We present a novel computational model employing hierarchical active inference to simulate reading and eye movements. The model characterizes linguistic processing as inference over a hierarchical generative model, facilitating predictions and inferences at various levels of granularity, from syllables to sentences. Our approach combines the strengths of large language models for realistic textual predictions and active inference for guiding eye movements to informative textual information, enabling the testing of predictions. The model exhibits proficiency in reading both known and unknown words and sentences, adhering to the distinction between lexical and nonlexical routes in dual-route theories of reading. Notably, our model permits the exploration of maladaptive inference effects on eye movements during reading, such as in dyslexia. To simulate this condition, we attenuate the contribution of priors during the reading process, leading to incorrect inferences and a more fragmented reading style, characterized by a greater number of shorter saccades. This alignment with empirical findings regarding eye movements in dyslexic individuals highlights the model's potential to aid in understanding the cognitive processes underlying reading and eye movements, as well as how reading deficits associated with dyslexia may emerge from maladaptive predictive processing. In summary, our model represents a significant advancement in comprehending the intricate cognitive processes involved in reading and eye movements, with potential implications for understanding and addressing dyslexia through the simulation of maladaptive inference. It may offer valuable insights into this condition and contribute to the development of more effective interventions for treatment.

Currently, the world seeks to find appropriate mitigation techniques to control and prevent the spread of the new SARS-CoV-2. In our paper herein, we present a peculiar Multi-Task Learning framework that jointly predicts the effect of SARS-CoV-2 as well as Personal-Protective-Equipment consumption in Community Health Centres for a given populace. Predicting the effect of the virus (SARS-CoV-2), via studies and analyses, enables us to understand the nature of SARS-CoV- 2 with reference to factors that promote its growth and spread. Therefore, these foster widespread awareness; and the populace can become more proactive and cautious so as to mitigate the spread of Corona Virus Disease 2019 (COVID- 19). Furthermore, understanding and predicting the demand for Personal Protective Equipment promotes the efficiency and safety of healthcare workers in Community Health Centres. Owing to the novel nature and strains of SARS-CoV-2, relatively few literature and research exist in this regard. These existing literature have attempted to solve the problem statement(s) using either Agent-based Models, Machine Learning Models, or Mathematical Models. In view of this, our work herein adds to existing literature via modeling our problem statements as Multi- Task Learning problems. Results from our research indicate that government actions and human factors are the most significant determinants that influence the spread of SARS-CoV-2.

In today's world, Wireless Connectivity, Sensing and Machine intelligence are at the heart of innovations in Smart Home, Smart City, Smart Factory and Smart Healthcare applications. Following the remarkable success of the Access IoT 2019, this year's edition will focus on those key technologies with lectures, presentations and demonstrations from some of the most progressive industry experts. Get first-hand insights into the rapidly evolving world of connectivity technologies – Wi-Fi 6, Bluetooth, proprietary mesh connectivity stacks, as well as new groundbreaking sensors, batteries and other relevant components. Get in touch with expert speakers from Panasonic Industry, Schaeffler, Nordic Semiconductor, Wirepas, Edge Impulse, Bluetooth SIG, CTC advanced and NXP, who will spotlight on the topic from different and exciting perspective, each backed with years of dedicated experience. Don't miss this specialist get-together and intense exchange of latest know-how on IoT connectivity.

The movement toward digital part manufacturing is part of a trend toward connecting all steps of the manufacturing process--from planning to production--with a single source of information, a so-called digital thread. By taking advantage of digitalization, manufacturers can embrace automation to achieve greater efficiency, reduced time to delivery and better production results. Here's what NX 12 offers to enable digitalization: "Transformative technologies like additive manufacturing and advanced robotics offer tremendous potential for manufacturers of all sizes to gain an advantage in today's competitive market," said Siemens' Zvi Feuer. "NX provides a fully integrated solution for part manufacturers to utilize these powerful technologies, creating the ability to improve overall business performance and helping to create a truly digital machine shop." To learn more about Siemens NX 12, visit the NX landing page.

Samsung is set to unveil the heavily leaked Galaxy S8 in just a few days, but before that, it's cleared up a bit of a mystery about those leaks. We finally know what that extra hardware button is all about. Bixby is Samsung's replacement for the woefully under-equipped S Voice platform. Samsung knows it doesn't have the machine learning or wealth of data necessary to compete with Google Assistant, so Bixby won't even try. Instead, Bixby lets you control apps by voice.

In a very short time, Machine Learning has gone from a theoretical study to (almost) mainstream commercial reality. Much of this growth in machine learning and artificial intelligence can be attributed to the Internet of Things which is creating mega-myriads of data that must be processed in a variety of ways. While the IoT DevCon provides practical training for developing IoT applications such as medical devices, robots, drones, point-of-sale equipment, and industrial automation, the Machine Learning DevCon will explore the hardware and software challenges of building up and debugging these complex machine learning and artificial intelligence systems. The 2017 Machine Learning Developers Conference is directed by Markus Levy, Conference Chairman.