Red Bull team principal Christian Horner has defended Max Verstappen after he pushed rival driver Esteban Ocon, saying: "Drivers aren't robots and we don't want them to be." Verstappen confronted Force India's Ocon following Sunday's Brazilian Grand Prix after a collision between the pair cost the 21-year-old Dutchman victory. Governing body the FIA has ordered him to do "two days of public service". "I don't think it got out of hand," said Horner. "Through the irresponsible actions of a backmarker we've lost a grand prix, and it just wasn't handled at all well by Ocon. It was totally irresponsible to be racing Max.

Machine learning has been an emerging tool for various aspects of infectious diseases including tuberculosis surveillance and detection. However, WHO provided no recommendations on using computer-aided tuberculosis detection software because of the small number of studies, methodological limitations, and limited generalizability of the findings. To quantify the generalizability of the machine-learning model, we developed a Deep Convolutional Neural Network (DCNN) model using a TB-specific CXR dataset of one population (National Library of Medicine Shenzhen No.3 Hospital) and tested it with non-TB-specific CXR dataset of another population (National Institute of Health Clinical Centers). The findings suggested that a supervised deep learning model developed by using the training dataset from one population may not have the same diagnostic performance in another population. Technical specification of CXR images, disease severity distribution, overfitting, and overdiagnosis should be examined before implementation in other settings.

Operating deep neural networks on devices with limited resources requires the reduction of their memory footprints and computational requirements. In this paper we introduce a training method, called look-up table quantization, LUT-Q, which learns a dictionary and assigns each weight to one of the dictionary's values. We show that this method is very flexible and that many other techniques can be seen as special cases of LUT-Q. For example, we can constrain the dictionary trained with LUT-Q to generate networks with pruned weight matrices or restrict the dictionary to powers-of-two to avoid the need for multiplications. In order to obtain fully multiplier-less networks, we also introduce a multiplier-less version of batch normalization. Extensive experiments on image recognition and object detection tasks show that LUT-Q consistently achieves better performance than other methods with the same quantization bitwidth.

Deep Learning algorithms have recently become the de-facto paradigm for various prediction problems, which include many privacy-preserving applications like online medical image analysis. Presumably, the privacy of data in a deep learning system is a serious concern. There have been several efforts to analyze and exploit the information leakages from deep learning architectures to compromise data privacy. In this paper, however, we attempt to provide an evaluation strategy for such information leakages through deep neural network architectures by considering a case study on Convolutional Neural Network (CNN) based image classifier. The approach takes the aid of low-level hardware information, provided by Hardware Performance Counters (HPCs), during the execution of a CNN classifier and a simple hypothesis testing in order to produce an alarm if there exists any information leakage on the actual input.

Abstract--Machine Learning has been steadily gaining traction for its use in Anomaly-based Network Intrusion Detection Systems (A-NIDS). Research into this domain is frequently performed using the KDD CUP 99 dataset as a benchmark. Several studies question its usability while constructing a contemporary NIDS, due to the skewed response distribution, nonstationarity, and failure to incorporate modern attacks. In this paper, we compare the performance for KDD-99 alternatives when trained using classification models commonly found in literature: Neural Network, Support Vector Machine, Decision Tree, Random Forest, Naive Bayes and K-Means. Applying the SMOTE oversampling technique and random undersampling, we create a balanced version of NSL-KDD and prove that skewed target classes in KDD-99 and NSL-KDD hamper the efficacy of classifiers on minority classes (U2R and R2L), leading to possible security risks. We explore UNSW-NB15, a modern substitute to KDD-99 with greater uniformity of pattern distribution. We benchmark this dataset before and after SMOTE oversampling to observe the effect on minority performance. Our results indicate that classifiers trained on UNSW-NB15 match or better the Weighted F1-Score of those trained on NSL-KDD and KDD-99 in the binary case, thus advocating UNSW-NB15 as a modern substitute to these datasets. Network security is an ever-evolving discipline where new types of attacks manifest and must be mitigated on a daily basis.

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep RL for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing applications. In pixelRL, each pixel has an agent, and the agent changes the pixel value by taking an action. We also propose an effective learning method for pixelRL that significantly improves the performance by considering not only the future states of the own pixel but also those of the neighbor pixels. The proposed method can be applied to some image processing tasks that require pixel-wise manipulations, where deep RL has never been applied. We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement. Our experimental results demonstrate that the proposed method achieves comparable or better performance, compared with the state-of-the-art methods based on supervised learning.

The prospect of UK firms implanting their staff with microchips in order to improve security and efficiency has raised concerns among trade unions. Several legal and financial firms in the UK are reportedly in discussions with a company responsible for fitting thousands of people with chips in Scandinavia. The chips, which are about the size of a grain of rice, are usually implanted beneath the skin between the thumb and forefinger and use radio-frequency identification (RFID) technology to allow people to replace physical key cards, IDs and even train tickets. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

The name, price and release date of Samsung's mysterious foldable smartphone have finally been revealed, according to a South Korean news agency. Samsung teased the device at its annual developer conference last week, saying it will serve as the "foundation of the smartphone of tomorrow" – though few details about the phone were actually revealed. The so-called Galaxy F will be released in March 2019 and is expected to cost 2 million won (£1,370), the Seoul-based Yonhap News Agency reported. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

A Japanese venture group have devised a wearable exoskeleton which can carry the burden of heavy lifting. The so-called Muscle Upper weighs just 17 pounds (8 kilograms) and has the power to raise a maximum of 66 pounds (30 kilograms), it's inventors say. The brainchild of Innophys - a company set-up by the Tokyo University of Science - it could relieve humans of muscle and joint damage through repetitive heavy-lifting. Specifically, it alleviates wear-and-tear on the back and arms, which means it could be ideal for those who work in factories. A video showcasing the frame reveals that compressed air is used to flex the'muscles', which are made of rubber.

The U.S. and China have been getting lots of attention lately for the AI-focused startups, talent and investment coming out of these two countries. AI startups in the U.S. and China are garnering eyebrow-raising funding rounds. Case in point: SenseTime, a Chinese startup, is now the most valuable AI startup in the world having raised $1.6 billion to date. In 2017 alone, AI-focused startups raised a staggering $12 billion, and investment shows no signs of slowing down in the years ahead. The Chinese government sees AI as a strategic imperative and is investing heavily into developing the industry.