Ptychographic hyperspectral spectromicroscopy with an extreme ultraviolet high harmonic comb

We demonstrate a new scheme of spectromicroscopy in the extreme ultraviolet (EUV) spectral range, where the spectral response of the sample at different wavelengths is imaged simultaneously. It is enabled by applying ptychographical information multiplexing (PIM) to a tabletop EUV source based on high harmonic generation, where four spectrally narrow harmonics near 30 nm form a spectral comb structure. Extending PIM from previously demonstrated visible wavelengths to the EUV/X-ray wavelengths promises much higher spatial resolution and more powerful spectral contrast mechanism, making PIM an attractive spectromicroscopy method in both the microscopy and the spectroscopy aspects. Besides the sample, the multicolor EUV beam is also imaged in situ, making our method a powerful beam characterization technique. No hardware is used to separate or narrow down the wavelengths, leading to efficient use of the EUV radiation

KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion

Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web. Although knowledge graph embedding (KGE) models have demonstrated superior predictive performance on KGC tasks, these models infer missing links in a black-box manner that lacks transparency and accountability, preventing researchers from developing accountable models. Existing KGE-based explanation methods focus on exploring key paths or isolated edges as explanations, which is information-less to reason target prediction. Additionally, the missing ground truth leads to these explanation methods being ineffective in quantitatively evaluating explored explanations. To overcome these limitations, we propose KGExplainer, a model-agnostic method that identifies connected subgraph explanations and distills an evaluator to assess them quantitatively. KGExplainer employs a perturbation-based greedy search algorithm to find key connected subgraphs as explanations within the local structure of target predictions. To evaluate the quality of the explored explanations, KGExplainer distills an evaluator from the target KGE model. By forwarding the explanations to the evaluator, our method can examine the fidelity of them. Extensive experiments on benchmark datasets demonstrate that KGExplainer yields promising improvement and achieves an optimal ratio of 83.3% in human evaluation.Comment: 13 pages, 7 figures, 11 tables. Under Revie

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic

Single-shot 3D coherent diffractive imaging of core-shell nanoparticles with elemental specificity.

We report 3D coherent diffractive imaging (CDI) of Au/Pd core-shell nanoparticles with 6.1 nm spatial resolution with elemental specificity. We measured single-shot diffraction patterns of the nanoparticles using intense x-ray free electron laser pulses. By exploiting the curvature of the Ewald sphere and the symmetry of the nanoparticle, we reconstructed the 3D electron density of 34 core-shell structures from these diffraction patterns. To extract 3D structural information beyond the diffraction signal, we implemented a super-resolution technique by taking advantage of CDI's quantitative reconstruction capabilities. We used high-resolution model fitting to determine the Au core size and the Pd shell thickness to be 65.0 ± 1.0 nm and 4.0 ± 0.5 nm, respectively. We also identified the 3D elemental distribution inside the nanoparticles with an accuracy of 3%. To further examine the model fitting procedure, we simulated noisy diffraction patterns from a Au/Pd core-shell model and a solid Au model and confirmed the validity of the method. We anticipate this super-resolution CDI method can be generally used for quantitative 3D imaging of symmetrical nanostructures with elemental specificity

Direct Visualization of Laser-Driven Electron Multiple Scattering and Tunneling Distance in Strong-Field Ionization

Using a simple model of strong-field ionization of atoms that generalizes the well-known 3-step model from 1D to 3D, we show that the experimental photoelectron angular distributions resulting from laser ionization of xenon and argon display prominent structures that correspond to electrons that pass by their parent ion more than once before strongly scattering. The shape of these structures can be associated with the specific number of times the electron is driven past its parent ion in the laser field before scattering. Furthermore, a careful analysis of the cutoff energy of the structures allows us to experimentally measure the distance between the electron and ion at the moment of tunnel ionization. This work provides new physical insight into how atoms ionize in strong laser fields and has implications for further efforts to extract atomic and molecular dynamics from strong-field physics

Single-shot 3D coherent diffractive imaging of core-shell nanoparticles with elemental specificity

We report 3D coherent diffractive imaging (CDI) of Au/Pd core-shell nanoparticles with 6.1 nm spatial resolution with elemental specificity. We measured single-shot diffraction patterns of the nanoparticles using intense x-ray free electron laser pulses. By exploiting the curvature of the Ewald sphere and the symmetry of the nanoparticle, we reconstructed the 3D electron density of 34 core-shell structures from these diffraction patterns. To extract 3D structural information beyond the diffraction signal, we implemented a super-resolution technique by taking advantage of CDI’s quantitative reconstruction capabilities. We used high-resolution model fitting to determine the Au core size and the Pd shell thickness to be 65.0 ± 1.0 nm and 4.0 ± 0.5 nm, respectively. We also identified the 3D elemental distribution inside the nanoparticles with an accuracy of 3%. To further examine the model fitting procedure, we simulated noisy diffraction patterns from a Au/Pd core-shell model and a solid Au model and confirmed the validity of the method. We anticipate this super-resolution CDI method can be generally used for quantitative 3D imaging of symmetrical nanostructures with elemental specificity

Internal Logic and Significance of Times in Xi Jinping's Important Exposition of Poverty Alleviation

Eliminating poverty is the essential requirement of socialism. Since the 18th National Congress of the Communist Party of China, targeted poverty alleviation has become a major strategy for poverty alleviation and development in China. Xi Jinping's important exposition of poverty alleviation is the theoretical basis and practical guide to direct the effective implementation of China's targeted poverty alleviation strategy. It has gradually developed into an innovative theoretical system for poverty alleviation and development in the new era, with meticulous internal logic and a reputation for the significance of the times at home and abroad. Xi Jinping's thought of targeted poverty alleviation is the development and innovation of the theory and practice of poverty alleviation and development with Chinese characteristics. It is an important guarantee for China to win the battle to get rid of poverty and build a well-off society in an all-round way, and has contributed China's wisdom and China's plan to reducing poverty in the world