Accurate and efficient question-answering systems are essential for high-quality patient care in the medical field. While Large Language Models (LLMs) have made remarkable strides across various domains, they still face challenges in medical question answering, particularly in understanding domain-specific terminology and performing complex reasoning, limiting their effectiveness in critical applications. To address this, we propose a multi-agent medical question-answering (MedQA) system incorporating similar case generation. We leverage the Llama3.1:70B model in a multi-agent architecture to enhance enhance zero-shot classification on the MedQA dataset, utilizing the model’s inherent medical knowledge and reasoning capabilities without additional training data. Experimental results show substantial gains over existing benchmark models, with improvements of 7% in both accuracy and F1-score across various medical QA tasks. Furthermore, we examine the model’s interpretability and reliability in addressing complex medical queries. This research not only offers a robust solution for medical question answering but also establishes a foundation for broader applications of LLMs in the medical domain.

In the era of big data, the landscape of data management and analytics has significantly transformed, presenting diverse challenges for cloud platforms. Modern data warehouses face increasing challenges in handling hybrid transactional and analytical processing (HTAP) workloads efficiently in cloud environments. Traditional shared-nothing architectures provide high-performance query execution but suffer from high storage costs and limited elasticity, while shared-storage approaches improve scalability but often struggle with query efficiency due to increased data movement and indexing overhead. Furthermore, existing execution engines lack optimized support for vectorized processing and real-time analytics, limiting their ability to handle large-scale workloads efficiently. To address these limitations, we introduce AnalyticDB-PG (ADB-PG), a cloud-native, high-performance data warehouse designed for modern analytical workloads. It integrates a unified architecture supporting both Shared-Nothing and Shared-Storage modes, allowing flexible deployment and seamless elasticity. In ADB-PG, we introduce Beam, a hybrid storage engine that efficiently balances row-based and columnar storage for real-time analytics, and Laser, an optimized execution engine leveraging vectorized execution and Just-In-Time compilation to accelerate query processing. The system further incorporates advanced indexing mechanisms, adaptive runtime filtering, and dictionary encoding to enhance performance. Extensive evaluations on TPC-H and TPC-DS benchmarks demonstrate that ADB-PG achieves significant performance improvements while reducing storage and operational costs, making it a compelling solution for modern cloud-based data analytics.

Due to the particularity of the simultaneous occurrence of multiple events in music sequences, compound Transformer is proposed to deal with the challenge of long sequences. However, there are two deficiencies in the compound Transformer. First, since the order of events is more important for music than natural language, the information provided by the original absolute position embedding is not precise enough. Second, there is an important correlation between the tokens in the compound word, which is ignored by the current compound Transformer. Therefore, in this work, we propose an improved compound Transformer model for music understanding. Specifically, we propose an attribute embedding fusion module and a novel position encoding scheme with absolute-relative consideration. In the attribute embedding fusion module, different attributes are fused through feature permutation by using a multi-head self-attention mechanism in order to capture rich interactions between attributes. In the novel position encoding scheme, we propose RoAR position encoding, which realizes rotational absolute position encoding, relative position encoding, and absolute-relative position interactive encoding, providing clear and rich orders for musical events. Empirical study on four typical music understanding tasks shows that our attribute fusion approach and RoAR position encoding brings large performance gains. In addition, we further investigate the impact of masked language modeling and casual language modeling pre-training on music understanding.

Optimizer is an essential component for the success of deep learning, which guides the neural network to update the parameters according to the loss on the training set. SGD and Adam are two classical and effective optimizers on which researchers have proposed many variants, such as SGDM and RAdam. In this paper, we innovatively combine the backward-looking and forward-looking aspects of the optimizer algorithm and propose a novel Admeta (**A** **D**ouble exponential **M**oving averag**E** **T**o **A**daptive and non-adaptive momentum) optimizer framework. For backward-looking part, we propose a DEMA variant scheme, which is motivated by a metric in the stock market, to replace the common exponential moving average scheme. While in the forward-looking part, we present a dynamic lookahead strategy which asymptotically approaches a set value, maintaining its speed at early stage and high convergence performance at final stage. Based on this idea, we provide two optimizer implementations, AdmetaR and AdmetaS, the former based on RAdam and the latter based on SGDM. Through extensive experiments on diverse tasks, we find that the proposed Admeta optimizer outperforms our base optimizers and shows advantages over recently proposed competitive optimizers. We also provide theoretical proof of these two algorithms, which verifies the convergence of our proposed Admeta.

Rice variety identification is important for genetic breeding classification and crop yield estimation. Traditional identification methods are time-consuming and inaccurate. This paper proposes a method for rice variety identification based on the hyperspectral characteristics of leaves. Hyperspectral data of rice leaves were collected using a geophysical spectrometer imaging system. To reduce the redundance among the hyperspectral data and save the identification cost, locality preserving projections (LPP) is first applied to extract low-dimensional representative features from the leaf hyperspectral data. Then, support vector machine (SVM) is combined for conducting the identification of rice varieties. The experimental results show that the identification rate of 10 varieties of early rice was found to be 91.67% and the identification rate of 10 varieties of late rice was 97.33%.

Grape is an important cash crop that is susceptible to diseases when growing, resulting in lower yield and quality. In recent years, transformers have achieved excellent performance in a variety of natural language processing and image recognition tasks through the self-attention mechanism. Therefore, this paper proposes a grape leaf disease recognition model named Dense Convolutional Transformer (DensCT). The compact convolutional transformer (CCT) is used as the backbone in this model, which improves the convolutional module of the original model by introducing densely connected modules, enhancing the transfer and reuse of features between networks. This also modifies the single-scale feature extraction method of the original model to multi-scale, which improves the feature extraction performance. Finally, the model was trained on two small-scale datasets from scratch, and the recognition accuracy of the final model on the test sets reached 89.19% and 93.92%. Compared with CCT, DenseNet121, ResNet50, MobileNetV3 and ViT, the recognition accuracy improved by 4.73%, 3.38%, 10.81%, 0.68% and 18.24% on the first dataset and 6.08%, 5.41%, 1.35%, 3.38% and 12.84% on the second dataset. The experimental results show that the proposed model can effectively identify grape leaf diseases, which can provide a reference for building disease leaf recognition models on small-scale datasets.

Watermelon is a crop susceptible to diseases. Rapid and effective detection of watermelon diseases is of great significance to ensure the yield of watermelon. Aiming at the interference of the environment and obstacles in the natural environment, resulting in low target detection accuracy and poor robustness, this paper takes watermelon leaves as the research object, considering anthracnose, leaf blight, leaf spot and normal leaves as examples. A disease recognition method based on deep learning is proposed. This paper has improved the pre-selected box setting formula of the SSD model and tested it in multiple SSD models. Experiments show that the average accuracy of the final SSD768 model is 92.4%, and the average accuracy of the IOU is 88.9%. It shows that this method can be used to detect watermelon diseases in natural environment.