Identification of fine-grained embryo developmental stages during In Vitro Fertilization (IVF) is crucial for assessing embryo viability. Although recent deep learning methods have achieved promising accuracy, existing discriminative models fail to utilize the distributional prior of embryonic development to improve accuracy. Moreover, their reliance on single-focal information leads to incomplete embryonic representations, making them susceptible to feature ambiguity under cell occlusions. To address these limitations, we propose EmbryoDiff, a two-stage diffusion-based framework that formulates the task as a conditional sequence denoising process. Specifically, we first train and freeze a frame-level encoder to extract robust multi-focal features. In the second stage, we introduce a Multi-Focal Feature Fusion Strategy that aggregates information across focal planes to construct a 3D-aware morphological representation, effectively alleviating ambiguities arising from cell occlusions. Building on this fused representation, we derive complementary semantic and boundary cues and design a Hybrid Semantic-Boundary Condition Block to inject them into the diffusion-based denoising process, enabling accurate embryonic stage classification. Extensive experiments on two benchmark datasets show that our method achieves state-of-the-art results. Notably, with only a single denoising step, our model obtains the best average test performance, reaching 82.8% and 81.3% accuracy on the two datasets, respectively.

Deep clustering nowadays has proven to significantly surpass the classical clustering method, so it has been widely used in diverse applications. One current branch of deep clustering methods enhances the primary task through auxiliary tasks, among which the most prevalent is over-clustering, i.e., jointly training clustering with different numbers of clusters in a multi task manner. However, existing approaches typically treat these auxiliary tasks in isolation and neglect the inherent correlations among their cluster assignments. In this paper, we interpret the cluster assignment memberships of samples generated by all clustering tasks as correlated pseudo-labels. Motivated by this observation, we propose to explicitly exploit such correlation knowledge to improve clustering performance. To achieve this, we can formulate the collection of samples with pseudo-labels as a pseudo-multi-label learning problem, and solve it by employing any off-the-shelf multi-label learning methods which enable to capture correlations between pseudo-labels. Based on this idea, beyond the clustering tasks, we propose a correlation learning auxiliary task, namely Self-supervised Multi-Label Learning (SMLL); and we then specify a novel deep clustering method with SMLL, namely DCSL3. We conduct several experiments to examine the performance of DCSL3on benchmark datasets. Empirical results demonstrate the superiority of DCSL3over the existing deep clustering baseline methods.

Graph contrastive learning (GCL), which captures essential features from augmented graphs to address data sparsity issues, has recently demonstrated promising potential in improving recommendation performance. Most GCL-based recommendation methods learn consistent entity representations from user-item bipartite graphs through structural perturbations. However, these approaches impose an additional computational cost and have been shown to be insensitive to various graph augmentations, resulting in limited improvements in long-tail recommendation scenarios. To address this issue, we propose a novel framework for recommendation,Knowledge-Enhanced graphContrastiveLearning (KECL), which adopts knowledge graph-based embedding augmentation instead of graph enhancement to construct views for GCL. Specifically, we introduce a knowledge aggregation module with a heterogeneous attentive aggregator to capture relation heterogeneity in the knowledge graph. Furthermore, we propose a knowledge-based augmentation GCL model that adds knowledge-aware embeddings to the learned representations for more efficient representation-level augmentation. Extensive experiments on real-world datasets demonstrate that the knowledge-based augmentation approach effectively enhances recommendation performance and shows superiority over state-of-the-art methods.

ABSTRACT Active domain adaptation (active DA) provides an effective solution by selectively labelling a limited number of target samples to significantly enhance adaptation performance. However, existing active DA methods often struggle in real‐world scenarios where, due to data privacy concerns, only a pre‐trained source model is available, rather than the source samples. To address this issue, we propose a novel method called the structure‐based uncertainty estimation model (SUEM) for source‐free active domain adaptation (SFADA). To be specific, we introduce an innovative active sample selection strategy that combines both uncertainty and diversity sampling to identify the most informative samples. We assess the uncertainty in target samples using structure‐wise probabilities and implement a diversity selection method to minimise redundancy. For the selected samples, we not only apply standard‐supervised loss but also conduct interpolation consistency training to further explore the structural information of the target domain. Extensive experiments across four widely used datasets demonstrate that our method is comparable to or outperforms current UDA and active DA methods.

High-sensitivity geoscience and remote sensing instruments employing large reflector antennas face a significant threat from radio frequency interferences (RFIs). In the presence of sufficiently strong RFI, the front-end components of the receiver are driven into nonlinear operation, potentially resulting in permanent damage to the receiver. In this article, we propose utilizing a wideband true-time delay active reconfigurable intelligent surface (WTTD-ARIS) to mitigate wideband RFI encroaching on the mainlobe of a large reflector antenna before it enters the RF front end of the receiver chain. The formulated optimization problem minimizes the sum of RFI power and noise power by jointly optimizing the refractive beamforming at the WTTD-ARIS and the wideband performance of the wireless channel. To tackle this high-dimensional mathematically intractable optimization problem, a two-stage scalable partition deep reinforcement learning (DRL) algorithm is proposed to reduce the computational complexity while achieving robust optimization of the coefficients of the WTTD-ARIS. First, the large-scale WTTD-ARIS is partitioned into several subarrays and a noise power minimization guided codebook design method is proposed to obtain the preliminary coefficients of each subarray. Then, a robust feature-domain partition graph attention reinforcement learning (FPGA-RL) algorithm is developed to correlate the inherent pattern of the coefficients and the dynamic environment to obtain the final coefficients. Finally, the experimental results demonstrate the effectiveness and robustness of the proposed technique to mitigate the mainlobe wideband RFI for a large reflector antenna before it enters the RF front end.