Safe Online Gain Optimization for Variable Impedance Control

Kavli Affiliate: Xiang Zhang | First 5 Authors: Changhao Wang, Zhian Kuang, Xiang Zhang, Masayoshi Tomizuka, | Summary: Smooth behaviors are preferable for many contact-rich manipulation tasks. Impedance control arises as an effective way to regulate robot movements by mimicking a mass-spring-damping system. Consequently, the robot behavior can be determined by the impedance gains. However, […]


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A Quantum trajectory picture of single photon absorption and energy transport in photosystem II

Kavli Affiliate: K. Birgitta Whaley | First 5 Authors: Robert L. Cook, Liwen Ko, K. Birgitta Whaley, , | Summary: In this work we study the first step in photosynthesis for the limiting extreme case of a single photon interacting with photosystem II (PSII). We model our system using quantum trajectory theory, which allows us […]


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Density of States Prediction for Materials Discovery via Contrastive Learning from Probabilistic Embeddings

Kavli Affiliate: Jeffrey B. Neaton | First 5 Authors: Shufeng Kong, Francesco Ricci, Dan Guevarra, Jeffrey B. Neaton, Carla P. Gomes | Summary: Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density […]


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A Logarithmic Bayesian Approach to Quantum Error Detection

Kavli Affiliate: K. Birgitta Whaley | First 5 Authors: Ian Convy, K. Birgitta Whaley, , , | Summary: We consider the problem of continuous quantum error correction from a Bayesian perspective, proposing a pair of digital filters using logarithmic probabilities that are able to achieve near-optimal performance on a three-qubit bit-flip code while still being […]


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Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits

Kavli Affiliate: K. Birgitta Whaley | First 5 Authors: Ian Convy, Haoran Liao, Song Zhang, Sahil Patel, William P. Livingston | Summary: We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identity bit-flip errors from continuous noisy syndrome measurements. The algorithm […]


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Interplay of vibration- and environment-assisted energy transfer

Kavli Affiliate: K. Birgitta Whaley | First 5 Authors: Zeng-Zhao Li, Liwen Ko, Zhibo Yang, Mohan Sarovar, K. Birgitta Whaley | Summary: We study the interplay between two environmental influences on excited state energy transfer in photosynthetic light harvesting complexes, namely, vibrationally assisted energy transfer(VAET) and environment-assisted quantum transport (ENAQT), considering a dimeric chromophore donor-acceptor […]


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Self-Supervised Learning by Estimating Twin Class Distributions

Kavli Affiliate: Feng Wang | First 5 Authors: Feng Wang, Tao Kong, Rufeng Zhang, Huaping Liu, Hang Li | Summary: We present TWIST, a simple and theoretically explainable self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way. We employ a siamese network terminated by a softmax operation to produce twin class […]


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Graph-Guided Network for Irregularly Sampled Multivariate Time Series

Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik, | Summary: In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with varying time intervals between successive readouts and different subsets of variables (sensors) observed at different time points. Here, we introduce RAINDROP, a […]


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Online Graph Learning in Dynamic Environments

Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, , , , | Summary: Inferring the underlying graph topology that characterizes structured data is pivotal to many graph-based models when pre-defined graphs are not available. This paper focuses on learning graphs in the case of sequential data in dynamic environments. For sequential data, we […]


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Time-varying Graph Learning Under Structured Temporal Priors

Kavli Affiliate: Xiang Zhang | First 5 Authors: Xiang Zhang, Qiao Wang, , , | Summary: This paper endeavors to learn time-varying graphs by using structured temporal priors that assume underlying relations between arbitrary two graphs in the graph sequence. Different from many existing chain structure based methods in which the priors like temporal homogeneity […]


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