North China University of Science & Technology

Weakly supervised camouflaged object segmentation (WSCOS) aims to segment objects well embedded in surroundings via the supervision of sparse annotations. To compensate for the shortcomings of sparse annotations, existing methods design intricate loss functions with multiple regularization rules, not fully exploring the annotation information itself. Therefore, to address this issue, this paper proposes the long-range diffusion network (LRDNet) to diffuse the sparse annotations for improving WSCOS performance. Specifically, a novel gated local saliency coherence (GLSC) loss is designed to efficiently diffuse limited annotation information across the entire image to supplement the supervision by the unidirectional gating. Meanwhile, a two-stage training is introduced to make GLSC loss further improve the diffusion ability of background annotations and then produce the enhanced squeezing effect for sharp edges. Additionally, for capturing sufficient long-range dependencies, the Trans-decorator and the restoration upsampling (RUp) are designed to communicate global priors with convolutional modules by spatial tokens. Extensive experiments have been conducted and the experimental results demonstrate the effectiveness and the high versatility of our designed LRDNet. The code is available at https://github.com/Ray3417/LRDNet.

Major depressive disorder (MDD) impacts >300 million individuals worldwide, highlighting a significant public health issue. However, the uneven distribution of medical resources and the complexity of diagnostic methods have resulted in inadequate attention to this disorder in numerous countries and regions.

This paper introduces a high-performance MDD diagnosis tool named MDD-LLM, an AI-driven framework that utilizes fine-tuned large language models (LLMs) and extensive real-world samples to tackle challenges in MDD diagnosis. Specifically, we select 274,348 individual records from the UK Biobank cohort and design three tabular data transformation methods to create a large corpus for training and evaluating the proposed method. To illustrate the advantages of MDD-LLM, we perform comprehensive experiments and provide several comparative analyses against existing model-based solutions across multiple evaluation metrics.

Experimental results show that MDD-LLM (70B) achieves an accuracy of 0.8378 and an AUC of 0.8919 (95 % CI: 0.8799-0.9040), significantly outperforming existing machine and deep learning frameworks for MDD diagnosis. Given the limited exploration of LLMs in MDD diagnosis, we examine numerous factors that may influence the performance of our proposed method, including tabular data transformation techniques and different fine-tuning strategies. Furthermore, we also analyze the model's interpretability, requiring the MDD-LLM to explain its predictions and provide corresponding reasons.

This paper investigates the application of LLMs and large-scale training samples for diagnosing MDD. The findings indicate that LLMs-driven schemes offer significant potential for accuracy, robustness, and interpretability in MDD diagnosis compared to traditional model-based solutions.

A novel sensing material, La₂CuO₄, with high sensitivity to both NO and NO₂ was developed and incorporated into an impedimetric NO_x sensor with a dense/porous dual-layer Ce_0.8Gd_0.2O_2-δ (CGO) solid electrolyte, resulting in a sensor with consistent sensitivity direction to NO and NO₂.The performance of the sensor was tested to detect NO and NO₂ in the range of 25-700 ppm at 400°C-550°C.Among the various configurations tested, the sensor with 50 μl impregnation of the sensing electrode (SE) demonstrated superior performance in detecting both NO and NO₂ at 450°C and 0.1 Hz, and showed comparable sensitivity to both gases (0.036 and 0.039 °/ppm to 200-700 ppm NO and NO₂ resp.).The sensor also exhibited commendable reproducibility, stability, and selectivity for both NO and NO₂, while also displaying resistance to moisture and immunity to O₂ concentration fluctuations.The superior properties were attributed to the excellent adsorption and electrocatalytic properties of La₂CuO₄ for NO and NO₂.