Networks observed in the real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real-world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches. To the best of our knowledge, this is the first work that integrates statistical modeling of dynamic networks with deep learning for community detection and link prediction.

This paper introduces a deep learning based classifier for common skin ailments, to help people without easy access to dermatologists. We have confirmed that it can classify at approximately 80% accuracy on average, when primary care doctors are reported to have 53% success as per recent literature. Dermatological diseases are common in every population and have a wide spectrum in severity. With a shortage of dermatological experts being observed in many countries, machine learning solutions can offer timely medical advice regarding existence of common skin diseases. The paper implements supervised classification of nine distinct dermatological diseases which have high occurrence in East Asian countries. Our current attempt establishes that deep learning based techniques are viable avenues for preliminary information.

Learning using privileged information is an attractive problem setting that helps many learning scenarios in the real world. A state-of-the-art method of Gaussian process classification (GPC) with privileged information is GPC+, which incorporates privileged information into a noise term of the likelihood. A drawback of GPC+ is that it requires numerical quadrature to calculate the posterior distribution of the latent function, which is extremely time-consuming. To overcome this limitation, we propose a novel classification method with privileged information based on Gaussian processes, called "soft-label-transferred Gaussian process (SLT-GP)." Our basic idea is that we construct another learning task of predicting soft labels (continuous values) obtained from privileged information and we perform transfer learning from this task to the target task of predicting hard labels. We derive a PAC-Bayesian bound of our proposed method, which justifies optimizing hyperparameters by the empirical Bayes method. We also experimentally show the usefulness of our proposed method compared with GPC and GPC+.

The real-time crash likelihood prediction has been an important research topic. Various classifiers, such as support vector machine (SVM) and tree-based boosting algorithms, have been proposed in traffic safety studies. However, few research focuses on the missing data imputation in real-time crash likelihood prediction, although missing values are commonly observed due to breakdown of sensors or external interference. Besides, classifying imbalanced data is also a difficult problem in real-time crash likelihood prediction, since it is hard to distinguish crash-prone cases from non-crash cases which compose the majority of the observed samples. In this paper, principal component analysis (PCA) based approaches, including LS-PCA, PPCA, and VBPCA, are employed for imputing missing values, while two kinds of solutions are developed to solve the problem in imbalanced data. The results show that PPCA and VBPCA not only outperform LS-PCA and other imputation methods (including mean imputation and k-means clustering imputation), in terms of the root mean square error (RMSE), but also help the classifiers achieve better predictive performance. The two solutions, i.e., cost-sensitive learning and synthetic minority oversampling technique (SMOTE), help improve the sensitivity by adjusting the classifiers to Corresponding author Email address: chenxiqun@zju.edu.cn Keywords: Real-time crash likelihood prediction, PCA-based missing data imputation, cost-sensitive learning, SMOTE, support vector machine, AdaBoost 1. Introduction Prediction of traffic crash has been a major research topic in transportation safety studies. Crashes, especially on urban expressways, can trigger heavy traffic congestions, impose huge external costs, and reduce the level of service of transportation infrastructures. Therefore, the accurate and reliable prediction of crash risks is critical to the success of proactive safety management strategies on urban expressways. There have been fruitful studies in the domain of the real-time crash likelihood estimation (Abdel-Aty and Pemmanaboina, 2006; Abdel-Aty et al., 2007, 2008; Ahmed and Abdel-Aty, 2012). It has been reported that crash occurrence was affected by four major factors: real-time traffic state, drivers' behavior, environment factors, and road geometry (Ahmed and Abdel-Aty, 2013b).

Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recent advances in producing large drug screens against cancer cell lines provided an opportunity to apply machine learning methods for this purpose. In addition to cytotoxicity databases, considerable amount of drug-induced gene expression data has also become publicly available. Following this, several methods that exploit omics data were proposed to predict drug activity on cancer cells. However, due to the complexity of cancer drug mechanisms, none of the existing methods are perfect. One possible direction, therefore, is to combine the strengths of both the methods and the databases for improved performance. We demonstrate that integrating a large number of predictions by the proposed method improves the performance for this task. The predictors in the ensemble differ in several aspects such as the method itself, the number of tasks method considers (multi-task vs. single-task) and the subset of data considered (sub-sampling). We show that all these different aspects contribute to the success of the final ensemble. In addition, we attempt to use the drug screen data together with two novel signatures produced from the drug-induced gene expression profiles of cancer cell lines. Finally, we evaluate the method predictions by in vitro experiments in addition to the tests on data sets.The predictions of the methods, the signatures and the software are available from http://mtan.etu.edu.tr/drug-response-prediction/.

People in China are the world's most optimistic when it comes to the impact of artificial intelligence on the jobs market and improving their lives, a global survey has found. Some 65 per cent of Chinese respondents believed AI and robotics would create more jobs – rather than steal them – over the next five to 10 years, according to the Digital Society Index released by UK digital marketing firm Dentsu Aegis Network on Wednesday. That compared with the global average of 29 per cent. The company polled 20,000 people across 10 countries – Australia, China, France, Germany, Italy, Japan, Russia, Spain, the United Kingdom and United States – last summer. How China's AI technology can help Twitter's suicidal users Some 71 per cent of Chinese respondents also believed that emerging digital technologies would help to solve the world's most pressing challenges such as poverty, health and environmental issues.

The UAE Minister of Artificial Intelligence (AI) Omar bin Sultan Al Olama on Saturday announced that the UAE will focus on adopting AI in three lead sectors, including natural resources, tourism and logistics. He said the UAE will "lead the world" with fast-developing technologies in AI-Ready sectors.

The electric scooter Flow gets advanced artificial intelligence based software that can do many unheard things. It is priced at Rs. 74,740, ex-showroom. The bookings for the scooter has started already and the deliveries will start in the second quarter of 2018. Flow features many advanced features like cloud-server connectivity, AI learning based on the riding habits, inbuilt GPS that acts as security device too and much more. The scooter is powered by a 90 Nm electric motor.

This past week I led a group of 20 American tech investors to Israel in conjunction with the UJA and Israel's Ministry of Economy and Industry. We witnessed firsthand the innovation that has produced more than $22 billion of investments and acquisitions within the past year. We met with the University that produced Mobileye, with the investor that believed in its founder, and the network of every multinational company supporting the startup ecosystem. Mechatronics is blooming in the desert from the CyberTech Convention in Tel Aviv to the robotic labs at Capsula to the latest in autonomous driving inventions in the hills of Jerusalem. Sitting in a suspended conference room that floats three stories above the ground enclosed within the "greenest building in the Middle East," I had the good fortune to meet Torr Polakow of Curiosity Lab.

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