0000000000821413
AUTHOR
Samuel Kaski
showing 6 related works from this author
The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoenc…
2022
Funding Information: This work was financially supported by the Academy of Finland (Finnish Center of Excellence in Computational Inference Research COIN and grants #292334, #294238 to SK; #255349, #315553 to RS; #257576 to JK; #286405 funding for TM), the Sigrid Jusélius Foundation (grant to RS), the Finnish Cultural Foundation (grant to ML), the Swedish Cultural Foundation in Finland (grant to ML), the Maud Kuistila Memorial Foundation (grant to ML), and Aalto Brain Center. Publisher Copyright: Copyright © 2022 Mononen, Kujala, Liljeström, Leppäaho, Kaski and Salmelin. Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relat…
Identification of multiplicatively acting modulatory mutational signatures in cancer
2022
Abstract Background A deep understanding of carcinogenesis at the DNA level underpins many advances in cancer prevention and treatment. Mutational signatures provide a breakthrough conceptualisation, as well as an analysis framework, that can be used to build such understanding. They capture somatic mutation patterns and at best identify their causes. Most studies in this context have focused on an inherently additive analysis, e.g. by non-negative matrix factorization, where the mutations within a cancer sample are explained by a linear combination of independent mutational signatures. However, other recent studies show that the mutational signatures exhibit non-additive interactions. Resu…
Entity Recommendation for Everyday Digital Tasks
2021
| openaire: EC/H2020/826266/EU//CO-ADAPT Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitor…
EntityBot: Supporting Everyday Digital Tasks with Entity Recommendations
2021
Everyday digital tasks can highly benefit from systems that recommend the right information to use at the right time. However, existing solutions typically support only specific applications and tasks. In this demo, we showcase EntityBot, a system that captures context across application boundaries and recommends information entities related to the current task. The user’s digital activity is continuously monitored by capturing all content on the computer screen using optical character recognition. This includes all applications and services being used and specific to individuals’ computer usages such as instant messaging, emailing, web browsing, and word processing. A linear model is then …
EntityBot: Actionable Entity Recommendations for Everyday Digital Task
2022
Our everyday digital tasks require access to information from a wide range of applications and systems. Although traditional search systems can help find information, they usually operate within one application (e.g., email client or web browser) and require the user's cognitive effort and attention to formulate proper search queries. In this paper, we demonstrate EntityBot, a system that proactively provides useful and supporting entities across application boundaries without requiring explicit query formulation. Our methodology is to exploit the context from screen frames captured every 2 seconds to recommend relevant entities for the current task. Recommendations are not restricted to on…
Additional file 1 of Identification of multiplicatively acting modulatory mutational signatures in cancer
2023
Additional file 1. Supplementary Figures.