342 research outputs found
Joint Data and Feature Augmentation for Self-Supervised Representation Learning on Point Clouds
To deal with the exhausting annotations, self-supervised representation
learning from unlabeled point clouds has drawn much attention, especially
centered on augmentation-based contrastive methods. However, specific
augmentations hardly produce sufficient transferability to high-level tasks on
different datasets. Besides, augmentations on point clouds may also change
underlying semantics. To address the issues, we propose a simple but efficient
augmentation fusion contrastive learning framework to combine data
augmentations in Euclidean space and feature augmentations in feature space. In
particular, we propose a data augmentation method based on sampling and graph
generation. Meanwhile, we design a data augmentation network to enable a
correspondence of representations by maximizing consistency between augmented
graph pairs. We further design a feature augmentation network that encourages
the model to learn representations invariant to the perturbations using an
encoder perturbation. We comprehensively conduct extensive object
classification experiments and object part segmentation experiments to validate
the transferability of the proposed framework. Experimental results demonstrate
that the proposed framework is effective to learn the point cloud
representation in a self-supervised manner, and yields state-of-the-art results
in the community. The source code is publicly available at:
https://zhiyongsu.github.io/Project/AFSRL.html
Optimal precoding for a QoS optimization problem in two-user MISO-NOMA downlink
In this letter, based on the non-orthogonal multiple access (NOMA) concept, a quality-of-service optimization problem for two-user multiple-input-single-output broadcast systems is considered, given a pair of target interference levels. The minimal power and the optimal precoding vectors are obtained by considering its Lagrange dual problem and via Newton's iterative algorithm, respectively. Moreover, the closed-form expressions of the minimal transmission power for some special cases are also derived. One of these cases is termed quasi-degraded, which is the key point and will be discussed in detail in this letter. Our analysis further figures out that the proposed NOMA scheme can approach nearly the same performance as optimal dirty paper coding, as verified by computer simulations
An Optimization Perspective of the Superiority of NOMA Compared to Conventional OMA
While existing works about non-orthogonal multiple access (NOMA) have
indicated that NOMA can yield a significant performance gain over orthogonal
multiple access (OMA) with fixed resource allocation, it is not clear whether
such a performance gain will diminish when optimal resource
(Time/Frequency/Power) allocation is carried out. In this paper, the
performance comparison between NOMA and conventional OMA systems is
investigated, from an optimization point of view. Firstly, by using the idea of
power splitting, a closed-form expression for the optimum sum rate of NOMA
systems is derived. Then, with rigorous mathematical proofs, we reveal the fact
that NOMA can always outperform conventional OMA systems, even if both are
equipped with the optimal resource allocation policies. Finally, computer
simulations are conducted to validate the accuracy of the analytical results.Comment: 28 pages, 8 figures, submitted to IEEE Transactions on Signal
Processin
On the application of quasi-degradation to MISO-NOMA downlink
In this paper, the design of non-orthogonal multiple access (NOMA) in a multiple-input-single-output (MISO) downlink scenario is investigated. The impact of the recently developed concept, quasi-degradation, on NOMA downlink transmission is first studied. Then, a Hybrid NOMA (H-NOMA) precoding algorithm, based on this concept, is proposed. By exploiting the properties of H-NOMA precoding, a low-complexity sequential user pairing algorithm is consequently developed, to further improve the overall system performance. Both analytical and numerical results are provided to demonstrate the performance of the H-NOMA precoding through the average power consumption and outage probability, while conventional schemes, as dirty-paper coding and zero-forcing beamforming, are used as benchmarking
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations
Point cloud segmentation with scene-level annotations is a promising but
challenging task. Currently, the most popular way is to employ the class
activation map (CAM) to locate discriminative regions and then generate
point-level pseudo labels from scene-level annotations. However, these methods
always suffer from the point imbalance among categories, as well as the sparse
and incomplete supervision from CAM. In this paper, we propose a novel weighted
hypergraph convolutional network-based method, called WHCN, to confront the
challenges of learning point-wise labels from scene-level annotations. Firstly,
in order to simultaneously overcome the point imbalance among different
categories and reduce the model complexity, superpoints of a training point
cloud are generated by exploiting the geometrically homogeneous partition.
Then, a hypergraph is constructed based on the high-confidence superpoint-level
seeds which are converted from scene-level annotations. Secondly, the WHCN
takes the hypergraph as input and learns to predict high-precision point-level
pseudo labels by label propagation. Besides the backbone network consisting of
spectral hypergraph convolution blocks, a hyperedge attention module is learned
to adjust the weights of hyperedges in the WHCN. Finally, a segmentation
network is trained by these pseudo point cloud labels. We comprehensively
conduct experiments on the ScanNet and S3DIS segmentation datasets.
Experimental results demonstrate that the proposed WHCN is effective to predict
the point labels with scene annotations, and yields state-of-the-art results in
the community. The source code is available at
http://zhiyongsu.github.io/Project/WHCN.html
Fluoxetine Protects against Big Endothelin-1 Induced Anti-Apoptosis by Rescuing Kv1.5 Channels in Human Pulmonary Arterial Smooth Muscle Cells
∙ The authors have no financial conflicts of interest. © Copyright: Yonsei University College of Medicine 2012 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens
- …