Stability of Magnetic Fluids in Magnetic Fields

Stability of magnetic fluids in magnetic fields is one of the major factors determining the possibility of their practical use and resource of their exploitation. This paper examines the stability of magnetic fluids based on kerosene in constant and variable magnetic fields. It is shown that the synthesized magnetic flu-ids are stable during long-term exposure to magnetic fields and can be used as the working fluid in a num-ber of magnetic fluid devices. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3635

Colloidal Stability and Thermal Stability of Magnetic Fluids

Colloidal and thermal stabilities of magnetic fluids define the service life of magneto-liquid equipment. The results of the research into colloidal and thermal stabilities of original synthesized magnetic fluids based on kerosene, siloxane fluid and synthetic hydrocarbon oil are presented. The method of carrying agent substitution was used in the research into colloidal stability. The thermal tests were conducted in the research into thermal stability. The conclusions about the prospects of synthesized magnetic fluids using in technical equipment are made on the basis of received experimental data. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3366

Study of the 10 micron continuum of water vapor

Radiation attenuation by atmospheric water vapor is considered. A formula based on laboratory data is recommended for approximating continuous absorption in the spectra region in question. Data of full scale measurements and laboratory experiments are compared. It was concluded that only molecular absorption need be taken into account under clear atmospheric conditions during the warm part of the year, while in winter or in cloudy conditions, the effect of aerosol can be significant

HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings

We present our system for semantic frame induction that showed the best performance in Subtask B.1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019). Our approach separates this task into two independent steps: verb clustering using word and their context embeddings and role labeling by combining these embeddings with syntactical features. A simple combination of these steps shows very competitive results and can be extended to process other datasets and languages.Comment: 5 pages, 3 tables, accepted at SemEval 201

Negative sampling improves hypernymy extraction based on projection learning

We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of positive examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages

Communication research between working capacity of hardalloy cutting tools and fractal dimension of their wear

The results of communication research between the wear resistance of the Kapplicability hard-alloy cutting tools and the fractal dimension of the wear surface,which is formed on a back side of the cutting edge when processing the materialsshowing high adhesive activity are presented in the paper. It has been established thatthe wear resistance of tested cutting tools samples increases according to a fractaldimension increase of their wear surface

RUSSE'2018 : a shared task on word sense induction for the Russian language

The paper describes the results of the first shared task on word sense induction (WSI) for the Russian language. While similar shared tasks were conducted in the past for some Romance and Germanic languages, we explore the performance of sense induction and disambiguation methods for a Slavic language that shares many features with other Slavic languages, such as rich morphology and free word order. The participants were asked to group contexts with a given word in accordance with its senses that were not provided beforehand. For instance, given a word “bank” and a set of contexts with this word, e.g. “bank is a financial institution that accepts deposits” and “river bank is a slope beside a body of water”, a participant was asked to cluster such contexts in the unknown in advance number of clusters corresponding to, in this case, the “company” and the “area” senses of the word “bank”. For the purpose of this evaluation campaign, we developed three new evaluation datasets based on sense inventories that have different sense granularity. The contexts in these datasets were sampled from texts of Wikipedia, the academic corpus of Russian, and an explanatory dictionary of Russian. Overall 18 teams participated in the competition submitting 383 models. Multiple teams managed to substantially outperform competitive state-of-the-art baselines from the previous years based on sense embeddings

Multilingual Substitution-based Word Sense Induction

Word Sense Induction (WSI) is the task of discovering senses of an ambiguous word by grouping usages of this word into clusters corresponding to these senses. Many approaches were proposed to solve WSI in English and a few other languages, but these approaches are not easily adaptable to new languages. We present multilingual substitution-based WSI methods that support any of 100 languages covered by the underlying multilingual language model with minimal to no adaptation required. Despite the multilingual capabilities, our methods perform on par with the existing monolingual approaches on popular English WSI datasets. At the same time, they will be most useful for lower-resourced languages which miss lexical resources available for English, thus, have higher demand for unsupervised methods like WSI