Towards a System Theoretic Approach to Wireless Network Capacity in Finite Time and Space

In asymptotic regimes, both in time and space (network size), the derivation of network capacity results is grossly simplified by brushing aside queueing behavior in non-Jackson networks. This simplifying double-limit model, however, lends itself to conservative numerical results in finite regimes. To properly account for queueing behavior beyond a simple calculus based on average rates, we advocate a system theoretic methodology for the capacity problem in finite time and space regimes. This methodology also accounts for spatial correlations arising in networks with CSMA/CA scheduling and it delivers rigorous closed-form capacity results in terms of probability distributions. Unlike numerous existing asymptotic results, subject to anecdotal practical concerns, our transient one can be used in practical settings: for example, to compute the time scales at which multi-hop routing is more advantageous than single-hop routing

中国の為替制度と人民元の国際化に関する研究

早大学位記番号:新6728早稲田大

Why torus-unstable solar filaments experience failed eruption?

To investigate the factors that control the success and/or failure of solar eruptions, we study the magnetic field and 3-Dimensional (3D) configuration of 16 filament eruptions during 2010 July - 2013 February. All these events, i.e., erupted but failed to be ejected to become a coronal mass ejection (CME), are failed eruptions with the filament maximum height exceeding $100 Mm$. The magnetic field of filament source regions is approximated by a potential field extrapolation method. The filament 3D configuration is reconstructed from three vantage points by the observations of STEREO Ahead/Behind and SDO spacecraft. We calculate the decay index at the apex of these failed filaments and find that in 7 cases, their apex decay indexes exceed the theoretical threshold ($n_{crit} = 1.5$) of the torus instability. We further determine the orientation change or rotation angle of each filament top during the eruption. Finally, the distribution of these events in the parameter space of rotation angle versus decay index is established. Four distinct regimes in the parameter space are empirically identified. We find that, all the torus-unstable cases (decay index $n > 1.5$), have a large rotation angles ranging from $50^\circ - 130^\circ$. The possible mechanisms leading to the rotation and failed eruption are discussed. These results imply that, besides the torus instability, the rotation motion during the eruption may also play a significant role in solar eruptions

Comprehensive analysis reveals signal and molecular mechanism of mitochondrial energy metabolism pathway in pancreatic cancer

Pancreatic cancer (PAAD) is one of the most malignant tumors with the worst prognosis. The abnormalities in the mitochondrial energy metabolism pathway are intimately correlated with the occurrence and progression of cancer. For the diagnosis and treatment of pancreatic cancer, abnormal genes in the mitochondrial energy metabolism system may offer new targets and biomarkers. In this study, we compared the dysregulated mitochondrial energy metabolism-associated pathways in PAAD based on pancreatic cancer samples in the Cancer Genome Atlas (TCGA) database and normal pancreas samples from the Genotype Tissue Expression project (GTEx) database. Then identified 32 core genes of mitochondrial energy metabolism pathway-related genes (MMRG) were based on the gene set enrichment analysis (GSEA). We found most of these genes were altered among different clinical characteristic groups, and showed significant prognostic value and association with immune infiltration, suggesting critical roles of MMRG involve tumor genesis of PAAD. Therefore, we constructed a four-gene (LDHA, ALDH3B1, ALDH3A1, and ADH6) prognostic biomarker after eliminating redundant factors, and confirming its efficiency and independence. Further analysis indicated the potential therapeutic compounds based on the mitochondrial energy metabolism-associated prognostic biomarker. All of the above analyses dissected the critical role of mitochondrial energy metabolism signaling in pancreatic cancer and gave a better understanding of the clinical intervention of PAAD

ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation

Breast cancer is one of the common cancers that endanger the health of women globally. Accurate target lesion segmentation is essential for early clinical intervention and postoperative follow-up. Recently, many convolutional neural networks (CNNs) have been proposed to segment breast tumors from ultrasound images. However, the complex ultrasound pattern and the variable tumor shape and size bring challenges to the accurate segmentation of the breast lesion. Motivated by the selective kernel convolution, we introduce an enhanced selective kernel convolution for breast tumor segmentation, which integrates multiple feature map region representations and adaptively recalibrates the weights of these feature map regions from the channel and spatial dimensions. This region recalibration strategy enables the network to focus more on high-contributing region features and mitigate the perturbation of less useful regions. Finally, the enhanced selective kernel convolution is integrated into U-net with deep supervision constraints to adaptively capture the robust representation of breast tumors. Extensive experiments with twelve state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets demonstrate that our method has a more competitive segmentation performance in breast ultrasound images.Comment: 12 pages, 8 figure