Crossing Lilium Orientals of different ploidy creates Fusarium-resistant hybrid

Oriental hybrid lily is of great commercial value, but it is susceptible to Fusarium disease that causes a significant loss to the production. A diploid Oriental hybrid resistant to Fusarium, Cai-74, was diploidized from triploid obtained from the offspring of tetraploid (from ‘Star Fighter’) and diploid (‘Con Amore’, ‘Acapulco’) by screening the hybrids of different cross combinations following inoculating Fusarium oxysporum to the tissue cultured plantlets in a greenhouse. By analyzing saponins content in bulbs of a number of lily genotypes with a known Fusarium resistance, it was found that the mutant Cai-74 had a much higher content of saponin than its parents. Highly resistant wild _L. dauricum_ had the highest level (4.59mg/g), followed by the resistant Cai-74 with 4.01mg/g. The resistant OT cultivars ‘Conca d’or’ and ‘Robina’ had a higher saponins content (3.70 mg/g) and 2.83 mg/g, than the susceptible Oriental lily cultivars ‘Sorbonne’, ‘Siberia’ and ‘Tiber’. The hybrid Cai-74 had a different karyotype compared with the normal Lilium Oriental hybrid cultivars. The results suggested that Cai-74 carries a chromosomal variation correlated to Fusarium resistance. Cai-74 might be used as a genetic resource for breeding of Fusarium resistant cultivars of Oriental hybrid lilies

Neighborhood Matching Network for Entity Alignment

Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural heterogeneity challenge. NMN estimates the similarities between entities to capture both the topological structure and the neighborhood difference. It provides two innovative components for better learning representations for entity alignment. It first uses a novel graph sampling method to distill a discriminative neighborhood for each entity. It then adopts a cross-graph neighborhood matching module to jointly encode the neighborhood difference for a given entity pair. Such strategies allow NMN to effectively construct matching-oriented entity representations while ignoring noisy neighbors that have a negative impact on the alignment task. Extensive experiments performed on three entity alignment datasets show that NMN can well estimate the neighborhood similarity in more tough cases and significantly outperforms 12 previous state-of-the-art methods.Comment: 11 pages, accepted by ACL 202

Exotic nonaxial-octupole shapes in N=184N=184 isotones from covariant density functional theories

The nonaxial octupole shape in some nuclei with $N = 184$, namely, $^{284}$Fm, $^{286}$No, $^{288}$Rf, and $^{290}$Sg, is investigated using covariant density functional theories. Employing the density-dependent point-coupling covariant density functional theory with the parameter set DD-PC1 in the particle-hole channel, it is found that the ground states of $^{284}$Fm, $^{286}$No, $^{288}$Rf, and $^{290}$Sg have pure nonaxial octupole shapes with deformation parameters $\beta_{31} \approx 0.08$ and $\beta_{33} \approx -0.01 \sim -0.03$. The energy gain due to the $\beta_{31}$ and $\beta_{33}$ distortion is $\sim$ 1 MeV. The occurrence of the nonaxial octupole correlations is mainly from the proton orbitals $1i_{13/2}$ and $2f_{7/2}$, which are close to the proton Fermi surface. The dependence of the nonaxial octupole effects on the form of the energy density functional and on the parameter set is also studied.Comment: 7 pages, 4 figures, accepted for publication in Phys. Rev.

HotStuff-2 vs. HotStuff: The Difference and Advantage

Byzantine consensus protocols are essential in blockchain technology. The widely recognized HotStuff protocol uses cryptographic measures for efficient view changes and reduced communication complexity. Recently, the main authors of HotStuff introduced an advanced iteration named HotStuff-2. This paper aims to compare the principles and analyze the effectiveness of both protocols, hoping to depict their key differences and assess the potential enhancements offered by HotStuff-2

Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of region-proposing stage which is used to filter a majority of backgrounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art real-time detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.Comment: 8 pages, 6 figure

Energy bands and Landau levels of ultracold fermions in the bilayer honeycomb optical lattice

We investigate the spectrum and eigenstates of ultracold fermionic atoms in the bilayer honeycomb optical lattice. In the low energy approximation, the dispersion relation has parabolic form and the quasiparticles are chiral. In the presence of the effective magnetic field, which is created for the system with optical means, the energy spectrum shows an unconventional Landau level structure. Furthermore, the experimental detection of the spectrum is proposed with the Bragg scattering techniques.Comment: To appear in Journal of Modern Optic

Hardening mechanism of commercially pure Mg processed by high pressure torsion at room temperature

Coarse-grained Mg in the as-cast condition and fine-grained Mg in the extruded condition were processed by high pressure torsion (HPT) at room temperature for up to 16 turns. Microstructure observation and texture analysis indicate that to fulfil the Von Mises criterion, the non-basal slip is activated in the as-cast Mg and tension twinning is activated in the as-extruded Mg. Although the deformation mechanism is different in the as-cast Mg and the as-extruded Mg during HPT, their hardening evolutions are similar, i.e. after 1/8 turn of HPT, microhardness of the as-cast Mg and the extruded Mg both show a significant increase and further HPT processing does not significantly further increase the microhardness. Texture strengthening can explain the rapid hardening. Hardness anisotropy and texture data results suggest that texture strengthening plays an important role for both types of samples. Texture strengthening weakens with decreasing grain size

Coherent manipulation of spin wave vector for polarization of photons in an atomic ensemble

We experimentally demonstrate the manipulation of two-orthogonal components of a spin wave in an atomic ensemble. Based on Raman two-photon transition and Larmor spin precession induced by magnetic field pulses, the coherent rotations between the two components of the spin wave is controllably achieved. Successively, the two manipulated spin-wave components are mapped into two orthogonal polarized optical emissions, respectively. By measuring Ramsey fringes of the retrieved optical signals, the \pi/2-pulse fidelity of ~96% is obtained. The presented manipulation scheme can be used to build an arbitrary rotation for qubit operations in quantum information processing based on atomic ensembles

BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection

Arbitrary shape scene text detection is of great importance in scene understanding tasks. Due to the complexity and diversity of text in natural scenes, existing scene text algorithms have limited accuracy for detecting arbitrary shape text. In this paper, we propose a novel arbitrary shape scene text detector through boundary points dynamic optimization(BPDO). The proposed model is designed with a text aware module (TAM) and a boundary point dynamic optimization module (DOM). Specifically, the model designs a text aware module based on segmentation to obtain boundary points describing the central region of the text by extracting a priori information about the text region. Then, based on the idea of deformable attention, it proposes a dynamic optimization model for boundary points, which gradually optimizes the exact position of the boundary points based on the information of the adjacent region of each boundary point. Experiments on CTW-1500, Total-Text, and MSRA-TD500 datasets show that the model proposed in this paper achieves a performance that is better than or comparable to the state-of-the-art algorithm, proving the effectiveness of the model.Comment: Accepted to ICASSP 202