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Remote sensing image change captioning (RSICC) aims at generating human-like language to describe the semantic changes between bi-temporal remote sensing image pairs. It provides valuable insights into environmental dynamics and land management. Unlike conventional change captioning task, RSICC involves not only retrieving relevant information across different modalities and generating fluent captions, but also mitigating the impact of pixel-level differences on terrain change localization. The pixel problem due to long time span decreases the accuracy of generated caption. Inspired by the remarkable generative power of diffusion model, we propose a probabilistic diffusion model for RSICC to solve the aforementioned problems. In training process, we construct a noise predictor conditioned on cross modal features to learn the distribution from the real caption distribution to the standard Gaussian distribution under the Markov chain. Meanwhile, a cross-mode fusion and a stacking self-attention module are designed for noise predictor in the reverse process. In testing phase, the well-trained noise predictor helps to estimate the mean value of the distribution and generate change captions step by step. Extensive experiments on the LEVIR-CC dataset demonstrate the effectiveness of our Diffusion-RSCC and its individual components. The quantitative results showcase superior performance over existing methods across both traditional and newly augmented metrics. The code and materials will be available online at https://github.com/Fay-Y/Diffusion-RSCC.

Here, using unique in-situ liquid secondary ion mass spectroscopy on isotope-labelled solid-electrolyte-interphase (SEI), assisted by cryogenic transmission electron microscopy and constrained ab initio molecular dynamics simulation, for the first time we answer the question regarding Li+ transport mechanism across SEI, and quantitatively determine the Li+-mobility therein. We unequivocally unveil that Li+ transport in SEI follows a mechanism of successive displacement, rather than "direct-hopping". We further reveal, in accordance with spatial-dependence of SEI structure across the thickness, the apparent Li+ self-diffusivity varies from 6.7*10-19 m2/s to 1.0*10-20 m2/s, setting a quantitative gauging of ionic transport behavior of SEI layer against the underlining electrode as well as the rate limiting step of battery operation. This direct study on Li+ kinetics in SEI fills part of the decade-long knowledge gap about the most important component in advanced batteries and provides more precise guidelines to the tailoring of interphasial chemistries for future battery chemistries., Comment: 25 pages, 4 figures

Diamond has superlative material properties for a broad range of quantum and electronic technologies. However, heteroepitaxial growth of single crystal diamond remains limited, impeding integration and evolution of diamond-based technologies. Here, we directly bond single-crystal diamond membranes to a wide variety of materials including silicon, fused silica, sapphire, thermal oxide, and lithium niobate. Our bonding process combines customized membrane synthesis, transfer, and dry surface functionalization, allowing for minimal contamination while providing pathways for near unity yield and scalability. We generate bonded crystalline membranes with thickness as low as 10 nm, sub-nm interfacial regions, and nanometer-scale thickness variability over 200 by 200 $\mu m^2$ areas. We measure spin coherence times $T_2$ for nitrogen-vacancy centers in bonded membranes of up to 623(21) $\mu$s, suitable for advanced quantum applications. We demonstrate multiple methods for integrating high quality factor nanophotonic cavities with the diamond heterostructures, highlighting the platform versatility in quantum photonic applications. Furthermore, we show that our ultra-thin diamond membranes are compatible with total internal reflection fluorescence (TIRF) microscopy, which enables interfacing coherent diamond quantum sensors with living cells while rejecting unwanted background luminescence. The processes demonstrated herein provide a full toolkit to synthesize heterogeneous diamond-based hybrid systems for quantum and electronic technologies., Comment: 64 pages, 25 figures

Diamond nanocrystals can harbor spin qubit sensors capable of probing the physical properties of biological systems with nanoscale spatial resolution. These diamond nanosensors can readily be delivered into intact cells and even living organisms. However, applications beyond current proof-of-principle experiments require a substantial increase in sensitivity, which is generally limited by surface-noise-induced spin dephasing and relaxation. In this work, we significantly reduce magnetic surface noise by engineering core-shell structures, which in combination with dynamical decoupling result in qubit coherence times (T2) ranging from 52us to 87us - a drastic improvement over the 1.1us to 35us seen in bare particles. This improvement in spin coherence, combined with an overall increase in particle fluorescence, corresponds to a two-order-of-magnitude reduction in integration time. Probing qubit dynamics at a single particle level, furthermore, reveals that the noise characteristics fundamentally change from a bath with spins that rearrange their spatial configuration during the course of an experiment to a more dilute static bath. The observed results shed light on the underlying mechanisms governing spin dephasing in diamond nanocrystals and offer an effective noise mitigation strategy based on engineered core-shell structures., Comment: Main text 10 pages 4 main figures and 10 extended data figures

Quantum metrology enables some of the most precise measurements. In the life sciences, diamond-based quantum sensing has enabled a new class of biophysical sensors and diagnostic devices that are being investigated as a platform for cancer screening and ultra-sensitive immunoassays. However, a broader application in the life sciences based on nanoscale nuclear magnetic resonance spectroscopy has been hampered by the need to interface highly sensitive quantum bit (qubit) sensors with their biological targets. Here, we demonstrate a new approach that combines quantum engineering with single-molecule biophysics to immobilize individual proteins and DNA molecules on the surface of a bulk diamond crystal that hosts coherent nitrogen vacancy qubit sensors. Our thin (sub-5 nm) functionalization architecture provides precise control over protein adsorption density and results in near-surface qubit coherence approaching 100 {\mu}s. The developed architecture remains chemically stable under physiological conditions for over five days, making our technique compatible with most biophysical and biomedical applications.

Quantum sensing is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use optimization techniques to search for encoder and decoder circuits that scalably improve sensitivity under given application and noise characteristics. Our approach uses a variational algorithm that can learn a quantum sensing circuit based on platform-specific control capacity, noise, and signal distribution. The quantum circuit is composed of an encoder which prepares the optimal sensing state and a decoder which gives an output distribution containing information of the signal. We optimize the full circuit to maximize the Signal-to-Noise Ratio (SNR). Furthermore, this learning algorithm can be run on real hardware scalably by using the "parameter-shift" rule which enables gradient evaluation on noisy quantum circuits, avoiding the exponential cost of quantum system simulation. We demonstrate up to 13.12x SNR improvement over existing fixed protocol (GHZ), and 3.19x Classical Fisher Information (CFI) improvement over the classical limit on 15 qubits using IBM quantum computer. More notably, our algorithm overcomes the decreasing performance of existing entanglement-based protocols with increased system sizes., Comment: 12 pages, 11 figures