Interactive level-of-detail rendering of large graphs

Fig. 1 . Application of our visualization technique on a hierarchical data set, zooming from overview (left) to a region of interest (right). The density-based node aggregation field (blue color) guides edge aggregation (orange/red color) to reveal visual patterns at different levels of detail. Abstract-We propose a technique that allows straight-line graph drawings to be rendered interactively with adjustable level of detail. The approach consists of a novel combination of edge cumulation with density-based node aggregation and is designed to exploit common graphics hardware for speed. It operates directly on graph data and does not require precomputed hierarchies or meshes. As proof of concept, we present an implementation that scales to graphs with millions of nodes and edges, and discuss several example applications

GEMv2 : Multilingual NLG benchmarking in a single line of code

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe

GEMv2 : Multilingual NLG benchmarking in a single line of code

Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal as better alternatives arise. This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims. To make following best model evaluation practices easier, we introduce GEMv2. The new version of the Generation, Evaluation, and Metrics Benchmark introduces a modular infrastructure for dataset, model, and metric developers to benefit from each others work. GEMv2 supports 40 documented datasets in 51 languages. Models for all datasets can be evaluated online and our interactive data card creation and rendering tools make it easier to add new datasets to the living benchmark.Peer reviewe

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Kritisk pedagogikk – et svar på dagens kunstpedagogiske utfordringer?

Artikkelen undersøker opprinnelsen og utviklingen av den kritiske pedagogikken og dens relevans for kunstnerisk utdanning i dag. Innledningsvis skisseres kunstfagenes vilkår i dagens utdanningslandskap, som er preget av nyliberal instrumentell tenkning og med økende krav om økonomisering, standardisering og målstyring. Ut fra en oppfatning om at mange av dagens utdanningspolitiske føringer strider mot kunstfagenes egenart og begrenser fagenes mulighet for å utdanne kritiske og myndige mennesker, argumenteres det for en kritisk pedagogisk tilnærming til kunstfagene i skole og utdanning. Kritisk pedagogikk beskrives som en pedagogisk retning med mange opphav, med sentrale aktører i Latin- og Nord-Amerika og i Tyskland. Mens den amerikanske linjen, som tar utgangspunkt i Paulo Freires idé om empowerment, primært er opptatt av sosial utjevning og marginaliserte gruppers rettigheter, har den tyske tradisjonen, i hovedsak representert ved Frankfurterskolen, en mer filosofisk orientering. Den har imidlertid også inspirert en rekke pedagogiske og didaktiske retninger i det tyskspråklige og det skandinaviske rommet. I forlengelsen av de historiske linjene drøfter artikkelen spørsmålet om en kritisk pedagogisk nyorientering i kunstfagene, eksemplifisert ved musikkfaget, kan bidra til å svare på sosiale og utdanningspolitiske utfordringer i det 21. århundre. Søkeord: Kritisk pedagogikk; kunstpedagogikk; empowerment; Paulo Freire; Frankfurterskole

Skapende og tenkende – Kritisk kunstpedagogikk i Freires fotspor

Teksten gir bakgrunnet til temanummeret samt en kort tematisk oversikt over artiklene

Interactive Level-of-Detail Rendering of Large Graphs

We propose a technique that allows straight-line graph drawings to be rendered interactively with adjustable level of detail. The approach consists of a novel combination of edge cumulation with density-based node aggregation and is designed to exploit common graphics hardware for speed. It operates directly on graph data and does not require precomputed hierarchies or meshes. As proof of concept, we present an implementation that scales to graphs with millions of nodes and edges, and discuss several example applications