The Impact of Health-Related Quality of Life on Retention in Drug Treatment Courts [English and Spanish versions]

A Spanish translation of this publication is available to download under Additional Files. This Research in the Works describes Ekaterina Pivovarova’s new study The Impact of Health-Related Quality of Life on Retention in Drug Treatment Courts. Past research has focused on Drug Treatment Court dropouts as a function of participant characteristics (e.g., age, criminal history) or treatment program features (e.g., frequency of DTC hearings). Identifying individuals most likely to dropout and helping them to remain in treatment programs is critical to decreasing substance use in this population. This study proposes to shift the focus to health-related Quality of Life (QOL) and its impact on DTC dropout

Visual Topic Modelling for NewsImage Task at MediaEval 2021

We present the Visual Topic Model (VTM)—a model able to generate a topic distribution for an image, without using any text during inference. The model is applied to an image-text matching task at MediaEval 2021. Though results for this specific task are negative (the model works worse than a baseline), we demonstrate that VTM produces meaningful results and can be used in other applications

Multilingual and Multimodal Topic Modelling with Pretrained Embeddings

This paper presents M3L-Contrast—a novel multimodal multilingual (M3L) neural topic model for comparable data that maps texts from multiple languages and images into a shared topic space. Our model is trained jointly on texts and images and takes advantage of pretrained document and image embeddings to abstract the complexities between different languages and modalities. As a multilingual topic model, it produces aligned language-specific topics and as multimodal model, it infers textual representations of semantic concepts in images. We demonstrate that our model is competitive with a zero-shot topic model in predicting topic distributions for comparable multilingual data and significantly outperforms a zero-shot model in predicting topic distributions for comparable texts and images. We also show that our model performs almost as well on unaligned embeddings as it does on aligned embeddings.Peer reviewe

RuShiftEval: A Shared Task on Semantic Shift Detection for Russian

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Employment management policies in single-industry towns in the light of existing issues of precarious employment

Purpose: The article aims to study the precarious employment in single-industry towns and to assess the effectiveness of government measures aimed at reducing it and ensuring the protection of economic and social rights of workers. Design/Methodology/Approach: It was revealed that the transition to an information-digital society, which continues to be formatted at the junction of changes in communication technologies and the motivation of employment behavior, has significantly changed the labor market in Russia. There was a massive introduction of non-standard forms of employment, which has not only positive effects, but also negative risks that are most acute in single-industry towns. To minimize these risks, it is necessary to find mechanisms to increase the economic and social security of workers with flexible employment. Findings: The paper proposes to develop an employment management policy in single-industry towns regarding the precarious employment and include measures of solving the issues related to self-employment of the population. Practical Implications: The practical results of the study can be used to develop assumptions for regional authorities to reduce precarious employment in single-industry towns. Originality / Value: The main contribution of this study is that single-industry cities should reduce inefficient “social employment” by creating highly efficient jobs and developing self-employmenpeer-reviewe

Three-part diachronic semantic change dataset for Russian

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Comparison of Representations of Named Entities for Multi-label Document Classification with Convolutional Neural Networks

Peer reviewe

Central IRBs: Enhanced Protections for Research Participants [English and Spanish versions]

A Spanish translation of this publication is available for download under Additional Files below. Institutional Review Boards (IRBs) play a critical role in research, and assure safety and fairness to participants enrolled in research studies. Multisite studies are often reviewed by multiple IRBs (an IRB review at each site participating in the study), which can slow down study approval, result in duplication of effort, and occasionally produce contradictory decisions by different IRBs. To address these problems, the federal government has promoted the use of single IRBs (referred to as Central IRBs or CIRBs), where a single IRB is responsible for the review of all sites where the research study is conducted. The National Institutes of Health (NIH) has recently announced that beginning in 2017 all research conducted at multiple sites must be reviewed by a CIRB. This CIRB process is new and requires careful study to understand its pitfalls and benefits. As such, UMass Medical School and Columbia University received a (NIH) grant to study how different institutions conduct reviews of research involving multiple sites