With the COVID-19 pandemic, behavioural scientists aimed to illuminate reasons why people comply with (or not) large-scale cooperative activities. Here we investigated the motives that underlie support for COVID-19 pr...
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With the COVID-19 pandemic, behavioural scientists aimed to illuminate reasons why people comply with (or not) large-scale cooperative activities. Here we investigated the motives that underlie support for COVID-19 preventive behaviours in a sample of 12,758 individuals from 34 countries. We hypothesized that the associations of empathic prosocial concern and fear of disease with support towards preventive COVID-19 behaviours would be moderated by trust in the government. Results suggest that the association between fear of disease and support for COVID-19 preventive behaviours was strongest when trust in the government was weak (both at individual- and country-level). Conversely, the association with empathic prosocial concern was strongest when trust in the government was high, but this moderation was only found at individual-level scores of governmental trust. We discuss how motivations may be shaped by socio-cultural context, and outline how findings may contribute to a better understanding of collective action during global crises.
Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous ef...
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Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecula...
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A teachable agent is a learning companion that students teach about a domain they are trying to master. While most teachable agents have been virtual, there may be advantages to having students teach an agent with a p...
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ISBN:
(纸本)9781467390422
A teachable agent is a learning companion that students teach about a domain they are trying to master. While most teachable agents have been virtual, there may be advantages to having students teach an agent with a physical form (i.e., a robot). The robot may better engage students in the learning activity, and if students take embodied action in order to instruct the robot, they may develop deeper knowledge. In this paper, we investigate these two hypotheses using the rTAG system, a teachable robot for geometry learning. In a study with 37 4th-6th grade participants, we compare rTAG to two other conditions, one where students use embodied action to teach a virtual agent, and one where students teach a virtual agent on a personal computer. We find that while there are no significant learning differences between conditions, students' perceptions of the agent are influenced by condition and prior knowledge.
Obsessive–compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,4...
We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize “perturbation modularity,” defined as the...
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We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize “perturbation modularity,” defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the “Markov stability” method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., “relevance networks” or “functional networks”). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.
Concept elicitation is a fundamental methodology for knowledge extraction and representation in cognitive robot learning. Traditional machine learning technologies deal with object identification, cluster classificati...
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ISBN:
(纸本)9781509038473
Concept elicitation is a fundamental methodology for knowledge extraction and representation in cognitive robot learning. Traditional machine learning technologies deal with object identification, cluster classification, functional regression, and behavior acquisition. This paper presents a supervised machine knowledge learning methodology for concept elicitation from sample dictionaries in natural languages. Formal concepts are autonomously generated based on collective intention of attributes and collective extension of objects elicited from informal definitions in dictionaries. A system of formal concept generation for a cognitive robot is implemented by the Algorithm of Machine Concept Elicitation (AMCE) in MATLAB. Experiments on machine learning for creating a set of twenty formal concepts reveal that the cognitive robot is able to learn synergized concepts in human knowledge in order to build its own cognitive knowledge base. The results of machine-generated concepts demonstrate that the AMCE algorithm can over perform human knowledge expressions in dictionaries in terms of relevance, accuracy, quantitativeness, and cohesiveness.
Legal decision-making support is key for legal security in any legal system. This paper describes a new scope and method for Business Intelligence applied in the legal domain. We describe a legal case processing model...
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ISBN:
(纸本)9781467395793
Legal decision-making support is key for legal security in any legal system. This paper describes a new scope and method for Business Intelligence applied in the legal domain. We describe a legal case processing model based on the transformation of relevant data from a legal case file by a judge, to produce what we call the judge's perception of the case. That is a representation of the case that stresses (1) the relevance of the different attributes of the case, and (2) the relationships among attributes that the judge considers relevant for argumentation purposes.
A folksonomy is ostensibly an information structure built up by the “wisdom of the crowd”, but is the “crowd”really doing the work? Tagging is in fact a sharply skewed process in which a small minority of “supert...
A folksonomy is ostensibly an information structure built up by the “wisdom of the crowd”, but is the “crowd”
really doing the work? Tagging is in fact a sharply skewed process in which a small minority of “supertagger” users
generate an overwhelming majority of the annotations. Using data from three large-scale social tagging platforms,
we explore (a) how to best quantify the imbalance in tagging behavior and formally define a supertagger, (b) how
supertaggers differ from other users in their tagging patterns, and (c) if effects of motivation and expertise inform
our understanding of what makes a supertagger. Our results indicate that such prolific users not only tag more
than their counterparts, but in quantifiably different ways. Specifically, we find that supertaggers are more likely
to label content in the long tail of less popular items, that they show differences in patterns of content tagged
and terms utilized, and are measurably different with respect to tagging expertise and motivation. These findings
suggest we should question the extent to which folksonomies achieve crowdsourced classification via the “wisdom
of the crowd”, especially for broad folksonomies like *** as opposed to narrow folksonomies like Flickr.
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