The Knowledge Graph-to-Text Generation task aims to convert structured knowledge graphs into coherent and human-readable natural language text. Recent efforts in this field have focused on enhancing pre-trained language models (PLMs) by incorporating graph structure information to capture the intricate structure details of knowledge graphs. However, most of these approaches tend to capture only single-granularity structure information, concentrating either on the relationships between entities within the original graph or on the relationships between words within the same entity or across different entities. This narrow focus results in a significant limitation: models that concentrate solely on entity-level structure fail to capture the nuanced semantic relationships between words, while those that focus only on word-level structure overlook the broader relationships between original entire entities. To overcome these limitations, this paper introduces the Multi-granularity Graph Structure Attention (MGSA), which is based on PLMs. The encoder of the model architecture features an entity-level structure encoding module, a word-level structure encoding module, and an aggregation module that synthesizes information from both structure. This multi-granularity structure encoding approach allows the model to simultaneously capture both entity-level and word-level structure information, providing a more comprehensive understanding of the knowledge graph's structure information, thereby significantly improving the quality of the generated text. We conducted extensive evaluations of the MGSA model using two widely recognized KG-to-Text Generation benchmark datasets, WebNLG and EventNarrative, where it consistently outperformed models that rely solely on single-granularity structure information, demonstrating the effectiveness of our approach.

Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates and data quantity. On the other hand, access time to the most advanced ARPES instruments remains strictly limited, calling for fast, effective, and on-the-fly data analysis tools to exploit this time. In response to this need, we introduce ARPESNet, a versatile autoencoder network that efficiently summmarises and compresses ARPES datasets. We train ARPESNet on a large and varied dataset of 2-dimensional ARPES data extracted by cutting standard 3-dimensional ARPES datasets along random directions in $\mathbf{k}$. To test the data representation capacity of ARPESNet, we compare $k$-means clustering quality between data compressed by ARPESNet, data compressed by discrete cosine transform, and raw data, at different noise levels. ARPESNet data excels in clustering quality despite its high compression ratio.

In this study, we employ a classification approach to show that different categories of literary "quality" display unique linguistic profiles, leveraging a corpus that encompasses titles from the Norton Anthology, Penguin Classics series, and the Open Syllabus project, contrasted against contemporary bestsellers, Nobel prize winners and recipients of prestigious literary awards. Our analysis reveals that canonical and so called high-brow texts exhibit distinct textual features when compared to other quality categories such as bestsellers and popular titles as well as to control groups, likely responding to distinct (but not mutually exclusive) models of quality. We apply a classic machine learning approach, namely Random Forest, to distinguish quality novels from "control groups", achieving up to 77\% F1 scores in differentiating between the categories. We find that quality category tend to be easier to distinguish from control groups than from other quality categories, suggesting than literary quality features might be distinguishable but shared through quality proxies.

We've all been there – you meet an adorable baby and immediately find yourself using an exaggerated, high-pitched, singsong voice. Now, a study has revealed that this'baby talk' is the same in every language, with people around the world transforming their voices when they speak to infants. Researchers from the University of York and Aarhus University studied baby talk across 36 languages and found similarities in pitch, melody, and articulation rates. Christopher Cox, who led the study, said: 'We use a higher pitch, more melodious phrases, and a slower articulation rate when talking to infants compared to how we talk to adults, and this appears to be the same across most languages.' We've all been there – you meet an adorable baby and immediately find yourself using an exaggerated, high-pitched singsong voice Baby talk is a style of speech employed by adults when talking to an infant.

No matter how conscientious the consumer, by the time the material gets to the end of the conveyor belt at the recycling plant, most plastics end up mixed together. Due to the rather rudimentary sorting techniques in use, only a small percentage of the plastic we try to recycle ends up getting recycled. "The ordinary consumer, with the best intentions--and also the correct procedure--puts everything in the plastic bin. We get it all," said Mogens Hinge, an associate professor in the department of biological and chemical engineering and process and materials engineering at Denmark's Aarhus University, and co-author of the paper "Plastic classification via in-line hyperspectral camera analysis and unsupervised machine learning," which appeared in Vibrational Spectroscopy this year. "Now we have a problem: we can wash it, but we can't unmix it. And plastic is not just plastic."

Scientists are combining artificial intelligence and advanced computer technology with biological know how to identify insects with supernatural speed. Insects are the most diverse group of animals on Earth and only a small fraction of these have been found and formally described. In fact, there are so many species that discovering all of them in the near future is unlikely. This enormous diversity among insects also means that they have very different life histories and roles in the ecosystems. For instance, a hoverfly in Greenland lives a very different life than a mantid in the Brazilian rainforest.

We expect the applicant to have experience in the fields of statistics, data science, genetics, and software development. The successful candidate will contribute to and lead one or more major research projects focused on improving polygenic risk scores and other genetic analyses. The successful applicant is then expected to write up the results, present them at international conferences, and publish in peer-reviewed scientific articles. A senior researcher position at Aarhus University is vacant from May 1st 2022, or soon thereafter. We are seeking a highly motivated person to develop and apply statistical and machine learning methods for genetic analyses and genetic risk prediction.

You can read more about how to apply in the application guide and find the rules and regulations of the PhD education here. You can read more about the assessment procedure and committees appointed by the dean here. Applicants should specify in the application whether they are interested in one of the specific projects outlined below. Applicants are very much encouraged to supply documentation as to how they rank compared to their class (both Bachelor's and Master's degrees). This may for example be in the form of a letter from your University stating either how you rank as an individual student compared to other students the year you graduated ("ranks as no.

One position in Graph Management and Semantic Search is available in the Data-Intensive Systems group, Department of Computer Science (https://cs.au.dk) at

Doctors know that we need smarter medicines to target the bad guys only. One hope is that tiny robots on the scale of a billionth of a metre can come to the rescue, delivering drugs directly to rogue cancer cells. To make these nanorobots, researchers in Europe are turning to the basic building blocks of life – DNA. Today robots come in all shapes and sizes. One of the strongest industrial robots can lift cars weighing over two tons.