arXiv subjects

Zheng Cui

Publications and source records attributed to Zheng Cui.

17 recordsLinked to original sources

arXiv2026

Quantum Imaging via Kurtosis-Difference Weighted Covariance on 2D Camera

Camera-based quantum imaging detects spatially correlated photon pairs from spontaneous parametric down-conversion (SPDC). Conventional covariance methods typically require tens of thousands of frames to extract weak correlations from noise. While thick crystals can increase photon flux, they generate photon pairs from multiple emission positions within the crystal, producing multiple correlation centers with complex pairing geometries. In addition, conventional covariance methods assume a single pre-selected correlation center and cannot fully exploit these distributed correlations. We demonstrate that kurtosis difference, a fourth-order statistic measuring tail similarity, effectively discriminates correlated pixel pairs even when correlation coefficients remain low. Weighting covariance by an exponential function of absolute kurtosis difference can select symmetric pixels while preserving true coincidences. This kurtosis weighting automatically identifies correlated pairs within a broad search region and accommodates multiple pairing geometries without requiring precise correlation center calibration. At 5000 frames, our method yields a contrast-to-noise ratio (CNR) exceeding 7, whereas standard covariance remains below 2. Compared with standard covariance, the method reduces the acquisition time by 40-fold and could enable practical quantum imaging in sparse correlated-photon regimes.

quant-ph↗

arXiv2026

YCDa: YCbCr Decoupled Attention for Real-time Realistic Camouflaged Object Detection

Human vision exhibits remarkable adaptability in perceiving objects under camouflage. When color cues become unreliable, the visual system instinctively shifts its reliance from chrominance (color) to luminance (brightness and texture), enabling more robust perception in visually confusing environments. Drawing inspiration from this biological mechanism, we propose YCDa, an efficient early-stage feature processing strategy that embeds this "chrominance-luminance decoupling and dynamic attention" principle into modern real-time detectors. Specifically, YCDa separates color and luminance information in the input stage and dynamically allocates attention across channels to amplify discriminative cues while suppressing misleading color noise. The strategy is plug-and-play and can be integrated into existing detectors by simply replacing the first downsampling layer. Extensive experiments on multiple baselines demonstrate that YCDa consistently improves performance with negligible overhead as shown in Fig. Notably, YCDa-YOLO12s achieves a 112% improvement in mAP over the baseline on COD10K-D and sets new state-of-the-art results for real-time camouflaged object detection across COD-D datasets.

cs.CV↗

arXiv2025

PRNet: Original Information Is All You Have

Small object detection in aerial images suffers from severe information degradation during feature extraction due to limited pixel representations, where shallow spatial details fail to align effectively with semantic information, leading to frequent misses and false positives. Existing FPN-based methods attempt to mitigate these losses through post-processing enhancements, but the reconstructed details often deviate from the original image information, impeding their fusion with semantic content. To address this limitation, we propose PRNet, a real-time detection framework that prioritizes the preservation and efficient utilization of primitive shallow spatial features to enhance small object representations. PRNet achieves this via two modules:the Progressive Refinement Neck (PRN) for spatial-semantic alignment through backbone reuse and iterative refinement, and the Enhanced SliceSamp (ESSamp) for preserving shallow information during downsampling via optimized rearrangement and convolution. Extensive experiments on the VisDrone, AI-TOD, and UAVDT datasets demonstrate that PRNet outperforms state-of-the-art methods under comparable computational constraints, achieving superior accuracy-efficiency trade-offs.

cs.CV↗

arXiv2025

DAOpt: Modeling and Evaluation of Data-Driven Optimization under Uncertainty with LLMs

Recent advances in large language models (LLMs) have accelerated research on automated optimization modeling. While real-world decision-making is inherently uncertain, most existing work has focused on deterministic optimization with known parameters, leaving the application of LLMs in uncertain settings largely unexplored. To that end, we propose the DAOpt framework including a new dataset OptU, a multi-agent decision-making module, and a simulation environment for evaluating LLMs with a focus on out-of-sample feasibility and robustness. Additionally, we enhance LLMs' modeling capabilities by incorporating few-shot learning with domain knowledge from stochastic and robust optimization.

cs.LG↗

arXiv2025

Fragility-aware Classification for Understanding Risk and Improving Generalization

Classification models play a central role in data-driven decision-making applications such as medical diagnosis, recommendation systems, and risk assessment. Traditional performance metrics, such as accuracy and AUC, focus on overall error rates but fail to account for the confidence of incorrect predictions, i.e., the risk of confident misjudgments. This limitation is particularly consequential in safety-critical and cost-sensitive settings, where overconfident errors can lead to severe outcomes. To address this issue, we propose the Fragility Index (FI), a novel performance metric that evaluates classifiers from a risk-averse perspective by capturing the tail risk of confident misjudgments. We formulate FI within a robust satisficing (RS) framework to ensure robustness under distributional uncertainty. Building on this, we develop a tractable training framework that directly targets FI via a surrogate loss, and show that models trained under this framework admit provable bounds on FI. We further derive exact reformulations for a broad class of loss functions, including cross-entropy, hinge-type, and Lipschitz losses, and extend the approach to deep neural networks. Empirical results on real-world medical diagnosis tasks demonstrate that FI complements existing metrics by revealing error tail risk and improving decision quality. FI-based models achieve competitive accuracy and AUC while consistently reducing confident misjudgments and associated operational costs, offering a practical tool for improving robustness and reliability in risk-critical applications.

cs.LG↗

arXiv2024

Capturing dynamics and thermodynamics of a three-level quantum heat engine via programmable quantum circuits

This research employs the Kraus representation and Sz.-Nagy dilation theorem to model a three-level quantum heat on quantum circuits, investigating its dynamic evolution and thermodynamic performance. The feasibility of the dynamic model is validated by tracking the changes of population. On the basis of reinforcement learning algorithm, the optimal cycle of the quantum heat engine for maximal average power is proposed and verified by the thermodynamic model. The stability of quantum circuit simulations is scrutinized through a comparative analysis of theoretical and simulated results, predicated on an orthogonal test. These results affirm the practicality of simulating quantum heat engines on quantum circuits, offering potential for substantially curtailing the experimental expenses associated with the construction of such engines.

quant-ph↗

arXiv2024

IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion

Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge and reflecting the dynamic nature of the real world. Typically, TKGs contain complex geometric structures, with various geometric structures interwoven. However, existing Temporal Knowledge Graph Completion (TKGC) methods either model TKGs in a single space or neglect the heterogeneity of different curvature spaces, thus constraining their capacity to capture these intricate geometric structures. In this paper, we propose a novel Integrating Multi-curvature shared and specific Embedding (IME) model for TKGC tasks. Concretely, IME models TKGs into multi-curvature spaces, including hyperspherical, hyperbolic, and Euclidean spaces. Subsequently, IME incorporates two key properties, namely space-shared property and space-specific property. The space-shared property facilitates the learning of commonalities across different curvature spaces and alleviates the spatial gap caused by the heterogeneous nature of multi-curvature spaces, while the space-specific property captures characteristic features. Meanwhile, IME proposes an Adjustable Multi-curvature Pooling (AMP) approach to effectively retain important information. Furthermore, IME innovatively designs similarity, difference, and structure loss functions to attain the stated objective. Experimental results clearly demonstrate the superior performance of IME over existing state-of-the-art TKGC models.

cs.AI↗

arXiv2024

Experimental demonstration of steady-state dynamics of three-level quantum heat engine using superconducting quantum circuits

The three-level system represents the smallest quantum system capable of autonomous cycling in quantum heat engines. This study proposes a method to simulate the steady-state dynamics of a three-level quantum heat engine by designing and implementing superconducting quantum circuits. Following error mitigation, the outcomes from the quantum circuit model designed in this study, when executed on a real quantum device, closely align with theoretical predictions, thereby validating the effectiveness of the circuit model. This study offers a novel approach for investigating three-level quantum heat engines, enabling the verification of theoretical research findings while also reducing the complexity and cost of experimental procedures.

quant-ph↗

arXiv2023

Exploring the Optimal Cycle for Quantum Heat Engine using Reinforcement Learning

Quantum thermodynamic relationships in emerging nanodevices are significant but often complex to deal with. The application of machine learning in quantum thermodynamics has provided a new perspective. This study employs reinforcement learning to output the optimal cycle of quantum heat engine. Specifically, the soft actor-critic algorithm is adopted to optimize the cycle of three-level coherent quantum heat engine with the aim of maximal average power. The results show that the optimal average output power of the coherent three-level heat engine is 1.28 times greater than the original cycle (steady limit). Meanwhile, the efficiency of the optimal cycle is greater than the Curzon-Ahlborn efficiency as well as reporting by other researchers. Notably, this optimal cycle can be fitted as an Otto-like cycle by applying the Boltzmann function during the compression and expansion processes, which illustrates the effectiveness of the method.

quant-ph↗

arXiv2023

Continuous Three-level Quantum Heat Engine with High Performance Under Medium Temperature Difference

The possibility of utilizing quantum effects to enhance the performance of quantum heat engines has been an active topic of research, but how to enhance the performance by optimizing the engine parameters needs to be further studied. In this study, the temperature difference and dissipation modes affecting the performance of a three-level quantum heat engine were analyzed using an orthogonal test. The results indicated that the dissipation mode dominated the performance of the quantum heat engine. The quantum heat engine performs best when there is only resonance and no detuning; however, when detuning exists, a lower resonance can improve the efficiency by reducing energy losses. Regarding the temperature difference, the best performance was achieved at medium temperature difference owing to the decreasing heat leakage. Finally, the quantum friction caused by the detuning could make the maximal efficiency lower than the Carnot efficiency.

quant-ph↗

arXiv2022

LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation

Multimodal Machine Translation (MMT) focuses on enhancing text-only translation with visual features, which has attracted considerable attention from both natural language processing and computer vision communities. Recent advances still struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world. In other words, the multilingual multimodal machine translation (Multilingual MMT) task has not been investigated, which aims to handle the aforementioned issues by providing a shared semantic space for multiple languages. Besides, the image modality has no language boundaries, which is superior to bridging the semantic gap between languages. To this end, we first propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages. Then, an effective baseline LVP-M3 using visual prompts is proposed to support translations between different languages, which includes three stages (token encoding, language-aware visual prompt generation, and language translation). Extensive experimental results on our constructed benchmark datasets demonstrate the effectiveness of LVP-M3 method for Multilingual MMT.

cs.CL↗

arXiv2019

Semiconductor - Ferromagnetic Insulator - Superconductor Nanowires: Stray Field and Exchange Field

Nanowires can serve as flexible substrates for hybrid epitaxial growth on selected facets, allowing for design of heterostructures with complex material combinations and geometries. In this work we report on hybrid epitaxy of semiconductor - ferromagnetic insulator - superconductor (InAs/EuS/Al) nanowire heterostructures. We study the crystal growth and complex epitaxial matching of wurtzite InAs / rock-salt EuS interfaces as well as rock-salt EuS / face-centered cubic Al interfaces. Because of the magnetic anisotropy originating from the nanowire shape, the magnetic structure of the EuS phase are easily tuned into single magnetic domains. This effect efficiently ejects the stray field lines along the nanowires. With tunnel spectroscopy measurements of the density of states, we show the material has a hard induced superconducting gap, and magnetic hysteretic evolution which indicates that the magnetic exchange fields are not negligible. These hybrid nanowires fulfil key material requirements for serving as a platform for spin-based quantum applications, such as scalable topological quantum computing.

quant-ph↗

arXiv2019

Imaging anisotropic vortex dynamics in FeSe

Strong vortex pinning in FeSe could be useful for technological applications and could provide clues about the coexistence of superconductivity and nematicity. To characterize the pinning of individual, isolated vortices, we simultaneously apply a local magnetic field and image the vortex motion with scanning SQUID susceptibility. We find that the pinning is highly anisotropic: the vortices move easily along directions that are parallel to the orientations of twin domain walls and pin strongly in a perpendicular direction. These results are consistent with a scenario in which the anisotropy arises from vortex pinning on domain walls and quantify the dynamics of individual vortex pinning in FeSe.

cond-mat.supr-con↗

arXiv2019

Cryogen-free variable temperature scanning SQUID microscope

Scanning Superconducting QUantum Interference Device (SQUID) microscopy is a powerful tool for imaging local magnetic properties of materials and devices, but it requires a low-vibration cryogenic environment, traditionally achieved by thermal contact with a bath of liquid helium or the mixing chamber of a "wet" dilution refrigerator. We mount a SQUID microscope on the 3 K plate of a Bluefors cryocooler and characterize its vibration spectrum by measuring SQUID noise in a region of sharp flux gradient. By implementing passive vibration isolation, we reduce relative sensor-sample vibrations to 20 nm in-plane and 15 nm out-of-plane. A variable-temperature sample stage that is thermally isolated from the SQUID sensor enables measurement at sample temperatures from 2.8 K to 110 K. We demonstrate these advances by imaging inhomogeneous diamagnetic susceptibility and vortex pinning in optimally-doped YBCO above 90 K.

cond-mat.supr-con↗

arXiv2019

Current-phase relations of InAs nanowire Josephson junctions: from interacting to multi-mode regimes

Gate-tunable semiconductor-superconductor nanowires with superconducting leads form exotic Josephson junctions that are a highly desirable platform for two types of qubits: those with topological superconductivity (Majorana qubits) and those based on tunable anharmonicity (gatemon qubits). Controlling their behavior, however, requires understanding their electrostatic environment and electronic structure. Here we study gated InAs nanowires with epitaxial aluminum shells. By measuring current-phase relations (CPR) and comparing them with analytical and numerical calculations, we show that we can tune the number of modes, determine the transparency of each mode, and tune into regimes in which electron-electron interactions are apparent, indicating the presence of a quantum dot. To take into account electrostatic and geometrical effects, we perform microscopic self-consistent Schrodinger-Poisson numerical simulations, revealing the energy spectrum of Andreev states in the junction as well as their spatial distribution. Our work systematically demonstrates the effect of device geometry, gate voltage and phase bias on mode behavior, providing new insights into ongoing experimental efforts and predictive device design.

cond-mat.mes-hall↗

arXiv2015

Suspended graphene devices with local gate control on an insulating substrate

We present a fabrication process for graphene-based devices where a graphene monolayer is suspended above a local metallic gate placed in a trench. As an example we detail the fabrication steps of a graphene field-effect transistor. The devices are built on a bare high-resistivity silicon substrate. At temperatures of 77~K and below, we observe the field-effect modulation of the graphene resistivity by a voltage applied to the gate. This fabrication approach enables new experiments involving graphene-based superconducting qubits and nano-electromechanical resonators. The method is applicable to other two-dimensional materials.

cond-mat.mes-hall↗

arXiv2006

Magnetoresistance in Thin Permalloy Film (10nm-thick and 30-200nm-wide) Nanocontacts Fabricated by e-Beam Lithography

In this paper we show spin dependent transport experiments in nanoconstrictions ranging from 30 to 200nm. These nanoconstrictions were fabricated combining electron beam lithography and thin film deposition techniques. Two types of geometries have been fabricated and investigated. We compare the experimental results with the theoretical estimation of the electrical resistance. Finally we show that the magnetoresistance for the different geometries does not scale with the resistance of the structure and obtain drops in voltage of 20mV at 20Oe.

cond-mat.mtrl-sci↗