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Yang Zou

Yang Zou appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
quant-phComputer VisionMachine LearningRoboticscond-mat.mes-hallcond-mat.mtrl-scicond-mat.str-elcond-mat.supr-conCryptography and Securityeess.SYGraphicsMultimedianucl-thphysics.app-phphysics.opticsSystems and Control

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Published work

19 published item(s)

preprint2026arXiv

CM-EVS: Sparse Panoramic RGB-D-Pose Data for Complete Scene Coverage

Modern 3D visual learning relies on observations sampled from metric 3D assets, yet existing scans, meshes, point clouds, simulations, and reconstructions do not directly provide a sparse, comparable, and geometry-consistent panoramic training interface. Dense trajectories duplicate nearby views, source-specific rendering policies yield heterogeneous annotations, and sparse heuristics may miss important regions or introduce depth-inconsistent observations. We study how to convert 3D assets into sparse panoramic RGB-D-pose data that preserves complete scene coverage with low redundancy and auditable provenance. We propose COVER (Coverage-Oriented Viewpoint curation with ERP Range-depth warping), a training-free ERP viewpoint curator that projects geometry observed from selected views into candidate ERP probes, scores incremental coverage, and penalizes depth conflicts. Under bounded proxy error, its greedy coverage proxy preserves the standard coverage-style approximation behavior up to an additive error term. Using COVER, we build CM-EVS (Coverage-curated Metric ERP View Set), a panoramic RGB-D-pose dataset with 36,373 curated ERP frames from 1,275 indoor scenes across Blender indoor, HM3D, and ScanNet++, complemented by outdoor panoramas from TartanGround and OB3D re-encoded into the same schema. Each frame provides full-sphere RGB, metric range depth, calibrated pose; COVER-produced indoor frames include per-step provenance logs. With a median of only 25 frames per indoor scene, CM-EVS covers all 13 unified room types while maintaining compact scene-level coverage. Experiments show that COVER improves the coverage-conflict trade-off, making CM-EVS a sparse, compact, and auditable RGB-D-pose resource for geometry-consistent panoramic 3D learning.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion

With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks. Despite its popularity, the inherent disparities in how different sources depict scene content make fusion a challenging problem. Current fusion methodologies identify shared characteristics between the two modalities and integrate them within this shared domain using either iterative optimization or deep learning architectures, which often neglect the intricate semantic relationships between modalities, resulting in a superficial understanding of inter-modal connections and, consequently, suboptimal fusion outcomes. To address this, we introduce a text-guided multi-modality image fusion method that leverages the high-level semantics from textual descriptions to integrate semantics from infrared and visible images. This method capitalizes on the complementary characteristics of diverse modalities, bolstering both the accuracy and robustness of object detection. The codebook is utilized to enhance a streamlined and concise depiction of the fused intra- and inter-domain dynamics, fine-tuned for optimal performance in detection tasks. We present a bilevel optimization strategy that establishes a nexus between the joint problem of fusion and detection, optimizing both processes concurrently. Furthermore, we introduce the first dataset of paired infrared and visible images accompanied by text prompts, paving the way for future research. Extensive experiments on several datasets demonstrate that our method not only produces visually superior fusion results but also achieves a higher detection mAP over existing methods, achieving state-of-the-art results.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we present a new dataset as well as a new self-supervised learning method for ImageNet pre-training to improve anomaly detection and segmentation in 1-class and 2-class 5/10/high-shot training setups. We release the Visual Anomaly (VisA) Dataset consisting of 10,821 high-resolution color images (9,621 normal and 1,200 anomalous samples) covering 12 objects in 3 domains, making it the largest industrial anomaly detection dataset to date. Both image and pixel-level labels are provided. We also propose a new self-supervised framework - SPot-the-difference (SPD) - which can regularize contrastive self-supervised pre-training, such as SimSiam, MoCo and SimCLR, to be more suitable for anomaly detection tasks. Our experiments on VisA and MVTec-AD dataset show that SPD consistently improves these contrastive pre-training baselines and even the supervised pre-training. For example, SPD improves Area Under the Precision-Recall curve (AU-PR) for anomaly segmentation by 5.9% and 6.8% over SimSiam and supervised pre-training respectively in the 2-class high-shot regime. We open-source the project at http://github.com/amazon-research/spot-diff .

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Heuristic Method for Load Retrievals Route Programming in Puzzle-based Storage Systems

Recently, many enterprises are facing the difficulties brought out by the limitation of warehouse land and the increase of loan cost. As a promising approach to improve space utilization rate, puzzle-based storage systems (PBSSs) are drawing more attention from logistics researchers. In previous research about PBSS, concentration has been paid to single target item problems. However, there are no consensus algorithms to solve load retrievals route programming in PBSSs with multiple target items. In this paper, a heuristic algorithm is proposed to solve load retrievals route programming in PBSSs with multiple target items, multiple escorts and multiple IOs. In this paper, new concepts about the proposed algorithm are defined, including target IOs, target position of escorts, number of escorts required, et al. Then, the decision procedures are designed according to these concepts. Based on Markov decision process, the proposed algorithm makes the decision of the next action by analyzing the current status, until all the target items arrive at the IOs. The case study results have shown the effectiveness and efficiency of the proposed heuristic algorithm.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Neutronics Analysis for MSR Cell with Different Fuel Salt Channel Geometry

The neutronic properties of Molten Salt Reactor are different from that of traditional solid-fuel reactors due to its nuclear fuel particularity. Based upon MCNP code, the influence of the size and shape of fuel salt channel on neutron physics of MSR cell was studied systematically in this work. The results show that the infinite multiplication factors increases first and then decreases with the change of graphite cell size under the condition of given fuel volume fraction. In the case of the same FVF and average chord length, when the average chord length is relatively small, the k values with different fuel salt channel shapes are in good agreement; when the average chord length is relatively large, the k values with different fuel salt channel shapes are greatly different. In addition, some examples of practical application of this work are illustrated in the end, including cell selection for the core and thermal expansion displacement analysis of the cell.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Confidence Regularized Self-Training

Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation. These methods often involve an iterative process of predicting on target domain and then taking the confident predictions as pseudo-labels for retraining. However, since pseudo-labels can be noisy, self-training can put overconfident label belief on wrong classes, leading to deviated solutions with propagated errors. To address the problem, we propose a confidence regularized self-training (CRST) framework, formulated as regularized self-training. Our method treats pseudo-labels as continuous latent variables jointly optimized via alternating optimization. We propose two types of confidence regularization: label regularization (LR) and model regularization (MR). CRST-LR generates soft pseudo-labels while CRST-MR encourages the smoothness on network output. Extensive experiments on image classification and semantic segmentation show that CRSTs outperform their non-regularized counterpart with state-of-the-art performance. The code and models of this work are available at https://github.com/yzou2/CRST.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification

Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge domain gaps. Recently, there has been a growing interest in using unsupervised domain adaptation to address this scalability issue. Existing methods typically conduct adaptation on the representation space that contains both id-related and id-unrelated factors, thus inevitably undermining the adaptation efficacy of id-related features. In this paper, we seek to improve adaptation by purifying the representation space to be adapted. To this end, we propose a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id-related feature space exclusively. Our model involves a disentangling module that encodes cross-domain images into a shared appearance space and two separate structure spaces, and an adaptation module that performs adversarial alignment and self-training on the shared appearance space. The two modules are co-designed to be mutually beneficial. Extensive experiments demonstrate that the proposed joint learning framework outperforms the state-of-the-art methods by clear margins.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning

While being deployed in many critical applications as core components, machine learning (ML) models are vulnerable to various security and privacy attacks. One major privacy attack in this domain is membership inference, where an adversary aims to determine whether a target data sample is part of the training set of a target ML model. So far, most of the current membership inference attacks are evaluated against ML models trained from scratch. However, real-world ML models are typically trained following the transfer learning paradigm, where a model owner takes a pretrained model learned from a different dataset, namely teacher model, and trains her own student model by fine-tuning the teacher model with her own data. In this paper, we perform the first systematic evaluation of membership inference attacks against transfer learning models. We adopt the strategy of shadow model training to derive the data for training our membership inference classifier. Extensive experiments on four real-world image datasets show that membership inference can achieve effective performance. For instance, on the CIFAR100 classifier transferred from ResNet20 (pretrained with Caltech101), our membership inference achieves $95\%$ attack AUC. Moreover, we show that membership inference is still effective when the architecture of target model is unknown. Our results shed light on the severity of membership risks stemming from machine learning models in practice.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Sliced Wasserstein Kernels for Probability Distributions

Optimal transport distances, otherwise known as Wasserstein distances, have recently drawn ample attention in computer vision and machine learning as a powerful discrepancy measure for probability distributions. The recent developments on alternative formulations of the optimal transport have allowed for faster solutions to the problem and has revamped its practical applications in machine learning. In this paper, we exploit the widely used kernel methods and provide a family of provably positive definite kernels based on the Sliced Wasserstein distance and demonstrate the benefits of these kernels in a variety of learning tasks. Our work provides a new perspective on the application of optimal transport flavored distances through kernel methods in machine learning tasks.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Storage of multiple single-photon pulses emitted from a quantum dot in a solid-state quantum memory

Quantum repeaters are critical components for distributing entanglement over long distances in presence of unavoidable optical losses during transmission. Stimulated by Duan-Lukin-Cirac-Zoller protocol, many improved quantum-repeater protocols based on quantum memories have been proposed, which commonly focus on the entanglement-distribution rate. Among these protocols, the elimination of multi-photons (multi-photon-pairs) and the use of multimode quantum memory are demonstrated to have the ability to greatly improve the entanglement-distribution rate. Here, we demonstrate the storage of deterministic single photons emitted from a quantum dot in a polarization-maintaining solid-state quantum memory; in addition, multi-temporal-mode memory with $1$, $20$ and $100$ narrow single-photon pulses is also demonstrated. Multi-photons are eliminated, and only one photon at most is contained in each pulse. Moreover, the solid-state properties of both sub-systems make this configuration more stable and easier to be scalable. Our work will be helpful in the construction of efficient quantum repeaters based on all-solid-state devices

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Unconventional superconductivity in the layered iron germanide YFe$_2$Ge$_2$

Since the discovery of superconductivity in LaFePO in 2006, numerous iron-based superconductors have been identified within diverse structure families, all of which combine iron with a group-V (pnictogen) or group-VI (chalco- gen) element. Unconventional superconductivity is extremely rare among transition metal compounds outside these layered iron systems and the cuprates, and it is almost universally associated with highly anisotropic electronic properties and nearly 2D Fermi surface geometries. The iron-based intermetallic YFe$_2$Ge$_2$ features a 3D Fermi surface and a strongly enhanced low temperature heat capacity, which signals strong electronic correlations. We present data from a new generation of high quality samples of YFe$_2$Ge$_2$, which show superconducting transition anomalies below 1.8 K in thermodynamic as well as transport measurements, establishing that superconductivity is intrinsic in this layered iron compound outside the known superconducting iron pnictide or chalcogenide families. The Fermi surface geometry of YFe$_2$Ge$_2$ resembles that of KFe$_2$As$_2$ in the high pressure collapsed tetragonal phase, in which superconductivity at temperatures as high as 10 K has recently been reported, suggesting an underlying connection between the two systems.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Experimental test of the tradeoff relation in weak measurement

An upper bound between the information gain and state reversibility of weak measurement was first developed by Y. K. Cheong and S. W. Lee [Phys. Rev. Lett. 109, 150402 (2012)]. Their results are valid for arbitrary d-level quantum systems. In light of the commonly used qubit system in quantum information, a sharp tradeoff relation can be obtained. In this letter, this tradeoff relation is experimentally verified with polarization encoded single photons from a quantum dot. Furthermore, a complete traversal of weak measurement operators is realized, and the mapping to the least upper bound of this tradeoff relation is obtained. Our results complement the theoretical work and provide a universal ruler for the characterization of weak measurements.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Evidence for Interlayer Electronic Coupling in Multilayer Epitaxial Graphene from Polarization Dependent Coherently Controlled Photocurrent Generation

Most experimental studies to date of multilayer epitaxial graphene on C-face SiC have indicated that the electronic states of different layers are decoupled as a consequence of rotational stacking. We have measured the third order nonlinear tensor in epitaxial graphene as a novel approach to probe interlayer electronic coupling, by studying THz emission from coherently controlled photocurrents as a function of the optical pump and THz beam polarizations. We find that the polarization dependence of the coherently controlled THz emission expected from perfectly uncoupled layers, i.e. a single graphene sheet, is not observed. We hypothesize that the observed angular dependence arises from weak coupling between the layers; a model calculation of the angular dependence treating the multilayer structure as a stack of independent bilayers with variable interlayer coupling qualitatively reproduces the polarization dependence, providing evidence for coupling.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Experimental demonstration of nonlocal effects of the partial collapse measurement and reversal process

We experimentally demonstrate the nonlocal reversal of a partial-collapse quantum measurement on two-photon entangled state. Both the partial measurement and the reversal operation are implemented in linear optics with two displaced Sagnac interferometers, which are characterized by single qubit quantum process tomography. The recovered state is measured by quantum state tomography and its nonlocality is characterized by testing the Bell inequality. Our result will be helpful in quantum communication and quantum error correction.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Non-classical correlation of cascaded photon pairs emitted from quantum dot

We studied the quantum correlation between the photon pairs generated by biexciton cascade decays of self-assembled quantum dots, and determined the temperature behavior associated with so-called sudden change of the quantum correlation. The relationship between the fine structure splitting and the sudden change temperature is also provided. Our study indicates that this correlation behavior sudden change temperature is independent on the back ground noise in the system and far lower than entanglement sudden death temperature, therefore it should be easier to observe the phenomenon of correlation sudden change in experiments than to observe entanglement sudden death.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Entanglement dynamics of photon pairs emitted from quantum dot

We present a model to derive the state of the photon pairs generated by the biexciton cascade decay of a self-assembled quantum dot, which agrees well with the experimental result. Furthermore we calculate the concurrence and entanglement sudden death is found in this system with temperature increasing, which prevents quantum dot emits entangled photon pairs at a high temperature. The relationship between the fine structure splitting and the sudden death temperature is provided too.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Non-Markovian Entanglement Sudden Death and Rebirth of a Two-Qubit System in the Presence of System-Bath Coherence

We present a detailed study of the entanglement dynamics of a two-qubit system coupled to independent non-Markovian environments, employing hierarchy equations. This recently developed theoretical treatment can conveniently solve non-Markovian problems and take into consideration the correlation between the system and bath in an initial state. We concentrate on calculating the death and rebirth time points of the entanglement to obtain a general view of the concurrence curve and explore the behavior of entanglement dynamics with respect to the coupling strength, the characteristic frequency of the noise bath and the environment temperature.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Phase Compensation Enhancement of Photon Pair Entanglement Generated from Biexciton Decays in Quantum Dots

Exciton fine-structure splittings within quantum dots introduce phase differences between the two biexciton decay paths that greatly reduce the entanglement of photon pairs generated via biexciton recombination. We analyze this problem in the frequency domain and propose a practicable method to compensate the phase difference by inserting a spatial light modulator, which substantially improves the entanglement of the photon pairs without any loss.

0 citations0 reviewsNo code linkedOpen access