SLIC: Self-Conditioned Adaptive Transform with Large-Scale Receptive Fields for Learned Image Compression

Learned image compression has achieved remarkable performance. Transform, plays an important role in boosting the RD performance. Analysis transform converts the input image to a compact latent representation. The more compact the latent representation is, the fewer bits we need to compress it. When designing better transform, some previous works adopt Swin-Transformer. The success of the Swin-Transformer in image compression can be attributed to the dynamic weights and large receptive field.However,the LayerNorm adopted in transformers is not suitable for image compression.We find CNN-based modules can also be dynamic and have large receptive-fields. The CNN-based modules can also work with GDN/IGDN. To make the CNN-based modules dynamic, we generate the weights of kernels conditioned on the input feature. We scale up the size of each kernel for larger receptive fields. To reduce complexity, we make the CNN-module channel-wise connected. We call this module Dynamic Depth-wise convolution. We replace the self-attention module with the proposed Dynamic Depth-wise convolution, replace the embedding layer with a depth-wise residual bottleneck for non-linearity and replace the FFN layer with an inverted residual bottleneck for more interactions in the spatial domain. The interactions among channels of dynamic depth-wise convolution are limited. We design the other block, which replaces the dynamic depth-wise convolution with channel attention. We equip the proposed modules in the analysis and synthesis transform and receive a more compact latent representation and propose the learned image compression model SLIC, meaning Self-Conditioned Adaptive Transform with Large-Scale Receptive Fields for Learned Image Compression Learned Image Compression. Thanks to the proposed transform modules, our proposed SLIC achieves 6.35% BD-rate reduction over VVC when measured in PSNR on Kodak dataset.Comment: Submitted to TCSV

MLIC: Multi-Reference Entropy Model for Learned Image Compression

Recently, learned image compression has achieved remarkable performance. The entropy model, which estimates the distribution of the latent representation, plays a crucial role in boosting rate-distortion performance. However, most entropy models only capture correlations in one dimension, while the latent representation contain channel-wise, local spatial, and global spatial correlations. To tackle this issue, we propose the Multi-Reference Entropy Model (MEM) and the advanced version, MEM$^+$. These models capture the different types of correlations present in latent representation. Specifically, We first divide the latent representation into slices. When decoding the current slice, we use previously decoded slices as context and employ the attention map of the previously decoded slice to predict global correlations in the current slice. To capture local contexts, we introduce two enhanced checkerboard context capturing techniques that avoids performance degradation. Based on MEM and MEM$^+$, we propose image compression models MLIC and MLIC$^+$. Extensive experimental evaluations demonstrate that our MLIC and MLIC+ models achieve state-of-the-art performance, reducing BD-rate by $8.05\%$ and $11.39\%$ on the Kodak dataset compared to VTM-17.0 when measured in PSNR.Comment: Fixed some typos and re-organized the pape

Variability of fibre quality on Chinese Alashan Left Banner White Cashmere goat

The heritability and the phenotypic and genetic correlations of down weight (DW), down fibre diameter (DFD), and coefficient of variation of the down fibre diameter (CVDFD) of Chinese Alashan Left Banner White Cashmere goat were estimated on 1375 one-year-old animals, born in 2009, 2011 and 2013 and bred at the Station for Livestock Improvement of Alashan (Left Banner, Inner Mongolia, P.R. China). For all traits, significant effects were for sex, cohort and sex–cohort interaction (p < .001). The heritability for DFD and CVDFD was high, 0.41 ± 0.08 and 0.52 ± 0.06, respectively. Heritability for the DW was low (0.12 ± 0.03). Phenotypic correlation calculated by Pearson's coefficient showed that DFD is positively correlated with both CVDFD (0.29 ± 0.07) and DW (0.20 ± 0.05). The phenotypic correlation between CVDFD and DW was negative (−0.11 ± 0.06). The genetic correlations between DW and CVDFD and between DFD and CVDFD were both high and positive (0.63 ± 0.16 and 0.39 ± 0.1, respectively) while the DW showed a negative genetic correlation with DFD (−0.27 ± 0.2). Our results suggest that the selection for reducing DFD and its CVDFD is possible and a genetic progress can be achieved quickly in the Chinese Alashan Left Banner White Cashmere goat

Self-appearance-aided Differential Evolution for Motion Transfer

Image animation transfers the motion of a driving video to a static object in a source image, while keeping the source identity unchanged. Great progress has been made in unsupervised motion transfer recently, where no labelled data or ground truth domain priors are needed. However, current unsupervised approaches still struggle when there are large motion or viewpoint discrepancies between the source and driving images. In this paper, we introduce three measures that we found to be effective for overcoming such large viewpoint changes. Firstly, to achieve more fine-grained motion deformation fields, we propose to apply Neural-ODEs for parametrizing the evolution dynamics of the motion transfer from source to driving. Secondly, to handle occlusions caused by large viewpoint and motion changes, we take advantage of the appearance flow obtained from the source image itself ("self-appearance"), which essentially "borrows" similar structures from other regions of an image to inpaint missing regions. Finally, our framework is also able to leverage the information from additional reference views which help to drive the source identity in spite of varying motion state. Extensive experiments demonstrate that our approach outperforms the state-of-the-arts by a significant margin (~40%), across six benchmarks varying from human faces, human bodies to robots and cartoon characters. Model generality analysis indicates that our approach generalises the best across different object categories as well.Comment: 10 pages, 6 figure

New Reassortant H5N6 Highly Pathogenic Avian Influenza Viruses in Southern China, 2014

New reassortant H5N6 highly pathogenic avian influenza viruses were isolated from apparently healthy domestic ducks in Southern China in 2014. Our results show that the viruses grew efficiently in eggs and replicated systemically in chickens. They were completely lethal in chicken (100% mortality), and the mean death time (MDT) was 6 to 7 days post-inoculation (DPI). The viruses could transmit in chickens by naïve contact. BLAST analysis revealed that their HA gene was most closely related to A/wild duck/Shangdong/628/2011 (H5N1), and their NA genes were most closely related to A/swine/Guangdong/K6/2010 (H6N6). The other genes had the highest identity with A/wild duck/Fujian/1/2011(H5N1). The results of phylogenetic analysis showed that their HA genes clustered into clade 2.3.4.4 of the H5N1 viruses and all genes derived from H5 were Mix-like or H6-like viruses. Thus, the new H5N6 viruses were reassortanted of H5N1 and H6N6 virus. Therefore, the circulation of the new H5N6 avian influenza viruses may become a threat to poultry and human health

A Unified Model for Tracking and Image-Video Detection Has More Power

Objection detection (OD) has been one of the most fundamental tasks in computer vision. Recent developments in deep learning have pushed the performance of image OD to new heights by learning-based, data-driven approaches. On the other hand, video OD remains less explored, mostly due to much more expensive data annotation needs. At the same time, multi-object tracking (MOT) which requires reasoning about track identities and spatio-temporal trajectories, shares similar spirits with video OD. However, most MOT datasets are class-specific (e.g., person-annotated only), which constrains a model's flexibility to perform tracking on other objects. We propose TrIVD (Tracking and Image-Video Detection), the first framework that unifies image OD, video OD, and MOT within one end-to-end model. To handle the discrepancies and semantic overlaps across datasets, TrIVD formulates detection/tracking as grounding and reasons about object categories via visual-text alignments. The unified formulation enables cross-dataset, multi-task training, and thus equips TrIVD with the ability to leverage frame-level features, video-level spatio-temporal relations, as well as track identity associations. With such joint training, we can now extend the knowledge from OD data, that comes with much richer object category annotations, to MOT and achieve zero-shot tracking capability. Experiments demonstrate that TrIVD achieves state-of-the-art performances across all image/video OD and MOT tasks.Comment: (13 pages, 4 figures

Ubiquitin-Proteasome-Collagen (CUP) Pathway in Preterm Premature Rupture of Fetal Membranes

Spontaneous preterm birth (sPTB) occurs before 37 gestational weeks, with preterm premature rupture of the membranes (PPROM) and spontaneous preterm labor (sPTL) as the predominant adverse outcomes. Previously, we identified altered expression of long non-coding RNAs (lncRNAs) and message RNAs (mRNAs) related to the ubiquitin proteasome system (UPS) in human placentas following pregnancy loss and PTB. We therefore hypothesized that similar mechanisms might underlie PPROM. In the current study, nine pairs of ubiquitin-proteasome-collagen (CUP) pathway–related mRNAs and associated lncRNAs were found to be differentially expressed in PPROM and sPTL. Pathway analysis showed that the functions of their protein products were inter-connected by ring finger protein. Twenty variants including five mutations were identified in CUP-related genes in sPTL samples. Copy number variations were found in COL19A1, COL28A1, COL5A1, and UBAP2 of sPTL samples. The results reinforced our previous findings and indicated the association of the CUP pathway with the development of sPTL and PPROM. This association was due not only to the genetic variation, but also to the epigenetic regulatory function of lncRNAs. Furthermore, the findings suggested that the loss of collagen content in PPROM could result from degradation and/or suppressed expression of collagens

BrLAS, a GRAS Transcription Factor From Brassica rapa, Is Involved in Drought Stress Tolerance in Transgenic Arabidopsis

GRAS proteins belong to a plant-specific transcription factor family and play roles in diverse physiological processes and environmental signals. In this study, we identified and characterized a GRAS transcription factor gene in Brassica rapa, BrLAS, an ortholog of Arabidopsis AtLAS. BrLAS was primarily expressed in the roots and axillary meristems, and localized exclusively in the nucleus of B. rapa protoplast cells. qRT-PCR analysis indicated that BrLAS was upregulated by exogenous abscisic acid (ABA) and abiotic stress treatment [polyethylene glycol (PEG), NaCl, and H2O2]. BrLAS-overexpressing Arabidopsis plants exhibited pleiotropic characteristics, including morphological changes, delayed bolting and flowering time, reduced fertility and delayed senescence. Transgenic plants also displayed significantly enhanced drought resistance with decreased accumulation of ROS and increased antioxidant enzyme activity under drought treatment compared with the wild-type. Increased sensitivity to exogenous ABA was also observed in the transgenic plants. qRT-PCR analysis further showed that expression of several genes involved in stress responses and associated with leaf senescence were also modified. These findings suggest that BrLAS encodes a stress-responsive GRASs transcription factor that positively regulates drought stress tolerance, suggesting a role in breeding programs aimed at improving drought tolerance in plants

BrRLP48, Encoding a Receptor-Like Protein, Involved in Downy Mildew Resistance in Brassica rapa

Downy mildew, caused by Hyaloperonospora parasitica, is a major disease of Brassica rapa that causes large economic losses in many B. rapa-growing regions of the world. The genotype used in this study was based on a double haploid population derived from a cross between the Chinese cabbage line BY and a European turnip line MM, susceptible and resistant to downy mildew, respectively. We initially located a locus Br-DM04 for downy mildew resistance in a region about 2.7 Mb on chromosome A04, which accounts for 22.3% of the phenotypic variation. Using a large F2 mapping population (1156 individuals) we further mapped Br-DM04 within a 160 kb region, containing 17 genes encoding proteins. Based on sequence annotations for these genes, four candidate genes related to disease resistance, BrLRR1, BrLRR2, BrRLP47, and BrRLP48 were identified. Overexpression of both BrRLP47 and BrRLP48 using a transient expression system significantly enhanced the downy mildew resistance of the susceptible line BY. But only the leaves infiltrated with RNAi construct of BrRLP48 could significantly reduce the disease resistance in resistant line MM. Furthermore, promoter sequence analysis showed that one salicylic acid (SA) and two jasmonic acid-responsive transcript elements were found in BrRLP48 from the resistant line, but not in the susceptible one. Real-time PCR analysis showed that the expression level of BrRLP48 was significantly induced by inoculation with downy mildew or SA treatment in the resistant line MM. Based on these findings, we concluded that BrRLP48 was involved in disease resistant response and the disease-inducible expression of BrRLP48 contributed to the downy mildew resistance. These findings led to a new understanding of the mechanisms of resistance and lay the foundation for marker-assisted selection to improve downy mildew resistance in Brassica rapa