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

Portfolio selection with uncertainty measures consistent with additive shifts

Assuming a non-satiable risk-averse investor, the standard approach to portfolio selection suggests discarding of all ineffi cient investment in terms of mean return and its standard deviation ratio within its fi rst step. However, in literature we can fi nd many alternative dispersion and risk measures that can help us to identify the most suitable investment opportunity. In this work two new dispersion measures, fulfi lling the condition that “more is better than less” are proposed. Moreover, their distinct characteristics are analysed and empirically compared. In particular, starting from the defi nition of dispersion measures, we discuss the property of consistency with respect to additive shifts and we examine two dispersion measures that satisfy this property. Finally, we empirically compare the proposed dispersion measures with the standard deviation and the conditional value at risk on the US stock market. Moreover, within the empirical example the so called “alarm” is incorporated in order to predict potential fails of the market.Web of Science24116

Long-term Stabilized Amorphous Calcium Carbonate – an Ink for Bio-inspired 3D Printing

Amorphous Calcium Carbonate (ACC) is a highly unstable amorphous precursor many organisms utilize for the formation of crystals with intricate morphology and improved mechanical properties. Herein, we report for the first-time high-yield long-term stabilization of ACC, achieved via its co-precipitation in the presence of high amounts of Mg and an acetone-based storage protocol. A novel use of the formed high-Mg ACC paste as an ink for 3D printing techniques allows the formation of bio-inspired intricately shaped calcium carbonate geometries. The obtained ink can dry, though retains its amorphous nature, at a variety of temperatures ranging from 25 to 150˚C enabling various applications such as cultural heritage reconstruction and artificial reefs formation. We also show the on-demand low-temperature crystallization of the 3D printed ACC models, similar to what is achieved by organisms in nature. Using this bio-inspired crystallization route via transient amorphous precursor also enables the presence of high Mg levels within the calcite crystalline lattice, far beyond the thermodynamically stable solubility level. High levels of Mg incorporation, in turns, encompasses a great promise for the enhancement in the mechanical properties of the crystallized calcite 3D objects akin naturally found crystalline CaCO3