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An Efficient Smoothing and Thresholding Image Segmentation Framework with Weighted Anisotropic-Isotropic Total Variation

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Communications on Applied Mathematics and Computation 2024年第2期6卷 1369-1405页

作者： Kevin Bui Yifei Lou Fredrick Park jack xin Department of Mathematics University of CaliforniaIrvineIrvineCA 92697-3875USA Department of Mathematics University of North CarolinaChapel HillChapel HillNC 27599.USA Department of Mathematics and Computer Science Whittier CollegeWhittierCA 90602USA

In this paper,we design an efficient,multi-stage image segmentation framework that incorporates a weighted difference of anisotropic and isotropic total variation(AITV).The segmentation framework generally consists of two stages:smoothing and thresholding,thus referred to as smoothing-and-thresholding(SaT).In the first stage,a smoothed image is obtained by an AITV-regularized Mumford-Shah(MS)model,which can be solved efficiently by the alternating direction method of multipliers(ADMMs)with a closed-form solution of a proximal operator of the l_(1)-αl_(2) *** convergence of the ADMM algorithm is *** the second stage,we threshold the smoothed image by K-means clustering to obtain the final segmentation *** experiments demonstrate that the proposed segmentation framework is versatile for both grayscale and color images,effcient in producing high-quality segmentation results within a few seconds,and robust to input images that are corrupted with noise,blur,or *** compare the AITV method with its original convex TV and nonconvex TVP(O

关键词： Image segmentation Non-convex optimization Mumford-Shah(MS)model Alternating direction method of multipliers(ADMMs) Proximal operator

ITERATIVE l1 MINIMIZATION FOR NON-CONVEX COMPRESSED SENSING

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Journal of Computational Mathematics 2017年第4期35卷 439-451页

作者： Penghang Yin jack xin Department of Mathematics University of California Los Angeles CA 90095 USA Department of Mathematics University of California Irvine CA 92697 USA

An algorithmic framework, based on the difference of convex functions algorithm （D- CA）, is proposed for minimizing a class of concave sparse metrics for compressed sensing problems. The resulting algorithm iterates a sequence ofl1 minimization problems. An exact sparse recovery theory is established to show that the proposed framework always improves on the basis pursuit （l1 minimization） and inherits robustness from it. Numerical examples on success rates of sparse solution recovery illustrate further that, unlike most existing non-convex compressed sensing solvers in the literature, our method always out- performs basis pursuit, no matter how ill-conditioned the measurement matrix is. Moreover, the iterative l1 （ILl） algorithm lead by a wide margin the state-of-the-art algorithms on l1/2 and logarithimic minimizations in the strongly coherent （highly ill-conditioned） regime, despite the same objective functions. Last but not least, in the application of magnetic resonance imaging （MRI）, IL1 algorithm easily recovers the phantom image with just 7 line projections.

关键词： Compressed sensing Non-convexity Difference of convex functions algorithm,Iterative l1 minimization.

A TIME DOMAIN BLIND DECORRELATION METHOD OF CONVOLUTIVE MIXTURES BASED ON AN IIR MODEL

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Journal of Computational Mathematics 2010年第3期28卷 371-385页

作者： Jie Liu jack xin Yingyong Qi Department of Mathematics National University of Singapore Singapore Department of Mathematics University of California Irvine USA Department of Mathematics University of California Irvine USAand School of Information Engineering Shangdong University at Weihai China

We study a time domain decorrelation method of source signal separation from convolutive sound mixtures based on an infinite impulse response （IIR） model. The IIR model uses fewer parameters to capture the physical mixing process and is useful for finding low dimensional separating solutions. We present inversion formulas to decorrelate the mixture signals and derive filter equations involving second order time lagged statistics of mixtures. We then formulate an 11 constrained minimization problem and solve it by an iterative method. Numerical experiments on recorded sound mixtures show that our method is capable of sound separation in low dimensional parameter spaces with good perceptual quality and low correlation coefficient comparable to the known infomax method.

关键词： Blind Decorrelation Convolutive Mixtures IIR Modeling l1 ConstrainedMinimization

QUANTIZATION AND TRAINING OF LOW BIT-WIDTH CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION

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Journal of Computational Mathematics 2019年第3期37卷 349-359页

作者： Penghang Yin Shuai Zhang Yingyong Qi jack xin Department of Mathematics University of CaliforniaLos AngelesCA 90095USA Department of Mathematics University of CaliforniaIrvineCA 92697USA

We presen t LBW-Net,an efficient optimization based method for qua nt ization and training of the low bit-width convolutional neural networks(CNNs).Specifically,we quantize the weights to zero or powers of 2 by minimizing the Euclidean distance between full-precision weights and quantized weights during backpropagation(weight learning).We characterize the combinatorial nature of the low bit-width quantization *** 2-bit(ternary)CNNs,the quantization of N weights can be done by an exact formula in O(N log N)*** the bit-width is 3 and above,we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally *** free parameter is further determined by network retraining and object detection *** LBW-Net has several desirable advantages over full-precision CNNs,including considerable memory savings,energy efficiency,and faster *** experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart,the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks,and can even do better in real world visual scenes,while empirically enjoying more than 4× faster deployment.

关键词： Quantization Low bit width deep neural networks Exact and approximate analytical formulas Network training Object detection

Deep Learning for Real-Time Crime Forecasting and Its Ternarization

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Chinese Annals of Mathematics,Series B 2019年第6期40卷 949-966页

作者： Bao WANG Penghang YIN Andrea Louise BERTOZZI P.Jeffrey BRANTINGHAM Stanley Joel OSHER jack xin Department of Mathematics University of CaliforniaLos AngelesWestwoodLos AngelesCA 90095USA Department of Anthropology University of CaliforniaLos AngelesWestwoodLos AngelesCA 90095USA Department of Mathematics University of CaliforniaIrvineIrvineCA 92697USA

Real-time crime forecasting is ***,accurate prediction of when and where the next crime will happen is *** known physical model provides a reasonable approximation to such a complex *** crime data are sparse in both space and time and the signal of interests is *** this work,the authors first present a proper representation of crime *** authors then adapt the spatial temporal residual network on the well represented data to predict the distribution of crime in Los Angeles at the scale of hours in neighborhood-sized *** experiments as well as comparisons with several existing approaches to prediction demonstrate the superiority of the proposed model in terms of ***,the authors present a ternarization technique to address the resource consumption issue for its deployment in real *** work is an extension of our short conference proceeding paper[Wang,B.,Zhang,D.,Zhang,D.H.,et al.,Deep learning for real time Crime forecasting,2017,ar Xiv:1707.03340].

关键词： Crime representation Spatial-temporal deep learning Real-time forecasting Ternarization

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《回到哈莱姆》中的文化冲突

盐城工学院学报（社会科学版） 2024年第4期37卷 50-54页

作者：辛珏如安鑫广东财经大学外国语学院广东广州510320 邯郸学院经济管理学院河北邯郸056006

克劳德·麦凯的《回到哈莱姆》是对20世纪早期城市黑人生活的一个全景刻画。通过杰克和雷这两个角色之间的友谊和微妙心理冲突,探讨了不同的文化认同取向,展示了哈莱姆这一黑人文化熔炉中的复杂性。小说中餐车主厨和下属之间的冲突,则... 详细信息

克劳德·麦凯的《回到哈莱姆》是对20世纪早期城市黑人生活的一个全景刻画。通过杰克和雷这两个角色之间的友谊和微妙心理冲突,探讨了不同的文化认同取向,展示了哈莱姆这一黑人文化熔炉中的复杂性。小说中餐车主厨和下属之间的冲突,则触及更深层的黑人阶层分化以及由此而来的潜在暴力冲突。以塞林的文化冲突理论来分析以上隐性和显性的文化冲突,揭示了哈莱姆文艺复兴时期黑人社区内部的复杂性。

关键词：《回到哈莱姆》麦凯文化冲突

利用CRISPR/Cas9技术编辑GmBADH1基因改变大豆耐盐性

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作物学报 2024年第1期50卷 100-109页

作者：石宇欣刘欣玥孙建强李晓菲郭潇阳周雅邱丽娟东北农业大学农学院黑龙江哈尔滨150030 农作物基因资源与遗传改良国家重大科学工程/农业农村部种质资源利用重点实验室/中国农业科学院作物科学研究所北京100081

耐盐基因功能鉴定对于大豆品种改良以及盐碱地的开发利用至关重要。盐害胁迫下作物通过合成和积累甜菜碱作为渗透保护剂,减小盐害对作物产量的影响。BADH基因已在多种植物中被证实调节植物对逆境胁迫的响应,但在大豆中的调控机制尚不清... 详细信息

耐盐基因功能鉴定对于大豆品种改良以及盐碱地的开发利用至关重要。盐害胁迫下作物通过合成和积累甜菜碱作为渗透保护剂,减小盐害对作物产量的影响。BADH基因已在多种植物中被证实调节植物对逆境胁迫的响应,但在大豆中的调控机制尚不清晰。本研究克隆了GmBADH1基因,qRT-PCR显示该基因在根、茎、叶中均有表达,尤其在根中的表达丰度最高。通过农杆菌介导的大豆遗传转化技术将CRISPR/Cas9系统构建的表达载体转入到大豆品种jack中,产生了3种可以调控大豆耐盐性状的靶向突变,均发生在编码区,分别为缺失19 bp、插入1 bp、插入9bp替换2 bp。前两者,过早出现的终止密码子,使其均产生截断的BADH1蛋白,后者发生移码突变。在出苗期和苗期分别对突变纯合植株进行盐处理,结果表明,出苗期耐盐性与野生型相比显著下降,苗期与野生型相比无明显差异。这说明GmBADH1基因可能主要调控出苗期的耐盐性状。本研究为深入挖掘大豆耐盐基因和培育大豆耐盐品种提供了依据。

关键词：大豆 GmBADH1 CRISPR/Cas9 耐盐性

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砂土中桩靴插桩对临近筒型基础的影响研究

太阳能学报 2024年第2期45卷 95-101页

作者：乐丛欢胡灏王昕任建宇张浦阳丁红岩天津大学水利工程智能建设与运维全国重点实验室天津300350 天津大学建筑工程学院天津300350

通过物理模型试验和有限元法研究砂土中风电安装船桩靴插桩对临近筒型基础的影响。首先通过物理模型试验,验证有限元模型的合理性,初步确定桩靴在砂土中贯入的影响范围大致在2.5倍桩靴直径。基于有限元CEL方法进一步研究桩靴和筒型基础... 详细信息

通过物理模型试验和有限元法研究砂土中风电安装船桩靴插桩对临近筒型基础的影响。首先通过物理模型试验,验证有限元模型的合理性,初步确定桩靴在砂土中贯入的影响范围大致在2.5倍桩靴直径。基于有限元CEL方法进一步研究桩靴和筒型基础的净距与桩靴直径之比(S/D)对筒型基础在位稳定性的影响。结果表明:筒型基础的倾斜率、最大竖向位移以及筒壁应力随着S/D的增大而逐渐减小,插桩过程中,靠近桩靴一侧的筒裙和分舱板应力远大于远离桩靴一侧的筒型基础应力,且筒顶的土压力远小于筒底土压力。

关键词：海上风电自升式安装船数值模拟插桩筒型基础

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黏土中大直径钢圆筒在桩靴贯入时的稳定性研究

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