pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Historical Hourly Traffic Volumes via Machine Learning and Vehicle Probe Data: A Maryland Case Study
Authors: Przemysław Sekuła, Nikola Marković, Zachary Vander Laan, Kaveh Farokhi Sadabadi
Date: 2017-11-02 00:00:00
Tasks: BIG-bench Machine Learning

This paper focuses on the problem of estimating historical traffic volumes
between sparsely-located traffic sensors, which transportation agencies need to
accurately compute statewide performance measures. To this end, the paper
examines applications of vehicle probe data, automatic traffic recorder counts,
and neural network models to estimate hourly volumes in the Maryland highway
network, and proposes a novel approach that combines neural networks with an
existing profiling method. On average, the proposed approach yields 24% more
accurate estimates than volume profiles, which are currently used by
transportation agencies across the US to compute statewide performance
measures. The paper also quantifies the value of using vehicle probe data in
estimating hourly traffic volumes, which provides important managerial insights
to transportation agencies interested in acquiring this type of data. For
example, results show that volumes can be estimated with a mean absolute
percent error of about 21% at locations where average number of observed probes
is between 30 and 47 vehicles/hr, which provides a useful guideline for
assessing the value of probe vehicle data from different vendors.
Overcoming data scarcity with transfer learning
Authors: Maxwell L. Hutchinson, Erin Antono, Brenna M. Gibbons, Sean Paradiso, Julia Ling, Bryce Meredig
Date: 2017-11-02 00:00:00
Tasks: Transfer Learning

Despite increasing focus on data publication and discovery in materials
science and related fields, the global view of materials data is highly sparse.
This sparsity encourages training models on the union of multiple datasets, but
simple unions can prove problematic as (ostensibly) equivalent properties may
be measured or computed differently depending on the data source. These hidden
contextual differences introduce irreducible errors into analyses,
fundamentally limiting their accuracy. Transfer learning, where information
from one dataset is used to inform a model on another, can be an effective tool
for bridging sparse data while preserving the contextual differences in the
underlying measurements. Here, we describe and compare three techniques for
transfer learning: multi-task, difference, and explicit latent variable
architectures. We show that difference architectures are most accurate in the
multi-fidelity case of mixed DFT and experimental band gaps, while multi-task
most improves classification performance of color with band gaps. For
activation energies of steps in NO reduction, the explicit latent variable
method is not only the most accurate, but also enjoys cancellation of errors in
functions that depend on multiple tasks. These results motivate the publication
of high quality materials datasets that encode transferable information,
independent of industrial or academic interest in the particular labels, and
encourage further development and application of transfer learning methods to
materials informatics problems.
Adaptive coordination of working-memory and reinforcement learning in non-human primates performing a trial-and-error problem solving task
Authors: Guillaume Viejo, Benoît Girard, Emmanuel Procyk, Mehdi Khamassi
Date: 2017-11-02 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Accumulating evidence suggest that human behavior in trial-and-error learning
tasks based on decisions between discrete actions may involve a combination of
reinforcement learning (RL) and working-memory (WM). While the understanding of
brain activity at stake in this type of tasks often involve the comparison with
non-human primate neurophysiological results, it is not clear whether monkeys
use similar combined RL and WM processes to solve these tasks. Here we analyzed
the behavior of five monkeys with computational models combining RL and WM. Our
model-based analysis approach enables to not only fit trial-by-trial choices
but also transient slowdowns in reaction times, indicative of WM use. We found
that the behavior of the five monkeys was better explained in terms of a
combination of RL and WM despite inter-individual differences. The same
coordination dynamics we used in a previous study in humans best explained the
behavior of some monkeys while the behavior of others showed the opposite
pattern, revealing a possible different dynamics of WM process. We further
analyzed different variants of the tested models to open a discussion on how
the long pretraining in these tasks may have favored particular coordination
dynamics between RL and WM. This points towards either inter-species
differences or protocol differences which could be further tested in humans.
A Universal Marginalizer for Amortized Inference in Generative Models
Authors: Laura Douglas, Iliyan Zarov, Konstantinos Gourgoulias, Chris Lucas, Chris Hart, Adam Baker, Maneesh Sahani, Yura Perov, Saurabh Johri
Date: 2017-11-02 00:00:00

We consider the problem of inference in a causal generative model where the
set of available observations differs between data instances. We show how
combining samples drawn from the graphical model with an appropriate masking
function makes it possible to train a single neural network to approximate all
the corresponding conditional marginal distributions and thus amortize the cost
of inference. We further demonstrate that the efficiency of importance sampling
may be improved by basing proposals on the output of the neural network. We
also outline how the same network can be used to generate samples from an
approximate joint posterior via a chain decomposition of the graph.
Statistical evaluation of visual quality metrics for image denoising
Authors: Karen Egiazarian, Mykola Ponomarenko, Vladimir Lukin, Oleg Ieremeiem
Date: 2017-11-02 00:00:00
Tasks: Denoising, Image Denoising

This paper studies the problem of full reference visual quality assessment of
denoised images with a special emphasis on images with low contrast and
noise-like texture. Denoising of such images together with noise removal often
results in image details loss or smoothing. A new test image database, FLT,
containing 75 noise-free "reference" images and 300 filtered ("distorted")
images is developed. Each reference image, corrupted by an additive white
Gaussian noise, is denoised by the BM3D filter with four different values of
threshold parameter (four levels of noise suppression). After carrying out a
perceptual quality assessment of distorted images, the mean opinion scores
(MOS) are obtained and compared with the values of known full reference quality
metrics. As a result, the Spearman Rank Order Correlation Coefficient (SROCC)
between PSNR values and MOS has a value close to zero, and SROCC between values
of known full-reference image visual quality metrics and MOS does not exceed
0.82 (which is reached by a new visual quality metric proposed in this paper).
The FLT dataset is more complex than earlier datasets used for assessment of
visual quality for image denoising. Thus, it can be effectively used to design
new image visual quality metrics for image denoising.
Extracting an English-Persian Parallel Corpus from Comparable Corpora
Authors: Akbar Karimi, Ebrahim Ansari, Bahram Sadeghi Bigham
Date: 2017-11-02 00:00:00
Proceeding: LREC 2018 5
Tasks: Machine Translation, Translation

Parallel data are an important part of a reliable Statistical Machine
Translation (SMT) system. The more of these data are available, the better the
quality of the SMT system. However, for some language pairs such as
Persian-English, parallel sources of this kind are scarce. In this paper, a
bidirectional method is proposed to extract parallel sentences from English and
Persian document aligned Wikipedia. Two machine translation systems are
employed to translate from Persian to English and the reverse after which an IR
system is used to measure the similarity of the translated sentences. Adding
the extracted sentences to the training data of the existing SMT systems is
shown to improve the quality of the translation. Furthermore, the proposed
method slightly outperforms the one-directional approach. The extracted corpus
consists of about 200,000 sentences which have been sorted by their degree of
similarity calculated by the IR system and is freely available for public
access on the Web.
Understanding and Predicting The Attractiveness of Human Action Shot
Authors: Bin Dai, Baoyuan Wang, Gang Hua
Date: 2017-11-02 00:00:00

Selecting attractive photos from a human action shot sequence is quite
challenging, because of the subjective nature of the "attractiveness", which is
mainly a combined factor of human pose in action and the background. Prior
works have actively studied high-level image attributes including
interestingness, memorability, popularity, and aesthetics. However, none of
them has ever studied the "attractiveness" of human action shot. In this paper,
we present the first study of the "attractiveness" of human action shots by
taking a systematic data-driven approach. Specifically, we create a new
action-shot dataset composed of about 8000 high quality action-shot photos. We
further conduct rich crowd-sourced human judge studies on Amazon Mechanical
Turk(AMT) in terms of global attractiveness of a single photo, and relative
attractiveness of a pair of photos. A deep Siamese network with a novel hybrid
distribution matching loss was further proposed to fully exploit both types of
ratings. Extensive experiments reveal that (1) the property of action shot
attractiveness is subjective but predicable (2) our proposed method is both
efficient and effective for predicting the attractive human action shots.
Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging
Authors: Karteek Popuri, Rakesh Balachandar, Kathryn Alpert, Donghuan Lu, Mahadev Bhalla, Ian Mackenzie, Robin Ging-Yuek Hsiung, Lei Wang, Mirza Faisal Beg, the Alzhemier's Disease Neuroimaging Initiative
Date: 2017-11-02 00:00:00
Tasks: General Classification

Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D
topographic brain glucose metabolism patterns from normal controls (NC) and
individuals with dementia of Alzheimer's type (DAT) are used to train a novel
multi-scale ensemble classification model. This ensemble model outputs a
FDG-PET DAT score (FPDS) between 0 and 1 denoting the probability of a subject
to be clinically diagnosed with DAT based on their metabolism profile. A novel
7 group image stratification scheme is devised that groups images not only
based on their associated clinical diagnosis but also on past and future
trajectories of the clinical diagnoses, yielding a more continuous
representation of the different stages of DAT spectrum that mimics a real-world
clinical setting. The potential for using FPDS as a DAT biomarker was validated
on a large number of FDG-PET images (N=2984) obtained from the Alzheimer's
Disease Neuroimaging Initiative (ADNI) database taken across the proposed
stratification, and a good classification AUC (area under the curve) of 0.78
was achieved in distinguishing between images belonging to subjects on a DAT
trajectory and those images taken from subjects not progressing to a DAT
diagnosis. Further, the FPDS biomarker achieved state-of-the-art performance on
the mild cognitive impairment (MCI) to DAT conversion prediction task with an
AUC of 0.81, 0.80, 0.77 for the 2, 3, 5 years to conversion windows
respectively.
Dimensionality reduction methods for molecular simulations
Authors: Stefan Doerr, Igor Ariz-Extreme, Matthew J. Harvey, Gianni de Fabritiis
Date: 2017-10-29 00:00:00
Tasks: Clustering, Dimensionality Reduction, Protein Folding

Molecular simulations produce very high-dimensional data-sets with millions
of data points. As analysis methods are often unable to cope with so many
dimensions, it is common to use dimensionality reduction and clustering methods
to reach a reduced representation of the data. Yet these methods often fail to
capture the most important features necessary for the construction of a Markov
model. Here we demonstrate the results of various dimensionality reduction
methods on two simulation data-sets, one of protein folding and another of
protein-ligand binding. The methods tested include a k-means clustering
variant, a non-linear auto encoder, principal component analysis and tICA. The
dimension-reduced data is then used to estimate the implied timescales of the
slowest process by a Markov state model analysis to assess the quality of the
projection. The projected dimensions learned from the data are visualized to
demonstrate which conformations the various methods choose to represent the
molecular process.
Concave losses for robust dictionary learning
Authors: Rafael Will M de Araujo, Roberto Hirata, Alain Rakotomamonjy
Date: 2017-11-02 00:00:00
Tasks: Dictionary Learning

Traditional dictionary learning methods are based on quadratic convex loss
function and thus are sensitive to outliers. In this paper, we propose a
generic framework for robust dictionary learning based on concave losses. We
provide results on composition of concave functions, notably regarding
super-gradient computations, that are key for developing generic dictionary
learning algorithms applicable to smooth and non-smooth losses. In order to
improve identification of outliers, we introduce an initialization heuristic
based on undercomplete dictionary learning. Experimental results using
synthetic and real data demonstrate that our method is able to better detect
outliers, is capable of generating better dictionaries, outperforming
state-of-the-art methods such as K-SVD and LC-KSVD.
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem
Authors: Lei Han, Yiheng Huang, Tong Zhang
Date: 2017-11-02 00:00:00
Proceeding: ICML 2018 7
Tasks: General Classification, Multi-class Classification

This paper proposes a method for multi-class classification problems, where
the number of classes K is large. The method, referred to as Candidates vs.
Noises Estimation (CANE), selects a small subset of candidate classes and
samples the remaining classes. We show that CANE is always consistent and
computationally efficient. Moreover, the resulting estimator has low
statistical variance approaching that of the maximum likelihood estimator, when
the observed label belongs to the selected candidates with high probability. In
practice, we use a tree structure with leaves as classes to promote fast beam
search for candidate selection. We further apply the CANE method to estimate
word probabilities in learning large neural language models. Extensive
experimental results show that CANE achieves better prediction accuracy over
the Noise-Contrastive Estimation (NCE), its variants and a number of the
state-of-the-art tree classifiers, while it gains significant speedup compared
to standard O(K) methods.
Sentence Correction Based on Large-scale Language Modelling
Authors: Ji Wen
Date: 2017-09-22 00:00:00
Tasks: Imputation, Language Modeling, Language Modelling, Sentence

With the further development of informatization, more and more data is stored
in the form of text. There are some loss of text during their generation and
transmission. The paper aims to establish a language model based on the
large-scale corpus to complete the restoration of missing text. In this paper,
we introduce a novel measurement to find the missing words, and a way of
establishing a comprehensive candidate lexicon to insert the correct choice of
words. The paper also introduces some effective optimization methods, which
largely improve the efficiency of the text restoration and shorten the time of
dealing with 1000 sentences into 3.6 seconds. \keywords{ language model,
sentence correction, word imputation, parallel optimization
Improved Dynamic Regret for Non-degenerate Functions
Authors: Lijun Zhang, Tianbao Yang, Jin-Feng Yi, Rong Jin, Zhi-Hua Zhou
Date: 2016-08-13 00:00:00
Proceeding: NeurIPS 2017 12

Recently, there has been a growing research interest in the analysis of
dynamic regret, which measures the performance of an online learner against a
sequence of local minimizers. By exploiting the strong convexity, previous
studies have shown that the dynamic regret can be upper bounded by the
path-length of the comparator sequence. In this paper, we illustrate that the
dynamic regret can be further improved by allowing the learner to query the
gradient of the function multiple times, and meanwhile the strong convexity can
be weakened to other non-degenerate conditions. Specifically, we introduce the
squared path-length, which could be much smaller than the path-length, as a new
regularity of the comparator sequence. When multiple gradients are accessible
to the learner, we first demonstrate that the dynamic regret of strongly convex
functions can be upper bounded by the minimum of the path-length and the
squared path-length. We then extend our theoretical guarantee to functions that
are semi-strongly convex or self-concordant. To the best of our knowledge, this
is the first time that semi-strong convexity and self-concordance are utilized
to tighten the dynamic regret.
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
Authors: Kristofer E. Bouchard, Alejandro F. Bujan, Farbod Roosta-Khorasani, Shashanka Ubaru, Prabhat, Antoine M. Snijders, Jian-Hua Mao, Edward F. Chang, Michael W. Mahoney, Sharmodeep Bhattacharyya
Date: 2017-05-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Model Selection, Prediction

The increasing size and complexity of scientific data could dramatically
enhance discovery and prediction for basic scientific applications. Realizing
this potential, however, requires novel statistical analysis methods that are
both interpretable and predictive. We introduce Union of Intersections (UoI), a
flexible, modular, and scalable framework for enhanced model selection and
estimation. Methods based on UoI perform model selection and model estimation
through intersection and union operations, respectively. We show that UoI-based
methods achieve low-variance and nearly unbiased estimation of a small number
of interpretable features, while maintaining high-quality prediction accuracy.
We perform extensive numerical investigation to evaluate a UoI algorithm
($UoI_{Lasso}$) on synthetic and real data. In doing so, we demonstrate the
extraction of interpretable functional networks from human electrophysiology
recordings as well as accurate prediction of phenotypes from genotype-phenotype
data with reduced features. We also show (with the $UoI_{L1Logistic}$ and
$UoI_{CUR}$ variants of the basic framework) improved prediction parsimony for
classification and matrix factorization on several benchmark biomedical data
sets. These results suggest that methods based on the UoI framework could
improve interpretation and prediction in data-driven discovery across
scientific fields.
Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device
Authors: Xin Zhang, Weixuan Kou, Eric I-Chao Chang, He Gao, Yubo Fan, Yan Xu
Date: 2017-11-02 00:00:00
Tasks: Automatic Sleep Stage Classification, General Classification, Sleep Staging

This paper proposes a practical approach for automatic sleep stage
classification based on a multi-level feature learning framework and Recurrent
Neural Network (RNN) classifier using heart rate and wrist actigraphy derived
from a wearable device. The feature learning framework is designed to extract
low- and mid-level features. Low-level features capture temporal and frequency
domain properties and mid-level features learn compositions and structural
information of signals. Since sleep staging is a sequential problem with
long-term dependencies, we take advantage of RNNs with Bidirectional Long
Short-Term Memory (BLSTM) architectures for sequence data learning. To simulate
the actual situation of daily sleep, experiments are conducted with a resting
group in which sleep is recorded in resting state, and a comprehensive group in
which both resting sleep and non-resting sleep are included.We evaluate the
algorithm based on an eight-fold cross validation to classify five sleep stages
(W, N1, N2, N3, and REM). The proposed algorithm achieves weighted precision,
recall and F1 score of 58.0%, 60.3%, and 58.2% in the resting group and 58.5%,
61.1%, and 58.5% in the comprehensive group, respectively. Various comparison
experiments demonstrate the effectiveness of feature learning and BLSTM. We
further explore the influence of depth and width of RNNs on performance. Our
method is specially proposed for wearable devices and is expected to be
applicable for long-term sleep monitoring at home. Without using too much prior
domain knowledge, our method has the potential to generalize sleep disorder
detection.
Automatic Estimation of Fetal Abdominal Circumference from Ultrasound Images
Authors: Jaeseong Jang, Yejin Park, Bukweon Kim, Sung Min Lee, Ja-Young Kwon, Jin Keun Seo
Date: 2017-02-09 00:00:00

Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal
biometry, and owing to its time-consuming process, there has been a great
demand for automatic estimation. However, the automated analysis of ultrasound
images is complicated because they are patient-specific, operator-dependent,
and machine-specific. Among various types of fetal biometry, the accurate
estimation of abdominal circumference (AC) is especially difficult to perform
automatically because the abdomen has low contrast against surroundings,
non-uniform contrast, and irregular shape compared to other parameters.We
propose a method for the automatic estimation of the fetal AC from 2D
ultrasound data through a specially designed convolutional neural network
(CNN), which takes account of doctors' decision process, anatomical structure,
and the characteristics of the ultrasound image. The proposed method uses CNN
to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical
vein) and Hough transformation for measuring AC. We test the proposed method
using clinical ultrasound data acquired from 56 pregnant women. Experimental
results show that, with relatively small training samples, the proposed CNN
provides sufficient classification results for AC estimation through the Hough
transformation. The proposed method automatically estimates AC from ultrasound
images. The method is quantitatively evaluated, and shows stable performance in
most cases and even for ultrasound images deteriorated by shadowing artifacts.
As a result of experiments for our acceptance check, the accuracies are 0.809
and 0.771 with the expert 1 and expert 2, respectively, while the accuracy
between the two experts is 0.905. However, for cases of oversized fetus, when
the amniotic fluid is not observed or the abdominal area is distorted, it could
not correctly estimate AC.
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder
Authors: Daehyung Park, Yuuna Hoshi, Charles C. Kemp
Date: 2017-11-02 00:00:00
Tasks: Anomaly Detection

The detection of anomalous executions is valuable for reducing potential
hazards in assistive manipulation. Multimodal sensory signals can be helpful
for detecting a wide range of anomalies. However, the fusion of
high-dimensional and heterogeneous modalities is a challenging problem. We
introduce a long short-term memory based variational autoencoder (LSTM-VAE)
that fuses signals and reconstructs their expected distribution. We also
introduce an LSTM-VAE-based detector using a reconstruction-based anomaly score
and a state-based threshold. For evaluations with 1,555 robot-assisted feeding
executions including 12 representative types of anomalies, our detector had a
higher area under the receiver operating characteristic curve (AUC) of 0.8710
than 5 other baseline detectors from the literature. We also show the
multimodal fusion through the LSTM-VAE is effective by comparing our detector
with 17 raw sensory signals versus 4 hand-engineered features.