pwc-archive/papers-with-abstracts huggingface dataset:
Single-View and Multi-View Depth Fusion
Authors: José M. Fácil, Alejo Concha, Luis Montesano, Javier Civera
Date: 2016-11-22 00:00:00
Tasks: Depth Estimation

Dense and accurate 3D mapping from a monocular sequence is a key technology
for several applications and still an open research area. This paper leverages
recent results on single-view CNN-based depth estimation and fuses them with
multi-view depth estimation. Both approaches present complementary strengths.
Multi-view depth is highly accurate but only in high-texture areas and
high-parallax cases. Single-view depth captures the local structure of
mid-level regions, including texture-less areas, but the estimated depth lacks
global coherence. The single and multi-view fusion we propose is challenging in
several aspects. First, both depths are related by a deformation that depends
on the image content. Second, the selection of multi-view points of high
accuracy might be difficult for low-parallax configurations. We present
contributions for both problems. Our results in the public datasets of NYUv2
and TUM shows that our algorithm outperforms the individual single and
multi-view approaches. A video showing the key aspects of mapping in our Single
and Multi-view depth proposal is available at https://youtu.be/ipc5HukTb4k
Preserving Differential Privacy Between Features in Distributed Estimation
Authors: Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen
Date: 2017-03-01 00:00:00

Privacy is crucial in many applications of machine learning. Legal, ethical
and societal issues restrict the sharing of sensitive data making it difficult
to learn from datasets that are partitioned between many parties. One important
instance of such a distributed setting arises when information about each
record in the dataset is held by different data owners (the design matrix is
"vertically-partitioned").
  In this setting few approaches exist for private data sharing for the
purposes of statistical estimation and the classical setup of differential
privacy with a "trusted curator" preparing the data does not apply. We work
with the notion of $(\epsilon,\delta)$-distributed differential privacy which
extends single-party differential privacy to the distributed,
vertically-partitioned case. We propose PriDE, a scalable framework for
distributed estimation where each party communicates perturbed random
projections of their locally held features ensuring
$(\epsilon,\delta)$-distributed differential privacy is preserved. For
$\ell_2$-penalized supervised learning problems PriDE has bounded estimation
error compared with the optimal estimates obtained without privacy constraints
in the non-distributed setting. We confirm this empirically on real world and
synthetic datasets.
Robust Video-Based Eye Tracking Using Recursive Estimation of Pupil Characteristics
Authors: Terence Brouns
Date: 2017-06-25 00:00:00
Tasks: Image Segmentation, Pupil Detection, Semantic Segmentation

Video-based eye tracking is a valuable technique in various research fields.
Numerous open-source eye tracking algorithms have been developed in recent
years, primarily designed for general application with many different camera
types. These algorithms do not, however, capitalize on the high frame rate of
eye tracking cameras often employed in psychophysical studies. We present a
pupil detection method that utilizes this high-speed property to obtain
reliable predictions through recursive estimation about certain pupil
characteristics in successive camera frames. These predictions are subsequently
used to carry out novel image segmentation and classification routines to
improve pupil detection performance. Based on results from hand-labelled eye
images, our approach was found to have a greater detection rate, accuracy and
speed compared to other recently published open-source pupil detection
algorithms. The program's source code, together with a graphical user
interface, can be downloaded at https://github.com/tbrouns/eyestalker
PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem
Authors: Claudio Sanhueza, Francia Jimenez, Regina Berretta, Pablo Moscato
Date: 2017-06-27 00:00:00
Tasks: Evolutionary Algorithms

Multi-Objective Optimization Problems (MOPs) have attracted growing attention
during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have
been extensively used to address MOPs because are able to approximate a set of
non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment
Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP
which has been extensively studied, and used in several real-life applications.
The mQAP is defined as having as input several flows between the facilities
which generate multiple cost functions that must be optimized simultaneously.
In this study, we propose PasMoQAP, a parallel asynchronous memetic algorithm
to solve the Multi-Objective Quadratic Assignment Problem. PasMoQAP is based on
an island model that structures the population by creating sub-populations. The
memetic algorithm on each island individually evolve a reduced population of
solutions, and they asynchronously cooperate by sending selected solutions to
the neighboring islands. The experimental results show that our approach
significatively outperforms all the island-based variants of the
multi-objective evolutionary algorithm NSGA-II. We show that PasMoQAP is a
suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.
Two-Stage Hybrid Day-Ahead Solar Forecasting
Authors: Mohana Alanazi, Mohsen Mahoor, Amin Khodaei
Date: 2017-06-27 00:00:00
Tasks: Vocal Bursts Valence Prediction

Power supply from renewable resources is on a global rise where it is
forecasted that renewable generation will surpass other types of generation in
a foreseeable future. Increased generation from renewable resources, mainly
solar and wind, exposes the power grid to more vulnerabilities, conceivably due
to their variable generation, thus highlighting the importance of accurate
forecasting methods. This paper proposes a two-stage day-ahead solar
forecasting method that breaks down the forecasting into linear and nonlinear
parts, determines subsequent forecasts, and accordingly, improves accuracy of
the obtained results. To further reduce the error resulted from nonstationarity
of the historical solar radiation data, a data processing approach, including
pre-process and post-process levels, is integrated with the proposed method.
Numerical simulations on three test days with different weather conditions
exhibit the effectiveness of the proposed two-stage model.
Controlled Tactile Exploration and Haptic Object Recognition
Authors: Massimo Regoli, Nawid Jamali, Giorgio Metta, Lorenzo Natale
Date: 2017-06-27 00:00:00
Tasks: Object, Object Recognition, Position

In this paper we propose a novel method for in-hand object recognition. The
method is composed of a grasp stabilization controller and two exploratory
behaviours to capture the shape and the softness of an object. Grasp
stabilization plays an important role in recognizing objects. First, it
prevents the object from slipping and facilitates the exploration of the
object. Second, reaching a stable and repeatable position adds robustness to
the learning algorithm and increases invariance with respect to the way in
which the robot grasps the object. The stable poses are estimated using a
Gaussian mixture model (GMM). We present experimental results showing that
using our method the classifier can successfully distinguish 30 objects.We also
compare our method with a benchmark experiment, in which the grasp
stabilization is disabled. We show, with statistical significance, that our
method outperforms the benchmark method.
Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering
Authors: Grzegorz Chlebus, Hans Meine, Jan Hendrik Moltz, Andrea Schenk
Date: 2017-06-02 00:00:00
Tasks: Lesion Segmentation, Liver Segmentation, Organ Segmentation, Segmentation, Tumor Segmentation

We present a fully automatic method employing convolutional neural networks
based on the 2D U-net architecture and random forest classifier to solve the
automatic liver lesion segmentation problem of the ISBI 2017 Liver Tumor
Segmentation Challenge (LiTS). In order to constrain the ROI in which the
tumors could be located, a liver segmentation is performed first. For the organ
segmentation, an ensemble of convolutional networks is trained to segment a
liver using a set of 179 liver CT datasets from liver surgery planning. Inside
of the liver ROI a neural network, trained using 127 challenge training
datasets, identifies tumor candidates, which are subsequently filtered with a
random forest classifier yielding the final tumor segmentation. The evaluation
on the 70 challenge test cases resulted in a mean Dice coefficient of 0.65,
ranking our method in the second place.
Large-scale Datasets: Faces with Partial Occlusions and Pose Variations in the Wild
Authors: Tarik Alafif, Zeyad Hailat, Melih Aslan, Xue-wen Chen
Date: 2017-06-27 00:00:00
Tasks: Face Detection, Face Recognition

Face detection methods have relied on face datasets for training. However,
existing face datasets tend to be in small scales for face learning in both
constrained and unconstrained environments. In this paper, we first introduce
our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy
Large-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large
number of unconstrained multi-view and partially occluded faces. The faces have
many variations in color and grayscale, image quality, image resolution, image
illumination, image background, image illusion, human face, cartoon face,
facial expression, light and severe partial facial occlusion, make up, gender,
age, and race. Many of these faces are partially occluded with accessories such
as tattoos, hats, glasses, sunglasses, hands, hair, beards, scarves,
microphones, or other objects or persons. The LSLF dataset is currently the
largest labeled face image dataset in the literature in terms of the number of
labeled images and the number of individuals compared to other existing labeled
face image datasets. Second, we introduce our CrowedFaces and CrowedNonFaces
image datasets. The crowedFaces and CrowedNonFaces datasets include faces and
non-faces images from crowed scenes. These datasets essentially aim for
researchers to provide a large number of training examples with many variations
for large scale face learning and face recognition tasks.
Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain
Authors: Martim Brandao, Yukitoshi Minami Shiguematsu, Kenji Hashimoto, Atsuo Takanishi
Date: 2017-06-27 00:00:00
Tasks: Friction, Material Recognition

In this paper we tackle the problem of visually predicting surface friction
for environments with diverse surfaces, and integrating this knowledge into
biped robot locomotion planning. The problem is essential for autonomous robot
locomotion since diverse surfaces with varying friction abound in the real
world, from wood to ceramic tiles, grass or ice, which may cause difficulties
or huge energy costs for robot locomotion if not considered. We propose to
estimate friction and its uncertainty from visual estimation of material
classes using convolutional neural networks, together with probability
distribution functions of friction associated with each material. We then
robustly integrate the friction predictions into a hierarchical (footstep and
full-body) planning method using chance constraints, and optimize the same
trajectory costs at both levels of the planning method for consistency. Our
solution achieves fully autonomous perception and locomotion on slippery
terrain, which considers not only friction and its uncertainty, but also
collision, stability and trajectory cost. We show promising friction prediction
results in real pictures of outdoor scenarios, and planning experiments on a
real robot facing surfaces with different friction.
Memory-augmented Chinese-Uyghur Neural Machine Translation
Authors: Shiyue Zhang, Gulnigar Mahmut, Dong Wang, Askar Hamdulla
Date: 2017-06-27 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Neural machine translation (NMT) has achieved notable performance recently.
However, this approach has not been widely applied to the translation task
between Chinese and Uyghur, partly due to the limited parallel data resource
and the large proportion of rare words caused by the agglutinative nature of
Uyghur. In this paper, we collect ~200,000 sentence pairs and show that with
this middle-scale database, an attention-based NMT can perform very well on
Chinese-Uyghur/Uyghur-Chinese translation. To tackle rare words, we propose a
novel memory structure to assist the NMT inference. Our experiments
demonstrated that the memory-augmented NMT (M-NMT) outperforms both the vanilla
NMT and the phrase-based statistical machine translation (SMT). Interestingly,
the memory structure provides an elegant way for dealing with words that are
out of vocabulary.
Non-Convex Weighted Lp Nuclear Norm based ADMM Framework for Image Restoration
Authors: Zhiyuan Zha, Xinggan Zhang, Yu Wu, Qiong Wang, Lan Tang
Date: 2017-04-24 00:00:00
Tasks: Compressive Sensing, Deblurring, Image Deblurring, Image Inpainting, Image Restoration

Since the matrix formed by nonlocal similar patches in a natural image is of
low rank, the nuclear norm minimization (NNM) has been widely used in various
image processing studies. Nonetheless, nuclear norm based convex surrogate of
the rank function usually over-shrinks the rank components and makes different
components equally, and thus may produce a result far from the optimum. To
alleviate the above-mentioned limitations of the nuclear norm, in this paper we
propose a new method for image restoration via the non-convex weighted Lp
nuclear norm minimization (NCW-NNM), which is able to more accurately enforce
the image structural sparsity and self-similarity simultaneously. To make the
proposed model tractable and robust, the alternative direction multiplier
method (ADMM) is adopted to solve the associated non-convex minimization
problem. Experimental results on various types of image restoration problems,
including image deblurring, image inpainting and image compressive sensing (CS)
recovery, demonstrate that the proposed method outperforms many current
state-of-the-art methods in both the objective and the perceptual qualities.
Proceedings of the First International Workshop on Deep Learning and Music
Authors: Dorien Herremans, Ching-Hua Chuan
Date: 2017-06-27 00:00:00
Tasks: Deep Learning

Proceedings of the First International Workshop on Deep Learning and Music,
joint with IJCNN, Anchorage, US, May 17-18, 2017
Fast and robust tensor decomposition with applications to dictionary learning
Authors: Tselil Schramm, David Steurer
Date: 2017-06-27 00:00:00
Tasks: Dictionary Learning, Tensor Decomposition

We develop fast spectral algorithms for tensor decomposition that match the
robustness guarantees of the best known polynomial-time algorithms for this
problem based on the sum-of-squares (SOS) semidefinite programming hierarchy.
  Our algorithms can decompose a 4-tensor with $n$-dimensional orthonormal
components in the presence of error with constant spectral norm (when viewed as
an $n^2$-by-$n^2$ matrix). The running time is $n^5$ which is close to linear
in the input size $n^4$.
  We also obtain algorithms with similar running time to learn sparsely-used
orthogonal dictionaries even when feature representations have constant
relative sparsity and non-independent coordinates.
  The only previous polynomial-time algorithms to solve these problem are based
on solving large semidefinite programs. In contrast, our algorithms are easy to
implement directly and are based on spectral projections and tensor-mode
rearrangements.
  Or work is inspired by recent of Hopkins, Schramm, Shi, and Steurer (STOC'16)
that shows how fast spectral algorithms can achieve the guarantees of SOS for
average-case problems. In this work, we introduce general techniques to capture
the guarantees of SOS for worst-case problems.
A divide and conquer method for symbolic regression
Authors: Changtong Luo, Chen Chen, Zonglin Jiang
Date: 2017-05-23 00:00:00
Tasks: regression, Symbolic Regression

Symbolic regression aims to find a function that best explains the
relationship between independent variables and the objective value based on a
given set of sample data. Genetic programming (GP) is usually considered as an
appropriate method for the problem since it can optimize functional structure
and coefficients simultaneously. However, the convergence speed of GP might be
too slow for large scale problems that involve a large number of variables.
Fortunately, in many applications, the target function is separable or
partially separable. This feature motivated us to develop a new method, divide
and conquer (D&C), for symbolic regression, in which the target function is
divided into a number of sub-functions and the sub-functions are then
determined by any of a GP algorithm. The separability is probed by a new
proposed technique, Bi-Correlation test (BiCT). D&C powered GP has been tested
on some real-world applications, and the study shows that D&C can help GP to
get the target function much more rapidly.
Fast and accurate classification of echocardiograms using deep learning
Authors: Ali Madani, Ramy Arnaout, Mohammad Mofrad, Rima Arnaout
Date: 2017-06-27 00:00:00
Tasks: Classification, Deep Learning, General Classification, Overall - Test

Echocardiography is essential to modern cardiology. However, human
interpretation limits high throughput analysis, limiting echocardiography from
reaching its full clinical and research potential for precision medicine. Deep
learning is a cutting-edge machine-learning technique that has been useful in
analyzing medical images but has not yet been widely applied to
echocardiography, partly due to the complexity of echocardiograms' multi view,
multi modality format. The essential first step toward comprehensive computer
assisted echocardiographic interpretation is determining whether computers can
learn to recognize standard views. To this end, we anonymized 834,267
transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51
percent female, 26 percent obese) seen between 2000 and 2017 and labeled them
according to standard views. Images covered a range of real world clinical
variation. We built a multilayer convolutional neural network and used
supervised learning to simultaneously classify 15 standard views. Eighty
percent of data used was randomly chosen for training and 20 percent reserved
for validation and testing on never seen echocardiograms. Using multiple images
from each clip, the model classified among 12 video views with 97.8 percent
overall test accuracy without overfitting. Even on single low resolution
images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5
percent for board-certified echocardiographers. Confusional matrices, occlusion
experiments, and saliency mapping showed that the model finds recognizable
similarities among related views and classifies using clinically relevant image
features. In conclusion, deep neural networks can classify essential
echocardiographic views simultaneously and with high accuracy. Our results
provide a foundation for more complex deep learning assisted echocardiographic
interpretation.
A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud
Authors: Xin Wang, Lejun Zou, Xiaohua Shen, Yupeng Ren, Yi Qin
Date: 2017-06-27 00:00:00

Conventional manual surveys of rock mass fractures usually require large
amounts of time and labor; yet, they provide a relatively small set of data
that cannot be considered representative of the study region. Terrestrial laser
scanners are increasingly used for fracture surveys because they can
efficiently acquire large area, high-resolution, three-dimensional (3D) point
clouds from outcrops. However, extracting fractures and other planar surfaces
from 3D outcrop point clouds is still a challenging task. No method has been
reported that can be used to automatically extract the full extent of every
individual fracture from a 3D outcrop point cloud. In this study, we propose a
method using a region-growing approach to address this problem; the method also
estimates the orientation of each fracture. In this method, criteria based on
the local surface normal and curvature of the point cloud are used to initiate
and control the growth of the fracture region. In tests using outcrop point
cloud data, the proposed method identified and extracted the full extent of
individual fractures with high accuracy. Compared with manually acquired field
survey data, our method obtained better-quality fracture data, thereby
demonstrating the high potential utility of the proposed method.
Dense Non-rigid Structure-from-Motion Made Easy - A Spatial-Temporal Smoothness based Solution
Authors: Yuchao Dai, Huizhong Deng, Mingyi He
Date: 2017-06-27 00:00:00

This paper proposes a simple spatial-temporal smoothness based method for
solving dense non-rigid structure-from-motion (NRSfM). First, we revisit the
temporal smoothness and demonstrate that it can be extended to dense case
directly. Second, we propose to exploit the spatial smoothness by resorting to
the Laplacian of the 3D non-rigid shape. Third, to handle real world noise and
outliers in measurements, we robustify the data term by using the $L_1$ norm.
In this way, our method could robustly exploit both spatial and temporal
smoothness effectively and make dense non-rigid reconstruction easy. Our method
is very easy to implement, which involves solving a series of least squares
problems. Experimental results on both synthetic and real image dense NRSfM
tasks show that the proposed method outperforms state-of-the-art dense
non-rigid reconstruction methods.
Do Deep Neural Networks Suffer from Crowding?
Authors: Anna Volokitin, Gemma Roig, Tomaso Poggio
Date: 2017-06-26 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Object Recognition

Crowding is a visual effect suffered by humans, in which an object that can
be recognized in isolation can no longer be recognized when other objects,
called flankers, are placed close to it. In this work, we study the effect of
crowding in artificial Deep Neural Networks for object recognition. We analyze
both standard deep convolutional neural networks (DCNNs) as well as a new
version of DCNNs which is 1) multi-scale and 2) with size of the convolution
filters change depending on the eccentricity wrt to the center of fixation.
Such networks, that we call eccentricity-dependent, are a computational model
of the feedforward path of the primate visual cortex. Our results reveal that
the eccentricity-dependent model, trained on target objects in isolation, can
recognize such targets in the presence of flankers, if the targets are near the
center of the image, whereas DCNNs cannot. Also, for all tested networks, when
trained on targets in isolation, we find that recognition accuracy of the
networks decreases the closer the flankers are to the target and the more
flankers there are. We find that visual similarity between the target and
flankers also plays a role and that pooling in early layers of the network
leads to more crowding. Additionally, we show that incorporating the flankers
into the images of the training set does not improve performance with crowding.