40,972 research outputs found
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method
Liver lesion segmentation is an important step for liver cancer diagnosis,
treatment planning and treatment evaluation. LiTS (Liver Tumor Segmentation
Challenge) provides a common testbed for comparing different automatic liver
lesion segmentation methods. We participate in this challenge by developing a
deep convolutional neural network (DCNN) method. The particular DCNN model
works in 2.5D in that it takes a stack of adjacent slices as input and produces
the segmentation map corresponding to the center slice. The model has 32 layers
in total and makes use of both long range concatenation connections of U-Net
[1] and short-range residual connections from ResNet [2]. The model was trained
using the 130 LiTS training datasets and achieved an average Dice score of 0.67
when evaluated on the 70 test CT scans, which ranked first for the LiTS
challenge at the time of the ISBI 2017 conference.Comment: Submission for ISBI'2017 LiTS Challenge ISIC201
On Kernel Mengerian Orientations of Line Multigraphs
We present a polyhedral description of kernels in orientations of line
multigraphs. Given a digraph , let denote the fractional kernel
polytope defined on , and let denote the linear system
defining . A digraph is called kernel perfect if every induced
subdigraph has a kernel, called kernel ideal if is
integral for each induced subdigraph , and called kernel Mengerian if
is TDI for each induced subdigraph . We show
that an orientation of a line multigraph is kernel perfect iff it is kernel
ideal iff it is kernel Mengerian. Our result strengthens the theorem of Borodin
et al. [3] on kernel perfect digraphs and generalizes the theorem of Kiraly and
Pap [7] on stable matching problem.Comment: 12 pages, corrected and slightly expanded versio
On the coherent Hopf 2-algebras
We construct a coherent Hopf 2-algebra as quantization of a coherent 2-group,
which consists of two Hopf coquasigroups and a coassociator. For this
constructive method, if we replace Hopf coquasigroups by Hopf algebras, we can
construct a strict Hoft 2-algebra, which is a quantisation of 2-group. We also
study the crossed comodule of Hopf algebras, which is shown to be a strict Hopf
2-algebra under some conditions. As an example, a quasi coassociative Hopf
coquasigroup is employed to build a special coherent Hopf 2-algebra with
nontrivial coassociator. Following this we study functions on Cayley algebra
basis.Comment: 36 page
NDT: Neual Decision Tree Towards Fully Functioned Neural Graph
Though traditional algorithms could be embedded into neural architectures
with the proposed principle of \cite{xiao2017hungarian}, the variables that
only occur in the condition of branch could not be updated as a special case.
To tackle this issue, we multiply the conditioned branches with Dirac symbol
(i.e. ), then approximate Dirac symbol with the continuous
functions (e.g. ). In this way, the gradients of
condition-specific variables could be worked out in the back-propagation
process, approximately, making a fully functioned neural graph. Within our
novel principle, we propose the neural decision tree \textbf{(NDT)}, which
takes simplified neural networks as decision function in each branch and
employs complex neural networks to generate the output in each leaf. Extensive
experiments verify our theoretical analysis and demonstrate the effectiveness
of our model.Comment: This is the draft paper. I will refine the paper until accepte
R_b Constraints on Littlest Higgs Model with T-parity
In the framework of the littlest Higgs model with T-parity (LHT), we study
the contributions of the T-even and T-odd particles to the branching ratio R_b.
We find that the precision data of R_b can give strong constraints on the
masses of T-odd fermions.Comment: 11 pages, 5 figure
Knowledge Recognition Algorithm enables P = NP
This paper introduces a knowledge recognition algorithm (KRA) that is both a
Turing machine algorithm and an Oracle Turing machine algorithm. By definition
KRA is a non-deterministic language recognition algorithm. Simultaneously it
can be implemented as a deterministic Turing machine algorithm. KRA applies
mirrored perceptual-conceptual languages to learn member-class relations
between the two languages iteratively and retrieve information through
deductive and reductive recognition from one language to another. The novelty
of KRA is that the conventional concept of relation is adjusted. The
computation therefore becomes efficient bidirectional string mapping
Margin-Based Feed-Forward Neural Network Classifiers
Margin-Based Principle has been proposed for a long time, it has been proved
that this principle could reduce the structural risk and improve the
performance in both theoretical and practical aspects. Meanwhile, feed-forward
neural network is a traditional classifier, which is very hot at present with a
deeper architecture. However, the training algorithm of feed-forward neural
network is developed and generated from Widrow-Hoff Principle that means to
minimize the squared error. In this paper, we propose a new training algorithm
for feed-forward neural networks based on Margin-Based Principle, which could
effectively promote the accuracy and generalization ability of neural network
classifiers with less labelled samples and flexible network. We have conducted
experiments on four UCI open datasets and achieved good results as expected. In
conclusion, our model could handle more sparse labelled and more high-dimension
dataset in a high accuracy while modification from old ANN method to our method
is easy and almost free of work.Comment: This paper has been published in ICANN 2015: International Conference
on Artificial Neural Networks, Amsterdam, The Netherlands, (May 14-15, 2015
Finiteness of small factor analysis models
We consider small factor analysis models with one or two factors. Fixing the
number of factors, we prove a finiteness result about the covariance matrix
parameter space when the size of the covariance matrix increases. According to
this result, there exists a distinguished matrix size starting at which one can
determine whether a given covariance matrix belongs to the parameter space by
determining whether all principal submatrices of the distinguished size belong
to the corresponding parameter space. We show that the distinguished matrix
size is equal to four in the one-factor model and six with two factors
Smoothness of Gaussian conditional independence models
Conditional independence in a multivariate normal (or Gaussian) distribution
is characterized by the vanishing of subdeterminants of the distribution's
covariance matrix. Gaussian conditional independence models thus correspond to
algebraic subsets of the cone of positive definite matrices. For statistical
inference in such models it is important to know whether or not the model
contains singularities. We study this issue in models involving up to four
random variables. In particular, we give examples of conditional independence
relations which, despite being probabilistically representable, yield models
that non-trivially decompose into a finite union of several smooth submodels
Max-Entropy Feed-Forward Clustering Neural Network
The outputs of non-linear feed-forward neural network are positive, which
could be treated as probability when they are normalized to one. If we take
Entropy-Based Principle into consideration, the outputs for each sample could
be represented as the distribution of this sample for different clusters.
Entropy-Based Principle is the principle with which we could estimate the
unknown distribution under some limited conditions. As this paper defines two
processes in Feed-Forward Neural Network, our limited condition is the
abstracted features of samples which are worked out in the abstraction process.
And the final outputs are the probability distribution for different clusters
in the clustering process. As Entropy-Based Principle is considered into the
feed-forward neural network, a clustering method is born. We have conducted
some experiments on six open UCI datasets, comparing with a few baselines and
applied purity as the measurement . The results illustrate that our method
outperforms all the other baselines that are most popular clustering methods.Comment: This paper has been published in ICANN 201
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