349 research outputs found
Objective Evaluation Criteria for Shooting Quality of Stereo Cameras over Short Distance
Stereo cameras are the basic tools used to obtain stereoscopic image pairs, which can lead to truly great image quality. However, some inappropriate shooting conditions may cause discomfort while viewing stereo images. It is therefore considerably necessary to establish the perceptual criteria that can be used to evaluate the shooting quality of stereo cameras. This article proposes objective quality evaluation criteria based on the characteristics of parallel and toed-in camera configurations. Considering the different internal structures and basic shooting principles, this paper focuses on short-distance shooting conditions and establishes assessment criteria for both parallel and toed-in camera configurations. Experimental results show that the proposed evaluation criteria can predict the visual perception of stereoscopic images and effectively evaluate stereoscopic image quality
Smaller Sensitivity of Precipitation to Surface Temperature under Massive Atmospheres
Precipitation and its response to forcings is an important aspect of
planetary climate system. In this study, we examine the strength of
precipitation in the experiments with different atmospheric masses and their
response to surface warming, using three global atmospheric general circulation
models (GCMs) and one regional cloud-resolving model (CRM). We find that
precipitation is weaker when atmospheric mass is larger for a given surface
temperature. Furthermore, the increasing rate of precipitation with increasing
surface temperature under a larger atmospheric mass is smaller than that under
a smaller atmospheric mass. These behaviors can be understood based on
atmospheric or surface energy balance. Atmospheric mass influences Rayleigh
scattering, multiple scattering in the atmosphere, pressure broadening, lapse
rate, and thereby precipitation strength. These results have important
implications on the climate and habitability of early Earth, early Mars, and
exoplanets with oceans
Cooperation in Multi-Agent Reinforcement Learning
As progress in reinforcement learning (RL) gives rise to increasingly general and powerful artificial intelligence, society needs to anticipate a possible future in which multiple RL agents must learn and interact in a shared multi-agent environment. When a single principal has oversight of the multi-agent system, how should agents learn to cooperate via centralized training to achieve individual and global objectives? When agents belong to self-interested principals with imperfectly-aligned objectives, how can cooperation emerge from fully-decentralized learning? This dissertation addresses both questions by proposing novel methods for multi-agent reinforcement learning (MARL) and demonstrating the empirical effectiveness of these methods in high-dimensional simulated environments.
To address the first case, we propose new algorithms for fully-cooperative MARL in the paradigm of centralized training with decentralized execution. Firstly, we propose a method based on multi-agent curriculum learning and multi-agent credit assignment to address the setting where global optimality is defined as the attainment of all individual goals. Secondly, we propose a hierarchical MARL algorithm to discover and learn interpretable and useful skills for a multi-agent team to optimize a single team objective. Extensive experiments with ablations show the strengths of our approaches over state-of-the-art baselines.
To address the second case, we propose learning algorithms to attain cooperation within a population of self-interested RL agents. We propose the design of a new agent who is equipped with the new ability to incentivize other RL agents and explicitly account for the other agents' learning process. This agent overcomes the challenging limitation of fully-decentralized training and generates emergent cooperation in difficult social dilemmas. Then, we extend and apply this technique to the problem of incentive design, where a central incentive designer explicitly optimizes a global objective only by intervening on the rewards of a population of independent RL agents. Experiments on the problem of optimal taxation in a simulated market economy demonstrate the effectiveness of this approach.Ph.D
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement
To improve the execution speed and efficiency of neural networks in embedded
systems, it is crucial to decrease the model size and computational complexity.
In addition to conventional compression techniques, e.g., weight pruning and
quantization, removing unimportant activations can reduce the amount of data
communication and the computation cost. Unlike weight parameters, the pattern
of activations is directly related to input data and thereby changes
dynamically. To regulate the dynamic activation sparsity (DAS), in this work,
we propose a generic low-cost approach based on winners-take-all (WTA) dropout
technique. The network enhanced by the proposed WTA dropout, namely
\textit{DASNet}, features structured activation sparsity with an improved
sparsity level. Compared to the static feature map pruning methods, DASNets
provide better computation cost reduction. The WTA technique can be easily
applied in deep neural networks without incurring additional training
variables. More importantly, DASNet can be seamlessly integrated with other
compression techniques, such as weight pruning and quantization, without
compromising on accuracy. Our experiments on various networks and datasets
present significant run-time speedups with negligible accuracy loss
- …