14,280 research outputs found
Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images
Breast cancer is the most diagnosed cancer and the most predominant cause of
death in women worldwide. Imaging techniques such as the breast cancer
pathology helps in the diagnosis and monitoring of the disease. However
identification of malignant cells can be challenging given the high
heterogeneity in tissue absorbotion from staining agents. In this work, we
present a novel approach for Invasive Ductal Carcinoma (IDC) cells
discrimination in histopathology slides. We propose a model derived from the
Inception architecture, proposing a multi-level batch normalization module
between each convolutional steps. This module was used as a base block for the
feature extraction in a CNN architecture. We used the open IDC dataset in which
we obtained a balanced accuracy of 0.89 and an F1 score of 0.90, thus
surpassing recent state of the art classification algorithms tested on this
public dataset.Comment: 4 pages, 5 figure
Liver lesion segmentation informed by joint liver segmentation
We propose a model for the joint segmentation of the liver and liver lesions
in computed tomography (CT) volumes. We build the model from two fully
convolutional networks, connected in tandem and trained together end-to-end. We
evaluate our approach on the 2017 MICCAI Liver Tumour Segmentation Challenge,
attaining competitive liver and liver lesion detection and segmentation scores
across a wide range of metrics. Unlike other top performing methods, our model
output post-processing is trivial, we do not use data external to the
challenge, and we propose a simple single-stage model that is trained
end-to-end. However, our method nearly matches the top lesion segmentation
performance and achieves the second highest precision for lesion detection
while maintaining high recall.Comment: Late upload of conference version (ISBI
An Unsupervised Autoregressive Model for Speech Representation Learning
This paper proposes a novel unsupervised autoregressive neural model for
learning generic speech representations. In contrast to other speech
representation learning methods that aim to remove noise or speaker
variabilities, ours is designed to preserve information for a wide range of
downstream tasks. In addition, the proposed model does not require any phonetic
or word boundary labels, allowing the model to benefit from large quantities of
unlabeled data. Speech representations learned by our model significantly
improve performance on both phone classification and speaker verification over
the surface features and other supervised and unsupervised approaches. Further
analysis shows that different levels of speech information are captured by our
model at different layers. In particular, the lower layers tend to be more
discriminative for speakers, while the upper layers provide more phonetic
content.Comment: Accepted to Interspeech 2019. Code available at:
https://github.com/iamyuanchung/Autoregressive-Predictive-Codin
Distributed Algorithms for Learning and Cognitive Medium Access with Logarithmic Regret
The problem of distributed learning and channel access is considered in a
cognitive network with multiple secondary users. The availability statistics of
the channels are initially unknown to the secondary users and are estimated
using sensing decisions. There is no explicit information exchange or prior
agreement among the secondary users. We propose policies for distributed
learning and access which achieve order-optimal cognitive system throughput
(number of successful secondary transmissions) under self play, i.e., when
implemented at all the secondary users. Equivalently, our policies minimize the
regret in distributed learning and access. We first consider the scenario when
the number of secondary users is known to the policy, and prove that the total
regret is logarithmic in the number of transmission slots. Our distributed
learning and access policy achieves order-optimal regret by comparing to an
asymptotic lower bound for regret under any uniformly-good learning and access
policy. We then consider the case when the number of secondary users is fixed
but unknown, and is estimated through feedback. We propose a policy in this
scenario whose asymptotic sum regret which grows slightly faster than
logarithmic in the number of transmission slots.Comment: Submitted to IEEE JSAC on Advances in Cognitive Radio Networking and
Communications, Dec. 2009, Revised May 201
Contactless Remote Induction of Shear Waves in Soft Tissues Using a Transcranial Magnetic Stimulation Device
This study presents the first observation of shear wave induced remotely
within soft tissues. It was performed through the combination of a transcranial
magnetic stimulation device and a permanent magnet. A physical model based on
Maxwell and Navier equations was developed. Experiments were performed on a
cryogel phantom and a chicken breast sample. Using an ultrafast ultrasound
scanner, shear waves of respective amplitude of 5 and 0.5 micrometers were
observed. Experimental and numerical results were in good agreement. This study
constitutes the framework of an alternative shear wave elastography method
Research on Prediction of Instability Failure for Gassy Coal Sample in Different Confining Pressure Based on Microseism
AbstractAccording to the rock failure damage theories and the gas-solid coupling method, used RFPA2D-GasFlow soft code to study the characteristics of microseism in time and space for gassy coal sample in different confining pressure. It is shown that there is some microseism precursory before main shock. Basing on the characteristics of microseism precursory events amount in time can be warned in advance, and basing on the characteristics of microseism precursory events produced by tension stress in space,there is can divide potential failure danger zone. It is shown that the divided results for potential failure danger zone are exactly, and with the increment of confining pressure, the accuracy of divided results on potential failure danger zone become higher. It is found that the potential failure danger zone consistent with crack initiatory zone by contrasted potential failure danger zone to crack development process, and with the increment of confining pressure, the consistency become higher
Pinning control of fractional-order weighted complex networks
In this paper, we consider the pinning control problem of fractional-order weighted complex dynamical networks. The well-studied integer-order complex networks are the special cases of the fractional-order ones. The network model considered can represent both directed and undirected weighted networks. First, based on the eigenvalue analysis and fractional-order stability theory, some local stability properties of such pinned fractional-order networks are derived and the valid stability regions are estimated. A surprising finding is that the fractional-order complex networks can stabilize itself by reducing the fractional-order q without pinning any node. Second, numerical algorithms for fractional-order complex networks are introduced in detail. Finally, numerical simulations in scale-free complex networks are provided to show that the smaller fractional-order q, the larger control gain matrix D, the larger tunable weight parameter , the larger overall coupling strength c, the more capacity that the pinning scheme may possess to enhance the control performance of fractional-order complex networks
Coexistence of heterogenous predator-prey systems with density-dependent dispersal
This paper is concerned with existence, non-existence and uniqueness of
positive (coexistence) steady states to a predator-prey system with
density-dependent dispersal. To overcome the analytical obstacle caused by the
cross-diffusion structure embedded in the density-dependent dispersal, we use a
variable transformation to convert the problem into an elliptic system without
cross-diffusion structure. The transformed system and pre-transformed system
are equivalent in terms of the existence or non-existence of positive
solutions. Then we employ the index theory alongside the method of the
principle eigenvalue to give a nearly complete classification for the existence
and non-existence of positive solutions. Furthermore we show the uniqueness of
positive solutions and characterize the asymptotic profile of solutions for
small or large diffusion rates of species. Our results pinpoint the positive
role of density-dependent dispersal on the population dynamics for the first
time by showing that the density-dependent dispersal is a beneficial strategy
promoting the coexistence of species in the predator-prey system by increasing
the chance of predator's survival.Comment: 28 pages, 2 figure
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