pwc-archive/papers-with-abstracts huggingface dataset:
Control-Oriented Learning on the Fly
Authors: Melkior Ornik, Arie Israel, Ufuk Topcu
Date: 2017-09-14 00:00:00

This paper focuses on developing a strategy for control of systems whose
dynamics are almost entirely unknown. This situation arises naturally in a
scenario where a system undergoes a critical failure. In that case, it is
imperative to retain the ability to satisfy basic control objectives in order
to avert an imminent catastrophe. A prime example of such an objective is the
reach-avoid problem, where a system needs to move to a certain state in a
constrained state space. To deal with limitations on our knowledge of system
dynamics, we develop a theory of myopic control. The primary goal of myopic
control is to, at any given time, optimize the current direction of the system
trajectory, given solely the information obtained about the system until that
time. We propose an algorithm that uses small perturbations in the control
effort to learn local dynamics while simultaneously ensuring that the system
moves in a direction that appears to be nearly optimal, and provide hard bounds
for its suboptimality. We additionally verify the usefulness of the algorithm
on a simulation of a damaged aircraft seeking to avoid a crash, as well as on
an example of a Van der Pol oscillator.
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning
Authors: Daniel J. Luckett, Eric B. Laber, Anna R. Kahkoska, David M. Maahs, Elizabeth Mayer-Davis, Michael R. Kosorok
Date: 2016-11-10 00:00:00
Tasks: Decision Making, Reinforcement Learning

The vision for precision medicine is to use individual patient
characteristics to inform a personalized treatment plan that leads to the best
healthcare possible for each patient. Mobile technologies have an important
role to play in this vision as they offer a means to monitor a patient's health
status in real-time and subsequently to deliver interventions if, when, and in
the dose that they are needed. Dynamic treatment regimes formalize
individualized treatment plans as sequences of decision rules, one per stage of
clinical intervention, that map current patient information to a recommended
treatment. However, existing methods for estimating optimal dynamic treatment
regimes are designed for a small number of fixed decision points occurring on a
coarse time-scale. We propose a new reinforcement learning method for
estimating an optimal treatment regime that is applicable to data collected
using mobile technologies in an outpatient setting. The proposed method
accommodates an indefinite time horizon and minute-by-minute decision making
that are common in mobile health applications. We show the proposed estimators
are consistent and asymptotically normal under mild conditions. The proposed
methods are applied to estimate an optimal dynamic treatment regime for
controlling blood glucose levels in patients with type 1 diabetes.
Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
Authors: Nikita Mokrov, Maxim Panov, Boris A. Gutman, Joshua I. Faskowitz, Neda Jahanshad, Paul M. Thompson
Date: 2017-10-14 00:00:00
Tasks: Classification, General Classification

This paper considers the problem of brain disease classification based on
connectome data. A connectome is a network representation of a human brain. The
typical connectome classification problem is very challenging because of the
small sample size and high dimensionality of the data. We propose to use
simultaneous approximate diagonalization of adjacency matrices in order to
compute their eigenstructures in more stable way. The obtained approximate
eigenvalues are further used as features for classification. The proposed
approach is demonstrated to be efficient for detection of Alzheimer's disease,
outperforming simple baselines and competing with state-of-the-art approaches
to brain disease classification.
SuperSpike: Supervised learning in multi-layer spiking neural networks
Authors: Friedemann Zenke, Surya Ganguli
Date: 2017-05-31 00:00:00

A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico. Here we revisit the problem of supervised learning
in temporally coding multi-layer spiking neural networks. First, by using a
surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based
three factor learning rule capable of training multi-layer networks of
deterministic integrate-and-fire neurons to perform nonlinear computations on
spatiotemporal spike patterns. Second, inspired by recent results on feedback
alignment, we compare the performance of our learning rule under different
credit assignment strategies for propagating output errors to hidden units.
Specifically, we test uniform, symmetric and random feedback, finding that
simpler tasks can be solved with any type of feedback, while more complex tasks
require symmetric feedback. In summary, our results open the door to obtaining
a better scientific understanding of learning and computation in spiking neural
networks by advancing our ability to train them to solve nonlinear problems
involving transformations between different spatiotemporal spike-time patterns.
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction
Authors: Eunhee Kang, junhong Min, Jong Chul Ye
Date: 2016-10-31 00:00:00
Tasks: CT Reconstruction, Denoising, Low-Dose X-Ray Ct Reconstruction

Due to the potential risk of inducing cancers, radiation dose of X-ray CT
should be reduced for routine patient scanning. However, in low-dose X-ray CT,
severe artifacts usually occur due to photon starvation, beamhardening, etc,
which decrease the reliability of diagnosis. Thus, high quality reconstruction
from low-dose X-ray CT data has become one of the important research topics in
CT community. Conventional model-based denoising approaches are, however,
computationally very expensive, and image domain denoising approaches hardly
deal with CT specific noise patterns. To address these issues, we propose an
algorithm using a deep convolutional neural network (CNN), which is applied to
wavelet transform coefficients of low-dose CT images. Specifically, by using a
directional wavelet transform for extracting directional component of artifacts
and exploiting the intra- and inter-band correlations, our deep network can
effectively suppress CT specific noises. Moreover, our CNN is designed to have
various types of residual learning architecture for faster network training and
better denoising. Experimental results confirm that the proposed algorithm
effectively removes complex noise patterns of CT images, originated from the
reduced X-ray dose. In addition, we show that wavelet domain CNN is efficient
in removing the noises from low-dose CT compared to an image domain CNN. Our
results were rigorously evaluated by several radiologists and won the second
place award in 2016 AAPM Low-Dose CT Grand Challenge. To the best of our
knowledge, this work is the first deep learning architecture for low-dose CT
reconstruction that has been rigorously evaluated and proven for its efficacy.
Microaneurysm Detection in Fundus Images Using a Two-step Convolutional Neural Networks
Authors: Noushin Eftekheri, Mojtaba Masoudi, Hamid-Reza Pourreza, Kamaledin Ghiasi Shirazi, Ehsan Saeedi
Date: 2017-10-14 00:00:00

Diabetic Retinopathy (DR) is a prominent cause of blindness in the world. The
early treatment of DR can be conducted from detection of microaneurysms (MAs)
which appears as reddish spots in retinal images. An automated microaneurysm
detection can be a helpful system for ophthalmologists. In this paper, deep
learning, in particular convolutional neural network (CNN), is used as a
powerful tool to efficiently detect MAs from fundus images. In our method a new
technique is used to utilise a two-stage training process which results in an
accurate detection, while decreasing computational complexity in comparison
with previous works. To validate our proposed method, an experiment is
conducted using Keras library to implement our proposed CNN on two standard
publicly available datasets. Our results show a promising sensitivity value of
about 0.8 at the average number of false positive per image greater than 6
which is a competitive value with the state-of-the-art approaches.
A graphical, scalable and intuitive method for the placement and the connection of biological cells
Authors: Nicolas P. Rougier
Date: 2017-10-14 00:00:00

We introduce a graphical method originating from the computer graphics domain
that is used for the arbitrary and intuitive placement of cells over a
two-dimensional manifold. Using a bitmap image as input, where the color
indicates the identity of the different structures and the alpha channel
indicates the local cell density, this method guarantees a discrete
distribution of cell position respecting the local density function. This
method scales to any number of cells, allows to specify several different
structures at once with arbitrary shapes and provides a scalable and versatile
alternative to the more classical assumption of a uniform non-spatial
distribution. Furthermore, several connection schemes can be derived from the
paired distances between cells using either an automatic mapping or a
user-defined local reference frame, providing new computational properties for
the underlying model. The method is illustrated on a discrete homogeneous
neural field, on the distribution of cones and rods in the retina and on a
coronal view of the basal ganglia.
Director Field Analysis (DFA): Exploring Local White Matter Geometric Structure in diffusion MRI
Authors: Jian Cheng, Peter J. Basser
Date: 2017-06-06 00:00:00
Tasks: Diffusion  MRI

In Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion
Imaging (HARDI), a tensor field or a spherical function field (e.g., an
orientation distribution function field), can be estimated from measured
diffusion weighted images. In this paper, inspired by the microscopic
theoretical treatment of phases in liquid crystals, we introduce a novel
mathematical framework, called Director Field Analysis (DFA), to study local
geometric structural information of white matter based on the reconstructed
tensor field or spherical function field: 1) We propose a set of mathematical
tools to process general director data, which consists of dyadic tensors that
have orientations but no direction. 2) We propose Orientational Order (OO) and
Orientational Dispersion (OD) indices to describe the degree of alignment and
dispersion of a spherical function in a single voxel or in a region,
respectively; 3) We also show how to construct a local orthogonal coordinate
frame in each voxel exhibiting anisotropic diffusion; 4) Finally, we define
three indices to describe three types of orientational distortion (splay, bend,
and twist) in a local spatial neighborhood, and a total distortion index to
describe distortions of all three types. To our knowledge, this is the first
work to quantitatively describe orientational distortion (splay, bend, and
twist) in general spherical function fields from DTI or HARDI data. The
proposed DFA and its related mathematical tools can be used to process not only
diffusion MRI data but also general director field data, and the proposed
scalar indices are useful for detecting local geometric changes of white matter
for voxel-based or tract-based analysis in both DTI and HARDI acquisitions. The
related codes and a tutorial for DFA will be released in DMRITool.
Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion
Authors: Runmin Cong, Jianjun Lei, Changqing Zhang, Qingming Huang, Xiaochun Cao, Chunping Hou
Date: 2017-10-14 00:00:00
Tasks: graph construction, Saliency Detection

Stereoscopic perception is an important part of human visual system that
allows the brain to perceive depth. However, depth information has not been
well explored in existing saliency detection models. In this letter, a novel
saliency detection method for stereoscopic images is proposed. Firstly, we
propose a measure to evaluate the reliability of depth map, and use it to
reduce the influence of poor depth map on saliency detection. Then, the input
image is represented as a graph, and the depth information is introduced into
graph construction. After that, a new definition of compactness using color and
depth cues is put forward to compute the compactness saliency map. In order to
compensate the detection errors of compactness saliency when the salient
regions have similar appearances with background, foreground saliency map is
calculated based on depth-refined foreground seeds selection mechanism and
multiple cues contrast. Finally, these two saliency maps are integrated into a
final saliency map through weighted-sum method according to their importance.
Experiments on two publicly available stereo datasets demonstrate that the
proposed method performs better than other 10 state-of-the-art approaches.
Co-saliency Detection for RGBD Images Based on Multi-constraint Feature Matching and Cross Label Propagation
Authors: Runmin Cong, Jianjun Lei, Huazhu Fu, Qingming Huang, Xiaochun Cao, Chunping Hou
Date: 2017-10-14 00:00:00
Tasks: Co-Salient Object Detection, Saliency Detection

Co-saliency detection aims at extracting the common salient regions from an
image group containing two or more relevant images. It is a newly emerging
topic in computer vision community. Different from the most existing
co-saliency methods focusing on RGB images, this paper proposes a novel
co-saliency detection model for RGBD images, which utilizes the depth
information to enhance identification of co-saliency. First, the intra saliency
map for each image is generated by the single image saliency model, while the
inter saliency map is calculated based on the multi-constraint feature
matching, which represents the constraint relationship among multiple images.
Then, the optimization scheme, namely Cross Label Propagation (CLP), is used to
refine the intra and inter saliency maps in a cross way. Finally, all the
original and optimized saliency maps are integrated to generate the final
co-saliency result. The proposed method introduces the depth information and
multi-constraint feature matching to improve the performance of co-saliency
detection. Moreover, the proposed method can effectively exploit any existing
single image saliency model to work well in co-saliency scenarios. Experiments
on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed
model.
Sparse Representation Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral Spatial Hyperspectral Image Classification
Authors: Faxian Cao, Zhijing Yang, Jinchang Ren, Wing-Kuen Ling
Date: 2017-09-12 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Although extreme learning machine (ELM) has been successfully applied to a
number of pattern recognition problems, it fails to pro-vide sufficient good
results in hyperspectral image (HSI) classification due to two main drawbacks.
The first is due to the random weights and bias of ELM, which may lead to
ill-posed problems. The second is the lack of spatial information for
classification. To tackle these two problems, in this paper, we propose a new
framework for ELM based spectral-spatial classification of HSI, where
probabilistic modelling with sparse representation and weighted composite
features (WCF) are employed respectively to derive the op-timized output
weights and extract spatial features. First, the ELM is represented as a
concave logarithmic likelihood function under statistical modelling using the
maximum a posteriori (MAP). Second, the sparse representation is applied to the
Laplacian prior to effi-ciently determine a logarithmic posterior with a unique
maximum in order to solve the ill-posed problem of ELM. The variable splitting
and the augmented Lagrangian are subsequently used to further reduce the
computation complexity of the proposed algorithm and it has been proven a more
efficient method for speed improvement. Third, the spatial information is
extracted using the weighted compo-site features (WCFs) to construct the
spectral-spatial classification framework. In addition, the lower bound of the
proposed method is derived by a rigorous mathematical proof. Experimental
results on two publicly available HSI data sets demonstrate that the proposed
methodology outperforms ELM and a number of state-of-the-art approaches.
A Large-scale Dataset and Benchmark for Similar Trademark Retrieval
Authors: Osman Tursun, Cemal Aker, Sinan Kalkan
Date: 2017-01-20 00:00:00
Tasks: Retrieval, Trademark Retrieval

Trademark retrieval (TR) has become an important yet challenging problem due
to an ever increasing trend in trademark applications and infringement
incidents. There have been many promising attempts for the TR problem, which,
however, fell impracticable since they were evaluated with limited and mostly
trivial datasets. In this paper, we provide a large-scale dataset with
benchmark queries with which different TR approaches can be evaluated
systematically. Moreover, we provide a baseline on this benchmark using the
widely-used methods applied to TR in the literature. Furthermore, we identify
and correct two important issues in TR approaches that were not addressed
before: reversal of contrast, and presence of irrelevant text in trademarks
severely affect the TR methods. Lastly, we applied deep learning, namely,
several popular Convolutional Neural Network models, to the TR problem. To the
best of the authors, this is the first attempt to do so.
An Improved Modified Cholesky Decomposition Method for Precision Matrix Estimation
Authors: Xiaoning Kang, Xinwei Deng
Date: 2017-10-14 00:00:00

The modified Cholesky decomposition is commonly used for precision matrix estimation given a specified order of random variables. However, the order of variables is often not available or cannot be pre-determined. In this work, we propose to address the variable order issue in the modified Cholesky decomposition for sparse precision matrix estimation. The key idea is to effectively combine a set of estimates obtained from multiple permutations of variable orders, and to efficiently encourage the sparse structure for the resultant estimate by the thresholding technique on the ensemble Cholesky factor matrix. The consistent property of the proposed estimate is established under some weak regularity conditions. Simulation studies are conducted to evaluate the performance of the proposed method in comparison with several existing approaches. The proposed method is also applied into linear discriminant analysis of real data for classification.
Anomaly Detection by Robust Statistics
Authors: Peter J. Rousseeuw, Mia Hubert
Date: 2017-07-31 00:00:00
Tasks: Anomaly Detection, Clustering, General Classification, Outlier Detection, regression

Real data often contain anomalous cases, also known as outliers. These may
spoil the resulting analysis but they may also contain valuable information. In
either case, the ability to detect such anomalies is essential. A useful tool
for this purpose is robust statistics, which aims to detect the outliers by
first fitting the majority of the data and then flagging data points that
deviate from it. We present an overview of several robust methods and the
resulting graphical outlier detection tools. We discuss robust procedures for
univariate, low-dimensional, and high-dimensional data, such as estimating
location and scatter, linear regression, principal component analysis,
classification, clustering, and functional data analysis. Also the challenging
new topic of cellwise outliers is introduced.
BrainSegNet : A Segmentation Network for Human Brain Fiber Tractography Data into Anatomically Meaningful Clusters
Authors: Tushar Gupta, Shreyas Malakarjun Patil, Mukkaram Tailor, Daksh Thapar, Aditya Nigam
Date: 2017-10-14 00:00:00
Tasks: Classification, General Classification, Management

The segregation of brain fiber tractography data into distinct and
anatomically meaningful clusters can help to comprehend the complex brain
structure and early investigation and management of various neural disorders.
We propose a novel stacked bidirectional long short-term memory(LSTM) based
segmentation network, (BrainSegNet) for human brain fiber tractography data
classification. We perform a two-level hierarchical classification a) White vs
Grey matter (Macro) and b) White matter clusters (Micro). BrainSegNet is
trained over three brain tractography data having over 250,000 fibers each. Our
experimental evaluation shows that our model achieves state-of-the-art results.
We have performed inter as well as intra class testing over three patient's
brain tractography data and achieved a high classification accuracy for both
macro and micro levels both under intra as well as inter brain testing
scenario.
GHCLNet: A Generalized Hierarchically tuned Contact Lens detection Network
Authors: Avantika Singh, Vishesh Mistry, Dhananjay Yadav, Aditya Nigam
Date: 2017-10-14 00:00:00

Iris serves as one of the best biometric modality owing to its complex,
unique and stable structure. However, it can still be spoofed using fabricated
eyeballs and contact lens. Accurate identification of contact lens is must for
reliable performance of any biometric authentication system based on this
modality. In this paper, we present a novel approach for detecting contact lens
using a Generalized Hierarchically tuned Contact Lens detection Network
(GHCLNet) . We have proposed hierarchical architecture for three class oculus
classification namely: no lens, soft lens and cosmetic lens. Our network
architecture is inspired by ResNet-50 model. This network works on raw input
iris images without any pre-processing and segmentation requirement and this is
one of its prodigious strength. We have performed extensive experimentation on
two publicly available data-sets namely: 1)IIIT-D 2)ND and on IIT-K data-set
(not publicly available) to ensure the generalizability of our network. The
proposed architecture results are quite promising and outperforms the available
state-of-the-art lens detection algorithms.
Hierarchical semantic segmentation using modular convolutional neural networks
Authors: Sagi Eppel
Date: 2017-10-14 00:00:00
Tasks: Segmentation, Semantic Segmentation

Image recognition tasks that involve identifying parts of an object or the
contents of a vessel can be viewed as a hierarchical problem, which can be
solved by initial recognition of the main object, followed by recognition of
its parts or contents. To achieve such modular recognition, it is necessary to
use the output of one recognition method (which identifies the general object)
as the input for a second method (which identifies the parts or contents). In
recent years, convolutional neural networks have emerged as the dominant method
for segmentation and classification of images. This work examines a method for
serially connecting convolutional neural networks for semantic segmentation of
materials inside transparent vessels. It applies one fully convolutional neural
net to segment the image into vessel and background, and the vessel region is
used as an input for a second net which recognizes the contents of the glass
vessel. Transferring the segmentation map generated by the first nets to the
second net was performed using the valve filter attention method that involves
using different filters on different segments of the image. This modular
semantic segmentation method outperforms a single step method in which both the
vessel and its contents are identified using a single net. An advantage of the
modular neural net is that it allows networks to be built from existing trained
modules, as well the transfer and reuse of trained net modules without the need
for any retraining of the assembled net.