161 research outputs found
Image quality metrics and optimum focus criteria for visual optical systems
Image quality metrics for visual instruments were examined in terms of their through focus behavior in the presence of various aberrations, and their correlations with available subjective performance data. The contrast sensitivity measurements were performed using rotationally symmetric, variable contrast difference-of-gaussians (DOG) targets, viewed through specially designed telescopes that presented various amounts of monochromatic aberrations. Then the contrast sensitivity ratios were correlated with the image quality metrics of the telescopes. The results show that an appropriately defined integral of the instrument-observer MTF (called MTFa) correlates well with subjective performance in most cases and predicts the optimum focus best; the radius that encircles 84% of the energy of the point spread function (called Rg4) gives good correlation in some cases including the DOG experimen
A Computationally Efficient Hybrid Neural Network Architecture for Porous Media: Integrating CNNs and GNNs for Improved Permeability Prediction
Subsurface fluid flow, essential in various natural and engineered processes,
is largely governed by a rock's permeability, which describes its ability to
allow fluid passage. While convolutional neural networks (CNNs) have been
employed to estimate permeability from high-resolution 3D rock images, our
novel visualization technology reveals that they occasionally miss higher-level
characteristics, such as nuanced connectivity and flow paths, within porous
media. To address this, we propose a novel fusion model to integrate CNN with
the graph neural network (GNN), which capitalizes on graph representations
derived from pore network model to capture intricate relational data between
pores. The permeability prediction accuracy of the fusion model is superior to
the standalone CNN, whereas its total parameter number is nearly two orders of
magnitude lower than the latter. This innovative approach not only heralds a
new frontier in the research of digital rock property predictions, but also
demonstrates remarkable improvements in prediction accuracy and efficiency,
emphasizing the transformative potential of hybrid neural network architectures
in subsurface fluid flow research
Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving
In the context of autonomous driving, the iterative linear quadratic
regulator (iLQR) is known to be an efficient approach to deal with the
nonlinear vehicle models in motion planning problems. Particularly, the
constrained iLQR algorithm has shown noteworthy advantageous outcomes of
computation efficiency in achieving motion planning tasks under general
constraints of different types. However, the constrained iLQR methodology
requires a feasible trajectory at the first iteration as a prerequisite. Also,
the methodology leaves open the possibility for incorporation of fast,
efficient, and effective optimization methods (i.e., fast-solvers) to further
speed up the optimization process such that the requirements of real-time
implementation can be successfully fulfilled. In this paper, a well-defined and
commonly-encountered motion planning problem is formulated under nonlinear
vehicle dynamics and various constraints, and an alternating direction method
of multipliers (ADMM) is developed to determine the optimal control actions.
With this development, the approach is able to circumvent the feasibility
requirement of the trajectory at the first iteration. An illustrative example
of motion planning in autonomous vehicles is then investigated with different
driving scenarios taken into consideration. As clearly observed from the
simulation results, the significance of this work in terms of obstacle
avoidance is demonstrated. Furthermore, a noteworthy achievement of high
computation efficiency is attained; and as a result, real-time computation and
implementation can be realized through this framework, and thus it provides
additional safety to the on-road driving tasks.Comment: 9 pages, 8 figure
Neural Network iLQR: A New Reinforcement Learning Architecture
As a notable machine learning paradigm, the research efforts in the context
of reinforcement learning have certainly progressed leaps and bounds. When
compared with reinforcement learning methods with the given system model, the
methodology of the reinforcement learning architecture based on the unknown
model generally exhibits significantly broader universality and applicability.
In this work, a new reinforcement learning architecture is developed and
presented without the requirement of any prior knowledge of the system model,
which is termed as an approach of a "neural network iterative linear quadratic
regulator (NNiLQR)". Depending solely on measurement data, this method yields a
completely new non-parametric routine for the establishment of the optimal
policy (without the necessity of system modeling) through iterative refinements
of the neural network system. Rather importantly, this approach significantly
outperforms the classical iterative linear quadratic regulator (iLQR) method in
terms of the given objective function because of the innovative utilization of
further exploration in the methodology. As clearly indicated from the results
attained in two illustrative examples, these significant merits of the NNiLQR
method are demonstrated rather evidently.Comment: 13 pages, 9 figure
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
Large language models (LLMs), such as ChatGPT, are prone to generate
hallucinations, i.e., content that conflicts with the source or cannot be
verified by the factual knowledge. To understand what types of content and to
which extent LLMs are apt to hallucinate, we introduce the Hallucination
Evaluation benchmark for Large Language Models (HaluEval), a large collection
of generated and human-annotated hallucinated samples for evaluating the
performance of LLMs in recognizing hallucination. To generate these samples, we
propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering.
Besides, we also hire some human labelers to annotate the hallucinations in
ChatGPT responses. The empirical results suggest that ChatGPT is likely to
generate hallucinated content in specific topics by fabricating unverifiable
information (i.e., about responses). Moreover, existing LLMs face
great challenges in recognizing the hallucinations in texts. However, our
experiments also prove that providing external knowledge or adding reasoning
steps can help LLMs recognize hallucinations. Our benchmark can be accessed at
https://github.com/RUCAIBox/HaluEval.Comment: Accepted to EMNLP 2023 Main Conference (Long Paper
Fully packed quantum loop model on the square lattice: phase diagram and application for Rydberg atoms
The quantum dimer and loop models attract great attentions, partially because
the fundamental importance in the novel phases and phase transitions emerging
in these prototypical constrained systems; and partially due to their intimate
relevance towards the on-going experiments on Rydberg atom arrays in which the
blockade mechanism naturally enforces the local constraint. Here we show, by
means of the sweeping cluster quantum Monte Carlo method, the complete ground
state phase diagram of the fully packed quantum loop model on the square
lattice. We find between the lattice nematic (LN) phase with strong dimer
attraction and the staggered phase (SP) with strong dimer repulsion, there
emerges a resonating plaquette (RP) phase with off-diagonal translational
symmetry breaking. Such a novel phase is separated from the LN via a first
order transition and from the SP by the famous Rokhsar-Kivelson point. Our
renormalization group analysis reveals the different flow directions, fully
consistent with the order parameter histogram in Monte Carlo simulations. The
realization and implication of our phase diagram in Rydberg experiments are
proposed.Comment: 6+2 pages, 5+2 figure
Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints
In the recent literature, significant and substantial efforts have been
dedicated to the important area of multi-agent decision-making problems.
Particularly here, the model predictive control (MPC) methodology has
demonstrated its effectiveness in various applications, such as mobile robots,
unmanned vehicles, and drones. Nevertheless, in many specific scenarios
involving the MPC methodology, accurate and effective system identification is
a commonly encountered challenge. As a consequence, the overall system
performance could be significantly weakened in outcome when the traditional MPC
algorithm is adopted under such circumstances. To cater to this rather major
shortcoming, this paper investigates an alternate data-driven approach to solve
the multi-agent decision-making problem. Utilizing an innovative modified
methodology with suitable closed-loop input/output measurements that comply
with the appropriate persistency of excitation condition, a non-parametric
predictive model is suitably constructed. This non-parametric predictive model
approach in the work here attains the key advantage of alleviating the rather
heavy computational burden encountered in the optimization procedures typical
in alternative methodologies requiring open-loop input/output measurement data
collection and parametric system identification. Then with a conservative
approximation of probabilistic chance constraints for the MPC problem, a
resulting deterministic optimization problem is formulated and solved
efficiently and effectively. In the work here, this intuitive data-driven
approach is also shown to preserve good robustness properties. Finally, a
multi-drone system is used to demonstrate the practical appeal and highly
effective outcome of this promising development in achieving very good system
performance.Comment: 10 pages, 6 figure
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