Reduced Density Matrix and Quantum Correlation in Two-Qubit Quantum Rabi Model

International Journal of Theoretical Physics - In this work, we give an analytical derivation of the reduced density matrix between two qubits in a cavity field, which is described by the quantum...     

Magnetoresistance and Quantum Oscillations in WTe2 Semimetal

Physics of the Solid State - The discovery of extreme magnetoresistance (MR) in nonmagnetic materials attracted attention to WTe2 semimetal. We studied MR in a single crystal of tungsten...     

Multi-targeted molecular docking,drug-likeness and ADMET studies of derivatives of few quinoline- and acridine-based FDA-approved drugs for anti-breast cancer activity

Structural Chemistry - Quinoline- and acridine-based drugs are widely used as anti-breast cancer agents. These drugs act through various mechanisms of action; for example, neratinib acts on...     

Finding any given 2‐factor in sparse pseudorandom graphs efficiently

Given an $n$‐vertex pseudorandom graph $G$ and an $n$‐vertex graph $H$ with maximum degree at most two, we wish to find a copy of $H$ in $G$, that is, an embedding $\phi :V\left(H\right)\to V\left(G\right)$ so that $\phi \left(u\right)\phi \left(v\right)\in E\left(G\right)$ for all $uv\in E\left(H\right)$. Particular instances of this problem include finding a triangle‐factor and finding a Hamilton cycle in $G$. Here, we provide a deterministic polynomial time algorithm that finds a given $H$ in any suitably pseudorandom graph $G$. The pseudorandom graphs we consider are $\left(p,\lambda \right)$‐bijumbled graphs of minimum degree which is a constant proportion of the average degree, that is, $\mathrm{\Omega }\left(pn\right)$. A $\left(p,\lambda \right)$‐bijumbled graph is characterised through the discrepancy property: $|e\left(A,B\right)?p|A||B||<\lambda \sqrt{|A||B|}$ for any two sets of vertices $A$ and $B$. Our condition $\lambda =O(p^{2}n/\log n)$ on bijumbledness is within a log factor from being tight and provides a positive answer to a recent question of Nenadov. We combine novel variants of the absorption‐reservoir method, a powerful tool from extremal graph theory and random graphs. Our approach builds on our previous work, incorporating the work of Nenadov, together with additional ideas and simplifications.     

Hybrid control strategy of delayed neural networks and its application to sampled-data systems: an impulsive-based bilateral looped-functional approach

Nonlinear Dynamics - In this work, the problem of hybrid control strategy for delayed neural networks is investigated via an impulsive-based bilateral looped-functional (IBBLF) approach. Firstly, a...     

Uniform local well-posedness and inviscid limit for the Benjamin-Ono-Burgers equation

In this paper, we study the Cauchy problem for the Benjamin-Ono-Burgers equation \({\partial _t}u - \epsilon \partial _x^2u + {\cal H}\partial _x^2u + u{u_x} = 0\), where \({\cal H}\) denotes the Hilbert transform operator. We obtain that it is uniformly locally well-posed for small data in the refined Sobolev space \({\tilde H^\sigma }(\mathbb{R})\,\,(\sigma \geqslant 0)\), which is a subspace of L²(ℝ). It is worth noting that the low-frequency part of \({\tilde H^\sigma }(\mathbb{R})\) is scaling critical, and thus the small data is necessary. The high-frequency part of \({\tilde H^\sigma }(\mathbb{R})\) is equal to the Sobolev space H^σ (ℝ) (σ ⩾ 0) and reduces to L²(ℝ). Furthermore, we also obtain its inviscid limit behavior in \({\tilde H^\sigma }(\mathbb{R})\) (σ ⩾ 0).

     

A Generalization of Lappan’s Theorem to Higher Dimensional Complex Projective Space

In this paper, the authors discuss a generalization of Lappan’s theorem to higher dimensional complex projective space and get the following result: Let f be a holomorphic mapping of ? into Pⁿ(C), and let H₁, · · · , H_q be hyperplanes in general position in Pⁿ(C).Assume that sup {(1 ? |z|²)f^?(z) : z ∈ q[ j=1 f^?1(H_j )o < ∞,if q ≥ 2n² + 3, then f is normal.     

基于相同参数抛物镜面旋转阵列的太阳能槽式聚光器聚焦特性研究

针对吉林一号某轻型空间遥感相机的任务需求，以碳纤维复合材料的薄壁筒与桁架杆一体式成型为设计思路，基于拓扑优化、尺寸优化以及铺层优化设计了一种高稳定性的次镜支撑结构。工程分析和实验结果表明一体式碳纤维次镜支撑结构具有较好的结构稳定性，整体质量仅为1.3 kg,其在轨成像质量良好，这进一步验证了一体式碳纤维次镜支撑结构的可靠性和设计方法的正确性。     

Mechanistic Investigation and Conductance Modulation of a Metal-Molecule-Metal Junction via Extra Acid Addition

One important prerequisite for the fabrication of molecular functional device strongly relies on the understanding the conducting behaviors of the metal-molecule-metal junction that can respond to an external stimulus. The model Lewis basic molecule 4,4′-(pyridine-3,5-diyl)dibenzonitrile (DBP), which can react with Lewis acid and protic acid, was synthesized. Then, the molecular conducting behavior of DBP, DBP-B(C₆F₅)₃, and DBP-TfOH (DBP-B(C₆F₅)₃, and DBP-TfOH were produced by Lewis acid and protonic acid treatment of DBP) was researched and compared. Given that their identical physical paths for DBP, DBP-B(C₆F₅)₃, and DBP-TfOH to sustain charge transport, our results indicate that modifying the molecular electronic structure, even not directly changing the conductive physical backbone, can tune the charge transporting ability by nearly one order of magnitude. Furthermore, the addition of another Lewis base triethylamine (of stronger alkaline than DBP), to Lewis acid-base pair reverts the electrical properties back to that of a single DBP junction, that is constructive to propose a useful but simple strategy for the design and construction of reversible and controllable molecular device based on pyridine derived molecule.     

Sentinel-2卫星的多光谱轻量级船舶目标检测算法

近年来深度卷积神经网络在可见光船舶检测方面取得了显著的进展，然而，大多数相关研究是通过改进大型的网络结构来提高检测性能，因此加大了对更高计算机性能的需求。此外，可见光图像难以在云、雾、海杂波、黑夜等复杂场景检测到船舶。针对以上问题，提出了一种融合红（red, R）、绿（green, G）、蓝（blue, B）和近红外（NIR）4个波段光谱信息的由粗到精细的轻量型船舶检测算法。与现有的方法中根据光谱特性利用水体检测算法提取水体区域不同之处是该算法是利用改进的水体检测算法来提取船舶候选区域。为获取更准确的候选区域，对船舶、厚云、薄云、平静海面、杂波海面5种场景中4个波段的像素值进行了统计分析，选取近红外大于阈值作为辅助判断，并以其中心点获取候选区域32×32大小的切片，并对切片进行非极大值抑制，由此获得了船舶粗检测结果。随后构建了轻量级LSGFNet网络对船舶候选区域切片进行精细识别。构建的网络融合了1×1卷积提取的波谱特征与3×3的提取几何特征，为防止光谱特征与几何特征的信息在融合时“信息不流通”，在LSGFNet网络中引入了ShuffleNet中的通道打乱机制，并减小了模型结构，与典型的轻量级网络相比具有更好的效果且模型较小。最后，利用Sentinel-2卫星多光谱10 m分辨率数据构建了512×512大小的1 120组数据进行粗检测，以及32×32大小的6 014组数据进行精细网络训练，其中候选区域粗提取的查全率为98.99%，精细识别网络精确度为96.04%，不同场景下的平均精确度为92.98%。实验表明该算法在抑制云层、海浪杂波等干扰的复杂背景下具有较高的检测效率，且训练时间短、计算机性能需求低。