On the Randić Index of Acyclic Conjugated Molecules

The Randić index of an organic molecule whose molecular graph is G is the sum of the weights (d(u)d(v))^−1/2 of all edges uv of G, where d(u) and d(v) are the degrees of the vertices u and v in G. We give a sharp lower bound on the Randić index of conjugated trees (trees with a perfect matching) in terms of the number of vertices. A sharp lower bound on the Randić index of trees with a given size of matching is also given Mei Lu: Partially supported by NNSFC (No. 60172005) Lian-zhu Zhang: Partially supported by NNSFC (No. 10271105) Feng Tian: Partially supported by NNSFC (No. 10431020)     

Fast folding and comparison of RNA secondary structures

Summary Computer codes for computation and comparison of RNA secondary structures, the Vienna RNA package, are presented, that are based on dynamic programming algorithms and aim at predictions of structures with minimum free energies as well as at computations of the equilibrium partition functions and base pairing probabilities.An efficient heuristic for the inverse folding problem of RNA is introduced. In addition we present compact and efficient programs for the comparison of RNA secondary structures based on tree editing and alignment.All computer codes are written in ANSI C. They include implementations of modified algorithms on parallel computers with distributed memory. Performance analysis carried out on an Intel Hypercube shows that parallel computing becomes gradually more and more efficient the longer the sequences are.

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Schnelle Faltung und Vergleich von Sekundärstrukturen von RNA

Zusammenfassung Die im Vienna RNA package enthaltenen Computer Programme für die Berechnung und den Vergleich von RNA Sekundärstrukturen werden präsentiert. Ihren Kern bilden Algorithmen zur Vorhersage von Strukturen minimaler Energie sowie zur Berechnung von Zustandssumme und Basenpaarungswahrscheinlichkeiten mittels dynamischer Programmierung.Ein effizienter heuristischer Algorithmus für das inverse Faltungsproblem wird vorgestellt. Darüberhinaus präsentieren wir kompakte und effiziente Programme zum Vergleich von RNA Sekundärstrukturen durch Baum-Editierung und Alignierung.Alle Programme sind in ANSI C geschrieben, darunter auch eine Implementation des Faltungs-algorithmus für Parallelrechner mit verteiltem Speicher. Wie Tests auf einem Intel Hypercube zeigen, wird das Parallelrechnen umso effizienter je länger die Sequenzen sind.

     

N叉树上的离散时间量子随机行走

通过离散时间量子随机行走的框架,我们研究了在N叉树上的离散时间量子随机行走,该框架不需要硬币空间,仅仅只需要选择一个除了酉性再无其它限制的演化算子,并且包含了使用再生结构的轨道枚举和z变换.作为结果,我们在封闭形式中计算了在根处的振幅的生成函数.     

基于新型核与灰度序列的时滞GM(1,N)模型及其应用

为了解决GM(1,N)模型在新型核与灰度的基础上,对驱动项的延迟作用机理不明确的问题,将时滞参数引入到GM(1,N)模型的驱动项中,构建了基于新型核与灰度的时滞GM(1,N)模型,分析了时滞参数的辨识方法,讨论了新模型的建模机理。为了更好地对该模型的有效性进行验证,将优化的时滞GM(1,N)模型对南京市的雾霾进行预测分析,选择GM(1,N)模型、一元回归模型与文中的优化模型进行对比。结果显示,优化模型对PM₁₀浓度的拟合精度更高,且误差均控制在5%之内,从而验证了提出的优化模型适用于具有时滞特征数据的模拟和预测。     

Estimation of a quantile in some nonstandard cases

A necessary condition for the asymptotic normality of the sample quantile estimator isf(Q(p))=F(Q(p))>0, whereQ(p) is thep-th quantile of the distribution functionF(x). In this paper, we estimate a quantile by a kernel quantile estimator when this condition is violated. We have shown that the kernel quantile estimator is asymptotically normal in some nonstandard cases. The optimal convergence rate of the mean squared error for the kernel estimator is obtained with respect to the asymptotically optimal bandwidth. A law of the iterated logarithm is also established.This research was partially supported by the new faculty award from the University of Oregon.     

基于稀疏采样传播数据和决策树算法的蒸发波导反演

蒸发波导的超视距传播特性是影响海上无线电系统性能的重要因素,准确预测蒸发波导是进行系统 评估的基础。文中提出一种基于稀疏采样传播数据和决策树轻量梯度提升机(Light Gradient Boosting Machine, LightGBM)算法的蒸发波导反演方法。首先,采用抛物方程方法仿真得到稀疏采样传播数据并构建训练集和测试 集;其次,使用决策树LightGBM 算法搭建反演模型,通过不断调参改进模型的精度以达到较高的反演准确度;最后, 调用训练好的LightGBM 模型进行蒸发波导反演,并对反演结果的概率分布进行了分析。结果表明,基于稀疏采样 传播数据的蒸发波导反演方法能够有效且快速地实现蒸发波导反演,为海上蒸发波导预测提供了一种新途径。     

Learning knowledge representation with meta knowledge distillation for single image super-resolution

Although the deep CNN-based super-resolution methods have achieved outstanding performance, their memory cost and computational complexity severely limit their practical employment. Knowledge distillation (KD), which can efficiently transfer knowledge from a cumbersome network (teacher) to a compact network (student), has demonstrated its advantages in some computer vision applications. The representation of knowledge is vital for knowledge transferring and student learning, which is generally defined in hand-crafted manners or uses the intermediate features directly. In this paper, we propose a model-agnostic meta knowledge distillation method under the teacher–student architecture for the single image super-resolution task. It provides a more flexible and accurate way to help teachers transmit knowledge in accordance with the abilities of students via knowledge representation networks (KRNets) with learnable parameters. Specifically, the texture-aware dynamic kernels are generated from local information to decompose the distillation problem into texture-wise supervision for further promoting the recovery quality of high-frequency details. In addition, the KRNets are optimized in a meta-learning manner to ensure the knowledge transferring and the student learning are beneficial to improving the reconstructed quality of the student. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation-related distillation methods and can help super-resolution algorithms achieve better reconstruction quality without introducing any extra inference complexity.     

TETRA通信与干扰信号智能识别技术

陆上集群无线电(Terrestrial Trunked Radio,TETRA)数字集群通信系统因其开放性易受到内外部电磁干扰。干扰信号的类型多种多样,针对不同的干扰样式,采取的抗干扰措施也各不相同,因此干扰信号的识别具有重大意义。基于此,提出了一种干扰信号智能识别技术。该技术首先对受到不同干扰后的TETRA音频数据进行特征提取,并筛选出具有分类能力的特征,其次使用决策树、支持向量机和随机森林3种分类模型对特征提取后的待测试信号进行智能分类识别。实验结果表明,使用的这3种模型能够有效判断TETRA系统中的信号是否受到干扰,以及受到何种样式的干扰,可为后续TETRA系统中的信号干扰识别提供参考。     

多尺度混合注意力胶囊网络的海洋鱼类识别

针对胶囊网络(capsule network,CapsNet)特 征提取结构单一和数据处理中参数量过大的问题,提出 多尺度混合注意力胶囊网络 模型。首先,在网络初始端添加不同尺度的卷积核来多角度提取 特征,并引 入混合注意力机制,通过聚焦更具分辨性的特征区域来降低复杂背景干扰。其次,采用局部 剪枝算法优 化动态路由,减少参数量,缩短模型训练时间。最后,在海洋鱼类数据集F4K(Fish4Knowled ge)上验证, 结果表明,与传统残差网络(residual network50,ResNet-50)、双线性网络(bilinear convo l utional neural network,B-CNN)、分层精简双线性注意力网络(spatial transformation netw ork and hierarchical compact bilinear pooling,STN-H-CBP)以及CapsNet模型相比,该算法 识别精度为98.65%,比ResNet-50模型提升 了5.92%;训练时间为2.2 h,相比于CapsNet 缩短了近40 min,验证了该算法的可行性。     

某通信设备激励模块自检偶发报故的分析与解决措施

简单介绍了某通信设备激励模块工作原理,针对某批次激励模块自检偶发报故的问题,通过对故障原因的详细分析,发现采样时间与指示信号有效时间临界的问题。提出了在充分考虑指示信号有效时间范围的前提下,找到最佳采样时间段,对软件采样时间的设计进行优化的解决措施。