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.

Contextualizing language technologies beyond a single language kindled embracing multiple modalities and languages. Individually, each of these directions undoubtedly proliferated into several NLP tasks. Despite this momentum, most of the multimodal research is primarily centered around English and multilingual research is primarily centered around contexts from text modality. Challenging this conventional setup, researchers studied the unification of multilingual and multimodal (MultiX) streams. The main goal of this work is to catalogue and characterize these works by charting out the categories of tasks, datasets and methods to address MultiX scenarios. To this end, we review the languages studied, gold or silver data with parallel annotations, and understand how these modalities and languages interact in modeling. We present an account of the modeling approaches along with their strengths and weaknesses to better understand what scenarios they can be used reliably. Following this, we present the high-level trends in the overall paradigm of the field. Finally, we conclude by presenting a road map of challenges and promising research directions.

Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integration of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called MALI (Manifold alignment with label information) that learns a correspondence between two distinct domains. MALI can be considered as belonging to a middle ground between the more commonly addressed semi-supervised manifold alignment problem with some known correspondences between the two domains, and the purely unsupervised case, where no known correspondences are provided. To do this, MALI learns the manifold structure in both domains via a diffusion process and then leverages discrete class labels to guide the alignment. By aligning two distinct domains, MALI recovers a pairing and a common representation that reveals related samples in both domains. Additionally, MALI can be used for the transfer learning problem known as domain adaptation. We show that MALI outperforms the current state-of-the-art manifold alignment methods across multiple datasets.

We propose the first character-level white-box adversarial attack method against transformer models. The intuition of our method comes from the observation that words are split into subtokens before being fed into the transformer models and the substitution between two close subtokens has a similar effect to the character modification. Our method mainly contains three steps. First, a gradient-based method is adopted to find the most vulnerable words in the sentence. Then we split the selected words into subtokens to replace the origin tokenization result from the transformer tokenizer. Finally, we utilize an adversarial loss to guide the substitution of attachable subtokens in which the Gumbel-softmax trick is introduced to ensure gradient propagation. Meanwhile, we introduce the visual and length constraint in the optimization process to achieve minimum character modifications. Extensive experiments on both sentence-level and token-level tasks demonstrate that our method could outperform the previous attack methods in terms of success rate and edit distance. Furthermore, human evaluation verifies our adversarial examples could preserve their origin labels.