Spontaneous Echo Contrast (SEC) is a swirling smoke-like echo phenomenon in Transesophageal Echocardiography (TEE) videos caused by slow blood flow and hypercoagulable states. It is a significant indicator for assessing thromboembolic risk. However, current SEC identification requires extensive manual intervention, leading to low accuracy, high costs, and subjectivity. To address these issues, we propose TEENet, an effective clinical detection network for identifying SEC in TEE videos. Specifically, TEENet first generates attention maps for the input clips to highlight important regions and integrates Convolutional Neural Network with the Multi-Head Self-Attention to capture spatiotemporal representations. Furthermore, to enhance the classification performance across different SEC severity grades, we introduce an auxiliary classification module, which simultaneously utilizes the main classification head and auxiliary classification heads. Notably, we constructed a comprehensive dataset of 1106 TEE videos collected during clinical examinations performed at the First Affiliated Hospital of Soochow University from 2018 to 2023, providing a solid foundation for the development and validation of TEENet. Extensive experimental results demonstrate that our proposed network achieves the highest SEC identification accuracy of 92.4$\pm$1.3% compared to other spatiotemporal representation networks such as SlowFastR50 (89.6$\pm$0.7%) and TimeSformer (74.9$\pm$1.8%), which shows strong potential for effective auxiliary diagnosis in clinical practice.

Large AI models, connected to terminal devices via high-speed mobile communication networks, enable task collaboration and resource sharing, forming an intelligent framework for the Symbiotic Internet of Things (SIoT) paradigm in industry applications. Despite large language models hold significant potential for psychological intervention, their emotional interaction capabilities remain limited. This paper introduces a SIoT framework for psychological intervention and proposes an emotion-enhanced human-machine interaction architecture incorporating behavioral information captured by IoT devices. The system leverages ubiquitous sensing devices such as cameras and microphones, along with technologies like speech recognition and generation, as well as hyper-realistic digital humans, to create a natural interaction interface. Additionally, we introduce the end-of-utterance detection method and the behavior pattern control algorithm to facilitate smoother and more goal-oriented conversations. The proposed methods and prototype system have been validated through subjective and objective experiments, with results demonstrating their feasibility and suggesting that this approach could become one of the primary forms of humanmachine interaction for psychological intervention in the future.

Inverse molecular generation is an essential task for drug discovery, and generative models offer a very promising avenue, especially when diffusion models are used. Despite their great success, existing methods are inherently limited by the lack of a semantic latent space that can not be navigated and perform targeted exploration to generate molecules with desired properties. Here, we present a property-guided diffusion model for generating desired molecules, which incorporates a sophisticated diffusion process capturing intricate interactions of nodes and edges within molecular graphs and leverages a time-dependent molecular property classifier to integrate desired properties into the diffusion sampling process. Furthermore, we extend our model to a multi-property-guided paradigm. Experimental results underscore the competitiveness of our approach in molecular generation, highlighting its superiority in generating desired molecules without the need for additional optimization steps.

Text matching systems have become a fundamental service in most Searching platforms. For instance, they are responsible for matching user queries to relevant candidate items, or rewriting the user-input query to a pre-selected high-performing one for a better search experience. In practice, both the queries and items often contain multiple attributes, such as the category of the item and the location mentioned in the query, which represent condensed key information that is helpful for matching. However, most of the existing works downplay the effectiveness of attributes by integrating them into text representations as supplementary information. Hence, in this work, we focus on exploring the relationship between the attributes from two sides. Since attributes from two ends are often not aligned in terms of number and type, we propose to exploit the benefit of attributes by multiple-intent modeling. The intents extracted from attributes summarize the diverse needs of queries and provide rich content of items, which are more refined and abstract, and can be aligned for paired inputs. Concretely, we propose a multi-intent attribute-aware matching model (MIM), which consists of three main components: attribute-aware encoder, multi-intent modeling, and intent-aware matching. In the attribute-aware encoder, the text and attributes are weighted and processed through a scaled attention mechanism with regard to the attributes' importance. Afterward, the multi-intent modeling extracts intents from two ends and aligns them. Herein, we come up with a distribution loss to ensure the learned intents are diverse but concentrated, and a kullback-leibler divergence loss that aligns the learned intents. Finally, in the intent-aware matching, the intents are evaluated by a self-supervised masking task, and then incorporated to output the final matching result. Extensive experiments on three real-world datasets from different matching scenarios show that MIM significantly outperforms state-of-the-art matching baselines. MIM is also tested by online A/B test, which brings significant improvements over three business metrics in query rewriting and query-item relevance tasks compared with the online baseline in Alipay App.

Generating molecular graphs using deep graph generative models is a challenging task that involves optimizing a given target within an enormous search space while adhering to chemical valence rules. Despite promising results, existing models mainly focus on learning molecular graph structures at the individual level while ignoring inter-molecular relationships regarding molecular characterization features and molecular activity. This can lead to the generation of molecules that are unresponsive to their true neighbors possessing similar characterization features, resulting in a divergence between the learned generation distribution and the actual molecular distribution. In this paper, we propose a distribution preserving model, designed to maintain the inter-molecular relationships of the original distribution within the generated space. Specifically, the model operates on a student-teacher paradigm, where the teacher module learns the inter-molecular relationship dynamics of the original distribution, and imparts this knowledge to the student module, which is responsible for generating molecules. Experimental results on two benchmark datasets demonstrate that our model outperforms the state-of-the-art models in generating valid, novel and unique molecules. Moreover, our model is verified on preserving molecule distribution in the generation space.

Due to the growing importance of using graph neural networks in high-stakes applications, there is a pressing need to interpret the predicted results of these models. Existing methods for explanation have mainly focused on generating sub-graphs comprising important edges for a specific prediction. However, these methods face two issues. Firstly, they lack counterfactual validity as removing the subgraph may not affect the prediction, and generating plausible counterfactual examples has not been adequately explored. Secondly, they cannot be extended to heterogeneous graphs as the complex information involved in such graphs increases the difficulty of generating interpretations. This paper proposes a novel counterfactual learning method, named CF-HGExplainer, for heterogeneous graphs. The method incorporates a semantic-aware attentive pooling strategy for the heterogeneous graph classifier and designs a heterogeneous decision boundaries extraction module to find the common logic for similar graphs based on the extracted graph embeddings from the classifier. Additionally, we propose to greedily perturb nodes and edges based on the distribution of node features and edge plausibility to train a neural network for heterogeneous edge weight learning. Extensive experiments on two public academic datasets demonstrate the effectiveness of CF-HGExplainer compared to state-of-the-art methods on the graph classification task and graph interpretation task.

Molecular graph generation via deep generative models has attracted increasing attention. This is a challenging problem because it requires optimizing a given objective under a huge search space while obeying the chemical valence rules. Although recently developed molecular generation models have achieved promising results on generating novel, valid and unique molecules, few efforts have been made toward interpretable molecular graph generation. In this work, we propose DEMO, a flow-based model for DisEntangled Molecular graph generatiOn in a completely unsupervised manner, which is able to generate molecular graphs w.r.t. the learned disentangled latent factors that are relevant to molecular semantic features and interpretable structural patterns. Specifically, DEMO is composed of a VAE-encoder and a flow-generator. The VAE-encoder focuses on extracting global features of molecular graphs, and the flow-generator aims at disentangling these features to be corresponding to certain types of understandable molecular structure features while learning data distributions. To generate molecular graphs, DEMO simply runs the flow-generator in the reverse order due to the reversibility of the flow-based models. Extensive experimental results on two benchmark datasets demonstrate that DEMO outperforms the state-of-the-art methods in molecular generation, and takes the first step in interpretable molecular graph generation.

Generating novel molecules with desired properties is a fundamental problem in modern drug discovery. This is a challenging problem because it requires the optimization of the given objectives while obeying the rules of chemical valence. An effective approach is to incorporate the molecular graph with deep generative models. However, recent generative models with high-performance are still computationally expensive. In this paper, we propose GF-VAE, a flow-based variational autoencoder (VAE) model for molecular graph generation. Specifically, the model equips VAE a lightweight flow model as its decoder, in which, the encoder aims to accelerate the training process of the decoder, while the decoder in turns to optimize the performance of the encoder. Thanks to the invertibility of flow model, the generation process is easily accomplished by reversing the decoder. Additionally, the final generated molecules are processed by validity correction. Therefore, our GF-VAE inherits the advantages of both VAE and flow-based methods. We validate our model on molecule generation and reconstruction, smoothness of learned latent space, property optimization and constrained property optimization. The results show that our model achieves state-of-the-arts performance on these tasks. Moreover, the time performance of GF-VAE on two classical datasets can achieve 31.3% and 62.9% improvements separately than the state-of-the-art model.

When taking care of complex patients with multiple morbidities, accurately predicting the occurrence of each cause-specific event is critical for designing optimal treatment plans. However, standard survival analysis cannot deal with the multiple (usually competing) adverse events and views those competing events as censored. This will result in biased estimation of the incidence rate. In this paper, we propose a deep learning based survival analysis algorithm called DeepComp to jointly predict the progress of the competing events, which can thus inform the doctors which event is more likely to hit the patient first. DeepComp constructs a multi-task recurrent neural network (RNN) and views the conditional probability of each competing event at each time point as the output of each RNN cell. Then the probability chain rule is utilized to combine them together. In this way, the survival probability and the risk for each competing event over the time space are obtained. The multitask structure not only prevents the model from unreasonable censoring but also aids the model in capturing the complex hidden association among the competing events. A novel penalty is added to the loss function to better discriminate the competing risks for each particular patient, which could benefit treatment decision-making. We conduct comprehensive experiments on two real-world clinical data sets and one synthetic data set. The proposed DeepComp method achieves significant performance improvement compared to the state-of-the-art baseline methods.

Duhuo-Jisheng-Tang is a Chinese herbal formula designed to treat rheumatoid arthritis (RA) with syndrome dual deficiency of liver and kidney. Although its therapeutic clinical effect is widely accepted over one thousand years, its therapeutic agents are still obscure. In this study, we explored the potential therapeutic agents of Duhuo-Jisheng-Tang for RA by integrating leading knowledge databases of different biomedical domains. The molecule mechanism includes compositional herbal medicine, chemical compound, target protein, RA OMIM genes, FDA approved RA drug targets, and pathways. As a result, Duhuo-Jisheng-Tang's 11 target proteins can be found in RA's OMIM genes (e.g., UGT1, UGT1A9, GNT1, and JAK3) and FDA approved RA drug targets (e.g., CMKBR2/5, CCR2/5, APRF, and STAT1/3). What's more, these 11 genes/proteins are mainly enriched in pathways of disease, immune system, signal transduction, and metabolism. It demonstrates that Duhuo-Jisheng-Tang may regulate a wide range of different systems associated with RA which might indicate that Chinese herbal formula's therapeutic effects are demonstrated through system-level regulation rather than targeting disease specified genes/proteins.