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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., Comment: 4 pages, 4 figures, The International Geoscience and Remote Sensing Symposium (IGARSS2023)

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.

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.

Digital Elevation Model (DEM) plays a fundamental role in remote sensing and photogrammetry. Enhancing the quality of DEM is crucial for various applications. Although multiple types of defects may appear simultaneously in the same DEM, they are commonly addressed separately. Most existing approaches only aim to fill the DEM voids, or apply super-resolution to the intact DEM. This paper introduces a unified generative model that simultaneously addresses voids and low-resolution problems, rather than taking two separate measures. The proposed approach presents the DEM Stochastic Differential Equation (DEM-SDE) for unified DEM quality enhancement. The DEM degradation of downsampling and random voids adding is modeled as the SDE forwarding, and the restoration is achieved by simulating the corresponding revert process. Conditioned on the terrain feature, and adopting efficient submodules with lightweight channel attention, DEM-SDE simultaneously enhances the DEM quality with an efficient process for training. The experiments show that DEM-SDE method achieves highly competitive performance in simultaneous super-resolution and void filling compared to the state-of-the-art work. DEM-SDE also manifests robustness for larger DEM patches.

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.

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.

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., Comment: 13 pages, 4 figures

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.

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.

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.

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.