pwc-archive/papers-with-abstracts huggingface dataset:
PolyNeuron: Automatic Neuron Discovery via Learned Polyharmonic Spline Activations
Authors: Andrew Hryniowski, Alexander Wong
Date: 2018-11-10 00:00:00

Automated deep neural network architecture design has received a significant
amount of recent attention. However, this attention has not been equally shared
by one of the fundamental building blocks of a deep neural network, the
neurons. In this study, we propose PolyNeuron, a novel automatic neuron
discovery approach based on learned polyharmonic spline activations. More
specifically, PolyNeuron revolves around learning polyharmonic splines,
characterized by a set of control points, that represent the activation
functions of the neurons in a deep neural network. A relaxed variant of
PolyNeuron, which we term PolyNeuron-R, loosens the constraints imposed by
PolyNeuron to reduce the computational complexity for discovering the neuron
activation functions in an automated manner. Experiments show both PolyNeuron
and PolyNeuron-R lead to networks that have improved or comparable performance
on multiple network architectures (LeNet-5 and ResNet-20) using different
datasets (MNIST and CIFAR10). As such, automatic neuron discovery approaches
such as PolyNeuron is a worthy direction to explore.
Coronary Calcium Detection using 3D Attention Identical Dual Deep Network Based on Weakly Supervised Learning
Authors: Yuankai Huo, James G. Terry, Jiachen Wang, Vishwesh Nath, Camilo Bermudez, Shunxing Bao, Prasanna Parvathaneni, J. Jeffery Carr, Bennett A. Landman
Date: 2018-11-10 00:00:00
Tasks: General Classification, Weakly-supervised Learning

Coronary artery calcium (CAC) is biomarker of advanced subclinical coronary
artery disease and predicts myocardial infarction and death prior to age 60
years. The slice-wise manual delineation has been regarded as the gold standard
of coronary calcium detection. However, manual efforts are time and resource
consuming and even impracticable to be applied on large-scale cohorts. In this
paper, we propose the attention identical dual network (AID-Net) to perform CAC
detection using scan-rescan longitudinal non-contrast CT scans with weakly
supervised attention by only using per scan level labels. To leverage the
performance, 3D attention mechanisms were integrated into the AID-Net to
provide complementary information for classification tasks. Moreover, the 3D
Gradient-weighted Class Activation Mapping (Grad-CAM) was also proposed at the
testing stage to interpret the behaviors of the deep neural network. 5075
non-contrast chest CT scans were used as training, validation and testing
datasets. Baseline performance was assessed on the same cohort. From the
results, the proposed AID-Net achieved the superior performance on
classification accuracy (0.9272) and AUC (0.9627).
IP Geolocation through Reverse DNS
Authors: Ovidiu Dan, Vaibhav Parikh, Brian D. Davison
Date: 2018-11-10 00:00:00

IP Geolocation databases are widely used in online services to map end user
IP addresses to their geographical locations. However, they use proprietary
geolocation methods and in some cases they have poor accuracy. We propose a
systematic approach to use publicly accessible reverse DNS hostnames for
geolocating IP addresses. Our method is designed to be combined with other
geolocation data sources. We cast the task as a machine learning problem where
for a given hostname, we generate and rank a list of potential location
candidates. We evaluate our approach against three state of the art academic
baselines and two state of the art commercial IP geolocation databases. We show
that our work significantly outperforms the academic baselines, and is
complementary and competitive with commercial databases. To aid
reproducibility, we open source our entire approach.
Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling
Authors: Hainan Xu, Shuoyang Ding, Shinji Watanabe
Date: 2018-11-10 00:00:00
Tasks: Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Most end-to-end speech recognition systems model text directly as a sequence
of characters or sub-words. Current approaches to sub-word extraction only
consider character sequence frequencies, which at times produce inferior
sub-word segmentation that might lead to erroneous speech recognition output.
We propose pronunciation-assisted sub-word modeling (PASM), a sub-word
extraction method that leverages the pronunciation information of a word.
Experiments show that the proposed method can greatly improve upon the
character-based baseline, and also outperform commonly used byte-pair encoding
methods.
The Method of Multimodal MRI Brain Image Segmentation Based on Differential Geometric Features
Authors: Yongpei Zhu, Zicong Zhou, Guojun Liao, Qianxi Yang, Kehong Yuan
Date: 2018-11-10 00:00:00
Tasks: Brain Image Segmentation, Brain Segmentation, Image Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a
diffcult task due to different types of brain abnormalities. Using information
and features from multimodal MRI including T1, T1-weighted inversion recovery
(T1-IR) and T2-FLAIR and differential geometric features including the Jacobian
determinant(JD) and the curl vector(CV) derived from T1 modality can result in
a more accurate analysis of brain images. In this paper, we use the
differential geometric information including JD and CV as image characteristics
to measure the differences between different MRI images, which represent local
size changes and local rotations of the brain image, and we can use them as one
CNN channel with other three modalities (T1-weighted, T1-IR and T2-FLAIR) to
get more accurate results of brain segmentation. We test this method on two
datasets including IBSR dataset and MRBrainS datasets based on the deep
voxelwise residual network, namely VoxResNet, and obtain excellent improvement
over single modality or three modalities and increases average
DSC(Cerebrospinal Fluid (CSF), Gray Matter (GM) and White Matter (WM)) by about
1.5% on the well-known MRBrainS18 dataset and about 2.5% on the IBSR dataset.
Moreover, we discuss that one modality combined with its JD or CV information
can replace the segmentation effect of three modalities, which can provide
medical conveniences for doctor to diagnose because only to extract T1-modality
MRI image of patients. Finally, we also compare the segmentation performance of
our method in two networks, VoxResNet and U-Net network. The results show
VoxResNet has a better performance than U-Net network with our method in brain
MRI segmentation. We believe the proposed method can advance the performance in
brain segmentation and clinical diagnosis.
Anomaly Detection via Graphical Lasso
Authors: Haitao Liu, Randy C. Paffenroth, Jian Zou, Chong Zhou
Date: 2018-11-10 00:00:00
Tasks: Anomaly Detection

Anomalies and outliers are common in real-world data, and they can arise from
many sources, such as sensor faults. Accordingly, anomaly detection is
important both for analyzing the anomalies themselves and for cleaning the data
for further analysis of its ambient structure. Nonetheless, a precise
definition of anomalies is important for automated detection and herein we
approach such problems from the perspective of detecting sparse latent effects
embedded in large collections of noisy data. Standard Graphical Lasso-based
techniques can identify the conditional dependency structure of a collection of
random variables based on their sample covariance matrix. However, classic
Graphical Lasso is sensitive to outliers in the sample covariance matrix. In
particular, several outliers in a sample covariance matrix can destroy the
sparsity of its inverse. Accordingly, we propose a novel optimization problem
that is similar in spirit to Robust Principal Component Analysis (RPCA) and
splits the sample covariance matrix $M$ into two parts, $M=F+S$, where $F$ is
the cleaned sample covariance whose inverse is sparse and computable by
Graphical Lasso, and $S$ contains the outliers in $M$. We accomplish this
decomposition by adding an additional $ \ell_1$ penalty to classic Graphical
Lasso, and name it "Robust Graphical Lasso (Rglasso)". Moreover, we propose an
Alternating Direction Method of Multipliers (ADMM) solution to the optimization
problem which scales to large numbers of unknowns. We evaluate our algorithm on
both real and synthetic datasets, obtaining interpretable results and
outperforming the standard robust Minimum Covariance Determinant (MCD) method
and Robust Principal Component Analysis (RPCA) regarding both accuracy and
speed.
Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning
Authors: Chao Yu, Tianpei Yang, Wenxuan Zhu, Dongxu Wang, Guangliang Li
Date: 2018-11-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Providing reinforcement learning agents with informationally rich human
knowledge can dramatically improve various aspects of learning. Prior work has
developed different kinds of shaping methods that enable agents to learn
efficiently in complex environments. All these methods, however, tailor human
guidance to agents in specialized shaping procedures, thus embodying various
characteristics and advantages in different domains. In this paper, we
investigate the interplay between different shaping methods for more robust
learning performance. We propose an adaptive shaping algorithm which is capable
of learning the most suitable shaping method in an on-line manner. Results in
two classic domains verify its effectiveness from both simulated and real human
studies, shedding some light on the role and impact of human factors in
human-robot collaborative learning.
Input Perturbations for Adaptive Control and Learning
Authors: Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
Date: 2018-11-10 00:00:00

This paper studies adaptive algorithms for simultaneous regulation (i.e., control) and estimation (i.e., learning) of Multiple Input Multiple Output (MIMO) linear dynamical systems. It proposes practical, easy to implement control policies based on perturbations of input signals. Such policies are shown to achieve a worst-case regret that scales as the square-root of the time horizon, and holds uniformly over time. Further, it discusses specific settings where such greedy policies attain the information theoretic lower bound of logarithmic regret. To establish the results, recent advances on self-normalized martingales together with a novel method of policy decomposition are leveraged.
Scene Text Detection and Recognition: The Deep Learning Era
Authors: Shangbang Long, Xin He, Cong Yao
Date: 2018-11-10 00:00:00
Tasks: Deep Learning, Scene Text Detection, Text Detection

With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, approach and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.
Formal Limitations on the Measurement of Mutual Information
Authors: David McAllester, Karl Stratos
Date: 2018-11-10 00:00:00
Proceeding: ICLR 2019 5

Measuring mutual information from finite data is difficult. Recent work has considered variational methods maximizing a lower bound. In this paper, we prove that serious statistical limitations are inherent to any method of measuring mutual information. More specifically, we show that any distribution-free high-confidence lower bound on mutual information estimated from N samples cannot be larger than O(ln N ).
Detecting Work Zones in SHRP 2 NDS Videos Using Deep Learning Based Computer Vision
Authors: Franklin Abodo, Robert Rittmuller, Brian Sumner, Andrew Berthaume
Date: 2018-11-10 00:00:00

Naturalistic driving studies seek to perform the observations of human driver
behavior in the variety of environmental conditions necessary to analyze,
understand and predict that behavior using statistical and physical models. The
second Strategic Highway Research Program (SHRP 2) funds a number of
transportation safety-related projects including its primary effort, the
Naturalistic Driving Study (NDS), and an effort supplementary to the NDS, the
Roadway Information Database (RID). This work seeks to expand the range of
answerable research questions that researchers might pose to the NDS and RID
databases. Specifically, we present the SHRP 2 NDS Video Analytics (SNVA)
software application, which extracts information from NDS-instrumented
vehicles' forward-facing camera footage and efficiently integrates that
information into the RID, tying the video content to geolocations and other
trip attributes. Of particular interest to researchers and other stakeholders
is the integration of work zone, traffic signal state and weather information.
The version of SNVA introduced in this paper focuses on work zone detection,
the highest priority. The ability to automate the discovery and cataloging of
this information, and to do so quickly, is especially important given the two
petabyte (2PB) size of the NDS video data set.
Deep Learning Approach for Building Detection in Satellite Multispectral Imagery
Authors: Geesara Prathap, Ilya Afanasyev
Date: 2018-11-10 00:00:00
Tasks: Deep Learning, Image Segmentation, Semantic Segmentation

Building detection from satellite multispectral imagery data is being a
fundamental but a challenging problem mainly because it requires correct
recovery of building footprints from high-resolution images. In this work, we
propose a deep learning approach for building detection by applying numerous
enhancements throughout the process. Initial dataset is preprocessed by 2-sigma
percentile normalization. Then data preparation includes ensemble modelling
where 3 models were created while incorporating OpenStreetMap data. Binary
Distance Transformation (BDT) is used for improving data labeling process and
the U-Net (Convolutional Networks for Biomedical Image Segmentation) is
modified by adding batch normalization wrappers. Afterwards, it is explained
how each component of our approach is correlated with the final detection
accuracy. Finally, we compare our results with winning solutions of SpaceNet 2
competition for real satellite multispectral images of Vegas, Paris, Shanghai
and Khartoum, demonstrating the importance of our solution for achieving higher
building detection accuracy.
A Bayesian Approach to Income Inference in a Communication Network
Authors: Martin Fixman, Ariel Berenstein, Jorge Brea, Martin Minnoni, Matias Travizano, Carlos Sarraute
Date: 2018-11-10 00:00:00

The explosion of mobile phone communications in the last years occurs at a
moment where data processing power increases exponentially. Thanks to those two
changes in a global scale, the road has been opened to use mobile phone
communications to generate inferences and characterizations of mobile phone
users. In this work, we use the communication network, enriched by a set of
users' attributes, to gain a better understanding of the demographic features
of a population. Namely, we use call detail records and banking information to
infer the income of each person in the graph.
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network
Authors: Md Zahangir Alom, Chris Yakopcic, Tarek M. Taha, Vijayan K. Asari
Date: 2018-11-10 00:00:00
Tasks: Cancer Classification, Classification, General Classification, Object Recognition

The Deep Convolutional Neural Network (DCNN) is one of the most powerful and
successful deep learning approaches. DCNNs have already provided superior
performance in different modalities of medical imaging including breast cancer
classification, segmentation, and detection. Breast cancer is one of the most
common and dangerous cancers impacting women worldwide. In this paper, we have
proposed a method for breast cancer classification with the Inception Recurrent
Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful
DCNN model that combines the strength of the Inception Network (Inception-v4),
the Residual Network (ResNet), and the Recurrent Convolutional Neural Network
(RCNN). The IRRCNN shows superior performance against equivalent Inception
Networks, Residual Networks, and RCNNs for object recognition tasks. In this
paper, the IRRCNN approach is applied for breast cancer classification on two
publicly available datasets including BreakHis and Breast Cancer Classification
Challenge 2015. The experimental results are compared against the existing
machine learning and deep learning-based approaches with respect to
image-based, patch-based, image-level, and patient-level classification. The
IRRCNN model provides superior classification performance in terms of
sensitivity, Area Under the Curve (AUC), the ROC curve, and global accuracy
compared to existing approaches for both datasets.
Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms
Authors: Geesara Prathap, Titus Nanda Kumara, Roshan Ragel
Date: 2018-11-10 00:00:00
Tasks: Dynamic Time Warping

Recognizing sEMG (Surface Electromyography) signals belonging to a particular
action (e.g., lateral arm raise) automatically is a challenging task as EMG
signals themselves have a lot of variation even for the same action due to
several factors. To overcome this issue, there should be a proper separation
which indicates similar patterns repetitively for a particular action in raw
signals. A repetitive pattern is not always matched because the same action can
be carried out with different time duration. Thus, a depth sensor (Kinect) was
used for pattern identification where three joint angles were recording
continuously which is clearly separable for a particular action while recording
sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving
Dynamic Time Warping) approach is introduced. This technique is allowed to
retrieve suspected motion of interest from raw signals. MDTW based on DTW
algorithm, but it will be moving through the whole dataset in a pre-defined
manner which is capable of picking up almost all the suspected segments inside
a given dataset an optimal way. Elevated bicep curl and lateral arm raise
movements are taken as motions of interest to show how the proposed technique
can be employed to achieve auto identification and labelling. The full
implementation is available at https://github.com/GPrathap/OpenBCIPython
Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention
Authors: Guyue Hu, Bo Cui, Shan Yu
Date: 2018-11-10 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Benefiting from its succinctness and robustness, skeleton-based action recognition has recently attracted much attention. Most existing methods utilize local networks (e.g., recurrent, convolutional, and graph convolutional networks) to extract spatio-temporal dynamics hierarchically. As a consequence, the local and non-local dependencies, which contain more details and semantics respectively, are asynchronously captured in different level of layers. Moreover, existing methods are limited to the spatio-temporal domain and ignore information in the frequency domain. To better extract synchronous detailed and semantic information from multi-domains, we propose a residual frequency attention (rFA) block to focus on discriminative patterns in the frequency domain, and a synchronous local and non-local (SLnL) block to simultaneously capture the details and semantics in the spatio-temporal domain. Besides, a soft-margin focal loss (SMFL) is proposed to optimize the learning whole process, which automatically conducts data selection and encourages intrinsic margins in classifiers. Our approach significantly outperforms other state-of-the-art methods on several large-scale datasets.
Towards Formula Translation using Recursive Neural Networks
Authors: Felix Petersen, Moritz Schubotz, Bela Gipp
Date: 2018-11-10 00:00:00
Tasks: Clustering, Position, Translation

While it has become common to perform automated translations on natural
language, performing translations between different representations of
mathematical formulae has thus far not been possible. We implemented the first
translator for mathematical formulae based on recursive neural networks. We
chose recursive neural networks because mathematical formulae inherently
include a structural encoding. In our implementation, we developed new
techniques and topologies for recursive tree-to-tree neural networks based on
multi-variate multi-valued Long Short-Term Memory cells. We propose a novel
approach for mini-batch training that utilizes clustering and tree traversal.
We evaluate our translator and analyze the behavior of our proposed topologies
and techniques based on a translation from generic LaTeX to the semantic LaTeX
notation. We use the semantic LaTeX notation from the Digital Library for
Mathematical Formulae and the Digital Repository for Mathematical Formulae at
the National Institute for Standards and Technology. We find that a simple
heuristics-based clustering algorithm outperforms the conventional clustering
algorithms on the task of clustering binary trees of mathematical formulae with
respect to their topology. Furthermore, we find a mask for the loss function,
which can prevent the neural network from finding a local minimum of the loss
function. Given our preliminary results, a complete translation from formula to
formula is not yet possible. However, we achieved a prediction accuracy of
47.05% for predicting symbols at the correct position and an accuracy of 92.3%
when ignoring the predicted position. Concluding, our work advances the field
of recursive neural networks by improving the training speed and quality of
training. In the future, we will work towards a complete translation allowing a
machine-interpretation of LaTeX formulae.
Efficient Spiking Neural Networks with Logarithmic Temporal Coding
Authors: Ming Zhang, Nenggan Zheng, De Ma, Gang Pan, Zonghua Gu
Date: 2018-11-10 00:00:00

A Spiking Neural Network (SNN) can be trained indirectly by first training an
Artificial Neural Network (ANN) with the conventional backpropagation
algorithm, then converting it into an SNN. The conventional rate-coding method
for SNNs uses the number of spikes to encode magnitude of an activation value,
and may be computationally inefficient due to the large number of spikes.
Temporal-coding is typically more efficient by leveraging the timing of spikes
to encode information. In this paper, we present Logarithmic Temporal Coding
(LTC), where the number of spikes used to encode an activation value grows
logarithmically with the activation value; and the accompanying
Exponentiate-and-Fire (EF) spiking neuron model, which only involves efficient
bit-shift and addition operations. Moreover, we improve the training process of
ANN to compensate for approximation errors due to LTC. Experimental results
indicate that the resulting SNN achieves competitive performance at
significantly lower computational cost than related work.