A pathway-based mean-field model for E. coli chemotaxis: Mathematical derivation and Keller-Segel limit

A pathway-based mean-field theory (PBMFT) was recently proposed for E. coli chemotaxis in [G. Si, T. Wu, Q. Quyang and Y. Tu, Phys. Rev. Lett., 109 (2012), 048101]. In this paper, we derived a new moment system of PBMFT by using the moment closure technique in kinetic theory under the assumption that the methylation level is locally concentrated. The new system is hyperbolic with linear convection terms. Under certain assumptions, the new system can recover the original model. Especially the assumption on the methylation difference made there can be understood explicitly in this new moment system. We obtain the Keller-Segel limit by taking into account the different physical time scales of tumbling, adaptation and the experimental observations. We also present numerical evidence to show the quantitative agreement of the moment system with the individual based E. coli chemotaxis simulator.Comment: 21 pages, 3 figure

Retrieval Oriented Masking Pre-training Language Model for Dense Passage Retrieval

Pre-trained language model (PTM) has been shown to yield powerful text representations for dense passage retrieval task. The Masked Language Modeling (MLM) is a major sub-task of the pre-training process. However, we found that the conventional random masking strategy tend to select a large number of tokens that have limited effect on the passage retrieval task (e,g. stop-words and punctuation). By noticing the term importance weight can provide valuable information for passage retrieval, we hereby propose alternative retrieval oriented masking (dubbed as ROM) strategy where more important tokens will have a higher probability of being masked out, to capture this straightforward yet essential information to facilitate the language model pre-training process. Notably, the proposed new token masking method will not change the architecture and learning objective of original PTM. Our experiments verify that the proposed ROM enables term importance information to help language model pre-training thus achieving better performance on multiple passage retrieval benchmarks.Comment: Search LM part of the "AliceMind SLM + HLAR" method in MS MARCO Passage Ranking Leaderboard Submissio

Research on Power Grid Resilience and Power Supply Restoration during Disasters-A Review

Electric power system plays an indispensable role in modern society, which supplies the energy to residential, commercial, and industrial consumers. However, the high-impact and low-probability natural disasters (i.e., windstorm, typhoon, and flood) come more frequent because of the climate change in the recent years, which may sequentially cause devastating damages to the infrastructure of power systems. The aim of this paper is mainly to explore and review the resilience of power grid system during the disaster and the power supply management strategies to recover the power grid. Firstly, the category of natural disasters and different influences on power grid are discussed. Then, the definition of power grid resilience is explored and the supply management strategies copying with disasters are introduced, such as microgrids and distributed generation systems. Specially, the electric vehicles (EVs) equipped with large-capacity battery pack in the transportation network can also be considered as the distributed power sources with mobility. Thus, the conceptual frameworks of integrating large-scale EVs into the power grid to fasten restoration of the power systems in the pre-disaster/post-disaster are emphatically investigated in this paper. Finally, the opportunities and challenges in further research on employing EVs for emergency power supply in the extreme weather events are also discussed

CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by jointly training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Local Feature Refinement Module (LFRM) to enhance the local facial structure information. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Comprehensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly.Comment: 12 pages, 10 figures, 8 table

Investigation of Wood Impact Properties Using Fractal Dimension Analysis

Fractal analysis is a research tool recently used to model various processes. However, this analysis has not been used for determining impact properties of wood. In this study, the transverse and longitudinal impact ductility of five species, ie white pine, poplar, pine, birch, and basswood, was experimentally determined. Based on the grid-cover method, photographs were taken of the fracture surfaces and edited by image graying using Photoshop CS5 (Adobe Systems Inc.). The yardstick δ was determined by adjusting the distance between the grid lines. The slope K of the regression equation of Log(1/δi) vs Log(N[δi]) was the fractal dimension DL of the fracture profile curve. Fractal dimension allows us to measure the complexity of fracture profiles after the specimens were broken by impacts. The results indicate that the average fractal dimension values were 2.023-2.075 on the fractures from transverse and longitudinal impacts. The longitudinal impact ductility was greater than the transverse for all tested species. The transverse and longitudinal impact ductility was linearly related to the fracture fractal dimension

STAT3 Partly Inhibits Cell Proliferation via Direct Negative Regulation of FST Gene Expression

Follistatin (FST) is a secretory glycoprotein and belongs to the TGF-β superfamily. Previously, we found that two single nucleotide polymorphisms (SNPs) of sheep FST gene were significantly associated with wool quality traits in Chinese Merino sheep (Junken type), indicating that FST is involved in the regulation of hair follicle development and hair trait formation. The transcription regulation of human and mouse FST genes has been widely investigated, and many transcription factors have been identified to regulate FST gene. However, to date, the transcriptional regulation of sheep FST is largely unknown. In the present study, genome walking was used to close the genomic gap upstream of the sheep genomic FST gene and to obtain the FST gene promoter sequence. Transcription factor binding site analysis showed sheep FST promoter region contained a conserved putative binding site for signal transducer and activator of transcription 3 (STAT3), located at nucleotides −423 to −416 relative to the first nucleotide (A, +1) of the initiation codon (ATG) of sheep FST gene. The dual-luciferase reporter assay demonstrated that STAT3 inhibited the FST promoter activity and that the mutation of the putative STAT3 binding site attenuated the inhibitory effect of STAT3 on the FST promoter activity. Additionally, chromatin immunoprecipitation assay (ChIP) exhibited that STAT3 is directly bound to the FST promoter. Cell proliferation assay displayed that FST and STAT3 played opposite roles in cell proliferation. Overexpression of sheep FST significantly promoted the proliferation of sheep fetal fibroblasts (SFFs) and human keratinocyte (HaCaT) cells, and overexpression of sheep STAT3 displayed opposite results, which was accompanied by a significantly reduced expression of FST gene (P < 0.05). Taken together, STAT3 directly negatively regulates sheep FST gene and depresses cell proliferation. Our findings may contribute to understanding molecular mechanisms that underlie hair follicle development and morphogenesis