Herb recommendation plays a crucial role in the therapeutic process of Traditional Chinese Medicine (TCM), which aims at recommending a set of herbs to treat patients with different symptoms. Previous works used many methods to discover regularities in prescriptions but rarely considered the actual therapeutic process in TCM and the information of herbs was ignored. In this work, we propose LAMGCN (Herb Recommendation via LSTMs with Attention Mechanisms and Graph Convolutional Networks), which takes the syndrome induction process and the herb descriptions into account. We utilize attention mechanisms and graph neural networks to capture the correlation between symptoms and herbs. Extensive experiments have been done and the results demonstrate the effectiveness of our proposed method.

With the introduction of the Adaptive Intelligence Engine (AIE), the Versal Adaptive Compute Acceleration Platform (Versal ACAP) has garnered great attention. However, the current focus of Vitis Libraries and limited research has mainly been on how to invoke AIE modules, without delving into a thorough discussion on effectively utilizing AIE in its typical use cases. As a result, the widespread adoption of Versal ACAP has been restricted. The Communication Avoidance (CA) algorithm is considered a typical application within the AIE architecture. Nevertheless, the effective utilization of AIE in CA applications remains an area that requires further exploration. We propose a top-down customized design framework, EA4RCA (Efficient AIE accelerator design framework for regular Communication-Avoiding Algorithm), specifically tailored for CA algorithms with regular communication patterns, and equipped with AIE Graph Code Generator software to accelerate the AIE design process. The primary objective of this framework is to maximize the performance of AIE while incorporating high-speed data streaming services. Experiments show that for the RCA algorithm Filter2D and Matrix Multiple (MM) with lower communication requirements and the RCA algorithm FFT with higher communication requirements, the accelerators implemented by the RA4RCA framework achieve the highest throughput improvements of 22.19×, 1.05×, and 3.88× compared with the current highest performance acceleration scheme (SOTA), and the highest energy efficiency improvements of 6.11×, 1.30× and 7.00×.

Abstract The technology for autonomous navigation on inland waterways is worth investigating, and navigable water surface segmentation is a key part of this technology. Semantic segmentation methods based on deep learning are able to distinguish between water surface areas and non‐water surface areas. However, existing semantic segmentation methods cannot meet the requirements of the water surface segmentation task in terms of both segmentation precision and real‐time performance. In this study, a Swap Attention Bilateral Segmentation Network (SA‐BiSeNet) is proposed to improve segmentation performance while ensuring model inference speed by better fusing the two features of the dual‐branch down‐sampling network using the attention mechanism. Specifically, an innovative Swap Attention Module is designed to model the dependency between the features of the spatial detail branch and the features of the semantic branches, thus expanding the receptive fields of the spatial detail and semantic branches to each other's global contexts. This design can effectively fuse features and thus enhance feature representation. Experiments were conducted on the inland waterway dataset USVInland to verify the performance of SA‐BiSeNet in terms of segmentation precision and inference speed, and SA‐BiSeNet achieved 93.65% Mean IoU and maintained the same level of fps as the baseline.

Abstract The sustainable development of marine fisheries depends on the accurate measurement of data on fish stocks. Semantic segmentation methods based on deep learning can be applied to automatically obtain segmentation masks of fish in images to obtain measurement data. However, general semantic segmentation methods cannot accurately segment fish objects in underwater images. In this study, a Dual Pooling‐aggregated Attention Network (DPANet) to adaptively capture long‐range dependencies through an efficient and computing‐friendly manner to enhance feature representation and improve segmentation performance is proposed. Specifically, a novel pooling‐aggregate position attention module and a pooling‐aggregate channel attention module are designed to aggregate contexts in the spatial dimension and channel dimension, respectively. These two modules adopt pooling operations along the channel dimension and along the spatial dimension to aggregate information, respectively, thus reducing computational costs. In these modules, attention maps are generated by four different paths and are aggregated into one. The authors conduct extensive experiments to validate the effectiveness of the DPANet and achieve new state‐of‐the‐art segmentation performance on the well‐known fish image dataset DeepFish as well as on the underwater image dataset SUIM, achieving a Mean IoU score of 91.08% and 85.39% respectively, while significantly reducing FLOPs of attention modules by about 93%.

A depth map can be used in many applications such as robotic navigation, driverless, video production and 3D reconstruction. Both passive stereo and time‐of‐flight (ToF) cameras can provide the depth map for the captured real scenes, but they both have innate limitations. Since ToF cameras and passive stereo are intrinsically complementary for certain scenes, it is desirable to appropriately leverage all the available information by ToF cameras and passive stereo. As a result, this study proposes an approach to integrate ToF cameras and passive stereo to obtain high‐accuracy depth maps. The main contributions are: the first step is to design an energy cost function to utilise the depth map from ToF cameras to guide the stereo matching of passive stereo and the second step is to design their weight function for depth maps pixel‐level fusion. The experiments show that the proposed approach achieves the improved results with high accuracy and robustness.