An alternative objective function for fitting regression trees to functional response variables

Analyses of systems that can be represented by functional responses are becoming common in many scientific disciplines. Functional regression trees (FRT) provide a methodology for modelling such systems. Recent work has focused on fitting models where the response variable is a probability density function, using a splitting criterion that is based on the sum of dissimilarities between the densities. We suggest a different criterion based on deviations of the densities from their mean. We provide motivation and justification for this criterion, and demonstrate its superior performance using an extensive simulation exercise. We discuss the computational aspects of the FRT procedure and show that substantial speed gains can be made through use of a dissimilarity matrix. Our results show that the proposed splitting criterion outperforms both the original and a splitting criterion based on Euclidean distance. Pointwise standard error curves for a predicted functional response can be generated through the fitting procedure, which we demonstrate in a case study with a forestry data set. Supplementary materials are available.     

An Analysis of Edge Detection by Using the Jensen-Shannon Divergence

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the method's sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.     

Improving Data Locality for Efficient In‐Core Path Tracing

In this paper, we investigate the efficiency of ray queries on the CPU in the context of path tracing, where ray distributions are mostly random. We show that existing schemes that exploit data locality to improve ray tracing efficiency fail to do so beyond the first diffuse bounce, and analyze the cause for this. We then present an alternative scheme inspired by the work of Pharr et al. in which we improve data locality by using a data‐centric breadth‐first approach. We show that our scheme improves on state‐of‐the‐art performance for ray distributions in a path tracer.     

级数sum from n=1 to ∞(α(α-1)…(α-n+1)x~n/n!)在x=±1处的敛散性研究

用比较原则及莱布尼兹判别法对函数f(x)=(1+x)α的马克劳林级数在x=±1的敛散情形给予讨论.     

燃气管网水力计算研究

燃气管网水力计算存在虚平衡现象、叠代计算不收敛、压缩机供气管网水力计算未考虑压缩机实际工作曲线等问题．为此，引入开环能量方程解决用解环状方程法进行定压多气源点供气管网水力计算时存在虚平衡的问题；通过分析找出摩擦阻力系数在流态分区点不连续是造成不收敛的原因，对管道中燃气流动状态分区点采用插值法，解决在叠代过程中可能因摩擦阻力系数不连续导致叠代流量在分界点来回振荡造成的不收敛问题；引用虚气源点法解决压缩机供气管网的水力计算问题．     

机器翻译中异化现象的分析及映射运算

本文从词汇－语义角度分析了机器翻译中与汉英语言相关的一些异化现象，探讨了语义－句法映射运算集的有效性和合理性．同时，本文讨论了通过变异映射集、变异类型指示器和参数合一机制解决机器翻译中异化现象的方法．这些方法提高了译准率，使生成的句子既能正确表达原中间语言的语义，又符合目标语言的表述习惯．     

判别无穷级数敛散性的一种方法

讨论了级数与广义积分的内在联系，通过例题给出了由此产生的一种级数敛散性的判别法。     

3.705 μm紧凑型光参量振荡器

报道了一种基于工程应用的3.705 μm紧凑型高稳定性光参量振荡器。采用半导体制冷、强制气体对流冷却和热管导热技术相结合的方式,对主振荡功率放大结构的掺Yb光纤泵浦模块泵浦过程中产生的废热进行有效管理,再将经过耦合后的泵浦光注入到使用温控炉精确控温的PPMgLN晶体,在重复频率为55.56 kHz时,获得了功率不稳定度优于1.5%的1.78 W,脉冲宽度120 ns,峰值波长3.705 μm的线偏中红外激光输出,光光转换效率为12.6%。     

Higher-order Hamilton dynamics and Hamilton–Jacobi divergence PDE

This paper, using a non-standard Legendrian duality, investigates the Hamiltonian dynamics and formulates a Hamilton–Jacobi type divergence PDE governed by higher-order Lagrangians.     

Improving branch divergence performance on GPGPU with a new PDOM stack and multi-level warp scheduling

General-purpose graphics processing unit (GPGPU) plays an important role in massive parallel computing nowadays. A GPGPU core typically holds thousands of threads, where hardware threads are organized into warps. With the single instruction multiple thread (SIMT) pipeline, GPGPU can achieve high performance. But threads taking different branches in the same warp violate SIMD style and cause branch divergence. To support this, a hardware stack is used to sequentially execute all branches. Hence branch divergence leads to performance degradation. This article represents the PDOM (post dominator) stack as a binary tree, and each leaf corresponds to a branch target. We propose a new PDOM stack called PDOM-ASI, which can schedule all the tree leaves. The new stack can hide more long operation latencies with more schedulable warps without the problem of warp over-subdivision. Besides, a multi-level warp scheduling policy is proposed, which lets part of the warps run ahead and creates more opportunities to hide the latencies. The simulation results show that our policies achieve 10.5% performance improvements over baseline policies with only 1.33% hardware area overhead.