Spatial multi-omics techniques generate extensive spot-level profiles without accompanying spot-type labels, forcing biologists into labor-intensive manual annotation. Although large language models (LLMs) promise automated annotation, they are poorly equipped to handle high-dimensional numeric inputs, struggle to convert complex spatial-omics structures into interpretable text, and lack the specialized biological knowledge needed to avoid hallucinations. Moreover, the intrinsic sparsity and heterogeneity of spatial omics data undermine robust feature extraction and accurate spot-level niche label assignment. To address these challenges, we propose SpotTypeLLM, a framework for annotating spatial multi-omics spot-level niche types using a Bi-view Retrieval-Augmented Generation (BiRAG) tailored for LLMs. Specifically, SpotTypeLLM encodes spatial multi-omics data through scLLM-based embeddings, graph convolutional networks (GCNs), and multi-view attention module, and then selects spot-specific genes to construct the prompts for LLMs. We validate the accuracy and practical utility of our framework by comparing it with ten state-of-the-art methods on labeled spatial multi-omics datasets. The results demonstrate the effectiveness of SpotTypeLLM in overcoming the limitations of current annotation methods and enhancing spatial multi-omics analysis.

RNA's diverse biological functions, including gene regulation, enzymatic catalysis, signal transduction, and even therapeutic activity, ultimately depend on its three-dimensional (3D) structure. Departing from the classical task of inferring structure directly from sequence, this study investigates how to efficiently encode and generate RNA 3D structures, thereby enabling systematic exploration of the molecules' vast folding landscape. We propose a generative framework, RNA-FrameEncoder (RNA-FE). First, a Vector-Quantised Variational AutoEncoder compresses continuous RNA backbones into sequences of discrete structural tokens, “verbalizing” the geometry while suppressing high-frequency noise. Next, a Transformer language model is trained on these token sequences to capture their joint distribution, and a decoder subsequently projects the tokens back into Cartesian coordinates. This design sidesteps the complexity of explicit equivariance modelling, lowers computational cost, and markedly enhances the representational capacity. Comprehensive benchmarks show that RNA-FE can unconditionally generate realistic, novel, and designable RNA structures without any sequence or structural priors. Overall, RNA-FE provides a scalable approach for efficiently charting RNA 3D structural space and opens a data-efficient avenue for a range of downstream applications. The code and pretrained models are publicly available at: https://github.com/users/Hanbo24-bit/RNA-FE.

Rice blast, caused by the fungal pathogen Magnaporthe oryzae (M. oryzae), remains one of the most devastating threats to global rice production, capable of reducing yields by up to 30%. Current integration methods for single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) treat tissues as static snapshots, overlooking the temporal dynamics crucial for host-pathogen analysis. Here, we introduce Rice Blast Temporal Graph Neural Network (RBTN), the first computational framework specifically designed to integrate scRNA-seq and ST data across the three-dimensional spot-cell-time manifold during rice blast infection. RBTN constructs a unified graph whose nodes represent either individual cell transcriptomes or spatial spots sampled at three infection time points (0 h,$\text{1 2 h}, \text{2 4 h}$). It learns stage-specific mappings via bidirectional cosine loss and enforces temporal smoothness through a cosinebased consistency term. Alternating self-attention and temporal-attention layers capture local spatial organization and cross-time transitions. When applied to a public rice blast dataset, RBTN consistently delivers high-fidelity spatial reconstructions across different sets of highly variable marker genes (HVGs) and produces interpretable embeddings that jointly encode spatial, cellular, and temporal relationships. RBTN therefore establishes a robust, temporally-aware computational platform for elucidating the spatiotemporal dynamics of host-pathogen interactions during rice blast infection. All code in this paper is available at https://github.com/zhuyuheng111/RBTN.

Modern network environments—spanning 5G cores, industrial IoT, and spine-leaf data-centers—offer rich hierarchical structure and multi-layer telemetry, yet deep learning models applied to these settings remain black-box predictors that ignore domain logic and struggle with scarce data. We introduce LogiK-Net, a neurosymbolic framework that bridges data and knowledge by decoupling the learning process into (i) a forward-discovery module based on Kolmogorov-Arnold Networks (KANs) that yields interpretable edge activations for feature pruning and rule mining, and (ii) a backward-validation module that employs differentiable first-order network logic to enforce domain axioms and the rules mined on-the-fly. This modular design allows practitioners to swap in richer feature extractors or stricter logical rule sets in the machine learning model as needed, scaling smoothly from supervised traffic-classification to unsupervised, open-world network management. Extensive experiments on reliable feature pruning, IoT threat detection, and topology discovery demonstrate LogiK-Net's generality, interpretability, and reliability, outperforming standard neural network baselines employed in network analysis.

Autonomous vehicles operate in dynamic, ever-changing environments where new scenarios and edge cases constantly emerge. As a result, static learning models are inadequate for ensuring safe and reliable operation. Continuous learning is essential for adapting to these evolving conditions and maintaining robust performance across diverse real-world settings. However, autonomous vehicles generate massive streams of visual data during operation, and existing continuous learning approaches typically rely on heuristic sampling methods that fail to capture temporal dynamics, often overlooking critical learning opportunities or selecting redundant frames. In this paper, we introduce FrameScope, a temporal data valuation framework for continuous learning in autonomous vehicles. FrameScope extends neural tangent kernel theory to temporal domains, enabling principled valuation of streaming visual data. Unlike cloud-centric methods that transmit all video data for processing, our approach performs principled, local frame selection on the vehicle and queries a cloud-based oracle model only for labels of those high-value frames. Extensive experiments across multiple domain shifts show that FrameScope consistently outperforms existing methods, achieving higher sample efficiency and significantly reducing catastrophic forgetting in autonomous vehicle perception. By valuing data on the vehicle and querying only labels for selected frames, FrameScope reduces bandwidth requirements, enabling scalable operation with a lightweight cloud labeling service.

MOTIVATION: Accurate prediction of RNA secondary structure remains challenging due to the presence of pseudoknots, long-range dependencies, and limited labeled data. RESULTS: We propose TVAE, a novel framework that integrates a Transformer encoder with a Variational Autoencoder (VAE). The Transformer captures global dependencies in the sequence, while the VAE models structural variability by learning a probabilistic latent space. Unlike deterministic models, TVAE generates diverse and biologically plausible secondary structures, enabling more comprehensive structure discovery. To obtain discrete predictions, we introduce GHA-Pairing, a fast and biologically constrained base-pairing algorithm. TVAE demonstrates strong generalization across different RNA families and achieves state-of-the-art performance on benchmark datasets, reaching an F1 score of 0.89 and 83% accuracy, surpassing existing methods by 10%. These results highlight the advantage of probabilistic modeling for RNA structure prediction and its potential to enhance biological insights. AVAILABILITY AND IMPLEMENTATION: Code and pretrained models are available at https://github.com/mei-rna/TVAE-RNA. The released version of the dataset and models can also be accessed via DOI: 10.5281/zenodo.16946114.

Deploying vision perception models in autonomous vehicles requires that we prioritize inference speeds, resulting in a model with shallower architectures and lesser model parameters (i.e., more pruned). Such small models do not generalize well, which could result in poor performance when encountered with novel scenarios. We propose a system that overcomes this by continuously retraining the vision models on the cloud with data uploaded by vehicles. We leverage the abundant compute resources, including machine learning accelerators, of the cloud to run a highly-accurate oracle model that will guide the retraining process of the on-vehicle model. This newly trained model is transmitted to the vehicle over the network and is utilized by the vehicle for perceptions, leading to improved inference accuracy over time.

Cloud computing plays an increasingly vital role in both commercial and personal services. In multi-tenant clouds, cloud providers encounter challenges such as physical machine failures and malicious tenant attacks. Ensuring the reliability and robustness of cloud remain significant and complex challenges for cloud providers to improve quality of service and profitability. Previous works either fail to strike a balance between these aspects or result in resource waste and increased costs. In this paper, we propose an innovative virtual machine placement solution without additional resource overhead. Specifically, we place tenants’ virtual machines (VMs) on physical machines (PMs) that meet the reliability requirements specified in the service level agreement, while also limiting the number of PMs to mitigate the impact of malicious attacks, thereby enhancing the robustness of the cloud. However, the dynamic nature of tenant traffic exacerbates the complexity of the problem. To tackle this challenge, we present KR-OPD, a two-stage algorithm with superior competitive ratios. Through large-scale simulations and small-scale testbed, KR-OPD outperforms existing state-of-the-art solutions. For example, our algorithm reduces the affected range of tenants by 47%-64% and the packet loss rate of PM nodes by over 70% compared with other alternatives.