94 research outputs found
Learning hypergraphs from signals with dual smoothness prior
Hypergraph structure learning, which aims to learn the hypergraph structures from the observed signals to capture the intrinsic high-order relationships among the entities, becomes crucial when a hypergraph topology is not readily available in the datasets. There are two challenges that lie at the heart of this problem: 1) how to handle the huge search space of potential hyperedges, and 2) how to define meaningful criteria to measure the relationship between the signals observed on nodes and the hypergraph structure. In this paper, for the first challenge, we adopt the assumption that the ideal hypergraph structure can be derived from a learnable graph structure that captures the pairwise relations within signals. Further, we propose a hypergraph structure learning framework HGSL with a novel dual smoothness prior that reveals a mapping between the observed node signals and the hypergraph structure, whereby each hyperedge corresponds to a subgraph with both node signal smoothness and edge signal smoothness in the learnable graph structure. Finally, we conduct extensive experiments to evaluate HGSL on both synthetic and real world datasets. Experiments show that HGSL can efficiently infer meaningful hypergraph topologies from observed signals
Hypergraph Structure Inference From Data Under Smoothness Prior
Hypergraphs are important for processing data with higher-order relationships
involving more than two entities. In scenarios where explicit hypergraphs are
not readily available, it is desirable to infer a meaningful hypergraph
structure from the node features to capture the intrinsic relations within the
data. However, existing methods either adopt simple pre-defined rules that fail
to precisely capture the distribution of the potential hypergraph structure, or
learn a mapping between hypergraph structures and node features but require a
large amount of labelled data, i.e., pre-existing hypergraph structures, for
training. Both restrict their applications in practical scenarios. To fill this
gap, we propose a novel smoothness prior that enables us to design a method to
infer the probability for each potential hyperedge without labelled data as
supervision. The proposed prior indicates features of nodes in a hyperedge are
highly correlated by the features of the hyperedge containing them. We use this
prior to derive the relation between the hypergraph structure and the node
features via probabilistic modelling. This allows us to develop an unsupervised
inference method to estimate the probability for each potential hyperedge via
solving an optimisation problem that has an analytical solution. Experiments on
both synthetic and real-world data demonstrate that our method can learn
meaningful hypergraph structures from data more efficiently than existing
hypergraph structure inference methods
Learning Hypergraphs From Signals With Dual Smoothness Prior
The construction of a meaningful hypergraph topology is the key to processing
signals with high-order relationships that involve more than two entities.
Learning the hypergraph structure from the observed signals to capture the
intrinsic relationships among the entities becomes crucial when a hypergraph
topology is not readily available in the datasets. There are two challenges
that lie at the heart of this problem: 1) how to handle the huge search space
of potential hyperedges, and 2) how to define meaningful criteria to measure
the relationship between the signals observed on nodes and the hypergraph
structure. In this paper, to address the first challenge, we adopt the
assumption that the ideal hypergraph structure can be derived from a learnable
graph structure that captures the pairwise relations within signals. Further,
we propose a hypergraph learning framework with a novel dual smoothness prior
that reveals a mapping between the observed node signals and the hypergraph
structure, whereby each hyperedge corresponds to a subgraph with both node
signal smoothness and edge signal smoothness in the learnable graph structure.
Finally, we conduct extensive experiments to evaluate the proposed framework on
both synthetic and real world datasets. Experiments show that our proposed
framework can efficiently infer meaningful hypergraph topologies from observed
signals.Comment: We have polished the paper and fixed some typos and the correct
number of the target hyperedges is given to the baseline in this versio
Hypergraph-Mlp: learning on hypergraphs without message passing
Hypergraphs are vital in modelling data with higher-order relations containing more than two entities, gaining prominence in machine learning and signal processing. Many hypergraph neural networks leverage message passing over hypergraph structures to enhance node representation learning, yielding impressive performances in tasks like hypergraph node classification. However, these message-passing-based models face several challenges, including oversmoothing as well as high latency and sensitivity to structural perturbations at inference time. To tackle those challenges, we propose an alternative approach where we integrate the information about hypergraph structures into training supervision without explicit message passing, thus also removing the reliance on it at inference. Specifically, we introduce Hypergraph-MLP, a novel learning framework for hypergraph-structured data, where the learning model is a straightforward multilayer perceptron (MLP) supervised by a loss function based on a notion of signal smoothness on hypergraphs. Experiments on hypergraph node classification tasks demonstrate that Hypergraph-MLP achieves competitive performance compared to existing baselines, and is considerably faster and more robust against structural perturbations at inference
A supramolecular radical cation: folding-enhanced electrostatic effect for promoting radical-mediated oxidation.
We report a supramolecular strategy to promote radical-mediated Fenton oxidation by the rational design of a folded host-guest complex based on cucurbit[8]uril (CB[8]). In the supramolecular complex between CB[8] and a derivative of 1,4-diketopyrrolo[3,4-c]pyrrole (DPP), the carbonyl groups of CB[8] and the DPP moiety are brought together through the formation of a folded conformation. In this way, the electrostatic effect of the carbonyl groups of CB[8] is fully applied to highly improve the reactivity of the DPP radical cation, which is the key intermediate of Fenton oxidation. As a result, the Fenton oxidation is extraordinarily accelerated by over 100 times. It is anticipated that this strategy could be applied to other radical reactions and enrich the field of supramolecular radical chemistry in radical polymerization, photocatalysis, and organic radical battery and holds potential in supramolecular catalysis and biocatalysis
Hypergraph transformer for semi-supervised classification
Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities. Hypergraph neural networks emerge as a powerful tool for processing hypergraph-structured data, delivering remarkable performance across various tasks, e.g., hypergraph node classification. However, these models struggle to capture global structural information due to their reliance on local message passing. To address this challenge, we propose a novel hypergraph learning framework, HyperGraph Transformer (HyperGT). HyperGT uses a Transformer-based neural network architecture to effectively consider global correlations among all nodes and hyperedges. To incorporate local structural information, HyperGT has two distinct designs: i) a positional encoding based on the hypergraph incidence matrix, offering valuable insights into node-node and hyperedge-hyperedge interactions; and ii) a hypergraph structure regularization in the loss function, capturing connectivities between nodes and hyperedges. Through these designs, HyperGT achieves comprehensive hypergraph representation learning by effectively incorporating global interactions while preserving local connectivity patterns. Extensive experiments conducted on real-world hypergraph node classification tasks showcase that HyperGT consistently outperforms existing methods, establishing new state-of-the-art benchmarks. Ablation studies affirm the effectiveness of the individual designs of our model
Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting
In multi-modal multi-agent trajectory forecasting, two major challenges have
not been fully tackled: 1) how to measure the uncertainty brought by the
interaction module that causes correlations among the predicted trajectories of
multiple agents; 2) how to rank the multiple predictions and select the optimal
predicted trajectory. In order to handle these challenges, this work first
proposes a novel concept, collaborative uncertainty (CU), which models the
uncertainty resulting from interaction modules. Then we build a general
CU-aware regression framework with an original permutation-equivariant
uncertainty estimator to do both tasks of regression and uncertainty
estimation. Further, we apply the proposed framework to current SOTA
multi-agent multi-modal forecasting systems as a plugin module, which enables
the SOTA systems to 1) estimate the uncertainty in the multi-agent multi-modal
trajectory forecasting task; 2) rank the multiple predictions and select the
optimal one based on the estimated uncertainty. We conduct extensive
experiments on a synthetic dataset and two public large-scale multi-agent
trajectory forecasting benchmarks. Experiments show that: 1) on the synthetic
dataset, the CU-aware regression framework allows the model to appropriately
approximate the ground-truth Laplace distribution; 2) on the multi-agent
trajectory forecasting benchmarks, the CU-aware regression framework steadily
helps SOTA systems improve their performances. Specially, the proposed
framework helps VectorNet improve by 262 cm regarding the Final Displacement
Error of the chosen optimal prediction on the nuScenes dataset; 3) for
multi-agent multi-modal trajectory forecasting systems, prediction uncertainty
is positively correlated with future stochasticity; and 4) the estimated CU
values are highly related to the interactive information among agents.Comment: arXiv admin note: text overlap with arXiv:2110.1394
Controllable Mind Visual Diffusion Model
Brain signal visualization has emerged as an active research area, serving as
a critical interface between the human visual system and computer vision
models. Although diffusion models have shown promise in analyzing functional
magnetic resonance imaging (fMRI) data, including reconstructing high-quality
images consistent with original visual stimuli, their accuracy in extracting
semantic and silhouette information from brain signals remains limited. In this
regard, we propose a novel approach, referred to as Controllable Mind Visual
Diffusion Model (CMVDM). CMVDM extracts semantic and silhouette information
from fMRI data using attribute alignment and assistant networks. Additionally,
a residual block is incorporated to capture information beyond semantic and
silhouette features. We then leverage a control model to fully exploit the
extracted information for image synthesis, resulting in generated images that
closely resemble the visual stimuli in terms of semantics and silhouette.
Through extensive experimentation, we demonstrate that CMVDM outperforms
existing state-of-the-art methods both qualitatively and quantitatively.Comment: 16 pages, 11 figure
IPDreamer: Appearance-Controllable 3D Object Generation with Image Prompts
Recent advances in text-to-3D generation have been remarkable, with methods
such as DreamFusion leveraging large-scale text-to-image diffusion-based models
to supervise 3D generation. These methods, including the variational score
distillation proposed by ProlificDreamer, enable the synthesis of detailed and
photorealistic textured meshes. However, the appearance of 3D objects generated
by these methods is often random and uncontrollable, posing a challenge in
achieving appearance-controllable 3D objects. To address this challenge, we
introduce IPDreamer, a novel approach that incorporates image prompts to
provide specific and comprehensive appearance information for 3D object
generation. Our results demonstrate that IPDreamer effectively generates
high-quality 3D objects that are consistent with both the provided text and
image prompts, demonstrating its promising capability in
appearance-controllable 3D object generation.Comment: 11 pages, 7 figure
Effect of mineral-based amendments on rice (Oryza sativa L.) growth and cadmium content in plant and Polluted soil
Agricultural soils can be contaminated by industrial activities such as mining and smelting. Contamination with cadmium (Cd) can significantly exceed average background values, which can lead to uptake by rice plant and even harm to humans through food chain. In Hunan province, southern China, rice (Oryza sativa L.) is the main cereal, and human exposure to metallic contaminants through rice pathway is of particular interest. Shortage of land for rice growing means that contaminated agricultural soil is still cultivated for rice in Hunan. In the present work, a field experiment was undertaken to remediate Cd-contaminated paddy soil with three mineral amendments, namely sepiolite, bone char, and a silicon-based product (normally used as fertilizer). Average Cd concentration in the paddy soil was 2.85 mg/kg, significantly exceeding Chinese soil quality standards of China. Cd content was 0.59 mg/kg in sepiolite, 0.28 mg/kg in bone char, and 0.44 mg/kg in silicon fertilizer, respectively. Distribution fractions of Cd in soil followed the order of exchangeable (FI) > organic matter-bound (FIII) > residual (FIV) > oxide-bound (FII) without treatment, while exchangeable (FI) > residual (FIV) > organic matter-bound (FIII) > oxide-bound (FII) after treatment. With addition of three amendments, soil pH values and rice growth such as plant height and ripening rate increased. Concentrations of Cd in the rice plant (straw, husk, and unpolished rice) decreased after treatment. However, among three amendments, only the bone char addition reduced Cd accumulation in the rice plant below the Chinese standard value (0.2 mg/kg) and in the husk to below the Chinese feed hygiene standard for food (0.5 mg/kg)
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