156 research outputs found
Computational Protein Design Using AND/OR Branch-and-Bound Search
The computation of the global minimum energy conformation (GMEC) is an
important and challenging topic in structure-based computational protein
design. In this paper, we propose a new protein design algorithm based on the
AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional
branch-and-bound search algorithm, to solve this combinatorial optimization
problem. By integrating with a powerful heuristic function, AOBB is able to
fully exploit the graph structure of the underlying residue interaction network
of a backbone template to significantly accelerate the design process. Tests on
real protein data show that our new protein design algorithm is able to solve
many prob- lems that were previously unsolvable by the traditional exact search
algorithms, and for the problems that can be solved with traditional provable
algorithms, our new method can provide a large speedup by several orders of
magnitude while still guaranteeing to find the global minimum energy
conformation (GMEC) solution.Comment: RECOMB 201
Token Imbalance Adaptation for Radiology Report Generation
Imbalanced token distributions naturally exist in text documents, leading
neural language models to overfit on frequent tokens. The token imbalance may
dampen the robustness of radiology report generators, as complex medical terms
appear less frequently but reflect more medical information. In this study, we
demonstrate how current state-of-the-art models fail to generate infrequent
tokens on two standard benchmark datasets (IU X-RAY and MIMIC-CXR) of radiology
report generation. % However, no prior study has proposed methods to adapt
infrequent tokens for text generators feeding with medical images. To solve the
challenge, we propose the \textbf{T}oken \textbf{Im}balance Adapt\textbf{er}
(\textit{TIMER}), aiming to improve generation robustness on infrequent tokens.
The model automatically leverages token imbalance by an unlikelihood loss and
dynamically optimizes generation processes to augment infrequent tokens. We
compare our approach with multiple state-of-the-art methods on the two
benchmarks. Experiments demonstrate the effectiveness of our approach in
enhancing model robustness overall and infrequent tokens. Our ablation analysis
shows that our reinforcement learning method has a major effect in adapting
token imbalance for radiology report generation.Comment: Accepted by CHIL202
Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation
Current state-of-the-art point cloud-based perception methods usually rely on
large-scale labeled data, which requires expensive manual annotations. A
natural option is to explore the unsupervised methodology for 3D perception
tasks. However, such methods often face substantial performance-drop
difficulties. Fortunately, we found that there exist amounts of image-based
datasets and an alternative can be proposed, i.e., transferring the knowledge
in the 2D images to 3D point clouds. Specifically, we propose a novel approach
for the challenging cross-modal and cross-domain adaptation task by fully
exploring the relationship between images and point clouds and designing
effective feature alignment strategies. Without any 3D labels, our method
achieves state-of-the-art performance for 3D point cloud semantic segmentation
on SemanticKITTI by using the knowledge of KITTI360 and GTA5, compared to
existing unsupervised and weakly-supervised baselines.Comment: 12 pages,4 figures,accepte
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