The separation of upgoing and downgoing waves in vertical seismic profiling (VSP) data is crucial for subsequent imaging, interpretation, and inversion. The interweaving of upgoing and downgoing waves and the presence of noise complicate the entire wave field, making it difficult to separate upgoing and downgoing waves. Various model-driven separation methods including low-rank approximation (LRA) and data-driven methods have achieved promising results. However, the performance of pure model-driven methods, such as F-K filtering and Radon transform, rely on domain transformation sparse representation for both upgoing and downgoing wavefield. Additionally, pure data-driven methods require a large number of precisely separated signals as training samples, which is a nontrivial task. To overcome these difficulties, this article proposes a model-data-driven VSP wavefield separation framework that iteratively completes the task of wavefield separation in an unsupervised manner. The key to this model is to use the powerful feature representation capability of the deep convolutional autoencoder (DCAE) to model the downgoing waves and use LRA to model the upgoing waves. By accurately modeling the upgoing and downgoing waves, we integrate the model-driven and data-driven methods together, while protecting both the upgoing and downgoing waves, and inheriting the advantages of the DCAE and LRA methods. Subsequently, we also proposed an alternating minimization optimization strategy to optimize the parameters of the model and iteratively obtain high-quality solutions. Comparative experiments on synthetic data and real data show that our method can achieve effective separation results while suppressing Gaussian noise.

MOTIVATION: Computational modeling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology, and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a lab. Recent advancements in single-cell RNA-sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico "synthetic" cells that faithfully mimic the temporal trajectories. RESULTS: Here we present OneSC, a platform that can simulate cell state transitions using systems of stochastic differential equations govern by a regulatory network of core transcription factors (TFs). Different from many current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and terminal cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes, and monocytes). Finally, through the in silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations. AVAILABILITY AND IMPLEMENTATION: OneSC is implemented as a Python package on GitHub (https://github.com/CahanLab/oneSC) and on Zenodo (https://zenodo.org/records/14052421).

Sparsity-regularized linear inverse problem has been served as the base in many disciplines, such as remote sensing imaging, image processing and analysis, seismic deconvolution, compressed sensing, medical imaging, and so forth. Iterative hard thresholding algorithm (IHTA) and iterative soft thresholding algorithm (ISTA) are two frequently used methods to solve sparsity-regularized linear inverse problems. They are also the basic unit of other more complex methods. IHTA and ISTA are derived under steepest descent method. I.e., iteratively perform gradient descent and thresholding shrinkage. Steepest descent method is a first order algorithm, which is a powerful way to solve optimization due to its relatively simple implementation. However, a known issue of first order method is possible poor convergence rate. Fast iterative thresholding-like algorithms have been proposed to overcome this issue in the existing literatures. In history, another alternative way is second order algorithms or quasi-second order algorithms. In this paper, we include a quasi-Newton’s method, i.e., DFP (Davidon-Fletcher-Powell) formulations in the framework of iterative thresholding-like algorithms to replace gradient descent to form a hybrid method to further increase convergence rate. The proposed method has been performed on two numerical examples and a real-life application in sparse-spike seismic deconvolution. The numerical examples and real-life application showed that it provides an effective alternative method to solve sparsity-regularized linear inverse problems.

Many out-of-GPU-memory systems are recently designed to support iterative processing of large-scale graphs. However, these systems still suffer from long time to converge because of inefficient propagation of active vertices’ new states along graph paths. To efficiently support out-of-GPU-memory graph processing, this work designs a system LargeGraph . Different from existing out-of-GPU-memory systems, LargeGraph proposes a dependency-aware data-driven execution approach , which can significantly accelerate active vertices’ state propagations along graph paths with low data access cost and also high parallelism. Specifically, according to the dependencies between the vertices, it only loads and processes the graph data associated with dependency chains originated from active vertices for smaller access cost. Because most active vertices frequently use a small evolving set of paths for their new states’ propagation because of power-law property, this small set of paths are dynamically identified and maintained and efficiently handled on the GPU to accelerate most propagations for faster convergence, whereas the remaining graph data are handled over the CPU. For out-of-GPU-memory graph processing, LargeGraph outperforms four cutting-edge systems: Totem (5.19–11.62×), Graphie (3.02–9.41×), Garaph (2.75–8.36×), and Subway (2.45–4.15×).