41 research outputs found
Mesh-MLP: An all-MLP Architecture for Mesh Classification and Semantic Segmentation
With the rapid development of geometric deep learning techniques, many
mesh-based convolutional operators have been proposed to bridge irregular mesh
structures and popular backbone networks. In this paper, we show that while
convolutions are helpful, a simple architecture based exclusively on
multi-layer perceptrons (MLPs) is competent enough to deal with mesh
classification and semantic segmentation. Our new network architecture, named
Mesh-MLP, takes mesh vertices equipped with the heat kernel signature (HKS) and
dihedral angles as the input, replaces the convolution module of a ResNet with
Multi-layer Perceptron (MLP), and utilizes layer normalization (LN) to perform
the normalization of the layers. The all-MLP architecture operates in an
end-to-end fashion and does not include a pooling module. Extensive
experimental results on the mesh classification/segmentation tasks validate the
effectiveness of the all-MLP architecture.Comment: 8 pages, 6 figure
Neural-IMLS: Self-supervised Implicit Moving Least-Squares Network for Surface Reconstruction
Surface reconstruction is very challenging when the input point clouds,
particularly real scans, are noisy and lack normals. Observing that the
Multilayer Perceptron (MLP) and the implicit moving least-square function
(IMLS) provide a dual representation of the underlying surface, we introduce
Neural-IMLS, a novel approach that directly learns the noise-resistant signed
distance function (SDF) from unoriented raw point clouds in a self-supervised
fashion. We use the IMLS to regularize the distance values reported by the MLP
while using the MLP to regularize the normals of the data points for running
the IMLS. We also prove that at the convergence, our neural network, benefiting
from the mutual learning mechanism between the MLP and the IMLS, produces a
faithful SDF whose zero-level set approximates the underlying surface. We
conducted extensive experiments on various benchmarks, including synthetic
scans and real scans. The experimental results show that {\em Neural-IMLS} can
reconstruct faithful shapes on various benchmarks with noise and missing parts.
The source code can be found at~\url{https://github.com/bearprin/Neural-IMLS}
Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation
Mapping two modalities, speech and text, into a shared representation space,
is a research topic of using text-only data to improve end-to-end automatic
speech recognition (ASR) performance in new domains. However, the length of
speech representation and text representation is inconsistent. Although the
previous method up-samples the text representation to align with acoustic
modality, it may not match the expected actual duration. In this paper, we
proposed novel representations match strategy through down-sampling acoustic
representation to align with text modality. By introducing a continuous
integrate-and-fire (CIF) module generating acoustic representations consistent
with token length, our ASR model can learn unified representations from both
modalities better, allowing for domain adaptation using text-only data of the
target domain. Experiment results of new domain data demonstrate the
effectiveness of the proposed method.Comment: Accepted by INTERSPEECH 2023. arXiv admin note: text overlap with
arXiv:2309.0143
Biological Bone Micro Grinding Temperature Field under Nanoparticle Jet Mist Cooling
Clinical neurosurgeons used micro grinding to remove bone tissues, and drip irrigation-type normal saline (NS) is used with low cooling efficiency. Osteonecrosis and irreversible thermal neural injury caused by excessively high grinding temperature are bottleneck problems in neurosurgery and have severely restricted the application of micro grinding in surgical procedures. Therefore, a nanoparticle jet mist cooling (NJMC) bio-bone micro grinding process is put forward in this chapter. The nanofluid convective heat transfer mechanism in the micro grinding zone is investigated, and heat transfer enhancement mechanism of solid nanoparticles and heat distribution mechanism in the micro grinding zone are revealed. On this basis, a temperature field model of NJMC bio-bone micro grinding is established. An experimental platform of NJMC bio-bone micro grinding is constructed, and bone micro grinding force and temperatures at different measuring points on the bone surface are measured. The results indicated that the model error of temperature field is 6.7%, theoretical analysis basically accorded with experimental results, thus certifying the correctness of the dynamic temperature field in NJMC bio-bone micro grinding
Intracellular TRPA1 mediates Ca2+ release from lysosomes in dorsal root ganglion neurons
Assessing changes in wind erosion climatic erosivity in China’s dryland region during 1961–2012
Roles of Soybean Plasma Membrane Intrinsic Protein GmPIP2;9 in Drought Tolerance and Seed Development
A Study on Impact Force Detection Method Based on Piezoelectric Sensing
Impact force refers to a transient phenomenon with a very short-acting time, but a large impulse. Therefore, the detection of impact vibration is critical for the reliability, stability, and overall life of mechanical parts. Accordingly, this paper proposes a method to indirectly characterize the impact force by using an impact stress wave. The LS-DYNA software is utilized to establish the model of a ball impacting the steel plate, and the impact force of the ball and the impact response of the detection point are obtained through explicit dynamic finite element analysis. In addition, on this basis, a correspondence between the impact force and the impact response is established, and finally, an experimental platform for impact force detection is built for experimental testing. The results obtained by the finite element method are in good agreement with the experimental measurement results, and it can be inferred that the detected piezoelectric signal can be used to characterize the impact force. The method proposed herein can guide the impact resistance design and safety assessment of structures in actual engineering applications