pwc-archive/papers-with-abstracts huggingface dataset:
The Dependence of Frequency Distributions on Multiple Meanings of Words, Codes and Signs
Authors: Xiao-Yong Yan, Petter Minnhagen
Date: 2017-09-28 00:00:00

The dependence of the frequency distributions due to multiple meanings of
words in a text is investigated by deleting letters. By coding the words with
fewer letters the number of meanings per coded word increases. This increase is
measured and used as an input in a predictive theory. For a text written in
English, the word-frequency distribution is broad and fat-tailed, whereas if
the words are only represented by their first letter the distribution becomes
exponential. Both distribution are well predicted by the theory, as is the
whole sequence obtained by consecutively representing the words by the first
L=6,5,4,3,2,1 letters. Comparisons of texts written by Chinese characters and
the same texts written by letter-codes are made and the similarity of the
corresponding frequency-distributions are interpreted as a consequence of the
multiple meanings of Chinese characters. This further implies that the
difference of the shape for word-frequencies for an English text written by
letters and a Chinese text written by Chinese characters is due to the coding
and not to the language per se.
Dilated Deep Residual Network for Image Denoising
Authors: Tianyang Wang, Mingxuan Sun, Kaoning Hu
Date: 2017-08-18 00:00:00
Tasks: Color Image Denoising, Denoising, image-classification, Image Classification, Image Denoising

Variations of deep neural networks such as convolutional neural network (CNN)
have been successfully applied to image denoising. The goal is to automatically
learn a mapping from a noisy image to a clean image given training data
consisting of pairs of noisy and clean images. Most existing CNN models for
image denoising have many layers. In such cases, the models involve a large
amount of parameters and are computationally expensive to train. In this paper,
we develop a dilated residual CNN for Gaussian image denoising. Compared with
the recently proposed residual denoiser, our method can achieve comparable
performance with less computational cost. Specifically, we enlarge receptive
field by adopting dilated convolution in residual network, and the dilation
factor is set to a certain value. We utilize appropriate zero padding to make
the dimension of the output the same as the input. It has been proven that the
expansion of receptive field can boost the CNN performance in image
classification, and we further demonstrate that it can also lead to competitive
performance for denoising problem. Moreover, we present a formula to calculate
receptive field size when dilated convolution is incorporated. Thus, the change
of receptive field can be interpreted mathematically. To validate the efficacy
of our approach, we conduct extensive experiments for both gray and color image
denoising with specific or randomized noise levels. Both of the quantitative
measurements and the visual results of denoising are promising comparing with
state-of-the-art baselines.
The detour problem in a stochastic environment: Tolman revisited
Authors: Pegah Fakhari, Arash Khodadadi, Jerome Busemeyer
Date: 2017-09-27 00:00:00
Tasks: Model-based Reinforcement Learning, Reinforcement Learning

We designed a grid world task to study human planning and re-planning
behavior in an unknown stochastic environment. In our grid world, participants
were asked to travel from a random starting point to a random goal position
while maximizing their reward. Because they were not familiar with the
environment, they needed to learn its characteristics from experience to plan
optimally. Later in the task, we randomly blocked the optimal path to
investigate whether and how people adjust their original plans to find a
detour. To this end, we developed and compared 12 different models. These
models were different on how they learned and represented the environment and
how they planned to catch the goal. The majority of our participants were able
to plan optimally. We also showed that people were capable of revising their
plans when an unexpected event occurred. The result from the model comparison
showed that the model-based reinforcement learning approach provided the best
account for the data and outperformed heuristics in explaining the behavioral
data in the re-planning trials.
A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images
Authors: Meghana Dinesh Kumar, Morteza Babaie, Shujin Zhu, Shivam Kalra, H. R. Tizhoosh
Date: 2017-09-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Despite the progress made in the field of medical imaging, it remains a large
area of open research, especially due to the variety of imaging modalities and
disease-specific characteristics. This paper is a comparative study describing
the potential of using local binary patterns (LBP), deep features and the
bag-of-visual words (BoVW) scheme for the classification of histopathological
images. We introduce a new dataset, \emph{KIMIA Path960}, that contains 960
histopathology images belonging to 20 different classes (different tissue
types). We make this dataset publicly available. The small size of the dataset
and its inter- and intra-class variability makes it ideal for initial
investigations when comparing image descriptors for search and classification
in complex medical imaging cases like histopathology. We investigate deep
features, LBP histograms and BoVW to classify the images via leave-one-out
validation. The accuracy of image classification obtained using LBP was 90.62\%
while the highest accuracy using deep features reached 94.72\%. The dictionary
approach (BoVW) achieved 96.50\%. Deep solutions may be able to deliver higher
accuracies but they need extensive training with a large number of (balanced)
image datasets.
Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives
Authors: Hamed Erfankhah, Mehran Yazdi, H. R. Tizhoosh
Date: 2017-09-27 00:00:00
Tasks: Content-Based Image Retrieval, Diagnostic, General Classification, Image Retrieval, Retrieval

Content-based image retrieval (CBIR) of medical images in large datasets to
identify similar images when a query image is given can be very useful in
improving the diagnostic decision of the clinical experts and as well in
educational scenarios. In this paper, we used two stage classification and
retrieval approach to retrieve similar images. First, the Gabor filters are
applied to Radon-transformed images to extract features and to train a
multi-class SVM. Then based on the classification results and using an
extracted Gabor barcode, similar images are retrieved. The proposed method was
tested on IRMA dataset which contains more than 14,000 images. Experimental
results show the efficiency of our approach in retrieving similar images
compared to other Gabor-Radon-oriented methods.
Skin Lesion Segmentation: U-Nets versus Clustering
Authors: Bill S. Lin, Kevin Michael, Shivam Kalra, H. R. Tizhoosh
Date: 2017-09-27 00:00:00
Tasks: Clustering, CPU, Lesion Segmentation, Segmentation, Skin Lesion Segmentation

Many automatic skin lesion diagnosis systems use segmentation as a
preprocessing step to diagnose skin conditions because skin lesion shape,
border irregularity, and size can influence the likelihood of malignancy. This
paper presents, examines and compares two different approaches to skin lesion
segmentation. The first approach uses U-Nets and introduces a histogram
equalization based preprocessing step. The second approach is a C-Means
clustering based approach that is much simpler to implement and faster to
execute. The Jaccard Index between the algorithm output and hand segmented
images by dermatologists is used to evaluate the proposed algorithms. While
many recently proposed deep neural networks to segment skin lesions require a
significant amount of computational power for training (i.e., computer with
GPUs), the main objective of this paper is to present methods that can be used
with only a CPU. This severely limits, for example, the number of training
instances that can be presented to the U-Net. Comparing the two proposed
algorithms, U-Nets achieved a significantly higher Jaccard Index compared to
the clustering approach. Moreover, using the histogram equalization for
preprocessing step significantly improved the U-Net segmentation results.
Learning Autoencoded Radon Projections
Authors: Aditya Sriram, Shivam Kalra, H. R. Tizhoosh, Shahryar Rahnamayan
Date: 2017-09-27 00:00:00
Tasks: Retrieval

Autoencoders have been recently used for encoding medical images. In this
study, we design and validate a new framework for retrieving medical images by
classifying Radon projections, compressed in the deepest layer of an
autoencoder. As the autoencoder reduces the dimensionality, a multilayer
perceptron (MLP) can be employed to classify the images. The integration of MLP
promotes a rather shallow learning architecture which makes the training
faster. We conducted a comparative study to examine the capabilities of
autoencoders for different inputs such as raw images, Histogram of Oriented
Gradients (HOG) and normalized Radon projections. Our framework is benchmarked
on IRMA dataset containing $14,410$ x-ray images distributed across $57$
different classes. Experiments show an IRMA error of $313$ (equivalent to
$\approx 82\%$ accuracy) outperforming state-of-the-art works on retrieval from
IRMA dataset using autoencoders.
KeyVec: Key-semantics Preserving Document Representations
Authors: Bin Bi, Hao Ma
Date: 2017-09-27 00:00:00
Tasks: BIG-bench Machine Learning, document understanding, Word Embeddings

Previous studies have demonstrated the empirical success of word embeddings
in various applications. In this paper, we investigate the problem of learning
distributed representations for text documents which many machine learning
algorithms take as input for a number of NLP tasks.
  We propose a neural network model, KeyVec, which learns document
representations with the goal of preserving key semantics of the input text. It
enables the learned low-dimensional vectors to retain the topics and important
information from the documents that will flow to downstream tasks. Our
empirical evaluations show the superior quality of KeyVec representations in
two different document understanding tasks.
A convergence analysis of the perturbed compositional gradient flow: averaging principle and normal deviations
Authors: Wenqing Hu, Chris Junchi Li
Date: 2017-09-02 00:00:00

We consider in this work a system of two stochastic differential equations
named the perturbed compositional gradient flow. By introducing a separation of
fast and slow scales of the two equations, we show that the limit of the slow
motion is given by an averaged ordinary differential equation. We then
demonstrate that the deviation of the slow motion from the averaged equation,
after proper rescaling, converges to a stochastic process with Gaussian inputs.
This indicates that the slow motion can be approximated in the weak sense by a
standard perturbed gradient flow or the continuous-time stochastic gradient
descent algorithm that solves the optimization problem for a composition of two
functions. As an application, the perturbed compositional gradient flow
corresponds to the diffusion limit of the Stochastic Composite Gradient Descent
(SCGD) algorithm for minimizing a composition of two expected-value functions
in the optimization literatures. For the strongly convex case, such an analysis
implies that the SCGD algorithm has the same convergence time asymptotic as the
classical stochastic gradient descent algorithm. Thus it validates, at the
level of continuous approximation, the effectiveness of using the SCGD
algorithm in the strongly convex case.
WHY: Natural Explanations from a Robot Navigator
Authors: Raj Korpan, Susan L. Epstein, Anoop Aroor, Gil Dekel
Date: 2017-09-27 00:00:00
Tasks: Robot Navigation, Text Generation

Effective collaboration between a robot and a person requires natural
communication. When a robot travels with a human companion, the robot should be
able to explain its navigation behavior in natural language. This paper
explains how a cognitively-based, autonomous robot navigation system produces
informative, intuitive explanations for its decisions. Language generation here
is based upon the robot's commonsense, its qualitative reasoning, and its
learned spatial model. This approach produces natural explanations in real time
for a robot as it navigates in a large, complex indoor environment.
Information Recovery in Shuffled Graphs via Graph Matching
Authors: Vince Lyzinski
Date: 2016-05-08 00:00:00
Tasks: Clustering, Graph Clustering, Graph Matching, Spectral Graph Clustering, Two-sample testing

While many multiple graph inference methodologies operate under the implicit
assumption that an explicit vertex correspondence is known across the vertex
sets of the graphs, in practice these correspondences may only be partially or
errorfully known. Herein, we provide an information theoretic foundation for
understanding the practical impact that errorfully observed vertex
correspondences can have on subsequent inference, and the capacity of graph
matching methods to recover the lost vertex alignment and inferential
performance. Working in the correlated stochastic blockmodel setting, we
establish a duality between the loss of mutual information due to an errorfully
observed vertex correspondence and the ability of graph matching algorithms to
recover the true correspondence across graphs. In the process, we establish a
phase transition for graph matchability in terms of the correlation across
graphs, and we conjecture the analogous phase transition for the relative
information loss due to shuffling vertex labels. We demonstrate the practical
effect that graph shuffling---and matching---can have on subsequent inference,
with examples from two sample graph hypothesis testing and joint spectral graph
clustering.
Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images
Authors: Heran Yang, Jian Sun, Huibin Li, Lisheng Wang, Zongben Xu
Date: 2017-09-27 00:00:00
Tasks: Image Segmentation, Left Ventricle Segmentation, Segmentation, Semantic Segmentation

Multi-atlas segmentation approach is one of the most widely-used image
segmentation techniques in biomedical applications. There are two major
challenges in this category of methods, i.e., atlas selection and label fusion.
In this paper, we propose a novel multi-atlas segmentation method that
formulates multi-atlas segmentation in a deep learning framework for better
solving these challenges. The proposed method, dubbed deep fusion net (DFN), is
a deep architecture that integrates a feature extraction subnet and a non-local
patch-based label fusion (NL-PLF) subnet in a single network. The network
parameters are learned by end-to-end training for automatically learning deep
features that enable optimal performance in a NL-PLF framework. The learned
deep features are further utilized in defining a similarity measure for atlas
selection. By evaluating on two public cardiac MR datasets of SATA-13 and LV-09
for left ventricle segmentation, our approach achieved 0.833 in averaged Dice
metric (ADM) on SATA-13 dataset and 0.95 in ADM for epicardium segmentation on
LV-09 dataset, comparing favorably with the other automatic left ventricle
segmentation methods. We also tested our approach on Cardiac Atlas Project
(CAP) testing set of MICCAI 2013 SATA Segmentation Challenge, and our method
achieved 0.815 in ADM, ranking highest at the time of writing.
How regularization affects the critical points in linear networks
Authors: Amirhossein Taghvaei, Jin W. Kim, Prashant G. Mehta
Date: 2017-09-27 00:00:00
Proceeding: NeurIPS 2017 12

This paper is concerned with the problem of representing and learning a
linear transformation using a linear neural network. In recent years, there has
been a growing interest in the study of such networks in part due to the
successes of deep learning. The main question of this body of research and also
of this paper pertains to the existence and optimality properties of the
critical points of the mean-squared loss function. The primary concern here is
the robustness of the critical points with regularization of the loss function.
An optimal control model is introduced for this purpose and a learning
algorithm (regularized form of backprop) derived for the same using the
Hamilton's formulation of optimal control. The formulation is used to provide a
complete characterization of the critical points in terms of the solutions of a
nonlinear matrix-valued equation, referred to as the characteristic equation.
Analytical and numerical tools from bifurcation theory are used to compute the
critical points via the solutions of the characteristic equation. The main
conclusion is that the critical point diagram can be fundamentally different
even with arbitrary small amounts of regularization.
A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks
Authors: Xiao Li, Yao Ma, Calin Belta
Date: 2017-09-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reward engineering is an important aspect of reinforcement learning. Whether
or not the user's intentions can be correctly encapsulated in the reward
function can significantly impact the learning outcome. Current methods rely on
manually crafted reward functions that often require parameter tuning to obtain
the desired behavior. This operation can be expensive when exploration requires
systems to interact with the physical world. In this paper, we explore the use
of temporal logic (TL) to specify tasks in reinforcement learning. TL formula
can be translated to a real-valued function that measures its level of
satisfaction against a trajectory. We take advantage of this function and
propose temporal logic policy search (TLPS), a model-free learning technique
that finds a policy that satisfies the TL specification. A set of simulated
experiments are conducted to evaluate the proposed approach.
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
Authors: Xingyi Cheng, Ruiqing Zhang, Jie zhou, Wei Xu
Date: 2017-09-27 00:00:00

Predicting traffic conditions has been recently explored as a way to relieve traffic congestion. Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to a lack of mining road topology. To address the effect attenuation problem, we suggest taking into account the traffic of surrounding locations(wider than the adjacent range). We propose an end-to-end framework called DeepTransport, in which Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are utilized to obtain spatial-temporal traffic information within a transport network topology. In addition, an attention mechanism is introduced to align spatial and temporal information. Moreover, we constructed and released a real-world large traffic condition dataset with a 5-minute resolution. Our experiments on this dataset demonstrate our method captures the complex relationship in the temporal and spatial domains. It significantly outperforms traditional statistical methods and a state-of-the-art deep learning method.
Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data
Authors: Manuel López-Radcenco, Ronan Fablet, Abdeldjalil Aïssa-El-Bey, Pierre Ailliot
Date: 2017-04-07 00:00:00
Tasks: Image Enhancement, Super-Resolution

Super-resolution is a classical problem in image processing, with numerous
applications to remote sensing image enhancement. Here, we address the
super-resolution of irregularly-sampled remote sensing images. Using an optimal
interpolation as the low-resolution reconstruction, we explore locally-adapted
multimodal convolutional models and investigate different dictionary-based
decompositions, namely based on principal component analysis (PCA), sparse
priors and non-negativity constraints. We consider an application to the
reconstruction of sea surface height (SSH) fields from two information sources,
along-track altimeter data and sea surface temperature (SST) data. The reported
experiments demonstrate the relevance of the proposed model, especially
locally-adapted parametrizations with non-negativity constraints, to outperform
optimally-interpolated reconstructions.
Neural networks for topology optimization
Authors: Ivan Sosnovik, Ivan Oseledets
Date: 2017-09-27 00:00:00
Tasks: Decoder, Deep Learning, Image Segmentation, Semantic Segmentation

In this research, we propose a deep learning based approach for speeding up
the topology optimization methods. The problem we seek to solve is the layout
problem. The main novelty of this work is to state the problem as an image
segmentation task. We leverage the power of deep learning methods as the
efficient pixel-wise image labeling technique to perform the topology
optimization. We introduce convolutional encoder-decoder architecture and the
overall approach of solving the above-described problem with high performance.
The conducted experiments demonstrate the significant acceleration of the
optimization process. The proposed approach has excellent generalization
properties. We demonstrate the ability of the application of the proposed model
to other problems. The successful results, as well as the drawbacks of the
current method, are discussed.
Traffic Optimization For a Mixture of Self-interested and Compliant Agents
Authors: Guni Sharon, Michael Albert, Tarun Rambha, Stephen Boyles, Peter Stone
Date: 2017-09-27 00:00:00

This paper focuses on two commonly used path assignment policies for agents
traversing a congested network: self-interested routing, and system-optimum
routing. In the self-interested routing policy each agent selects a path that
optimizes its own utility, while the system-optimum routing agents are assigned
paths with the goal of maximizing system performance. This paper considers a
scenario where a centralized network manager wishes to optimize utilities over
all agents, i.e., implement a system-optimum routing policy. In many real-life
scenarios, however, the system manager is unable to influence the route
assignment of all agents due to limited influence on route choice decisions.
Motivated by such scenarios, a computationally tractable method is presented
that computes the minimal amount of agents that the system manager needs to
influence (compliant agents) in order to achieve system optimal performance.
Moreover, this methodology can also determine whether a given set of compliant
agents is sufficient to achieve system optimum and compute the optimal route
assignment for the compliant agents to do so. Experimental results are
presented showing that in several large-scale, realistic traffic networks
optimal flow can be achieved with as low as 13% of the agent being compliant
and up to 54%.