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.
Example: Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.
Example solution: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Example explanation: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

Problem: Emphasis effects visual changes that make certain elements more
prominent are commonly used in information visualization to draw
the user's attention or to indicate importance. Although theoretical
frameworks of emphasis exist (that link visually diverse emphasis
effects through the idea of visual prominence compared to background
elements), most metrics for predicting how emphasis effects
will be perceived by users come from abstract models of human
vision which may not apply to visualization design. In particular,
it is difficult for designers to know, when designing a visualization,
how different emphasis effects will compare and what level of one
effect is equivalent to what level of another. To address this gap,
we carried out two studies that provide empirical evidence about
how users perceive different emphasis effects, using three visual
variables (colour, size, and blur/focus) and eight strength levels.
Results from gaze tracking, mouse clicks, and subjective responses
show that there are significant differences between visual variables
and between levels, and allow us to develop an initial understanding
of perceptual equivalence. We developed a model from the data in
our first study, and used it to predict the results in the second; the
model was accurate, with high correlations between predictions and
real values. Our studies and empirical models provide valuable new
information for designers who want to understand and control how
emphasis effects will be perceived by users.
Solution: Our studies and empirical models provide valuable new information for designers who want to understand and control how emphasis effects will be perceived by users