196 research outputs found
Large-Scale, Dynamic, Microscopic Simulation for Region-Wide Line Source Dispersion Modeling
Although a variety of modeling tools have been developed to predict potential public exposure to harmful transportation emissions at regional and sub-regional scales, computational efficiency remains a critical concern in the design of modeling tools. Microscale dispersion models run at high resolution and require extremely long runtimes for larger roadway networks and high-resolution receptor grids. Motivated by the challenges encountered in the previous modeling efforts, this work develops an advanced modeling framework for region-wide applications of line source dispersion models that integrates a high-performance emission rate lookup system (i.e., MOVES-Matrix), link screening, and innovative receptor site selection routines to further accelerate model implementation within distributed computing modeling framework. The case study in the 20-county metropolitan Atlanta area accounts for an extremely large number of link-receptor pairs demonstrates that the modeling system generates comparable concentration estimates to extremely-high-resolution processes, but with very high computational efficiency. The comprehensive modeling methodology presented in this work will make comparison of air quality impacts across complex project scenarios (and transportation development alternatives over large geographic areas) much more feasible. All these aspects should be of interest to a broad readership engaged in near-road air quality modeling for transportation planning and air quality conformity and for environmental analysis under the National Environmental Policy Act.Ph.D
Selective Token Generation for Few-shot Natural Language Generation
Natural language modeling with limited training data is a challenging
problem, and many algorithms make use of large-scale pretrained language models
(PLMs) for this due to its great generalization ability. Among them, additive
learning that incorporates a task-specific adapter on top of the fixed
large-scale PLM has been popularly used in the few-shot setting. However, this
added adapter is still easy to disregard the knowledge of the PLM especially
for few-shot natural language generation (NLG) since an entire sequence is
usually generated by only the newly trained adapter. Therefore, in this work,
we develop a novel additive learning algorithm based on reinforcement learning
(RL) that selectively outputs language tokens between the task-general PLM and
the task-specific adapter during both training and inference. This output token
selection over the two generators allows the adapter to take into account
solely the task-relevant parts in sequence generation, and therefore makes it
more robust to overfitting as well as more stable in RL training. In addition,
to obtain the complementary adapter from the PLM for each few-shot task, we
exploit a separate selecting module that is also simultaneously trained using
RL. Experimental results on various few-shot NLG tasks including question
answering, data-to-text generation and text summarization demonstrate that the
proposed selective token generation significantly outperforms the previous
additive learning algorithms based on the PLMs.Comment: COLING 202
FaceCLIPNeRF: Text-driven 3D Face Manipulation using Deformable Neural Radiance Fields
As recent advances in Neural Radiance Fields (NeRF) have enabled
high-fidelity 3D face reconstruction and novel view synthesis, its manipulation
also became an essential task in 3D vision. However, existing manipulation
methods require extensive human labor, such as a user-provided semantic mask
and manual attribute search unsuitable for non-expert users. Instead, our
approach is designed to require a single text to manipulate a face
reconstructed with NeRF. To do so, we first train a scene manipulator, a latent
code-conditional deformable NeRF, over a dynamic scene to control a face
deformation using the latent code. However, representing a scene deformation
with a single latent code is unfavorable for compositing local deformations
observed in different instances. As so, our proposed Position-conditional
Anchor Compositor (PAC) learns to represent a manipulated scene with spatially
varying latent codes. Their renderings with the scene manipulator are then
optimized to yield high cosine similarity to a target text in CLIP embedding
space for text-driven manipulation. To the best of our knowledge, our approach
is the first to address the text-driven manipulation of a face reconstructed
with NeRF. Extensive results, comparisons, and ablation studies demonstrate the
effectiveness of our approach.Comment: ICCV 202
WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting
Time series forecasting has become a critical task due to its high
practicality in real-world applications such as traffic, energy consumption,
economics and finance, and disease analysis. Recent deep-learning-based
approaches have shown remarkable success in time series forecasting.
Nonetheless, due to the dynamics of time series data, deep networks still
suffer from unstable training and overfitting. Inconsistent patterns appearing
in real-world data lead the model to be biased to a particular pattern, thus
limiting the generalization. In this work, we introduce the dynamic error
bounds on training loss to address the overfitting issue in time series
forecasting. Consequently, we propose a regularization method called WaveBound
which estimates the adequate error bounds of training loss for each time step
and feature at each iteration. By allowing the model to focus less on
unpredictable data, WaveBound stabilizes the training process, thus
significantly improving generalization. With the extensive experiments, we show
that WaveBound consistently improves upon the existing models in large margins,
including the state-of-the-art model.Comment: NeurIPS 202
CSGM Designer: a platform for designing cross-species intron-spanning genic markers linked with genome information of legumes.
BackgroundGenetic markers are tools that can facilitate molecular breeding, even in species lacking genomic resources. An important class of genetic markers is those based on orthologous genes, because they can guide hypotheses about conserved gene function, a situation that is well documented for a number of agronomic traits. For under-studied species a key bottleneck in gene-based marker development is the need to develop molecular tools (e.g., oligonucleotide primers) that reliably access genes with orthology to the genomes of well-characterized reference species.ResultsHere we report an efficient platform for the design of cross-species gene-derived markers in legumes. The automated platform, named CSGM Designer (URL: http://tgil.donga.ac.kr/CSGMdesigner), facilitates rapid and systematic design of cross-species genic markers. The underlying database is composed of genome data from five legume species whose genomes are substantially characterized. Use of CSGM is enhanced by graphical displays of query results, which we describe as "circular viewer" and "search-within-results" functions. CSGM provides a virtual PCR representation (eHT-PCR) that predicts the specificity of each primer pair simultaneously in multiple genomes. CSGM Designer output was experimentally validated for the amplification of orthologous genes using 16 genotypes representing 12 crop and model legume species, distributed among the galegoid and phaseoloid clades. Successful cross-species amplification was obtained for 85.3% of PCR primer combinations.ConclusionCSGM Designer spans the divide between well-characterized crop and model legume species and their less well-characterized relatives. The outcome is PCR primers that target highly conserved genes for polymorphism discovery, enabling functional inferences and ultimately facilitating trait-associated molecular breeding
Local 3D Editing via 3D Distillation of CLIP Knowledge
3D content manipulation is an important computer vision task with many
real-world applications (e.g., product design, cartoon generation, and 3D
Avatar editing). Recently proposed 3D GANs can generate diverse photorealistic
3D-aware contents using Neural Radiance fields (NeRF). However, manipulation of
NeRF still remains a challenging problem since the visual quality tends to
degrade after manipulation and suboptimal control handles such as 2D semantic
maps are used for manipulations. While text-guided manipulations have shown
potential in 3D editing, such approaches often lack locality. To overcome these
problems, we propose Local Editing NeRF (LENeRF), which only requires text
inputs for fine-grained and localized manipulation. Specifically, we present
three add-on modules of LENeRF, the Latent Residual Mapper, the Attention Field
Network, and the Deformation Network, which are jointly used for local
manipulations of 3D features by estimating a 3D attention field. The 3D
attention field is learned in an unsupervised way, by distilling the zero-shot
mask generation capability of CLIP to the 3D space with multi-view guidance. We
conduct diverse experiments and thorough evaluations both quantitatively and
qualitatively.Comment: conference: CVPR 202
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