Background: Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns.Main body: We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications.Conclusions: The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

South Asia has one of the world's largest populations—India, Pakistan, and Bangladesh alone are home to more than 20% of all people. The region is also one of the fastest-growing technology markets as a result of its increased infrastructure and growing affordability. Despite this progress, South Asia faces one of the largest online gender disparities in the world, i.e., women are 58% less likely to connect to the mobile Internet than men.1 We believe that, as the Internet becomes more globally accessible, it is imperative for technologists to intentionally examine the biases and inequities perpetuated in technology to truly enable gender equity online.

The Human-computer Interaction (HCI) community has the opportunity to foster the integration of research practices across the Global South and North to begin overcoming colonial relationships. In this paper, we focus on the case of Latin America (LATAM), where initiatives to increase the representation of HCI practitioners lack a consolidated understanding of the practices they employ, the factors that influence them, and the challenges that practitioners face. To address this knowledge gap, we employ a mixed-methods approach, comprising a survey (66 respondents) and in-depth interviews (19 interviewees). Our analyses characterize a set of research perspectives on how HCI is practiced in/about LATAM; a set of driving forces and tensions with a heavy reliance on diasporic dynamics; and a set of professional demands and associated structural limitations. We also offer a roadmap towards building connections across HCI communities, in an attempt to rebuild HCI as a pluriverse.

Over the last 20 years, the Latin American Human-Computer Interaction (HCI) community has been working to shed light on how the diverse populations in the region are adopting, using, and making sense of computational technologies. Latin America's tense socio-political context, plurality of languages, collectivist culture, and historical relationship with the Global North make it a unique and rich space for HCI research. Considering the growing number of studies about Latin American communities and the emergent efforts to contribute to the HCI literature, we propose to host a SIG meeting at the 2020 ACM CHI conference. Our goal is to consolidate these efforts to better promote HCI research in, by, and for Latin America, by (1) bringing together researchers, practitioners, and students who are interested in engaging with Latin America through their research and practice, (2) envisioning a shared research agenda, and (3) identifying strategies for making its contributions more visible and impactful in the international community.