pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Classification of Functional Gait Disorders
Authors: Djordje Slijepcevic, Matthias Zeppelzauer, Anna-Maria Gorgas, Caterine Schwab, Michael Schüller, Arnold Baca, Christian Breiteneder, Brian Horsak
Date: 2017-12-18 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

This article proposes a comprehensive investigation of the automatic
classification of functional gait disorders based solely on ground reaction
force (GRF) measurements. The aim of the study is twofold: (1) to investigate
the suitability of stateof-the-art GRF parameterization techniques
(representations) for the discrimination of functional gait disorders; and (2)
to provide a first performance baseline for the automated classification of
functional gait disorders for a large-scale dataset. The utilized database
comprises GRF measurements from 279 patients with gait disorders (GDs) and data
from 161 healthy controls (N). Patients were manually classified into four
classes with different functional impairments associated with the "hip",
"knee", "ankle", and "calcaneus". Different parameterizations are investigated:
GRF parameters, global principal component analysis (PCA)-based representations
and a combined representation applying PCA on GRF parameters. The
discriminative power of each parameterization for different classes is
investigated by linear discriminant analysis (LDA). Based on this analysis, two
classification experiments are pursued: (1) distinction between healthy and
impaired gait (N vs. GD) and (2) multi-class classification between healthy
gait and all four GD classes. Experiments show promising results and reveal
among others that several factors, such as imbalanced class cardinalities and
varying numbers of measurement sessions per patient have a strong impact on the
classification accuracy and therefore need to be taken into account. The
results represent a promising first step towards the automated classification
of gait disorders and a first performance baseline for future developments in
this direction.
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification
Authors: Qingjiu Zhang, Shiliang Sun
Date: 2017-12-24 00:00:00
Tasks: Active Learning, General Classification

Artificial neural network (ANN) is a very useful tool in solving learning
problems. Boosting the performances of ANN can be mainly concluded from two
aspects: optimizing the architecture of ANN and normalizing the raw data for
ANN. In this paper, a novel method which improves the effects of ANN by
preprocessing the raw data is proposed. It totally leverages the fact that
different features should play different roles. The raw data set is firstly
preprocessed by principle component analysis (PCA), and then its principle
components are weighted by their corresponding eigenvalues. Several aspects of
analysis are carried out to analyze its theory and the applicable occasions.
Three classification problems are launched by an active learning algorithm to
verify the proposed method. From the empirical results, conclusion comes to the
fact that the proposed method can significantly improve the performance of ANN.
Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor
Authors: Shiliang Sun, Chang-Shui Zhang, Yi Zhang
Date: 2017-12-24 00:00:00

A novel predictor for traffic flow forecasting, namely spatio-temporal
Bayesian network predictor, is proposed. Unlike existing methods, our approach
incorporates all the spatial and temporal information available in a
transportation network to carry our traffic flow forecasting of the current
site. The Pearson correlation coefficient is adopted to rank the input
variables (traffic flows) for prediction, and the best-first strategy is
employed to select a subset as the cause nodes of a Bayesian network. Given the
derived cause nodes and the corresponding effect node in the spatio-temporal
Bayesian network, a Gaussian Mixture Model is applied to describe the
statistical relationship between the input and output. Finally, traffic flow
forecasting is performed under the criterion of Minimum Mean Square Error
(M.M.S.E.). Experimental results with the urban vehicular flow data of Beijing
demonstrate the effectiveness of our presented spatio-temporal Bayesian network
predictor.
Co-occurrence Filter
Authors: Roy J Jevnisek, Shai Avidan
Date: 2017-03-12 00:00:00
Proceeding: CVPR 2017 7

Co-occurrence Filter (CoF) is a boundary preserving filter. It is based on
the Bilateral Filter (BF) but instead of using a Gaussian on the range values
to preserve edges it relies on a co-occurrence matrix. Pixel values that
co-occur frequently in the image (i.e., inside textured regions) will have a
high weight in the co-occurrence matrix. This, in turn, means that such pixel
pairs will be averaged and hence smoothed, regardless of their intensity
differences. On the other hand, pixel values that rarely co-occur (i.e., across
texture boundaries) will have a low weight in the co-occurrence matrix. As a
result, they will not be averaged and the boundary between them will be
preserved. The CoF therefore extends the BF to deal with boundaries, not just
edges. It learns co-occurrences directly from the image. We can achieve various
filtering results by directing it to learn the co-occurrence matrix from a part
of the image, or a different image. We give the definition of the filter,
discuss how to use it with color images and show several use cases.
Lectures on Randomized Numerical Linear Algebra
Authors: Petros Drineas, Michael W. Mahoney
Date: 2017-12-24 00:00:00

This chapter is based on lectures on Randomized Numerical Linear Algebra from
the 2016 Park City Mathematics Institute summer school on The Mathematics of
Data.
Optimal Weighting for Exam Composition
Authors: Sam Ganzfried, Farzana Yusuf
Date: 2017-12-24 00:00:00
Tasks: Multiple-choice

A problem faced by many instructors is that of designing exams that
accurately assess the abilities of the students. Typically these exams are
prepared several days in advance, and generic question scores are used based on
rough approximation of the question difficulty and length. For example, for a
recent class taught by the author, there were 30 multiple choice questions
worth 3 points, 15 true/false with explanation questions worth 4 points, and 5
analytical exercises worth 10 points. We describe a novel framework where
algorithms from machine learning are used to modify the exam question weights
in order to optimize the exam scores, using the overall class grade as a proxy
for a student's true ability. We show that significant error reduction can be
obtained by our approach over standard weighting schemes, and we make several
new observations regarding the properties of the "good" and "bad" exam
questions that can have impact on the design of improved future evaluation
methods.
How Intelligent is your Intelligent Robot?
Authors: Alan F. T. Winfield
Date: 2017-12-24 00:00:00

How intelligent is robot A compared with robot B? And how intelligent are
robots A and B compared with animals (or plants) X and Y? These are both
interesting and deeply challenging questions. In this paper we address the
question "how intelligent is your intelligent robot?" by proposing that
embodied intelligence emerges from the interaction and integration of four
different and distinct kinds of intelligence. We then suggest a simple
diagrammatic representation on which these kinds of intelligence are shown as
four axes in a star diagram. A crude qualitative comparison of the intelligence
graphs of animals and robots both exposes and helps to explain the chronic
intelligence deficit of intelligent robots. Finally we examine the options for
determining numerical values for the four kinds of intelligence in an effort to
move toward a quantifiable intelligence vector.
Blind Image Deblurring via Reweighted Graph Total Variation
Authors: Yuanchao Bai, Gene Cheung, Xian-Ming Liu, Wen Gao
Date: 2017-12-24 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring

Blind image deblurring, i.e., deblurring without knowledge of the blur
kernel, is a highly ill-posed problem. The problem can be solved in two parts:
i) estimate a blur kernel from the blurry image, and ii) given estimated blur
kernel, de-convolve blurry input to restore the target image. In this paper, by
interpreting an image patch as a signal on a weighted graph, we first argue
that a skeleton image---a proxy that retains the strong gradients of the target
but smooths out the details---can be used to accurately estimate the blur
kernel and has a unique bi-modal edge weight distribution. We then design a
reweighted graph total variation (RGTV) prior that can efficiently promote
bi-modal edge weight distribution given a blurry patch. However, minimizing a
blind image deblurring objective with RGTV results in a non-convex
non-differentiable optimization problem. We propose a fast algorithm that
solves for the skeleton image and the blur kernel alternately. Finally with the
computed blur kernel, recent non-blind image deblurring algorithms can be
applied to restore the target image. Experimental results show that our
algorithm can robustly estimate the blur kernel with large kernel size, and the
reconstructed sharp image is competitive against the state-of-the-art methods.
Stability and Fluctuations in a Simple Model of Phonetic Category Change
Authors: Benjamin Goodman, Paul Tupper
Date: 2017-04-20 00:00:00

In spoken languages, speakers divide up the space of phonetic possibilities
into different regions, corresponding to different phonemes. We consider a
simple exemplar model of how this division of phonetic space varies over time
among a population of language users. In the particular model we consider, we
show that, once the system is initialized with a given set of phonemes, that
phonemes do not become extinct: all phonemes will be maintained in the system
for all time. This is in contrast to what is observed in more complex models.
Furthermore, we show that the boundaries between phonemes fluctuate and we
quantitatively study the fluctuations in a simple instance of our model. These
results prepare the ground for more sophisticated models in which some phonemes
go extinct or new phonemes emerge through other processes.
Use of Generative Adversarial Network for Cross-Domain Change Detection
Authors: Yamaguchi Kousuke, Tanaka Kanji, Sugimoto Takuma
Date: 2017-12-24 00:00:00
Tasks: Change Detection, Generative Adversarial Network, Image-to-Image Translation, Translation, Visual Place Recognition

This paper addresses the problem of cross-domain change detection from a
novel perspective of image-to-image translation. In general, change detection
aims to identify interesting changes between a given query image and a
reference image of the same scene taken at a different time. This problem
becomes a challenging one when query and reference images involve different
domains (e.g., time of the day, weather, and season) due to variations in
object appearance and a limited amount of training examples. In this study, we
address the above issue by leveraging a generative adversarial network (GAN).
Our key concept is to use a limited amount of training data to train a
GAN-based image translator that maps a reference image to a virtual image that
cannot be discriminated from query domain images. This enables us to treat the
cross-domain change detection task as an in-domain image comparison. This
allows us to leverage the large body of literature on in-domain generic change
detectors. In addition, we also consider the use of visual place recognition as
a method for mining more appropriate reference images over the space of virtual
images. Experiments validate efficacy of the proposed approach.
Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application
Authors: Subhadeep Mukhopadhyay, Emanuel Parzen
Date: 2013-08-03 00:00:00
Tasks: All, Time Series, Time Series Analysis

A new comprehensive approach to nonlinear time series analysis and modeling
is developed in the present paper. We introduce novel data-specific
mid-distribution based Legendre Polynomial (LP) like nonlinear transformations
of the original time series Y(t) that enables us to adapt all the existing
stationary linear Gaussian time series modeling strategy and made it applicable
for non-Gaussian and nonlinear processes in a robust fashion. The emphasis of
the present paper is on empirical time series modeling via the algorithm
LPTime. We demonstrate the effectiveness of our theoretical framework using
daily S&P 500 return data between Jan/2/1963 - Dec/31/2009. Our proposed LPTime
algorithm systematically discovers all the `stylized facts' of the financial
time series automatically all at once, which were previously noted by many
researchers one at a time.
Neural Network Multitask Learning for Traffic Flow Forecasting
Authors: Feng Jin, Shiliang Sun
Date: 2017-12-24 00:00:00

Traditional neural network approaches for traffic flow forecasting are
usually single task learning (STL) models, which do not take advantage of the
information provided by related tasks. In contrast to STL, multitask learning
(MTL) has the potential to improve generalization by transferring information
in training signals of extra tasks. In this paper, MTL based neural networks
are used for traffic flow forecasting. For neural network MTL, a
backpropagation (BP) network is constructed by incorporating traffic flows at
several contiguous time instants into an output layer. Nodes in the output
layer can be seen as outputs of different but closely related STL tasks.
Comprehensive experiments on urban vehicular traffic flow data and comparisons
with STL show that MTL in BP neural networks is a promising and effective
approach for traffic flow forecasting.
Whatever Does Not Kill Deep Reinforcement Learning, Makes It Stronger
Authors: Vahid Behzadan, Arslan Munir
Date: 2017-12-23 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent developments have established the vulnerability of deep Reinforcement
Learning (RL) to policy manipulation attacks via adversarial perturbations. In
this paper, we investigate the robustness and resilience of deep RL to
training-time and test-time attacks. Through experimental results, we
demonstrate that under noncontiguous training-time attacks, Deep Q-Network
(DQN) agents can recover and adapt to the adversarial conditions by reactively
adjusting the policy. Our results also show that policies learned under
adversarial perturbations are more robust to test-time attacks. Furthermore, we
compare the performance of $\epsilon$-greedy and parameter-space noise
exploration methods in terms of robustness and resilience against adversarial
perturbations.
Transfer Regression via Pairwise Similarity Regularization
Authors: Aubrey Gress, Ian Davidson
Date: 2017-12-23 00:00:00
Tasks: regression, Transfer Learning

Transfer learning methods address the situation where little labeled training
data from the "target" problem exists, but much training data from a related
"source" domain is available. However, the overwhelming majority of transfer
learning methods are designed for simple settings where the source and target
predictive functions are almost identical, limiting the applicability of
transfer learning methods to real world data. We propose a novel, weaker,
property of the source domain that can be transferred even when the source and
target predictive functions diverge. Our method assumes the source and target
functions share a Pairwise Similarity property, where if the source function
makes similar predictions on a pair of instances, then so will the target
function. We propose Pairwise Similarity Regularization Transfer, a flexible
graph-based regularization framework which can incorporate this modeling
assumption into standard supervised learning algorithms. We show how users can
encode domain knowledge into our regularizer in the form of spatial continuity,
pairwise "similarity constraints" and how our method can be scaled to large
data sets using the Nystrom approximation. Finally, we present positive and
negative results on real and synthetic data sets and discuss when our Pairwise
Similarity transfer assumption seems to hold in practice.
Feature Extraction for Machine Learning Based Crackle Detection in Lung Sounds from a Health Survey
Authors: Morten Grønnesby, Juan Carlos Aviles Solis, Einar Holsbø, Hasse Melbye, Lars Ailo Bongo
Date: 2017-05-31 00:00:00
Tasks: BIG-bench Machine Learning

In recent years, many innovative solutions for recording and viewing sounds
from a stethoscope have become available. However, to fully utilize such
devices, there is a need for an automated approach for detecting abnormal lung
sounds, which is better than the existing methods that typically have been
developed and evaluated using a small and non-diverse dataset.
  We propose a machine learning based approach for detecting crackles in lung
sounds recorded using a stethoscope in a large health survey. Our method is
trained and evaluated using 209 files with crackles classified by expert
listeners. Our analysis pipeline is based on features extracted from small
windows in audio files. We evaluated several feature extraction methods and
classifiers. We evaluated the pipeline using a training set of 175 crackle
windows and 208 normal windows. We did 100 cycles of cross validation where we
shuffled training sets between cycles. For all the division between training
and evaluation was 70%-30%.
  We found and evaluated a 5-dimenstional vector with four features from the
time domain and one from the spectrum domain. We evaluated several classifiers
and found SVM with a Radial Basis Function Kernel to perform best. Our approach
had a precision of 86% and recall of 84% for classifying a crackle in a window,
which is more accurate than found in studies of health personnel. The
low-dimensional feature vector makes the SVM very fast. The model can be
trained on a regular computer in 1.44 seconds, and 319 crackles can be
classified in 1.08 seconds.
  Our approach detects and visualizes individual crackles in recorded audio
files. It is accurate, fast, and has low resource requirements. It can be used
to train health personnel or as part of a smartphone application for Bluetooth
stethoscopes.
Outlier-robust moment-estimation via sum-of-squares
Authors: Pravesh K. Kothari, David Steurer
Date: 2017-11-30 00:00:00

We develop efficient algorithms for estimating low-degree moments of unknown
distributions in the presence of adversarial outliers. The guarantees of our
algorithms improve in many cases significantly over the best previous ones,
obtained in recent works of Diakonikolas et al, Lai et al, and Charikar et al.
We also show that the guarantees of our algorithms match information-theoretic
lower-bounds for the class of distributions we consider. These improved
guarantees allow us to give improved algorithms for independent component
analysis and learning mixtures of Gaussians in the presence of outliers.
  Our algorithms are based on a standard sum-of-squares relaxation of the
following conceptually-simple optimization problem: Among all distributions
whose moments are bounded in the same way as for the unknown distribution, find
the one that is closest in statistical distance to the empirical distribution
of the adversarially-corrupted sample.
Truncated Variational Expectation Maximization
Authors: Jörg Lücke
Date: 2016-10-10 00:00:00

We derive a novel variational expectation maximization approach based on truncated posterior distributions. Truncated distributions are proportional to exact posteriors within subsets of a discrete state space and equal zero otherwise. The treatment of the distributions' subsets as variational parameters distinguishes the approach from previous variational approaches. The specific structure of truncated distributions allows for deriving novel and mathematically grounded results, which in turn can be used to formulate novel efficient algorithms to optimize the parameters of probabilistic generative models. Most centrally, we find the variational lower bounds that correspond to truncated distributions to be given by very concise and efficiently computable expressions, while update equations for model parameters remain in their standard form. Based on these findings, we show how efficient and easily applicable meta-algorithms can be formulated that guarantee a monotonic increase of the variational bound. Example applications of the here derived framework provide novel theoretical results and learning procedures for latent variable models as well as mixture models. Furthermore, we show that truncated variation EM naturally interpolates between standard EM with full posteriors and EM based on the maximum a-posteriori state (MAP). The approach can, therefore, be regarded as a generalization of the popular `hard EM' approach towards a similarly efficient method which can capture more of the true posterior structure.
Texture Synthesis with Recurrent Variational Auto-Encoder
Authors: Rohan Chandra, Sachin Grover, Kyungjun Lee, Moustafa Meshry, Ahmed Taha
Date: 2017-12-23 00:00:00
Tasks: Texture Synthesis

We propose a recurrent variational auto-encoder for texture synthesis. A
novel loss function, FLTBNK, is used for training the texture synthesizer. It
is rotational and partially color invariant loss function. Unlike L2 loss,
FLTBNK explicitly models the correlation of color intensity between pixels. Our
texture synthesizer generates neighboring tiles to expand a sample texture and
is evaluated using various texture patterns from Describable Textures Dataset
(DTD). We perform both quantitative and qualitative experiments with various
loss functions to evaluate the performance of our proposed loss function
(FLTBNK) --- a mini-human subject study is used for the qualitative evaluation.
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
Authors: Aljoša Ošep, Paul Voigtlaender, Jonathon Luiten, Stefan Breuers, Bastian Leibe
Date: 2017-12-23 00:00:00
Tasks: Autonomous Driving, Clustering, Object, Object Discovery, Object Tracking

We explore object discovery and detector adaptation based on unlabeled video
sequences captured from a mobile platform. We propose a fully automatic
approach for object mining from video which builds upon a generic object
tracking approach. By applying this method to three large video datasets from
autonomous driving and mobile robotics scenarios, we demonstrate its robustness
and generality. Based on the object mining results, we propose a novel approach
for unsupervised object discovery by appearance-based clustering. We show that
this approach successfully discovers interesting objects relevant to driving
scenarios. In addition, we perform self-supervised detector adaptation in order
to improve detection performance on the KITTI dataset for existing categories.
Our approach has direct relevance for enabling large-scale object learning for
autonomous driving.