Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation

Different from other sequential data, sentences in natural language are structured by linguistic grammars. Previous generative conversational models with chain-structured decoder ignore this structure in human language and might generate plausible responses with less satisfactory relevance and fluency. In this study, we aim to incorporate the results from linguistic analysis into the process of sentence generation for high-quality conversation generation. Specifically, we use a dependency parser to transform each response sentence into a dependency tree and construct a training corpus of sentence-tree pairs. A tree-structured decoder is developed to learn the mapping from a sentence to its tree, where different types of hidden states are used to depict the local dependencies from an internal tree node to its children. For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees. Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are finally flattened into sequences as the system output. Experimental results demonstrate that the proposed X2Tree framework outperforms baseline methods over 11.15% increase of acceptance ratio

Relationship between eating attitudes, depression, and insight in schizophrenic patients with and without type 2 diabetes mellitus: a comparative study in Guangdong, China

BackgroundSchizophrenia, a severe mental disorder, is often complicated by Type 2 Diabetes Mellitus (T2DM), which can further impact patients’ psychological health. This study investigated the differences in eating attitudes, depression, and insight between schizophrenic patients with and without comorbid T2DM and explored the correlations among these factors to provide empirical support for clinical interventions.MethodsThis case-control study was conducted in Guangdong Province, China. From December 2022 to May 2023, a total of 300 hospitalized patients with schizophrenia (92 with comorbid T2DM and 208 without T2DM) were recruited. Data were collected using the Personal Information Form, Eating Attitudes Test (EAT-26), Hamilton Depression Scale (HAMD), and Insight and Treatment Attitudes Questionnaire (ITAQ). Statistical analyses, including t-tests, ANOVA, and multiple linear regression, were performed to examine differences and predictive factors of eating attitudes among patients. This study was approved by the Ethics Committee of the Affiliated Brain Hospital of Guangzhou Medical University (approval number: 2020028), and written informed consent was obtained from all participants.ResultsPatients with schizophrenia and comorbid T2DM exhibited significantly higher risks of eating disorders (EAT-26: 12.54 ± 9.77 vs. 9.07 ± 7.90, P=0.003), more severe depression (HAMD: 14.71 ± 7.36 vs. 11.80 ± 6.04, P=0.001), and poorer insight (ITAQ: 10.46 ± 6.01 vs. 12.16 ± 6.09, P=0.025) compared to those without T2DM. Regression analysis revealed that gender, weekly exercise frequency, depression, and insight were significant predictors of eating attitudes among patients with T2DM. For patients without T2DM, weekly exercise frequency, smoking status, and insight were significant predictors.ConclusionSchizophrenic patients with comorbid T2DM are facing increasing risks related to eating attitudes, depression, and insight which highlight the need for targeted interventions. Regular psychological assessment and tailored support strategies might improve their mental health and quality of life. Future research should focus on longitudinal studies to clarify causal relationships and develop more effective interventions

Extracting Zero-shot Structured Information from Form-like Documents: Pretraining with Keys and Triggers

In this paper, we revisit the problem of extracting the values of a given set of key fields from form-like documents. It is the vital step to support many downstream applications, such as knowledge base construction, question answering, document comprehension and so on. Previous studies ignore the semantics of the given keys by considering them only as the class labels, and thus might be incapable to handle zero-shot keys. Meanwhile, although these models often leverage the attention mechanism, the learned features might not reflect the true proxy of explanations on why humans would recognize the value for the key, and thus could not well generalize to new documents. To address these issues, we propose a Key-Aware and Trigger-Aware (KATA) extraction model. With the input key, it explicitly learns two mappings, namely from key representations to trigger representations and then from trigger representations to values. These two mappings might be intrinsic and invariant across different keys and documents. With a large training set automatically constructed based on the Wikipedia data, we pre-train these two mappings. Experiments with the fine-tuning step to two applications show that the proposed model achieves more than 70% accuracy for the extraction of zero-shot keys while previous methods all fail

Robust Indoor Human Activity Recognition Using Wireless Signals

Wireless signals–based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions’ CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds

Reliability Optimization of Hybrid Systems Driven by Constraint Importance Measure Considering Different Cost Functions

The requirements of high reliability for hybrid systems are urgent for engineers to maximize the system reliability under the limited cost budget. The cost constraint importance measure (CIM) is an important tool to achieve the local optimal solution by considering the relationship between constraint conditions and objective functions in the optimization problem. To better consider the contribution of the CIM, this paper considers three different cost function forms, including power type, trigonometric type, and exponential type. Combining the global search ability of the arithmetic optimization algorithm (AOA) with the local search ability of the CIM, a CIM-based arithmetic optimization algorithm (CIAOA) is developed to analyze the contribution of the CIM. Through the numerical experiments, the optimal system reliability and convergence generation of the CIAOA and AOA under different cost function forms are regarded as the indexes to analyze algorithm performance. The experimental results show that the average system reliability improvement percentages under power type, trigonometric type, and exponential cost constraint are 8.07%, 0.14%, and 0.53%, respectively, while the average convergence improvement percentages under three cost forms are 37.30%, 0.08%, and 1.66%, respectively. Therefore, the CIAOA performs the best under power cost constraints. Finally, a numerical example of a hybrid power vehicle system is introduced to analyze the contribution of the CIM under different cost functions by considering the reliability improvement rate in the optimal solution and the ranking of the CIM. The higher prioritization components in the two rankings are similar, which shows that the component with higher a CIM is selected to improve its reliability

Mechanism-Aware Neural Machine for Dialogue Response Generation

To the same utterance, people's responses in everyday dialogue may be diverse largely in terms of content semantics, speaking styles, communication intentions and so on. Previous generative conversational models ignore these 1-to-n relationships between a post to its diverse responses, and tend to return high-frequency but meaningless responses. In this study we propose a mechanism-aware neural machine for dialogue response generation. It assumes that there exists some latent responding mechanisms, each of which can generate different responses for a single input post. With this assumption we model different responding mechanisms as latent embeddings, and develop a encoder-diverter-decoder framework to train its modules in an end-to-end fashion. With the learned latent mechanisms, for the first time these decomposed modules can be used to encode the input into mechanism-aware context, and decode the responses with the controlled generation styles and topics. Finally, the experiments with human judgements, intuitive examples, detailed discussions demonstrate the quality and diversity of the generated responses with 9.80% increase of acceptable ratio over the best of six baseline methods

Exercise-Induced Excessive Blood Pressure Elevation Is Associated with Cardiac Dysfunction in Male Patients with Essential Hypertension

Objective. Cardiopulmonary exercise testing (CPET) has been used to explore the blood pressure response and potential cardiovascular system structure and dysfunction in male patients with essential hypertension during exercise, to provide a scientific basis for safe and effective exercise rehabilitation and improvement of prognosis. Methods. A total of 100 male patients with essential hypertension (aged 18–60) who were admitted to the outpatient department of the Center for Diagnosis and Treatment of Cardiovascular Diseases of Jilin University from September 2018 to January 2021 were enrolled in this study. The patients had normal cardiac structure in resting state without clinical manifestations of heart failure or systematic regularization of treatment at the time of admission. Symptom-restricted CPET was performed and blood pressure was measured during and after exercise. According to Framingham criteria, male systolic blood pressure (SBP) ≥210 mmHg during exercise was defined as exercise hypertension (EH), and the subjects were divided into EH group (n = 47) and non-EH group (n = 53). Based on whether the oxygen pulse (VO2/HR) plateau appeared immediately after anaerobic threshold (AT), the EH group was further divided into the VO2/HR plateau immediately after AT (EH-ATP) group (n = 19) and EH-non-ATP group (n = 28). The basic clinical data and related parameters, key CPET indicators, were compared between groups. Result. Body mass index (BMI) visceral fat, resting SBP, and SBP variability in EH group were significantly higher than those in non-EH group. Moreover, VO2/HR at AT and the ratio of VO2/HR plateau appearing immediately after AT in EH group were significantly higher than those in the non-EH group. The resting SBP, 15-minute SBP variability, and the presence of VO2/HR plateau were independent risk factors for EH. In addition, work rate (WR) at AT but also WR, oxygen consumption per minute (VO2), VO2/kg, and VO2/HR at peak were significantly lower in the EH-ATP group compared to the EH-non-ATP group. Peak diastolic blood pressure (DBP) increment and decreased △VO2/△WR for AT to peak were independent risk factors for VO2/HR plateau appearing immediately after AT in EH patients. Conclusion. EH patients have impaired autonomic nervous function and are prone to exercise-induced cardiac dysfunction. EH patients with exercise-induced cardiac dysfunction have reduced peak cardiac output and exercise tolerance and impaired vascular diastolic function. CPET examination should be performed on EH patients and EH patients with exercise-induced cardiac dysfunction to develop precise drug therapy and effective individual exercise prescription, to avoid arteriosclerosis and exercise-induced cardiac damage. The retrospective study protocol was approved by medical ethics committee of the First Hospital of Jilin University (AF-IRB-032-06 No. 2021-015). The study was registered with the Chinese Clinical Trials Register, registration number: ChiCTR2100053140