pwc-archive/papers-with-abstracts huggingface dataset:
Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling
Authors: Vikram Venkatraghavan, Esther E. Bron, Wiro J. Niessen, Stefan Klein, for the Alzheimer's Disease Neuroimaging Initiative
Date: 2018-08-10 00:00:00
Tasks: Prognosis

Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming
abnormal, the pathophysiology of which is very complex and largely unknown.
Event-based modeling (EBM) is a data-driven technique to estimate the sequence
in which biomarkers for a disease become abnormal based on cross-sectional
data. It can help in understanding the dynamics of disease progression and
facilitate early diagnosis and prognosis. In this work we propose a novel
discriminative approach to EBM, which is shown to be more accurate than
existing state-of-the-art EBM methods. The method first estimates for each
subject an approximate ordering of events. Subsequently, the central ordering
over all subjects is estimated by fitting a generalized Mallows model to these
approximate subject-specific orderings. We also introduce the concept of
relative distance between events which helps in creating a disease progression
timeline. Subsequently, we propose a method to stage subjects by placing them
on the estimated disease progression timeline. We evaluated the proposed method
on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the
results with existing state-of-the-art EBM methods. We also performed extensive
experiments on synthetic data simulating the progression of Alzheimer's
disease. The event orderings obtained on ADNI data seem plausible and are in
agreement with the current understanding of progression of AD. The proposed
patient staging algorithm performed consistently better than that of
state-of-the-art EBM methods. Event orderings obtained in simulation
experiments were more accurate than those of other EBM methods and the
estimated disease progression timeline was observed to correlate with the
timeline of actual disease progression. The results of these experiments are
encouraging and suggest that discriminative EBM is a promising approach to
disease progression modeling.
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks
Authors: Fan Yang, ZhiYuan Chen
Date: 2018-08-10 00:00:00
Tasks: BIG-bench Machine Learning, Intrusion Detection, Malware Detection, Spam detection

Machine learning models have been widely used in security applications such
as intrusion detection, spam filtering, and virus or malware detection.
However, it is well-known that adversaries are always trying to adapt their
attacks to evade detection. For example, an email spammer may guess what
features spam detection models use and modify or remove those features to avoid
detection. There has been some work on making machine learning models more
robust to such attacks. However, one simple but promising approach called {\em
randomization} is underexplored. This paper proposes a novel
randomization-based approach to improve robustness of machine learning models
against evasion attacks. The proposed approach incorporates randomization into
both model training time and model application time (meaning when the model is
used to detect attacks). We also apply this approach to random forest, an
existing ML method which already has some degree of randomness. Experiments on
intrusion detection and spam filtering data show that our approach further
improves robustness of random-forest method. We also discuss how this approach
can be applied to other ML models.
How Complex is your classification problem? A survey on measuring classification complexity
Authors: Ana C. Lorena, Luís P. F. Garcia, Jens Lehmann, Marcilio C. P. Souto, Tin K. Ho
Date: 2018-08-10 00:00:00
Tasks: Classification, General Classification

Characteristics extracted from the training datasets of classification problems have proven to be effective predictors in a number of meta-analyses. Among them, measures of classification complexity can be used to estimate the difficulty in separating the data points into their expected classes. Descriptors of the spatial distribution of the data and estimates of the shape and size of the decision boundary are among the known measures for this characterization. This information can support the formulation of new data-driven pre-processing and pattern recognition techniques, which can in turn be focused on challenges highlighted by such characteristics of the problems. This paper surveys and analyzes measures which can be extracted from the training datasets in order to characterize the complexity of the respective classification problems. Their use in recent literature is also reviewed and discussed, allowing to prospect opportunities for future work in the area. Finally, descriptions are given on an R package named Extended Complexity Library (ECoL) that implements a set of complexity measures and is made publicly available.
Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning
Authors: Noah Frazier-Logue, Stephen José Hanson
Date: 2018-08-10 00:00:00
Tasks: parameter estimation

Multi-layer neural networks have lead to remarkable performance on many kinds
of benchmark tasks in text, speech and image processing. Nonlinear parameter
estimation in hierarchical models is known to be subject to overfitting and
misspecification. One approach to these estimation and related problems (local
minima, colinearity, feature discovery etc.) is called Dropout (Hinton, et al
2012, Baldi et al 2016). The Dropout algorithm removes hidden units according
to a Bernoulli random variable with probability $p$ prior to each update,
creating random "shocks" to the network that are averaged over updates. In this
paper we will show that Dropout is a special case of a more general model
published originally in 1990 called the Stochastic Delta Rule, or SDR (Hanson,
1990). SDR redefines each weight in the network as a random variable with mean
$\mu_{w_{ij}}$ and standard deviation $\sigma_{w_{ij}}$. Each weight random
variable is sampled on each forward activation, consequently creating an
exponential number of potential networks with shared weights. Both parameters
are updated according to prediction error, thus resulting in weight noise
injections that reflect a local history of prediction error and local model
averaging. SDR therefore implements a more sensitive local gradient-dependent
simulated annealing per weight converging in the limit to a Bayes optimal
network. Tests on standard benchmarks (CIFAR) using a modified version of
DenseNet shows the SDR outperforms standard Dropout in test error by approx.
$17\%$ with DenseNet-BC 250 on CIFAR-100 and approx. $12-14\%$ in smaller
networks. We also show that SDR reaches the same accuracy that Dropout attains
in 100 epochs in as few as 35 epochs.
Weakly- and Semi-Supervised Panoptic Segmentation
Authors: Qizhu Li, Anurag Arnab, Philip H. S. Torr
Date: 2018-08-10 00:00:00
Proceeding: ECCV 2018 9
Tasks: Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation, Weakly-supervised instance segmentation, Weakly-supervised panoptic segmentation, Weakly-Supervised Semantic Segmentation

We present a weakly supervised model that jointly performs both semantic- and
instance-segmentation -- a particularly relevant problem given the substantial
cost of obtaining pixel-perfect annotation for these tasks. In contrast to many
popular instance segmentation approaches based on object detectors, our method
does not predict any overlapping instances. Moreover, we are able to segment
both "thing" and "stuff" classes, and thus explain all the pixels in the image.
"Thing" classes are weakly-supervised with bounding boxes, and "stuff" with
image-level tags. We obtain state-of-the-art results on Pascal VOC, for both
full and weak supervision (which achieves about 95% of fully-supervised
performance). Furthermore, we present the first weakly-supervised results on
Cityscapes for both semantic- and instance-segmentation. Finally, we use our
weakly supervised framework to analyse the relationship between annotation
quality and predictive performance, which is of interest to dataset creators.
Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging
Authors: Michael R. Kellman, Emrah Bostan, Nicole Repina, Laura Waller
Date: 2018-08-10 00:00:00
Tasks: Experimental Design, Retrieval

Coded-illumination can enable quantitative phase microscopy of transparent
samples with minimal hardware requirements. Intensity images are captured with
different source patterns and a non-linear phase retrieval optimization
reconstructs the image. The non-linear nature of the processing makes
optimizing the illumination pattern designs complicated. Traditional techniques
for experimental design (e.g. condition number optimization, spectral analysis)
consider only linear measurement formation models and linear reconstructions.
Deep neural networks (DNNs) can efficiently represent the non-linear process
and can be optimized over via training in an end-to-end framework. However,
DNNs typically require a large amount of training examples and parameters to
properly learn the phase retrieval process, without making use of the known
physical models. Here, we aim to use both our knowledge of the physics and the
power of machine learning together. We develop a new data-driven approach to
optimizing coded-illumination patterns for a LED array microscope for a given
phase reconstruction algorithm. Our method incorporates both the physics of the
measurement scheme and the non-linearity of the reconstruction algorithm into
the design problem. This enables efficient parameterization, which allows us to
use only a small number of training examples to learn designs that generalize
well in the experimental setting without retraining. We show experimental
results for both a well-characterized phase target and mouse fibroblast cells
using coded-illumination patterns optimized for a sparsity-based phase
reconstruction algorithm. Our learned design results using 2 measurements
demonstrate similar accuracy to Fourier Ptychography with 69 measurements.
Densely Connected Convolutional Networks for Speech Recognition
Authors: Chia Yu Li, Ngoc Thang Vu
Date: 2018-08-10 00:00:00
Tasks: Acoustic Modelling, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper presents our latest investigation on Densely Connected
Convolutional Networks (DenseNets) for acoustic modelling (AM) in automatic
speech recognition. DenseN-ets are very deep, compact convolutional neural
networks, which have demonstrated incredible improvements over the
state-of-the-art results on several data sets in computer vision. Our
experimental results show that DenseNet can be used for AM significantly
outperforming other neural-based models such as DNNs, CNNs, VGGs. Furthermore,
results on Wall Street Journal revealed that with only a half of the training
data DenseNet was able to outperform other models trained with the full data
set by a large margin.
Greedy Algorithms for Approximating the Diameter of Machine Learning Datasets in Multidimensional Euclidean Space
Authors: Ahmad B. Hassanat
Date: 2018-08-10 00:00:00
Tasks: BIG-bench Machine Learning

Finding the diameter of a dataset in multidimensional Euclidean space is a
well-established problem, with well-known algorithms. However, most of the
algorithms found in the literature do not scale well with large values of data
dimension, so the time complexity grows exponentially in most cases, which
makes these algorithms impractical. Therefore, we implemented 4 simple greedy
algorithms to be used for approximating the diameter of a multidimensional
dataset; these are based on minimum/maximum l2 norms, hill climbing search,
Tabu search and Beam search approaches, respectively. The time complexity of
the implemented algorithms is near-linear, as they scale near-linearly with
data size and its dimensions. The results of the experiments (conducted on
different machine learning data sets) prove the efficiency of the implemented
algorithms and can therefore be recommended for finding the diameter to be used
by different machine learning applications when needed.
Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
Authors: N. Anantrasirichai, Alin Achim, David Bull
Date: 2018-08-10 00:00:00

This paper describes a new method for mitigating the effects of atmospheric
distortion on observed sequences that include large moving objects. In order to
provide accurate detail from objects behind the distorting layer, we solve the
space-variant distortion problem using recursive image fusion based on the Dual
Tree Complex Wavelet Transform (DT-CWT). The moving objects are detected and
tracked using the improved Gaussian mixture models (GMM) and Kalman filtering.
New fusion rules are introduced which work on the magnitudes and angles of the
DT-CWT coefficients independently to achieve a sharp image and to reduce
atmospheric distortion, respectively. The subjective results show that the
proposed method achieves better video quality than other existing methods with
competitive speed.
Band selection with Higher Order Multivariate Cumulants for small target detection in hyperspectral images
Authors: Przemysław Głomb, Krzysztof Domino, Michał Romaszewski, Michał Cholewa
Date: 2018-08-10 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

In the small target detection problem a pattern to be located is on the order
of magnitude less numerous than other patterns present in the dataset. This
applies both to the case of supervised detection, where the known template is
expected to match in just a few areas and unsupervised anomaly detection, as
anomalies are rare by definition. This problem is frequently related to the
imaging applications, i.e. detection within the scene acquired by a camera. To
maximize available data about the scene, hyperspectral cameras are used; at
each pixel, they record spectral data in hundreds of narrow bands.
  The typical feature of hyperspectral imaging is that characteristic
properties of target materials are visible in the small number of bands, where
light of certain wavelength interacts with characteristic molecules. A
target-independent band selection method based on statistical principles is a
versatile tool for solving this problem in different practical applications.
  Combination of a regular background and a rare standing out anomaly will
produce a distortion in the joint distribution of hyperspectral pixels. Higher
Order Cumulants Tensors are a natural `window' into this distribution, allowing
to measure properties and suggest candidate bands for removal. While there have
been attempts at producing band selection algorithms based on the 3 rd
cumulant's tensor i.e. the joint skewness, the literature lacks a systematic
analysis of how the order of the cumulant tensor used affects effectiveness of
band selection in detection applications. In this paper we present an analysis
of a general algorithm for band selection based on higher order cumulants. We
discuss its usability related to the observed breaking points in performance,
depending both on method order and the desired number of bands. Finally we
perform experiments and evaluate these methods in a hyperspectral detection
scenario.
Making effective use of healthcare data using data-to-text technology
Authors: Steffen Pauws, Albert Gatt, Emiel Krahmer, Ehud Reiter
Date: 2018-08-10 00:00:00

Healthcare organizations are in a continuous effort to improve health
outcomes, reduce costs and enhance patient experience of care. Data is
essential to measure and help achieving these improvements in healthcare
delivery. Consequently, a data influx from various clinical, financial and
operational sources is now overtaking healthcare organizations and their
patients. The effective use of this data, however, is a major challenge.
Clearly, text is an important medium to make data accessible. Financial reports
are produced to assess healthcare organizations on some key performance
indicators to steer their healthcare delivery. Similarly, at a clinical level,
data on patient status is conveyed by means of textual descriptions to
facilitate patient review, shift handover and care transitions. Likewise,
patients are informed about data on their health status and treatments via
text, in the form of reports or via ehealth platforms by their doctors.
Unfortunately, such text is the outcome of a highly labour-intensive process if
it is done by healthcare professionals. It is also prone to incompleteness,
subjectivity and hard to scale up to different domains, wider audiences and
varying communication purposes. Data-to-text is a recent breakthrough
technology in artificial intelligence which automatically generates natural
language in the form of text or speech from data. This chapter provides a
survey of data-to-text technology, with a focus on how it can be deployed in a
healthcare setting. It will (1) give an up-to-date synthesis of data-to-text
approaches, (2) give a categorized overview of use cases in healthcare, (3)
seek to make a strong case for evaluating and implementing data-to-text in a
healthcare setting, and (4) highlight recent research challenges.
Model Approximation Using Cascade of Tree Decompositions
Authors: Navid Tafaghodi Khajavi, Anthony Kuh
Date: 2018-08-10 00:00:00
Tasks: model

In this paper, we present a general, multistage framework for graphical model
approximation using a cascade of models such as trees. In particular, we look
at the problem of covariance matrix approximation for Gaussian distributions as
linear transformations of tree models. This is a new way to decompose the
covariance matrix. Here, we propose an algorithm which incorporates the
Cholesky factorization method to compute the decomposition matrix and thus can
approximate a simple graphical model using a cascade of the Cholesky
factorization of the tree approximation transformations. The Cholesky
decomposition enables us to achieve a tree structure factor graph at each
cascade stage of the algorithm which facilitates the use of the message passing
algorithm since the approximated graph has less loops compared to the original
graph. The overall graph is a cascade of factor graphs with each factor graph
being a tree. This is a different perspective on the approximation model, and
algorithms such as Gaussian belief propagation can be used on this overall
graph. Here, we present theoretical result that guarantees the convergence of
the proposed model approximation using the cascade of tree decompositions. In
the simulations, we look at synthetic and real data and measure the performance
of the proposed framework by comparing the KL divergences.
Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones
Authors: Santiago Cortés, Arno Solin, Juho Kannala
Date: 2018-08-10 00:00:00
Tasks: State Estimation

Strapdown inertial navigation systems are sensitive to the quality of the
data provided by the accelerometer and gyroscope. Low-grade IMUs in handheld
smart-devices pose a problem for inertial odometry on these devices. We propose
a scheme for constraining the inertial odometry problem by complementing
non-linear state estimation by a CNN-based deep-learning model for inferring
the momentary speed based on a window of IMU samples. We show the feasibility
of the model using a wide range of data from an iPhone, and present
proof-of-concept results for how the model can be combined with an inertial
navigation system for three-dimensional inertial navigation.
The Evolution of Sex Chromosomes through the Baldwin Effect
Authors: Larry Bull
Date: 2018-08-10 00:00:00

It has recently been suggested that the fundamental haploid-diploid cycle of eukaryotic sex exploits a rudimentary form of the Baldwin effect. Thereafter the other associated phenomena can be explained as evolution tuning the amount and frequency of learning experienced by an organism. Using the well-known NK model of fitness landscapes it is here shown that the emergence of sex determination systems can also be explained under this view of eukaryotic evolution.
TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification
Authors: Wenpeng Yin, Dan Roth
Date: 2018-08-10 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Claim Verification, Natural Language Inference, Vocal Bursts Valence Prediction

Determining whether a given claim is supported by evidence is a fundamental
NLP problem that is best modeled as Textual Entailment. However, given a large
collection of text, finding evidence that could support or refute a given claim
is a challenge in itself, amplified by the fact that different evidence might
be needed to support or refute a claim. Nevertheless, most prior work decouples
evidence identification from determining the truth value of the claim given the
evidence.
  We propose to consider these two aspects jointly. We develop TwoWingOS
(two-wing optimization strategy), a system that, while identifying appropriate
evidence for a claim, also determines whether or not the claim is supported by
the evidence. Given the claim, TwoWingOS attempts to identify a subset of the
evidence candidates; given the predicted evidence, it then attempts to
determine the truth value of the corresponding claim. We treat this challenge
as coupled optimization problems, training a joint model for it. TwoWingOS
offers two advantages: (i) Unlike pipeline systems, it facilitates
flexible-size evidence set, and (ii) Joint training improves both the claim
entailment and the evidence identification. Experiments on a benchmark dataset
show state-of-the-art performance. Code: https://github.com/yinwenpeng/FEVER
Facial Action Unit Detection Using Attention and Relation Learning
Authors: Zhiwen Shao, Zhilei Liu, Jianfei Cai, Yunsheng Wu, Lizhuang Ma
Date: 2018-08-10 00:00:00
Tasks: Action Unit Detection, Facial Action Unit Detection, Relation

Attention mechanism has recently attracted increasing attentions in the field of facial action unit (AU) detection. By finding the region of interest of each AU with the attention mechanism, AU-related local features can be captured. Most of the existing attention based AU detection works use prior knowledge to predefine fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose an end-to-end deep learning based attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise and spatial attentions are adaptively learned to select and extract AU-related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Without changing the network architecture, our framework can be easily extended for AU intensity estimation. Extensive experiments show that our framework (i) soundly outperforms the state-of-the-art methods for both AU detection and AU intensity estimation on the challenging BP4D, DISFA, FERA 2015 and BP4D+ benchmarks, (ii) can adaptively capture the correlated regions of each AU, and (iii) also works well under severe occlusions and large poses.