Zobrazeno 1 - 10
of 8 197
pro vyhledávání: '"Kelin A"'
Accurate and efficient prediction of polymer properties is of key importance for polymer design. Traditional experimental tools and density function theory (DFT)-based simulations for polymer property evaluation, are both expensive and time-consuming
Externí odkaz:
http://arxiv.org/abs/2410.04765
Autor:
Li, Kelin, Wagh, Shubham M, Sharma, Nitish, Bhadani, Saksham, Chen, Wei, Liu, Chang, Kormushev, Petar
Robotic manipulation is essential for the widespread adoption of robots in industrial and home settings and has long been a focus within the robotics community. Advances in artificial intelligence have introduced promising learning-based methods to a
Externí odkaz:
http://arxiv.org/abs/2409.11925
Smartphones have significantly enhanced our daily learning, communication, and entertainment, becoming an essential component of modern life. However, certain populations, including the elderly and individuals with disabilities, encounter challenges
Externí odkaz:
http://arxiv.org/abs/2409.09354
Autor:
Chen, Zhiqiang, Qi, Yuhua, Feng, Dapeng, Zhuang, Xuebin, Chen, Hongbo, Hu, Xiangcheng, Wu, Jin, Peng, Kelin, Lu, Peng
The ability to estimate pose and generate maps using 3D LiDAR significantly enhances robotic system autonomy. However, existing open-source datasets lack representation of geometrically degenerate environments, limiting the development and benchmarki
Externí odkaz:
http://arxiv.org/abs/2409.04961
Class imbalance significantly impacts the performance of multi-label classifiers. Oversampling is one of the most popular approaches, as it augments instances associated with less frequent labels to balance the class distribution. Existing oversampli
Externí odkaz:
http://arxiv.org/abs/2408.13078
With remarkable stability and exceptional optoelectronic properties, two-dimensional (2D) halide layered perovskites hold immense promise for revolutionizing photovoltaic technology. Presently, inadequate representations have substantially impeded th
Externí odkaz:
http://arxiv.org/abs/2407.16996
Recently, therapeutic peptides have demonstrated great promise for cancer treatment. To explore powerful anticancer peptides, artificial intelligence (AI)-based approaches have been developed to systematically screen potential candidates. However, th
Externí odkaz:
http://arxiv.org/abs/2407.08974
Autor:
Kurzer-Ogul, Kelin, Haines, Brian M., Montgomery, David S., Pandolfi, Silvia, Sauppe, Joshua P., Leong, Andrew F. T., Hodge, Daniel, Kozlowski, Pawel M., Marchesini, Stefano, Cunningham, Eric, Galtier, Eric, Khaghani, Dimitri, Lee, Hae Ja, Nagler, Bob, Sandberg, Richard L., Gleason, Arianna E., Aluie, Hussein, Shang, Jessica K.
Shock-bubble interactions (SBI) are important across a wide range of physical systems. In inertial confinement fusion, interactions between laser-driven shocks and micro-voids in both ablators and foam targets generate instabilities that are a major
Externí odkaz:
http://arxiv.org/abs/2403.02684
Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains
Externí odkaz:
http://arxiv.org/abs/2310.18760
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:26640-26660, 2024
Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, such as having erroneous or missi
Externí odkaz:
http://arxiv.org/abs/2310.16401