Extracting frequent words from a collection of texts is commonly performed in many subjects. However, as useful as it is to obtain a collection of commonly occurring words from texts, there is a need for more specific information to be obtained from texts in the form of most commonly occurring phrases. Despite this need, extracting frequent phrases is not commonly done due to inherent complications, the most significant being double-counting. Double-counting occurs when words or phrases are counted when they appear inside longer phrases that themselves are also counted, resulting in a selection of mostly meaningless phrases that are frequent only because they occur inside frequent super phrases. Several papers have been written on phrase mining that describe solutions to this issue; however, they either require a list of so-called quality phrases to be available to the extracting process, or they require human interaction to identify those quality phrases during the process. We present here a method that eliminates double-counting via a unique rectification process that does not require lists of quality phrases. In the context of a set of texts, we define a principal phrase as a phrase that does not cross punctuation marks, does not start with a stop word, with the exception of the stop words "not" and "no", does not end with a stop word, is frequent within those texts without being double counted, and is meaningful to the user. Our method identifies such principal phrases independently without human input, and enables their extraction from any texts within a reasonable amount of time.

Manoeuvring in the presence of emergency vehicles is still a major issue for vehicle autonomy systems. Most studies that address this topic are based on rule-based methods, which cannot cover all possible scenarios that can take place in autonomous traffic. Multi-Agent Proximal Policy Optimisation (MAPPO) has recently emerged as a powerful method for autonomous systems because it allows for training in thousands of different situations. In this study, we present an approach based on MAPPO to guarantee the safe and efficient manoeuvring of autonomous vehicles in the presence of an emergency vehicle. We introduce a risk metric that summarises the potential risk of collision in a single index. The proposed method generates cooperative policies allowing the emergency vehicle to go at $15 \%$ higher average speed while maintaining high safety distances. Moreover, we explore the trade-off between safety and traffic efficiency and assess the performance in a competitive scenario.

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server's side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model.

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind powe has been developing in the direction of digitization and intelligence. It is of great significance to carry ou research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit fo the reduction of the operation and maintenance costs, the improvement of the power generation efficiency improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of "offshore wind power engineering and biological and environment", the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of powe equipment, and digital platforms.

Recent advances in large pretrained language models have increased attention to zero-shot text classification. In particular, models finetuned on natural language inference datasets have been widely adopted as zero-shot classifiers due to their promising results and off-the-shelf availability. However, the fact that such models are unfamiliar with the target task can lead to instability and performance issues. We propose a plug-and-play method to bridge this gap using a simple self-training approach, requiring only the class names along with an unlabeled dataset, and without the need for domain expertise or trial and error. We show that fine-tuning the zero-shot classifier on its most confident predictions leads to significant performance gains across a wide range of text classification tasks, presumably since self-training adapts the zero-shot model to the task at hand.

The recent decade has seen an enormous rise in the popularity of deep learning and neural networks. These algorithms have broken many previous records and achieved remarkable results. Their outstanding performance has significantly sped up the progress of AI, and so far various milestones have been achieved earlier than expected. However, in the case of relatively small datasets, the performance of Deep Neural Networks (DNN) may suffer from reduced accuracy compared to other Machine Learning models. Furthermore, it is difficult to construct prediction intervals or evaluate the uncertainty of predictions when dealing with regression tasks. In this paper, we propose an ensemble method that attempts to estimate the uncertainty of predictions, increase their accuracy and provide an interval for the expected variation. Compared with traditional DNNs that only provide a prediction, our proposed method can output a prediction interval by combining DNNs, extreme gradient boosting (XGBoost) and dissimilarity computation techniques. Albeit the simple design, this approach significantly increases accuracy on small datasets and does not introduce much complexity to the architecture of the neural network. The proposed method is tested on various datasets, and a significant improvement in the performance of the neural network model is seen. The model's prediction interval can include the ground truth value at an average rate of 71% and 78% across training sizes of 90% and 55%, respectively. Finally, we highlight other aspects and applications of the approach in experimental error estimation, and the application of transfer learning.

In many lower-and-middle income countries including South Africa, data access in health facilities is restricted due to patient privacy and confidentiality policies. Further, since clinical data is unique to individual institutions and laboratories, there are insufficient data annotation standards and conventions. As a result of the scarcity of textual data, natural language processing (NLP) techniques have fared poorly in the health sector. A cause of death (COD) is often determined by a verbal autopsy (VA) report in places without reliable death registration systems. A non-clinician field worker does a VA report using a set of standardized questions as a guide to uncover symptoms of a COD. This analysis focuses on the textual part of the VA report as a case study to address the challenge of adapting NLP techniques in the health domain. We present a system that relies on two transfer learning paradigms of monolingual learning and multi-source domain adaptation to improve VA narratives for the target task of the COD classification. We use the Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) models pre-trained on the general English and health domains to extract features from the VA narratives. Our findings suggest that this transfer learning system improves the COD classification tasks and that the narrative text contains valuable information for figuring out a COD. Our results further show that combining binary VA features and narrative text features learned via this framework boosts the classification task of COD.

Russia's blockade of grain exports makes it "impossible" for fully loaded ships to leave port, Ukraine charged Sunday after Moscow claimed drone attacks on its Crimea fleet had exploited the grain corridor safe zone. Kyiv's maritime grain exports were halted after Russia pulled out of a landmark agreement that allowed the vital shipments. The July deal to unlock grain exports signed between Russia and Ukraine and brokered by Turkey and the United Nations, is critical to easing the global food crisis caused by the conflict. "(A) bulk carrier loaded with 40 tons of grain was supposed to leave the Ukraine port today," Infrastructure Minister Oleksandr Kubrakov tweeted. "These foodstuffs were intended for Ethiopians, that are on the verge of famine. But due to the blockage of the'grain corridor' by Russia the export is impossible," the Ukrainian minister said.

Western governments are calling on Russia to reverse its decision to pull out of a UN-brokered grain deal, a move that undermines efforts to ease a global food crisis, with Ukraine saying Moscow had planned the move well in advance. The Turkey and UN-brokered deal was signed between Russia and Ukraine in July under which Moscow allowed the grain ships to leave Ukrainian Black Sea ports. The agreement has already allowed more than 9 million tonnes of Ukrainian grain to be exported and was due to be renewed on November 19. Moscow suspended its participation in the deal on Saturday, effectively blocking shipments from Ukraine, one of the world's top grain exporters, in response to what it called a major Ukrainian drone attack earlier in the day on its Black Sea Fleet headquarters near the port of Sevastopol in Russian-annexed Crimea. "Russia's decision to suspend participation in the Black Sea deal puts at risk the main export route of much needed grain and fertilisers to address the global food crisis caused by its war against Ukraine," European Union foreign policy chief Josep Borrell said on Twitter on Sunday.

This survey shows how concepts in arithmetic circuit complexity and structured matrices can be used to solve a (theoretical) problem motivated by practical applications in machine learning (especially deep learning). Since each of the areas of arithmetic (circuit) complexity, structured matrices and deep learning have been explored in great depth and this survey clearly cannot do any justice to all the great work in each of the these areas, we will spend most of the introduction clarifying what this survey is not about. Algebraic circuit complexity or more generally algebraic complexity theory [11] studies the power of algebraic algorithms (as opposed to the Turing machine/RAM model). The arithmetic circuit model (or the straight-line programs) are one of the standard models of computation in algebraic complexity theory [11, Chapter 4]. In this survey we will ignore pretty much everything in this literature except for results on the arithmetic circuit complexity of the linear map i.e. functions of the form x Wx (where x is a vector over some field F and W is a matrix over the same field) [11, Chapter 13]. We would like to stress that this survey will only scratch the surface of the literature on the algebraic circuit complexity of the linear map. Just to give a sense of the breadth of this seemingly'specialized' topic, we remark that the study of matrix rigidity [22], which has seen a lot of recent research activity [1, 2, 3, 19, 10], is a part of this topic. We note that originally, the topic of matrix rigidity was proposed by Valiant [42] as a way to prove super-linear lower bounds, by constructing matrices that are rigid. However, our goal in this survey is to prove upper bounds-i.e.