PeakNet: Bragg peak finding in X-ray crystallography experiments with U-Net

Serial crystallography at X-ray free electron laser (XFEL) sources has experienced tremendous progress in achieving high data rate in recent times. While this development offers potential to enable novel scientific investigations, such as imaging molecular events at logarithmic timescales, it also poses challenges in regards to real-time data analysis, which involves some degree of data reduction to only save those features or images pertaining to the science on disks. If data reduction is not effective, it could directly result in a substantial increase in facility budgetary requirements, or even hinder the utilization of ultra-high repetition imaging techniques making data analysis unwieldy. Furthermore, an additional challenge involves providing real-time feedback to users derived from real-time data analysis. In the context of serial crystallography, the initial and critical step in real-time data analysis is finding X-ray Bragg peaks from diffraction images. To tackle this challenge, we present PeakNet, a Bragg peak finder that utilizes neural networks and runs about four times faster than Psocake peak finder, while delivering significantly better indexing rates and comparable number of indexed events. We formulated the task of peak finding into a semantic segmentation problem, which is implemented as a classical U-Net architecture. A key advantage of PeakNet is its ability to scale linearly with respect to data volume, making it well-suited for real-time serial crystallography data analysis at high data rates

On Exact Inversion of DPM-Solvers

Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality significantly, but have posed challenges to find the exact inverse (i.e., finding the initial noise from the given image). Here we investigate the exact inversions for DPM-solvers and propose algorithms to perform them when samples are generated by the first-order as well as higher-order DPM-solvers. For each explicit denoising step in DPM-solvers, we formulated the inversions using implicit methods such as gradient descent or forward step method to ensure the robustness to large classifier-free guidance unlike the prior approach using fixed-point iteration. Experimental results demonstrated that our proposed exact inversion methods significantly reduced the error of both image and noise reconstructions, greatly enhanced the ability to distinguish invisible watermarks and well prevented unintended background changes consistently during image editing. Project page: \url{https://smhongok.github.io/inv-dpm.html}.Comment: 16 page

SpeckleNN: A unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples

With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or "speckles", to extract single hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high data rate facilities like European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments

A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography

Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.open0

Graphitic carbon growth on crystalline and amorphous oxide substrates using molecular beam epitaxy

We report graphitic carbon growth on crystalline and amorphous oxide substrates by using carbon molecular beam epitaxy. The films are characterized by Raman spectroscopy and X-ray photoelectron spectroscopy. The formations of nanocrystalline graphite are observed on silicon dioxide and glass, while mainly sp2 amorphous carbons are formed on strontium titanate and yttria-stabilized zirconia. Interestingly, flat carbon layers with high degree of graphitization are formed even on amorphous oxides. Our results provide a progress toward direct graphene growth on oxide materials

β-Caryophyllene attenuates dextran sulfate sodium-induced colitis in mice via modulation of gene expression associated mainly with colon inflammation

AbstractWe examined the modulatory activity of β-caryophyllene (CA) and gene expression in colitic colon tissues in a dextran sulfate sodium (DSS)-induced colitis model. Experimental colitis was induced by exposing male BALB/c mice to 5% DSS in drinking water for 7 days. CA (30 or 300mg/kg) was administered orally once a day together with DSS. CA administration attenuated the increases in the disease activity index, colon weight/length ratio, inflammation score, and myeloperoxidase activity in DSS-treated mice. Microarray analysis showed that CA administration regulated the expression in colon tissue of inflammation-related genes including those for cytokines and chemokines (Ccl2, Ccl7, Ccl11, Ifitm3, IL-1β, IL-28, Tnfrsf1b, Tnfrsf12a); acute-phase proteins (S100a8, Saa3, Hp); adhesion molecules (Cd14, Cd55, Cd68, Mmp3, Mmp10, Sema6b, Sema7a, Anax13); and signal regulatory proteins induced by DSS. CA significantly suppressed NF-κB activity, which mediates the expression of a different set of genes. These results suggest that CA attenuates DSS-induced colitis, possibly by modulating the expression of genes associated mainly with colon inflammation through inhibition of DSS-induced NF-κB activity

Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics

Advanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address the limitations, we introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS) measurements for spin fluctuations. Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. The capability of automatic differentiation from the neural network model is further leveraged for more robust and accurate parameter estimation. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time. Although focusing on XPFS and spin fluctuations, our method can be adapted to other experiments, facilitating more efficient data collection and accelerating scientific discoveries