STT的总体架构如上图所示。在Pixel-Wise Supervision模块(绿色虚线框)中,首先通过Spatial Transformer Network(STN)对低分辨率的文本图像进行校正,以解决错位问题。校正后的图像进入一系列基于Transformer的超分辨率网络(TBSRN),然后通过像素变换上采样到超分辨率的文本图像。在Position-Aware模块(红色虚线帧)中,以相应的HR...
Su, "Exploring spatial-temporal trajectory model for location prediction," in MDM, vol. 1, Lulea, Sweden, June 2011, pp. 58-67.QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction[J] . Po-Ruey Lei,Shou-Chung Li,Wen-Chih Peng.Distributed and Parallel...
1.1 mm (mouse - ultra-high sensitivity), 0.6 mm (mouse - ultra-high resolution) and 1 mm for rats PET parameters: 0.9 mm spatial resolution (reconstructed voxel options: 0.3, 0.4 or 0.6 mm) CT parameters: 10 µm minimum voxel size for a small field-of-view (e.g. mouse vertebra)...
By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state specific dynamics and spatial state-transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Overall, STT provides a consistent...
SpatialTranscriptomics example: PIPELINE="standard" Rscript $STTKIT/st_snormalize.R --infile $SAMPLE/${PIPELINE}_pipeline/${SAMPLE}_${PIPELINE}_ensembl_adjusted.tsv \ --sampleid $SAMPLE \ --hejpeg {SAMPLE}_HE_bw_scaled.jpg \ --outprefix OUTDIR/${PIPELINE}/$SAMPLE/normalize/$SAMPLE \ ...
参考链接:CVPR2018论文:Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking1、Github代码下载地址:https://github.com/lifeng9472/STRCF2、从如下地址下载matconvnet,并解压到external_libs/matconvnet/ 路径 http 单目标跟踪 ide
• Well defined spatial radiation pattern• Viewing angles: Major axis 70° Minor axis 35°• High luminous output• Red and Amber intensity are available for: AlInGaP(Bright) AlInGaP II (Brightest)• Colors: 626 nm red 630 nm red 590 nm amber 592 nm amber• Superior resistance ...
• Well defined spatial radiation pattern • Viewing angles: Major axis 70° Minor axis 35° • High luminous output • Red and Amber intensity are available for: AlInGaP(Bright) AlInGaP II (Brightest) • Colors: 626 nm red
These runs had up to 29 colliding bunches with a large spatial separation between the collid- ing bunches, and with the probability of an inelastic interac- tion per bunch crossing being much smaller than one. Only data where the inner detector was operating nominally are included. For five ...
Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks 2016 ACM/IEEE 43rd annual international symposium on computer architecture (ISCA) (2016) P. Wang et al. Designing scratchpad memory architecture with emerging stt-ram memory technologies ISCAS (2013)View more...