CATVI: conditional and adaptively truncated variational inference for hierarchical Bayesian nonparametric models

Current variational inference methods for hierarchical Bayesian nonparametric models can neither characterize the correlation struc- ture among latent variables due to the mean- eld setting, nor infer the true posterior dimension because of the universal trunca- tion. To overcome these limitations, we pro- pose the conditional and adaptively trun- cated variational inference method (CATVI) by maximizing the nonparametric evidence lower bound and integrating Monte Carlo into the variational inference framework. CATVI enjoys several advantages over tra- ditional methods, including a smaller diver- gence between variational and true posteri- ors, reduced risk of undertting or overt- ting, and improved prediction accuracy. Em- pirical studies on three large datasets re- veal that CATVI applied in Bayesian non- parametric topic models substantially out- performs competing models, providing lower perplexity and clearer topic-words clustering

Adaptive Shape Kernel-Based Mean Shift Tracker in Robot Vision System

This paper proposes an adaptive shape kernel-based mean shift tracker using a single static camera for the robot vision system. The question that we address in this paper is how to construct such a kernel shape that is adaptive to the object shape. We perform nonlinear manifold learning technique to obtain the low-dimensional shape space which is trained by training data with the same view as the tracking video. The proposed kernel searches the shape in the low-dimensional shape space obtained by nonlinear manifold learning technique and constructs the adaptive kernel shape in the high-dimensional shape space. It can improve mean shift tracker performance to track object position and object contour and avoid the background clutter. In the experimental part, we take the walking human as example to validate that our method is accurate and robust to track human position and describe human contour

EEGNN: edge enhanced graph neural network with a Bayesian nonparametric graph model

Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may suffer from the number of hidden message-passing layers. The literature has focused on the proposals of over-smoothing and under-reaching to explain the performance deterioration of deep GNNs. In this paper, we propose a new explanation for such deteriorated performance phenomenon, mis-simplification, that is, mistakenly simplifying graphs by preventing self-loops and forcing edges to be unweighted. We show that such simplifying can reduce the potential of message-passing layers to capture the structural information of graphs. In view of this, we propose a new framework, edge enhanced graph neural network (EEGNN). EEGNN uses the structural information extracted from the proposed Dirichlet mixture Poisson graph model (DMPGM), a Bayesian nonparametric model for graphs, to improve the performance of various deep message-passing GNNs. We propose a Markov chain Monte Carlo inference framework for DMPGM. Experiments over different datasets show that our method achieves considerable performance increase compared to baselines

Core Point Pixel-Level Localization by Fingerprint Features in Spatial Domain

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples

Effect of acupuncture inclusion in the enhanced recovery after surgery protocol on tumor patient gastrointestinal function: a systematic review and meta-analysis of randomized controlled studies

IntroductionAcupuncture has been shown to be effective in restoring gastrointestinal function in tumor patients receiving the enhanced recovery after surgery (ERAS) protocol. The present systematic review and meta-analysis aimed to evaluate the rationality and efficacy of integrating acupuncture in the ERAS strategy to recuperate gastrointestinal function.MethodsWe searched eleven databases for relevant randomized clinical trials (RCTs) of acupuncture for the treatment of gastrointestinal dysfunction in tumor patients treated with the ERAS protocol. The quality of each article was assessed using the Cochrane Collaboration risk of bias criteria and the modified Jadad Scale. As individual symptoms, the primary outcomes were time to postoperative oral food intake, time to first flatus, time to first distension and peristaltic sound recovery time (PSRT). Pain control, adverse events, and acupoint names reported in the included studies were also investigated.ResultsOf the 211 reviewed abstracts, 9 studies (702 patients) met eligibility criteria and were included in the present systematic review and meta‑analysis. Compared to control groups, acupuncture groups showed a significant reduction in time to postoperative oral food intake [standardized mean difference (SMD) = -0.77, 95% confidence interval (CI) -1.18 to -0.35], time to first flatus (SMD=-0.81, 95% CI -1.13 to -0.48), time to first defecation (SMD=-0.91, 95% CI -1.41 to -0.41, PSRT (SMD=-0.92, 95% CI -1.93 to 0.08), and pain intensity (SMD=-0.60, 95% CI -0.83 to -0.37).The Zusanli (ST36) and Shangjuxu (ST37) acupoints were used in eight of the nine included studies. Adverse events related to acupuncture were observed in two studies, and only one case of bruising was reported. DiscussionThe present systematic review and meta‑analysis suggested that acupuncture significantly improves recovery of gastrointestinal function and pain control in tumor patients receiving the ERAS protocol compared to the control group. Moreover, ST36 and ST37 were the most frequently used acupoints. Although the safety of acupuncture was poorly described in the included studies, the available data suggested that acupuncture is a safe treatment with only mild side effects. These findings provide evidence-based recommendations for the inclusion of acupuncture in the ERAS protocol for tumor patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/ PROSPERO, identifier CRD42023430211

A novel flow-guide device for uniform exhaust in a central air exhaust ventilation system

Exhaust ventilation system with one central fan and multiple terminals has been widely used for the heat and contaminant removal in building environment. Conventional design without pressure balancing leads to uneven distribution of exhaust airflow rate among the multiple outlets. Existed balancing methods usually uses dampers (constant-air-volume valve or regulating valve), tapered duct, or varied inlet area. However, these methods result in higher fan energy consumption, or complicated construction and on-site commissioning. In this paper, a flow-guide device was developed for adjusting the pressure distribution of duct branches. This new device is integrated with the interflow Tee-junction and does not need any commissioning or regulating. The resistance performance of the device responding to the structural parameter was derived using the CFD simulation and experiment. The negative direct resistance featured by the device was found to effectively benefit exhaust at the outlets farther away from the central fan. The ductwork hydraulic model based on the Bernoulli's law of airflow and the fitted resistance correlations were further proposed to fulfill the parametric design. Finally, full-scale test was carried out for a central exhaust system installed with the flow-guide devices referring to a factory workshop with heat and contaminant sources. Compared to the system without the devices, the total rate of the system increased by 25%. Discrepancy of exhaust rate decreased by 78% and uneven degree decreased by 82%, which well meets the engineering balancing requirement. Meanwhile, total resistance of the system reduced 23.8% owing to the negative loss the devices bring

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during admission between 13 July 2012 and 11 July 2018. The area under the receiver operating characteristic curve (AUROC) was 0.86 for Random Forest and 0.85 for LASSO. Based on Youden’s Index, a probability cutoff of \u3e0.15 provided sensitivity and specificity of 0.80 and 0.79, respectively. AKI risk could be successfully predicted in 91% patients who required dialysis. The model predicted AKI an average of 2.3 days before it developed. Conclusions: The proposed simpler machine learning model utilizing data available at 24 h of admission is promising for early AKI risk stratification. It requires external validation and evaluation of effects of risk prediction on clinician behavior and patient outcomes