EFFECT OF PROTEIN HYDROLYSATE, ALVESIN AND PHYSIOLOGICAL SALINE ON GLYCEMIA IN EXPERIMENTAL DIABETES CAUSED BY STREPTOSOTOCIN

No abstrac

BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

The BLOOM model is a large publicly available multilingual language model, but its pretraining was limited to 46 languages. To extend the benefits of BLOOM to other languages without incurring prohibitively large costs, it is desirable to adapt BLOOM to new languages not seen during pretraining. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages in a resource-constrained setting. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, we find that adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at https://github.com/bigscience-workshop/multilingual-modeling

Novel merwinite/akermanite ceramics: in vitro bioactivity

The ceramics in the system CaO – MgO – SiO2 has recently received a great deal of attention because they exhibit good in vitro bioactivity and have potential use as bone implants. Biphasic calcium-magnesium-silicate ceramics was prepared by a sol-gel method. The dried gel with chemical composition 3CaO.MgO.2SiO2 was thermally treated at 1300°C for 2 h. The structural behavior of the synthesized ceramics was examined by means of X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM). Merwinite, as the main crystalline phase, and akermanite, as the minor phase, were identified. The in vitro bioactivity of the synthesized ceramic samples was recorded in Simulated Body Fluid (SBF) for different times of soaking. The apatite formation on the surface of the immersed samples was detected by FTIR, SEM and Energy Dispersive Spectroscopy (EDS) techniques. The ion concentrations in the SBF solutions after the in vitro test were evaluated by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). On the basis of the results obtained, the ability of the biphasic ceramics to deposit apatite layer was found. The peculiarities of the formation of apatite layer depending on the phase composition were analyzed and discussed

Creating language resources for under-resourced languages: methodologies, and experiments with Arabic

Language resources are important for those working on computational methods to analyse and study languages. These resources are needed to help advancing the research in fields such as natural language processing, machine learning, information retrieval and text analysis in general. We describe the creation of useful resources for languages that currently lack them, taking resources for Arabic summarisation as a case study. We illustrate three different paradigms for creating language resources, namely: (1) using crowdsourcing to produce a small resource rapidly and relatively cheaply; (2) translating an existing gold-standard dataset, which is relatively easy but potentially of lower quality; and (3) using manual effort with appropriately skilled human participants to create a resource that is more expensive but of high quality. The last of these was used as a test collection for TAC-2011. An evaluation of the resources is also presented

The Structure of the EU Mediasphere

Background. A trend towards automation of scientific research has recently resulted in what has been termed “data-driven inquiry” in various disciplines, including physics and biology. The automation of many tasks has been identified as a possible future also for the humanities and the social sciences, particularly in those disciplines concerned with the analysis of text, due to the recent availability of millions of books and news articles in digital format. In the social sciences, the analysis of news media is done largely by hand and in a hypothesis-driven fashion: the scholar needs to formulate a very specific assumption about the patterns that might be in the data, and then set out to verify if they are present or not. Methodology/Principal Findings. In this study, we report what we think is the first large scale content-analysis of cross-linguistic text in the social sciences, by using various artificial intelligence techniques. We analyse 1.3 M news articles in 22 languages detecting a clear structure in the choice of stories covered by the various outlets. This is significantly affected by objective national, geographic, economic and cultural relations among outlets and countries, e.g., outlets from countries sharing strong economic ties are more likely to cover the same stories. We also show that the deviation from average content is significantly correlated with membership to the eurozone, as well as with the year of accession to the EU. Conclusions/Significance. While independently making a multitude of small editorial decisions, the leading media of the 27 EU countries, over a period of six months, shaped the contents of the EU mediasphere in a way that reflects its deep geographic, economic and cultural relations. Detecting these subtle signals in a statistically rigorous way would be out of the reach of traditional methods. This analysis demonstrates the power of the available methods for significant automation of media content analysis

Turkish information retrieval: Past changes future

One of the most exciting accomplishments of computer science in the lifetime of this generation is the World Wide Web. The Web is a global electronic publishing medium. Its size has been growing with an enormous speed for over a decade. Most of its content is objectionable, but it also contains a huge amount of valuable information. The Web adds a new dimension to the concept of information explosion and tries to solve the very same problem by information retrieval systems known as Web search engines. We briefly review the information explosion problem and information retrieval systems, convey the past and state of the art in Turkish information retrieval research, illustrate some recent developments, and propose some future actions in this research area in Turkey. © Springer-Verlag Berlin Heidelberg 2006