pwc-archive/papers-with-abstracts huggingface dataset:
Neural Turing Machines: Convergence of Copy Tasks
Authors: Janez Aleš
Date: 2016-12-07 00:00:00

The architecture of neural Turing machines is differentiable end to end and
is trainable with gradient descent methods. Due to their large unfolded depth
Neural Turing Machines are hard to train and because of their linear access of
complete memory they do not scale. Other architectures have been studied to
overcome these difficulties. In this report we focus on improving the quality
of prediction of the original linear memory architecture on copy and repeat
copy tasks. Copy task predictions on sequences of length six times larger than
those the neural Turing machine was trained on prove to be highly accurate and
so do predictions of repeat copy tasks for sequences with twice the repetition
number and twice the sequence length neural Turing machine was trained on.
Pano2Vid: Automatic Cinematography for Watching 360$^{\circ}$ Videos
Authors: Yu-Chuan Su, Dinesh Jayaraman, Kristen Grauman
Date: 2016-12-07 00:00:00

We introduce the novel task of Pano2Vid $-$ automatic cinematography in
panoramic 360$^{\circ}$ videos. Given a 360$^{\circ}$ video, the goal is to
direct an imaginary camera to virtually capture natural-looking normal
field-of-view (NFOV) video. By selecting "where to look" within the panorama at
each time step, Pano2Vid aims to free both the videographer and the end viewer
from the task of determining what to watch. Towards this goal, we first compile
a dataset of 360$^{\circ}$ videos downloaded from the web, together with
human-edited NFOV camera trajectories to facilitate evaluation. Next, we
propose AutoCam, a data-driven approach to solve the Pano2Vid task. AutoCam
leverages NFOV web video to discriminatively identify space-time "glimpses" of
interest at each time instant, and then uses dynamic programming to select
optimal human-like camera trajectories. Through experimental evaluation on
multiple newly defined Pano2Vid performance measures against several baselines,
we show that our method successfully produces informative videos that could
conceivably have been captured by human videographers.
Robust Low-Complexity Randomized Methods for Locating Outliers in Large Matrices
Authors: Xingguo Li, Jarvis Haupt
Date: 2016-12-07 00:00:00
Tasks: Computational Efficiency, Missing Elements

This paper examines the problem of locating outlier columns in a large,
otherwise low-rank matrix, in settings where {}{the data} are noisy, or where
the overall matrix has missing elements. We propose a randomized two-step
inference framework, and establish sufficient conditions on the required sample
complexities under which these methods succeed (with high probability) in
accurately locating the outliers for each task. Comprehensive numerical
experimental results are provided to verify the theoretical bounds and
demonstrate the computational efficiency of the proposed algorithm.
Extend natural neighbor: a novel classification method with self-adaptive neighborhood parameters in different stages
Authors: Ji Feng, Qingsheng Zhu, Jinlong Huang, LiJun Yang
Date: 2016-12-07 00:00:00
Tasks: Classification, General Classification

Various kinds of k-nearest neighbor (KNN) based classification methods are
the bases of many well-established and high-performance pattern-recognition
techniques, but both of them are vulnerable to their parameter choice.
Essentially, the challenge is to detect the neighborhood of various data sets,
while utterly ignorant of the data characteristic. This article introduces a
new supervised classification method: the extend natural neighbor (ENaN)
method, and shows that it provides a better classification result without
choosing the neighborhood parameter artificially. Unlike the original KNN based
method which needs a prior k, the ENaNE method predicts different k in
different stages. Therefore, the ENaNE method is able to learn more from
flexible neighbor information both in training stage and testing stage, and
provide a better classification result.
Fair task allocation in transportation
Authors: Qing Chuan Ye, Yingqian Zhang, Rommert Dekker
Date: 2015-05-27 00:00:00
Tasks: Fairness

Task allocation problems have traditionally focused on cost optimization.
However, more and more attention is being given to cases in which cost should
not always be the sole or major consideration. In this paper we study a fair
task allocation problem in transportation where an optimal allocation not only
has low cost but more importantly, it distributes tasks as even as possible
among heterogeneous participants who have different capacities and costs to
execute tasks. To tackle this fair minimum cost allocation problem we analyze
and solve it in two parts using two novel polynomial-time algorithms. We show
that despite the new fairness criterion, the proposed algorithms can solve the
fair minimum cost allocation problem optimally in polynomial time. In addition,
we conduct an extensive set of experiments to investigate the trade-off between
cost minimization and fairness. Our experimental results demonstrate the
benefit of factoring fairness into task allocation. Among the majority of test
instances, fairness comes with a very small price in terms of cost.
A Novel Artificial Fish Swarm Algorithm for Pattern Recognition with Convex Optimization
Authors: Lei Shi, Rui Guo, Yuchen Ma
Date: 2016-12-01 00:00:00

Image pattern recognition is an important area in digital image processing.
An efficient pattern recognition algorithm should be able to provide correct
recognition at a reduced computational time. Off late amongst the machine
learning pattern recognition algorithms, Artificial fish swarm algorithm is one
of the swarm intelligence optimization algorithms that works based on
population and stochastic search. In order to achieve acceptable result, there
are many parameters needs to be adjusted in AFSA. Among these parameters,
visual and step are very significant in view of the fact that artificial fish
basically move based on these parameters. In standard AFSA, these two
parameters remain constant until the algorithm termination. Large values of
these parameters increase the capability of algorithm in global search, while
small values improve the local search ability of the algorithm. In this paper,
we empirically study the performance of the AFSA and different approaches to
balance between local and global exploration have been tested based on the
adaptive modification of visual and step during algorithm execution. The
proposed approaches have been evaluated based on the four well-known benchmark
functions. Experimental results show considerable positive impact on the
performance of AFSA. A Convex optimization has been integrated into the
proposed work to have an ideal segmentation of the input image which is a MR
brain image.
On a Well-behaved Relational Generalisation of Rough Set Approximations
Authors: Alexa Gopaulsingh
Date: 2016-12-05 00:00:00
Tasks: Survey

We examine non-dual relational extensions of rough set approximations and
find an extension which satisfies surprisingly many of the usual rough set
properties. We then use this definition to give an explanation for an
observation made by Samanta and Chakraborty in their recent paper [P. Samanta
and M.K. Chakraborty. Interface of rough set systems and modal logics: A
survey. Transactions on Rough Sets XIX, pages 114-137, 2015].
Stochastic Function Norm Regularization of Deep Networks
Authors: Amal Rannen Triki, Matthew B. Blaschko
Date: 2016-05-30 00:00:00
Tasks: Small Data Image Classification

Deep neural networks have had an enormous impact on image analysis. State-of-the-art training methods, based on weight decay and DropOut, result in impressive performance when a very large training set is available. However, they tend to have large problems overfitting to small data sets. Indeed, the available regularization methods deal with the complexity of the network function only indirectly. In this paper, we study the feasibility of directly using the $L_2$ function norm for regularization. Two methods to integrate this new regularization in the stochastic backpropagation are proposed. Moreover, the convergence of these new algorithms is studied. We finally show that they outperform the state-of-the-art methods in the low sample regime on benchmark datasets (MNIST and CIFAR10). The obtained results demonstrate very clear improvement, especially in the context of small sample regimes with data laying in a low dimensional manifold. Source code of the method can be found at \url{https://github.com/AmalRT/DNN_Reg}.
Knowledge Representation in Graphs using Convolutional Neural Networks
Authors: Armando Vieira
Date: 2016-12-07 00:00:00
Tasks: Knowledge Graphs

Knowledge Graphs (KG) constitute a flexible representation of complex
relationships between entities particularly useful for biomedical data. These
KG, however, are very sparse with many missing edges (facts) and the
visualisation of the mesh of interactions nontrivial. Here we apply a
compositional model to embed nodes and relationships into a vectorised semantic
space to perform graph completion. A visualisation tool based on Convolutional
Neural Networks and Self-Organised Maps (SOM) is proposed to extract high-level
insights from the KG. We apply this technique to a subset of CTD, containing
interactions of compounds with human genes / proteins and show that the
performance is comparable to the one obtained by structural models.
Optimized Linear Imputation
Authors: Yehezkel S. Resheff, Daphna Weinshall
Date: 2015-11-17 00:00:00
Tasks: Imputation, Missing Values, regression

Often in real-world datasets, especially in high dimensional data, some
feature values are missing. Since most data analysis and statistical methods do
not handle gracefully missing values, the first step in the analysis requires
the imputation of missing values. Indeed, there has been a long standing
interest in methods for the imputation of missing values as a pre-processing
step. One recent and effective approach, the IRMI stepwise regression
imputation method, uses a linear regression model for each real-valued feature
on the basis of all other features in the dataset. However, the proposed
iterative formulation lacks convergence guarantee. Here we propose a closely
related method, stated as a single optimization problem and a block
coordinate-descent solution which is guaranteed to converge to a local minimum.
Experiments show results on both synthetic and benchmark datasets, which are
comparable to the results of the IRMI method whenever it converges. However,
while in the set of experiments described here IRMI often does not converge,
the performance of our methods is shown to be markedly superior in comparison
with other methods.
A simple and efficient SNN and its performance & robustness evaluation method to enable hardware implementation
Authors: Anmol Biswas, Sidharth Prasad, Sandip Lashkare, Udayan Ganguly
Date: 2016-12-07 00:00:00
Tasks: General Classification

Spiking Neural Networks (SNN) are more closely related to brain-like
computation and inspire hardware implementation. This is enabled by small
networks that give high performance on standard classification problems. In
literature, typical SNNs are deep and complex in terms of network structure,
weight update rules and learning algorithms. This makes it difficult to
translate them into hardware. In this paper, we first develop a simple
2-layered network in software which compares with the state of the art on four
different standard data-sets within SNNs and has improved efficiency. For
example, it uses lower number of neurons (3 x), synapses (3.5 x) and epochs for
training (30 x) for the Fisher Iris classification problem. The efficient
network is based on effective population coding and synapse-neuron co-design.
Second, we develop a computationally efficient (15000 x) and accurate
(correlation of 0.98) method to evaluate the performance of the network without
standard recognition tests. Third, we show that the method produces a
robustness metric that can be used to evaluate noise tolerance.
Exploring the potential of combining time of flight and thermal infrared cameras for person detection
Authors: Wim Abbeloos, Toon Goedemé
Date: 2016-12-07 00:00:00
Tasks: Camera Calibration, Human Detection, Position, Sensitivity

Combining new, low-cost thermal infrared and time-of-flight range sensors
provides new opportunities. In this position paper we explore the possibilities
of combining these sensors and using their fused data for person detection. The
proposed calibration approach for this sensor combination differs from the
traditional stereo camera calibration in two fundamental ways. A first
distinction is that the spectral sensitivity of the two sensors differs
significantly. In fact, there is no sensitivity range overlap at all. A second
distinction is that their resolution is typically very low, which requires
special attention. We assume a situation in which the sensors' relative
position is known, but their orientation is unknown. In addition, some of the
typical measurement errors are discussed, and methods to compensate for them
are proposed. We discuss how the fused data could allow increased accuracy and
robustness without the need for complex algorithms requiring large amounts of
computational power and training data.
A Communication-Efficient Parallel Method for Group-Lasso
Authors: Binghong Chen, Jun Zhu
Date: 2016-12-07 00:00:00
Tasks: regression

Group-Lasso (gLasso) identifies important explanatory factors in predicting
the response variable by considering the grouping structure over input
variables. However, most existing algorithms for gLasso are not scalable to
deal with large-scale datasets, which are becoming a norm in many applications.
In this paper, we present a divide-and-conquer based parallel algorithm
(DC-gLasso) to scale up gLasso in the tasks of regression with grouping
structures. DC-gLasso only needs two iterations to collect and aggregate the
local estimates on subsets of the data, and is provably correct to recover the
true model under certain conditions. We further extend it to deal with
overlappings between groups. Empirical results on a wide range of synthetic and
real-world datasets show that DC-gLasso can significantly improve the time
efficiency without sacrificing regression accuracy.
Process Monitoring of Extrusion Based 3D Printing via Laser Scanning
Authors: Matthias Faes, Wim Abbeloos, Frederik Vogeler, Hans Valkenaers, Kurt Coppens, Toon Goedemé, Eleonora Ferraris
Date: 2016-12-07 00:00:00

Extrusion based 3D Printing (E3DP) is an Additive Manufacturing (AM)
technique that extrudes thermoplastic polymer in order to build up components
using a layerwise approach. Hereby, AM typically requires long production times
in comparison to mass production processes such as Injection Molding. Failures
during the AM process are often only noticed after build completion and
frequently lead to part rejection because of dimensional inaccuracy or lack of
mechanical performance, resulting in an important loss of time and material. A
solution to improve the accuracy and robustness of a manufacturing technology
is the integration of sensors to monitor and control process state-variables
online. In this way, errors can be rapidly detected and possibly compensated at
an early stage. To achieve this, we integrated a modular 2D laser triangulation
scanner into an E3DP machine and analyzed feedback signals. A 2D laser
triangulation scanner was selected here owing to the very compact size,
achievable accuracy and the possibility of capturing geometrical 3D data. Thus,
our implemented system is able to provide both quantitative and qualitative
information. Also, in this work, first steps towards the development of a
quality control loop for E3DP processes are presented and opportunities are
discussed.
Embedded Line Scan Image Sensors: The Low Cost Alternative for High Speed Imaging
Authors: Stef Van Wolputte, Wim Abbeloos, Stijn Helsen, Abdellatif Bey-Temsamani, Toon Goedemé
Date: 2016-12-07 00:00:00

In this paper we propose a low-cost high-speed imaging line scan system. We
replace an expensive industrial line scan camera and illumination with a
custom-built set-up of cheap off-the-shelf components, yielding a measurement
system with comparative quality while costing about 20 times less. We use a
low-cost linear (1D) image sensor, cheap optics including a LED-based or
LASER-based lighting and an embedded platform to process the images. A
step-by-step method to design such a custom high speed imaging system and
select proper components is proposed. Simulations allowing to predict the final
image quality to be obtained by the set-up has been developed. Finally, we
applied our method in a lab, closely representing the real-life cases. Our
results shows that our simulations are very accurate and that our low-cost line
scan set-up acquired image quality compared to the high-end commercial vision
system, for a fraction of the price.
Image and Video Mining through Online Learning
Authors: Andrew Gilbert, Richard Bowden
Date: 2016-09-09 00:00:00
Tasks: Action Recognition, Active Learning, feature selection, Temporal Action Localization, Video Recognition

Within the field of image and video recognition, the traditional approach is
a dataset split into fixed training and test partitions. However, the labelling
of the training set is time-consuming, especially as datasets grow in size and
complexity. Furthermore, this approach is not applicable to the home user, who
wants to intuitively group their media without tirelessly labelling the
content. Our interactive approach is able to iteratively cluster classes of
images and video. Our approach is based around the concept of an image
signature which, unlike a standard bag of words model, can express
co-occurrence statistics as well as symbol frequency. We efficiently compute
metric distances between signatures despite their inherent high dimensionality
and provide discriminative feature selection, to allow common and distinctive
elements to be identified from a small set of user labelled examples. These
elements are then accentuated in the image signature to increase similarity
between examples and pull correct classes together. By repeating this process
in an online learning framework, the accuracy of similarity increases
dramatically despite labelling only a few training examples. To demonstrate
that the approach is agnostic to media type and features used, we evaluate on
three image datasets (15 scene, Caltech101 and FG-NET), a mixed text and image
dataset (ImageTag), a dataset used in active learning (Iris) and on three
action recognition datasets (UCF11, KTH and Hollywood2). On the UCF11 video
dataset, the accuracy is 86.7% despite using only 90 labelled examples from a
dataset of over 1200 videos, instead of the standard 1122 training videos. The
approach is both scalable and efficient, with a single iteration over the full
UCF11 dataset of around 1200 videos taking approximately 1 minute on a standard
desktop machine.
STC: A Simple to Complex Framework for Weakly-supervised Semantic Segmentation
Authors: Yunchao Wei, Xiaodan Liang, Yunpeng Chen, Xiaohui Shen, Ming-Ming Cheng, Jiashi Feng, Yao Zhao, Shuicheng Yan
Date: 2015-09-10 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Recently, significant improvement has been made on semantic object
segmentation due to the development of deep convolutional neural networks
(DCNNs). Training such a DCNN usually relies on a large number of images with
pixel-level segmentation masks, and annotating these images is very costly in
terms of both finance and human effort. In this paper, we propose a simple to
complex (STC) framework in which only image-level annotations are utilized to
learn DCNNs for semantic segmentation. Specifically, we first train an initial
segmentation network called Initial-DCNN with the saliency maps of simple
images (i.e., those with a single category of major object(s) and clean
background). These saliency maps can be automatically obtained by existing
bottom-up salient object detection techniques, where no supervision information
is needed. Then, a better network called Enhanced-DCNN is learned with
supervision from the predicted segmentation masks of simple images based on the
Initial-DCNN as well as the image-level annotations. Finally, more pixel-level
segmentation masks of complex images (two or more categories of objects with
cluttered background), which are inferred by using Enhanced-DCNN and
image-level annotations, are utilized as the supervision information to learn
the Powerful-DCNN for semantic segmentation. Our method utilizes $40$K simple
images from Flickr.com and 10K complex images from PASCAL VOC for step-wisely
boosting the segmentation network. Extensive experimental results on PASCAL VOC
2012 segmentation benchmark well demonstrate the superiority of the proposed
STC framework compared with other state-of-the-arts.
Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia
Authors: Evrim Acar, Yuri Levin-Schwartz, Vince D. Calhoun, Tülay Adalı
Date: 2016-12-07 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Neuroimaging modalities such as functional magnetic resonance imaging (fMRI)
and electroencephalography (EEG) provide information about neurological
functions in complementary spatiotemporal resolutions; therefore, fusion of
these modalities is expected to provide better understanding of brain activity.
In this paper, we jointly analyze fMRI and multi-channel EEG signals collected
during an auditory oddball task with the goal of capturing brain activity
patterns that differ between patients with schizophrenia and healthy controls.
Rather than selecting a single electrode or matricizing the third-order tensor
that can be naturally used to represent multi-channel EEG signals, we preserve
the multi-way structure of EEG data and use a coupled matrix and tensor
factorization (CMTF) model to jointly analyze fMRI and EEG signals. Our
analysis reveals that (i) joint analysis of EEG and fMRI using a CMTF model can
capture meaningful temporal and spatial signatures of patterns that behave
differently in patients and controls, and (ii) these differences and the
interpretability of the associated components increase by including multiple
electrodes from frontal, motor and parietal areas, but not necessarily by
including all electrodes in the analysis.
Fusion of Range and Thermal Images for Person Detection
Authors: Wim Abbeloos, Toon Goedemé
Date: 2016-12-07 00:00:00
Tasks: Human Detection

Detecting people in images is a challenging problem. Differences in pose,
clothing and lighting, along with other factors, cause a lot of variation in
their appearance. To overcome these issues, we propose a system based on fused
range and thermal infrared images. These measurements show considerably less
variation and provide more meaningful information. We provide a brief
introduction to the sensor technology used and propose a calibration method.
Several data fusion algorithms are compared and their performance is assessed
on a simulated data set. The results of initial experiments on real data are
analyzed and the measurement errors and the challenges they present are
discussed. The resulting fused data are used to efficiently detect people in a
fixed camera set-up. The system is extended to include person tracking.