5,029 research outputs found
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
Molecular representation learning is crucial for the problem of molecular
property prediction, where graph neural networks (GNNs) serve as an effective
solution due to their structure modeling capabilities. Since labeled data is
often scarce and expensive to obtain, it is a great challenge for GNNs to
generalize in the extensive molecular space. Recently, the training paradigm of
"pre-train, fine-tune" has been leveraged to improve the generalization
capabilities of GNNs. It uses self-supervised information to pre-train the GNN,
and then performs fine-tuning to optimize the downstream task with just a few
labels. However, pre-training does not always yield statistically significant
improvement, especially for self-supervised learning with random structural
masking. In fact, the molecular structure is characterized by motif subgraphs,
which are frequently occurring and influence molecular properties. To leverage
the task-related motifs, we propose a novel paradigm of "pre-train, prompt,
fine-tune" for molecular representation learning, named molecule continuous
prompt tuning (MolCPT). MolCPT defines a motif prompting function that uses the
pre-trained model to project the standalone input into an expressive prompt.
The prompt effectively augments the molecular graph with meaningful motifs in
the continuous representation space; this provides more structural patterns to
aid the downstream classifier in identifying molecular properties. Extensive
experiments on several benchmark datasets show that MolCPT efficiently
generalizes pre-trained GNNs for molecular property prediction, with or without
a few fine-tuning steps
MOCAST 2021
The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications
Engineering data compendium. Human perception and performance. User's guide
The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use
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