Sommer, M. Boehm, A. V. Evfimievski, B. Reinwald, P. J. Haas: MNC: Structure- Exploiting Sparsity Estimation for Matrix Expressions. SIGMOD 2019] 15 706.015 Introduction to Scientific Writing – 03 Experiments Sommer, M. Boehm, A. V. Evfimievski, B. Reinwald, P. J. Haas: MNC: Structure- Exploiting Sparsity Estimation for Matrix Expressions. SIGMOD 2019] 27 706.015 Introduction to Scientific Writing – 03 Experiments

primitive shapes updated deprecated ipo and curve methods in keyframe export code improved FPS estimation on import check ipo curve completeness on export (solves the “NoneType has no evaluate attribute” wire drawtype Import: full skinning support (but still no animation) Import: better bone length estimation, automatic alignment Version 1.3 (Jan 21, 2006) Import/Export: Vertex key animation support (geometry

primitive shapes • updated deprecated IPO and curve methods in keyframe export code • improved FPS estimation on import • check IPO curve completeness on export (solves the “NoneType has no evaluate attribute” 0a0.dev4 • Import: full skinning support (but still no animation) • Import: better bone length estimation, automatic alignment 5.89 Version 1.3 (Jan 21, 2006) • Import/Export: Vertex key animation support

primitive shapes • updated deprecated ipo and curve methods in keyframe export code • improved FPS estimation on import • check ipo curve completeness on export (solves the “NoneType has no evaluate attribute” 0a0.dev4 • Import: full skinning support (but still no animation) • Import: better bone length estimation, automatic alignment 5.89 Version 1.3 (Jan 21, 2006) • Import/Export: Vertex key animation support

primitive shapes updated deprecated IPO and curve methods in keyframe export code improved FPS estimation on import check IPO curve completeness on export (solves the “NoneType has no evaluate attribute” wire draw type Import: full skinning support (but still no animation) Import: better bone length estimation, automatic alignment Version 1.3 (Jan 21, 2006) Import/Export: Vertex key animation support (geometry

devices and models >> 2 HW Platforms ZCU102 ZCU104 Ultra96 PYNQ Face detection Pose estimation Video analytics Lane detection Object detection Segmentation

of alpha = 0.4, radius = 0.6. For a more blatant effect try alpha = 0.5, radius = 1.0 . Optimal estimation smoothing. (1.0 <= alpha <= 2.0) This type of filter applies a smoothing filter adaptively over optimal estimation filter, then some subtle edge enhancement. Note that using edge enhancement is only likely to be useful after one of the non-linear filters (alpha trimmed mean or optimal estimation filter) optimal estimation filter (alpha 1.2, radius 1.0), a pass of the median filter (alpha 0.5, radius 0.55), and possibly a pass of the edge enhancement filter. Several passes of the optimal estimation filter

effect try Alpha = 1.0, Radius = 1.0 . 674 CHAPTER 17. FILTERS 17.4. ENHANCE FILTERS Optimal estimation This type of filter applies a smoothing filter adaptively over the image. For each pixel the variance optimal estimation filter, then some subtle edge enhancement. Note that using edge enhancement is only likely to be useful after one of the non-linear filters (alpha trimmed mean or optimal estimation filter) in images (i.e. turning .gif files back into 24 bit files) you could try a pass of the optimal estimation filter (Alpha = 0.2, Radius = 1.0), a pass of the median filter (Alpha = 1.0, Radius = 0.55), and

algorithms). PARAMETERS NL Filter provides three different filters: Alpha Trimmed Mean, Optimal Estimation and Edge Enhancement. The Radius slider controls the strength of the filter. Alpha determines Trimmed Mean and set the Alpha to 0.5 when removing single noise spots in the image. • Optimal Estimation is very good for reducing dithering noise. Low Alpha values make the smoothing subtle and high p Us er ’ s M a n u a l E nha nce Fil t er s • Edge Enhancement is the opposite of Optimal Estimation. Edge Enhancement sharpens edges instead of blurring them. Radius controls the effectiveness of

些是不可建模的不确定性，你不知道下⾯会发⽣什么。但有⼀些是你能确定的，能排除掉⼀些东西 的，这是⼀个通⽤的、对世界去进⾏建模的模型。有很多历史学家来对这个事情做过研究，⽐如 Density
Estimation（密度统计），⼤模型本质是在做这样⼀个事情。但当时我只意识到这是个重要的 问题，⽽没有意识到是唯⼀要解决的问题。 36氪：那是什么时候让你改变主意了？
杨植麟：2020年GPT-3出