Smart Pacing for Effective Online Ad Campaign Optimization

In targeted online advertising, advertisers look for maximizing campaign performance under delivery constraint within budget schedule. Most of the advertisers typically prefer to impose the delivery constraint to spend budget smoothly over the time in order to reach a wider range of audiences and have a sustainable impact. Since lots of impressions are traded through public auctions for online advertising today, the liquidity makes price elasticity and bid landscape between demand and supply change quite dynamically. Therefore, it is challenging to perform smooth pacing control and maximize campaign performance simultaneously. In this paper, we propose a smart pacing approach in which the delivery pace of each campaign is learned from both offline and online data to achieve smooth delivery and optimal performance goals. The implementation of the proposed approach in a real DSP system is also presented. Experimental evaluations on both real online ad campaigns and offline simulations show that our approach can effectively improve campaign performance and achieve delivery goals.Comment: KDD'15, August 10-13, 2015, Sydney, NSW, Australi

Visualization of Surfaces and 3D Vector Fields

Visualization of trivariate functions and vector fields with three components in scientific computation is still a hard problem in compute graphic area. People build their own visualization packages for their special purposes. And there exist some general-purpose packages (MatLab, Vis5D), but they all require extensive user experience on setting all the parameters in order to generate images. We present a simple package to produce simplified but productive images of 3-D vector fields. We used this method to render the magnetic field and current as solutions of the Ginzburg-Landau equations on a 3-D domain

High Performance Architecture using Speculative Threads and Dynamic Memory Management Hardware

With the advances in very large scale integration (VLSI) technology, hundreds of billions of transistors can be packed into a single chip. With the increased hardware budget, how to take advantage of available hardware resources becomes an important research area. Some researchers have shifted from control flow Von-Neumann architecture back to dataflow architecture again in order to explore scalable architectures leading to multi-core systems with several hundreds of processing elements. In this dissertation, I address how the performance of modern processing systems can be improved, while attempting to reduce hardware complexity and energy consumptions. My research described here tackles both central processing unit (CPU) performance and memory subsystem performance. More specifically I will describe my research related to the design of an innovative decoupled multithreaded architecture that can be used in multi-core processor implementations. I also address how memory management functions can be off-loaded from processing pipelines to further improve system performance and eliminate cache pollution caused by runtime management functions

LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation

Semantic map construction under bird's-eye view (BEV) plays an essential role in autonomous driving. In contrast to camera image, LiDAR provides the accurate 3D observations to project the captured 3D features onto BEV space inherently. However, the vanilla LiDAR-based BEV feature often contains many indefinite noises, where the spatial features have little texture and semantic cues. In this paper, we propose an effective LiDAR-based method to build semantic map. Specifically, we introduce a BEV feature pyramid decoder that learns the robust multi-scale BEV features for semantic map construction, which greatly boosts the accuracy of the LiDAR-based method. To mitigate the defects caused by lacking semantic cues in LiDAR data, we present an online Camera-to-LiDAR distillation scheme to facilitate the semantic learning from image to point cloud. Our distillation scheme consists of feature-level and logit-level distillation to absorb the semantic information from camera in BEV. The experimental results on challenging nuScenes dataset demonstrate the efficacy of our proposed LiDAR2Map on semantic map construction, which significantly outperforms the previous LiDAR-based methods over 27.9% mIoU and even performs better than the state-of-the-art camera-based approaches. Source code is available at: https://github.com/songw-zju/LiDAR2Map.Comment: Accepted by CVPR202

The Tissue Response and Degradation of Electrospun Poly( ε

Due to the advantage of controllability on the mechanical property and the degradation rates, electrospun PCL/PTMC nanofibrous scaffold could be appropriate for vascular tissue engineering. However, the tissue response and degradation of electrospun PCL/PTMC scaffold in vivo have never been evaluated in detail. So, electrospun PCL/PTMC scaffolds with different blend ratios were prepared in this study. Mice subcutaneous implantation showed that the continuous degradation of PCL/PTMC scaffolds induced a lasted macrophage-mediated foreign body reaction, which could be in favor of the tissue regeneration in graft

Dynamic Transcriptome Analysis Reveals Potential Long Non-coding RNAs Governing Postnatal Pineal Development in Pig

Postnatal development and maturation of pineal gland is a highly dynamic period of tissue remodeling and phenotype maintenance, which is genetically controlled by programmed gene expression regulations. However, limited molecular characterization, particularly regarding long noncoding RNAs (lncRNA), is available for postnatal pineal at a whole transcriptome level. The present study first characterized the comprehensive pineal transcriptome profiles using strand-specific RNA-seq to illustrate the dynamic mRNA/lncRNA expression at three developmental stages (infancy, puberty, and adulthood). The results showed that 21,448 mRNAs and 8,166 novel lncRNAs were expressed in pig postnatal pineal gland. Among these genes, 3,573 mRNAs and 851 lncRNAs, including the 5-hydroxytryptamine receptors, exhibited significant dynamic regulation along maturation process, while the expression of homeobox genes didn’t show significant differences. Gene Ontology analysis revealed that the differentially expressed genes (DEGs) were significantly enriched in ion transport and synaptic transmission, highlighting the critical role of calcium signaling in postnatal pineal development. Additionally, co-expression analysis revealed the DEGs could be grouped into 12 clusters with distinct expression patterns. Many differential lncRNAs were functionally enriched in co-expressed clusters of genes related to ion transport, transcription regulation, DNA binding, and visual perception. Our study first provided an overview of postnatal pineal transcriptome dynamics in pig and demonstrated that dynamic lncRNA regulation of developmental transitions impact pineal physiology

Box-supervised Instance Segmentation with Level Set Evolution

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which integrates the classical level set model with deep neural network delicately. Specifically, our proposed method iteratively learns a series of level sets through a continuous Chan-Vese energy-based function in an end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance. Both the input image and its deep features are employed as the input data to evolve the level set curves, where a box projection function is employed to obtain the initial boundary. By minimizing the fully differentiable energy function, the level set for each instance is iteratively optimized within its corresponding bounding box annotation. The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach to robust instance segmentation in various scenarios. The code is available at: https://github.com/LiWentomng/boxlevelset.Comment: 17 page, 4figures, ECCV202