Early-fusion based pulsar identification with smart under-sampling

The discovery of pulsars is of great significance in the field of physics and astronomy. As the astronomical equipment produces a large number of pulsar data, an algorithm for automatically identifying pulsars becomes urgent. We propose a deep learning framework for pulsar recognition. In response to the extreme imbalance between positive and negative examples and the hard negative sample issue presented in the High Time Resolution Universe Medlat Training Data, there are two coping strategies in our framework: the smart under-sampling and the improved loss function. We also apply the early-fusion strategy to integrate features obtained from different attributes before classification to improve the performance. To our best knowledge, this is the first study that integrates these strategies and techniques in pulsar recognition. The experiment results show that our framework outperforms previous works with respect to either the training time or F1 score. We can not only speed up the training time by 10 × compared with the state-of-the-art work, but also get a competitive result in terms of F1 score.

A Hybrid Deep Learning and Optimized Machine Learning Approach for Rose Leaf Disease Classification

Analysis of the symptoms of rose leaves can identify up to 15 different diseases. This research aims to develop Convolutional Neural Network models for classifying the diseases on rose leaves using hybrid deep learning techniques with Support Vector Machine (SVM). The developed models were based on the VGG16 architecture and early or late fusion techniques were applied to concatenate the output from a fully connected layer. The results showed that the developed models based on early fusion performed better than the developed models on either late fusion or VGG16 alone. In addition, it was found that the models using the SVM classifier had better efficiency in classifying the diseases appearing on rose leaves than the models using the softmax function classifier. In particular, a hybrid deep learning model based on early fusion and SVM, which applied the categorical hinge loss function, yielded a validation accuracy of 88.33% and a validation loss of 0.0679, which were higher than the ones of the other models. Moreover, this model was evaluated by 10-fold cross-validation with 90.26% accuracy, 90.59% precision, 92.44% recall, and 91.50% F1-score for disease classification on rose leaves.

Differentially Private Attributed Network Releasing Based on Early Fusion

Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.

The impact of smoking on outcomes following anterior cervical fusion-nonfusion hybrid surgery: a retrospective single-center cohort study

Background There is mixed evidence for the impact of cigarette smoking on outcomes following anterior cervical surgery. It has been reported to have a negative impact on healing after multilevel anterior cervical discectomy and fusion, however, segmental mobility has been suggested to be superior in smokers who underwent one- or two-level cervical disc replacement. Hybrid surgery, including anterior cervical discectomy and fusion and cervical disc replacement, has emerged as an alternative procedure for multilevel cervical degenerative disc disease. This study aimed to examine the impact of smoking on intermediate-term outcomes following hybrid surgery. Methods Radiographical and clinical outcomes of 153 patients who had undergone continuous two- or three-level hybrid surgery were followed-up to a minimum of 2-years post-operatively. The early fusion effect, 1-year fusion rate, the incidence of bone loss and heterotopic ossification, as well as the clinical outcomes were compared across three smoking status groups: (1) current smokers; (2) former smokers; (3) nonsmokers. Results Clinical outcomes were comparable among the three groups. However, the current smoking group had a poorer early fusion effect and 1-year fusion rate (P < 0.001 and P < 0.035 respectively). Both gender and smoking status were considered as key factors for 1-year fusion rate. Upon multivariable analysis, male gender (OR = 6.664, 95% CI: 1.248–35.581, P = 0.026) and current smoking status (OR = 0.009, 95% CI: 0.020–0.411, P = 0.002) were significantly associated with 1-year fusion rate. A subgroup analysis demonstrated statistically significant differences in both early fusion process (P < 0.001) and the 1-year fusion rate (P = 0.006) across the three smoking status groups in female patients. Finally, non-smoking status appeared to be protective against bone loss (OR = 0.427, 95% CI: 0.192–0.947, P = 0.036), with these patients likely to have at least one grade lower bone loss than current smokers. Conclusions Smoking is associated with poor outcomes following hybrid surgery for multilevel cervical disc disease. Current smokers had the poorest fusion rate and most bone loss, but no statistically significant differences were seen in clinical outcomes across the three groups.

Patient-specific COVID-19 resource utilization prediction using fusion AI model

The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1–86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.