Multi-view clustering (MVC) has recently garnered increasing attention for its ability to partition unlabeled samples into distinct clusters by leveraging complementary and consistent information from different views. Existing MVC methods primarily combine deep neural networks with contrastive learning for cross-view representation learning, yet often overlook the inherent global-local structural relationships among samples. While GNN-based methods capture local structures, they struggle to model global dependencies, leading to inferior inter-cluster separability. In contrast, Transformer-based methods excel at global aggregation but suffer from quadratic complexity, and their attention smoothing effect weakens fine-grained local structures, resulting in suboptimal intra-cluster compactness. To address these limitations, we propose a novel end-to-end MVC framework called Mamba-Driven Multi-View Discriminative Clustering via Global-Local Cross-View Sequence Modeling (MGLC). By flexibly constructing multi-view sequences, MGLC fully exploits the efficient sequence modeling capabilities of Mamba to jointly model cross-view dependencies and global-local structural relationships among samples. Furthermore, MGLC introduces a Cross-Mamba Fusion module to dynamically integrate cross-view and global-local structural representations. Additionally, MGLC incorporates a Dual Calibration Contrastive Learning module, guided by high-confidence pseudo-labels, that adaptively refines both feature and semantic representations while mitigating false negatives among semantically similar samples. Extensive comparative experiments and ablation studies demonstrate the effectiveness of MGLC.

Diffusion models have recently shown strong potential for reconstr-uction-based unsupervised anomaly detection (UAD). However, industrial UAD across diverse object categories remains challenging: subtle defects often overlap with intrinsic structural details, and commonly used uniform or semantically agnostic perturbations can induce two failure modes—identity shortcut (copying uncorrupted content, resulting in misleading residuals) and semantic drift (over-smoothed yet structurally inconsistent restorations). We propose SaF-AD, a saliency-adaptive and feature-consistent diffusion framework to mitigate these issues. First, Saliency-Adaptive Perturbation Masking (SAPM) applies soft, saliency-guided masking to adaptively corrupt informative regions while avoiding hard boundaries, encouraging structure-aware reconstruction instead of background redundancy. Second, Progressive Anchor Decoupling (PAD) progressively adjusts the masking preference during training to reduce persistent anchors and prevent shortcut learning, forcing reconstruction of salient structures from diverse contextual cues. Third, Hierarchical Semantic Feature Consistency (HSFC) regularizes multi-level features on corrupted regions using a frozen backbone, improving semantic coherence while preserving fine-grained details. Experiments on MVTec-AD and VisA show that SaF-AD achieves competitive image-level detection performance and more consistent gains on pixel-level anomaly localization.

Industrial anomaly detection is typically trained under the assumption of clean normal data, yet real-world manufacturing datasets are often contaminated by unlabeled defects and background outliers. Global memory-based detectors are especially fragile in such noisy regimes: semantic mismatch across regions can induce erroneous retrieval, while boundary-crossing patches yield ambiguous pseudo supervision. We propose SAND, a semantic-aware framework for unsupervised anomaly detection under noisy training data that leverages training-only semantic priors derived from pre-computed semantic region partitions. SAND introduces a region-consistent memory design that constrains retrieval to the same semantic region, with a global fallback when region assignment is uncertain, reducing cross-region aliasing. To further stabilize learning, we develop purity-aware soft weighting with adaptive thresholds to downweight boundary-ambiguous pseudo labels. We additionally propose a region-wise representation regularizer that enforces intra-region compactness and inter-region separation, thereby mitigating anomaly contamination of prototypes. Extensive experiments on MVTec AD and VisA under multiple noise protocols, including realistic background perturbations, demonstrate consistent improvements in both image-level detection and pixel-level localization, particularly in multi-object scenarios. Notably, SAND achieves these gains without semantic priors or test-time retrieval, supporting efficient inference for practical deployment. Our results highlight the importance of semantic provenance in noisy anomaly detection and suggest a practical pathway toward robust industrial deployment.

3D scene inpainting aims to restore geometrically and texturally consistent content after object removal, enabling immersive scene editing and virtual content creation. Despite rapid progress in neural 3D reconstruction and rendering (e.g., Neural Radiance Fields and 3D Gaussian Splatting), achieving accurate and artifact-free 3D completion remains challenging. In particular, (i) imprecise 2D masks yield unreliable inpainting scopes, (ii) selecting high-quality 2D reference views for lifting to 3D is difficult due to view-dependent perceptual fidelity, and (iii) integrating 2D priors into 3D often introduces blurred textures and structural artifacts. These issues can accumulate and amplify as inconsistencies are fused into the 3D representation. We propose Ref2Inpaint, a geometry-aware and reference-guided framework for high-quality 3D scene inpainting. First, our Visibility-Aware Mask Refinement aggregates cross-view visibility cues to suppress erroneous masked regions and establish a spatially consistent inpainting scope. Second, our VLM-Guided Reference Retrieval combines geometric filtering with VLM-based quality ranking to select high-fidelity, cross-view consistent references for 3D initialization and inpainting guidance. Finally, a Two-Stage Structural Densification progressively reconstructs missing geometry from coarse layouts to fine-grained details, reducing floaters and boundary artifacts while improving structural plausibility. Our work demonstrates that advanced retrieval mechanisms can significantly alleviate the texture inconsistency issue in generative 3D tasks. Extensive experiments on both real and synthetic scenes demonstrate that Ref2Inpaint achieves superior visual fidelity, geometric coherence, and multi-view consistency compared to state-of-the-art methods.

Rendering high-fidelity 3D scenes is crucial for immersive applications like virtual reality and digital twins. However, standard 3D Gaussian Splatting (3DGS) relies heavily on multi-view consistency, making it fragile in real-world scenarios plagued by glass reflections. These reflections often manifest as geometric "floaters" or severe texture artifacts, obscuring the true background. Existing solutions, which typically employ single-image priors or NeRF-based in-painting, often lack explicit 3D constraints or rely on synthetic data, failing to generalize to complex environments. To address these challenges, we first present a novel benchmark dataset of 8 real-world scenes, capturing physically paired reflective and reflection-free images. Building on this, we propose GlassSplat, a robust framework designed to eliminate view-dependent artifacts and recover clean transmission geometry. Our method initializes with a reflection prior and introduces an Affine-Based Exposure Correction module to align global photometric inconsistencies. To distinguishing valid geometry from virtual outliers, we incorporate an Epipolar Consistency Loss and an uncertainty-weighted Depth Regularization. Finally, to physically purge residual noise, we devise a Visibility-Aware Pruning strategy that dynamically filters artifacts based on multi-view statistics. Extensive experiments demonstrate that GlassSplat significantly outperforms state-of-the-art approaches, effectively recovering a clean, artifact-free 3D scene representation.

The rapid growth of Internet of Things (IoT) devices has imposed higher demands on computational capabilities, which traditional cloud computing struggles to meet in real-time scenarios due to latency issues. Mobile edge computing (MEC) addresses these challenges by processing data at the network edge, thereby reducing latency and enhancing computational efficiency. However, MEC alone is insufficient for handling complex tasks, requiring more robust solutions. This paper proposes a hybrid cloud-edge computing framework that enhances system performance by integrating cloud and edge computing. A game-theoretic model is used to optimize wireless bandwidth allocation, and a Stackelberg game mechanism is introduced to incentivize task offloading. This approach orchestrates resource allocation and task offloading dynamics through game theory, ensuring cost minimization and delay requirements are met while fostering cloud-edge collaboration. Theoretical analysis demonstrates the existence of Nash equilibria in both layers of the game, ensuring the system’s stability and effectiveness in complex environments. Based on this, the GA-based resource allocation and offloading (GRAO) algorithm, and the iterative game-theoretic offloading (IGTO) algorithm are proposed. Experimental results validate the proposed algorithms, showing that the IGTO algorithm reduces the average cost for mobile devices (MDs) by 49.8% compared to the best baseline, while enhancing overall performance for both MEC servers and the cloud.

Incomplete multi-view clustering (IMVC) has attracted increasing attention in recent years, owing to the prevalence of missing data in real-world multi-view scenarios. Existing imputation-based IMVC methods partially mitigate the impact of missing information but still face three key limitations: (i) overlooking latent structural relationships among samples, which leads to imputed representations deviating from the true distribution; (ii) decoupling imputation from clustering, which reduces the discriminability of the recovered representations; and (iii) exhibiting low efficiency, which makes it difficult to balance recovery quality and inference speed under complex missing scenarios. To address these issues, we propose a Structure-Aware Conditional Diffusion Generation (SACDG) framework. During training, SACDG first models local structural relationships via adaptive neighborhood graphs and injects them as conditional priors into the diffusion model, where a cross-attention mechanism integrates these priors into the noise prediction process to learn structure-aware generative capability. Meanwhile, a semantic distribution alignment module is introduced to leverage pseudo-labels for enforcing cross-view consistency, thereby enhancing semantic discriminability. During inference, SACDG integrates cross-view structural information through cross-view adjacency fusion to guide the reverse denoising trajectory, and employs deterministic DDIM sampling to efficiently and stably recover the representations of missing views. Extensive comparative experiments and ablation studies on multiple benchmark datasets demonstrate that SACDG achieves superior clustering performance and improved efficiency over state-of-the-art methods. Our code is available athttps://github.com/zhangyuanyang21/SACDG.

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved significant improvements, they still encounter notable limitations: 1) heavy reliance on paired data for training the data recovery module, which is impractical in real scenarios with high missing data rates; 2) the generated data often lacks diversity and discriminability, resulting in suboptimal clustering results. To address these shortcomings, we propose a novel IMVC method called Diffusion Contrastive Generation (DCG). Motivated by the consistency between the diffusion and clustering processes, DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data. By performing contrastive learning on a limited set of paired multi-view samples, DCG can align the generated views with the real views, facilitating accurate recovery of views across arbitrary missing view scenarios. Additionally, DCG integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data, achieving robust and end-to-end clustering. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches.

Previous successful approaches to missing modality completion rely on carefully designed fusion techniques and extensive pre-training on complete data, which can limit their generalizability in out-of-domain (OOD) scenarios. In this study, we pose a new challenge: can we develop a missing modality completion model that is both resource-efficient and robust to OOD generalization? To address this, we present a training-free framework for missing modality completion that leverages large multimodal model (LMM). Our approach, termed the "Knowledge Bridger", is modality-agnostic and integrates generation and ranking of missing modalities. By defining domain-specific priors, our method automatically extracts structured information from available modalities to construct knowledge graphs. These extracted graphs connect the missing modality generation and ranking modules through the LMM, resulting in high-quality imputations of missing modalities. Experimental results across both general and medical domains show that our approach consistently outperforms competing methods, including in OOD generalization. Additionally, our knowledge-driven generation and ranking techniques demonstrate superiority over variants that directly employ LMMs for generation and ranking, offering insights that may be valuable for applications in other domains.

3D scene segmentation is crucial for 3D vision tasks and serves as a foundational step for downstream applications such as semantic understanding, object removal, and object editing. However, due to the complexity of 3D scene representation, existing methods often face challenges such as blurred edges, artifacts, and inaccurate segmentation. To address these issues, we propose a novel interactive Scale-Controlled 3D Object Segmentation method based on Gaussian Splatting, called SCGS. Specifically, SCGS first performs multiview 3D reconstruction through Gaussian Splatting, followed by object segmentation guided by visual prompts. During the reconstruction phase, we control the scale variation of 3D Gaussians to promote fine-grained flattening and better alignment with the object surface, while incorporating depth priors to enhance spatial understanding and improve object boundary recognition within the 3D scene. In the segmentation phase, we introduce an improved scale-aware contrastive training strategy that combines a scale-gated network with triplet loss to optimize the learning of scale-gated affinity features, thus improving the accuracy and robustness of the segmentation model. Extensive experimental results demonstrate that our method outperforms existing approaches on the NVOS, SPIn-NeRF, and MipNeRF360 datasets, significantly improving both the quality and accuracy of 3D scene segmentation.

With the rapid advancement of deepfake technology, its application in multimodal content such as video and audio has posed severe threats to digital information security and privacy protection. Although existing deepfake detection methods have made significant progress, three critical challenges remain: efficient fusion of cross-modal information, precise extraction of fine-grained visual features, and effective discrimination between the distributions of authentic and forged samples. To address these challenges, this paper innovatively proposes FUME, a crossmodal deepfake detection framework integrating facial UV maps with momentum contrastive learning. Specifically, the method introduces a Texture-Aware Video Transformer (TAViT) to achieve deep fusion of facial UV maps and spatiotemporal video features, while employing an Audio Spectrogram Transformer (AST) for multi-scale feature modeling of speech signals. Additionally, we design a momentum contrastive learning based cross-modal alignment mechanism, which achieves semantic-level matching of audiovisual representations through dynamic construction of positive and negative sample pairs, while incorporating a OneClass softmax loss function to enhance generalization capability against unseen deepfake generation techniques. Extensive experiments on datasets including DeepfakeTIMIT, DFDC, and KoDF validate our method’s superiority. Notably, on the KoDF dataset, our approach achieves $97.59 \%$ accuracy and $98.23 \%$ AUC, surpassing the current audio-visual state-of-the-art by 1.91 % and $2.99 \%$.

Multiview clustering (MVC) has gained significant attention as it enables the partitioning of samples into their respective categories through unsupervised learning. However, there are a few issues as follows: 1) many existing deep clustering methods use the same latent features to achieve the conflict objectives, namely, reconstruction and view consistency. The reconstruction objective aims to preserve view-specific features for each individual view, while the view-consistency objective strives to obtain common features across all views; 2) some deep embedded clustering (DEC) approaches adopt view-wise fusion to obtain consensus feature representation. However, these approaches overlook the correlation between samples, making it challenging to derive discriminative consensus representations; and 3) many methods use contrastive learning (CL) to align the view's representations; however, they do not take into account cluster information during the construction of sample pairs, which can lead to the presence of false negative pairs. To address these issues, we propose a novel multiview representation learning network, called anchor-sharing and clusterwise CL (CwCL) network for multiview representation learning. Specifically, we separate view-specific learning and view-common learning into different network branches, which addresses the conflict between reconstruction and consistency. Second, we design an anchor-sharing feature aggregation (ASFA) module, which learns the sharing anchors from different batch data samples, establishes the bipartite relationship between anchors and samples, and further leverages it to improve the samples' representations. This module enhances the discriminative power of the common representation from different samples. Third, we design CwCL module, which incorporates the learned transition probability into CL, allowing us to focus on minimizing the similarity between representations from negative pairs with a low transition probability. It alleviates the conflict in previous sample-level contrastive alignment. Experimental results demonstrate that our method outperforms the state-of-the-art performance.

With the rapid development of artificial intelligence, a substantial number of computing-intensive applications have emerged in Internet of Things (IoT) devices. The mobile edge computing (MEC) architecture enables the provision of abundant computing and storage resources in close proximity to end users (EUs), thereby effectively enhancing their quality of experience (QoE). Nonetheless, both the MEC server and EUs are self-interests, it is crucial to establish suitable incentive mechanism to promote active engagement from both parties in the offloading process. Therefore, we employ the Stackelberg game to describe the interaction process between EUs and the MEC server, and an optimal relationship between bandwidth and offloading task size is established to simplify the decision problem for EUs. Then, the optimal strategies for the MEC server and EUs are solved using reverse induction. Given the limited resources of the MEC server, we propose a dynamic programming-based resource allocation (DPRA) algorithm to maximize the revenue of the MEC server while ensuring the cost of each EU. The simulation results demonstrate that the DPRA algorithm can reduce latency and energy consumption costs, significantly outperforming other comparative strategies in terms of performance at both EUs and the MEC server.

The widespread adoption of the Internet of Things (IoT) has increased demand for task processing via mobile edge computing (MEC). In this study, we designed a directed acyclic graph (DAG) task offloading workflow in MEC. Traditional task offloading often does not simultaneously take into account task upload delay and task communication delay, failing to accurately reflect real-world issues. The constraints between task execution delay, upload delay and communication delay were introduced to model system response time and energy consumption for optimization. To satisfy task dependencies, the edge rank_u sorting (ERS) algorithm is used to generate specific offloading queues. A federated deep q-network (FDQN) algorithm addresses the offloading issue. It is different from the traditional approach of uploading task information data to the edge and facing data privacy risks. FDQN deploies the model locally and only collects model parameters for aggregation to update the local model. The algorithm improves the performance and stability of the model while protecting user privacy. To automatically tune hyperparameters for multiple devices, we used the tree of parzen estimators (TPE) algorithm, and named the whole process federated DQN with automated hyperparameter optimization (FDAHO). Experimental results show that FDAHO outperforms other algorithms in scenarios of different task number, task types, and user numbers, with consideration of benchmarks.

Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views via view-wise aggregation way, where they ignore structure relationship of all samples. In this paper, we propose a novel multi-view clustering network to address these problems, called Global and Cross-view Feature Aggregation for Multi-View Clustering (GCFAggMVC). Specifically, the consensus data presentation from multiple views is obtained via cross-sample and cross-view feature aggregation, which fully explores the complementary of similar samples. Moreover, we align the consensus representation and the view-specific representation by the structure-guided contrastive learning module, which makes the view-specific representations from different samples with high structure relationship similar. The proposed module is a flexible multi-view data representation module, which can be also embedded to the incomplete multi-view data clustering task via plugging our module into other frameworks. Extensive experiments show that the proposed method achieves excellent performance in both complete multi-view data clustering tasks and incomplete multi-view data clustering tasks.

Data mining techniques have been proposed to predict mortality for ICU patients using their demographic data, measurements and notes from doctors and nurses. Most of these techniques suffer from two main drawbacks. First, they model the mortality prediction problem as a binary classification problem, while ignoring the time of death as continuous values. Second, they use topic models to analyze the notes, while ignoring the relationship between measurements, notes and mortality/discharge outcomes. In this paper we propose a novel model called the survival topic model (SVTM), which models patients' measurements, notes and mortality/discharge jointly, and predicts the probability of mortality/discharge as functions of time. The idea is that each patient has a latent distribution of disease conditions, which we call topics. These conditions generate the measurements and notes and determine the patients' mortality. We derive a mean-field variational inference algorithm for this model. We fitted the SVTM with two outcomes on Medical Information Mart for Intensive Care III (MIMIC III) trauma patients data and obtained some important topics. Also, we demonstrated the relationships between these topics.

The Cox model has been widely used in time-to-outcome predictions, particularly in studies of medical patients, where prediction of the time of death is desired. In addition, the cure model has been proposed to model times of death for discharged patients. However, neither the Cox model nor the cure model allow explicit cure information and prediction of patient cure times (discharge times). In this paper we propose a new model, the "cure time model", which models the static data for dying patients, surviving patients, and their death/cure times jointly. It models (1) mortality via logistic regression and (2) death and discharge times via Cox models. We extend the cure time model to situations with censored data, where neither time of death nor discharge time are known, as well as to multiple (>2) outcomes. In addition, we propose a joint log-odds ratio which can predict the mortality of patients using the information from both the logistic regression and Cox models. We compare our model with the Cox and cure models on a trauma patient dataset from UCSF/San Francisco General Hospital. Our results show that the cure time model more accurately predicts both mortality and time-to-mortality for patients from these datasets.

With the increasing of physical-layer (PHY) data rate in modern wireless local area networks (WLANs) (e.g., 802.11n), the overhead of media access control (MAC) progressively degrades data throughput efficiency. This trend reflects a fundamental aspect of the current MAC protocol, which allocates the channel as a single resource at a time. This paper argues that, in a high data rate WLAN, the channel should be divided into separate subchannels whose width is commensurate with the PHY data rate and typical frame size. Multiple stations can then contend for and use subchannels simultaneously according to their traffic demands, thereby increasing overall efficiency. We introduce FICA, a fine-grained channel access method that embodies this approach to media access using two novel techniques. First, it proposes a new PHY architecture based on orthogonal frequency division multiplexing (OFDM) that retains orthogonality among subchannels while relying solely on the coordination mechanisms in existing WLAN, carrier sensing and broadcasting. Second, FICA employs a frequency-domain contention method that uses physical-layer Request to Send/Clear to Send (RTS/CTS) signaling and frequency domain backoff to efficiently coordinate subchannel access. We have implemented FICA, both MAC and PHY layers, using a software radio platform, and our experiments demonstrate the feasibility of the FICA design. Furthermore, our simulation results show FICA can improve the efficiency of WLANs from a few percent to 600% compared to existing 802.11.

Today's WiFi access points (APs) are ubiquitous, and provide critical connectivity for a wide range of mobile networking devices. Many management tasks, e.g. optimizing AP placement and detecting rogue APs, require a user to efficiently determine the location of wireless APs. Unlike prior localization techniques that require either specialized equipment or extensive outdoor measurements, we propose a way to locate APs in real-time using commodity smartphones. Our insight is that by rotating a wireless receiver (smartphone) around a signal-blocking obstacle (the user's body), we can effectively emulate the sensitivity and functionality of a directional antenna. Our measurements show that we can detect these signal strength artifacts on multiple smartphone platforms for a variety of outdoor environments. We develop a model for detecting signal dips caused by blocking obstacles, and use it to produce a directional analysis technique that accurately predicts the direction of the AP, along with an associated confidence value. The result is Borealis, a system that provides accurate directional guidance and leads users to a desired AP after a few measurements. Detailed measurements show that Borealis is significantly more accurate than other real-time localization systems, and is nearly as accurate as offline approaches using extensive wireless measurements.

Modern communication technologies are steadily advancing the physical layer (PHY) data rate in wireless LANs, from hundreds of Mbps in current 802.11n to over Gbps in the near future. As PHY data rates increase, however, the overhead of media access control (MAC) progressively degrades data throughput efficiency. This trend reflects a fundamental aspect of the current MAC protocol, which allocates the channel as a single resource at a time.