In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Ex Input:
The available resolution in our visual world is extremely high, if not infinite. Existing CNNs can be applied in a fully convolutional way to images of arbitrary resolution, but as the size of the input increases, they can not capture contextual information. In addition, computational requirements scale linearly to the number of input pixels, and resources are allocated uniformly across the input, no matter how informative different image regions are. We attempt to address these problems by proposing a novel architecture that traverses an image pyramid in a top-down fashion, while it uses a hard attention mechanism to selectively process only the most informative image parts. We conduct experiments on MNIST and ImageNet datasets, and we show that our models can significantly outperform fully convolutional counterparts, when the resolution of the input is that big that the receptive field of the baselines can not adequately cover the objects of interest. Gains in performance come for less FLOPs, because of the selective processing that we follow. Furthermore, our attention mechanism makes our predictions more interpretable, and creates a trade-off between accuracy and complexity that can be tuned both during training and testing time.

Ex Output:
We propose a novel architecture that traverses an image pyramid in a top-down fashion, while it visits only the most informative regions along the way.


Ex Input:
This paper presents a new Graph Neural Network (GNN) type using feature-wise linear modulation (FiLM). Many standard GNN variants propagate information along the edges of a graph by computing ``messages'' based only on the representation of the source of each edge. In GNN-FiLM, the representation of the target node of an edge is additionally used to compute a transformation that can be applied to all incoming messages, allowing feature-wise modulation of the passed information.

 Results of experiments comparing different GNN architectures on three tasks from the literature are presented, based on re-implementations of baseline methods. Hyperparameters for all methods were found using extensive search, yielding somewhat surprising results: differences between baseline models are smaller than reported in the literature. Nonetheless, GNN-FiLM outperforms baseline methods on a regression task on molecular graphs and performs competitively on other tasks.


Ex Output:
new GNN formalism + extensive experiments; showing differences between GGNN/GCN/GAT are smaller than thought


Ex Input:
Breathing exercises are an accessible way to manage stress and many mental illness symptoms. Traditionally, learning breathing exercises involved in-person guidance or audio recordings. The shift to mobile devices has led to a new way of learning and engaging in breathing exercises as seen in the rise of multiple mobile applications with different breathing representations. However, limited work has been done to investigate the effectiveness of these visual representations in supporting breathing pace as measured by synchronization. We utilized a within-subjects study to evaluate four common breathing visuals to understand which is most effective in providing breathing exercise guidance. Through controlled lab studies and interviews, we identified two representations with clear advantages over the others. In addition, we found that auditory guidance was not preferred by all users. We identify potential usability issues with the representations and suggest design guidelines for future development of app-supported breathing training.

Ex Output:

We utilized a within-subjects study to evaluate four paced breathing visuals common in mobile apps to understand which is most effective in providing breathing exercise guidance.