Abstract--Mining causality from text is a complex and crucial natural language understanding task. Most of the early attempts at its solution can group into two categories: 1) utilizing co-occurrence frequency and world knowledge for causality detection; 2) extracting cause-effect pairs by using connectives and syntax patterns directly. However, because causality has various linguistic expressions, the noisy data and ignoring implicit expressions problems induced by these methods cannot be avoided. In this paper, we present a neural causality detection model, namely Multilevel Causality Detection Network (MCDN), to address this problem. Specifically, we adopt multi-head self-attention to acquire semantic feature at word level and integrate a novel Relation Network to infer causality at segment level. To the best of our knowledge, in touch with the causality tasks, this is the first time that the Relation Network is applied. The experimental results on the AltLex dataset, demonstrate that: a) MCDN is highly effective for the ambiguous and implicit causality inference; b) comparing with the regular text classification task, causality detection requires stronger inference capability; c) the proposed approach achieved state-of- the-art performance. I. Introduction Automatic text causality mining is a critical but difficult task because causality is thought to play an essential role in human cognition when making decisions [1]. Thus, automatic text causality has been studied extensively in a wide range of areas, such as industry [2], physics [3] and healthcare [4], etc. A tool to automatically scour the plethora of textual content on the web and extract meaningful causal relations could help us construct causal chains to unveil previously unknown relationships between events [5] and accelerates the discovery of the intrinsic logic of the events [6]. Many research efforts have been made to mine causality from text corpus with complex sentence structures in the books or newspapers. In Causal-TimeBank [7] authors introduced "CLINK" and "C-SIGNAL" tag to mark events causal relation and causal signals respectively based on specific templates (e.g., "A happened because of B").

A paramedic gurney flies through the trauma bay carrying an unconscious elderly gentleman. He is already intubated and has a hive of doctors and nurses running alongside, placing intravenous lines and injecting medicine into his blood stream. He's suffered a serious head injury in a car accident. It was a cold winter afternoon in 2017, and the patient had been taken to a major regional hospital. When he arrived, the neurosurgeon on call had minutes to counsel the family on the man's prognosis, and together they needed to decide whether to operate; surgery could save the patient's life, but it could also commit him to a life dependent on a ventilator and a feeding tube, trapped in a coma or with limited brain function.

The U.S. government is spending millions of dollars on creating intelligent missiles - which will determine for targets for themselves. The Cannon-Delivered Area Effects Munition (C-DAEM) system will use GPS to identify enemy tanks and armoured shells, which will be scanned in advance from the skies. According to sources, the Pentagon will invest vast sums into the AI-guided munitions, which could be ready by 2021. They will replace the Dual-Purpose Improved Conventional Munition (DPICM) artillery rounds, which were introduced in the 1980s. Cannon-Delivered Area Effects Munition system: The U.S. government is spending millions of dollars on creating intelligent missiles - which will determine for targets for themselves C-DAEM is a 155-millimeter artillery shell, and will be available for the M777 towed howitzer, the M109A6 Paladin self-propelled howitzer, and the new XM1299 self-propelled howitzer, which has a range of up to 43 miles.

While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities organized in Telegram channels from June 17, 2018 to February 26, 2019, and discover patterns in crypto-markets associated with pump-and-dump schemes. We then build a model that predicts the pump likelihood of all coins listed in a crypto-exchange prior to a pump. The model exhibits high precision as well as robustness, and can be used to create a simple, yet very effective trading strategy, which we empirically demonstrate can generate a return as high as 60% on small retail investments within a span of two and half months. The study provides a proof of concept for strategic crypto-trading and sheds light on the application of machine learning for crime detection.

In March 2017, I joined the MathWorks Student Competitions team to focus on supporting university-level robotics competitions. The competition I spend most time with is RoboCup, which is great because RoboCup contains a variety of leagues and skill levels that keeps me sharp with almost everything going on in the field. Today I will talk about my experience in this role, and what it's been like returning to robotics and academia after more than 5 years away from the field. Let me start with a personal history lesson about my experience in robotics. I am a mechanical engineer with a background in controls, dynamics, and systems.

Scientists have found eight more mysterious repeating radio bursts emanating from deep space, which more than quadruples the known number of signals from earlier this year. The new signals were found by the Canadian Hydrogen Intensity Mapping Experiment (CHIME) radio telescope, and give scientists a much broader data set that they hope may help finally unlock their origin. With the discovery, described in a paper submitted to The Astrophysical Journal Letters, the number of repeating radio bursts signals has climbed to 11. The new signals will aid scientists in their efforts to trace the origin and cause of mysterious radio bursts from deep space. According to Nature, the results of a separate observation from researchers in Australia have yet to be published, but bring the number of findings this month alone to nine total.

Not all voice assistants can handle the same requests. We put Siri, Alexa and Google to the test. Gone are the days when you'd write a note on a piece of paper to remind someone to do something. Instead, you now leave reminders on your smart speaker. Google Assistant devices in the U.S., U.K. and Australia will get an upgrade over the next few weeks to allow you to set reminders for other people.

Convolutional Neural Networks (CNNs) are often described as black boxes due to the difficulty in explaining how they reach their final output for a given task. Consequently a number of techniques have been developed to aid in the process of explainability. These techniques range from the scoring of individual pixels to reflect their impact on the networks decision making, to the scoring of larger regions of the image. Scoring larger regions allows for the results to be more easily interpreted. A popular technique for explaining images is LIME [10]. This uses superpixels, contiguous regions for visualisation, allowing a level of interpretability that may not be present in individual pixel scoring. However, this increased interpretability comes at a cost. The LIME technique relies on perturbing the input image and repeatedly passing it to the network to build an understanding of how important each superpixel region is to the final classification. This requires multiple perturbed images to be passed through the network, by default 1000 in the released code.

Counterfactual thinking describes a psychological phenomenon that people re-infer the possible results with different solutions about things that have already happened. It helps people to gain more experience from mistakes and thus to perform better in similar future tasks. This paper investigates the counterfactual thinking for agents to find optimal decision-making strategies in multi-agent reinforcement learning environments. In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for agents. To this end, our model generates several possible actions (intent actions) with a parallel policy structure and estimates the rewards and regrets for these intent actions based on its current understanding of the environment. Our model incorporates a scenario-based framework to link the estimated regrets with its inner policies. During the iterations, our model updates the parallel policies and the corresponding scenario-based regrets for agents simultaneously. To verify the effectiveness of our proposed model, we conduct extensive experiments on two different environments with real-world applications. Experimental results show that counterfactual thinking can actually benefit the agents to obtain more accumulative rewards from the environments with fair information by comparing to their opponents while keeping high performing efficiency.

Jaguar Land Rover is trailing an in-car system that changes temperature, music and lighting in response to a driver's mood. The system gauges a driver's mood with a driver-facing camera and biometric sensing, and adjusts the heating, ventilation and air conditioning, media and ambient lighting to help tackle stress and tiredness. "Personalisation settings could include changing the ambient lighting to calming colours if the system detects the driver is under stress, selecting a favourite playlist if signs of weariness are identified, and lowering the temperature in response to yawning or other signs of tiring," the company said. The systems uses AI to get to know the owners moods better over time, Jaguar Land Rover added. "In time the system will learn a driver's preference and make increasingly tailored adjustments," the company said.