30 research outputs found
mixiTUI:A Tangible Sequencer for Electronic Live Performances
With the rise of crowdsourcing and mobile crowdsensing techniques, a large
number of crowdsourcing applications or platforms (CAP) have appeared. In the
mean time, CAP-related models and frameworks based on different research
hypotheses are rapidly emerging, and they usually address specific issues from
a certain perspective. Due to different settings and conditions, different
models are not compatible with each other. However, CAP urgently needs to
combine these techniques to form a unified framework. In addition, these models
needs to be learned and updated online with the extension of crowdsourced data
and task types, thus requiring a unified architecture that integrates lifelong
learning concepts and breaks down the barriers between different modules. This
paper draws on the idea of ubiquitous operating systems and proposes a novel OS
(CrowdOS), which is an abstract software layer running between native OS and
application layer. In particular, based on an in-depth analysis of the complex
crowd environment and diverse characteristics of heterogeneous tasks, we
construct the OS kernel and three core frameworks including Task Resolution and
Assignment Framework (TRAF), Integrated Resource Management (IRM), and Task
Result quality Optimization (TRO). In addition, we validate the usability of
CrowdOS, module correctness and development efficiency. Our evaluation further
reveals TRO brings enormous improvement in efficiency and a reduction in energy
consumption
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
Protein structure-based property prediction has emerged as a promising
approach for various biological tasks, such as protein function prediction and
sub-cellular location estimation. The existing methods highly rely on
experimental protein structure data and fail in scenarios where these data are
unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were
utilized as alternatives. However, we observed that current practices, which
simply employ accurately predicted structures during inference, suffer from
notable degradation in prediction accuracy. While similar phenomena have been
extensively studied in general fields (e.g., Computer Vision) as model
robustness, their impact on protein property prediction remains unexplored. In
this paper, we first investigate the reason behind the performance decrease
when utilizing predicted structures, attributing it to the structure embedding
bias from the perspective of structure representation learning. To study this
problem, we identify a Protein 3D Graph Structure Learning Problem for Robust
Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present
a protein Structure embedding Alignment Optimization framework (SAO) to
mitigate the problem of structure embedding bias between the predicted and
experimental protein structures. Extensive experiments have shown that our
framework is model-agnostic and effective in improving the property prediction
of both predicted structures and experimental structures. The benchmark
datasets and codes will be released to benefit the community