36,254 research outputs found
WPU-Net: Boundary Learning by Using Weighted Propagation in Convolution Network
Deep learning has driven a great progress in natural and biological image
processing. However, in material science and engineering, there are often some
flaws and indistinctions in material microscopic images induced from complex
sample preparation, even due to the material itself, hindering the detection of
target objects. In this work, we propose WPU-net that redesigns the
architecture and weighted loss of U-Net, which forces the network to integrate
information from adjacent slices and pays more attention to the topology in
boundary detection task. Then, the WPU-net is applied into a typical material
example, i.e., the grain boundary detection of polycrystalline material.
Experiments demonstrate that the proposed method achieves promising performance
and outperforms state-of-the-art methods. Besides, we propose a new method for
object tracking between adjacent slices, which can effectively reconstruct 3D
structure of the whole material. Finally, we present a material microscopic
image dataset with the goal of advancing the state-of-the-art in image
processing for material science.Comment: technical repor
git2net - Mining Time-Stamped Co-Editing Networks from Large git Repositories
Data from software repositories have become an important foundation for the
empirical study of software engineering processes. A recurring theme in the
repository mining literature is the inference of developer networks capturing
e.g. collaboration, coordination, or communication from the commit history of
projects. Most of the studied networks are based on the co-authorship of
software artefacts defined at the level of files, modules, or packages. While
this approach has led to insights into the social aspects of software
development, it neglects detailed information on code changes and code
ownership, e.g. which exact lines of code have been authored by which
developers, that is contained in the commit log of software projects.
Addressing this issue, we introduce git2net, a scalable python software that
facilitates the extraction of fine-grained co-editing networks in large git
repositories. It uses text mining techniques to analyse the detailed history of
textual modifications within files. This information allows us to construct
directed, weighted, and time-stamped networks, where a link signifies that one
developer has edited a block of source code originally written by another
developer. Our tool is applied in case studies of an Open Source and a
commercial software project. We argue that it opens up a massive new source of
high-resolution data on human collaboration patterns.Comment: MSR 2019, 12 pages, 10 figure
Are all the frames equally important?
In this work, we address the problem of measuring and predicting temporal
video saliency - a metric which defines the importance of a video frame for
human attention. Unlike the conventional spatial saliency which defines the
location of the salient regions within a frame (as it is done for still
images), temporal saliency considers importance of a frame as a whole and may
not exist apart from context. The proposed interface is an interactive
cursor-based algorithm for collecting experimental data about temporal
saliency. We collect the first human responses and perform their analysis. As a
result, we show that qualitatively, the produced scores have very explicit
meaning of the semantic changes in a frame, while quantitatively being highly
correlated between all the observers. Apart from that, we show that the
proposed tool can simultaneously collect fixations similar to the ones produced
by eye-tracker in a more affordable way. Further, this approach may be used for
creation of first temporal saliency datasets which will allow training
computational predictive algorithms. The proposed interface does not rely on
any special equipment, which allows to run it remotely and cover a wide
audience.Comment: CHI'20 Late Breaking Work
Learning Combinatorial Embedding Networks for Deep Graph Matching
Graph matching refers to finding node correspondence between graphs, such
that the corresponding node and edge's affinity can be maximized. In addition
with its NP-completeness nature, another important challenge is effective
modeling of the node-wise and structure-wise affinity across graphs and the
resulting objective, to guide the matching procedure effectively finding the
true matching against noises. To this end, this paper devises an end-to-end
differentiable deep network pipeline to learn the affinity for graph matching.
It involves a supervised permutation loss regarding with node correspondence to
capture the combinatorial nature for graph matching. Meanwhile deep graph
embedding models are adopted to parameterize both intra-graph and cross-graph
affinity functions, instead of the traditional shallow and simple parametric
forms e.g. a Gaussian kernel. The embedding can also effectively capture the
higher-order structure beyond second-order edges. The permutation loss model is
agnostic to the number of nodes, and the embedding model is shared among nodes
such that the network allows for varying numbers of nodes in graphs for
training and inference. Moreover, our network is class-agnostic with some
generalization capability across different categories. All these features are
welcomed for real-world applications. Experiments show its superiority against
state-of-the-art graph matching learning methods.Comment: ICCV2019 oral. Code available at
https://github.com/Thinklab-SJTU/PCA-G
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