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