Recent advances in interactive text-to-image retrieval (I-TIR) use diffusion models to bridge the modality gap between the textual information need and the images to be searched. However, existing frameworks fuse multi-modal user feedback by simple embedding addition. In this work, we show that this basic fusion strategy indiscriminately incorporates generative noise produced by the diffusion model, leading to performance degradation for up to 55.62% of samples. We further propose ADaFuSE (Adaptive Diffusion-Text Fusion with Semantic-aware Experts), a lightweight fusion model designed to align and calibrate multi-modal views for diffusion-augmented I-TIR, which can be plugged into existing frameworks without modifying the backbone encoder. Specifically, we introduce a dual-branch fusion mechanism that employs an adaptive gating branch to dynamically balance modality reliability, alongside a semantic-aware mixture-of-experts branch to capture fine-grained cross-modal nuances. Via thorough evaluation over four standard I-TIR benchmarks, ADaFuSE achieves state-of-the-art performance, surpassing the DAR model by up to 3.49% in Hits@10 with only a 5.29% parameter increase, while exhibiting stronger robustness to noisy and longer interactive queries. These results show that generative augmentation coupled with principled fusion provides a simple, generalizable alternative to fine-tuning for tasks like product search.

Fault diagnosis is crucial in the industrial Internet of Things (IIoT), but unknown fault types lead to out-of-distribution (OOD) problems, making label prediction challenging. Various domain adaptation (DA) methods often rely heavily on prior knowledge of the target domain. This article proposes a universal DA (UDA) fault diagnosis method capable of effectively handling various DA settings. The method proposed enables diagnosis without considering the label in the target domain. This eliminates the need to switch between different diagnostic models, greatly enhancing the generalization capability of crossing various tasks and reducing both time and operational costs in engineering applications. The method utilizes clustering algorithms to leverage the changes in data density information, and then address the long-tailed imbalanced data problem to some extent. Through cycle-matching at the category and sample levels, potential unknown categories in the target domain are identified, and samples of shared classes are aligned using contrastive domain discrepancy loss. To mitigate misclassifications during the clustering, extreme-value theory (EVT) models are constructed using source domain samples to filter out incorrect samples. The proposed method is evaluated by constructing experiments with imbalanced data and cross-domain experiments under different working conditions on the WT-planetary gearbox dataset and the twin spool engine (TSE) datasets. Simultaneously, we visualize the analysis of the results. The experimental results demonstrate that the proposed approach can accurately identify unknown fault samples and make improvements in both accuracy and H-score.

Vision-based industrial inspection (VII) aims to locate defects quickly and accurately. Supervised learning under a close-set setting and industrial anomaly detection, as two common paradigms in VII, face different problems in practical applications. The former is that various and sufficient defects are difficult to obtain, while the latter is that specific defects cannot be located. To solve these problems, in this article, we focus on the few-shot semantic segmentation (FSS) method, which can locate unseen defects conditioned on a few annotations without retraining. Compared to common objects in natural images, the defects in VII are small. This brings two problems to current FSS methods: first, distortion of target semantics and second, many false positives for backgrounds. To alleviate these problems, we propose a small object few-shot segmentation (SOFS) model. The key idea for alleviating, first, is to avoid the resizing of the original image and correctly indicate the intensity of target semantics. SOFS achieves this idea via the nonresizing procedure and the prototype intensity downsampling of support annotations. To alleviate, second, we design an abnormal prior map in SOFS to guide the model in reducing false positives and propose a mixed normal dice loss to prevent the model from predicting false positives preferentially. SOFS can achieve FSS and few-shot anomaly detection determined by support masks. Diverse experiments substantiate the superior performance of SOFS.

Existing data-driven fault diagnosis methods assume that the label sets of the training data and test data are consistent, which is usually not applicable for real applications since the fault modes that occur in the test phase are unpredictable. To address this problem, open set fault diagnosis (OSFD), where the test label set consists of a portion of the training label set and some unknown classes, is studied in this article. Considering the changeable operating conditions of machinery, OSFD tasks are further divided into shared-domain open set fault diagnosis (SOSFD) and cross-domain open set fault diagnosis (COSFD) in this article. For SOSFD, 1-D convolutional neural networks are trained for learning discriminative features and recognizing fault modes. For COSFD, due to the distribution discrepancy between the source and target domains, the deep model needs to learn domain-invariant features of shared classes and separate features of outlier classes. Thus, by utilizing the output of an additional domain classifier, a model named bilateral weighted adversarial networks is proposed to assign large weights to shared classes and small weights to outlier classes during the feature alignment. In the test phase, samples are classified according to the outputs of the deep model and unknown-class samples are rejected by the extreme value theory model. Experimental results on two bearing datasets demonstrate the effectiveness and superiority of the proposed method.

The cutting tool is a key part of a machine system, which plays an important role in modern manufacturing systems. To avoid an unexpected tool failure, it is necessary to carry out health condition assessment of cutting tools. In this paper, a deep coupled restricted Boltzmann machine (DCRBM) is proposed for health state assessment of cutting tools based on fusion of vibration signals and acoustic emission (AE) signals. Because of the complementary of multisensory signals, it is necessary to develop a fusion strategy for the fusion of multisource signals. The proposed DCRBM is symmetric with each side consisting of several hidden layers and one coupled layer, which is constructed by two basic restricted Boltzmann machines with similarity constraints. Vibration signals and AE signals, which are connected with the two sides of DCRBM, respectively, are mapped into a feature space, where similar representations are learned. The parameters of the deep architecture are learned by optimizing the new objective function. Experimental results on fusion of vibration signals and AE signals demonstrate the promising performance of DCRBM for health state assessment of cutting tools compared with other fusion strategies.

As HPC systems scale toward exascale, it becomes critical to manage the underlying resource more effectively. While almost all existing resource management systems schedule jobs in a queuing fashion and have drawbacks of making isolated scheduling decisions that would compromise system performance even with backfilling, plan-based schedulers have the potential to generate better job schedules by producing an execution plan of all waiting jobs but do not receive enough attention. In this paper, we present a novel plan-based scheduling system that utilizes simulated annealing as the optimization engine to support effective resource management on HPC systems. As demonstrated by extensive trace-based simulations with workload traces collected from a wide range of production supercomputers, in comparison with the queue-based scheduling system using FCFS with EASY backfilling, our plan-based scheduling system can reduce the job wait time by 40%, reduce the job response time by 30%, while slightly improving system utilization at the same time. Moreover, our plan-based system is able to run online by solving the scheduling problem at each scheduling iteration within one second, making it practical for production HPC systems.