Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation

Few-shot image generation aims to train generative models using a small number of training images. When there are few images available for training (e.g. 10 images), Learning From Scratch (LFS) methods often generate images that closely resemble the training data while Transfer Learning (TL) methods try to improve performance by leveraging prior knowledge from GANs pre-trained on large-scale datasets. However, current TL methods may not allow for sufficient control over the degree of knowledge preservation from the source model, making them unsuitable for setups where the source and target domains are not closely related. To address this, we propose a novel pipeline called Peer is your Pillar (PIP), which combines a target few-shot dataset with a peer dataset to create a data-unbalanced conditional generation. Our approach includes a class embedding method that separates the class space from the latent space, and we use a direction loss based on pre-trained CLIP to improve image diversity. Experiments on various few-shot datasets demonstrate the advancement of the proposed PIP, especially reduces the training requirements of few-shot image generation.Comment: Under Revie

Relationship of SARS-CoV to other pathogenic RNA viruses explored by tetranucleotide usage profiling

BACKGROUND: The exact origin of the cause of the Severe Acute Respiratory Syndrome (SARS) is still an open question. The genomic sequence relationship of SARS-CoV with 30 different single-stranded RNA (ssRNA) viruses of various families was studied using two non-standard approaches. Both approaches began with the vectorial profiling of the tetra-nucleotide usage pattern V for each virus. In approach one, a distance measure of a vector V, based on correlation coefficient was devised to construct a relationship tree by the neighbor-joining algorithm. In approach two, a multivariate factor analysis was performed to derive the embedded tetra-nucleotide usage patterns. These patterns were subsequently used to classify the selected viruses. RESULTS: Both approaches yielded relationship outcomes that are consistent with the known virus classification. They also indicated that the genome of RNA viruses from the same family conform to a specific pattern of word usage. Based on the correlation of the overall tetra-nucleotide usage patterns, the Transmissible Gastroenteritis Virus (TGV) and the Feline CoronaVirus (FCoV) are closest to SARS-CoV. Surprisingly also, the RNA viruses that do not go through a DNA stage displayed a remarkable discrimination against the CpG and UpA di-nucleotide (z = -77.31, -52.48 respectively) and selection for UpG and CpA (z = 65.79,49.99 respectively). Potential factors influencing these biases are discussed. CONCLUSION: The study of genomic word usage is a powerful method to classify RNA viruses. The congruence of the relationship outcomes with the known classification indicates that there exist phylogenetic signals in the tetra-nucleotide usage patterns, that is most prominent in the replicase open reading frames

A Design of Focal-plane Compensation of Aviation Imaging Equipment Based on MS5534C

AbstractThis paper proposes an auto-compensation method for defocusing distance caused by temperature and pressure in aviation imaging equipment. As the host computer, the TMS320F2812 is the core controller and the digital pressure sensor MS5534C from Intersema Company is used as slave computer. The controller acquires the output of the temperature and the pressure from the sensor through MCBSP interface. By the change of temperature and pressure which results in defocusing distance, the software is adopted to compensate the defocusing distance and thereby keeps the stabilization of focal plane in aviation imaging equipment. The design proposal and the software flow is shown in the paper, furthermore the new system has simple interface, small size and real-time function. With many flight tests, the defocusing distance after the compensation of temperature and pressure is far less than the half focal depth of the optical system and it is fully satisfied with the requirements of imaging

A semi-analytical method for the dynamic analysis of cylindrical shells with arbitrary boundaries

The dynamic behavior of cylindrical shells with arbitrary boundaries is studied in this paper. Love's shell theory and Hamilton's principle are employed to derive the motion equations for cylindrical shells. A semi-analytical methodology, which incorporates Durbin's inverse Laplace transform, differential quadrature method and Fourier series expansion technique, is proposed to investigate this phenomenon. The use of the differential quadrature method provides a solution in terms of the axial direction whereas the use of Durbin's numerical inversion method generates a solution in the time domain. Comparison of calculated frequency parameters to that derived from the literature illustrates the effectiveness of the method. Specifically, convergence tests indicate that the present approach has a rapid convergence, the time-history response and the Navier's solution are in great agreement. Comparisons between time-history responses derived by two shell theories show that the results fit well with each other when the thickness-radius ratios are small enough. An analysis of the influences of boundaries on the time-history response of cylindrical shells indicates that the peak displacement is closely related to the degrees of freedom of boundaries. The influences of the length-radius ratios and the thickness-radius ratios on the peak displacement are further investigated

Accelerating Sparse DNNs Based on Tiled GEMM

Network pruning can reduce the computation cost of deep neural network (DNN) models. However, sparse models often produce randomly-distributed weights to maintain accuracy, leading to irregular computations. Consequently, unstructured sparse models cannot achieve meaningful speedup on commodity hardware built for dense matrix computations. Accelerators are usually modified or designed with structured sparsity-optimized architectures for exploiting sparsity. For example, the Ampere architecture introduces a sparse tensor core, which adopts the 2:4 sparsity pattern. We propose a pruning method that builds upon the insight that matrix multiplication generally breaks the large matrix into multiple smaller tiles for parallel execution. We present the tile-wise sparsity pattern, which maintains a structured sparsity pattern at the tile level for efficient execution but allows for irregular pruning at the global scale to maintain high accuracy. In addition, the tile-wise sparsity is implemented at the global memory level, and the 2:4 sparsity executes at the register level inside the sparse tensor core. We can combine these two patterns into a tile-vector-wise (TVW) sparsity pattern to explore more fine-grained sparsity and further accelerate the sparse DNN models. We evaluate the TVW on the GPU, achieving averages of $1.85\times$, $2.75\times$, and $22.18\times$ speedups over the dense model, block sparsity, and unstructured sparsity.Comment: Accepted by IEEE Transactions on Computers. arXiv admin note: substantial text overlap with arXiv:2008.1300

Combined analysis of mRNA expression of ERCC1, BAG-1, BRCA1, RRM1 and TUBB3 to predict prognosis in patients with non-small cell lung cancer who received adjuvant chemotherapy

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to investigate prognostic value of excision repair cross-complementing 1 (ERCC1), BCL2-associated athanogene (BAG-1), the breast and ovarian cancer susceptibility gene 1 (BRCA1), ribonucleotide reductase subunit M1 (RRM1) and class III β-tubulin (TUBB3) in patients with non-small cell lung cancer (NSCLC) who received platinum- based adjuvant chemotherapy.</p> <p>Methods</p> <p>Messenger RNA expressions of these genes were examined in 85 tumor tissues and 34 adjacent tissue samples using semi-quantitative RT-PCR. The expressions of these five genes were analyzed in relation to chemotherapy and progression-free survival (PFS) and overall survival (OS). Seventy-four patients were enrolled into chemotherapy.</p> <p>Results</p> <p>Patients with ERCC1 or BAG-1 negative expression had a significantly longer PFS (<it>P </it>= 0.001 and <it>P </it>= 0.001) and OS (<it>P </it>= 0.001 and <it>P </it>= 0.001) than those with positive expression. Patients with negative ERCC1 and BAG-1 expression benefited more from platinum regimen (<it>P </it>= 0.001 and <it>P </it>= 0.002). Patients with BRCA1 negative expression might have a longer OS (<it>P </it>= 0.052), but not PFS (<it>P </it>= 0.088) than those with BRCA1 positive expression. A significant relationship was observed between the mRNA expression of ERCC1 and BAG-1 (<it>P </it>= 0.042). In multivariate analysis, ERCC1 and BAG-1 were significantly favorable factors for PFS (<it>P </it>= 0.018 and <it>P </it>= 0.017) and OS (<it>P </it>= 0.027 and <it>P </it>= 0.022).</p> <p>Conclusions</p> <p>ERCC1 and BAG-1 are determinants of survival after surgical treatment of NSCLC, and its mRNA expression in tumor tissues could be used to predict the prognosis of NSCLC treated by platinum.</p

Characterization of the early fiber development gene, Ligon-lintless 1 (Li1), using microarray

AbstractCotton fiber length is a key factor in determining fiber quality in the textile industry throughout the world. Understanding the molecular basis of fiber elongation would allow for improvement of fiber length. Ligon-lintless 1 (Li1) is a monogenic dominant mutation that results in short fibers. This mutant provides an excellent model system to study the molecular mechanisms of cotton fiber elongation. Microarray technology and quantitative real time PCR (qRT-PCR) were used to evaluate differentially expressed genes (DEGs) in the Ligon-lintless 1 (Li1) mutant compared to the wild-type. Although the results showed only a few differentially expressed genes at −1, 3 and 7days post anthesis (DPA); at 5 DPA, there were 1915 DEGs, including 984 up-regulated genes and 931 down-regulated genes. The critical stage for early termination of Li1 fiber elongation was 5 DPA, as there were the most differentially expressed genes in this sample. The transcription factors and other proteins identified might contribute to understanding the molecular basis of early fiber elongation. Gene ontology analysis identified some key GO terms that impact the regulation of fiber development during early elongation. These results provide some fundamental information about the TFs that might provide new insight into understanding the molecular mechanisms governing cotton fiber development

Conserved transcription factor binding sites of cancer markers derived from primary lung adenocarcinoma microarrays

Gene transcription in a set of 49 human primary lung adenocarcinomas and 9 normal lung tissue samples was examined using Affymetrix GeneChip technology. A total of 3442 genes, called the set M AD, were found to be either up- or down-regulated by at least 2-fold between the two phenotypes. Genes assigned to a particular gene ontology term were found, in many cases, to be significantly unevenly distributed between the genes in and outside M AD. Terms that were overrepresented in M AD included functions directly implicated in the cancer cell metabolism. Based on their functional roles and expression profiles, genes in M AD were grouped into likely co-regulated gene sets. Highly conserved sequences in the 5 kb region upstream of the genes in these sets were identified with the motif discovery tool, MoDEL. Potential oncogenic transcription factors and their corresponding binding sites were identified in these conserved regions using the TRANSFAC 8.3 database. Several of the transcription factors identified in this study have been shown elsewhere to be involved in oncogenic processes. This study searched beyond phenotypic gene expression profiles in cancer cells, in order to identify the more important regulatory transcription factors that caused these aberrations in gene expressio