5,067 research outputs found
Pushing the Limits: Cognitive, Affective, and Neural Plasticity Revealed by an Intensive Multifaceted Intervention.
Scientific understanding of how much the adult brain can be shaped by experience requires examination of how multiple influences combine to elicit cognitive, affective, and neural plasticity. Using an intensive multifaceted intervention, we discovered that substantial and enduring improvements can occur in parallel across multiple cognitive and neuroimaging measures in healthy young adults. The intervention elicited substantial improvements in physical health, working memory, standardized test performance, mood, self-esteem, self-efficacy, mindfulness, and life satisfaction. Improvements in mindfulness were associated with increased degree centrality of the insula, greater functional connectivity between insula and somatosensory cortex, and reduced functional connectivity between posterior cingulate cortex (PCC) and somatosensory cortex. Improvements in working memory and reading comprehension were associated with increased degree centrality of a region within the middle temporal gyrus (MTG) that was extensively and predominately integrated with the executive control network. The scope and magnitude of the observed improvements represent the most extensive demonstration to date of the considerable human capacity for change. These findings point to higher limits for rapid and concurrent cognitive, affective, and neural plasticity than is widely assumed
JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions
Commonsense reasoning simulates the human ability to make presumptions about
our physical world, and it is an essential cornerstone in building general AI
systems. We propose a new commonsense reasoning dataset based on human's
Interactive Fiction (IF) gameplay walkthroughs as human players demonstrate
plentiful and diverse commonsense reasoning. The new dataset provides a natural
mixture of various reasoning types and requires multi-hop reasoning. Moreover,
the IF game-based construction procedure requires much less human interventions
than previous ones. Experiments show that the introduced dataset is challenging
to previous machine reading models with a significant 20% performance gap
compared to human experts.Comment: arXiv admin note: text overlap with arXiv:2010.0978
How to tell stories using visualization: strategies towards narrative visualization
Os benefícios da utilização das narrativas são desde há muito conhecidos e o seu potencial
para simplificar conceitos, transmitir valores culturais e experiências, criar ligações emocionais
e capacidade para ajudar a reter a informação tem sido explorado em diferentes áreas.
As narrativas não são só a principal forma como as pessoas obtêm o sentido do mundo, mas
também a forma mais fácil que encontrámos para partilhar informações complexas.
Devido ao seu potencial, as narrativas foram recentemente abordadas na área da Visualização
de Informação e do Conhecimento, muitas vezes apelidada de Visualização Narrativa.
Esta questão é particularmente importante para os media, uma das áreas que tem impulsionado
a investigação em Visualização Narrativa. A necessidade de incorporar histórias nas visualizações
surge da necessidade de partilhar dados complexos de um modo envolvente. Hoje em dia
somos confrontados com a elevada quantidade de informação disponível, um desafio difícil de
resolver. Os avanços da tecnologia permitiram ir além das formas tradicionais de narrativa e
de representação de dados, dando-nos meios mais atraentes e sofisticados para contar histórias.
Nesta tese, exploro os benefícios da introdução de narrativas nas visualizações. Adicionalmente
também exploro formas de combinar histórias com a visualizações e métodos
eficientes para representar e dar sentido aos dados de uma forma que permite que as pessoas se
relacionem com a informação. Esta investigação está bastante próxima da área do jornalismo,
no entanto estas técnicas podem ser aplicadas em diferente áreas (educação, visualização científica,
etc.). Para explorar ainda mais este tema foi adotada um avaliação que utiliza diferentes
metodologias como a tipologia, vários casos de estudo, um estudo com grupos de foco, e ainda
estudos de design e análise de técnicas.The benefits of storytelling are long-known and its potential to simplify concepts,
convey cultural values and experiences, create emotional connection, and capacity to help retain
information has been explored in di erent areas, such as journalism, education, marketing,
and others. Narratives not only have been the main way people make sense of the world, but
also the easiest way humans found out to share complex information.
Due to its potential narratives have also recently been approached in the area of Information
and Knowledge Visualization, several times being referred to as Narrative Visualization.
This matter is also particularly important for news media, one of the areas that has been pushing
the research on Narrative Visualization. The necessity to incorporate storytelling in visualizations
arises from the need to share complex data in a way that is engaging. Nowadays we also
have the challenge of the high amount of information available, which can be hard to cope with.
Advances in technology have enabled us to go beyond the traditional forms of storytelling and
representing data, giving us more attractive and sophisticated means to tell stories.
In this dissertation, I explore the benefits of infusing visualizations with narratives. In
addition I also present ways of combining storytelling with visualization and e cient methods
to represent and make sense of data in a way that allows people to relate with the information.
This research is closely related to journalism, but these techniques can be applied to completely
di erent areas (education, scientific visualization, etc.). To further explore this topic a mixedmethod
evaluation that consists of a typology, several case studies and a focus group study
was chosen, as well as design studies and techniques review. This dissertation is intended to
contribute to the evolving understanding of the field of narrative visualization
TC-GAT: Graph Attention Network for Temporal Causality Discovery
The present study explores the intricacies of causal relationship extraction,
a vital component in the pursuit of causality knowledge. Causality is
frequently intertwined with temporal elements, as the progression from cause to
effect is not instantaneous but rather ensconced in a temporal dimension. Thus,
the extraction of temporal causality holds paramount significance in the field.
In light of this, we propose a method for extracting causality from the text
that integrates both temporal and causal relations, with a particular focus on
the time aspect. To this end, we first compile a dataset that encompasses
temporal relationships. Subsequently, we present a novel model, TC-GAT, which
employs a graph attention mechanism to assign weights to the temporal
relationships and leverages a causal knowledge graph to determine the adjacency
matrix. Additionally, we implement an equilibrium mechanism to regulate the
interplay between temporal and causal relations. Our experiments demonstrate
that our proposed method significantly surpasses baseline models in the task of
causality extraction.Comment: Accepted by IJCNN 202
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