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Recent experimental findings on limit-cycle-oscillations indicate that the nonlinear driving of the turbulent poloidal Reynolds stress to zonal flows is not a significant factor in the toroidal geometry, sparking fundamental controversial issues within the fusion community. By using the global nonlinear gyrokinetic simulations, we propose a unified mesoscale picture of nonlinear driving of zonal flows in the ion-temperature-gradient turbulence. Zonal flows are nonlinearly driven by the turbulent energy flux and the turbulent poloidal Reynolds stress. The turbulent energy flux is not shielded by the toroidal geometry effect in nonlinearly driving zonal flows. The turbulent poloidal Reynolds stress is not shielded on the time scale shorter than the ion bounce period; however, on the time scale longer than the ion bounce period, the turbulent poloidal Reynolds stress is indeed shielded by the toroidal geometry effect., Comment: 16 pages, 4 figures

Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic nature of the graph structure, resulting in sub-optimal node and graph representations. To address this limitation, we propose a novel Graph structure Prompt Learning method (GPL) to enhance the training of GNNs, which is inspired by prompt mechanisms in natural language processing. GPL employs task-independent graph structure losses to encourage GNNs to learn intrinsic graph characteristics while simultaneously solving downstream tasks, producing higher-quality node and graph representations. In extensive experiments on eleven real-world datasets, after being trained by GPL, GNNs significantly outperform their original performance on node classification, graph classification, and edge prediction tasks (up to 10.28%, 16.5%, and 24.15%, respectively). By allowing GNNs to capture the inherent structural prompts of graphs in GPL, they can alleviate the issue of over-smooth and achieve new state-of-the-art performances, which introduces a novel and effective direction for GNN research with potential applications in various domains.

Despite the Graph Neural Networks' (GNNs) proficiency in analyzing graph data, achieving high-accuracy and interpretable predictions remains challenging. Existing GNN interpreters typically provide post-hoc explanations disjointed from GNNs' predictions, resulting in misrepresentations. Self-explainable GNNs offer built-in explanations during the training process. However, they cannot exploit the explanatory outcomes to augment prediction performance, and they fail to provide high-quality explanations of node features and require additional processes to generate explainable subgraphs, which is costly. To address the aforementioned limitations, we propose a self-explained and self-supervised graph neural network (SES) to bridge the gap between explainability and prediction. SES comprises two processes: explainable training and enhanced predictive learning. During explainable training, SES employs a global mask generator co-trained with a graph encoder and directly produces crucial structure and feature masks, reducing time consumption and providing node feature and subgraph explanations. In the enhanced predictive learning phase, mask-based positive-negative pairs are constructed utilizing the explanations to compute a triplet loss and enhance the node representations by contrastive learning., Comment: Accepted as a conference paper at ICDE 2024

Since the DIII-D resonant magnetic perturbation experiment [Nucl. Fusion $\bm{59}$, 126010 (2019)] suggests that the neoclassical toroidal viscosity due to the collisional effects associated with the non-resonant magnetic perturbations is not enough to explain the observed toroidal rotation, it is of interest to investigate the toroidal rotation induced by the anomalous diffusion due to the resonant magnetic perturbations. Gyrokinetic simulation of the toroidal rotation of plasma in a stochastic magnetic field is carried out to investigate the resonant magnetic perturbations effects on toroidal rotation. The simulation results suggest that, in a stochastic magnetic field, resonant magnetic perturbations drive the plasma to toroidally rotate through the ambipolar radial electric field. It is found that this spontaneous flow driven on the time scale less than an ion-ion collision time is the parallel return flow of the $\bm{E}_r\times\bm{B}_0$ drift, which is due to the the ambipolar radial electric field induced by the non-ambipolar radial diffusion in the stochastic magnetic field. Collisional effect changes the plasma toroidal rotation from the return flow to the rigid-body flow after a few ion-ion collision times. The toroidal rotation observed in DIII-D resonant magnetic perturbation experiment [Nucl. Fusion $\bm{59}$, 126010 (2019)], can be explained by the rigid-body rotation driven by the ambipolar radial electric field generated by the stochastic magnetic field layer.

De-aliasing is an essential procedure for eliminating the aliasing error in nonlinear simulations, such as nonlinear gyrokinetic turbulence simulations. An ideal approach to de-aliasing in the periodic dimension is Fourier truncation. Finite difference low-pass filtering applied in the non-periodic direction strongly dampens aliasing modes. At the same time, it induces numerical dissipation in the region of the physically realistic solution. It is shown analytically that the long-wave dissipation coefficient is proportional to the (Np-3) power of the wavenumber under desirable constraints satisfying the highest order accuracy, where Np is the number of filter points. Numerical results after applying the optimised low-pass filtering to the nonlinear gyrokinetic turbulence simulation suggest that the nine-point format preserves intact mesoscopic zonal structures in Tokamak plasmas, and is therefore suitable for long-time nonlinear turbulence simulations.

A new computational method to solve the hyperbolic (Vlasov) equation coupled to the elliptic (Poisson-like) equation at the polar axis is proposed. It is shown that the value of a scalar function at the polar axis can be predicted by its neighbouring values based on the continuity condition. This continuity condition systematically solves the pole problems including the singular factor 1/r in the hyperbolic equation and the inner boundary in the elliptic equation. The proposed method is applied to the global gyrokinetic simulation of the tokamak plasma with the magnetic axis included., Comment: 26 pages, 8 figures, 1 table

Designing protein mutants of both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants of improved stability and activity without any prior experimental mutagenesis data of the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive power compared to current state-of-the-art models on the public mutagenesis dataset over 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the top 30-45 single-site mutations' impact on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize non-natural nucleic acid or resilience to extreme alkaline conditions. Remarkably, over 30% of the AI-recommended mutants exhibited superior performance compared to their pre-mutation counterparts across all proteins and desired properties. Moreover, we have developed an efficient, and successful method based on PRIME to rapidly obtain multi-site mutants with enhanced activity and stability. Hence, PRIME demonstrates the general applicability in protein engineering., Comment: arXiv admin note: text overlap with arXiv:2304.03780

Camouflaged objects are typically assimilated into their backgrounds and exhibit fuzzy boundaries. The complex environmental conditions and the high intrinsic similarity between camouflaged targets and their surroundings pose significant challenges in accurately locating and segmenting these objects in their entirety. While existing methods have demonstrated remarkable performance in various real-world scenarios, they still face limitations when confronted with difficult cases, such as small targets, thin structures, and indistinct boundaries. Drawing inspiration from human visual perception when observing images containing camouflaged objects, we propose a three-stage model that enables coarse-to-fine segmentation in a single iteration. Specifically, our model employs three decoders to sequentially process subsampled features, cropped features, and high-resolution original features. This proposed approach not only reduces computational overhead but also mitigates interference caused by background noise. Furthermore, considering the significance of multi-scale information, we have designed a multi-scale feature enhancement module that enlarges the receptive field while preserving detailed structural cues. Additionally, a boundary enhancement module has been developed to enhance performance by leveraging boundary information. Subsequently, a mask-guided fusion module is proposed to generate fine-grained results by integrating coarse prediction maps with high-resolution feature maps. Our network surpasses state-of-the-art CNN-based counterparts without unnecessary complexities. Upon acceptance of the paper, the source code will be made publicly available at https://github.com/clelouch/BTSNet.

Modern AI tools, such as generative adversarial networks, have transformed our ability to create and modify visual data with photorealistic results. However, one of the deleterious side-effects of these advances is the emergence of nefarious uses in manipulating information in visual data, such as through the use of deep fakes. We propose a novel architecture for preserving the provenance of semantic information in images to make them less susceptible to deep fake attacks. Our architecture includes semantic signing and verification steps. We apply this architecture to verifying two types of semantic information: individual identities (faces) and whether the photo was taken indoors or outdoors. Verification accounts for a collection of common image transformation, such as translation, scaling, cropping, and small rotations, and rejects adversarial transformations, such as adversarially perturbed or, in the case of face verification, swapped faces. Experiments demonstrate that in the case of provenance of faces in an image, our approach is robust to black-box adversarial transformations (which are rejected) as well as benign transformations (which are accepted), with few false negatives and false positives. Background verification, on the other hand, is susceptible to black-box adversarial examples, but becomes significantly more robust after adversarial training.