180 research outputs found
ENV-636: SOIL AQUIFER TREATMENT OF SECONDARY EFFLUENTS AND CSOS IN SOUTHWESTERN ONTARIO
Wastewater reclamation is becoming an important alternative for sustainable water resources management and building climate change resiliency in many regions around the world. The purpose of this research was to investigate the polishing of secondary effluents and Combine Sewer Overflows (CSOs) by a laboratory scale Soil Aquifer Treatment (SAT) considering local sub-surface geology and wastewater characteristics. Results show that characteristic soils of southwestern Ontario can effectively polish secondary effluents in terms of BOD5 (64.9% to 100%), e-coli (100%) and total coliforms (100%). However, low removals of DOC (22.81%) and Nitrate (15.17 %) were achieved. Furthermore, low to moderate improvements of CSOs quality were observed with maximum removals of 54.26 % for BOD5, 36.67% for e-coli, 58.15% for total coliforms and 44.83 % for Total Nitrogen. Additionally, de-nitrification of secondary effluents was greatly improved (46.1 % to 100%) by the addition of readily available organic matter, which supports the importance of protecting recharge wetlands for groundwater quality protection. SAT in southwestern Ontario is a feasible alternative for the recharge of non-potable and potable aquifers with secondary effluents. However, for potable aquifers further treatment of wastewater effluents may be required
Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors
Visual localization is an attractive problem that estimates the camera
localization from database images based on the query image. It is a crucial
task for various applications, such as autonomous vehicles, assistive
navigation and augmented reality. The challenging issues of the task lie in
various appearance variations between query and database images, including
illumination variations, dynamic object variations and viewpoint variations. In
order to tackle those challenges, Panoramic Annular Localizer into which
panoramic annular lens and robust deep image descriptors are incorporated is
proposed in this paper. The panoramic annular images captured by the single
camera are processed and fed into the NetVLAD network to form the active deep
descriptor, and sequential matching is utilized to generate the localization
result. The experiments carried on the public datasets and in the field
illustrate the validation of the proposed system.Comment: Accepted by ITSC 201
Training Energy-Based Models with Diffusion Contrastive Divergences
Energy-Based Models (EBMs) have been widely used for generative modeling.
Contrastive Divergence (CD), a prevailing training objective for EBMs, requires
sampling from the EBM with Markov Chain Monte Carlo methods (MCMCs), which
leads to an irreconcilable trade-off between the computational burden and the
validity of the CD. Running MCMCs till convergence is computationally
intensive. On the other hand, short-run MCMC brings in an extra non-negligible
parameter gradient term that is difficult to handle. In this paper, we provide
a general interpretation of CD, viewing it as a special instance of our
proposed Diffusion Contrastive Divergence (DCD) family. By replacing the
Langevin dynamic used in CD with other EBM-parameter-free diffusion processes,
we propose a more efficient divergence. We show that the proposed DCDs are both
more computationally efficient than the CD and are not limited to a
non-negligible gradient term. We conduct intensive experiments, including both
synthesis data modeling and high-dimensional image denoising and generation, to
show the advantages of the proposed DCDs. On the synthetic data learning and
image denoising experiments, our proposed DCD outperforms CD by a large margin.
In image generation experiments, the proposed DCD is capable of training an
energy-based model for generating the Celab-A dataset, which is
comparable to existing EBMs
Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
Due to the ease of training, ability to scale, and high sample quality,
diffusion models (DMs) have become the preferred option for generative
modeling, with numerous pre-trained models available for a wide variety of
datasets. Containing intricate information about data distributions,
pre-trained DMs are valuable assets for downstream applications. In this work,
we consider learning from pre-trained DMs and transferring their knowledge to
other generative models in a data-free fashion. Specifically, we propose a
general framework called Diff-Instruct to instruct the training of arbitrary
generative models as long as the generated samples are differentiable with
respect to the model parameters. Our proposed Diff-Instruct is built on a
rigorous mathematical foundation where the instruction process directly
corresponds to minimizing a novel divergence we call Integral Kullback-Leibler
(IKL) divergence. IKL is tailored for DMs by calculating the integral of the KL
divergence along a diffusion process, which we show to be more robust in
comparing distributions with misaligned supports. We also reveal non-trivial
connections of our method to existing works such as DreamFusion, and generative
adversarial training. To demonstrate the effectiveness and universality of
Diff-Instruct, we consider two scenarios: distilling pre-trained diffusion
models and refining existing GAN models. The experiments on distilling
pre-trained diffusion models show that Diff-Instruct results in
state-of-the-art single-step diffusion-based models. The experiments on
refining GAN models show that the Diff-Instruct can consistently improve the
pre-trained generators of GAN models across various settings
3D-GPT: Procedural 3D Modeling with Large Language Models
In the pursuit of efficient automated content creation, procedural
generation, leveraging modifiable parameters and rule-based systems, emerges as
a promising approach. Nonetheless, it could be a demanding endeavor, given its
intricate nature necessitating a deep understanding of rules, algorithms, and
parameters. To reduce workload, we introduce 3D-GPT, a framework utilizing
large language models~(LLMs) for instruction-driven 3D modeling. 3D-GPT
positions LLMs as proficient problem solvers, dissecting the procedural 3D
modeling tasks into accessible segments and appointing the apt agent for each
task. 3D-GPT integrates three core agents: the task dispatch agent, the
conceptualization agent, and the modeling agent. They collaboratively achieve
two objectives. First, it enhances concise initial scene descriptions, evolving
them into detailed forms while dynamically adapting the text based on
subsequent instructions. Second, it integrates procedural generation,
extracting parameter values from enriched text to effortlessly interface with
3D software for asset creation. Our empirical investigations confirm that
3D-GPT not only interprets and executes instructions, delivering reliable
results but also collaborates effectively with human designers. Furthermore, it
seamlessly integrates with Blender, unlocking expanded manipulation
possibilities. Our work highlights the potential of LLMs in 3D modeling,
offering a basic framework for future advancements in scene generation and
animation.Comment: Project page: https://chuny1.github.io/3DGPT/3dgpt.htm
- …