1,670 research outputs found
Semantic Part Segmentation using Compositional Model combining Shape and Appearance
In this paper, we study the problem of semantic part segmentation for
animals. This is more challenging than standard object detection, object
segmentation and pose estimation tasks because semantic parts of animals often
have similar appearance and highly varying shapes. To tackle these challenges,
we build a mixture of compositional models to represent the object boundary and
the boundaries of semantic parts. And we incorporate edge, appearance, and
semantic part cues into the compositional model. Given part-level segmentation
annotation, we develop a novel algorithm to learn a mixture of compositional
models under various poses and viewpoints for certain animal classes.
Furthermore, a linear complexity algorithm is offered for efficient inference
of the compositional model using dynamic programming. We evaluate our method
for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has
pixelwise part labels. Experimental results demonstrate the effectiveness of
our method
Edge-as-a-Service: Towards Distributed Cloud Architectures
We present an Edge-as-a-Service (EaaS) platform for realising distributed
cloud architectures and integrating the edge of the network in the computing
ecosystem. The EaaS platform is underpinned by (i) a lightweight discovery
protocol that identifies edge nodes and make them publicly accessible in a
computing environment, and (ii) a scalable resource provisioning mechanism for
offloading workloads from the cloud on to the edge for servicing multiple user
requests. We validate the feasibility of EaaS on an online game use-case to
highlight the improvement in the QoS of the application hosted on our
cloud-edge platform. On this platform we demonstrate (i) low overheads of less
than 6%, (ii) reduced data traffic to the cloud by up to 95% and (iii)
minimised application latency between 40%-60%.Comment: 10 pages; presented at the EdgeComp Symposium 2017; will appear in
Proceedings of the International Conference on Parallel Computing, 201
FreePSI: an alignment-free approach to estimating exon-inclusion ratios without a reference transcriptome.
Alternative splicing plays an important role in many cellular processes of eukaryotic organisms. The exon-inclusion ratio, also known as percent spliced in, is often regarded as one of the most effective measures of alternative splicing events. The existing methods for estimating exon-inclusion ratios at the genome scale all require the existence of a reference transcriptome. In this paper, we propose an alignment-free method, FreePSI, to perform genome-wide estimation of exon-inclusion ratios from RNA-Seq data without relying on the guidance of a reference transcriptome. It uses a novel probabilistic generative model based on k-mer profiles to quantify the exon-inclusion ratios at the genome scale and an efficient expectation-maximization algorithm based on a divide-and-conquer strategy and ultrafast conjugate gradient projection descent method to solve the model. We compare FreePSI with the existing methods on simulated and real RNA-seq data in terms of both accuracy and efficiency and show that it is able to achieve very good performance even though a reference transcriptome is not provided. Our results suggest that FreePSI may have important applications in performing alternative splicing analysis for organisms that do not have quality reference transcriptomes. FreePSI is implemented in C++ and freely available to the public on GitHub
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