A Diverse Domain Generative Adversarial Network for Style Transfer on Face Photographs.

The applications of style transfer on real time photographs are very trending now. This is used in various applications especially in social networking sites such as SnapChat and beauty cameras. A number of style transfer algorithms have been proposed but they are computationally expensive and generate artifacts in output image. Besides, most of research work only focuses on some traditional painting style transfer on real photographs. However, our work is unique as it considers diverse style domains to be transferred on real photographs by using one model. In this paper, we propose a Diverse Domain Generative Adversarial Network (DD-GAN) which performs fast diverse domain style translation on human face images. Our work is highly efficient and focused on applying different attractive and unique painting styles to human photographs while keeping the content preserved after translation. Moreover, we adopt a new loss function in our model and use PReLU activation function which improves and fastens the training procedure and helps in achieving high accuracy rates. Our loss function helps the proposed model in achieving better reconstructed images. The proposed model also occupies less memory space during training. We use various evaluation parameters to inspect the accuracy of our model. The experimental results demonstrate the effectiveness of our method as compared to state-of-the-art results

A Diverse Domain Generative Adversarial Network for Style Transfer on Face Photographs

The applications of style transfer on real time photographs are very trending now. This is used in various applications especially in social networking sites such as SnapChat and beauty cameras. A number of style transfer algorithms have been proposed but they are computationally expensive and generate artifacts in output image. Besides, most of research work only focuses on some traditional painting style transfer on real photographs. However, our work is unique as it considers diverse style domains to be transferred on real photographs by using one model. In this paper, we propose a Diverse Domain Generative Adversarial Network (DD-GAN) which performs fast diverse domain style translation on human face images. Our work is highly efficient and focused on applying different attractive and unique painting styles to human photographs while keeping the content preserved after translation. Moreover, we adopt a new loss function in our model and use PReLU activation function which improves and fastens the training procedure and helps in achieving high accuracy rates. Our loss function helps the proposed model in achieving better reconstructed images. The proposed model also occupies less memory space during training. We use various evaluation parameters to inspect the accuracy of our model. The experimental results demonstrate the effectiveness of our method as compared to state-of-the-art results

Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Remote sensing target detection has been widely used in industries. In various application scenarios, complicated contexts may inhibit target identification and reduce detection accuracy, especially in multi-target detection tasks. In this paper, a new remote sensing target detection method based on structural reasoning is proposed to improve target detection performance by integrating inter-object relationships and scene information. Based on inter-object information, a relation structure graph is designed to reduce errors and missed targets. To establish contextual constraints, semantic is used as a prior information for Bayesian criterion based on scene information. Experiments conducted on HRRSD dataset show that the average accuracy of the proposed method is 10.7 % higher than the state-of-the-art algorithms. The experimental results confirm that the proposed algorithm can achieve significant improvements and adapt to complex scenes in remote sensing by mining contextual information at both feature and semantic levels

An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing

Epilepsy is one kind of brain diseases, and its sudden unpredictability is the main cause of disability and even death. Thus, it is of great significance to identify electroencephalogram (EEG) during the seizure quickly and accurately. With the rise of cloud computing and edge computing, the interface between local detection and cloud recognition is established, which promotes the development of portable EEG detection and diagnosis. Thus, we construct a framework for identifying EEG signals in epileptic seizure based on cloud-edge computing. The EEG signals are obtained in real time locally, and the horizontal viewable model is established at the edge to enhance the internal correlation of the signals. The Takagi-Sugeno-Kang (TSK) fuzzy system is established to analyze the epileptic signals. In the cloud, the fusion of clinical features and signal features is established to establish a deep learning framework. Through local signal acquisition, edge signal processing and cloud signal recognition, the diagnosis of epilepsy is realized, which can provide a new idea for the real-time diagnosis and feedback of EEG during epileptic seizure

An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing.

Epilepsy is one kind of brain diseases, and its sudden unpredictability is the main cause of disability and even death. Thus, it is of great significance to identify electroencephalogram (EEG) during the seizure quickly and accurately. With the rise of cloud computing and edge computing, the interface between local detection and cloud recognition is established, which promotes the development of portable EEG detection and diagnosis. Thus, we construct a framework for identifying EEG signals in epileptic seizure based on cloud-edge computing. The EEG signals are obtained in real time locally, and the horizontal viewable model is established at the edge to enhance the internal correlation of the signals. The Takagi-Sugeno-Kang (TSK) fuzzy system is established to analyze the epileptic signals. In the cloud, the fusion of clinical features and signal features is established to establish a deep learning framework. Through local signal acquisition, edge signal processing and cloud signal recognition, the diagnosis of epilepsy is realized, which can provide a new idea for the real-time diagnosis and feedback of EEG during epileptic seizure

Innovation policy and firm patent value: evidence from China

This study aims to contribute to the empirical literature that evaluates the impact of the Science & Technology (S&T) Outline, a Chinese innovation policy implemented in 2006, measured by the scale of patent value. We first create a comprehensive patent valuation model (CPVM), derived from the extended patent renewal model and a variety of feature indices, to measure a firm’s patent value. From a database with over 700,000 Chinese patents from 1985 to 2013, we find that the patent value increases after the release of the S&T Outline, and the scale of patent value after 2006 is about 26.52 times more than that before 2006. Further, we use a quasi-difference in differences (DID) model to estimate the growth effect caused by the innovation policy. The results indicate that the S&T Outline had a significant effect on the promotion of patent value, in industries with high patent intensity. Considering the lag effect of the S&T Outline, we construct innovation correlation networks to visualise and compare its promotion effect. We find that regional networks have a gathering tendency after policy implementation, while industrial networks have a decentralising tendenc

Interpretable Risk Assessment Methods for Medical Image Processing via Dynamic Dilated Convolution and a Knowledge Base on Location Relations

Existing approaches to image risk assessment start with the uncertainty of the model, yet ignore the uncertainty that exists in the data itself. In addition, the decisions made by the models still lack interpretability, even with the ability to assess the credibility of the decisions. This paper proposes a risk assessment model that unites a model, a sample and an external knowledge base, which includes: 1. The uncertainty of the data is constructed by masking the different decision-related parts of the image data with a random mask of probabilities. 2. A dynamically distributed dilated convolution method based on random directional field perturbations is proposed to construct the uncertainty of the model. The method evaluates the impact of different components on the decisions within the local region by locally perturbing the attention region of the dilated convolution. 3. A triadic external knowledge base with relative interpretability is presented to reason and validate the model's decisions. The experiments are implemented on the dataset of CT images of the stomach, which shows that our proposed method outperforms current state-of-the-art methods

Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations

Water adsorption and dissociation processes on pristine low-index TiO$_{2}$ interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO$_{2}$ surfaces, based on three density-functional-theory approximations. Here we show the water dissociation free energies on seven pristine TiO$_{2}$ surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase (101) and rutile (100) have mostly molecular adsorption, while the simulations of rutile (110) sensitively depend on the slab thickness and molecular adsorption is preferred with thick slabs. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. These mechanisms can be rationalized based on the arrangements of water molecules on the different surfaces. Our finding thus demonstrates that the different pristine TiO$_{2}$ surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces

TG/HDL-C Ratio and ba-PWV in Patients with Essential Hypertension

BackgroundDyslipidemia is a major risk factor for cardiovascular disease. Triglyceride /high-density lipoprotein cholesterol (TG/HDL-C) ratio has been proved to be more effective in predicting cardiovascular events than a blood lipid index or low-density lipoprotein cholesterol (LDL-C) /HDL-C ratio. There are few clinical studies about TG/HDL-C ratio assessing arterial stiffness in hypertensive populations. Moreover, studies on the characteristics of ambulatory blood pressure parameters by TG/HDL-C ratio are also rare.ObjectiveTo explore the association of fasting TG/HDL-C ratio with brachial-ankle pulse wave velocity (ba-PWV) in essential hypertensive patients based on analyzing these patients' clinical features, ambulatory blood pressure and ba-PWV.MethodsA total of 439 essential hypertension patients (aged 18-80 years) were recruited from Hypertension Department, Beijing Anzhen Hospital, Capital Medical University from August 2014 to December 2015. Data were collected, including sex, age and medical history (diabetes history, hyperlipidemia history, smoking status, drinking status) , height, weight, BMI, serum creatinine, total cholesterol, TG, LDL-C, HDL-C, serum uric acid, fasting blood glucose, calculated TG/HDL-C ratio, 24 h ambulatory blood pressure parameters〔mean systolic blood pressure (SBP) , diastolic blood pressure (DBP) and heart rate in 24 h, in the daytime and at the nighttime, prevalence of nocturnal fall in SBP and DBP, and dipper blood pressure pattern〕 during hospitalization, and mean heart rate. Arterial stiffness was assessed by ba-PWV. The above-mentioned indicators were compared between lower (n=219) and higher quantile groups (n=220) divided by TG/HDL-C ratio. The influencing factors of ba-PWV were investigated by multiple linear regression analysis.Results(1) Higher quantile group had higher male proportion, higher prevalence of hyperlipidemia and smokers, higher mean BMI, serum creatinine, total cholesterol, TG, serum uric acid, and fasting blood glucose, as well as lower men age, and HDL-C than lower quantile group (P<0.05) . (2) Higher quantile group had higher mean 24-hour SBP and DBP, daytime SBP and DBP, nighttime SBP and DBP, nighttime heart rate and ba-PWV, and lower prevalence of nocturnal fall in SBP and DBP, and dippers than lower quantile group (P<0.05) . (3) Multiple linear regression analysis showed that, age〔β=12.35, 95%CI (10.307, 14.401) 〕, fasting blood glucose〔β=20.69, 95%CI (1.532, 39.854) 〕, TG/HDL-C ratio〔β=20.99, 95%CI (6.176, 35.810) 〕 and 24 hour mean SBP〔β=7.57, 95%CI (5.656, 9.493) 〕 were associated with ba-PWV (P<0.05) .ConclusionIn essential hypertension patients, elevated 24-hour SBP and DBP were found in those with higher TG/HDL-C ratio, and TG/HDL-C ratio may be independently associated with ba-PWV. Monitoring TG/HDL-C ratio helps early detection of arteriosclerosis and elevated blood pressure, promoting comprehensive management of cardiovascular risk factors