Recent text-to-image (T2I) diffusion models have demonstrated remarkable capabilities in visual synthesis, yet their performance heavily relies on the quality of input prompts. However, optimizing discrete prompts remains challenging because the discrete nature of tokens prevents the direct application of the gradient descent method and the vast search space of possible token combinations. As a result, existing approaches either suffer from quantization errors when employing continuous optimization techniques or be- come trapped in local optima due to coordinate-wise greedy search. In this paper, we propose STEPS, a novel Sequential probability Tensor Estimation approach for hard Prompt Search. Our method reformulates discrete prompt optimization as a sequential probability tensor estimation problem, leveraging the inherent low-rank characteristics to address the curse of dimensionality. To further improve the computational efficiency, we develop a memory-bounded sampling approach that shrinks the prompt space without the iteration step dependency while preserving sequential optimization dynamics. Extensive experiments on various public datasets demonstrate that our method consistently outperforms existing approaches in T2I generation, cross-model prompt transferability, and harmful prompt optimization, validating the effectiveness of the proposed framework.

Despite the growing success of transform-based tensor models such as the t-product, their underlying geometric principles remain poorly understood. Classical differential geometry, built on real-valued function spaces, is not well suited to capture the algebraic and spectral structure induced by transform-based tensor operations. In this work, we take an initial step toward a geometric framework for tensors equipped with tube-wise multiplication via orthogonal transforms. We introduce the notion of smooth t-manifolds, defined as topological spaces locally modeled on structured tensor modules over a commutative t-scalar ring. This formulation enables transform-consistent definitions of geometric objects, including metrics, gradients, Laplacians, and geodesics, thereby bridging discrete and continuous tensor settings within a unified algebraic-geometric perspective. On this basis, we develop a statistical procedure for testing whether tensor data lie near a low-dimensional t-manifold, and provide nonasymptotic guarantees for manifold fitting under noise. We further establish approximation bounds for tensor neural networks that learn smooth functions over t-manifolds, with generalization rates determined by intrinsic geometric complexity. This framework offers a theoretical foundation for geometry-aware learning in structured tensor spaces and supports the development of models that align with transform-based tensor representations.

In daily clinical practice, clinicians exhibit robustness in identifying polyps with both location and size variations. It is uncertain if deep segmentation models can achieve comparable robustness in automated colonoscopic analysis. To benchmark the model robustness, we focus on evaluating the segmentation models on the polyps with various attributes (e.g. location and size) and healthy samples. Based on the Latent Diffusion Model, we perform attribute editing on real polyps and build a new dataset named Polyp-E. Our synthetic dataset boasts exceptional realism, to the extent that clinical experts find it challenging to discern them from real data. We evaluate various existing polyp segmentation models on the proposed benchmark. The results reveal most of the models are highly sensitive to attribute variations. As a novel data augmentation technique, the proposed editing pipeline can improve both in-distribution and out-ofdistribution generalization ability. The code and datasets has been released at https://github.com/RunpuWei/Polyp-E-Benchmark.

Deep Multi-view Clustering (DMVC) stands out as a widely adopted technique aiming at enhanced clustering performance by leveraging diverse data sources. However, the critical issue of vulnerability to adversarial attacks is unexplored due to the lack of well-defined attack objectives. To fill this crucial gap, this paper is the first work to investigate the possibility of adversarial attacks on DMVC models. Specifically, we introduce an adversarial attack with Generative Adversarial Networks (GANs) with the aim to maximally change the complementarity and consistency of multiple views, thus leading to wrong clustering. Building upon this adversarial context, in the realm of defense, we propose a novel Adversarially Robust Deep Multi-View Clustering by leveraging adversarial training. Based on the analysis from an information-theoretic perspective, we design an Attack Mitigator that provides a foundation to guarantee the adversarial robustness of our DMVC models. Experiments conducted on multi-view datasets confirmed that our attack framework effectively reduces the clustering performance of the target model. Furthermore, our proposed adversarially robust method is also demonstrated to be an effective defense against such attacks. This work is a pioneer in exploring adversarial threats and advancing both theoretical understanding and practical strategies for robust multi-view clustering. Code is available at https://github.com/libertyhhn/AR-DMVC.

The introduction of edge computing provides a broad application scenario for the Internet of Vehicles. Service programs that were not able to be handled timely by On-Board Unit (OBU) can now be placed on Road Side Unit (RSU) to meet users' requirements of End-to-End (E2E) latency and reliability. However, based on the microservice architecture, services are decomposed of multiple microservices, and the complex dependencies between microservices pose new challenges to their placement. To tackle this problem, we first model the dependencies as a directed acyclic graph (DAG), and the long-term interference-aware placement model is then established to depict the load balance between RSUs and network. After that, we formulate it as an integer linear programming (ILP) problem with the aim to achieve a tradeoff between node load cost and transmission cost while reducing the E2E latencies. Considering the local features of DAG topology, an iterative two-phase heuristic microservice placement algorithm is then proposed. Finally, a simulation environment based on real-world electric taxis trajectory data is constructed, and intensive experiments with several baseline algorithms are conducted to verify the superiority of our proposed algorithm.

Learning a comprehensive representation from multiview data is crucial in many real-world applications. Multiview representation learning (MRL) based on nonnegative matrix factorization (NMF) has been widely adopted by projecting high-dimensional space into a lower order dimensional space with great interpretability. However, most prior NMF-based MRL techniques are shallow models that ignore hierarchical information. Although deep matrix factorization (DMF)-based methods have been proposed recently, most of them only focus on the consistency of multiple views and have cumbersome clustering steps. To address the above issues, in this article, we propose a novel model termed deep autoencoder-like NMF for MRL (DANMF-MRL), which obtains the representation matrix through the deep encoding stage and decodes it back to the original data. In this way, through a DANMF-based framework, we can simultaneously consider the multiview consistency and complementarity, allowing for a more comprehensive representation. We further propose a one-step DANMF-MRL, which learns the latent representation and final clustering labels matrix in a unified framework. In this approach, the two steps can negotiate with each other to fully exploit the latent clustering structure, avoid previous tedious clustering steps, and achieve optimal clustering performance. Furthermore, two efficient iterative optimization algorithms are developed to solve the proposed models both with theoretical convergence analysis. Extensive experiments on five benchmark datasets demonstrate the superiority of our approaches against other state-of-the-art MRL methods.

Since real‐world multiview data frequently contains numerous samples that are not observed from some viewpoints, the incomplete multiview clustering (IMC) issue has received a great deal of attention recently. However, most existing IMC methods choose to zero‐fill the missing instances, which leads to the failure to exploit information hidden in the missing instances, and high‐order interactions between various views. To tackle these problems, we proposed an effective IMC method using low‐rank tensor ring completion, which was demonstrated to be powerful in exploiting high‐order correlation. Specifically, we first stack the incomplete similarity graphs of all views into a 3rd‐order incomplete tensor and then restore it via the tensor ring decomposition. Next, using an adaptive weighting technique, we apply multiview spectral clustering to all entire graphs in order to balance the contributions of different viewpoints and identify the consensus representation for grouping. Finally, we employ the alternating direction method of multipliers (ADMM) to optimize the suggested model. Numerous experimental findings on numerous different datasets show that the suggested approach is superior to other cutting‐edge approaches.

The importance of microbe-drug associations (MDA) prediction is evidenced in research. Since traditional wet-lab experiments are both time-consuming and costly, computational methods are widely adopted. However, existing research has yet to consider the cold-start scenarios that commonly seen in clinical research and practices where confirmed MDA data are highly sparse. Therefore, we aim to contribute by developing two novel computational approaches, the GNAEMDA (Graph Normalized Auto-Encoder to predict MDA), and its variational extension (called VGNAEMDA), to provide effective and efficient solutions for well-annotated cases and cold-start scenarios. Multi-modal attribute graphs are constructed by collecting multiple features of microbes and drugs, and then input into a graph normalized convolutional network, where a $\ell _{2}$-normalization is introduced to avoid the norm-towards-zero tendency of isolated nodes in embedding space. Then the reconstructed graph output by the network is used to infer undiscovered MDA. The difference between the two proposed models lays in the way to generate the latent variables in network. To verify their effectiveness, we conduct a series of experiments on three benchmark datasets in comparison with six state-of-the-art methods. The comparison results indicate that both GNAEMDA and VGNAEMDA have strong prediction performances in all cases, especially in identifying associations for new microbes or drugs. In addition, we conduct case studies on two drugs and two microbes and find that more than 75% of the predicted associations have been reported in PubMed. The comprehensive experimental results validate the reliability of our models in accurately inferring potential MDA.

Since a large proportion of real-world data is made of different representations or views, learning on data represented with multiple views (e.g., numerous types of features or modalities) has garnered considerable attention recently. Nonnegative matrix factorization (NMF) has been widely adopted for multi-view learning due to its great interpretability. We focus on unsupervised multi-view data representation in this paper and propose a novel framework termed Deep Autoencoder-like NMF (DANMF-MDR), which learns an intact representation by simultaneously exploring multi-view complementary and consistent information. Furthermore, an efficient iterative optimization algorithm is developed to solve the proposed model. Experimental results on three real-world multi-view datasets demonstrate that ours performs better than the SOTA multi-view NMF-based MDR approaches.