Camera identification has recently attracted considerable attention in the image forensic field of research. Several algorithms have been established based on the hand-crafted features and deep learning, through analysis of the traces achieved by the digital image acquisition process. Although these approaches have led to a breakthrough in the image forensics, some important problems still remain unsolved. For instance, extracting the hand-crafted features with human efforts is a difficult and time-consuming process, while data-driven deep learning methods tend to learn features that represent image contents rather than cameras' characteristics. To fully take advantages of both hand-crafted and data-driven technologies, we propose a domain knowledge-driven method, which consists of one pre-processing module, one feature extractor, and one hierarchical multi-task learning procedure. The pre-processing module can introduce the domain knowledge to the subsequent deep learning network. Moreover, for device-level identification, hierarchical multi-task learning can provide more supervise information from the brand and model. The proposed framework is evaluated on three different tasks, i.e., the brand, model, and device-level identification using original and manipulated images. Our classification results demonstrate that the proposed method is effective and robust. To evaluate the robustness of the proposed method, we create a new database for the cell-phone identification and evaluate the proposed method. It is found that the accuracy of the cell-phone device identification can reach 84.3%, which is much higher than that of the camera identification. Moreover, the t-distributed stochastic neighbor embedding visualization results confirm that the features of different cell-phone devices are visually more separable than cameras.INDEX TERMS Camera identification, image forensic filed, domain knowledge-driven, multi-task learning, cell-phone identification.
MSFT is a rare type of mediastinal tumor, and the diagnosis requires pathological and immunohistochemical analysis. Surgical treatment is preferred, and a complete resection can achieve a good prognosis; however, postoperative adjuvant radiotherapy might be necessary for cases with extensive external invasion.
Objectives To explore whether lower outdoor temperature increases cardio-cerebrovascular disease risk through regulating blood pressure and whether indoor heating in winter is beneficial to prevent cardio-cerebrovascular disease in cold areas.
MethodsWe analyzed the data of 38589 participants in Harbin from the China Kadoorie Biobank (CKB) during 2004-2008, with an average of 7.14-year follow-up.Linear regression analysis was performed to estimate the relationship between outdoor temperature and blood pressure. Cox regression analysis and Logistic regression analysis were used to analyze the association of blood pressure with cardio-cerebrovascular events risk. Mediation analysis was performed to explore the role of blood pressure in the association between outdoor pressure and cardio-cerebrovascular events risk.
ResultsThere was an increase of 6.7 mmHg in SBP and 2.1 mmHg in DBP for each 10℃ decrease in outdoor temperature when outdoor temperature was higher than 5℃.There was an inverse association between outdoor temperature and cardio-cerebrovascular events morbidity. The increases in blood pressure and cardio-cerebrovascular events morbidity were attenuated in months when central heating was fully provided. Participants with hypertension have higher risks of
Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen’s kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.
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