183 research outputs found
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
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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
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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
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