pwc-archive/papers-with-abstracts huggingface dataset:
Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation
Authors: Antonio Lieto, Antonio Chella, Marcello Frixione
Date: 2017-01-02 00:00:00

During the last decades, many cognitive architectures (CAs) have been
realized adopting different assumptions about the organization and the
representation of their knowledge level. Some of them (e.g. SOAR [Laird
(2012)]) adopt a classical symbolic approach, some (e.g. LEABRA [O'Reilly and
Munakata (2000)]) are based on a purely connectionist model, while others (e.g.
CLARION [Sun (2006)] adopt a hybrid approach combining connectionist and
symbolic representational levels. Additionally, some attempts (e.g. biSOAR)
trying to extend the representational capacities of CAs by integrating
diagrammatical representations and reasoning are also available [Kurup and
Chandrasekaran (2007)]. In this paper we propose a reflection on the role that
Conceptual Spaces, a framework developed by Peter G\"ardenfors [G\"ardenfors
(2000)] more than fifteen years ago, can play in the current development of the
Knowledge Level in Cognitive Systems and Architectures. In particular, we claim
that Conceptual Spaces offer a lingua franca that allows to unify and
generalize many aspects of the symbolic, sub-symbolic and diagrammatic
approaches (by overcoming some of their typical problems) and to integrate them
on a common ground. In doing so we extend and detail some of the arguments
explored by G\"ardenfors [G\"ardenfors (1997)] for defending the need of a
conceptual, intermediate, representation level between the symbolic and the
sub-symbolic one.
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
Authors: Guillermo Cabrera-Vives, Ignacio Reyes, Francisco Förster, Pablo A. Estévez, Juan-Carlos Maureira
Date: 2017-01-02 00:00:00
Tasks: Feature Engineering, Survey

We introduce Deep-HiTS, a rotation invariant convolutional neural network
(CNN) model for classifying images of transients candidates into artifacts or
real sources for the High cadence Transient Survey (HiTS). CNNs have the
advantage of learning the features automatically from the data while achieving
high performance. We compare our CNN model against a feature engineering
approach using random forests (RF). We show that our CNN significantly
outperforms the RF model reducing the error by almost half. Furthermore, for a
fixed number of approximately 2,000 allowed false transient candidates per
night we are able to reduce the miss-classified real transients by
approximately 1/5. To the best of our knowledge, this is the first time CNNs
have been used to detect astronomical transient events. Our approach will be
very useful when processing images from next generation instruments such as the
Large Synoptic Survey Telescope (LSST). We have made all our code and data
available to the community for the sake of allowing further developments and
comparisons at https://github.com/guille-c/Deep-HiTS.
Retrieving Similar X-Ray Images from Big Image Data Using Radon Barcodes with Single Projections
Authors: Morteza Babaie, H. R. Tizhoosh, Shujin Zhu, M. E. Shiri
Date: 2017-01-02 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

The idea of Radon barcodes (RBC) has been introduced recently. In this paper,
we propose a content-based image retrieval approach for big datasets based on
Radon barcodes. Our method (Single Projection Radon Barcode, or SP-RBC) uses
only a few Radon single projections for each image as global features that can
serve as a basis for weak learners. This is our most important contribution in
this work, which improves the results of the RBC considerably. As a matter of
fact, only one projection of an image, as short as a single SURF feature
vector, can already achieve acceptable results. Nevertheless, using multiple
projections in a long vector will not deliver anticipated improvements. To
exploit the information inherent in each projection, our method uses the
outcome of each projection separately and then applies more precise local
search on the small subset of retrieved images. We have tested our method using
IRMA 2009 dataset a with 14,400 x-ray images as part of imageCLEF initiative.
Our approach leads to a substantial decrease in the error rate in comparison
with other non-learning methods.
Pointer Networks
Authors: Oriol Vinyals, Meire Fortunato, Navdeep Jaitly
Date: 2015-06-09 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Combinatorial Optimization, Point Cloud Completion

We introduce a new neural architecture to learn the conditional probability
of an output sequence with elements that are discrete tokens corresponding to
positions in an input sequence. Such problems cannot be trivially addressed by
existent approaches such as sequence-to-sequence and Neural Turing Machines,
because the number of target classes in each step of the output depends on the
length of the input, which is variable. Problems such as sorting variable sized
sequences, and various combinatorial optimization problems belong to this
class. Our model solves the problem of variable size output dictionaries using
a recently proposed mechanism of neural attention. It differs from the previous
attention attempts in that, instead of using attention to blend hidden units of
an encoder to a context vector at each decoder step, it uses attention as a
pointer to select a member of the input sequence as the output. We call this
architecture a Pointer Net (Ptr-Net). We show Ptr-Nets can be used to learn
approximate solutions to three challenging geometric problems -- finding planar
convex hulls, computing Delaunay triangulations, and the planar Travelling
Salesman Problem -- using training examples alone. Ptr-Nets not only improve
over sequence-to-sequence with input attention, but also allow us to generalize
to variable size output dictionaries. We show that the learnt models generalize
beyond the maximum lengths they were trained on. We hope our results on these
tasks will encourage a broader exploration of neural learning for discrete
problems.
An affective computational model for machine consciousness
Authors: Rohitash Chandra
Date: 2017-01-02 00:00:00
Tasks: model, speech-recognition, Speech Recognition

In the past, several models of consciousness have become popular and have led
to the development of models for machine consciousness with varying degrees of
success and challenges for simulation and implementations. Moreover, affective
computing attributes that involve emotions, behavior and personality have not
been the focus of models of consciousness as they lacked motivation for
deployment in software applications and robots. The affective attributes are
important factors for the future of machine consciousness with the rise of
technologies that can assist humans. Personality and affection hence can give
an additional flavor for the computational model of consciousness in humanoid
robotics. Recent advances in areas of machine learning with a focus on deep
learning can further help in developing aspects of machine consciousness in
areas that can better replicate human sensory perceptions such as speech
recognition and vision. With such advancements, one encounters further
challenges in developing models that can synchronize different aspects of
affective computing. In this paper, we review some existing models of
consciousnesses and present an affective computational model that would enable
the human touch and feel for robotic systems.
Global Hypothesis Generation for 6D Object Pose Estimation
Authors: Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother
Date: 2016-12-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: 6D Pose Estimation using RGB, Object, Pose Estimation

This paper addresses the task of estimating the 6D pose of a known 3D object
from a single RGB-D image. Most modern approaches solve this task in three
steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii)
Select and refine a pose from the pool. This work focuses on the second step.
While all existing approaches generate the hypotheses pool via local reasoning,
e.g. RANSAC or Hough-voting, we are the first to show that global reasoning is
beneficial at this stage. In particular, we formulate a novel fully-connected
Conditional Random Field (CRF) that outputs a very small number of
pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian,
we give a new and efficient two-step optimization procedure, with some
guarantees for optimality. We utilize our global hypotheses generation
procedure to produce results that exceed state-of-the-art for the challenging
"Occluded Object Dataset".
Assessing Uncertainties in X-ray Single-particle Three-dimensional reconstructions
Authors: Stefan Engblom, Carl Nettelblad, Jing Liu
Date: 2017-01-02 00:00:00

Modern technology for producing extremely bright and coherent X-ray laser
pulses provides the possibility to acquire a large number of diffraction
patterns from individual biological nanoparticles, including proteins, viruses,
and DNA. These two-dimensional diffraction patterns can be practically
reconstructed and retrieved down to a resolution of a few \angstrom. In
principle, a sufficiently large collection of diffraction patterns will contain
the required information for a full three-dimensional reconstruction of the
biomolecule. The computational methodology for this reconstruction task is
still under development and highly resolved reconstructions have not yet been
produced.
  We analyze the Expansion-Maximization-Compression scheme, the current state
of the art approach for this very challenging application, by isolating
different sources of uncertainty. Through numerical experiments on synthetic
data we evaluate their respective impact. We reach conclusions of relevance for
handling actual experimental data, as well as pointing out certain improvements
to the underlying estimation algorithm.
  We also introduce a practically applicable computational methodology in the
form of bootstrap procedures for assessing reconstruction uncertainty in the
real data case. We evaluate the sharpness of this approach and argue that this
type of procedure will be critical in the near future when handling the
increasing amount of data.
Challenges ahead Electron Microscopy for Structural Biology from the Image Processing point of view
Authors: Carlos Oscar S. Sorzano, Jose Maria Carazo
Date: 2017-01-02 00:00:00
Tasks: Drug Design, Single Particle Analysis

Since the introduction of Direct Electron Detectors (DEDs), the resolution
and range of macromolecules amenable to this technique has significantly
widened, generating a broad interest that explains the well over a dozen
reviews in top journal in the last two years. Similarly, the number of job
offers to lead EM groups and/or coordinate EM facilities has exploded, and FEI
(the main microscope manufacturer for Life Sciences) has received more than 100
orders of high-end electron microscopes by summer 2016. Strategic corporate
movements are also happening, with very big players entering the market through
key acquisitions (Thermo Fisher has recently bought FEI for \$4.2B), partly
attracted by new Pharma interest in the field, now perceived to be in a
position to impact structure-based drug design. The scientific perspectives are
indeed extremely positive but, in these moments of well-founded generalized
optimists, we want to make a reflection on some of the hurdles ahead us, since
they certainly exist and they indeed limit the informational content of cryoEM
projects. Here we focus on image processing aspects, particularly in the
so-called area of Single Particle Analysis, discussing some of the current
resolution and high-throughput limiting factors.
Deep learning for plasma tomography using the bolometer system at JET
Authors: Francisco A. Matos, Diogo R. Ferreira, Pedro J. Carvalho, JET Contributors
Date: 2017-01-02 00:00:00

Deep learning is having a profound impact in many fields, especially those
that involve some form of image processing. Deep neural networks excel in
turning an input image into a set of high-level features. On the other hand,
tomography deals with the inverse problem of recreating an image from a number
of projections. In plasma diagnostics, tomography aims at reconstructing the
cross-section of the plasma from radiation measurements. This reconstruction
can be computed with neural networks. However, previous attempts have focused
on learning a parametric model of the plasma profile. In this work, we use a
deep neural network to produce a full, pixel-by-pixel reconstruction of the
plasma profile. For this purpose, we use the overview bolometer system at JET,
and we introduce an up-convolutional network that has been trained and tested
on a large set of sample tomograms. We show that this network is able to
reproduce existing reconstructions with a high level of accuracy, as measured
by several metrics.
The Geodesic Distance between $\mathcal{G}_I^0$ Models and its Application to Region Discrimination
Authors: José Naranjo-Torres, Juliana Gambini, Alejandro C. Frery
Date: 2017-01-01 00:00:00

The $\mathcal{G}_I^0$ distribution is able to characterize different regions
in monopolarized SAR imagery. It is indexed by three parameters: the number of
looks (which can be estimated in the whole image), a scale parameter and a
texture parameter. This paper presents a new proposal for feature extraction
and region discrimination in SAR imagery, using the geodesic distance as a
measure of dissimilarity between $\mathcal{G}_I^0$ models. We derive geodesic
distances between models that describe several practical situations, assuming
the number of looks known, for same and different texture and for same and
different scale. We then apply this new tool to the problems of (i)~identifying
edges between regions with different texture, and (ii)~quantify the
dissimilarity between pairs of samples in actual SAR data. We analyze the
advantages of using the geodesic distance when compared to stochastic
distances.
Spatial best linear unbiased prediction: A computational mathematics approach for high dimensional massive datasets
Authors: Julio E. Castrillon-Candas
Date: 2017-01-01 00:00:00
Tasks: Uncertainty Quantification, Vocal Bursts Intensity Prediction

With the advent of massive data sets much of the computational science and engineering community has moved toward data-intensive approaches in regression and classification. However, these present significant challenges due to increasing size, complexity and dimensionality of the problems. In particular, covariance matrices in many cases are numerically unstable and linear algebra shows that often such matrices cannot be inverted accurately on a finite precision computer. A common ad hoc approach to stabilizing a matrix is application of a so-called nugget. However, this can change the model and introduce error to the original solution. It is well known from numerical analysis that ill-conditioned matrices cannot be accurately inverted. In this paper we develop a multilevel computational method that scales well with the number of observations and dimensions. A multilevel basis is constructed adapted to a kD-tree partitioning of the observations. Numerically unstable covariance matrices with large condition numbers can be transformed into well conditioned multilevel ones without compromising accuracy. Moreover, it is shown that the multilevel prediction exactly solves the Best Linear Unbiased Predictor (BLUP) and Generalized Least Squares (GLS) model, but is numerically stable. The multilevel method is tested on numerically unstable problems of up to 25 dimensions. Numerical results show speedups of up to 42,050 times for solving the BLUP problem, but with the same accuracy as the traditional iterative approach. For very ill-conditioned cases the speedup is infinite. In addition, decay estimates of the multilevel covariance matrices are derived based on high dimensional interpolation techniques from the field of numerical analysis. This work lies at the intersection of statistics, uncertainty quantification, high performance computing and computational applied mathematics.
Outlier Robust Online Learning
Authors: Jiashi Feng, Huan Xu, Shie Mannor
Date: 2017-01-01 00:00:00

We consider the problem of learning from noisy data in practical settings
where the size of data is too large to store on a single machine. More
challenging, the data coming from the wild may contain malicious outliers. To
address the scalability and robustness issues, we present an online robust
learning (ORL) approach. ORL is simple to implement and has provable robustness
guarantee -- in stark contrast to existing online learning approaches that are
generally fragile to outliers. We specialize the ORL approach for two concrete
cases: online robust principal component analysis and online linear regression.
We demonstrate the efficiency and robustness advantages of ORL through
comprehensive simulations and predicting image tags on a large-scale data set.
We also discuss extension of the ORL to distributed learning and provide
experimental evaluations.
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content
Authors: Wei Fang, Jui-Yang Hsu, Hung-Yi Lee, Lin-shan Lee
Date: 2016-08-28 00:00:00
Tasks: Reading Comprehension

Multimedia or spoken content presents more attractive information than plain
text content, but the former is more difficult to display on a screen and be
selected by a user. As a result, accessing large collections of the former is
much more difficult and time-consuming than the latter for humans. It's
therefore highly attractive to develop machines which can automatically
understand spoken content and summarize the key information for humans to
browse over. In this endeavor, a new task of machine comprehension of spoken
content was proposed recently. The initial goal was defined as the listening
comprehension test of TOEFL, a challenging academic English examination for
English learners whose native languages are not English. An Attention-based
Multi-hop Recurrent Neural Network (AMRNN) architecture was also proposed for
this task, which considered only the sequential relationship within the speech
utterances. In this paper, we propose a new Hierarchical Attention Model (HAM),
which constructs multi-hopped attention mechanism over tree-structured rather
than sequential representations for the utterances. Improved comprehension
performance robust with respect to ASR errors were obtained.
Classification of Smartphone Users Using Internet Traffic
Authors: Andrey Finkelstein, Ron Biton, Rami Puzis, Asaf Shabtai
Date: 2017-01-01 00:00:00
Tasks: Classification, General Classification

Today, smartphone devices are owned by a large portion of the population and
have become a very popular platform for accessing the Internet. Smartphones
provide the user with immediate access to information and services. However,
they can easily expose the user to many privacy risks. Applications that are
installed on the device and entities with access to the device's Internet
traffic can reveal private information about the smartphone user and steal
sensitive content stored on the device or transmitted by the device over the
Internet. In this paper, we present a method to reveal various demographics and
technical computer skills of smartphone users by their Internet traffic
records, using machine learning classification models. We implement and
evaluate the method on real life data of smartphone users and show that
smartphone users can be classified by their gender, smoking habits, software
programming experience, and other characteristics.
A Geometric Analysis of Phase Retrieval
Authors: Ju Sun, Qing Qu, John Wright
Date: 2016-02-22 00:00:00
Tasks: GPR, Retrieval

Can we recover a complex signal from its Fourier magnitudes? More generally,
given a set of $m$ measurements, $y_k = |\mathbf a_k^* \mathbf x|$ for $k = 1,
\dots, m$, is it possible to recover $\mathbf x \in \mathbb{C}^n$ (i.e.,
length-$n$ complex vector)? This **generalized phase retrieval** (GPR) problem
is a fundamental task in various disciplines, and has been the subject of much
recent investigation. Natural nonconvex heuristics often work remarkably well
for GPR in practice, but lack clear theoretical explanations. In this paper, we
take a step towards bridging this gap. We prove that when the measurement
vectors $\mathbf a_k$'s are generic (i.i.d. complex Gaussian) and the number of
measurements is large enough ($m \ge C n \log^3 n$), with high probability, a
natural least-squares formulation for GPR has the following benign geometric
structure: (1) there are no spurious local minimizers, and all global
minimizers are equal to the target signal $\mathbf x$, up to a global phase;
and (2) the objective function has a negative curvature around each saddle
point. This structure allows a number of iterative optimization methods to
efficiently find a global minimizer, without special initialization. To
corroborate the claim, we describe and analyze a second-order trust-region
algorithm.
Using Artificial Neural Networks (ANN) to Control Chaos
Authors: Ibrahim Ighneiwaa, Salwa Hamidatoua, Fadia Ben Ismaela
Date: 2017-01-01 00:00:00

Controlling Chaos could be a big factor in getting great stable amounts of
energy out of small amounts of not necessarily stable resources. By definition,
Chaos is getting huge changes in the system's output due to unpredictable small
changes in initial conditions, and that means we could take advantage of this
fact and select the proper control system to manipulate system's initial
conditions and inputs in general and get a desirable output out of otherwise a
Chaotic system. That was accomplished by first building some known chaotic
circuit (Chua circuit) and the NI's MultiSim was used to simulate the ANN
control system. It was shown that this technique can also be used to stabilize
some hard to stabilize electronic systems.
A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data
Authors: Hamid Hamraz, Marco A. Contreras, Jun Zhang
Date: 2017-01-01 00:00:00

This paper presents a non-parametric approach for segmenting trees from
airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the
approach collects crown information such as steepness and height on-the-fly to
delineate crown boundaries, and most importantly, does not require a priori
assumptions of crown shape and size. The approach segments trees iteratively
starting from the tallest within a given area to the smallest until all trees
have been segmented. To evaluate its performance, the approach was applied to
the University of Kentucky Robinson Forest, a deciduous closed-canopy forest
with complex terrain and vegetation conditions. The approach identified 94% of
dominant and co-dominant trees with a false detection rate of 13%. About 62% of
intermediate, overtopped, and dead trees were also detected with a false
detection rate of 15%. The overall segmentation accuracy was 77%. Correlations
of the segmentation scores of the proposed approach with local terrain and
stand metrics was not significant, which is likely an indication of the
robustness of the approach as results are not sensitive to the differences in
terrain and stand structures.