pwc-archive/papers-with-abstracts huggingface dataset:
Toponym Identification in Epidemiology Articles - A Deep Learning Approach
Authors: MohammadReza Davari, Leila Kosseim, Tien D. Bui
Date: 2019-04-24 00:00:00
Tasks: Articles, Deep Learning, Epidemiology, Toponym Recognition

When analyzing the spread of viruses, epidemiologists often need to identify
the location of infected hosts. This information can be found in public
databases, such as GenBank, however, information provided in these databases
are usually limited to the country or state level. More fine-grained
localization information requires phylogeographers to manually read relevant
scientific articles. In this work we propose an approach to automate the
process of place name identification from medical (epidemiology) articles. The
focus of this paper is to propose a deep learning based model for toponym
detection and experiment with the use of external linguistic features and
domain specific information. The model was evaluated using a collection of 105
epidemiology articles from PubMed Central provided by the recent SemEval task
12. Our best detection model achieves an F1 score of $80.13\%$, a significant
improvement compared to the state of the art of $69.84\%$. These results
underline the importance of domain specific embedding as well as specific
linguistic features in toponym detection in medical journals.
The Commute Trip Sharing Problem
Authors: Mohd. Hafiz Hasan, Pascal Van Hentenryck, Antoine Legrain
Date: 2019-04-24 00:00:00

Parking pressure has been steadily increasing in cities as well as in university and corporate campuses. To relieve this pressure, this paper studies a car-pooling platform that would match riders and drivers, while guaranteeing a ride back and exploiting spatial and temporal locality. In particular, the paper formalizes the Commute Trip Sharing Problem (CTSP) to find a routing plan that maximizes ride sharing for a set of commute trips. The CTSP is a generalization of the vehicle routing problem with routes that satisfy time window, capacity, pairing, precedence, ride duration, and driver constraints. The paper introduces two exact algorithms for the CTPS: A route-enumeration algorithm and a branch-and-price algorithm. Experimental results show that, on a high-fidelity, real-world dataset of commute trips from a mid-size city, both algorithms optimally solve small and medium-sized problems and produce high-quality solutions for larger problem instances. The results show that car pooling, if widely adopted, has the potential to reduce vehicle usage by up to 57% and decrease vehicle miles traveled by up to 46% while only incurring a 22% increase in average ride time per commuter for the trips considered.
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors
Authors: Nicholas Ruiz, Mattia Antonino Di Gangi, Nicola Bertoldi, Marcello Federico
Date: 2019-04-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Machine Translation, NMT, Sentence, speech-recognition, Speech Recognition, Translation

Machine translation systems are conventionally trained on textual resources
that do not model phenomena that occur in spoken language. While the evaluation
of neural machine translation systems on textual inputs is actively researched
in the literature , little has been discovered about the complexities of
translating spoken language data with neural models. We introduce and motivate
interesting problems one faces when considering the translation of automatic
speech recognition (ASR) outputs on neural machine translation (NMT) systems.
We test the robustness of sentence encoding approaches for NMT encoder-decoder
modeling, focusing on word-based over byte-pair encoding. We compare the
translation of utterances containing ASR errors in state-of-the-art NMT
encoder-decoder systems against a strong phrase-based machine translation
baseline in order to better understand which phenomena present in ASR outputs
are better represented under the NMT framework than approaches that represent
translation as a linear model.
PAN: Path Integral Based Convolution for Deep Graph Neural Networks
Authors: Zheng Ma, Ming Li, Yuguang Wang
Date: 2019-04-24 00:00:00

Convolution operations designed for graph-structured data usually utilize the
graph Laplacian, which can be seen as message passing between the adjacent
neighbors through a generic random walk. In this paper, we propose PAN, a new
graph convolution framework that involves every path linking the message sender
and receiver with learnable weights depending on the path length, which
corresponds to the maximal entropy random walk. PAN generalizes the graph
Laplacian to a new transition matrix we call \emph{maximal entropy transition}
(MET) matrix derived from a path integral formalism. Most previous graph
convolutional network architectures can be adapted to our framework, and many
variations and derivatives based on the path integral idea can be developed.
Experimental results show that the path integral based graph neural networks
have great learnability and fast convergence rate, and achieve state-of-the-art
performance on benchmark tasks.
A Robust Approach for Securing Audio Classification Against Adversarial Attacks
Authors: Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich
Date: 2019-04-24 00:00:00
Tasks: Audio Classification, BIG-bench Machine Learning, Classification, Dimensionality Reduction, General Classification

Adversarial audio attacks can be considered as a small perturbation unperceptive to human ears that is intentionally added to the audio signal and causes a machine learning model to make mistakes. This poses a security concern about the safety of machine learning models since the adversarial attacks can fool such models toward the wrong predictions. In this paper we first review some strong adversarial attacks that may affect both audio signals and their 2D representations and evaluate the resiliency of the most common machine learning model, namely deep learning models and support vector machines (SVM) trained on 2D audio representations such as short time Fourier transform (STFT), discrete wavelet transform (DWT) and cross recurrent plot (CRP) against several state-of-the-art adversarial attacks. Next, we propose a novel approach based on pre-processed DWT representation of audio signals and SVM to secure audio systems against adversarial attacks. The proposed architecture has several preprocessing modules for generating and enhancing spectrograms including dimension reduction and smoothing. We extract features from small patches of the spectrograms using speeded up robust feature (SURF) algorithm which are further used to generate a codebook using the K-Means++ algorithm. Finally, codewords are used to train a SVM on the codebook of the SURF-generated vectors. All these steps yield to a novel approach for audio classification that provides a good trade-off between accuracy and resilience. Experimental results on three environmental sound datasets show the competitive performance of proposed approach compared to the deep neural networks both in terms of accuracy and robustness against strong adversarial attacks.
Construction of the similarity matrix for the spectral clustering method: numerical experiments
Authors: Paola Favati, Grazia Lotti, Ornella Menchi, Francesco Romani
Date: 2019-04-24 00:00:00
Tasks: Clustering

Spectral clustering is a powerful method for finding structure in a dataset
through the eigenvectors of a similarity matrix. It often outperforms
traditional clustering algorithms such as $k$-means when the structure of the
individual clusters is highly non-convex. Its accuracy depends on how the
similarity between pairs of data points is defined. Two important items
contribute to the construction of the similarity matrix: the sparsity of the
underlying weighted graph, which depends mainly on the distances among data
points, and the similarity function. When a Gaussian similarity function is
used, the choice of the scale parameter $\sigma$ can be critical. In this paper
we examine both items, the sparsity and the selection of suitable $\sigma$'s,
based either directly on the graph associated to the dataset or on the minimal
spanning tree (MST) of the graph. An extensive numerical experimentation on
artificial and real-world datasets has been carried out to compare the
performances of the methods.
The utility of a convolutional neural network for generating a myelin volume index map from rapid simultaneous relaxometry imaging
Authors: Yasuhiko Tachibana, Akifumi Hagiwara, Masaaki Hori, Jeff Kershaw, Misaki Nakazawa, Tokuhiko Omatsu, Riwa Kishimoto, Kazumasa Yokoyama, Nobutaka Hattori, Shigeki Aoki, Tatsuya Higashi, Takayuki Obata
Date: 2019-04-24 00:00:00

Background and Purpose: A current algorithm to obtain a synthetic myelin
volume fraction map (SyMVF) from rapid simultaneous relaxometry imaging (RSRI)
has a potential problem, that it does not incorporate information from
surrounding pixels. The purpose of this study was to develop a method that
utilizes a convolutional neural network (CNN) to overcome this problem.
Methods: RSRI and magnetization transfer images from 20 healthy volunteers were
included. A CNN was trained to reconstruct RSRI-related metric maps into a
myelin volume-related index (generated myelin volume index: GenMVI) map using
the myelin volume index map calculated from magnetization transfer images
(MTMVI) as reference. The SyMVF and GenMVI maps were statistically compared by
testing how well they correlated with the MTMVI map. The correlations were
evaluated based on: (i) averaged values obtained from 164 atlas-based ROIs, and
(ii) pixel-based comparison for ROIs defined in four different tissue types
(cortical and subcortical gray matter, white matter, and whole brain). Results:
For atlas-based ROIs, the overall correlation with the MTMVI map was higher for
the GenMVI map than for the SyMVF map. In the pixel-based comparison,
correlation with the MTMVI map was stronger for the GenMVI map than for the
SyMVF map, and the difference in the distribution for the volunteers was
significant (Wilcoxon sign-rank test, P<.001) in all tissue types. Conclusion:
The proposed method is useful, as it can incorporate more specific information
about local tissue properties than the existing method.
Crop yield probability density forecasting via quantile random forest and Epanechnikov Kernel function
Authors: Samuel Asante Gyamerah, Philip Ngare, Dennis Ikpe
Date: 2019-04-23 00:00:00
Tasks: Decision Making, Density Estimation, Feature Importance, Prediction, Prediction Intervals, quantile regression, regression

A reliable and accurate forecasting model for crop yields is of crucial importance for efficient decision-making process in the agricultural sector. However, due to weather extremes and uncertainties, most forecasting models for crop yield are not reliable and accurate. For measuring the uncertainty and obtaining further information of future crop yields, a probability density forecasting model based on quantile random forest and Epanechnikov kernel function (QRF-SJ) is proposed. The nonlinear structure of random forest is applied to change the quantile regression model for building the probabilistic forecasting model. Epanechnikov kernel function and solve-the equation plug-in approach of Sheather and Jones are used in the kernel density estimation. A case study using the annual crop yield of groundnut and millet in Ghana is presented to illustrate the efficiency and robustness of the proposed technique. The values of the prediction interval coverage probability and prediction interval normalized average width for the two crops show that the constructed prediction intervals capture the observed yields with high coverage probability. The probability density curves show that QRF-SJ method has a very high ability to forecast quality prediction intervals with a higher coverage probability. The feature importance gave a score of the importance of each weather variable in building the quantile regression forest model. The farmer and other stakeholders are able to realize the specific weather variable that affect the yield of a selected crop through feature importance. The proposed method and its application on crop yield dataset are the first of its kind in literature.
Corticospinal Tract (CST) reconstruction based on fiber orientation distributions(FODs) tractography
Authors: Youshan Zhang
Date: 2019-04-23 00:00:00

The Corticospinal Tract (CST) is a part of pyramidal tract (PT), and it can
innervate the voluntary movement of skeletal muscle through spinal interneurons
(the 4th layer of the Rexed gray board layers), and anterior horn motorneurons
(which control trunk and proximal limb muscles). Spinal cord injury (SCI) is a
highly disabling disease often caused by traffic accidents. The recovery of CST
and the functional reconstruction of spinal anterior horn motor neurons play an
essential role in the treatment of SCI. However, the localization and
reconstruction of CST are still challenging issues; the accuracy of the
geometric reconstruction can directly affect the results of the surgery. The
main contribution of this paper is the reconstruction of the CST based on the
fiber orientation distributions (FODs) tractography. Differing from
tensor-based tractography in which the primary direction is a determined
orientation, the direction of FODs tractography is determined by the
probability. The spherical harmonics (SPHARM) can be used to approximate the
efficiency of FODs tractography. We manually delineate the three ROIs (the
posterior limb of the internal capsule, the cerebral peduncle, and the anterior
pontine area) by the ITK-SNAP software, and use the pipeline software to
reconstruct both the left and right sides of the CST fibers. Our results
demonstrate that FOD-based tractography can show more and correct anatomical
CST fiber bundles.
DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis
Authors: Arash Kalatian, Bilal Farooq
Date: 2019-04-16 00:00:00
Tasks: Autonomous Vehicles, feature selection, Survival Analysis

Pedestrian's road crossing behaviour is one of the important aspects of urban dynamics that will be affected by the introduction of autonomous vehicles. In this study we introduce DeepSurvival, a novel framework for estimating pedestrian's waiting time at unsignalized mid-block crosswalks in mixed traffic conditions. We exploit the strengths of deep learning in capturing the nonlinearities in the data and develop a cox proportional hazard model with a deep neural network as the log-risk function. An embedded feature selection algorithm for reducing data dimensionality and enhancing the interpretability of the network is also developed. We test our framework on a dataset collected from 160 participants using an immersive virtual reality environment. Validation results showed that with a C-index of 0.64 our proposed framework outperformed the standard cox proportional hazard-based model with a C-index of 0.58.
A novel method for extracting interpretable knowledge from a spiking neural classifier with time-varying synaptic weights
Authors: Abeegithan Jeyasothy, Suresh Sundaram, Savitha Ramasamy, Narasimhan Sundararajan
Date: 2019-02-28 00:00:00
Tasks: Classification, General Classification

This paper presents a novel method for information interpretability in an
MC-SEFRON classifier. To develop a method to extract knowledge stored in a
trained classifier, first, the binary-class SEFRON classifier developed earlier
is extended to handle multi-class problems. MC-SEFRON uses the population
encoding scheme to encode the real-valued input data into spike patterns.
MC-SEFRON is trained using the same supervised learning rule used in the
SEFRON. After training, the proposed method extracts the knowledge for a given
class stored in the classifier by mapping the weighted postsynaptic potential
in the time domain to the feature domain as Feature Strength Functions (FSFs).
A set of FSFs corresponding to each output class represents the extracted
knowledge from the classifier. This knowledge encoding method is derived to
maintain consistency between the classification in the time domain and the
feature domain. The correctness of the FSF is quantitatively measured by using
FSF directly for classification tasks. For a given input, each FSF is sampled
at the input value to obtain the corresponding feature strength value (FSV).
Then the aggregated FSVs obtained for each class are used to determine the
output class labels during classification. FSVs are also used to interpret the
predictions during the classification task. Using ten UCI datasets and the
MNIST dataset, the knowledge extraction method, interpretation and the
reliability of the FSF are demonstrated. Based on the studies, it can be seen
that on an average, the difference in the classification accuracies using the
FSF directly and those obtained by MC-SEFRON is only around 0.9% & 0.1\% for
the UCI datasets and the MNIST dataset respectively. This clearly shows that
the knowledge represented by the FSFs has acceptable reliability and the
interpretability of classification using the classifier's knowledge has been
justified.
Deep Generative Models for Reject Inference in Credit Scoring
Authors: Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen
Date: 2019-04-12 00:00:00

Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this research, we use deep generative models to develop two new semi-supervised Bayesian models for reject inference in credit scoring, in which we model the data generating process to be dependent on a Gaussian mixture. The goal is to improve the classification accuracy in credit scoring models by adding reject applications. Our proposed models infer the unknown creditworthiness of the rejected applications by exact enumeration of the two possible outcomes of the loan (default or non-default). The efficient stochastic gradient optimization technique used in deep generative models makes our models suitable for large data sets. Finally, the experiments in this research show that our proposed models perform better than classical and alternative machine learning models for reject inference in credit scoring.
Probability density distillation with generative adversarial networks for high-quality parallel waveform generation
Authors: Ryuichi Yamamoto, Eunwoo Song, Jae-Min Kim
Date: 2019-04-09 00:00:00

This paper proposes an effective probability density distillation (PDD)
algorithm for WaveNet-based parallel waveform generation (PWG) systems.
Recently proposed teacher-student frameworks in the PWG system have
successfully achieved a real-time generation of speech signals. However, the
difficulties optimizing the PDD criteria without auxiliary losses result in
quality degradation of synthesized speech. To generate more natural speech
signals within the teacher-student framework, we propose a novel optimization
criterion based on generative adversarial networks (GANs). In the proposed
method, the inverse autoregressive flow-based student model is incorporated as
a generator in the GAN framework, and jointly optimized by the PDD mechanism
with the proposed adversarial learning method. As this process encourages the
student to model the distribution of realistic speech waveform, the perceptual
quality of the synthesized speech becomes much more natural. Our experimental
results verify that the PWG systems with the proposed method outperform both
those using conventional approaches, and also autoregressive generation systems
with a well-trained teacher WaveNet.
A Graph-CNN for 3D Point Cloud Classification
Authors: Yingxue Zhang, Michael Rabbat
Date: 2018-11-28 00:00:00
Tasks: 3D Object Classification, 3D Point Cloud Classification, Classification, General Classification, Point Cloud Classification

Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to
handle data that is supported on a graph. Major challenges when working with
data on graphs are that the support set (the vertices of the graph) do not
typically have a natural ordering, and in general, the topology of the graph is
not regular (i.e., vertices do not all have the same number of neighbors).
Thus, Graph-CNNs have huge potential to deal with 3D point cloud data which has
been obtained from sampling a manifold. In this paper, we develop a Graph-CNN
for classifying 3D point cloud data, called PointGCN. The architecture combines
localized graph convolutions with two types of graph downsampling operations
(also known as pooling). By the effective exploration of the point cloud local
structure using the Graph-CNN, the proposed architecture achieves competitive
performance on the 3D object classification benchmark ModelNet, and our
architecture is more stable than competing schemes.
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
Authors: Zhe Liu, Shunbo Zhou, Chuanzhe Suo, Yingtian Liu, Peng Yin, Hesheng Wang, Yun-hui Liu
Date: 2018-12-11 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Place Recognition, Point Cloud Retrieval, Retrieval, Visual Place Recognition

Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and it's even harder in the large-scale dynamic environments. In this paper, we develop a novel deep neural network, named LPD-Net (Large-scale Place Description Network), which can extract discriminative and generalizable global descriptors from the raw 3D point cloud. Two modules, the adaptive local feature extraction module and the graph-based neighborhood aggregation module, are proposed, which contribute to extract the local structures and reveal the spatial distribution of local features in the large-scale point cloud, with an end-to-end manner. We implement the proposed global descriptor in solving point cloud based retrieval tasks to achieve the large-scale place recognition. Comparison results show that our LPD-Net is much better than PointNetVLAD and reaches the state-of-the-art. We also compare our LPD-Net with the vision-based solutions to show the robustness of our approach to different weather and light conditions.
Arbor -- a morphologically-detailed neural network simulation library for contemporary high-performance computing architectures
Authors: Nora Abi Akar, Ben Cumming, Vasileios Karakasis, Anne Küsters, Wouter Klijn, Alexander Peyser, Stuart Yates
Date: 2019-01-17 00:00:00
Tasks: GPU, Neural Network simulation

We introduce Arbor, a performance portable library for simulation of large
networks of multi-compartment neurons on HPC systems. Arbor is open source
software, developed under the auspices of the HBP. The performance portability
is by virtue of back-end specific optimizations for x86 multicore, Intel KNL,
and NVIDIA GPUs. When coupled with low memory overheads, these optimizations
make Arbor an order of magnitude faster than the most widely-used comparable
simulation software. The single-node performance can be scaled out to run very
large models at extreme scale with efficient weak scaling.
  HPC, GPU, neuroscience, neuron, software