Several new sites such as this are in the pipeline in the UK. Several new sites such as this are in the pipeline in the UK. 'A lot of this is speculative': faith and fear mix amid $3tn global datacentre boom The global investment spree in artificial intelligence is producing some remarkable numbers and a projected $3tn (£2.3tn) spend on datacentres is one of them. These vast warehouses are the central nervous system of AI tools such as OpenAI's ChatGPT and Google's Veo 3, underpinning the training and operation of a technology into which investors have poured vast sums of money. Despite concerns that the AI boom could be a bubble waiting to burst, there are few signs of it at the moment.

Precision soil greenhouse gas (GHG) flux prediction is essential in agricultural systems for assessing environmental impacts, developing emission mitigation strategies and promoting sustainable agriculture. Due to the lack of advanced sensor and network technologies on majority of farms, there are challenges in obtaining comprehensive and diverse agricultural data. As a result, the scarcity of agricultural data seriously obstructs the application of machine learning approaches in precision soil GHG flux prediction. This research proposes a knowledge-guided graph neural network framework that addresses the above challenges by integrating knowledge embedded in an agricultural process-based model and graph neural network techniques. Specifically, we utilise the agricultural process-based model to simulate and generate multi-dimensional agricultural datasets for 47 countries that cover a wide range of agricultural variables. To extract key agricultural features and integrate correlations among agricultural features in the prediction process, we propose a machine learning framework that integrates the autoencoder and multi-target multi-graph based graph neural networks, which utilises the autoencoder to selectively extract significant agricultural features from the agricultural process-based model simulation data and the graph neural network to integrate correlations among agricultural features for accurately predict fertilisation-oriented soil GHG fluxes. Comprehensive experiments were conducted with both the agricultural simulation dataset and real-world agricultural dataset to evaluate the proposed approach in comparison with well-known baseline and state-of-the-art regression methods. The results demonstrate that our proposed approach provides superior accuracy and stability in fertilisation-oriented soil GHG prediction.

Sentence transformers are language models designed to perform semantic search. This study investigates the capacity of sentence transformers, fine-tuned on general question-answering datasets for asymmetric semantic search, to associate descriptions of human-generated routes across Great Britain with queries often used to describe hiking experiences. We find that sentence transformers have some zero-shot capabilities to understand quasi-geospatial concepts, such as route types and difficulty, suggesting their potential utility for routing recommendation systems.

A computational theory for clustering and a semi-supervised clustering algorithm is presented. Clustering is defined to be the obtainment of groupings of data such that each group contains no anomalies with respect to a chosen grouping principle and measure; all other examples are considered to be fringe points, isolated anomalies, anomalous clusters or unknown clusters. More precisely, after appropriate modelling under the assumption of uniform random distribution, any example whose expectation of occurrence is <1 with respect to a group is considered an anomaly; otherwise it is assigned a membership of that group. Thus, clustering is conceived as the dual of anomaly detection. The representation of data is taken to be the Euclidean distance of a point to a cluster median. This is due to the robustness properties of the median to outliers, its approximate location of centrality and so that decision boundaries are general purpose. The kernel of the clustering method is Mohammad's anomaly detection algorithm, resulting in a parameter-free, fast, and efficient clustering algorithm. Acknowledging that clustering is an interactive and iterative process, the algorithm relies on a small fraction of known relationships between examples. These relationships serve as seeds to define the user's objectives and guide the clustering process. The algorithm then expands the clusters accordingly, leaving the remaining examples for exploration and subsequent iterations. Results are presented on synthetic and realworld data sets, demonstrating the advantages over the most widely used clustering methods.

Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture factual knowledge in their parameters. We argue that storing large amounts of knowledge in the model parameters is sub-optimal given the ever-growing amounts of knowledge and resource requirements. We posit that a more efficient alternative is to provide explicit access to contextually relevant structured knowledge to the model and train it to use that knowledge. We present LM-CORE -- a general framework to achieve this -- that allows \textit{decoupling} of the language model training from the external knowledge source and allows the latter to be updated without affecting the already trained model. Experimental results show that LM-CORE, having access to external knowledge, achieves significant and robust outperformance over state-of-the-art knowledge-enhanced language models on knowledge probing tasks; can effectively handle knowledge updates; and performs well on two downstream tasks. We also present a thorough error analysis highlighting the successes and failures of LM-CORE.

Lettuce is a valuable crop in Europe and the USA, but labor shortages make it difficult to harvest this valuable field vegetable, as sourcing sufficient seasonal labor to meet harvesting commitments is one of the sector's biggest challenges. Moreover, with wage inflation rising faster than producer prices, margins are very tight. In England, agricultural technology and machinery experts are working with IDS Imaging Development Systems GmbH, based in Obersulm, Germany, to develop a robotic solution to automate lettuce harvesting. The team is working on a project funded by Innovate UK and includes experts from the Grimme agricultural machinery factory, the Agri-EPI Centre in Edinburgh, UK, Harper Adams University in Newport, UK, the Centre for Machine Vision at the University of the West of England in Bristol and two of the UK's largest salad producers, G's Fresh and PDM Produce. Within the project, existing leek harvesting machinery is adapted to lift the lettuce clear from the ground and grip it in between pinch belts.

The proliferation and application of machine learning based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber attacks in Industrial Control Systems (ICS). However, the introduction of such IDSs has also created an additional attack vector; the learning models may also be subject to cyber attacks, otherwise referred to as Adversarial Machine Learning (AML). Such attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life. This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples using the Jacobian-based Saliency Map attack and exploring classification behaviours. The analysis also includes the exploration of how such samples can support the robustness of supervised models using adversarial training. An authentic power system dataset was used to support the experiments presented herein. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 16 and 20 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.

A timeline for putting the first Europeans on the surface of the moon will be in place by the end of the year, the European Space Agency has announced. David Parker, the agency's Director of Human and Robotic Exploration, made the comments during a space conference in Wales earlier this week. Plans for a European moonshot are already in motion, he added, part of which would see the construction of a space station in orbit above the lunar surface. The lunar gateway would provide not only a stepping stone to the moon but also to Mars, as well as providing a test bed to explore how space living impacts humans. Dr Parker -- who is also the former head of the UK Space Agency -- discussed the plans during the 2019 UK Space Conference that was held in Newport, Wales, from September 24–26.

In this paper we consider the problem of clustering collections of very short texts using subspace clustering. This problem arises in many applications such as product categorisation, fraud detection, and sentiment analysis. The main challenge lies in the fact that the vectorial representation of short texts is both high-dimensional, due to the large number of unique terms in the corpus, and extremely sparse, as each text contains a very small number of words with no repetition. We propose a new, simple subspace clustering algorithm that relies on linear algebra to cluster such datasets. Experimental results on identifying product categories from product names obtained from the US Amazon website indicate that the algorithm can be competitive against state-of-the-art clustering algorithms.

Useful quantum computers are closer to becoming a reality as some of the world's biggest corporations try to bring the technology from the lab into the practical world. A quantum computer utilizes subatomic particles called qubits to speed up the solving of complex computations. Near-term expectations for quantum computers range from solving optimization problems to quantum-encrypted communications, and more. With the help of CB Insights' investment, acquisition, and partnership data, we identified 18 corporate groups involved in the development of commercialized quantum computing hardware and software. They are a diverse group of players, ranging from tech industry behemoths to defense contractors to national telecommunications companies.