EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention

Thanks to the advancement of deep learning technology, vision transformer has demonstrated competitive performance in various computer vision tasks. Unfortunately, vision transformer still faces some challenges such as high computational complexity and absence of desirable inductive bias. To alleviate these problems, a novel Bi-Fovea Self-Attention (BFSA) is proposed, inspired by the physiological structure and characteristics of bi-fovea vision in eagle eyes. This BFSA can simulate the shallow fovea and deep fovea functions of eagle vision, enable the network to extract feature representations of targets from coarse to fine, facilitate the interaction of multi-scale feature representations. Additionally, a Bionic Eagle Vision (BEV) block based on BFSA is designed in this study. It combines the advantages of CNNs and Vision Transformers to enhance the ability of global and local feature representations of networks. Furthermore, a unified and efficient general pyramid backbone network family is developed by stacking the BEV blocks in this study, called Eagle Vision Transformers (EViTs). Experimental results on various computer vision tasks including image classification, object detection, instance segmentation and other transfer learning tasks show that the proposed EViTs perform effectively by comparing with the baselines under same model size and exhibit higher speed on graphics processing unit than other models. Code is available at https://github.com/nkusyl/EViT.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Strain and Damage Self-Sensing of Basalt Fiber Reinforced Polymer Laminates Fabricated with Carbon Nanofibers/Epoxy Composites Under Tension

This study investigated the strain and damage self-sensing capabilities of basalt fiber reinforced polymer (BFRP) laminates fabricated with carbon nanofibers (CNFs)/epoxy composites subjected to tensile loadings. The conduction mechanisms based on the tunnel conduction and percolation conduction theories as well as the damage evolution were also explored. A compensation circuit with a half-bridge configuration was proposed. The results indicated the resistivity of the CNFs/BFRP laminates and CNFs/epoxy composites exhibited similar change rule, indicating that the conductive networks of CNFs/BFRP laminates were governed by CNFs/epoxy composites. With the increase of strain under monotonic tensile loading, the electrical resistance response could be classified into three stages corresponding to different damage modes. This confirmed CNFs/BFRP laminates have excellent self-sensing abilities to monitor their internal damages. Moreover, stable and repeatable strain self-sensing capacity of the CNFs/BFRP laminates was verified under cyclic tensile loading because the electrical resistance varied synchronously with the applied strain

Properties and Mechanisms of Self-Sensing Carbon Nanofibers/Epoxy Composites for Structural Health Monitoring

In this paper, carbon nanofibers (CNFs) with high aspect ratio were dispersed into epoxy matrix via mechanical stirring and ultrasonic treatment to fabricate self-sensing CNFs/epoxy composites. The mechanical, electrical and piezoresistive properties of the nanocomposites filled with different contents of CNFs were investigated. Based on the tunneling conduction and percolation conduction theories, the mechanisms of piezoresistive property of the nanocomposites were also explored. The experimental results show that adding CNFs can effectively enhance the compressive strengths and elastic moduli of the composites. The percolation threshold of the CNFs/epoxy composites is 0.186 vol% according to the modified General Effective Media Equation. Moreover, the stable and sensitive piezoresistive response of CNFs/epoxy composites was observed under monotonic and cyclic loadings. It can be demonstrated that adding CNFs into epoxy-based composites provides an innovative means of self-sensing, and the high sensitivity and stable piezoresistivity endow the CNFs/epoxy composites with considerable potentials as efficient compressive strain sensors for structural health monitoring of civil infrastructures

GC-MS analysis of essential oil from Anethum graveolens L (dill) seeds extracted by supercritical carbon dioxide

Purpose: To conduct gas chromatography-mass spectrometric (GC-MS) analysis of the chemical compositions of dill seed essential oil (DSEO) obtained by supercritical CO2. Methods: The impact on extraction yield were examined by single factor test, the particle size of dill seed, extraction temperature, time, pressure, as well as CO2 flux. The best extraction conditions were obtained by an orthogonal test. The chemical configurations of essential oil were examined by GC-MS analysis. Results: The optimal extraction conditions included an extraction time of 120 min, particle size of 60 mesh, CO2 flow of 25 L/h, temperature of 40oC, and pressure of 20 MPa. Under these conditions, the yield of essential oil was 6.7 %. Out of 38 recognized compounds, the main ones were D-carvone (40.36 %), D-limonene (19.31 %), apiol (17.50 %), α-pinene (6.43 %), 9-octadecenoic acid (9.00 %) as well as 9,12-octadecadienoic acid (2.44 %). Conclusion: A total of 38 constituents of the essential oil obtained by supercritical CO2 were identified. The findings may provide a theoretical basis for comprehensive utilization of dill seed essential oil (DSEO) from China

Self-distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach

Text recognition methods are gaining rapid development. Some advanced techniques, e.g., powerful modules, language models, and un- and semi-supervised learning schemes, consecutively push the performance on public benchmarks forward. However, the problem of how to better optimize a text recognition model from the perspective of loss functions is largely overlooked. CTC-based methods, widely used in practice due to their good balance between performance and inference speed, still grapple with accuracy degradation. This is because CTC loss emphasizes the optimization of the entire sequence target while neglecting to learn individual characters. We propose a self-distillation scheme for CTC-based model to address this issue. It incorporates a framewise regularization term in CTC loss to emphasize individual supervision, and leverages the maximizing-a-posteriori of latent alignment to solve the inconsistency problem that arises in distillation between CTC-based models. We refer to the regularized CTC loss as Distillation Connectionist Temporal Classification (DCTC) loss. DCTC loss is module-free, requiring no extra parameters, longer inference lag, or additional training data or phases. Extensive experiments on public benchmarks demonstrate that DCTC can boost text recognition model accuracy by up to 2.6%, without any of these drawbacks.Comment: Ziyin Zhang and Ning Lu are co-first author

The fast light of CsI(Na) crystals

The responds of different common alkali halide crystals to alpha-rays and gamma-rays are tested in our research. It is found that only CsI(Na) crystals have significantly different waveforms between alpha and gamma scintillations, while others have not this phenomena. It is suggested that the fast light of CsI(Na) crystals arises from the recombination of free electrons with self-trapped holes of the host crystal CsI. Self-absorption limits the emission of fast light of CsI(Tl) and NaI(Tl) crystals.Comment: 5 pages, 11 figures Submit to Chinese Physics

Population genomic analysis reveals that homoploid hybrid speciation can be a lengthy process

This work was supported by grants from National key research and development program (2017YFC0505203), National Natural Science Foundation of China (grant numbers 31590821, 31670665, 91731301), National Key Project for Basic Research (2014CB954100), CAS “Light of West China” Program and Graduate Student’s Research and Innovation Fund of Sichuan University (2018YJSY007).An increasing number of species are thought to have originated by homoploid hybrid speciation (HHS), but in only a handful of cases are details of the process known. A previous study indicated that Picea purpurea, a conifer in the Qinghai–Tibet Plateau (QTP), originated through HHS from P. likiangensis and P. wilsonii. To investigate this origin in more detail, we analysed transcriptome data for 114 individuals collected from 34 populations of the three Picea species from their core distributions in the QTP. Phylogenetic, principal component and admixture analyses of nuclear SNPs showed the species to be delimited genetically and that P. purpurea was admixed with approximately 60% of its ancestry derived from P. wilsonii and 40% from P. likiangensis. Coalescent simulations revealed the best‐fitting model of origin involved formation of an intermediate hybrid lineage between P. likiangensis and P. wilsonii approximately 6 million years ago (mya), which backcrossed to P. wilsonii to form P. purpurea approximately one mya. The intermediate hybrid lineage no longer exists and is referred to as a “ghost” lineage. Our study emphasizes the power of population genomic analysis combined with coalescent analysis for reconstructing the stages involved in the origin of a homoploid hybrid species over an extended period. In contrast to other studies, we show that these stages can in some instances span a relatively long period of evolutionary time.PostprintPeer reviewe

Reticulate evolution within a spruce (Picea) species complex revealed by population genomic analysis

This work was supported by grants from National key research and development program (2017YFC0505203), National Natural Science Foundation of China (grant numbers 31590821, 31670665, 91731301), National Key Project for Basic Research (2014CB954100), “1000 Youth Talents Plan” of Yunnan Province and CAS “Light of West China” Program.The role of reticulation in the rapid diversification of organisms is attracting greater attention in evolutionary biology. Evidence of genetic exchange between diverging taxa is reported frequently, although most studies fail to show how hybridization and introgression contribute to the adaptation and differentiation of introgressed taxa. Here, we report a population genomics approach to test the role of hybridization and introgression in the evolution of the Picea likiangensis species complex, which comprises four taxa occurring in the biodiversity hotspot of the Hengduan-Himalayan mountains. Based on 84,793 SNPs detected in transcriptomes of 82 trees collected from 35 localities, we identified 18 hybrids (including backcrosses) distributed within the range boundaries of the four taxa. Coalescent simulations, for each pair of taxa and for all taxa taken together, rejected several tree-like divergence models and supported instead a reticulate evolution model with secondary contacts occurring during Pleistocene glacial cycles after initial divergence in the late Pliocene. Significant gene flow occurred among some taxa after secondary contact according to an analysis based on modified ABBA-BABA statistics that accommodated a rapid diversification scenario. A novel finding was that introgression between certain taxa can contribute to increasing divergence (and possibly reproductive isolation) between those taxa and other taxa within a complex at some loci. These results illuminate the reticulate nature of evolution within the P. likiangensis complex and highlight the value of population genomic data in detecting the effects of introgression in the rapid diversification of related taxa.PostprintPeer reviewe