Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract

Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic feature and prototype representation suffer from coarse segmentation granularity and train-set overfitting. In this work, we design Hierarchically Decoupled Matching Network (HDMNet) mining pixel-level support correlation based on the transformer architecture. The self-attention modules are used to assist in establishing hierarchical dense features, as a means to accomplish the cascade matching between query and support features. Moreover, we propose a matching module to reduce train-set overfitting and introduce correlation distillation leveraging semantic correspondence from coarse resolution to boost fine-grained segmentation. Our method performs decently in experiments. We achieve 50.0% mIoU on COCO dataset one-shot setting and 56.0% on five-shot segmentation, respectively. The code will be available on the project website. We hope our work can benefit broader industrial applications where novel classes with limited annotations are required to be decently identified.Comment: Accepted to CVPR 2023 VISION Workshop, Oral. The extended abstract of Hierarchical Dense Correlation Distillation for Few-Shot Segmentation. arXiv admin note: substantial text overlap with arXiv:2303.1465

SCMA Codebook Design Based on Decomposition of the Superposed Constellation for AWGN Channel

In this study, we propose a method named decomposition of the superposed constellation (DCSC) to design sparse code multiple access (SCMA) codebooks for the additive white Gaussian noise (AWGN) channel. We prove that the power of the user symbols (USs) is accurately determined by the power of the superposed constellation (SC). Thus, we select quadrature amplitude modulation (QAM) constellations as the SC and decompose the SC into several groups of USs with power diversity. The minimum Euclidean distance (MED) between superposed symbols (SS-MED) in the receiver is determined by the selected QAM and MED between the multi-dimensional codewords (CW-MED) is optimized by matching the symbols on different dimensions. We propose a simplified DCSC (S-DCSC) by modifying the factor graph and avoiding the transmission of USs with low power, which greatly reduces the complexity of the message passing algorithm (MPA). The simulations show that the SS-MEDs of DCSC and S-DCSC are larger than those in previous papers and the BER performance of the proposed codebooks is better than others

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models

A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models

Cellular localization of NLRP2 protein.

<p>Confocal microscopic images of oocytes and preimplantation embryos. Each sample was counterstained with DAPI to visualize DNA (blue). The original magnification was ×200.</p

Development of parthenogenetic embryos derived from EP control, control siRNA and <i>Nlrp2</i> siRNA-treated groups (20, 40 and 60 nM).

<p>(A) Parthenogenetic activation rate of electroporated oocytes. (B) Morphological appearance of parthenogenetic embryos after being cultured for 3.5 days. The original magnification was ×100. (C) Percentage of parthenogenetic embryos at different stages after being cultured for 3.5 days.</p

Subcellular localization of NLRP2 protein.

<p>(A) Subcellular localization of NLRP2 protein in immature mouse oocytes. Using an anti-NLRP2 antibody and ultrathin ovarian sections of 10-day-old mice, immunogold reactions were examined by transmission electron microscopy. Black spots with arrows are immunogold particles indicating the presence of NLRP2 protein. a, Oocytes and surrounding granulosa cells (×4,000). The positions of nucleus (i), cytoplasm (ii) and granulosa cells (iii) are indicated. b and c, Oocyte cytoplasm with immunogold particles (×50,000). d, Nucleus with immunogold particles (×50,000). e, Nuclear pore with immunogold particles nearby (×50,000). f, Control oocyte without immunogold particles in the absence of the primary antibody (×50,000). (B) Subcellular localization of NLRP2 protein in mouse granulosa cells. a, Granulosa cell (×15,000). The positions of the nucleus (i) and cytoplasm (ii). b and c, Cytoplasm with immunogold particles (×50,000). d, Nucleus with immunogold particles (×50,000). e, Nucleus and nuclear pore with immunogold particles (×50,000). f, Granulosa cell without immunogold particles in the absence of primary antibody (×50,000).</p

GV-stage oocyte maturation after electroporation with <i>Nlrp2</i> siRNA.

<p>(A) Oocyte maturation rate following electroporation (EP) of GV-stage oocytes in the presence or absence of control and <i>Nlrp2</i> siRNA. The numbers on top of each bar indicate the numbers of oocyte matured/numbers of oocytes electroporated. (B) The relative abundance of <i>Nlrp2</i> transcripts after electroporation with <i>Nlrp2</i> siRNA. The data have been normalized to untreated oocytes. Statistical comparisons were made using ANOVA and LSD tests (* p<0.05). (C) <i>Nlrp2</i>, <i>Nlrp4f</i>, <i>Nlrp5</i>, <i>Nlrp9c</i> and <i>Nlrp14</i> gene expression by qRT–PCR in mouse oocytes at 24 h after electroporation with <i>Nlrp2</i> siRNA (60 nM). Results were normalized to control siRNA (100 nM) group. * p<0.05. (D) Immunoblots of mouse oocytes at 24 h after electroporation in the presence or absence of control and <i>Nlrp2</i> siRNA.</p

Primer sequences for quantitative real-time PCR.

a<p>Annealing temperature.</p>b<p>Forward primer.</p>c<p>Reverse primer.</p