2,093 research outputs found
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review
Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene
representation has taken the field of Computer Vision by storm. As a novel view
synthesis and 3D reconstruction method, NeRF models find applications in
robotics, urban mapping, autonomous navigation, virtual reality/augmented
reality, and more. Since the original paper by Mildenhall et al., more than 250
preprints were published, with more than 100 eventually being accepted in tier
one Computer Vision Conferences. Given NeRF popularity and the current interest
in this research area, we believe it necessary to compile a comprehensive
survey of NeRF papers from the past two years, which we organized into both
architecture, and application based taxonomies. We also provide an introduction
to the theory of NeRF based novel view synthesis, and a benchmark comparison of
the performance and speed of key NeRF models. By creating this survey, we hope
to introduce new researchers to NeRF, provide a helpful reference for
influential works in this field, as well as motivate future research directions
with our discussion section
3DPCT: 3D Point Cloud Transformer with Dual Self-attention
Transformers have resulted in remarkable achievements in the field of image
processing. Inspired by this great success, the application of Transformers to
3D point cloud processing has drawn more and more attention. This paper
presents a novel point cloud representational learning network, 3D Point Cloud
Transformer with Dual Self-attention (3DPCT) and an encoder-decoder structure.
Specifically, 3DPCT has a hierarchical encoder, which contains two local-global
dual-attention modules for the classification task (three modules for the
segmentation task), with each module consisting of a Local Feature Aggregation
(LFA) block and a Global Feature Learning (GFL) block. The GFL block is dual
self-attention, with both point-wise and channel-wise self-attention to improve
feature extraction. Moreover, in LFA, to better leverage the local information
extracted, a novel point-wise self-attention model, named as Point-Patch
Self-Attention (PPSA), is designed. The performance is evaluated on both
classification and segmentation datasets, containing both synthetic and
real-world data. Extensive experiments demonstrate that the proposed method
achieved state-of-the-art results on both classification and segmentation
tasks.Comment: 10 pages, 5 figures, 4 table
Mini Baja CVT Optimization
The goal of this project is to design and optimize an eCVT setup for The University of Akron’s Zips Baja SAE car. The current CVT is a centrifugal CVT which changes its gear ratio through a series of weights and springs, while an eCVT uses a motor to change the gear ratio.The advantage of an eCVT is that the motor can be programmed to adjust in real-time based on the engine rpm. A centrifugal CVT can only be tuned ahead of time. It is necessary to compare the new acceleration performance to a tuned centrifugal CVT to determine if it would be an improvement over the Baja team’s current CVT design
DISCO: Distilling Phrasal Counterfactuals with Large Language Models
Models trained with counterfactually augmented data learn representations of
the causal structure of tasks, enabling robust generalization. However,
high-quality counterfactual data is scarce for most tasks and not easily
generated at scale. When crowdsourced, such data is typically limited in scale
and diversity; when generated using supervised methods, it is computationally
expensive to extend to new counterfactual dimensions. In this work, we
introduce DISCO (DIStilled COunterfactual Data), a new method for automatically
generating high quality counterfactual data at scale. DISCO engineers prompts
to generate phrasal perturbations with a large general language model. Then, a
task-specific teacher model filters these generations to distill high-quality
counterfactual data. While task-agnostic, we apply our pipeline to the task of
natural language inference (NLI) and find that on challenging evaluations such
as the NLI stress test, comparatively smaller student models trained with DISCO
generated counterfactuals are more robust (6% absolute) and generalize better
across distributions (2%) compared to models trained without data augmentation.
Furthermore, DISCO augmented models are 10% more consistent between
counterfactual pairs on three evaluation sets, demonstrating that DISCO
augmentation enables models to more reliably learn causal representations. Our
repository is available at: https://github.com/eric11eca/discoComment: ACL 2023 camera read
Analyzing Learned Molecular Representations for Property Prediction
Advancements in neural machinery have led to a wide range of algorithmic
solutions for molecular property prediction. Two classes of models in
particular have yielded promising results: neural networks applied to computed
molecular fingerprints or expert-crafted descriptors, and graph convolutional
neural networks that construct a learned molecular representation by operating
on the graph structure of the molecule. However, recent literature has yet to
clearly determine which of these two methods is superior when generalizing to
new chemical space. Furthermore, prior research has rarely examined these new
models in industry research settings in comparison to existing employed models.
In this paper, we benchmark models extensively on 19 public and 16 proprietary
industrial datasets spanning a wide variety of chemical endpoints. In addition,
we introduce a graph convolutional model that consistently matches or
outperforms models using fixed molecular descriptors as well as previous graph
neural architectures on both public and proprietary datasets. Our empirical
findings indicate that while approaches based on these representations have yet
to reach the level of experimental reproducibility, our proposed model
nevertheless offers significant improvements over models currently used in
industrial workflows
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short) has
emerged as a desirable property of deep neural networks. Intuitively,
robustness means that small perturbations to an input do not cause the network
to perform misclassifications. In this paper, we present a novel algorithm for
verifying robustness properties of neural networks. Our method synergistically
combines gradient-based optimization methods for counterexample search with
abstraction-based proof search to obtain a sound and ({\delta}-)complete
decision procedure. Our method also employs a data-driven approach to learn a
verification policy that guides abstract interpretation during proof search. We
have implemented the proposed approach in a tool called Charon and
experimentally evaluated it on hundreds of benchmarks. Our experiments show
that the proposed approach significantly outperforms three state-of-the-art
tools, namely AI^2 , Reluplex, and Reluval
A proposed agglomerate model for oxygen reduction in the catalyst layer of proton exchange membrane fuel cells
Oxygen diffusion and reduction in the catalyst layer of PEM fuel cell is an important process in fuel cell modelling, but models able to link the reduction rate to catalyst-layer structure are lack; this paper makes such an effort. We first link the average reduction rate over the agglomerate within a catalyst layer to a probability that an oxygen molecule, which is initially on the agglomerate surface, will enter and remain in the agglomerate at any time in the absence of any electrochemical reaction. We then propose a method to directly calculate distribution function of this probability and apply it to two catalyst layers with contrasting structures. A formula is proposed to describe these calculated distribution functions, from which the agglomerate model is derived. The model has two parameters and both can be independently calculated from catalyst layer structures. We verify the model by first showing that it is an improvement and able to reproduce what the spherical model describes, and then testing it against the average oxygen reductions directly calculated from pore-scale simulations of oxygen diffusion and reaction in the two catalyst layers. The proposed model is simple, but significant as it links the average oxygen reduction to catalyst layer structures, and its two parameters can be directly calculated rather than by calibration
Method to improve catalyst layer model for modelling proton exchange membrane fuel cell
Correctly describing oxygen reduction within the cathode catalyst layer (CL) in modelling proton exchange membrane fuel cell is an important issue remaining unresolved. In this paper we show how to derive an agglomerate model for calculating oxygen reactions by describing dissolved oxygen in the agglomerates using two independent random processes. The first one is the probability that an oxygen molecule, which dissolves in the ionomer film on the agglomerate surface, moves into and then remains in the agglomerates; the second one is the probability of the molecule being consumed in reactions. The first probability depends on CL structure and can be directly calculated; the second one is derived by assuming that the oxygen reduction is first-order kinetic. It is found that the distribution functions of the first process can be fitted to a generalised gamma distribution function, which enables us to derive an analytical agglomerate model. We also expend the model to include oxygen dissolution in the ionomer film, and apply it to simulate cathode electrodes. The results reveal that the resistance to oxygen diffusion in ionomer film and agglomerate in modern CL is minor, and that the main potential loss is due to oxygen dissolution in the ionomer film
Modelling water intrusion and oxygen diffusion in a reconstructed microporous layer of PEM fuel cells
The hydrophobic microporous layer (MPL) in PEM fuel cell improves water management but reduces oxygen transport. We investigate these conflict impacts using nanotomography and pore-scale modelling. The binary image of a MPL is acquired using FIB/SEM tomography. The water produced at the cathode is assumed to condense in the catalyst layer (CL), and then builds up a pressure before moving into the MPL. Water distribution in the MPL is calculated from its pore geometry, and oxygen transport through it is simulated using pore-scale models considering both bulk and Knudsen diffusions. The simulated oxygen concentration and flux at all voxels are volumetrically averaged to calculate the effective diffusion coefficients. For water flow, we found that when the MPL is too hydrophobic, water is unable to move through it and must find alternative exits. For oxygen diffusion, we found that the interaction of the bulk and Knudsen diffusions at pore scale creates an extra resistance after the volumetric average, and that the conventional dusty model substantially overestimates the effective diffusion coefficient
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