Recent studies have demonstrated that machine learning-based generative models can create novel molecules with desirable properties. Among them, Conditional Variational Autoencoder (CVAE) is a powerful approach to generate molecules with desired physiochemical and pharmacological properties. However, the CVAE’s latent space is still a black-box, making it difficult to understand the relationship between the latent space and molecular properties. To address this issue, we propose the Interpretable Conditional Variational Autoencoder (ICVAE), which introduces a modified loss function that correlates the latent value with molecular properties. ICVAE established a linear mapping between latent variables and molecular properties. This linearity is not only crucial for improving interpretability, by assigning clear semantic meaning to latent dimensions, but also provides a practical advantage. It enables direct manipulation of molecular attributes through simple coordinate shifts in latent space, rather than relying on opaque, black-box optimization algorithms. Our experimental results show that the ICVAE can linearly relate one or multiple molecular properties with the latent value and generate molecules with precise properties by controlling the latent values. The ICVAE’s interpretability allows us to gain insight into the molecular generation process, making it a promising approach in drug discovery and material design.

SAR ship detection is of great significance in marine safety, fisheries management, and maritime traffic. At present, many deep learning-based ship detection methods have improved the detection accuracy but also increased the complexity and computational cost. To address the issue, a lightweight prior feature fusion network (LPFFNet) is proposed to better improve the performance of SAR ship detection. A perception lightweight backbone network (PLBNet) is designed to reduce model complexity, and a multi-channel feature enhancement module (MFEM) is introduced to enhance the SAR ship localization capability. Moreover, a channel prior feature fusion network (CPFFNet) is designed to enhance the perception ability of ships of different sizes. Meanwhile, the residual channel focused attention module (RCFA) and the multi-kernel adaptive pooling local attention network (MKAP-LAN) are integrated to improve feature extraction capability. In addition, the enhanced ghost convolution (EGConv) is used to generate more reliable gradient information. And finally, the detection performance is improved by focusing on difficult samples through a smooth weighted focus loss function (SWF Loss). The experimental results have verified the effectiveness of the proposed model.

DeepPHSI is expected to be applied to the quality control and online detection of P. cablin , and it is a transferable, universal and potential method for hyperspectral analysis.

Accurate segmentation of nuclei is essential for structured analysis in digital pathology, directly influencing cancer diagnosis, grading, and prognosis. However, obtaining high-quality pixel-level annotations remains labor-intensive and time-consuming. Existing self-supervised learning methods primarily rely on low-level appearance representations to construct proxy tasks, which leads to limited sensitivity to nuclei morphology, suboptimal boundary discrimination, and insufficient modeling of spatial continuity. To mitigate these problems, a Self-Supervised Nuclei Segmentation Network with Saliency Guidance and Contextual Boundary Attention (SCB-Net) is proposed for instance segmentation of H&E stained images. First, SCB-Net incorporates the nuclei saliency guided self-supervision mechanism to generate structure-aware pseudo labels, which leverages cytological priors as structural driving signals to extract salient features by enhancing edge gradients and the structural continuity of nuclei. Then, during the segmentation stage, to enhance the structural expressiveness of the model in complex nuclei clusters and ambiguous boundary regions, the Contextual Boundary Attention (CBA) mechanism is designed to strengthen boundary directionality, reinforce edge gradients, and optimize cross-channel information fusion. Furthermore, the Efficient Triple Fusion (ETF) module is employed to concurrently fuse three complementary feature types (semantic, geometric structural, and visual boundary) to achieve cross-scale semantic alignment and geometrically consistent representations. Evaluations on the MoNuSeg, CPM17, and CryoNuSeg datasets demonstrate that SCB-Net attains AJI scores of 0.5609, 0.5102, and 0.4740, respectively. These results are comparable to weakly supervised methods despite requiring no ground-truth labels, and significantly surpass existing self-supervised approaches.