Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.

Effective management of public shared spaces such as car parking space, is one challenging transformational aspect for many cities, especially in the developing World. By leveraging sensing technologies, cloud computing, and Artificial Intelligence, Cities are increasingly being managed smartly. Smart Cities not only bring convenience to City dwellers, but also improve their quality of life as advocated for by United Nations in the 2030 Sustainable Development Goal on Sustainable Cities and Communities. Through integration of Internet of Things and Cloud Computing, this paper presents a successful proof-of-concept implementation of a framework for managing public car parking spaces. Reservation of parking slots is done through a cloud-hosted application, while access to and out of the parking slot is enabled through Radio Frequency Identification (RFID) technology which in real-time, accordingly triggers update of the parking slot availability in the cloud-hosted database. This framework could bring considerable convenience to City dwellers since motorists only have to drive to a parking space when sure of a vacant parking slot, an important stride towards realization of sustainable smart cities and communities.

While flying cars have long been a vision of science fiction movies, many companies, including NASA, have started turning them into a reality. However, the US space agency have left one'destroyed beyond expectations' after crashing it into the ground on purpose. This test was completed to see how the electric vertical takeoff and landing vehicle (eVTOL) would respond to such an event. Simulating a'severe crash', NASA engineers dropped a mock eVTOL containing six crash test dummies from a height. NASA has completed a crash test of its electric vertical takeoff and landing vehicle to test its response to such an event.

Note: The AI-made puzzle is near the end of this story. We don't need to wax poetic about ChatGPT's skills -- the world's already seeing it in action, as it churns out essays, 'inspirational' LinkedIn posts, even phishing emails that'd sucker the best of us. The AI chatbot is poised to replace the entire content writing industry, so there's a nagging question on every writer's mind: what can't it do? One of the answers, it turns out, is making a good word game. Now crosswords aren't easy things to build--apart from the actual'crossing' (interconnecting) of words, setters have to keep'product thinking' in mind.

Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.

We introduce an approach for the answer-aware question generation problem. Instead of only relying on the capability of strong pre-trained language models, we observe that the information of answers and questions can be found in some relevant sentences in the context. Based on that, we design a model which includes two modules: a selector and a generator. The selector forces the model to more focus on relevant sentences regarding an answer to provide implicit local information. The generator generates questions by implicitly combining local information from the selector and global information from the whole context encoded by the encoder. The model is trained jointly to take advantage of latent interactions between the two modules. Experimental results on two benchmark datasets show that our model is better than strong pre-trained models for the question generation task. The code is also available (shorturl.at/lV567).

Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals. However, such systems rely on costly manually labeled dialogs which are not available in practical scenarios. In this paper, we present our models for Track 2 of the SereTOD 2022 challenge, which is the first challenge of building semi-supervised and reinforced TOD systems on a large-scale real-world Chinese TOD dataset MobileCS. We build a knowledge-grounded dialog model to formulate dialog history and local KB as input and predict the system response. And we perform semi-supervised pre-training both on the labeled and unlabeled data. Our system achieves the first place both in the automatic evaluation and human interaction, especially with higher BLEU (+7.64) and Success (+13.6\%) than the second place.

Access to data, repetition and continued testing are preconditions for machine learning and – with the breadth and depth of data it consumes – the medical and healthcare industry provides AI with plenty of practice. Public health also happens to be where AI can make the most difference in terms of clinical need and cost-effectiveness of treatments. This of course is balanced with a need for rigorous testing because we're talking about human health rather than lower-stakes activities like gaming. As most biotech companies know, ethical and clinical testing is foundational to ensuring the safety of a therapy before it is unleashed on the public. COVID-19 taught us that population-level health solutions are the way of the future and AI, which works best at scale, can be used to optimise and speed up research and drug development and assist in the deployment of mass public health interventions.

For the past decade, temporal annotation has been sparse: only a small portion of event pairs in a text was annotated. We present NarrativeTime, the first timeline-based annotation framework that achieves full coverage of all possible TLinks. To compare with the previous SOTA in dense temporal annotation, we perform full re-annotation of TimeBankDense corpus, which shows comparable agreement with a significant increase in density. We contribute TimeBankNT corpus (with each text fully annotated by two expert annotators), extensive annotation guidelines, open-source tools for annotation and conversion to TimeML format, baseline results, as well as quantitative and qualitative analysis of inter-annotator agreement.

Recurrent neural networks (RNN) are the backbone of many text and speech applications. These architectures are typically made up of several computationally complex components such as; non-linear activation functions, normalization, bi-directional dependence and attention. In order to maintain good accuracy, these components are frequently run using full-precision floating-point computation, making them slow, inefficient and difficult to deploy on edge devices. In addition, the complex nature of these operations makes them challenging to quantize using standard quantization methods without a significant performance drop. We present a quantization-aware training method for obtaining a highly accurate integer-only recurrent neural network (iRNN). Our approach supports layer normalization, attention, and an adaptive piecewise linear (PWL) approximation of activation functions, to serve a wide range of state-of-the-art RNNs. The proposed method enables RNN-based language models to run on edge devices with $2\times$ improvement in runtime, and $4\times$ reduction in model size while maintaining similar accuracy as its full-precision counterpart.