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Found 10 papers, 2 papers with code
Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study
Excessive energy intake increased mortality rapidly in the early period of the acute phase.
Hybrid Learning with New Value Function for the Maximum Common Subgraph Problem
Branch-and-Bound (BnB) is the basis of a class of efficient algorithms for MCS, consisting in successively selecting vertices to match and pruning when it is discovered that a solution better than the best solution found so far does not exist.
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
In this work, we propose a meta-learning-based LR tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains.
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech
Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems.
A Deep Learning Approach to Predicting Ventilator Parameters for Mechanically Ventilated Septic Patients
We develop a deep learning approach to predicting a set of ventilator parameters for a mechanically ventilated septic patient using a long and short term memory (LSTM) recurrent neural network (RNN) model.
Tie-line Security Regions in High Dimension for Renewable Accommodations
However, a tie-line security region is a high-dimension polytope due to multiple time periods and border buses inherently in power system operations, leading to the considerable computational burden.
Real space topological invariant and higher-order topological Anderson insulator in two-dimensional non-Hermitian systems
We study the characterization and realization of higher-order topological Anderson insulator (HOTAI) in non-Hermitian systems, where the non-Hermitian mechanism ensures extra symmetries as well as gain and loss disorder. We illuminate that the quadrupole moment $Q_{xy}$ can be used as the real space topological invariant of non-Hermitian higher-order topological insulator (HOTI).
Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics
Quantum Spin-Valley Hall Kink States: From Concept to Materials Design
We propose a general and tunable platform to realize high-density arrays of quantum spin-valley Hall kink (QSVHK) states with spin-valley-momentum locking based on a two-dimensional hexagonal topological insulator.
Mesoscale and Nanoscale Physics Materials Science
A Semi-Grand Canonical Kinetic Monte Carlo study of Single-Walled Carbon Nanotubes growth
In this contribution, we establish a simple model for the prediction of the growth rates of carbon nanotubes of different chiralities, as a function of energies characterizing the carbon nanotube-catalyst interface and of parameters of the synthesis.
Mesoscale and Nanoscale Physics Statistical Mechanics
A Learning based Branch and Bound for Maximum Common Subgraph Problems
Branch-and-bound (BnB) algorithms are widely used to solve combinatorial problems, and the performance crucially depends on its branching heuristic. In this work, we consider a typical problem of maximum common subgraph (MCS), and propose a branching heuristic inspired from reinforcement learning with a goal of reaching a tree leaf as early as possible to greatly reduce the search tree size. Extensive experiments show that our method is beneficial and outperforms current best BnB algorithm for the MCS.
