Alzheimer’s disease (AD) is a neurological illness that causes cognitive impairment. Computer-aided diagnosis can help diagnose Alzheimer’s disease early before clinical symptoms appear. Currently, many deep learning methods show good performance in AD diagnosis. Still, most of these methods are based on single/multimodal neuroimaging, leading to a one-sided approach to disease modelling. Combining neuroimaging, cognitive tests, and demographics can significantly improve model performance and reduce the negative impact of noise. This study proposes a multimodal model that introduces contrastive learning, extracting and fusing feature representations separately from cognitive tests and neuroimaging data. After that, contrastive learning based on similarity is employed for both modalities’ features, assisting the network in learning cross-modal features. Moreover, the hybrid attention mechanism of the Transformer encoder is explored for feature fusion. Experimental results on 2082 cases from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset validate the effectiveness of our proposed multimodal model.

Sodium-glucose cotransporter 2 (SGLT2) plays a key role in the reabsorption of glucose in the kidney. Most of the marketed SGLT2 inhibitors have side effects such as hypovolemia and easy induction of urinary and reproductive system infections. Therefore, the development of new SGLT2 inhibitors is of great significance. Iridoids have certain hypoglycemic effects, but their relationship with SGLT2 targets is unclear. In this paper, molecular docking and pharmacophore models of virtual methods are utilized. In this study, 12 iridoid compounds were found to have a high matching ability. Especially, compounds 71 and 100 had higher scores in the two screening methods. Their target protein binding patterns are highly similar to the positive control Empagliflozin. The results showed that iridoid compounds may have potential SGLT2 inhibitory activity, especially No.71 and No.100 molecules have high potential research value, which are worthy of further study.

Medical imaging research has seen advances in deep learning, but temporal aspects in time-series medical imaging data are often overlooked, leading to diagnostic limitations. This study proposes a spatial-temporal fusion approach for medical image segmentation by integrating a 3D UNet spatial network with a novel temporal network, DTransformer, capable of handling irregularly spaced sequences. The 3D UNet extracts spatial features, while DTransformer processes temporal information with time distance considerations using a novel self-attention mechanism. Experiments on a lung CT dataset show significant segmentation accuracy improvements with the fusion approach. DTransformer proves effective for unequally spaced sequences and boosts performance. And spatial-temporal fusion enhances medical image segmentation. Moveover, DTransformer's ability to manage temporal context and time distance holds promise for various tasks, indicating a new avenue for research.

The emergence of big data has made knowledge graphs (KGs) an effective means of representing structured knowledge, and few-shot knowledge graph completion (FKGC) has recently received increasing attention, which attempts to forecast missing information for relations with few-shot related facts. In this regard, several deep learning-based and embedding-based methods have been proposed for FKGC. However, most existing methods overlook multi-hop path information and only utilize the immediate neighbors of relevant entities when encoding and matching entity pairs, potentially limiting their performance. In this paper, we propose an Enhancing Path Information with Reinforcement Learning (EPIRL) approach for FKGC. Specifically, we introduce a reinforcement learning framework to construct a reasoning subgraph, aiming to thoroughly uncover the inferential path rules between support and query triples. Then, we utilize an interaction focused matching model to capture the inherent connections among these reasoning paths. To further improve performance, we incorporate a relational attention mechanism aimed at emphasizing the influence of pivotal paths. Extensive experiments demonstrate that our model outperforms several state-of-the-art methods on the frequently-used benchmark datasets FB15k237-One and NELL-One.

The current emotion-based text categorization method incorporates a lot of deep learning, such as LSTM (Long short term memory) and CNN (Convolutional neural network) algorithms. The traditional algorithm extracts relatively few text features, so the performance of the algorithm can be improved. Based on this fact, this paper decided to adopt a text sentiment prediction method based on CNN and LSTM. First, the user's words are converted into vectors by the frequency of occurrence of the words, and the user's words are convoluted by the CNN to extract the feature information in the user text. Then, further feature extraction of the CNN convolved data by LSTM enables more dimensional information of the text to be used for classification. The experimental results show that the model constructed by this method is more effective in extracting multidimensional features on user text, and effectively optimizes the traditional algorithm. Compared with the CNN model and the LSTM model, the performance is improved.

Abstract Few‐shot knowledge graph (KG) reasoning is the main focus in the field of knowledge graph reasoning. In order to expand the application fields of the knowledge graph, a large number of studies are based on a large number of training samples. However, we have learnt that there are actually many missing relationships or entities in the knowledge graph, and in most cases, there are not many training instances when implementing new relationships. To tackle it, in this study, the authors aim to predict a new entity given few reference instances, even only one training instance. A few‐shot learning framework based on a hybrid attention mechanism is proposed. The framework employs traditional embedding models to extract knowledge, and uses an attenuated attention network and a self‐attention mechanism to obtain the hidden attributes of entities. Thus, it can learn a matching metric by considering both the learnt embeddings and one‐hop graph structures. The experimental results present that the model has achieved significant performance improvements on the NELL‐One and Wiki‐One datasets.

Generating pictures from text is an interesting, classic, and challenging task. Benefited from the development of generative adversarial networks (GAN), the generation quality of this task has been greatly improved. Many excellent cross modal GAN models have been put forward. These models add extensive layers and constraints to get impressive generation pictures. However, complexity and computation of existing cross modal GANs are too high to be deployed in mobile terminal. To solve this problem, this paper designs a compact cross modal GAN based on canonical polyadic decomposition. We replace an original convolution layer with three small convolution layers and use an autoencoder to stabilize and speed up training. The experimental results show that our model achieves 20% times of compression in both parameters and FLOPs without loss of quality on generated images.

The success of CNNs is accompanied by deep models and heavy storage costs. For compressing CNNs, we propose an efficient and robust pruning approach, cross-entropy pruning (CEP). Given a trained CNN model, connections were divided into groups in a group-wise way according to their corresponding output neurons. All connections with their cross-entropy errors below a grouping threshold were then removed. A sparse model was obtained and the number of parameters in the baseline model significantly reduced. This letter also presents a highest cross-entropy pruning (HCEP) method that keeps a small portion of weights with the highest CEP. This method further improves the accuracy of CEP. To validate CEP, we conducted the experiments on low redundant networks that are hard to compress. For the MNIST data set, CEP achieves an 0.08% accuracy drop required by LeNet-5 benchmark with only 16% of original parameters. Our proposed CEP also reduces approximately 75% of the storage cost of AlexNet on the ILSVRC 2012 data set, increasing the top-1 errorby only 0.4% and top-5 error by only 0.2%. Compared with three existing methods on LeNet-5, our proposed CEP and HCEP perform significantly better than the existing methods in terms of the accuracy and stability. Some computer vision tasks on CNNs such as object detection and style transfer can be computed in a high-performance way using our CEP and HCEP strategies.

Since service science is becoming more mature, many universities have now set up numbers of related courses. Service Science and Engineering Department, has some concrete teaching practice at School of Software, Dalian University of Technology. Our paper, analyses specific scientific knowledge systems for the integration of services in industry-specific systems, shows the new discipline will move to Financial Information Service when the service science is integrated into financial sectors, introduces a bachelor program of service science after presenting the relationship between service science and finance industry, analyses specific scientific knowledge systems for the integration of services in industry-specific systems, is to be expected that could establish knowledge system about finance industry on the basis of science-related courses which means that students in this program will take courses in two field, finance and service.