pwc-archive/papers-with-abstracts huggingface dataset:
Spatial Context based Angular Information Preserving Projection for Hyperspectral Image Classification
Authors: Minshan Cui, Saurabh Prasad
Date: 2016-07-15 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Robust classification

Dimensionality reduction is a crucial preprocessing for hyperspectral data
analysis - finding an appropriate subspace is often required for subsequent
image classification. In recent work, we proposed supervised angular
information based dimensionality reduction methods to find effective subspaces.
Since unlabeled data are often more readily available compared to labeled data,
we propose an unsupervised projection that finds a lower dimensional subspace
where local angular information is preserved. To exploit spatial information
from the hyperspectral images, we further extend our unsupervised projection to
incorporate spatial contextual information around each pixel in the image.
Additionally, we also propose a sparse representation based classifier which is
optimized to exploit spatial information during classification - we hence
assert that our proposed projection is particularly suitable for classifiers
where local similarity and spatial context are both important. Experimental
results with two real-world hyperspectral datasets demonstrate that our
proposed methods provide a robust classification performance.
Automatic Environmental Sound Recognition: Performance versus Computational Cost
Authors: Siddharth Sigtia, Adam M. Stark, Sacha Krstulovic, Mark D. Plumbley
Date: 2016-07-15 00:00:00
Tasks: General Classification, Sound Classification

In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed with limited consideration for computational cost, this article seeks
which AESR algorithm can make the most of a limited amount of computing power
by comparing the sound classification performance em as a function of its
computational cost. Results suggest that Deep Neural Networks yield the best
ratio of sound classification accuracy across a range of computational costs,
while Gaussian Mixture Models offer a reasonable accuracy at a consistently
small cost, and Support Vector Machines stand between both in terms of
compromise between accuracy and computational cost.
Neural Discourse Modeling of Conversations
Authors: John M. Pierre, Mark Butler, Jacob Portnoff, Luis Aguilar
Date: 2016-07-15 00:00:00

Deep neural networks have shown recent promise in many language-related tasks
such as the modeling of conversations. We extend RNN-based sequence to sequence
models to capture the long range discourse across many turns of conversation.
We perform a sensitivity analysis on how much additional context affects
performance, and provide quantitative and qualitative evidence that these
models are able to capture discourse relationships across multiple utterances.
Our results quantifies how adding an additional RNN layer for modeling
discourse improves the quality of output utterances and providing more of the
previous conversation as input also improves performance. By searching the
generated outputs for specific discourse markers we show how neural discourse
models can exhibit increased coherence and cohesion in conversations.
Spectral Echolocation via the Wave Embedding
Authors: Alexander Cloninger, Stefan Steinerberger
Date: 2016-07-15 00:00:00
Tasks: Dimensionality Reduction, Position

Spectral embedding uses eigenfunctions of the discrete Laplacian on a
weighted graph to obtain coordinates for an embedding of an abstract data set
into Euclidean space. We propose a new pre-processing step of first using the
eigenfunctions to simulate a low-frequency wave moving over the data and using
both position as well as change in time of the wave to obtain a refined metric
to which classical methods of dimensionality reduction can then applied. This
is motivated by the behavior of waves, symmetries of the wave equation and the
hunting technique of bats. It is shown to be effective in practice and also
works for other partial differential equations -- the method yields improved
results even for the classical heat equation.
Multi-body Non-rigid Structure-from-Motion
Authors: Suryansh Kumar, Yuchao Dai, Hongdong Li
Date: 2016-07-15 00:00:00
Tasks: 3D Reconstruction, Clustering

Conventional structure-from-motion (SFM) research is primarily concerned with
the 3D reconstruction of a single, rigidly moving object seen by a static
camera, or a static and rigid scene observed by a moving camera --in both cases
there are only one relative rigid motion involved. Recent progress have
extended SFM to the areas of {multi-body SFM} (where there are {multiple rigid}
relative motions in the scene), as well as {non-rigid SFM} (where there is a
single non-rigid, deformable object or scene). Along this line of thinking,
there is apparently a missing gap of "multi-body non-rigid SFM", in which the
task would be to jointly reconstruct and segment multiple 3D structures of the
multiple, non-rigid objects or deformable scenes from images. Such a multi-body
non-rigid scenario is common in reality (e.g. two persons shaking hands,
multi-person social event), and how to solve it represents a natural
{next-step} in SFM research. By leveraging recent results of subspace
clustering, this paper proposes, for the first time, an effective framework for
multi-body NRSFM, which simultaneously reconstructs and segments each 3D
trajectory into their respective low-dimensional subspace. Under our
formulation, 3D trajectories for each non-rigid structure can be well
approximated with a sparse affine combination of other 3D trajectories from the
same structure (self-expressiveness). We solve the resultant optimization with
the alternating direction method of multipliers (ADMM). We demonstrate the
efficacy of the proposed framework through extensive experiments on both
synthetic and real data sequences. Our method clearly outperforms other
alternative methods, such as first clustering the 2D feature tracks to groups
and then doing non-rigid reconstruction in each group or first conducting 3D
reconstruction by using single subspace assumption and then clustering the 3D
trajectories into groups.
Simplified Boardgames
Authors: Jakub Kowalski, Jakub Sutowicz, Marek Szykuła
Date: 2016-06-08 00:00:00
Tasks: Board Games

We formalize Simplified Boardgames language, which describes a subclass of
arbitrary board games. The language structure is based on the regular
expressions, which makes the rules easily machine-processable while keeping the
rules concise and fairly human-readable.
Nonrigid Optical Flow Ground Truth for Real-World Scenes with Time-Varying Shading Effects
Authors: Wenbin Li, Darren Cosker, Zhihan Lv, Matthew Brown
Date: 2016-03-26 00:00:00
Tasks: Optical Flow Estimation

In this paper we present a dense ground truth dataset of nonrigidly deforming
real-world scenes. Our dataset contains both long and short video sequences,
and enables the quantitatively evaluation for RGB based tracking and
registration methods. To construct ground truth for the RGB sequences, we
simultaneously capture Near-Infrared (NIR) image sequences where dense markers
- visible only in NIR - represent ground truth positions. This allows for
comparison with automatically tracked RGB positions and the formation of error
metrics. Most previous datasets containing nonrigidly deforming sequences are
based on synthetic data. Our capture protocol enables us to acquire real-world
deforming objects with realistic photometric effects - such as blur and
illumination change - as well as occlusion and complex deformations. A public
evaluation website is constructed to allow for ranking of RGB image based
optical flow and other dense tracking algorithms, with various statistical
measures. Furthermore, we present an RGB-NIR multispectral optical flow model
allowing for energy optimization by adoptively combining featured information
from both the RGB and the complementary NIR channels. In our experiments we
evaluate eight existing RGB based optical flow methods on our new dataset. We
also evaluate our hybrid optical flow algorithm by comparing to two existing
multispectral approaches, as well as varying our input channels across RGB, NIR
and RGB-NIR.
Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times
Authors: S. Senthilmurugan, Junaid Ansari, Petri Mähönen, T. G. Venkatesh, Marina Petrova
Date: 2016-07-15 00:00:00
Tasks: channel selection

We consider a multichannel Cognitive Radio Network (CRN), where secondary
users sequentially sense channels for opportunistic spectrum access. In this
scenario, the Channel Selection Algorithm (CSA) allows secondary users to find
a vacant channel with the minimal number of channel switches. Most of the
existing CSA literature assumes exponential ON-OFF time distribution for
primary users (PU) channel occupancy pattern. This exponential assumption might
be helpful to get performance bounds; but not useful to evaluate the
performance of CSA under realistic conditions. An in-depth analysis of
independent spectrum measurement traces reveals that wireless channels have
typically heavy-tailed PU OFF times. In this paper, we propose an extension to
the Predictive CSA framework and its generalization for heavy tailed PU OFF
time distribution, which represents realistic scenarios. In particular, we
calculate the probability of channel being idle for hyper-exponential OFF times
to use in CSA. We implement our proposed CSA framework in a wireless test-bed
and comprehensively evaluate its performance by recreating the realistic PU
channel occupancy patterns. The proposed CSA shows significant reduction in
channel switches and energy consumption as compared to Predictive CSA which
always assumes exponential PU ON-OFF times.Through our work, we show the impact
of the PU channel occupancy pattern on the performance of CSA in multichannel
CRN.
Higher-Order Block Term Decomposition for Spatially Folded fMRI Data
Authors: Christos Chatzichristos, Eleftherios Kofidis, Giannis Kopsinis, Sergios Theodoridis
Date: 2016-07-15 00:00:00

The growing use of neuroimaging technologies generates a massive amount of
biomedical data that exhibit high dimensionality. Tensor-based analysis of
brain imaging data has been proved quite effective in exploiting their multiway
nature. The advantages of tensorial methods over matrix-based approaches have
also been demonstrated in the characterization of functional magnetic resonance
imaging (fMRI) data, where the spatial (voxel) dimensions are commonly grouped
(unfolded) as a single way/mode of the 3-rd order array, the other two ways
corresponding to time and subjects. However, such methods are known to be
ineffective in more demanding scenarios, such as the ones with strong noise
and/or significant overlapping of activated regions. This paper aims at
investigating the possible gains from a better exploitation of the spatial
dimension, through a higher- (4 or 5) order tensor modeling of the fMRI signal.
In this context, and in order to increase the degrees of freedom of the
modeling process, a higher-order Block Term Decomposition (BTD) is applied, for
the first time in fMRI analysis. Its effectiveness is demonstrated via
extensive simulation results.
Photo Stylistic Brush: Robust Style Transfer via Superpixel-Based Bipartite Graph
Authors: Jiaying Liu, Wenhan Yang, Xiaoyan Sun, Wen-Jun Zeng
Date: 2016-06-13 00:00:00
Tasks: Style Transfer, Superpixels

With the rapid development of social network and multimedia technology,
customized image and video stylization has been widely used for various
social-media applications. In this paper, we explore the problem of
exemplar-based photo style transfer, which provides a flexible and convenient
way to invoke fantastic visual impression. Rather than investigating some fixed
artistic patterns to represent certain styles as was done in some previous
works, our work emphasizes styles related to a series of visual effects in the
photograph, e.g. color, tone, and contrast. We propose a photo stylistic brush,
an automatic robust style transfer approach based on Superpixel-based BIpartite
Graph (SuperBIG). A two-step bipartite graph algorithm with different
granularity levels is employed to aggregate pixels into superpixels and find
their correspondences. In the first step, with the extracted hierarchical
features, a bipartite graph is constructed to describe the content similarity
for pixel partition to produce superpixels. In the second step, superpixels in
the input/reference image are rematched to form a new superpixel-based
bipartite graph, and superpixel-level correspondences are generated by a
bipartite matching. Finally, the refined correspondence guides SuperBIG to
perform the transformation in a decorrelated color space. Extensive
experimental results demonstrate the effectiveness and robustness of the
proposed method for transferring various styles of exemplar images, even for
some challenging cases, such as night images.
Model-Driven Feed-Forward Prediction for Manipulation of Deformable Objects
Authors: Yinxiao Li, Yan Wang, Yonghao Yue, Danfei Xu, Michael Case, Shih-Fu Chang, Eitan Grinspun, Peter Allen
Date: 2016-07-15 00:00:00
Tasks: Object, Pose Estimation, Retrieval

Robotic manipulation of deformable objects is a difficult problem especially
because of the complexity of the many different ways an object can deform.
Searching such a high dimensional state space makes it difficult to recognize,
track, and manipulate deformable objects. In this paper, we introduce a
predictive, model-driven approach to address this challenge, using a
pre-computed, simulated database of deformable object models. Mesh models of
common deformable garments are simulated with the garments picked up in
multiple different poses under gravity, and stored in a database for fast and
efficient retrieval. To validate this approach, we developed a comprehensive
pipeline for manipulating clothing as in a typical laundry task. First, the
database is used for category and pose estimation for a garment in an arbitrary
position. A fully featured 3D model of the garment is constructed in real-time
and volumetric features are then used to obtain the most similar model in the
database to predict the object category and pose. Second, the database can
significantly benefit the manipulation of deformable objects via non-rigid
registration, providing accurate correspondences between the reconstructed
object model and the database models. Third, the accurate model simulation can
also be used to optimize the trajectories for manipulation of deformable
objects, such as the folding of garments. Extensive experimental results are
shown for the tasks above using a variety of different clothing.
Update Strength in EDAs and ACO: How to Avoid Genetic Drift
Authors: Dirk Sudholt, Carsten Witt
Date: 2016-07-14 00:00:00
Tasks: global-optimization

We provide a rigorous runtime analysis concerning the update strength, a
vital parameter in probabilistic model-building GAs such as the step size $1/K$
in the compact Genetic Algorithm (cGA) and the evaporation factor $\rho$ in
ACO. While a large update strength is desirable for exploitation, there is a
general trade-off: too strong updates can lead to genetic drift and poor
performance. We demonstrate this trade-off for the cGA and a simple MMAS ACO
algorithm on the OneMax function. More precisely, we obtain lower bounds on the
expected runtime of $\Omega(K\sqrt{n} + n \log n)$ and $\Omega(\sqrt{n}/\rho +
n \log n)$, respectively, showing that the update strength should be limited to
$1/K, \rho = O(1/(\sqrt{n} \log n))$. In fact, choosing $1/K, \rho \sim
1/(\sqrt{n}\log n)$ both algorithms efficiently optimize OneMax in expected
time $O(n \log n)$. Our analyses provide new insights into the stochastic
behavior of probabilistic model-building GAs and propose new guidelines for
setting the update strength in global optimization.
Low-complexity feedback-channel-free distributed video coding using Local Rank Transform
Authors: P Raj Bhagath, Kallol Mallick, Jayanta Mukherjee, Sudipta Mukopadhayay
Date: 2016-07-15 00:00:00
Tasks: Decoder, Motion Estimation

In this paper, we propose a new feedback-channel-free Distributed Video
Coding (DVC) algorithm using Local Rank Transform (LRT). The encoder computes
LRT by considering selected neighborhood pixels of Wyner-Ziv frame. The ranks
from the modified LRT are merged, and their positions are entropy coded and
sent to the decoder. In addition, means of each block of Wyner-Ziv frame are
also transmitted to assist motion estimation. Using these measurements, the
decoder generates side information (SI) by implementing motion estimation and
compensation in LRT domain. An iterative algorithm is executed on SI using LRT
to reconstruct the Wyner-Ziv frame. Experimental results show that the coding
efficiency of our codec is close to the efficiency of pixel domain distributed
video coders based on Low-Density Parity Check and Accumulate (LDPCA) or turbo
codes, with less encoder complexity.
DeepQA: Improving the estimation of single protein model quality with deep belief networks
Authors: Renzhi Cao, Debswapna Bhattacharya, Jie Hou, Jianlin Cheng
Date: 2016-07-15 00:00:00
Tasks: Protein Structure Prediction

Protein quality assessment (QA) by ranking and selecting protein models has
long been viewed as one of the major challenges for protein tertiary structure
prediction. Especially, estimating the quality of a single protein model, which
is important for selecting a few good models out of a large model pool
consisting of mostly low-quality models, is still a largely unsolved problem.
We introduce a novel single-model quality assessment method DeepQA based on
deep belief network that utilizes a number of selected features describing the
quality of a model from different perspectives, such as energy, physio-chemical
characteristics, and structural information. The deep belief network is trained
on several large datasets consisting of models from the Critical Assessment of
Protein Structure Prediction (CASP) experiments, several publicly available
datasets, and models generated by our in-house ab initio method. Our experiment
demonstrate that deep belief network has better performance compared to Support
Vector Machines and Neural Networks on the protein model quality assessment
problem, and our method DeepQA achieves the state-of-the-art performance on
CASP11 dataset. It also outperformed two well-established methods in selecting
good outlier models from a large set of models of mostly low quality generated
by ab initio modeling methods. DeepQA is a useful tool for protein single model
quality assessment and protein structure prediction. The source code,
executable, document and training/test datasets of DeepQA for Linux is freely
available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/.
Intrinsically Motivated Multimodal Structure Learning
Authors: Jay Ming Wong, Roderic A. Grupen
Date: 2016-07-15 00:00:00

We present a long-term intrinsically motivated structure learning method for
modeling transition dynamics during controlled interactions between a robot and
semi-permanent structures in the world. In particular, we discuss how
partially-observable state is represented using distributions over a Markovian
state and build models of objects that predict how state distributions change
in response to interactions with such objects. These structures serve as the
basis for a number of possible future tasks defined as Markov Decision
Processes (MDPs). The approach is an example of a structure learning technique
applied to a multimodal affordance representation that yields a population of
forward models for use in planning. We evaluate the approach using experiments
on a bimanual mobile manipulator (uBot-6) that show the performance of model
acquisition as the number of transition actions increases.
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Authors: Ruining He, Chen Fang, Zhaowen Wang, Julian McAuley
Date: 2016-07-15 00:00:00
Tasks: Recommendation Systems

Understanding users' interactions with highly subjective content---like
artistic images---is challenging due to the complex semantics that guide our
preferences. On the one hand one has to overcome `standard' recommender systems
challenges, such as dealing with large, sparse, and long-tailed datasets. On
the other, several new challenges present themselves, such as the need to model
content in terms of its visual appearance, or even social dynamics, such as a
preference toward a particular artist that is independent of the art they
create.
  In this paper we build large-scale recommender systems to model the dynamics
of a vibrant digital art community, Behance, consisting of tens of millions of
interactions (clicks and `appreciates') of users toward digital art.
Methodologically, our main contributions are to model (a) rich content,
especially in terms of its visual appearance; (b) temporal dynamics, in terms
of how users prefer `visually consistent' content within and across sessions;
and (c) social dynamics, in terms of how users exhibit preferences both towards
certain art styles, as well as the artists themselves.
SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods
Authors: Filipe Nunes Ribeiro, Matheus Araújo, Pollyanna Gonçalves, Fabrício Benevenuto, Marcos André Gonçalves
Date: 2015-12-06 00:00:00
Tasks: Articles, Sentence, Sentiment Analysis

In the last few years thousands of scientific papers have investigated
sentiment analysis, several startups that measure opinions on real data have
emerged and a number of innovative products related to this theme have been
developed. There are multiple methods for measuring sentiments, including
lexical-based and supervised machine learning methods. Despite the vast
interest on the theme and wide popularity of some methods, it is unclear which
one is better for identifying the polarity (i.e., positive or negative) of a
message. Accordingly, there is a strong need to conduct a thorough
apple-to-apple comparison of sentiment analysis methods, \textit{as they are
used in practice}, across multiple datasets originated from different data
sources. Such a comparison is key for understanding the potential limitations,
advantages, and disadvantages of popular methods. This article aims at filling
this gap by presenting a benchmark comparison of twenty-four popular sentiment
analysis methods (which we call the state-of-the-practice methods). Our
evaluation is based on a benchmark of eighteen labeled datasets, covering
messages posted on social networks, movie and product reviews, as well as
opinions and comments in news articles. Our results highlight the extent to
which the prediction performance of these methods varies considerably across
datasets. Aiming at boosting the development of this research area, we open the
methods' codes and datasets used in this article, deploying them in a benchmark
system, which provides an open API for accessing and comparing sentence-level
sentiment analysis methods.
Defensive Distillation is Not Robust to Adversarial Examples
Authors: Nicholas Carlini, David Wagner
Date: 2016-07-14 00:00:00

We show that defensive distillation is not secure: it is no more resistant to
targeted misclassification attacks than unprotected neural networks.