Bending down to pick up the final package of his day's round, delivery driver Richard Fuller felt a sudden stabbing pain in his lower back. 'It was as if someone had stuck a knife into my kidney,' recalls Richard, now 57, who managed to drive home but was'in absolute agony'. Assuming he'd pulled a muscle, he took some paracetamol and went to bed, hoping rest might help. The next day, however, his back was so stiff and painful that he could barely get up. 'I live alone, so my parents came over to help me as I could only get around bent over and shuffling about,' says Richard, who lives in Canterbury, Kent.

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering further advancements in this emerging field.

Dr Maurizio Tinnirello is an independent researcher, and visiting lecturer in International Relations, Conflict and Security at Northumbria University and the Amsterdam University of Applied Sciences. He has held academic positions in both the Global South and North, and he has also worked as an international researcher and policy consultant on global security and military corruption issues. He has been the Vice-Chair and Program Chair of the Science, Technology and Art in International Relations section at The International Studies Association since 2019. Dr Tinnirello holds a PhD from the School of Politics and International Relations, and an MA in International Conflict Analysis, from the University of Kent, UK. He was a recipient of a Marie Skłodowska-Curie Action Initial Training Award, and a visiting PhD fellow at Coimbra University.

The B.1.1.7 lineage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused fast-spreading outbreaks globally. Intrinsically, this variant has greater transmissibility than its predecessors, but this capacity has been amplified in some circumstances to tragic effect by a combination of human behavior and local immunity. What are the extrinsic factors that help or hinder the rapid dissemination of variants? Kraemer et al. explored the invasion dynamics of B.1.1.7. in fine detail, from its location of origin in Kent, UK, to its heterogenous spread around the country. A combination of mobile phone and virus data including more than 17,000 genomes shows how distinct phases of dispersal were related to intensity of mobility and the timing of lockdowns. As the local outbreaks grew, importation from the London source area became less important. Had B.1.1.7. emerged at a slightly different time of year, its impact might have been different. Science , abj0113, this issue p. [889][1] Understanding the causes and consequences of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is crucial to pandemic control yet difficult to achieve because they arise in the context of variable human behavior and immunity. We investigated the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data. We identified a multistage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7’s increased intrinsic transmissibility. We further explored how B.1.1.7 spread was shaped by nonpharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates. [1]: /lookup/doi/10.1126/science.abj0113

We introduce StockBabble, a conversational agent designed to support understanding and engagement with the stock market. StockBabble's value and novelty is in its ability to empower retail investors -- many of which may be new to investing -- and supplement their informational needs using a user-friendly agent. Users have the ability to query information on companies to retrieve a general and financial overview of a stock, including accessing the latest news and trading recommendations. They can also request charts which contain live prices and technical investment indicators, and add shares to a personal portfolio to allow performance monitoring over time. To evaluate our agent's potential, we conducted a user study with 15 participants. In total, 73% (11/15) of respondents said that they felt more confident in investing after using StockBabble, and all 15 would consider recommending it to others. These results are encouraging and suggest a wider appeal for such agents. Moreover, we believe this research can help to inform the design and development of future intelligent, financial personal assistants.

Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline correction and/or PCA as an essential step. Here we describe our unified solution for the identification of chemical species in which a convolutional neural network is trained to automatically identify substances according to their Raman spectrum without the need of ad-hoc preprocessing steps. We evaluated our approach using the RRUFF spectral database, comprising mineral sample data. Superior classification performance is demonstrated compared with other frequently used machine learning algorithms including the popular support vector machine.

This is so like something out of an action movie that it's sort of hard to believe what you're seeing is actually real life. The clip above was shared on the Spotted Portsmouth Facebook group on Sunday. It appears to show a police car responding to a crime scene in Kent, UK. "A police vehicle in Gillingham recreates Fast and Furious 9 when responding to a priority call!" reads the description. We're not sure if it's standard procedure, but you can't deny they make a memorable entrance. This mash-up of'Skyrim,' 'Zelda,' and'Counter-Strike' doesn't exist, but it needs to

This week's milestones in the history of technology include the birth of Apple Computer, the first release of Gmail, and IBM signing an agreement with Harvard to build one of the earliest computers, the Automatic Sequence Controlled Calculator (ASCC), later called Mark I. Guglielmo Marconi receives the first wireless signal transmitted across the English Channel, sent from Wimereux, France, to his ship-to-shore station at the South Foreland Lighthouse outside Dover, England. The signal was a test held at the request of the French Government which was considering licensing the invention in France. Bell Telephone Laboratories announces the invention of the phototransistor, a transistor operated by light rather than electric current, invented by John Northrup Shive. An entirely new type of "electric eye" much smaller and sturdier than present photo-electric cells and possibly cheaper-has been invented at the Laboratories. During the past quarter century, electric eyes have found widespread use in electronics because of their ability to control electric currents by the action of light.

This paper examines and extends the work of Linsker (1986) on self organising feature detectors. Linsker concentrates on the visual processing system, but infers that the weak assumptions made will allow the model to be used in the processing of other sensory information. This claim is examined here, with special attention paid to the auditory system, where there is much lower connectivity and therefore more statistical variability. Online training is utilised, to obtain an idea of training times. These are then compared to the time available to prenatal mammals for the formation of feature sensitive cells. 1 INTRODUCTION Within the last thirty years, a great deal of research has been carried out in an attempt to understand the development of cells in the pathways between the sensory apparatus and the cortex in mammals. For example, theories for the development of feature detectors were forwarded by Nass and Cooper (1975), by Grossberg (1976) and more recently Obermayer et al (1990). Hubel and Wiesel (1961) established the existence of several different types of feature sensitive cell in the visual cortex of cats. Various subsequent experiments have 1007 1008 Walton and Bisset shown that a considerable amount of development takes place before birth (i.e.

This paper examines and extends the work of Linsker (1986) on self organising feature detectors. Linsker concentrates on the visual processingsystem, but infers that the weak assumptions made will allow the model to be used in the processing of other sensory information. This claim is examined here, with special attention paid to the auditory system, where there is much lower connectivity andtherefore more statistical variability. Online training is utilised, to obtain an idea of training times. These are then compared tothe time available to prenatal mammals for the formation of feature sensitive cells. 1 INTRODUCTION Within the last thirty years, a great deal of research has been carried out in an attempt to understand the development of cells in the pathways between the sensory apparatus and the cortex in mammals. For example, theories for the development of feature detectors were forwarded by Nass and Cooper (1975), by Grossberg (1976) and more recently Obermayer et al (1990). Hubel and Wiesel (1961) established the existence of several different types of feature sensitivecell in the visual cortex of cats. Various subsequent experiments have 1007 1008 Walton and Bisset shown that a considerable amount of development takes place before birth (i.e.