reciprocal interaction acts as a relevant perturbation, decisively driving the system's critical exponents to flow from their non-mean-field values towards the mean-field related ones. This flow establishes non-reciprocity as a generic mechanism inducing mean-field criticality in conserved, non-equilibrium systems.

cond-mat.stat-mech↗

arXiv2026

Cross-Attention Speculative Decoding

Speculative decoding (SD) is a widely adopted approach for accelerating inference in large language models (LLMs), particularly when the draft and target models are well aligned. However, state-of-the-art SD methods typically rely on tightly coupled, self-attention-based Transformer decoders, often augmented with auxiliary pooling or fusion layers. This coupling makes them increasingly complex and harder to generalize across different models. We present Budget EAGLE (Beagle), the first, to our knowledge, cross-attention-based Transformer decoder SD model that achieves performance on par with leading self-attention SD models (EAGLE-v2) while eliminating the need for pooling or auxiliary components, simplifying the architecture, improving training efficiency, and maintaining stable memory usage during training-time simulation. To enable effective training of this novel architecture, we propose Two-Stage Block-Attention Training, a new method that achieves training stability and convergence efficiency in block-level attention scenarios. Extensive experiments across multiple LLMs and datasets show that Beagle achieves competitive inference speedups and higher training efficiency than EAGLE-v2, offering a strong alternative for architectures in speculative decoding.

cs.CL↗

arXiv2026

Helios: A Foundational Language Model for Smart Energy Knowledge Reasoning and Application

In the global drive toward carbon neutrality, deeply coordinated smart energy systems underpin industrial transformation. However, the interdisciplinary, fragmented, and fast-evolving expertise in this domain prevents general-purpose LLMs, which lack domain knowledge and physical-constraint awareness, from delivering precise engineering-aligned inference and generation. To address these challenges, we introduce Helios, a large language model tailored to the smart energy domain, together with a comprehensive suite of resources to advance LLM research in this field. Specifically, we develop Enersys, a multi-agent collaborative framework for end-to-end dataset construction, through which we produce: (1) a smart energy knowledge base, EnerBase, to enrich the model's foundational expertise; (2) an instruction fine-tuning dataset, EnerInstruct, to strengthen performance on domain-specific downstream tasks; and (3) an RLHF dataset, EnerReinforce, to align the model with human preferences and industry standards. Leveraging these resources, Helios undergoes large-scale pretraining, SFT, and RLHF. We also release EnerBench, a benchmark for evaluating LLMs in smart energy scenarios, and demonstrate that our approach significantly enhances domain knowledge mastery, task execution accuracy, and alignment with human preferences.

cs.AI↗

arXiv2026

High-capacity dual degrees of freedom quantum secret sharing protocol beyond the linear rate-distance bound

Quantum secret sharing (QSS) is the multipartite cryptographic primitive. Most of existing QSS protocols are limited by the linear rate-distance bound, and cannot realize the long-distance and high-capacity multipartite key distribution. This paper proposes a polarization (Pol) and phase (Ph) dual degrees of freedom (dual-DOF) QSS protocol based on the weak coherent pulse (WCP) sources. Our protocol combines the single-photon interference, two-photon interference and non-interference principles, and can resist the internal attack from the dishonest player. We develop simulation method to estimate its performance under the beam splitting attack. The simulation results show that our protocol can surpass the linear bound. Comparing with the differential-phase-shift twin-field QSS and WCP-Ph-QSS protocols, our protocol has stronger resistance against the beam splitting attack, and thus has longer maximal communication distance and higher key rate. By using the WCPs with high average photon number ($μ$ = 1.5), our protocol achieves a key rate about 5.4 times of that in WCP-Ph-QSS protocol. Its maximal communication distance (441.7 km) is about 7.9% longer than that of the WCP-Ph-QSS. Our protocol is highly feasible with current experimental technology and offers a promising approach for long-distance and high-capacity quantum networks.

quant-ph↗

arXiv2026

Reference-frame-independent Quantum secure direct communication

Current quantum secure direct communication (QSDC) protocols guarantee communication security by estimating the error rates of photons in the X and Z bases. This take the reference frame calibration between communicating parties as a necessary prerequisite. However, in mobile communications scenarios, achieving continuous and accurate reference frame calibration poses significant challenges. To address this issue, this paper proposes a reference-frame-independent (RFI) QSDC protocol. This protocol only requires ensuring the calibration accuracy of one direction of the reference frame, while allowing a misalignment angle $β$ in the other two directions. To improve the protocol's robustness against reference frame fluctuations, we introduce a $β$-independent parameter C into the security analysis framework and rederive the protocol's security bounds. Additionally, we construct a system model and optimize the pulse intensity of the signal states, enabling the protocol to achieve optimal performance under each level of channel attenuation. At an attenuation of 10 dB (corresponding to a communication distance of 25 km), the secrecy message capacities for $β= 0^{ \circ} $ and $45^{ \circ} $ are $8.765 \times10^{-6}$ bit/pulse and $4.150 \times10^{-6}$ bit/pulse, respectively. Compared with the single-photon-based QSDC, the communication distance of the protocol proposed in this paper is significantly extended. When $β= 0^{ \circ} $ and $45^{ \circ} $, the maximum transmission distances of the RFI QSDC protocol are 27.875 km and 26.750 km, which is about 155.9 % and 149.7 % of that of the single-photon-based QSDC protocol.

quant-ph↗

arXiv2026

Anatomy Aware Cascade Network: Bridging Epistemic Uncertainty and Geometric Manifold for 3D Tooth Segmentation

Accurate three-dimensional (3D) tooth segmentation from Cone-Beam Computed Tomography (CBCT) is a prerequisite for digital dental workflows. However, achieving high-fidelity segmentation remains challenging due to adhesion artifacts in naturally occluded scans, which are caused by low contrast and indistinct inter-arch boundaries. To address these limitations, we propose the Anatomy Aware Cascade Network (AACNet), a coarse-to-fine framework designed to resolve boundary ambiguity while maintaining global structural consistency. Specifically, we introduce two mechanisms: the Ambiguity Gated Boundary Refiner (AGBR) and the Signed Distance Map guided Anatomical Attention (SDMAA). The AGBR employs an entropy based gating mechanism to perform targeted feature rectification in high uncertainty transition zones. Meanwhile, the SDMAA integrates implicit geometric constraints via signed distance map to enforce topological consistency, preventing the loss of spatial details associated with standard pooling. Experimental results on a dataset of 125 CBCT volumes demonstrate that AACNet achieves a Dice Similarity Coefficient of 90.17 \% and a 95\% Hausdorff Distance of 3.63 mm, significantly outperforming state-of-the-art methods. Furthermore, the model exhibits strong generalization on an external dataset with an HD95 of 2.19 mm, validating its reliability for downstream clinical applications such as surgical planning. Code for AACNet is available at https://github.com/shiliu0114/AACNet.

cs.CV↗

arXiv2026

Irradiated Atmosphere V: Effects of Vertical-Mixing induced Energy Transport on the Inhomogeneity

Atmospheric variations over time and space boost planetary cooling, as outgoing internal flux responds to stellar radiation and opacity. Vertical mixing regulates this cooling. Our study examines how gravity waves or large-scale induced mixing interact with radiation transfer, affecting temperature inhomogeneity and internal flux. Through the radiative-convective-mixing equilibrium, mixing increases temperature inhomogeneity in the middle and lower atmospheres, redistributing internal flux. Stronger stellar radiation and mixing significantly reduce outgoing flux, slowing cooling. With constant infrared (IR) opacity, lower visible opacity and stronger mixing significantly reduce outgoing flux. Jensen's inequality implies that greater spatial disparities in stellar flux and opacity elevate the ratio of the average internal flux in inhomogeneous columns relative to that in homogeneous columns. This effect, particularly pronounced under high opacity contrasts, amplifies deep-layer temperature inhomogeneity and may enhance cooling. However, with mixing, overall cooling is weaker than without, as both the averaged internal flux of the inhomogeneous columns and that of the homogeneous column decline more sharply for the latter. Thus, while vertical mixing-induced inhomogeneity can enhance cooling, the overall cooling effect remains weaker than in the non-mixing case. Therefore, vertical mixing, by regulating atmospheric structure and flux, is key to understanding planetary cooling.

astro-ph.EP↗

arXiv2025

HyperLoad: A Cross-Modality Enhanced Large Language Model-Based Framework for Green Data Center Cooling Load Prediction

The rapid growth of artificial intelligence is exponentially escalating computational demand, inflating data center energy use and carbon emissions, and spurring rapid deployment of green data centers to relieve resource and environmental stress. Achieving sub-minute orchestration of renewables, storage, and loads, while minimizing PUE and lifecycle carbon intensity, hinges on accurate load forecasting. However, existing methods struggle to address small-sample scenarios caused by cold start, load distortion, multi-source data fragmentation, and distribution shifts in green data centers. We introduce HyperLoad, a cross-modality framework that exploits pre-trained large language models (LLMs) to overcome data scarcity. In the Cross-Modality Knowledge Alignment phase, textual priors and time-series data are mapped to a common latent space, maximizing the utility of prior knowledge. In the Multi-Scale Feature Modeling phase, domain-aligned priors are injected through adaptive prefix-tuning, enabling rapid scenario adaptation, while an Enhanced Global Interaction Attention mechanism captures cross-device temporal dependencies. The public DCData dataset is released for benchmarking. Under both data sufficient and data scarce settings, HyperLoad consistently surpasses state-of-the-art (SOTA) baselines, demonstrating its practicality for sustainable green data center management.

cs.LG↗

arXiv2025

A General Stability Approach to False Discovery Rate Control

Stability and reproducibility are essential considerations in various applications of statistical methods. False Discovery Rate (FDR) control methods are able to control false signals in scientific discoveries. However, many FDR control methods, such as Model-X knockoff and data-splitting approaches, yield unstable results due to the inherent randomness of the algorithms. To enhance the stability and reproducibility of statistical outcomes, we propose a general stability approach for FDR control in feature selection and multiple testing problems, named FDR Stabilizer. Taking feature selection as an example, our method first aggregates feature importance statistics obtained by multiple runs of the base FDR control procedure into a consensus ranking. Then, we construct a stabilized relaxed e-value for each feature and apply the e-BH procedure to these stabilized e-values to obtain the final selection set. We theoretically derive the finite-sample bounds for the FDR and the power of our method, and show that our method asymptotically controls the FDR without power loss. Moreover, we establish the stability of the proposed method, showing that the stabilized selection set converges to a deterministic limit as the number of repetitions increases. Extensive numerical experiments and applications to real datasets demonstrate that the proposed method generally outperforms existing alternatives.

stat.ME↗

arXiv2025

Entropic Uncertainty Relations with Quantum Memory in Accelerated Frames via Unruh-DeWitt Detectors

Quantum uncertainty is deeply linked to quantum correlations and relativistic motion. The entropic uncertainty relation with quantum memory offers a powerful way to study how shared entanglement affects measurement precision. However, under acceleration, the Unruh effect can degrade quantum correlations, raising questions about the reliability of QMA-EUR in such settings. Here, we investigate the QMA-EUR for two uniformly accelerating Unruh-DeWitt detectors coupled to a massless scalar field. Using the Kossakowski-Lindblad master equation, we calculate the entropic uncertainty, its lower bound, and the tightness of the relation under different Unruh temperatures. We find that acceleration does not always increase the lower bound on the uncertainty relation. Depending on the initial correlations between the detectors, it may either increase or decrease. This behavior results from the interplay between quantum discord and minimal missing information. Interestingly, a higher quantum discord does not necessarily lead to lower uncertainty.

quant-ph↗

arXiv2025

Robust hyperentanglement self testing

Hyperentanglement, which refers to entanglement encoded in two or more independent degrees of freedom (DOFs), is a valuable resource for the future high-capacity quantum network. Certifying hyperentanglement sources work as intended is critical for the hyperentanglement-based quantum information tasks. Self testing is the strongest certification method for quantum state and measurement under minimal assumptions, even without any knowledge of the devices' inner workings. However, the existing self testing protocols all focus on one-DOF entanglement, which cannot self test the multi-DOF entanglement. In the paper, we propose a hyperentanglement self testing framework. We take the self testing for the polarization-spatial-mode hyperentangled Bell states as an example. The self testing is based on the violation of two-dimension CHSH test in each DOF independently. The two-step swap isometry circuits are proposed for self testing the entanglement in spatial-mode and polarization DOFs, respectively. All the sixteen polarization-spatial-mode hyperentangled Bell states can be self tested. Our hyperentanglement self testing framework has three advantages. First, it is a general hyperentanglement self testing framework, and can be extended to self test multi-DOF hyperentanglement and multipartite hyperentanglement. Second, it can provide the robust hyperentanglement self testing and establish the relation between the lower bound of fidelity and the imperfect violation of Bell-like inequality in each DOF. Third, it is feasible with current experimental technology. Our hyperentanglement self testing framework provides a promising way to certify complex hyperentanglement sources, and has potential application in future high-capacity quantum network.

quant-ph↗

arXiv2025

Irradiated Atmospheres IV: Effect of Mixing Heat Flux on Chemistry

Vertical mixing disrupts the thermochemical equilibrium and introduces additional heat flux that alters exoplanetary atmospheric temperatures. We investigate how this mixing-induced heat flux affects atmospheric chemistry. Temperature increase in the lower atmosphere by the mixing-induced heat flux alters species abundances there and modifies those in the upper atmosphere through vertical transport. In the lower atmosphere, most species follow thermodynamic equilibrium with temperature changes. In the upper layers, species mixing ratios depend on the positions of quenching levels relative to the regions exhibiting significant mixing-induced temperature variations. When the quenching level resides within such region (e.g. CO, $\rm CH_4$, and $\rm H_2O$ with strong mixing), the mixing ratios in the upper atmosphere are modified due to changes in the quenched ratios affected by the temperature variation in the lower atmosphere. This alters the mixing ratio of other species (e.g. NO and $\rm CO_2$) through the chemical reaction network, whose quenching occurs in the region without much temperature change. The mixing ratios of $\rm CH_4$, $\rm H_2O$, and $\rm NH_3$ decrease in the lower atmosphere with increasing mixing heat flux, similarly reducing these ratios in the upper atmosphere. Conversely, the mixing ratios of CO, $\rm CO_2$, and NO rise in the lower atmosphere, with CO and $\rm CO_2$ also increasing in the upper levels, although NO decreases. Weaker host star irradiation lowers the overall temperature of the planet, allowing a smaller mixing to have a similar effect. We conclude that understanding the vertical mixing heat flux is essential for accurate atmospheric chemistry modeling and retrieval.

astro-ph.EP↗

arXiv2025

Global-focal Adaptation with Information Separation for Noise-robust Transfer Fault Diagnosis

Existing transfer fault diagnosis methods typically assume either clean data or sufficient domain similarity, which limits their effectiveness in industrial environments where severe noise interference and domain shifts coexist. To address this challenge, we propose an information separation global-focal adversarial network (ISGFAN), a robust framework for cross-domain fault diagnosis under noise conditions. ISGFAN is built on an information separation architecture that integrates adversarial learning with an improved orthogonal loss to decouple domain-invariant fault representation, thereby isolating noise interference and domain-specific characteristics. To further strengthen transfer robustness, ISGFAN employs a global-focal domain-adversarial scheme that constrains both the conditional and marginal distributions of the model. Specifically, the focal domain-adversarial component mitigates category-specific transfer obstacles caused by noise in unsupervised scenarios, while the global domain classifier ensures alignment of the overall distribution. Experiments conducted on three public benchmark datasets demonstrate that the proposed method outperforms other prominent existing approaches, confirming the superiority of the ISGFAN framework. Data and code are available at https://github.com/JYREN-Source/ISGFAN

cs.AI↗

arXiv2025

High-efficiency and long-distance quantum memory-assisted device-independent quantum secret sharing with single photon sources

Quantum secret sharing (QSS) plays a critical role in building the distributed quantum networks. Device-independent (DI) QSS provides the highest security level for QSS. However, the photon transmission loss and extremely low multipartite entanglement generation rate largely limit DI QSS's secure photon transmission distance (less than 1 km) and practical key generation efficiency. To address the above drawbacks, we propose the quantum memory-assisted (QMA) DI QSS protocol based on single photon sources (SPSs). The single photons from the SPSs are used to construct long-distance multipartite entanglement channels with the help of the heralded architecture. The heralded architecture enables our protocol to have an infinite secure photon transmission distance in theory. The QMA technology can not only increase the multi-photon synchronization efficiency, but also optimize the photon transmittance to maximize the construction efficiency of the multipartite entanglement channels. Our protocol achieves the practical key generation efficiency seven orders of magnitude higher than that of the existing DI QSS protocols based on cascaded spontaneous parametric down-conversion sources and six orders of magnitude higher than that of the DI QSS based on SPSs without QMA. Our protocol has modular characteristics and is feasible under the current experimental technical conditions. Combining with the advanced random key generation basis strategy, the requirement on experimental devices can be effectively reduced. Our protocol is expected to promote the development of long-distance and high-efficiency DI quantum network in the future.

quant-ph↗