This work gives introduction to traffic control by connected automated vehicles. The influence of vehicle control on vehicular traffic and traffic control strategies are discussed and compared. It is highlighted that vehicle-to-everything connectivity allows connected automated vehicles to access the state of the traffic behind them such that feedback can be utilized to mitigate evolving congestions. Numerical simulations demonstrate that such connectivity-based traffic control is beneficial for smoothness and energy efficiency of highway traffic. The dynamics and stability of traffic flow, under the proposed controllers, are analyzed in detail to construct stability charts that guide the selection of stabilizing control gains.

Turing never returned to the National Physical Laboratory after his research leave. Instead, in May 1948, he joined his friend Newman's Computing Machine Laboratory at the University of Manchester, where shortly afterwards the world's first electronic stored-program general-purpose digital computer, the Small-Scale Experimental Machine (commonly known as the Manchester Baby), ran its first program. Turing spent most of the remaining six years of his life continuing his research on AI. After completing the programming system of the expanded Manchester Mark I machine and the subsequent Ferranti Mark I, the world's first commercially available modern computer (manufactured by Ferranti Ltd), in early 1951 Turing began experimenting on the Ferranti. The early results of his computational modelling of biological growth were published in the paper'The Chemical Basis of Morphogenesis' (1952), which represented an important early contribution to research on artificial life.

For sequential data, a change point is a moment of abrupt regime switch in data streams. Such changes appear in different scenarios, including simpler data from sensors and more challenging video surveillance data. We need to detect disorders as fast as possible. Classic approaches for change point detection (CPD) might underperform for semi-structured sequential data because they cannot process its structure without a proper representation. We propose a principled loss function that balances change detection delay and time to a false alarm. It approximates classic rigorous solutions but is differentiable and allows representation learning for deep models. We consider synthetic sequences, real-world data sensors and videos with change points. We carefully labelled available data with change point moments for video data and released it for the first time. Experiments suggest that complex data require meaningful representations tailored for the specificity of the CPD task -- and our approach provides them outperforming considered baselines. For example, for explosion detection in video, the F1 score for our method is $0.53$ compared to baseline scores of $0.31$ and $0.35$.

EOS partners with QUANERGY to distribute false alarm-reducing LiDAR technology for security applications in the ANZ market. Quanergy Systems is a provider of LiDAR sensors and Smart 3D Computer Perception Software – the brains of Quanergy's 3D AI-powered LiDAR FlowManagement platform, aimed at increasing efficiency in response to security breaches and reducing costly false alarms. "Quanergy's LiDAR is the pinnacle technology for perimeter intrusion detection," said EOS MD, Patrick Cha. "Quanergy's platform is designed to increase efficiency in response to security breaches and drastically reduce costly false alarms. "We are proud to be a partner of Quanergy and we look forward to distributing the LiDAR sensor and 3D perception software to the ANZ market to further enhance electronic security systems for perimeter and smart city projects."

The remarkable progress in Natural Language Processing (NLP) brought about by deep learning, particularly with the recent advent of large pre-trained neural language models, is brought into scrutiny as several studies began to discuss and report potential biases in NLP applications. Bias in NLP is found to originate from latent historical biases encoded by humans into textual data which gets perpetuated or even amplified by NLP algorithm. We present a survey to comprehend bias in large pre-trained language models, analyze the stages at which they occur in these models, and various ways in which these biases could be quantified and mitigated. Considering wide applicability of textual affective computing based downstream tasks in real-world systems such as business, healthcare, education, etc., we give a special emphasis on investigating bias in the context of affect (emotion) i.e., Affective Bias, in large pre-trained language models. We present a summary of various bias evaluation corpora that help to aid future research and discuss challenges in the research on bias in pre-trained language models. We believe that our attempt to draw a comprehensive view of bias in pre-trained language models, and especially the exploration of affective bias will be highly beneficial to researchers interested in this evolving field. The examples provided in this paper may be offensive in nature and may hurt your moral beliefs.

Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their critical thinking and reasoning but also contribute to the effectiveness and civility in online communication. It is, however, not an easy task in online discussion settings where written words are the primary communication channel. In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts. To satisfy these design goals, we analyzed and built a labeled dataset of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and evidence) in 164 arguments with high ratings on persuasiveness from ChangeMyView, a popular online discussion forum. We then designed an interactive visual system, Persua, which provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments. In particular, the system constructs portfolios of arguments based on different persuasive strategies applied to a given discussion topic. It then presents concrete examples based on the difference between the portfolios of user input and high-quality arguments in the dataset. A between-subjects study shows suggestive evidence that Persua encourages users to submit more times for feedback and helps users improve more on the persuasiveness of their arguments than a baseline system. Finally, a set of design considerations was summarized to guide future intelligent systems that improve the persuasiveness in text.

Imagine a business world where employees are faster and more productive – where they can make smarter decisions and have the time to focus on strategy and being creative. This is all a near-reality with continued breakthroughs in artificial intelligence (AI) capabilities. AI is at the tipping point of becoming the next great technological disruptor. Improvements in computing power, the advent of big data and breakthroughs in machine learning have created the ideal environment for AI to flourish and augment human potential. At the very least, AI will transform our economies, reshape consumer expectations, and increase the speed and scale of business. At it's limit, it will help us understand humanity and human intelligence better.

The practical importance of coherent forecasts in hierarchical forecasting has inspired many studies on forecast reconciliation. Under this approach, so-called base forecasts are produced for every series in the hierarchy and are subsequently adjusted to be coherent in a second reconciliation step. Reconciliation methods have been shown to improve forecast accuracy, but will, in general, adjust the base forecast of every series. However, in an operational context, it is sometimes necessary or beneficial to keep forecasts of some variables unchanged after forecast reconciliation. In this paper, we formulate reconciliation methodology that keeps forecasts of a pre-specified subset of variables unchanged or "immutable". In contrast to existing approaches, these immutable forecasts need not all come from the same level of a hierarchy, and our method can also be applied to grouped hierarchies. We prove that our approach preserves unbiasedness in base forecasts. Our method can also account for correlations between base forecasting errors and ensure non-negativity of forecasts. We also perform empirical experiments, including an application to sales of a large scale online retailer, to assess the impacts of our proposed methodology.

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value and threat considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here, we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truth data for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly heterogeneous diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns, constituting a step forward toward automated biodiversity assessments.

"In my view, there is too much hype around A.I., road safety and self-driving vehicles -- it is super inflated," said David Ward, president of the Global New Car Assessment Program, a nonprofit based in London. The focus, he said, should be on "the low-hanging fruit, and not on some far-off utopian promise." Advocates like Mr. Ward look to beneficial, low-cost, intermediate technologies that are available now. A prime example is intelligent speed assistance, or I.S.A., which uses A.I. to manage a car's speed via in-vehicle cameras and maps. The technology will be mandatory in all new vehicles in the European Union beginning in July, but has yet to take hold in the United States. Acusensus, based in Australia, is among the companies that employ artificial intelligence to address road safety.