505 research outputs found
ET3D: Efficient Text-to-3D Generation via Multi-View Distillation
Recent breakthroughs in text-to-image generation has shown encouraging
results via large generative models. Due to the scarcity of 3D assets, it is
hardly to transfer the success of text-to-image generation to that of
text-to-3D generation. Existing text-to-3D generation methods usually adopt the
paradigm of DreamFusion, which conducts per-asset optimization by distilling a
pretrained text-to-image diffusion model. The generation speed usually ranges
from several minutes to tens of minutes per 3D asset, which degrades the user
experience and also imposes a burden to the service providers due to the high
computational budget.
In this work, we present an efficient text-to-3D generation method, which
requires only around 8 to generate a 3D asset given the text prompt on a
consumer graphic card. The main insight is that we exploit the images generated
by a large pre-trained text-to-image diffusion model, to supervise the training
of a text conditioned 3D generative adversarial network. Once the network is
trained, we are able to efficiently generate a 3D asset via a single forward
pass. Our method requires no 3D training data and provides an alternative
approach for efficient text-to-3D generation by distilling pre-trained image
diffusion models
The Wood Properties of an Intergeneric Hybrid - Taxodiomeria peizhongii (Taxodium mucronatum × Cryptomeria fortunei)
Taxodiomeria peizhongii is an intergeneric hybrid between Taxodium mucronatum and Cryptomeria fortunei. By more than 30 years investigation, it is found that the hybrid is well suited for the site and climate of Shanghai area, and it will be one of the main landscape trees in near few years. So it is necessary to know its basic wood properties. In this research, we harvested 6 sample trees of Taxodiomeria peizhongii and studied the elementary wood properties. The results showed that its mean annual ring width was 7.0mm, mean basic density 0.32g/cm3, and the mean percentage of latewood 24.3%. The mean treacheid length of latewood was 3.1mm, and mean treacheid width 35.1μm. Compared with other usual coniferous trees, the values of these indices were at a medium level. The period of juvenile wood was about 15 years, and the fast growing period appeared in first 10 years. The basic density, altering less in radial growth, showed a significant minus relation with annual ring width. The percentage of latewood did not related to wood density.OtherShinshu University International Symposium 2010 : Sustainable Agriculture and Environment : Asian Networks II 信州大学国際シンポジウム2010 : 持続的農業と環境 : アジアネットワークII ― アジアネットワークの発展をめざして―. 信州大学農学部, 2010, 65-70conference pape
Effective dosing of L-carnitine in the secondary prevention of cardiovascular disease: a systematic review and meta-analysis
BACKGROUND: L-carnitine supplementation has been associated with a significant reduction in all-cause mortality, ventricular arrhythmia, and angina in the setting of acute myocardial infarction (MI). However, on account of strict homeostatic regulation of plasma L-carnitine concentrations, higher doses of L-carnitine supplementation may not provide additional therapeutic benefits. This study aims to evaluate the effects of various oral maintenance dosages of L-carnitine on all-cause mortality and cardiovascular morbidities in the setting of acute MI. METHODS: After a systematic review of several major electronic databases (PubMed, EMBASE, and the Cochrane Library) up to November 2013, a meta-analysis of five controlled trials (n = 3108) was conducted to determine the effects of L-carnitine on all-cause mortality and cardiovascular morbidities in the setting of acute MI. RESULTS: The interaction test yielded no significant differences between the effects of the four daily oral maintenance dosages of L-carnitine (i.e., 2 g, 3 g, 4 g, and 6 g) on all-cause mortality (risk ratio [RR] = 0.77, 95% CI [0.57-1.03], P = 0.08) with a statistically insignificant trend favoring the 3 g dose (RR = 0.48) over the lower 2 g dose (RR = 0.62), which was favored over the higher 4 g and 6 g doses (RR = 0.78, 0.78). There was no significant differences between the effects of the daily oral maintenance dosages of 2 g and 6 g on heart failure (RR = 0.53, 95% CI [0.25-1.13], P = 0.10), unstable angina (RR = 0.90, 95% CI [0.51-1.58], P = 0.71), or myocardial reinfarction (RR = 0.74, 95% CI [0.30-1.80], P = 0.50). CONCLUSIONS: There appears to be no significant marginal benefit in terms of all-cause mortality, heart failure, unstable angina, or myocardial reinfarction in the setting of acute MI for oral L-carnitine maintenance doses of greater or less than 3 g per day
MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation
We introduce MVControl, a novel neural network architecture that enhances
existing pre-trained multi-view 2D diffusion models by incorporating additional
input conditions, e.g. edge maps. Our approach enables the generation of
controllable multi-view images and view-consistent 3D content. To achieve
controllable multi-view image generation, we leverage MVDream as our base
model, and train a new neural network module as additional plugin for
end-to-end task-specific condition learning. To precisely control the shapes
and views of generated images, we innovatively propose a new conditioning
mechanism that predicts an embedding encapsulating the input spatial and view
conditions, which is then injected to the network globally. Once MVControl is
trained, score-distillation (SDS) loss based optimization can be performed to
generate 3D content, in which process we propose to use a hybrid diffusion
prior. The hybrid prior relies on a pre-trained Stable-Diffusion network and
our trained MVControl for additional guidance. Extensive experiments
demonstrate that our method achieves robust generalization and enables the
controllable generation of high-quality 3D content. Code available at
https://github.com/WU-CVGL/MVControl/.Comment: Project page: https://lizhiqi49.github.io/MVControl
Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification
Multi-spectral vehicle re-identification aims to address the challenge of
identifying vehicles in complex lighting conditions by incorporating
complementary visible and infrared information. However, in harsh environments,
the discriminative cues in RGB and NIR modalities are often lost due to strong
flares from vehicle lamps or sunlight, and existing multi-modal fusion methods
are limited in their ability to recover these important cues. To address this
problem, we propose a Flare-Aware Cross-modal Enhancement Network that
adaptively restores flare-corrupted RGB and NIR features with guidance from the
flare-immunized thermal infrared spectrum. First, to reduce the influence of
locally degraded appearance due to intense flare, we propose a Mutual Flare
Mask Prediction module to jointly obtain flare-corrupted masks in RGB and NIR
modalities in a self-supervised manner. Second, to use the flare-immunized TI
information to enhance the masked RGB and NIR, we propose a Flare-Aware
Cross-modal Enhancement module that adaptively guides feature extraction of
masked RGB and NIR spectra with prior flare-immunized knowledge from the TI
spectrum. Third, to extract common informative semantic information from RGB
and NIR, we propose an Inter-modality Consistency loss that enforces semantic
consistency between the two modalities. Finally, to evaluate the proposed
FACENet in handling intense flare, we introduce a new multi-spectral vehicle
re-ID dataset, called WMVEID863, with additional challenges such as motion
blur, significant background changes, and particularly intense flare
degradation. Comprehensive experiments on both the newly collected dataset and
public benchmark multi-spectral vehicle re-ID datasets demonstrate the superior
performance of the proposed FACENet compared to state-of-the-art methods,
especially in handling strong flares. The code and dataset will be released
soon
Application of High-Resolution DNA Melting for Genotyping in Lepidopteran Non-Model Species: Ostrinia furnacalis (Crambidae)
Development of an ideal marker system facilitates a better understanding of the genetic diversity in lepidopteran non-model organisms, which have abundant species, but relatively limited genomic resources. Single nucleotide polymorphisms (SNPs) discovered within single-copy genes have proved to be desired markers, but SNP genotyping by current techniques remain laborious and expensive. High resolution melting (HRM) curve analysis represents a simple, rapid and inexpensive genotyping method that is primarily confined to clinical and diagnostic studies. In this study, we evaluated the potential of HRM analysis for SNP genotyping in the lepidopteran non-model species Ostrinia furnacalis (Crambidae). Small amplicon and unlabeled probe assays were developed for the SNPs, which were identified in 30 females of O. furnacalis from 3 different populations by our direct sequencing. Both assays were then applied to genotype 90 unknown female DNA by prior mixing with known wild-type DNA. The genotyping results were compared with those that were obtained using bi-directional sequencing analysis. Our results demonstrated the efficiency and reliability of the HRM assays. HRM has the potential to provide simple, cost-effective genotyping assays and facilitates genotyping studies in any non-model lepidopteran species of interest
Tomographic Alcock-Paczynski Method with Redshift Errors
The tomographic Alcock-Paczynski (AP) method is a promising method that uses
the redshift evolution of the anisotropic clustering in redshift space to
calibrate cosmology. It extends the applicable range of AP method to
substantially nonlinear scales, yielding very tight cosmological constraints.
For future stage-IV slitless spectroscopic surveys, the non-negligible redshift
errors might reduce the advantage of the tomographic AP method by suppressing
the resolution of the nonlinear structure along the line of sight. The present
work studies how redshift errors propagate to cosmological parameters in the
tomographic AP analysis. We use a formula to
model the redshift errors, with varying from 0.001 to 0.006 and
varying from 0.5 to 1.5. The redshift errors produce a signal of
anisotropic clustering that is similar to a strong finger-of-god effect, which
smears out both the AP signal and the contamination caused by the redshift
space distortions (RSD). For the target precision of the Chinese Space Station
Telescope optical survey (), the decrement of
constraining power on the dark energy equation of state is mild (), and the suppression of RSD contamination leads to a smaller
bias-to-signal ratio. Our results indicate that the tomographic AP method will
remain a useful and complementary tool for analyses of future slitless
spectroscopic surveys.Comment: 9 pages and 6 figure
Mixline: A Hybrid Reinforcement Learning Framework for Long-horizon Bimanual Coffee Stirring Task
Bimanual activities like coffee stirring, which require coordination of dual
arms, are common in daily life and intractable to learn by robots. Adopting
reinforcement learning to learn these tasks is a promising topic since it
enables the robot to explore how dual arms coordinate together to accomplish
the same task. However, this field has two main challenges: coordination
mechanism and long-horizon task decomposition. Therefore, we propose the
Mixline method to learn sub-tasks separately via the online algorithm and then
compose them together based on the generated data through the offline
algorithm. We constructed a learning environment based on the GPU-accelerated
Isaac Gym. In our work, the bimanual robot successfully learned to grasp, hold
and lift the spoon and cup, insert them together and stir the coffee. The
proposed method has the potential to be extended to other long-horizon bimanual
tasks.Comment: 10 pages, conferenc
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