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We develop a flat, analytic and nonlinear placement algorithm ePlace, which is more effective, generalized, simpler and faster than previous works. Based on the analogy between placement instance and electrostatic system, we develop a novel placement density function eDensity, which models every object as positive charge and the density cost as the potential energy of the electrostatic system. The electric potential and field distribution are coupled with density using a modified Poisson's equation, which is numerically solved by spectral methods using fast Fourier transform (FFT). Rather than conjugate gradient (CG) method by previous placers, we propose to use Nesterov's method for faster convergence. The efficiency bottleneck on line search is resolved by steplength prediction through an equation of Lipschitz constant. Through empirical validation, ePlace outperforms all prior placers with better quality and efficiency. On average of ISPD 2005 benchmarks, ePlace outperforms the leading placer BonnPlace with 2.83% shorter wirelength and runs 3.05x faster. On average of ISPD 2006 benchmarks, ePlace outperforms the leading placer MAPLE with 4.59% shorter wirelength and runs 2.84x faster. Based on the above placement prototype, we develop ePlace-MS, an electrostatics based placement algorithm for mixed-size circuits. The density function eDensity is extended to handle the mixed-size placement. We conduct detailed analysis on the correctness of the gradient formulation and the numerical solution, as well as the rationale of density equalization with its advantages over prior density functions. Nesterov's method is shown with high yet stable performance over mixed-size circuits. The steplength prediction methodology is enhanced with backtracking strategy to prevent overestimation. A nonlinear preconditioner is developed to minimize the topological and physical differences between large macros and standard cells. Besides, we devise a simulated annealer for direct macro

We develop a flat, analytic and nonlinear placement algorithm ePlace, which is more effective, generalized, simpler and faster than previous works. Based on the analogy between placement instance and electrostatic system, we develop a novel placement density function eDensity, which models every object as positive charge and the density cost as the potential energy of the electrostatic system. The electric potential and field distribution are coupled with density using a modified Poisson's equation, which is numerically solved by spectral methods using fast Fourier transform (FFT). Rather than conjugate gradient (CG) method by previous placers, we propose to use Nesterov's method for faster convergence. The efficiency bottleneck on line search is resolved by steplength prediction through an equation of Lipschitz constant. Through empirical validation, ePlace outperforms all prior placers with better quality and efficiency. On average of ISPD 2005 benchmarks, ePlace outperforms the leading placer BonnPlace with 2.83% shorter wirelength and runs 3.05x faster. On average of ISPD 2006 benchmarks, ePlace outperforms the leading placer MAPLE with 4.59% shorter wirelength and runs 2.84x faster. Based on the above placement prototype, we develop ePlace-MS, an electrostatics based placement algorithm for mixed-size circuits. The density function eDensity is extended to handle the mixed-size placement. We conduct detailed analysis on the correctness of the gradient formulation and the numerical solution, as well as the rationale of density equalization with its advantages over prior density functions. Nesterov's method is shown with high yet stable performance over mixed-size circuits. The steplength prediction methodology is enhanced with backtracking strategy to prevent overestimation. A nonlinear preconditioner is developed to minimize the topological and physical differences between large macros and standard cells. Besides, we devise a simulated annealer for direct macro

BackgroundUnderstanding how landscape factors, including suites of geographic and environmental variables, and both historical and contemporary ecological and evolutionary processes shape the distribution of genetic diversity is a primary goal of landscape and conservation genetics and may be particularly consequential for species involved in ecological restoration. In this study, we examine the factors that shape the distribution of genetic variation in a leguminous shrub (Caragana microphylla) important for restoration efforts on the Mongolian Plateau in China. This region houses several major bioclimatic gradients, and C. microphylla is an important restoration species because it stabilizes soils and prevents advancing desertification on the Inner Mongolia Plateau caused by ongoing climate change.ResultsWe assembled an expansive genomic dataset, consisting of 22 microsatellite loci, four cpDNA regions, and 5788 genome-wide SNPs from ten populations of C. microphylla. We then applied ecological niche modelling and linear and non-linear regression techniques to investigate the historical and contemporary forces that explain patterns of genetic diversity and population structure in C. microphylla on the Inner Mongolia Plateau. We found strong evidence that both geographic and environmental heterogeneity contribute to genetic differentiation and that the spatial distribution of genetic diversity in C. microphylla appears to result partly from the presence of a glacial refugium at the southwestern edge of its current range.ConclusionsThese results suggest that geographic, environmental, and historical factors have all contributed to spatial genetic variation in this ecologically important species. These results should guide restoration plans to sustain genetic diversity during plant translocations.

BackgroundUnderstanding how landscape factors, including suites of geographic and environmental variables, and both historical and contemporary ecological and evolutionary processes shape the distribution of genetic diversity is a primary goal of landscape and conservation genetics and may be particularly consequential for species involved in ecological restoration. In this study, we examine the factors that shape the distribution of genetic variation in a leguminous shrub (Caragana microphylla) important for restoration efforts on the Mongolian Plateau in China. This region houses several major bioclimatic gradients, and C. microphylla is an important restoration species because it stabilizes soils and prevents advancing desertification on the Inner Mongolia Plateau caused by ongoing climate change.ResultsWe assembled an expansive genomic dataset, consisting of 22 microsatellite loci, four cpDNA regions, and 5788 genome-wide SNPs from ten populations of C. microphylla. We then applied ecological niche modelling and linear and non-linear regression techniques to investigate the historical and contemporary forces that explain patterns of genetic diversity and population structure in C. microphylla on the Inner Mongolia Plateau. We found strong evidence that both geographic and environmental heterogeneity contribute to genetic differentiation and that the spatial distribution of genetic diversity in C. microphylla appears to result partly from the presence of a glacial refugium at the southwestern edge of its current range.ConclusionsThese results suggest that geographic, environmental, and historical factors have all contributed to spatial genetic variation in this ecologically important species. These results should guide restoration plans to sustain genetic diversity during plant translocations.

We propose a flat, analytic, mixed-size placement algorithm ePlace-3D for three-dimension integrated circuits (3D-ICs) using nonlinear optimization. Our contributions are (1) electrostatics based 3D density function with globally uniform smoothness (2) 3D numerical solution with improved spectral formulation (3) 3D nonlinear pre-conditioner for convergence acceleration (4) interleaved 2D-3D placement for efficiency enhancement. Our placer outperforms the leading work mPL6-3D and NTUplace3-3D with 6.44% and 37.15% shorter wirelength, 9.11% and 10.27% fewer 3D vertical interconnects (VI) on average of IBM-PLACE circuits. Validation on the large-scale modern mixed-size (MMS) 3D circuits shows high performance and scalability., Comment: 8 pages, 7 figures, ISPD 2016

We propose a flat nonlinear placement algorithm FFTPL using fast Fourier transform for density equalization. The placement instance is modeled as an electrostatic system with the analogy of density cost to the potential energy. A well-defined Poisson's equation is proposed for gradient and cost computation. Our placer outperforms state-of-the-art placers with better solution quality and efficiency.

Jingwei Lu, Vincent J Pompili, Hiranmoy DasCardiovascular Stem Cell Research Laboratory, The Dorothy M Davis Heart and Lung Research Institute, The Ohio State University Medical Center, Columbus, OH 43210, USAAbstract: Despite recent advances in cardiovascular medicine, ischemic heart disease remains the major cause of death in the United States and abroad. Cell-based therapy for degenerative diseases like myocardial ischemia using stem cells is currently under serious investigation. Various types of stem cells are being considered to be candidates for cell transplantation in cell-based therapy. Hematopoietic stem cells are one of the most promising cell types as several studies demonstrated their ability to improve ischemic cardiac functions by enhancing neovascularization and by reducing the total size of scar tissue. However, in order to procure sufficient numbers of functional stem cells, ex-vivo expansion technology became critically important. In this review, we focus on the state-of-the-art ex-vivo technology for the expansion of hematopoietic stem cells, and the underlying mechanisms regulating stem cell self-renewal as well as differentiation.Keywords: ischemic heart disease, ex-vivo expansion, hematopoietic stem cells, cytokines, nanofibers