arXiv subjects

Tongtong Zhang

Publications and source records attributed to Tongtong Zhang.

14 recordsLinked to original sources

arXiv2026

Integrated quantum-secured cryptographic memory in customizable single nanodiamonds

As advanced physical cloning and cyber-extraction techniques proliferate, achieving absolute hardware-level information security has become a paramount global challenge. Conventional architectures systems are fundamentally vulnerable because deterministic memory and cryptographic hardware are physically decoupled, exposing sensitive data to interception. While physical unclonable functions (PUFs) offer robust authentication, integrating them with high-density data writing within a unified nanoscale medium remains elusive. Here, we present an intrinsically secure all-in-one architecture that inextricably fuses high-dimensional data encoding with multiscale physical encryption within single nanodiamonds (NDs). Through precise focused-electron-beam irradiation, we achieve single-nanoparticle defect engineering, enabling deterministic base-36 data writing within individual NDs. Crucially, this customized memory layer is permanently locked within the medium's inherent stochasticity, combining macroscopic spatial PUFs with strictly irreproducible atomic-scale quantum fingerprints extracted via optically detected magnetic resonance (ODMR). By seamlessly embedding deterministic memory into an unclonable physical environment, our approach intrinsically eliminates the vulnerabilities of separated hardware, providing an unconditionally secure solid-state foundation for tamper-proof memory chips, zero-trust networks and next-generation data vaults.

cond-mat.mtrl-sci↗

arXiv2025

Wafer-scale integration of single nanodiamonds via electrostatic-trapping

Nanodiamonds (NDs) are key materials for building nanoscale quantum sensing, imaging and communication devices. Scalable configuration of single NDs on heterogeneous platforms, forming photonic quantum source arrays, will be an essential solution towards realizing next-generation practical and industrial quantum devices. However, NDs are challenging to manipulate because their size, shape and surface chemistry vary substantially. Here, we show a simple method based on electrostatic-trapping to rapidly and reliably pattern single ND arrays on arbitrary substrates at scale. Our method, which uses carefully engineered microscale hole templates and electrostatic force, captures single NDs across 8-inch wafers with 82.5% yields within 5 min. Systematic experimental and theoretical studies show the number of deposited NDs primarily depends on the diameter of the hole trap. The method is compatible with mature CMOS technologies, enabling the mass production of scalable and integrable quantum devices. This advancement is expected to accelerate the commercialization and industrial adoption of ND-based technologies.

physics.optics↗

arXiv2024

PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting

In urban computing, precise and swift forecasting of multivariate time series data from traffic networks is crucial. This data incorporates additional spatial contexts such as sensor placements and road network layouts, and exhibits complex temporal patterns that amplify challenges for predictive learning in traffic management, smart mobility demand, and urban planning. Consequently, there is an increasing need to forecast traffic flow across broader geographic regions and for higher temporal coverage. However, current research encounters limitations because of the inherent inefficiency of model and their unsuitability for large-scale traffic network applications due to model complexity. This paper proposes a novel framework, named PreMixer, designed to bridge this gap. It features a predictive model and a pre-training mechanism, both based on the principles of Multi-Layer Perceptrons (MLP). The PreMixer comprehensively consider temporal dependencies of traffic patterns in different time windows and processes the spatial dynamics as well. Additionally, we integrate spatio-temporal positional encoding to manage spatiotemporal heterogeneity without relying on predefined graphs. Furthermore, our innovative pre-training model uses a simple patch-wise MLP to conduct masked time series modeling, learning from long-term historical data segmented into patches to generate enriched contextual representations. This approach enhances the downstream forecasting model without incurring significant time consumption or computational resource demands owing to improved learning efficiency and data handling flexibility. Our framework achieves comparable state-of-the-art performance while maintaining high computational efficiency, as verified by extensive experiments on large-scale traffic datasets.

cs.LG↗

arXiv2024

Efficient Terrain Stochastic Differential Efficient Terrain Stochastic Differential Equations for Multipurpose Digital Elevation Model Restoration

Digital Elevation Models (DEMs) are indispensable in the fields of remote sensing and photogrammetry, with their refinement and enhancement being critical for a diverse array of applications. Numerous methods have been developed for enhancing DEMs, but most of them concentrate on tackling specific tasks individually. This paper presents a unified generative model for multipurpose DEM restoration, diverging from the conventional approach that typically targets isolated tasks. We modify the mean-reverting stochastic differential equation, to generally refine the DEMs by conditioning on the learned terrain priors. The proposed Efficient Terrain Stochastic Differential Equation (ET-SDE) models DEM degradation through SDE progression and restores it via a simulated reversal process. Leveraging efficient submodules with lightweight channel attention, this adapted SDE boosts DEM quality and streamlines the training process. The experiments show that ET-SDE achieves highly competitive restoration performance on super-resolution, void filling, denoising, and their combinations, compared to the state-of-the-art work. In addition to its restoration capabilities, ET-SDE also demonstrates faster inference speeds and the capacity to generalize across various tasks, particularly for larger patches of DEMs.

eess.IV↗

arXiv2024

A Nerf-Based Color Consistency Method for Remote Sensing Images

Due to different seasons, illumination, and atmospheric conditions, the photometric of the acquired image varies greatly, which leads to obvious stitching seams at the edges of the mosaic image. Traditional methods can be divided into two categories, one is absolute radiation correction and the other is relative radiation normalization. We propose a NeRF-based method of color consistency correction for multi-view images, which weaves image features together using implicit expressions, and then re-illuminates feature space to generate a fusion image with a new perspective. We chose Superview-1 satellite images and UAV images with large range and time difference for the experiment. Experimental results show that the synthesize image generated by our method has excellent visual effect and smooth color transition at the edges.

cs.CV↗

arXiv2024

Scalable Reshaping of Diamond Particles via Programmable Nanosculpting

Diamond particles have many interesting properties and possible applications. However, producing diamond particles with well-defined shapes at scale is challenging because diamonds are chemically inert and extremely hard. Here, we show air oxidation, a routine method for purifying diamonds, can be used to precisely shape diamond particles at scale. By exploiting the distinct reactivities of different crystal facets and defects inside the diamond, layer-by-layer outward-to-inward and inward-to-outward oxidation produced diverse diamond shapes including sphere, twisted surface, pyramidal islands, inverted pyramids, nano-flowers, and hollow polygons. The nanosculpted diamonds had more and finer features that enabled them to outperform the original raw diamonds in various applications. Using experimental observations and Monte Carlo simulations, we built a shape library that guides the design and fabrication of diamond particles with well-defined shapes and functional value. Our study presents a simple, economical and scalable way to produce shape-customized diamonds for various photonics, catalysis, quantum and information technology applications.

cond-mat.mtrl-sci↗

arXiv2024

A diamond heater-thermometer microsensor for measuring localized thermal conductivity: a case study in gelatin hydrogel

Understanding the microscopic thermal effects of the hydrogel is important for its application in diverse fields, including thermal-related studies in tissue engineering and thermal management for flexible electronic devices. In recent decades, localized thermal properties, such as thermal conductivity, have often been overlooked due to technical limitations. To tackle this, we propose a new hybrid diamond microsensor that is capable of simultaneous temperature control and readout in a decoupled manner. Specifically, the sensor consists of a silicon pillar (heater) at about 10 microns in length, topped by a micron-sized diamond particle that contains silicon-vacancy (SiV) centers (thermometer) with 1.29 K*Hz^(-1/2) temperature measurement sensitivity. Combining this innovative, scalable sensor with a newly established simulation model that can transform heating-laser-induced temperature change into thermal conductivity, we introduced an all-optical decoupled method with about 0.05 W/(m* K) precision, which can reduce laser crosstalk. For the first time, we track the thermal conductivity change of hydrogels during the gelation process and demonstrate the existence of variation. We introduce a rapid, undisturbed technique for measuring microscale thermal conductivity, potentially serving as a valuable tool for cellular thermometry and highlight the idea that decoupling can reduce crosstalk from different lasers, which is helpful for quantum sensing.

physics.optics↗

arXiv2024

psPRF:Pansharpening Planar Neural Radiance Field for Generalized 3D Reconstruction Satellite Imagery

Most current NeRF variants for satellites are designed for one specific scene and fall short of generalization to new geometry. Additionally, the RGB images require pan-sharpening as an independent preprocessing step. This paper introduces psPRF, a Planar Neural Radiance Field designed for paired low-resolution RGB (LR-RGB) and high-resolution panchromatic (HR-PAN) images from satellite sensors with Rational Polynomial Cameras (RPC). To capture the cross-modal prior from both of the LR-RGB and HR-PAN images, for the Unet-shaped architecture, we adapt the encoder with explicit spectral-to-spatial convolution (SSConv) to enhance the multimodal representation ability. To support the generalization ability of psRPF across scenes, we adopt projection loss to ensure strong geometry self-supervision. The proposed method is evaluated with the multi-scene WorldView-3 LR-RGB and HR-PAN pairs, and achieves state-of-the-art performance.

cs.CV↗

arXiv2024

Deep Extrinsic Manifold Representation for Vision Tasks

Non-Euclidean data is frequently encountered across different fields, yet there is limited literature that addresses the fundamental challenge of training neural networks with manifold representations as outputs. We introduce the trick named Deep Extrinsic Manifold Representation (DEMR) for visual tasks in this context. DEMR incorporates extrinsic manifold embedding into deep neural networks, which helps generate manifold representations. The DEMR approach does not directly optimize the complex geodesic loss. Instead, it focuses on optimizing the computation graph within the embedded Euclidean space, allowing for adaptability to various architectural requirements. We provide empirical evidence supporting the proposed concept on two types of manifolds, $SE(3)$ and its associated quotient manifolds. This evidence offers theoretical assurances regarding feasibility, asymptotic properties, and generalization capability. The experimental results show that DEMR effectively adapts to point cloud alignment, producing outputs in $ SE(3) $, as well as in illumination subspace learning with outputs on the Grassmann manifold.

cs.CV↗

arXiv2023

Effective electrical manipulation of topological antiferromagnet by orbital Hall effect

Electrical control of the non-trivial topology in Weyl antiferromagnet is of great interests to develop next-generation spintronic devices. Recent works suggest that spin Hall effect can switch the topological antiferromagnetic order. However, the switching efficiency remains relatively low. Here, we demonstrate effective manipulation of antiferromagnetic order in Weyl semimetal Mn3Sn by orbital Hall effect originated from metal Mn or oxide CuOx. While Mn3Sn is proven to be able to convert orbit current to spin current by itself, we find that inserting a heavy metal layer like Pt with proper thickness can effectively reduce the critical switching current density by one order of magnitude. In addition, we show that the memristor-like switching behavior of Mn3Sn can mimic the potentiation and depression processes of a synapse with high linearity, which is beneficial for constructing artificial neural network with high accuracy. Our work paves an alternative way to manipulate topological antiferromagnetic order and may inspire more high-performance antiferromagnetic functional devices.

cond-mat.mtrl-sci↗

arXiv2023

rpcPRF: Generalizable MPI Neural Radiance Field for Satellite Camera

Novel view synthesis of satellite images holds a wide range of practical applications. While recent advances in the Neural Radiance Field have predominantly targeted pin-hole cameras, and models for satellite cameras often demand sufficient input views. This paper presents rpcPRF, a Multiplane Images (MPI) based Planar neural Radiance Field for Rational Polynomial Camera (RPC). Unlike coordinate-based neural radiance fields in need of sufficient views of one scene, our model is applicable to single or few inputs and performs well on images from unseen scenes. To enable generalization across scenes, we propose to use reprojection supervision to induce the predicted MPI to learn the correct geometry between the 3D coordinates and the images. Moreover, we remove the stringent requirement of dense depth supervision from deep multiview-stereo-based methods by introducing rendering techniques of radiance fields. rpcPRF combines the superiority of implicit representations and the advantages of the RPC model, to capture the continuous altitude space while learning the 3D structure. Given an RGB image and its corresponding RPC, the end-to-end model learns to synthesize the novel view with a new RPC and reconstruct the altitude of the scene. When multiple views are provided as inputs, rpcPRF exerts extra supervision provided by the extra views. On the TLC dataset from ZY-3, and the SatMVS3D dataset with urban scenes from WV-3, rpcPRF outperforms state-of-the-art nerf-based methods by a significant margin in terms of image fidelity, reconstruction accuracy, and efficiency, for both single-view and multiview task.

cs.CV↗

arXiv2023

Fast Satellite Tensorial Radiance Field for Multi-date Satellite Imagery of Large Size

Existing NeRF models for satellite images suffer from slow speeds, mandatory solar information as input, and limitations in handling large satellite images. In response, we present SatensoRF, which significantly accelerates the entire process while employing fewer parameters for satellite imagery of large size. Besides, we observed that the prevalent assumption of Lambertian surfaces in neural radiance fields falls short for vegetative and aquatic elements. In contrast to the traditional hierarchical MLP-based scene representation, we have chosen a multiscale tensor decomposition approach for color, volume density, and auxiliary variables to model the lightfield with specular color. Additionally, to rectify inconsistencies in multi-date imagery, we incorporate total variation loss to restore the density tensor field and treat the problem as a denosing task.To validate our approach, we conducted assessments of SatensoRF using subsets from the spacenet multi-view dataset, which includes both multi-date and single-date multi-view RGB images. Our results clearly demonstrate that SatensoRF surpasses the state-of-the-art Sat-NeRF series in terms of novel view synthesis performance. Significantly, SatensoRF requires fewer parameters for training, resulting in faster training and inference speeds and reduced computational demands.

cs.CV↗

arXiv2023

Cross-correlated quantum thermometry using diamond containing dual-defect centers

The contactless temperature measurement at micro/nanoscale is vital to a broad range of fields in modern science and technology. The nitrogen vacancy (NV) center, a kind of diamond defect with unique spin-dependent photoluminescence, has been recognized as one of the most promising nanothermometers. However, this quantum thermometry technique has been prone to a number of possible perturbations, which will unavoidably degrade its actual temperature sensitivity. Here, for the first time, we have developed a cross-validated optical thermometry method using a bulk diamond sample containing both NV centers and silicon vacancy (SiV) centers. Particularly, the latter allowing all-optical method has been intrinsically immune to those influencing perturbations for the NV-based quantum thermometry, hence serving as a real-time cross validation system. As a proof-of-concept demonstration, we have shown a trustworthy temperature measurement under the influence of varying magnetic fields. This multi-modality approach allows a synchronized cross-validation of the measured temperature, which is required for micro/nanoscale quantum thermometry in complicated environments such as a living cell.

quant-ph↗

arXiv2022

SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding

Patient similarity assessment (PSA) is pivotal to evidence-based and personalized medicine, enabled by analyzing the increasingly available electronic health records (EHRs). However, machine learning approaches for PSA has to deal with inherent data deficiencies of EHRs, namely missing values, noise, and small sample sizes. In this work, an end-to-end discriminative learning framework, called SparGE, is proposed to address these data challenges of EHR for PSA. SparGE measures similarity by jointly sparse coding and graph embedding. First, we use low-rank constrained sparse coding to identify and calculate weight for similar patients, while denoising against missing values. Then, graph embedding on sparse representations is adopted to measure the similarity between patient pairs via preserving local relationships defined by distances. Finally, a global cost function is constructed to optimize related parameters. Experimental results on two private and public real-world healthcare datasets, namely SingHEART and MIMIC-III, show that the proposed SparGE significantly outperforms other machine learning patient similarity methods.

cs.LG↗