Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

On robust estimation of the location parameter

It is conventionally assumed that the growth of monodisperse colloidal nanocrystals requires a temporally discrete nucleation followed by monomer attachment onto the existing nuclei. However, recent studies have reported violations of this classical growth model, and have suggested that inter-particle interactions are also involved during the growth. Mechanisms of nanocrystal growth still remain controversial. Using in situ transmission electron microscopy, we show that platinum nanocrystals can grow either by monomer attachment from solution onto the existing particles or by coalescence between the particles. Surprisingly, an initially broad size distribution of the nanocrystals can spontaneously narrow. We suggest that nanocrystals take different pathways of growth based on their size- and morphology-dependent internal energies. These observations are expected to be highly relevant for other nanocrystal systems.

Observation of Single Colloidal Platinum Nanocrystal Growth Trajectories

Large-scale pre-training methods of learning cross-modal representations on image-text pairs are becoming popular for vision-language tasks. While existing methods simply concatenate image region features and text features as input to the model to be pre-trained and use self-attention to learn image-text semantic alignments in a brute force manner, in this paper, we propose a new learning method Oscar (Object-Semantics Aligned Pre-training), which uses object tags detected in images as anchor points to significantly ease the learning of alignments. Our method is motivated by the observation that the salient objects in an image can be accurately detected, and are often mentioned in the paired text. We pre-train an Oscar model on the public corpus of 6.5 million text-image pairs, and fine-tune it on downstream tasks, creating new state-of-the-arts on six well-established vision-language understanding and generation tasks.

Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

Scanning transmission electron microscopy (STEM) is widely used for imaging, diffraction, and spectroscopy of materials down to atomic resolution. Recent advances in detector technology and computational methods have enabled many experiments that record a full image of the STEM probe for many probe positions, either in diffraction space or real space. In this paper, we review the use of these four-dimensional STEM experiments for virtual diffraction imaging, phase, orientation and strain mapping, measurements of medium-range order, thickness and tilt of samples, and phase contrast imaging methods, including differential phase contrast, ptychography, and others.

/pdf/four-dimensional-scanning-transmission-electron-microscopy-zrn1qkqvia.pdf

Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM): From Scanning Nanodiffraction to Ptychography and Beyond.

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived “worst” case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL) by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named Reinforcement Learning for Grid Control (RLGC) has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Robustness of the developed DRL method to different simulation scenarios, model parameter uncertainty and noise in the observations is investigated. Extensive case studies performed in both the two-area, four-machine system and the IEEE 39-bus system have demonstrated excellent performance and robustness of the proposed schemes.

Adaptive Power System Emergency Control Using Deep Reinforcement Learning

Mutual information (MI) minimization has gained considerable interests in various machine learning tasks. However, estimating and minimizing MI in high-dimensional spaces remains a challenging problem, especially when only samples, rather than distribution forms, are accessible. Previous works mainly focus on MI lower bound approximation, which is not applicable to MI minimization problems. In this paper, we propose a novel Contrastive Log-ratio Upper Bound (CLUB) of mutual information. We provide a theoretical analysis of the properties of CLUB and its variational approximation. Based on this upper bound, we introduce an accelerated MI minimization training scheme, which bridges MI minimization with negative sampling. Simulation studies on Gaussian distributions show the reliable estimation ability of CLUB. Real-world MI minimization experiments, including domain adaptation and information bottleneck, further demonstrate the effectiveness of the proposed method.

/pdf/club-a-contrastive-log-ratio-upper-bound-of-mutual-3zb6ug683u.pdf

CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

Learning to navigate in a visual environment following natural-language instructions is a challenging task, because the multimodal inputs to the agent are highly variable, and the training data on a new task is often limited. In this paper, we present the first pre-training and fine-tuning paradigm for vision-and-language navigation (VLN) tasks. By training on a large amount of image-text-action triplets in a self-supervised learning manner, the pre-trained model provides generic representations of visual environments and language instructions. It can be easily used as a drop-in for existing VLN frameworks, leading to the proposed agent called Prevalent. It learns more effectively in new tasks and generalizes better in a previously unseen environment. The performance is validated on three VLN tasks. On the Room-to-Room benchmark, our model improves the state-of-the-art from 47% to 51% on success rate weighted by path length. Further, the learned representation is transferable to other VLN tasks. On two recent tasks, vision-and-dialog navigation and "Help, Anna!" the proposed Prevalent leads to significant improvement over existing methods, achieving a new state of the art.

Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named RLGC has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.

Adaptive Power System Emergency Control using Deep Reinforcement Learning.

Learning to navigate in a visual environment following natural-language instructions is a challenging task, because the multimodal inputs to the agent are highly variable, and the training data on a new task is often limited. In this paper, we present the first pre-training and fine-tuning paradigm for vision-and-language navigation (VLN) tasks. By training on a large amount of image-text-action triplets in a self-supervised learning manner, the pre-trained model provides generic representations of visual environments and language instructions. It can be easily used as a drop-in for existing VLN frameworks, leading to the proposed agent PREVALENT. It learns more effectively in new tasks and generalizes better in a previously unseen environment. The performance is validated on three VLN tasks. On the Room-to-Room benchmark, our model improves the state-of-the-art from 47\% to 51\% on success rate weighted by path length. Further, the learned representation is transferable to other VLN tasks. On two recent tasks, vision-and-dialog navigation and ``Help, Anna!'', the proposed PREVALENT leads to significant improvement over existing methods, achieving a new state of the art.

/pdf/towards-learning-a-generic-agent-for-vision-and-language-3d5nfzupoj.pdf

Weituo Hao

Papers

Adaptive Power System Emergency Control Using Deep Reinforcement Learning

CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information

Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training

Adaptive Power System Emergency Control using Deep Reinforcement Learning.

Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training