pwc-archive/papers-with-abstracts huggingface dataset:
RIDI: Robust IMU Double Integration
Authors: Hang Yan, Qi Shan, Yasutaka Furukawa
Date: 2017-12-25 00:00:00
Proceeding: ECCV 2018 9

This paper proposes a novel data-driven approach for inertial navigation,
which learns to estimate trajectories of natural human motions just from an
inertial measurement unit (IMU) in every smartphone. The key observation is
that human motions are repetitive and consist of a few major modes (e.g.,
standing, walking, or turning). Our algorithm regresses a velocity vector from
the history of linear accelerations and angular velocities, then corrects
low-frequency bias in the linear accelerations, which are integrated twice to
estimate positions. We have acquired training data with ground-truth motions
across multiple human subjects and multiple phone placements (e.g., in a bag or
a hand). The qualitatively and quantitatively evaluations have demonstrated
that our algorithm has surprisingly shown comparable results to full Visual
Inertial navigation. To our knowledge, this paper is the first to integrate
sophisticated machine learning techniques with inertial navigation, potentially
opening up a new line of research in the domain of data-driven inertial
navigation. We will publicly share our code and data to facilitate further
research.
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Authors: Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
Date: 2017-12-11 00:00:00
Tasks: Deep Learning

Recent developments in many-body potential energy representation via deep
learning have brought new hopes to addressing the accuracy-versus-efficiency
dilemma in molecular simulations. Here we describe DeePMD-kit, a package
written in Python/C++ that has been designed to minimize the effort required to
build deep learning based representation of potential energy and force field
and to perform molecular dynamics. Potential applications of DeePMD-kit span
from finite molecules to extended systems and from metallic systems to
chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the
most popular deep learning frameworks, making the training process highly
automatic and efficient. On the other end, DeePMD-kit is interfaced with
high-performance classical molecular dynamics and quantum (path-integral)
molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus,
upon training, the potential energy and force field models can be used to
perform efficient molecular simulations for different purposes. As an example
of the many potential applications of the package, we use DeePMD-kit to learn
the interatomic potential energy and forces of a water model using data
obtained from density functional theory. We demonstrate that the resulted
molecular dynamics model reproduces accurately the structural information
contained in the original model.
Exact Lower Bounds for the Agnostic Probably-Approximately-Correct (PAC) Machine Learning Model
Authors: Aryeh Kontorovich, Iosif Pinelis
Date: 2016-06-29 00:00:00
Tasks: BIG-bench Machine Learning

We provide an exact non-asymptotic lower bound on the minimax expected excess
risk (EER) in the agnostic probably-ap\-proximately-correct (PAC) machine
learning classification model and identify minimax learning algorithms as
certain maximally symmetric and minimally randomized "voting" procedures. Based
on this result, an exact asymptotic lower bound on the minimax EER is provided.
This bound is of the simple form $c_\infty/\sqrt{\nu}$ as $\nu\to\infty$, where
$c_\infty=0.16997\dots$ is a universal constant, $\nu=m/d$, $m$ is the size of
the training sample, and $d$ is the Vapnik--Chervonenkis dimension of the
hypothesis class. It is shown that the differences between these asymptotic and
non-asymptotic bounds, as well as the differences between these two bounds and
the maximum EER of any learning algorithms that minimize the empirical risk,
are asymptotically negligible, and all these differences are due to ties in the
mentioned "voting" procedures. A few easy to compute non-asymptotic lower
bounds on the minimax EER are also obtained, which are shown to be close to the
exact asymptotic lower bound $c_\infty/\sqrt{\nu}$ even for rather small values
of the ratio $\nu=m/d$. As an application of these results, we substantially
improve existing lower bounds on the tail probability of the excess risk. Among
the tools used are Bayes estimation and apparently new identities and
inequalities for binomial distributions.
Machine Learning for Building Energy and Indoor Environment: A Perspective
Authors: Zhijian Liu, Di wu, Hongyu Wei, Guoqing Cao
Date: 2017-12-31 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning is a promising technique for many practical applications. In
this perspective, we illustrate the development and application for machine
learning. It is indicated that the theories and applications of machine
learning method in the field of energy conservation and indoor environment are
not mature, due to the difficulty of the determination for model structure with
better prediction. In order to significantly contribute to the problems, we
utilize the ANN model to predict the indoor culturable fungi concentration,
which achieves the better accuracy and convenience. The proposal of hybrid
method is further expand the application fields of machine learning method.
Further, ANN model based on HTS was successfully applied for the optimization
of building energy system. We hope that this novel method could capture more
attention from investigators via our introduction and perspective, due to its
potential development with accuracy and reliability. However, its feasibility
in other fields needs to be promoted further.
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data
Authors: Qiang Zheng, Gregory Tasian, Yong Fan
Date: 2017-12-31 00:00:00
Tasks: Classification, General Classification, Transfer Learning

Classification of ultrasound (US) kidney images for diagnosis of congenital
abnormalities of the kidney and urinary tract (CAKUT) in children is a
challenging task. It is desirable to improve existing pattern classification
models that are built upon conventional image features. In this study, we
propose a transfer learning-based method to extract imaging features from US
kidney images in order to improve the CAKUT diagnosis in children.
Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is
adopted for transfer learning-based feature extraction from 3-channel feature
maps computed from US images, including original images, gradient features, and
distanced transform features. Support vector machine classifiers are then built
upon different sets of features, including the transfer learning features,
conventional imaging features, and their combination. Experimental results have
demonstrated that the combination of transfer learning features and
conventional imaging features yielded the best classification performance for
distinguishing CAKUT patients from normal controls based on their US kidney
images.
Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation
Authors: Qiang Zheng, Yong Fan
Date: 2017-12-31 00:00:00
Tasks: Hippocampus, Image Segmentation, regression, Segmentation, Semantic Segmentation

A novel multi-atlas based image segmentation method is proposed by
integrating a semi-supervised label propagation method and a supervised random
forests method in a pattern recognition based label fusion framework. The
semi-supervised label propagation method takes into consideration local and
global image appearance of images to be segmented and segments the images by
propagating reliable segmentation results obtained by the supervised random
forests method. Particularly, the random forests method is used to train a
regression model based on image patches of atlas images for each voxel of the
images to be segmented. The regression model is used to obtain reliable
segmentation results to guide the label propagation for the segmentation. The
proposed method has been compared with state-of-the-art multi-atlas based image
segmentation methods for segmenting the hippocampus in MR images. The
experiment results have demonstrated that our method obtained superior
segmentation performance.
Game-theoretic Network Centrality: A Review
Authors: Mateusz K. Tarkowski, Tomasz P. Michalak, Talal Rahwan, Michael Wooldridge
Date: 2017-12-31 00:00:00

Game-theoretic centrality is a flexible and sophisticated approach to
identify the most important nodes in a network. It builds upon the methods from
cooperative game theory and network theory. The key idea is to treat nodes as
players in a cooperative game, where the value of each coalition is determined
by certain graph-theoretic properties. Using solution concepts from cooperative
game theory, it is then possible to measure how responsible each node is for
the worth of the network.
  The literature on the topic is already quite large, and is scattered among
game-theoretic and computer science venues. We review the main game-theoretic
network centrality measures from both bodies of literature and organize them
into two categories: those that are more focused on the connectivity of nodes,
and those that are more focused on the synergies achieved by nodes in groups.
We present and explain each centrality, with a focus on algorithms and
complexity.
A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations
Authors: Ricardo Augusto Borsoi, Guilherme Holsbach Costa, José Carlos Moreira Bermudez
Date: 2017-06-14 00:00:00
Tasks: Super-Resolution, Video Super-Resolution

In this paper, a new video super-resolution reconstruction (SRR) method with
improved robustness to outliers is proposed. Although the R-LMS is one of the
SRR algorithms with the best reconstruction quality for its computational cost,
and is naturally robust to registration inaccuracies, its performance is known
to degrade severely in the presence of innovation outliers. By studying the
proximal point cost function representation of the R-LMS iterative equation, a
better understanding of its performance under different situations is attained.
Using statistical properties of typical innovation outliers, a new cost
function is then proposed and two new algorithms are derived, which present
improved robustness to outliers while maintaining computational costs
comparable to that of R-LMS. Monte Carlo simulation results illustrate that the
proposed method outperforms the traditional and regularized versions of LMS,
and is competitive with state-of-the-art SRR methods at a much smaller
computational cost.
Learning Continuous User Representations through Hybrid Filtering with doc2vec
Authors: Simon Stiebellehner, Jun Wang, Shuai Yuan
Date: 2017-12-31 00:00:00
Tasks: Language Modeling, Language Modelling

Players in the online ad ecosystem are struggling to acquire the user data
required for precise targeting. Audience look-alike modeling has the potential
to alleviate this issue, but models' performance strongly depends on quantity
and quality of available data. In order to maximize the predictive performance
of our look-alike modeling algorithms, we propose two novel hybrid filtering
techniques that utilize the recent neural probabilistic language model
algorithm doc2vec. We apply these methods to data from a large mobile ad
exchange and additional app metadata acquired from the Apple App store and
Google Play store. First, we model mobile app users through their app usage
histories and app descriptions (user2vec). Second, we introduce context
awareness to that model by incorporating additional user and app-related
metadata in model training (context2vec). Our findings are threefold: (1) the
quality of recommendations provided by user2vec is notably higher than current
state-of-the-art techniques. (2) User representations generated through hybrid
filtering using doc2vec prove to be highly valuable features in supervised
machine learning models for look-alike modeling. This represents the first
application of hybrid filtering user models using neural probabilistic language
models, specifically doc2vec, in look-alike modeling. (3) Incorporating context
metadata in the doc2vec model training process to introduce context awareness
has positive effects on performance and is superior to directly including the
data as features in the downstream supervised models.
Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
Authors: Wei Chen, Andrew L. Ferguson
Date: 2017-12-30 00:00:00

Macromolecular and biomolecular folding landscapes typically contain high
free energy barriers that impede efficient sampling of configurational space by
standard molecular dynamics simulation. Biased sampling can artificially drive
the simulation along pre-specified collective variables (CVs), but success
depends critically on the availability of good CVs associated with the
important collective dynamical motions. Nonlinear machine learning techniques
can identify such CVs but typically do not furnish an explicit relationship
with the atomic coordinates necessary to perform biased sampling. In this work,
we employ auto-associative artificial neural networks ("autoencoders") to learn
nonlinear CVs that are explicit and differentiable functions of the atomic
coordinates. Our approach offers substantial speedups in exploration of
configurational space, and is distinguished from exiting approaches by its
capacity to simultaneously discover and directly accelerate along data-driven
CVs. We demonstrate the approach in simulations of alanine dipeptide and
Trp-cage, and have developed an open-source and freely-available implementation
within OpenMM.
Camera-trap images segmentation using multi-layer robust principal component analysis
Authors: Jhony-Heriberto Giraldo-Zuluaga, Alexander Gomez, Augusto Salazar, Angélica Diaz-Pulido
Date: 2017-01-27 00:00:00
Tasks: Segmentation

The segmentation of animals from camera-trap images is a difficult task. To
illustrate, there are various challenges due to environmental conditions and
hardware limitation in these images. We proposed a multi-layer robust principal
component analysis (multi-layer RPCA) approach for background subtraction. Our
method computes sparse and low-rank images from a weighted sum of descriptors,
using color and texture features as case of study for camera-trap images
segmentation. The segmentation algorithm is composed of histogram equalization
or Gaussian filtering as pre-processing, and morphological filters with active
contour as post-processing. The parameters of our multi-layer RPCA were
optimized with an exhaustive search. The database consists of camera-trap
images from the Colombian forest taken by the Instituto de Investigaci\'on de
Recursos Biol\'ogicos Alexander von Humboldt. We analyzed the performance of
our method in inherent and therefore challenging situations of camera-trap
images. Furthermore, we compared our method with some state-of-the-art
algorithms of background subtraction, where our multi-layer RPCA outperformed
these other methods. Our multi-layer RPCA reached 76.17 and 69.97% of average
fine-grained F-measure for color and infrared sequences, respectively. To our
best knowledge, this paper is the first work proposing multi-layer RPCA and
using it for camera-trap images segmentation.
Why Adaptively Collected Data Have Negative Bias and How to Correct for It
Authors: Xinkun Nie, Xiaoying Tian, Jonathan Taylor, James Zou
Date: 2017-08-07 00:00:00

From scientific experiments to online A/B testing, the previously observed
data often affects how future experiments are performed, which in turn affects
which data will be collected. Such adaptivity introduces complex correlations
between the data and the collection procedure. In this paper, we prove that
when the data collection procedure satisfies natural conditions, then sample
means of the data have systematic \emph{negative} biases. As an example,
consider an adaptive clinical trial where additional data points are more
likely to be tested for treatments that show initial promise. Our surprising
result implies that the average observed treatment effects would underestimate
the true effects of each treatment. We quantitatively analyze the magnitude and
behavior of this negative bias in a variety of settings. We also propose a
novel debiasing algorithm based on selective inference techniques. In
experiments, our method can effectively reduce bias and estimation error.
Fractional Local Neighborhood Intensity Pattern for Image Retrieval using Genetic Algorithm
Authors: Shuvozit Ghose, Abhirup Das, Ayan Kumar Bhunia, Partha Pratim Roy
Date: 2017-12-30 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

In this paper, a new texture descriptor named "Fractional Local Neighborhood Intensity Pattern" (FLNIP) has been proposed for content based image retrieval (CBIR). It is an extension of the Local Neighborhood Intensity Pattern (LNIP)[1]. FLNIP calculates the relative intensity difference between a particular pixel and the center pixel of a 3x3 window by considering the relationship with adjacent neighbors. In this work, the fractional change in the local neighborhood involving the adjacent neighbors has been calculated first with respect to one of the eight neighbors of the center pixel of a 3x3 window. Next, the fractional change has been calculated with respect to the center itself. The two values of fractional change are next compared to generate a binary bit pattern. Both sign and magnitude information are encoded in a single descriptor as it deals with the relative change in magnitude in the adjacent neighborhood i.e., the comparison of the fractional change. The descriptor is applied on four multi-resolution images -- one being the raw image and the other three being filtered gaussian images obtained by applying gaussian filters of different standard deviations on the raw image to signify the importance of exploring texture information at different resolutions in an image. The four sets of distances obtained between the query and the target image are then combined with a genetic algorithm based approach to improve the retrieval performance by minimizing the distance between similar class images. The performance of the method has been tested for image retrieval on four popular databases. The precision and recall values observed on these databases have been compared with recent state-of-art local patterns. The proposed method has shown a significant improvement over many other existing methods.
Modular proximal optimization for multidimensional total-variation regularization
Authors: Álvaro Barbero, Suvrit Sra
Date: 2014-11-03 00:00:00
Tasks: Denoising, Image Deconvolution, Image Denoising, Video Denoising

We study \emph{TV regularization}, a widely used technique for eliciting
structured sparsity. In particular, we propose efficient algorithms for
computing prox-operators for $\ell_p$-norm TV. The most important among these
is $\ell_1$-norm TV, for whose prox-operator we present a new geometric
analysis which unveils a hitherto unknown connection to taut-string methods.
This connection turns out to be remarkably useful as it shows how our geometry
guided implementation results in efficient weighted and unweighted 1D-TV
solvers, surpassing state-of-the-art methods. Our 1D-TV solvers provide the
backbone for building more complex (two or higher-dimensional) TV solvers
within a modular proximal optimization approach. We review the literature for
an array of methods exploiting this strategy, and illustrate the benefits of
our modular design through extensive suite of experiments on (i) image
denoising, (ii) image deconvolution, (iii) four variants of fused-lasso, and
(iv) video denoising. To underscore our claims and permit easy reproducibility,
we provide all the reviewed and our new TV solvers in an easy to use
multi-threaded C++, Matlab and Python library.
A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market
Authors: Piero Mazzarisi, Paolo Barucca, Fabrizio Lillo, Daniele Tantari
Date: 2017-12-30 00:00:00

We propose a dynamic network model where two mechanisms control the
probability of a link between two nodes: (i) the existence or absence of this
link in the past, and (ii) node-specific latent variables (dynamic fitnesses)
describing the propensity of each node to create links. Assuming a Markov
dynamics for both mechanisms, we propose an Expectation-Maximization algorithm
for model estimation and inference of the latent variables. The estimated
parameters and fitnesses can be used to forecast the presence of a link in the
future. We apply our methodology to the e-MID interbank network for which the
two linkage mechanisms are associated with two different trading behaviors in
the process of network formation, namely preferential trading and trading
driven by node-specific characteristics. The empirical results allow to
recognise preferential lending in the interbank market and indicate how a
method that does not account for time-varying network topologies tends to
overestimate preferential linkage.
A Real-time and Registration-free Framework for Dynamic Shape Instantiation
Authors: Xiao-Yun Zhou, Guang-Zhong Yang, Su-Lin Lee
Date: 2017-12-30 00:00:00
Tasks: Anatomy

Real-time 3D navigation during minimally invasive procedures is an essential
yet challenging task, especially when considerable tissue motion is involved.
To balance image acquisition speed and resolution, only 2D images or
low-resolution 3D volumes can be used clinically. In this paper, a real-time
and registration-free framework for dynamic shape instantiation, generalizable
to multiple anatomical applications, is proposed to instantiate high-resolution
3D shapes of an organ from a single 2D image intra-operatively. Firstly, an
approximate optimal scan plane was determined by analyzing the pre-operative 3D
statistical shape model (SSM) of the anatomy with sparse principal component
analysis (SPCA) and considering practical constraints . Secondly, kernel
partial least squares regression (KPLSR) was used to learn the relationship
between the pre-operative 3D SSM and a synchronized 2D SSM constructed from 2D
images obtained at the approximate optimal scan plane. Finally, the derived
relationship was applied to the new intra-operative 2D image obtained at the
same scan plane to predict the high-resolution 3D shape intra-operatively. A
major feature of the proposed framework is that no extra registration between
the pre-operative 3D SSM and the synchronized 2D SSM is required. Detailed
validation was performed on studies including the liver and right ventricle
(RV) of the heart. The derived results (mean accuracy of 2.19mm on patients and
computation speed of 1ms) demonstrate its potential clinical value for
real-time, high-resolution, dynamic and 3D interventional guidance.
The origins of Zipf's meaning-frequency law
Authors: Ramon Ferrer-i-Cancho, Michael S. Vitevitch
Date: 2017-12-30 00:00:00

In his pioneering research, G. K. Zipf observed that more frequent words tend
to have more meanings, and showed that the number of meanings of a word grows
as the square root of its frequency. He derived this relationship from two
assumptions: that words follow Zipf's law for word frequencies (a power law
dependency between frequency and rank) and Zipf's law of meaning distribution
(a power law dependency between number of meanings and rank). Here we show that
a single assumption on the joint probability of a word and a meaning suffices
to infer Zipf's meaning-frequency law or relaxed versions. Interestingly, this
assumption can be justified as the outcome of a biased random walk in the
process of mental exploration.