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.

[Q]: We present Line-Storm, an interactive computer system for creative performance. The context we investigated was writing on paper using Line-Storm. We used self-report questionnaires as part of research involving human participants, to evaluate Line-Storm. Line-Storm consisted of a writing stylus and writing pad, augmented with electronics. The writing pad was connected to a contact microphone, and the writing stylus had a small micro-controller board and peripherals attached to it. The signals from these electronic augmentations were fed into the audio-synthesis environment Max/MSP to produce an interactive soundscape. We attempted to discover whether Line-Storm enhanced a self-reported sense of being present and engaged during a writing task, and we compared Line-Storm to a non-interactive control condition. After performing statistical analysis in SPSS, we were unable to support our research hypothesis, that presence and engagement were enhanced by Line-Storm. Participants reported they were, on average, no more present and engaged during the experimental condition than during the control condition. As creativity is subtle, and varies with person, time, context, space and so many other factors, this result was somewhat expected by us. A statistically significant result of our study is that some participants responded to Line-Storm more positively than others. These Preservers of Line-Storm were a group, distinct from other participants, who reported greater presence and engagement and who wrote more words with Line-Storm and during the control condition. We discuss the results of our research and place Line-Storm in an artistic-technological context, drawing upon writings by Martin Heidegger when considering the nature of Line-Storm. Future work includes modifying interactive components, improving aesthetics and using more miniaturized electronics, experimenting with a drawing task instead of a writing task, and collaborating with a composer of electronic music to make a more interesting, immersive, and engaging interactive soundscape for writing or drawing performance.
[A]: Interactive stylus based sound incorporating writing system

[Q]: Determining the optimal order in which data examples are presented to Deep Neural Networks during training is a non-trivial problem. However, choosing a non-trivial scheduling method may drastically improve convergence. In this paper, we propose a Self-Paced Learning (SPL)-fused Deep Metric Learning (DML) framework, which we call Learning Embeddings for Adaptive Pace (LEAP). Our method parameterizes mini-batches dynamically based on the extit{easiness} and extit{true diverseness} of the sample within a salient feature representation space. In LEAP, we train an extit{embedding} Convolutional Neural Network (CNN) to learn an expressive representation space by adaptive density discrimination using the Magnet Loss. The extit{student} CNN classifier dynamically selects samples to form a mini-batch based on the extit{easiness} from cross-entropy losses and extit{true diverseness} of examples from the representation space sculpted by the extit{embedding} CNN. We evaluate LEAP using deep CNN architectures for the task of supervised image classification on MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and SVHN. We show that the LEAP framework converges faster with respect to the number of mini-batch updates required to achieve a comparable or better test performance on each of the datasets.
[A]: LEAP combines the strength of adaptive sampling with that of mini-batch online learning and adaptive representation learning to formulate a representative self-paced strategy in an end-to-end DNN training protocol.

[Q]: Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples. As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed.
In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.
[A]:
We propose AGILE, a framework for training agents to perform instructions from examples of respective goal-states.