Pretrained language models (PLMs) have made remarkable progress in table-to-text generation tasks. However, the lack of domain-specific knowledge makes it challenging to bridge the topological gap between tabular data and text, especially in real-world applications with limited resources. To mitigate the limitation of insufficient labeled data, we propose a novel framework: Adapt-Prompt-to-Generate (AdaPTGen). The core insight of AdaPTGen is to adapt prompt templates of domain-specific knowledge into the model, which brings at least three benefits: (1) it injects representation of normal table-related descriptions to bridge the topological gap between tabular data and texts; (2) it enables us to use large amounts of unlabeled domain-specific knowledge fully, which can alleviate the PLMs' inherent shortcomings of lacking domain knowledge; (3) it allows us to design various tasks to explore the domain-specific knowledge. Extensive experiments and analyses are conducted on three open-domain few-shot natural language generation (NLG) data sets: Humans, Songs, and Books. Compared to previous state-of-the-art approaches, our model achieves superior performance in terms of both fluency and accuracy.

Ukraine has warned Vladimir Putin that more drone attacks coming -- just hours after a flying bot smashed into one of Moscow's skyscrapers for the second time in as many days. Although Kyiv refuses to officially take responsibility for such attacks inside Russia, this latest skirmish is considered to be part of a wider offensive aimed at shifting the focus of the conflict to the Kremlin's doorstep. Experts say the way Kyiv is looking to do this is with the help of drones in the air and by sea -- a'cheap', expendable technology which has been revolutionising modern warfare over the past two decades. It is certainly turning attention from the First World War-style trench warfare that has been raging throughout Ukraine since the conflict broke out - and there's a reason the rest of the world is watching. Here, MailOnline looks at how drones are changing the face of future conflict, and why Ukraine is ratcheting up the use of them in an attempt to win the propaganda war and turn the tide of Putin's invasion.

The common ancestor of Indo-European languages, which are now spoken by close to half the world's population, was spoken in the eastern Mediterranean around 8000 years ago, according to an analysis of related words. Indo-European languages, spanning from English to Sanskrit, have long been thought to share a common ancestor. The first linguist to make this link, William Jones, said in a lecture in 1786 that no linguist could examine Greek, Latin and Sanskrit together "without believing them to have sprung" from some common ancestor. But researchers have struggled to agree on the origin story of this so-called proto-Indo-European language, says Paul Heggarty, who is now at the Pontifical Catholic University of Peru. There are two main hypotheses, he says.

That may have been science fiction but scientists say the terrifying prophecy could soon become a reality. That's because new research warns that the Atlantic Ocean current which drives the Gulf Stream could collapse at'any time' from 2025 thanks to climate change. Known formally as the Atlantic Meridional Overturning Circulation (AMOC), the current is the driving force which brings warm water from the Gulf of Mexico up to the UK and is responsible for mild winters in Western Europe. If it collapsed, however, the impact would be devastating. Europe would be plunged into a deep freeze, while most of Africa, the Caribbean, and South American countries such as Colombia, Peru and Bolivia would experience rocketing temperatures.

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This investigation underscores the importance of using a model architecture that supports the communication of prediction uncertainty and the effective integration of relevant, multi-modal features.

The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains. However, considering regression problems, they are primarily designed to provide deterministic responses or model the uncertainty of the output with Gaussian or parametric distribution. In this work, we introduce TreeFlow, the tree-based approach that combines the benefits of using tree ensembles with the capabilities of modeling flexible probability distributions using normalizing flows. The main idea of the solution is to use a tree-based model as a feature extractor and combine it with a conditional variant of normalizing flow. Consequently, our approach is capable of modeling complex distributions for the regression outputs. We evaluate the proposed method on challenging regression benchmarks with varying volume, feature characteristics, and target dimensionality. We obtain the SOTA results for both probabilistic and deterministic metrics on datasets with multi-modal target distributions and competitive results on unimodal ones compared to tree-based regression baselines.

It is a well-known fact that current AI-based language technology -- language models, machine translation systems, multilingual dictionaries and corpora -- focuses on the world's 2-3% most widely spoken languages. Recent research efforts have attempted to expand the coverage of AI technology to `under-resourced languages.' The goal of our paper is to bring attention to a phenomenon that we call linguistic bias: multilingual language processing systems often exhibit a hardwired, yet usually involuntary and hidden representational preference towards certain languages. Linguistic bias is manifested in uneven per-language performance even in the case of similar test conditions. We show that biased technology is often the result of research and development methodologies that do not do justice to the complexity of the languages being represented, and that can even become ethically problematic as they disregard valuable aspects of diversity as well as the needs of the language communities themselves. As our attempt at building diversity-aware language resources, we present a new initiative that aims at reducing linguistic bias through both technological design and methodology, based on an eye-level collaboration with local communities.

The transition to a sustainable energy supply challenges the operation of electric power systems in manifold ways. Transmission grid loads increase as wind and solar power are often installed far away from the consumers. In extreme cases, system operators must intervene via countertrading or redispatch to ensure grid stability. In this article, we provide a data-driven analysis of congestion in the German transmission grid. We develop an explainable machine learning model to predict the volume of redispatch and countertrade on an hourly basis. The model reveals factors that drive or mitigate grid congestion and quantifies their impact. We show that, as expected, wind power generation is the main driver, but hydropower and cross-border electricity trading also play an essential role. Solar power, on the other hand, has no mitigating effect. Our results suggest that a change to the market design would alleviate congestion.

To enhance the quality of generated stories, recent story generation models have been investigating the utilization of higher-level attributes like plots or commonsense knowledge. The application of prompt-based learning with large language models (LLMs), exemplified by GPT-3, has exhibited remarkable performance in diverse natural language processing (NLP) tasks. This paper conducts a comprehensive investigation, utilizing both automatic and human evaluation, to compare the story generation capacity of LLMs with recent models across three datasets with variations in style, register, and length of stories. The results demonstrate that LLMs generate stories of significantly higher quality compared to other story generation models. Moreover, they exhibit a level of performance that competes with human authors, albeit with the preliminary observation that they tend to replicate real stories in situations involving world knowledge, resembling a form of plagiarism.

Russia launched another wave of attacks on the Black Sea port of Odesa early on Sunday, killing one person and wounding 18, including four children, according to Ukrainian officials. A Ukrainian drone attack on the annexed Crimean Peninsula on Saturday blew up an ammunition depot and prompted evacuations along a 5km (3 miles) radius, according to Moscow-installed officials. It also halted road traffic along a bridge connecting Crimea to Russia. Footage shared by state media showed a thick cloud of grey smoke at the site. Russian news agencies quoted the Health Ministry as saying 12 people required medical assistance and four were taken to hospital. Ukraine said its army destroyed an oil depot and Russian army warehouses in the "temporarily occupied" district of Oktiabrske in central Crimea.