728 research outputs found
Allometric models for aboveground biomass of ten tree species in northeast China
China contains 119 million hectares of natural forest, much of whichis secondary forest. An accurate estimation of the biomass of these forests is imperative because many studies conducted in northeast China have only used primary forest and this may have resulted in biased estimates. This study analyzed secondary forest in the area using information from a forest inventory to develop allometric models of the aboveground biomass (AGB). The parameter values of the diameter at breast height (DBH), tree height (H), and crown length (CL) were derived from a forest inventory of 2,733 trees in a 3.5 ha plot. The wood-specific gravity (WSG) was determined for 109 trees belonging to ten species. A partial sampling method was also used to determine the biomass of branches (including stem, bark and foliage) in 120 trees, which substantially ease the field works. The mean AGB was110,729 kg ha–1. We developed four allometric models from the investigation and evaluated the utility of other 19 published ones for AGB in the ten tree species. Incorporation of full range of variables with WSG-DBH-H-CL, significantly improved the precision of the models. Some of models were chosen that best fitted each tree species with high precision (R2 ≥ 0.939, SEE 0.167). At the latitude level, the estimated AGB of secondary forest was lower than that in mature primary forests, but higher than that in primary broadleaf forest and the average level in other types of forest likewise
Boosting the Cycle Counting Power of Graph Neural Networks with I-GNNs
Message Passing Neural Networks (MPNNs) are a widely used class of Graph
Neural Networks (GNNs). The limited representational power of MPNNs inspires
the study of provably powerful GNN architectures. However, knowing one model is
more powerful than another gives little insight about what functions they can
or cannot express. It is still unclear whether these models are able to
approximate specific functions such as counting certain graph substructures,
which is essential for applications in biology, chemistry and social network
analysis. Motivated by this, we propose to study the counting power of Subgraph
MPNNs, a recent and popular class of powerful GNN models that extract rooted
subgraphs for each node, assign the root node a unique identifier and encode
the root node's representation within its rooted subgraph. Specifically, we
prove that Subgraph MPNNs fail to count more-than-4-cycles at node level,
implying that node representations cannot correctly encode the surrounding
substructures like ring systems with more than four atoms. To overcome this
limitation, we propose I-GNNs to extend Subgraph MPNNs by assigning
different identifiers for the root node and its neighbors in each subgraph.
I-GNNs' discriminative power is shown to be strictly stronger than Subgraph
MPNNs and partially stronger than the 3-WL test. More importantly, I-GNNs
are proven capable of counting all 3, 4, 5 and 6-cycles, covering common
substructures like benzene rings in organic chemistry, while still keeping
linear complexity. To the best of our knowledge, it is the first linear-time
GNN model that can count 6-cycles with theoretical guarantees. We validate its
counting power in cycle counting tasks and demonstrate its competitive
performance in molecular prediction benchmarks
Plasmonic Nanomaterials for Optical Sensor and Energy Storage and Transfer
Nanomaterials including noble metal nanomaterials and some metal oxide nanomaterials exhibit very strong lightmatter interactions under resonant excitation. Very large absorption and scattering at the localized wavelengths can been achieved. Because of their attractive optical properties, optical NPs and nanostructures have been commonly used in various fields from nanophotonics, analytical chemistry, biotechnology, and information storage to energy applications including photovoltaics and photocatalysisphotocatalysi
DiffMorpher: Unleashing the Capability of Diffusion Models for Image Morphing
Diffusion models have achieved remarkable image generation quality surpassing
previous generative models. However, a notable limitation of diffusion models,
in comparison to GANs, is their difficulty in smoothly interpolating between
two image samples, due to their highly unstructured latent space. Such a smooth
interpolation is intriguing as it naturally serves as a solution for the image
morphing task with many applications. In this work, we present DiffMorpher, the
first approach enabling smooth and natural image interpolation using diffusion
models. Our key idea is to capture the semantics of the two images by fitting
two LoRAs to them respectively, and interpolate between both the LoRA
parameters and the latent noises to ensure a smooth semantic transition, where
correspondence automatically emerges without the need for annotation. In
addition, we propose an attention interpolation and injection technique and a
new sampling schedule to further enhance the smoothness between consecutive
images. Extensive experiments demonstrate that DiffMorpher achieves starkly
better image morphing effects than previous methods across a variety of object
categories, bridging a critical functional gap that distinguished diffusion
models from GANs
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