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Wei Zhong

Publications and source records attributed to Wei Zhong.

At least 37 records · Page 2Linked to original sources
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↗
arXiv2025

Basis-independent Coherence in Noninertial Frames

We investigate the behavior of basis-independent quantum coherence between two modes of a free Dirac field as observed by relatively accelerated observers. Our findings reveal three key results: (i) the basis-independent coherence between modes A and BI decreases with increasing acceleration but remains finite even in the limit of infinite acceleration; (ii) at zero acceleration, the coherence between modes $A$ and $B_II$ is nonzero contrasting with the behavior of basis-dependent coherence, which typically vanishes in this case; and (iii) the basis-independent coherence between modes BI and BII remains constant regardless of acceleration, exhibiting a freezing phenomenon. These results demonstrate the intrinsic robustness of basis-independent coherence under Unruh effects.

quant-ph↗
arXiv2025

DPSformer: A long-tail-aware model for improving heavy rainfall prediction

Accurate and timely forecasting of heavy rainfall remains a critical challenge for modern society. Precipitation exhibits a highly imbalanced distribution: most observations record no or light rain, while heavy rainfall events are rare. Such an imbalanced distribution obstructs deep learning models from effectively predicting heavy rainfall events. To address this challenge, we treat rainfall forecasting explicitly as a long-tailed learning problem, identifying the insufficient representation of heavy rainfall events as the primary barrier to forecasting accuracy. Therefore, we introduce DPSformer, a long-tail-aware model that enriches representation of heavy rainfall events through a high-resolution branch. For heavy rainfall events $ \geq $ 50 mm/6 h, DPSformer lifts the Critical Success Index (CSI) of a baseline Numerical Weather Prediction (NWP) model from 0.012 to 0.067. For the top 1% coverage of heavy rainfall events, its Fraction Skill Score (FSS) exceeds 0.45, surpassing existing methods. Our work establishes an effective long-tailed paradigm for heavy rainfall prediction, offering a practical tool to enhance early warning systems and mitigate the societal impacts of extreme weather events.

cs.LG↗
arXiv2025

FLAT: Fused Lasso Regression with Adaptive Minimum Spanning Tree with Applications on Thermohaline Circulation

This article introduces a new methodology model both discrete and continuous spatial heterogeneity simultaneously with an application in detection of hyper-plain in thermohaline circulation. To enable the data-driven detection of spatial boundaries with heterogeneity, we constructs an adaptive minimum spanning tree guided by both spatial proximity and coefficient dissimilarity, and combines both a spatial fused regularization and LASSO-type regularization to estimate the spatial coefficients under the framework of spatial regression. Numerical simulations demonstrate the effectiveness of proposed method in both estimation and heterogeneity detection. The usefulness of the approach is further illustrated via an analysis of oceanic data that provides new empirical finds about Atlantic with detected surfaces in temperature-salinity relationship.

stat.ME↗
arXiv2025

Signal Lasso with Non-Convex Penalties for Efficient Network Reconstruction and Topology Inference

Inferring network structures remains an interesting question for its importance on the understanding and controlling collective dynamics of complex systems. The existing shrinking methods such as Lasso-type estimation can not suitably reveal such property. A new method recently suggested, called by {\it signal lasso} (or its updating version: adaptive signal lasso) was proposed to solve the network reconstruction problem, where the signal parameter can be shrunk to either 0 or 1 in two different directions. The signal lasso or adaptive signal lasso employed the additive penalty of signal and non-signal terms which is a convex function and easily to complementation in computation. However their methods need tuning the one or two parameters to find an optimal solution, which is time cost for large size network. In this paper we propose new signal lasso method based on two penalty functions to estimate the signal parameter and uncovering network topology in complex network with a small amount of observations. The penalty functions we introduced are non-convex function, thus coordinate descent algorithms are suggested. We find in this method the tuning parameter can be set to a large enough values such that the signal parameter can be completely shrunk either 0 or 1. The extensive simulations are conducted in linear regression models with different assumptions, the evolutionary-game-based dynamic model and Kuramoto model of synchronization problem. The advantage and disadvantage of each method are fully discussed in various conditions. Finally a real example comes from behavioral experiment is used for illustration. Our results show that signal lasso with non-convex penalties is effective and fast in estimating signal parameters in linear regression model.

math.ST↗
arXiv2025

Parallel Network Reconstruction with Multi-directional Regularization

Reconstructing large-scale latent networks from observed dynamics is crucial for understanding complex systems. However, the existing methods based on compressive sensing are often rendered infeasible in practice by prohibitive computational and memory costs. To address this challenge, we introduce a new distributed computing framework for efficient large-scale network reconstruction with parallel computing, namely PALMS (Parallel Adaptive Lasso with Multi-directional Signals). The core idea of PALMS is to decompose the complex global problem by partitioning network nodes, enabling the parallel estimation of sub-networks across multiple computing units. This strategy substantially reduces the computational complexity and storage requirements of classic methods. By using the adaptive multi-directional regularization on each computing unit, we also establish the consistency of PALMS estimator theoretically. Extensive simulation studies and empirical analyses on several large-scale real-world networks validate the computational efficiency and robust reconstruction accuracy of our approach.

math.ST↗
arXiv2025

GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering

It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be directly applied. To learn the pseudo labels and select the discriminative features simultaneously, we propose a new unsupervised feature selection method, named GlObal and Local information combined Feature Selection (GOLFS), for high dimensional clustering problems. The GOLFS algorithm combines both local geometric structure via manifold learning and global correlation structure of samples via regularized self-representation to select the discriminative features. The combination improves the accuracy of both feature selection and clustering by exploiting more comprehensive information. In addition, an iterative algorithm is proposed to solve the optimization problem and the convergency is proved. Simulations and two real data applications demonstrate the excellent finite-sample performance of GOLFS on both feature selection and clustering.

stat.ML↗
arXiv2025

Relation between quantum illumination and quantum parameter estimation

Quantum illumination leverages entangled lights to detect the presence of low-reflectivity objects within a thermal environment. In a related vein, quantum parameter estimation utilizes nonclassical probes to precisely determine unknown system parameters. Although both fields have been studied extensively, their performances have traditionally been assessed using different figures of merit: signal-to-noise ratio for QI and quantum Fisher information for parameter estimation. In this paper, we reveal the intrinsic connection between these two measures in the context of target detection, thereby providing explicit operational criteria for identifying optimal measurements. We further apply this relationship to various target detection protocols that employ exotic non-Gaussian states derived from coherent states and two-mode squeezed vacuum states.

quant-ph↗
arXiv2025

Phase estimation in lossy optical interferometry without a reference beam

We investigate phase estimation in a lossy interferometer using entangled coherent states, with particular focus on a scenario where no reference beam is employed. By calculating the quantum Fisher information, we reveal two key results: (1) the metrological equivalence between scenarios with and without a reference beam, established under ideal lossless conditions for the two-phase-shifting configuration,breaks down in the presence of photon loss, and (2) the pronounced inferior performance of entangled coherent states relative to NOON states, observed in the presence of a reference beam, disappears in its absence.

quant-ph↗
arXiv2025

High-efficient long-distance device-independent quantum secret sharing based on single-photon sources

Device-independent quantum secret sharing (DI QSS) relaxes security assumptions of experimental devices to provide the highest security level for QSS. Previous DI QSS protocols require to generate multi-partite entangled states and then implement the long-distance entanglement distribution. The extremely low generation probability of current multi-partite entanglement source and photon transmission loss largely limit DI QSS's practical key generation rate and secure photon transmission distance. In the paper, we propose a high-efficient long-distance DI QSS protocol based on practical nearly on-demand single-photon sources (SPSs), which adopts the single photons and heralded architecture to construct long-distance multi-partite entanglement channels. Our SPS DI QSS protocol can automatically eliminate the negative influence from photon transmission loss and largely increase the secure photon transmission distance. Moreover, we adopt active improvement strategies such as random key generation basis, postselection, and advanced postselection to further increase the practical key generation rate and lower the experimental requirement. Comparing with previous DI QSS protocols based on entanglement sources, our SPS DI QSS protocol can increase the practical communication efficiency by six orders of magnitude, and increase the secure photon transmission distance by at least 151 times. This SPS DI QSS protocol is feasible with the current experimental technologies. Our protocol makes it possible to realize high-efficient long-distance DI network in the near future.

quant-ph↗
arXiv2025

Percolation in the two-dimensional Ising model

The study of the Ising model from a percolation perspective has played a significant role in the modern theory of critical phenomena. We consider the celebrated square-lattice Ising model and construct percolation clusters by placing bonds, with probability $p$, between any pair of parallel spins within an extended range beyond nearest neighbors. At the Ising criticality, we observe two percolation transitions as $p$ increases: starting from a disordered phase with only small clusters, the percolation system enters into a stable critical phase that persists over a wide range $p_{c_1} < p < p_{c_2}$, and then develops a long-ranged percolation order with giant clusters for both up and down spins. At $p_{c1}$ and for the stable critical phase, the critical behaviors agree well with those for the Fortuin-Kasteleyn random clusters and the spin domains of the Ising model, respectively. At $p_{c2}$, the fractal dimension of clusters and the scaling exponent along $p$ direction are estimated as $y_{h2} = 1.958\,0(6)$ and $y_{p2} = 0.552(9)$, of which the exact values remain unknown. These findings reveal interesting geometric properties of the two-dimensional Ising model that has been studied for more than 100 years.

cond-mat.stat-mech↗
arXiv2025

Irradiated Atmospheres III : Radiative-Convective-Mixing Equilibrium for Non-Grey Picket-Fence Model

The non-grey picket-fence model predicts more accurately the temperatures in low-density regions compared to semi-grey models. This study investigates how the vertical mixing and convection fluxes modify the picket-fence model. The usual radiative-convective-equilibrium (RCE) is now extended to radiative-convective-mixing-equilibrium (RCME). The temperature profile, characterized by an increase with pressure in the upper region and an inversion in the lower, is influenced by Rosseland opacity, spectral bands, and chemical composition. The atmosphere consists of five distinct layers: a pseudo-adiabatic zone shaped by mixing flux, two convective layers driven by convective flux with a smaller adiabatic gradient, and two radiative layers. In scenarios with lower Rosseland opacity, vertical mixing significantly reduces the width of temperature inversion, counteracting the cooling effect of the convective layers and driving the deep convective layer inward. The convective flux lowers the upper temperature and expands the upper convective layer. In the low-Rosseland-opacity five-band model, these fluxes significantly cool the mid-atmosphere when temperature increases with pressure, enlarging the pseudo-adiabatic region. Without TiO/VO, the pseudo-adiabatic region shrinks, indicating that TiO/VO enhances the mixing effect. Moreover, less mixing intensity is essential to maintain a stable five-layer structure. Therefore, future studies of chemical equilibrium with multi-frequency atmospheric opacity should clearly define the constraints on vertical mixing.

astro-ph.EP↗
arXiv2025

Irradiated Atmospheres II: Interplay Between Scattering and Vertical-Mixing induced Energy Transport

The scattering is crucial for the atmospheric thermal profiles. The energy transport by the vertical mixing plays an essential role for the greenhouse or anti-greenhouse effect. This work explores the interaction between scattering and vertical mixing, specifically whether these processes enhance or mitigate each other's effects on atmospheric temperature. The interaction between mixing flux and scattering is nonlinear. Our calculations indicate that thermal scattering intensifies the greenhouse effects caused by vertical mixing in the middle atmosphere but reduces it in the lower layers. In the middle atmosphere, increased vertical mixing enhances the warming effect of the thermal scattering while diminishing the cooling effect of visible scattering. In the lower atmosphere, it enhances the anti-greenhouse effect linked to visible scattering and diminishes the greenhouse effect produced by thermal scattering. The combined influence of thermal scattering and vertical mixing on the lower atmosphere's greenhouse effect is weaker than their separate impacts, akin to $1+1<2$. It is also interesting to note that the joint effect may also influence chemistry and cloud formation, altering the thermal structure.

astro-ph.EP↗
arXiv2025

Towards Long-Range ENSO Prediction with an Explainable Deep Learning Model

El Niño-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet's superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability.

physics.geo-ph↗
arXiv2025

Crime Forecasting: A Spatio-temporal Analysis with Deep Learning Models

This study uses deep-learning models to predict city partition crime counts on specific days. It helps police enhance surveillance, gather intelligence, and proactively prevent crimes. We formulate crime count prediction as a spatiotemporal sequence challenge, where both input data and prediction targets are spatiotemporal sequences. In order to improve the accuracy of crime forecasting, we introduce a new model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. We conducted a comparative analysis to access the effects of various data sequences, including raw and binned data, on the prediction errors of four deep learning forecasting models. Directly inputting raw crime data into the forecasting model causes high prediction errors, making the model unsuitable for real - world use. The findings indicate that the proposed CNN-LSTM model achieves optimal performance when crime data is categorized into 10 or 5 groups. Data binning can enhance forecasting model performance, but poorly defined intervals may reduce map granularity. Compared to dividing into 5 bins, binning into 10 intervals strikes an optimal balance, preserving data characteristics and surpassing raw data in predictive modelling efficacy.

cs.LG↗
arXiv2025

Observation of a $Pbca$ phase and robust metallicity in $\rm{RuO_2}$ under pressure

$\rm{RuO_2}$ stands as a quintessential rutile-type compound under ambient conditions, with its structural exploration under pressure bearing significant implications for both phase transition investigations and Earth science. Nonetheless, the precise phase transition sequence remains a debate. In this study, we disclose the emergence of the $Pbca$ phase alongside the enduring metallic character of $\rm{RuO_2}$ under megabar pressure. Employing state-of-the-art synchrotron X-ray diffraction, our observations delineate a phase transition trajectory progressing through rutile, $\rm{CaCl_2}$, and ultimately $Pbca$ phases. Notably, the $Pbca$ phase manifests immediately just after the rutile-$\rm{CaCl_2}$ transition, confining a narrow pressure regime for the pure $\rm{CaCl_2}$-type phase. Within the pressure range of 15.5 to 35.0 GPa, a coexistence of the $\rm{CaCl_2}$-type and $Pbca$ phases is observed, transforming to a sole presence of the $Pbca$ phase beyond 35.0 GPa. Electrical transport measurements conducted on both single crystal and powder samples confirm the enduring metallic conductivity of $\rm{RuO_2}$, persisting up to at least $\sim$120 GPa, albeit exhibiting a diminished conductivity at ultrahigh pressures due to a reduction in electronic density of states at the Fermi level. This study furnishes compelling evidence for the presence of the $Pbca$ phase across a broad pressure range, diverging from the previously widely acknowledged $Pa\bar{3}$ phase, thereby offering crucial insights into phase transition phenomena in other metal dioxides and advancing our comprehension of electronic behaviors within 4d and 5d electron systems.

cond-mat.mtrl-sci↗
arXiv2025

Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption

Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representation. Since 2018, the field has entered the deep learning era, with an increasing variety of approaches introducing a range of networks and loss functions to enhance visual performance. However, challenges such as data compatibility, perception accuracy, and efficiency remain. Unfortunately, there is a lack of recent comprehensive surveys that address this rapidly expanding domain. This paper fills that gap by providing a thorough survey covering a broad range of topics. We introduce a multi-dimensional framework to elucidate common learning-based IVIF methods, from visual enhancement strategies to data compatibility and task adaptability. We also present a detailed analysis of these approaches, accompanied by a lookup table clarifying their core ideas. Furthermore, we summarize performance comparisons, both quantitatively and qualitatively, focusing on registration, fusion, and subsequent high-level tasks. Beyond technical analysis, we discuss potential future directions and open issues in this area. For further details, visit our GitHub repository: https://github.com/RollingPlain/IVIF_ZOO.

cs.CV↗