70 research outputs found
An Algebraic Characterization of Highly Connected 2n-Manifolds
All surfaces, up to homeomorphism, can be formed by gluing the edges of a polygon. This process is generalized into the idea of a (n,2n)-cell complex: forming a space by attaching a (2n-1)-sphere into a wedge sum of n-spheres. In this paper, we classify oriented, (n-1)-connected, compact and closed 2n-manifolds up to homotopy by treating them as (n,2n)-cell complexes. To simplify the calculation, we create a basis called the Hilton basis for the homotopy class of the attaching map of the (n,2n)-cell complex. At the end, we show that two attaching maps give the same, up to homotopy, manifold if and only if their homotopy classes, when written in a Hilton basis, differ only by a change-of-basis matrix that is in the image of a certain map, which we define explicitly in the paper
Analytic Extension and Conformal Mapping in the Dual and the Double Planes
Many theorems in the complex plane have analogues in the dual (x+jy, j2=0) and the double (x+ky, k2=1) planes. In this paper, we prove that Schwarz reflection principle holds in the dual and the double planes. We also show that in these two planes the domain of an analytic function can usually be extended analytically to a larger region. In addition, we find that a certain class of regions can be mapped conformally to the upper half plane, which is analogous to the Riemann mapping theorem
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement
Adversarial training (AT) is currently one of the most effective ways to
obtain the robustness of deep neural networks against adversarial attacks.
However, most AT methods suffer from robust overfitting, i.e., a significant
generalization gap in adversarial robustness between the training and testing
curves. In this paper, we first identify a connection between robust
overfitting and the excessive memorization of noisy labels in AT from a view of
gradient norm. As such label noise is mainly caused by a distribution mismatch
and improper label assignments, we are motivated to propose a label refinement
approach for AT. Specifically, our Self-Guided Label Refinement first
self-refines a more accurate and informative label distribution from
over-confident hard labels, and then it calibrates the training by dynamically
incorporating knowledge from self-distilled models into the current model and
thus requiring no external teachers. Empirical results demonstrate that our
method can simultaneously boost the standard accuracy and robust performance
across multiple benchmark datasets, attack types, and architectures. In
addition, we also provide a set of analyses from the perspectives of
information theory to dive into our method and suggest the importance of soft
labels for robust generalization.Comment: Accepted to CVPR 202
DXVNet-ViT-Huge (JFT) Multimode Classification Network Based on Vision Transformer
Aiming at the problem that traditional CNN network is not good at extracting global features of images, Based on DXVNet network, Conditional Random Fields (CRF) component and pre-trained ViT-Huge (Vision Transformer) are adopted in this paper Transformer model expands and builds a brand new DXVNet-ViT-Huge (JFT) network. CRF component can help the network learn the constraint conditions of each word corresponding prediction label, improve the D-GRU method based word label prediction errors, and improve the accuracy of sequence annotation. The Transformer architecture of the ViT (Huge) model can extract the global feature information of the image, while CNN is better at extracting the local features of the image. Therefore, the ViT (Huge) Huge pre-training model and CNN pre-training model adopt the multi-modal feature fusion technology. Two complementary image feature information is fused by Bi-GRU to improve the performance of network classification. The experimental results show that the newly constructed Dxvnet-Vit-Huge (JFT) model achieves good performance, and the F1 values in the two real public data sets are 6.03% and 7.11% higher than the original DXVNet model, respectively
Future growth pattern projections under shared socioeconomic pathways: a municipal city bottom-up aggregated study based on a localised scenario and population projections for China
Precise multi-scenario projections of future economic outputs based
on localised interpretations of global scenarios and major growth
drivers are important for understanding long-term economic
changes. However, few studies have focussed on localised interpretations, and many assume regional uniformity or use key parameters that are recursive or extrapolated by mathematical methods.
This study provides a more intuitive and robust economic framework for projecting regional economic growth based on a neoclassical economic model and shared socioeconomic pathways (SSPs)
scenarios. A non-uniform version of SSP2 (the middle-of-the-road
scenario) was developed, and more detailed population projections
for China were adopted using municipal-level data for 340 districts
and parameter settings based on China’s recent development. The
results show that China’s GDP will vary substantially across SSPs by
2050. Per capita GDP ranges from 19,300 USD under SSP3 (fragmentation) to 41,100 USD under SSP5 (conventional development).
Per capita GDP under SSP1 (sustainability) is slightly higher than
under SSP2, but lower on average than under SSP5. However, SSP1
is a better choice overall because environmental quality and equity
are higher. Per capita GDP growth will generally be higher in relatively low-income regions by 2050, and the upper-middle-income
provinces will become China’s new engine for economic growth
Deciphering Tumor Ecosystems at Super Resolution From Spatial Transcriptomics With Tesla
Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases
Transcriptome analysis of germ cell changes in male Chinese mitten crabs (Eriocheir sinensis) induced by rhizocephalan parasite, Polyascus gregaria
The parasitism by Polyascus gregaria on Eriocheir sinensis induces feminization of the appearance of male crabs, misleading fishermen to bring them to the breeding ponds as female crabs to cultivate broodstock selection. However, there are few studies on whether P. gregaria feminizes the male germ cells, resulting in a decline in the fecundity of male crabs. Therefore, this study aims to clarify the changes in gene expression levels of male crab testes after being parasitized by P. gregaria through transcriptome sequencing to evaluate the change in fecundity. We selected parasitized and healthy male crabs from a pond culture for comparison of gene expression in germ cells. The results showed that, compared with healthy male crabs, there were 104 genes with significantly different expressions, of which 79 were up-regulated and 25 were down-regulated. These genes are mainly focused on the cytoskeleton pathway in cell components and cellular protein complex assembly in biological processes. Several spermatogenesis-related genes, such as Kazal-type protease inhibitor, which inhibits gelatinolytic activities of sperm proteases, and juvenile hormone esterase 6, which degrades methyl farnesoate, were up-regulated; while the down-regulated expression of certain heat shock proteins may lead to spermatogenic dysfunction. In addition, some immune-related genes, such as double whey acidic protein domain-containing protein and serine proteinase inhibitor 3, were significantly up-regulated. These results indicated that P. gregaria changed the development process and cell structure of male host germ cells to inhibit sperm proliferation and maturation, while multiple immune pathways in the hosts were activated to resist P. gregaria invasion
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