pwc-archive/papers-with-abstracts huggingface dataset:
Network Motifs Analysis of Croatian Literature
Authors: Hana Rizvić, Sanda Martinčić-Ipšić, Ana Meštrović
Date: 2014-11-18 00:00:00

In this paper we analyse network motifs in the co-occurrence directed
networks constructed from five different texts (four books and one portal) in
the Croatian language. After preparing the data and network construction, we
perform the network motif analysis. We analyse the motif frequencies and
Z-scores in the five networks. We present the triad significance profile for
five datasets. Furthermore, we compare our results with the existing results
for the linguistic networks. Firstly, we show that the triad significance
profile for the Croatian language is very similar with the other languages and
all the networks belong to the same family of networks. However, there are
certain differences between the Croatian language and other analysed languages.
We conclude that this is due to the free word-order of the Croatian language.
Designing Deep Networks for Surface Normal Estimation
Authors: Xiaolong Wang, David F. Fouhey, Abhinav Gupta
Date: 2014-11-18 00:00:00
Proceeding: CVPR 2015 6
Tasks: Scene Understanding, Surface Normal Estimation

In the past few years, convolutional neural nets (CNN) have shown incredible
promise for learning visual representations. In this paper, we use CNNs for the
task of predicting surface normals from a single image. But what is the right
architecture we should use? We propose to build upon the decades of hard work
in 3D scene understanding, to design new CNN architecture for the task of
surface normal estimation. We show by incorporating several constraints
(man-made, manhattan world) and meaningful intermediate representations (room
layout, edge labels) in the architecture leads to state of the art performance
on surface normal estimation. We also show that our network is quite robust and
show state of the art results on other datasets as well without any
fine-tuning.
Linguistic Descriptions for Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data
Authors: A. Ramos-Soto, A. Bugarín, S. Barro, J. Taboada
Date: 2014-11-18 00:00:00
Tasks: Text Generation

We present in this paper an application which automatically generates textual
short-term weather forecasts for every municipality in Galicia (NW Spain),
using the real data provided by the Galician Meteorology Agency (MeteoGalicia).
This solution combines in an innovative way computing with perceptions
techniques and strategies for linguistic description of data together with a
natural language generation (NLG) system. The application, named GALiWeather,
extracts relevant information from weather forecast input data and encodes it
into intermediate descriptions using linguistic variables and temporal
references. These descriptions are later translated into natural language texts
by the natural language generation system. The obtained forecast results have
been thoroughly validated by an expert meteorologist from MeteoGalicia using a
quality assessment methodology which covers two key dimensions of a text: the
accuracy of its content and the correctness of its form. Following this
validation GALiWeather will be released as a real service offering custom
forecasts for a wide public.
Do More Dropouts in Pool5 Feature Maps for Better Object Detection
Authors: Zhiqiang Shen, xiangyang xue
Date: 2014-09-24 00:00:00
Tasks: General Classification, image-classification, Image Classification, object-detection, Object Detection

Deep Convolutional Neural Networks (CNNs) have gained great success in image
classification and object detection. In these fields, the outputs of all layers
of CNNs are usually considered as a high dimensional feature vector extracted
from an input image and the correspondence between finer level feature vectors
and concepts that the input image contains is all-important. However, fewer
studies focus on this deserving issue. On considering the correspondence, we
propose a novel approach which generates an edited version for each original
CNN feature vector by applying the maximum entropy principle to abandon
particular vectors. These selected vectors correspond to the unfriendly
concepts in each image category. The classifier trained from merged feature
sets can significantly improve model generalization of individual categories
when training data is limited. The experimental results for
classification-based object detection on canonical datasets including VOC 2007
(60.1%), 2010 (56.4%) and 2012 (56.3%) show obvious improvement in mean average
precision (mAP) with simple linear support vector machines.
Towards Scene Understanding with Detailed 3D Object Representations
Authors: M. Zeeshan Zia, Michael Stark, Konrad Schindler
Date: 2014-11-18 00:00:00
Tasks: 3D Pose Estimation, Object, object-detection, Object Detection, Pose Estimation, Scene Understanding

Current approaches to semantic image and scene understanding typically employ
rather simple object representations such as 2D or 3D bounding boxes. While
such coarse models are robust and allow for reliable object detection, they
discard much of the information about objects' 3D shape and pose, and thus do
not lend themselves well to higher-level reasoning. Here, we propose to base
scene understanding on a high-resolution object representation. An object class
- in our case cars - is modeled as a deformable 3D wireframe, which enables
fine-grained modeling at the level of individual vertices and faces. We augment
that model to explicitly include vertex-level occlusion, and embed all
instances in a common coordinate frame, in order to infer and exploit
object-object interactions. Specifically, from a single view we jointly
estimate the shapes and poses of multiple objects in a common 3D frame. A
ground plane in that frame is estimated by consensus among different objects,
which significantly stabilizes monocular 3D pose estimation. The fine-grained
model, in conjunction with the explicit 3D scene model, further allows one to
infer part-level occlusions between the modeled objects, as well as occlusions
by other, unmodeled scene elements. To demonstrate the benefits of such
detailed object class models in the context of scene understanding we
systematically evaluate our approach on the challenging KITTI street scene
dataset. The experiments show that the model's ability to utilize image
evidence at the level of individual parts improves monocular 3D pose estimation
w.r.t. both location and (continuous) viewpoint.
A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly max-Oracle
Authors: Neel Shah, Vladimir Kolmogorov, Christoph H. Lampert
Date: 2014-08-28 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Segmentation, Pose Estimation, Semantic Segmentation, Structured Prediction

Structural support vector machines (SSVMs) are amongst the best performing
models for structured computer vision tasks, such as semantic image
segmentation or human pose estimation. Training SSVMs, however, is
computationally costly, because it requires repeated calls to a structured
prediction subroutine (called \emph{max-oracle}), which has to solve an
optimization problem itself, e.g. a graph cut.
  In this work, we introduce a new algorithm for SSVM training that is more
efficient than earlier techniques when the max-oracle is computationally
expensive, as it is frequently the case in computer vision tasks. The main idea
is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm
with efficient hyperplane caching, and (ii) use an automatic selection rule for
deciding whether to call the exact max-oracle or to rely on an approximate one
based on the cached hyperplanes.
  We show experimentally that this strategy leads to faster convergence to the
optimum with respect to the number of requires oracle calls, and that this
translates into faster convergence with respect to the total runtime when the
max-oracle is slow compared to the other steps of the algorithm.
  A publicly available C++ implementation is provided at
http://pub.ist.ac.at/~vnk/papers/SVM.html .
Exploiting Parallelism for Hard Problems in Abstract Argumentation: Technical Report
Authors: Federico Cerutti, Ilias Tachmazidis, Mauro Vallati, Sotirios Batsakis, Massimiliano Giacomin, Grigoris Antoniou
Date: 2014-11-11 00:00:00
Tasks: Abstract Argumentation

Abstract argumentation framework (\AFname) is a unifying framework able to
encompass a variety of nonmonotonic reasoning approaches, logic programming and
computational argumentation. Yet, efficient approaches for most of the decision
and enumeration problems associated to \AFname s are missing, thus potentially
limiting the efficacy of argumentation-based approaches in real domains. In
this paper, we present an algorithm for enumerating the preferred extensions of
abstract argumentation frameworks which exploits parallel computation. To this
purpose, the SCC-recursive semantics definition schema is adopted, where
extensions are defined at the level of specific sub-frameworks. The algorithm
shows significant performance improvements in large frameworks, in terms of
number of solutions found and speedup.
Music Data Analysis: A State-of-the-art Survey
Authors: Shubhanshu Gupta
Date: 2014-11-18 00:00:00
Tasks: No-Reference Image Quality Assessment, Survey

Music accounts for a significant chunk of interest among various online
activities. This is reflected by wide array of alternatives offered in music
related web/mobile apps, information portals, featuring millions of artists,
songs and events attracting user activity at similar scale. Availability of
large scale structured and unstructured data has attracted similar level of
attention by data science community. This paper attempts to offer current
state-of-the-art in music related analysis. Various approaches involving
machine learning, information theory, social network analysis, semantic web and
linked open data are represented in the form of taxonomy along with data
sources and use cases addressed by the research community.
Simple connectome inference from partial correlation statistics in calcium imaging
Authors: Antonio Sutera, Arnaud Joly, Vincent François-Lavet, Zixiao Aaron Qiu, Gilles Louppe, Damien Ernst, Pierre Geurts
Date: 2014-06-30 00:00:00

In this work, we propose a simple yet effective solution to the problem of
connectome inference in calcium imaging data. The proposed algorithm consists
of two steps. First, processing the raw signals to detect neural peak
activities. Second, inferring the degree of association between neurons from
partial correlation statistics. This paper summarises the methodology that led
us to win the Connectomics Challenge, proposes a simplified version of our
method, and finally compares our results with respect to other inference
methods.
The NLMS algorithm with time-variant optimum stepsize derived from a Bayesian network perspective
Authors: Christian Huemmer, Roland Maas, Walter Kellermann
Date: 2014-11-18 00:00:00
Tasks: Acoustic echo cancellation

In this article, we derive a new stepsize adaptation for the normalized least
mean square algorithm (NLMS) by describing the task of linear acoustic echo
cancellation from a Bayesian network perspective. Similar to the well-known
Kalman filter equations, we model the acoustic wave propagation from the
loudspeaker to the microphone by a latent state vector and define a linear
observation equation (to model the relation between the state vector and the
observation) as well as a linear process equation (to model the temporal
progress of the state vector). Based on additional assumptions on the
statistics of the random variables in observation and process equation, we
apply the expectation-maximization (EM) algorithm to derive an NLMS-like filter
adaptation. By exploiting the conditional independence rules for Bayesian
networks, we reveal that the resulting EM-NLMS algorithm has a stepsize update
equivalent to the optimal-stepsize calculation proposed by Yamamoto and
Kitayama in 1982, which has been adopted in many textbooks. As main difference,
the instantaneous stepsize value is estimated in the M step of the EM algorithm
(instead of being approximated by artificially extending the acoustic echo
path). The EM-NLMS algorithm is experimentally verified for synthesized
scenarios with both, white noise and male speech as input signal.
Cognitive Systems and Question Answering
Authors: Ulrich Furbach, Claudia Schon, Frieder Stolzenburg
Date: 2014-11-18 00:00:00
Tasks: Question Answering

This paper briefly characterizes the field of cognitive computing. As an
exemplification, the field of natural language question answering is introduced
together with its specific challenges. A possibility to master these challenges
is illustrated by a detailed presentation of the LogAnswer system, which is a
successful representative of the field of natural language question answering.
Automated Reasoning in Deontic Logic
Authors: Ulrich Furbach, Claudia Schon, Frieder Stolzenburg
Date: 2014-11-18 00:00:00
Tasks: Legal Reasoning, Philosophy

Deontic logic is a very well researched branch of mathematical logic and
philosophy. Various kinds of deontic logics are discussed for different
application domains like argumentation theory, legal reasoning, and acts in
multi-agent systems. In this paper, we show how standard deontic logic can be
stepwise transformed into description logic and DL- clauses, such that it can
be processed by Hyper, a high performance theorem prover which uses a
hypertableau calculus. Two use cases, one from multi-agent research and one
from the development of normative system are investigated.
Outlier-Robust Convex Segmentation
Authors: Itamar Katz, Koby Crammer
Date: 2014-11-17 00:00:00
Tasks: Segmentation

We derive a convex optimization problem for the task of segmenting sequential
data, which explicitly treats presence of outliers. We describe two algorithms
for solving this problem, one exact and one a top-down novel approach, and we
derive a consistency results for the case of two segments and no outliers.
Robustness to outliers is evaluated on two real-world tasks related to speech
segmentation. Our algorithms outperform baseline segmentation algorithms.
Sparse Generalized Eigenvalue Problem via Smooth Optimization
Authors: Junxiao Song, Prabhu Babu, Daniel P. Palomar
Date: 2014-08-28 00:00:00

In this paper, we consider an $\ell_{0}$-norm penalized formulation of the
generalized eigenvalue problem (GEP), aimed at extracting the leading sparse
generalized eigenvector of a matrix pair. The formulation involves maximization
of a discontinuous nonconcave objective function over a nonconvex constraint
set, and is therefore computationally intractable. To tackle the problem, we
first approximate the $\ell_{0}$-norm by a continuous surrogate function. Then
an algorithm is developed via iteratively majorizing the surrogate function by
a quadratic separable function, which at each iteration reduces to a regular
generalized eigenvalue problem. A preconditioned steepest ascent algorithm for
finding the leading generalized eigenvector is provided. A systematic way based
on smoothing is proposed to deal with the "singularity issue" that arises when
a quadratic function is used to majorize the nondifferentiable surrogate
function. For sparse GEPs with special structure, algorithms that admit a
closed-form solution at every iteration are derived. Numerical experiments show
that the proposed algorithms match or outperform existing algorithms in terms
of computational complexity and support recovery.
Smoothed Gradients for Stochastic Variational Inference
Authors: Stephan Mandt, David Blei
Date: 2014-06-13 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Stochastic Optimization, Variational Inference

Stochastic variational inference (SVI) lets us scale up Bayesian computation
to massive data. It uses stochastic optimization to fit a variational
distribution, following easy-to-compute noisy natural gradients. As with most
traditional stochastic optimization methods, SVI takes precautions to use
unbiased stochastic gradients whose expectations are equal to the true
gradients. In this paper, we explore the idea of following biased stochastic
gradients in SVI. Our method replaces the natural gradient with a similarly
constructed vector that uses a fixed-window moving average of some of its
previous terms. We will demonstrate the many advantages of this technique.
First, its computational cost is the same as for SVI and storage requirements
only multiply by a constant factor. Second, it enjoys significant variance
reduction over the unbiased estimates, smaller bias than averaged gradients,
and leads to smaller mean-squared error against the full gradient. We test our
method on latent Dirichlet allocation with three large corpora.
Faithful Variable Screening for High-Dimensional Convex Regression
Authors: Min Xu, Minhua Chen, John Lafferty
Date: 2014-11-07 00:00:00
Tasks: Additive models, regression, Variable Selection, Vocal Bursts Intensity Prediction

We study the problem of variable selection in convex nonparametric
regression. Under the assumption that the true regression function is convex
and sparse, we develop a screening procedure to select a subset of variables
that contains the relevant variables. Our approach is a two-stage quadratic
programming method that estimates a sum of one-dimensional convex functions,
followed by one-dimensional concave regression fits on the residuals. In
contrast to previous methods for sparse additive models, the optimization is
finite dimensional and requires no tuning parameters for smoothness. Under
appropriate assumptions, we prove that the procedure is faithful in the
population setting, yielding no false negatives. We give a finite sample
statistical analysis, and introduce algorithms for efficiently carrying out the
required quadratic programs. The approach leads to computational and
statistical advantages over fitting a full model, and provides an effective,
practical approach to variable screening in convex regression.
Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function
Authors: Michael R. Ferrier
Date: 2014-11-18 00:00:00
Tasks: Bayesian Inference, Template Matching

A wide range of evidence points toward the existence of a common algorithm
underlying the processing of information throughout the cerebral cortex.
Several hypothesized features of this cortical algorithm are reviewed,
including sparse distributed representation, Bayesian inference, hierarchical
organization composed of alternating template matching and pooling layers,
temporal slowness and predictive coding. Hierarchical Temporal Memory (HTM) is
a family of learning algorithms and corresponding theories of cortical function
that embodies these principles. HTM has previously been applied mainly to
perceptual tasks typical of posterior cortex. In order to evaluate HTM as a
candidate model of cortical function, it is necessary also to investigate its
compatibility with the requirements of frontal cortical function. To this end,
a variety of models of frontal cortical function are reviewed and integrated,
to arrive at the hypothesis that frontal functions including attention, working
memory and action selection depend largely upon the same basic algorithms as do
posterior functions, with the notable additions of a mechanism for the active
maintenance of representations and of multiple cortico-striato-thalamo-cortical
loops that allow communication between regions of frontal cortex to be gated in
an adaptive manner. Computational models of this system are reviewed. Finally,
there is a discussion of how HTM can contribute to the understanding of frontal
cortical function, and of what the requirements of frontal cortical function
mean for the future development of HTM.
Structured Hough Voting for Vision-based Highway Border Detection
Authors: Zhiding Yu, Wende Zhang, B. V. K. Vijaya Kumar, Dan Levi
Date: 2014-11-18 00:00:00

We propose a vision-based highway border detection algorithm using structured
Hough voting. Our approach takes advantage of the geometric relationship
between highway road borders and highway lane markings. It uses a strategy
where a number of trained road border and lane marking detectors are triggered,
followed by Hough voting to generate corresponding detection of the border and
lane marking. Since the initially triggered detectors usually result in large
number of positives, conventional frame-wise Hough voting is not able to always
generate robust border and lane marking results. Therefore, we formulate this
problem as a joint detection-and-tracking problem under the structured Hough
voting model, where tracking refers to exploiting inter-frame structural
information to stabilize the detection results. Both qualitative and
quantitative evaluations show the superiority of the proposed structured Hough
voting model over a number of baseline methods.
Feedback Solution to Optimal Switching Problems with Switching Cost
Authors: Ali Heydari
Date: 2014-11-17 00:00:00

The problem of optimal switching between nonlinear autonomous subsystems is
investigated in this study where the objective is not only bringing the states
to close to the desired point, but also adjusting the switching pattern, in the
sense of penalizing switching occurrences and assigning different preferences
to utilization of different modes. The mode sequence is unspecified and a
switching cost term is used in the cost function for penalizing each switching.
It is shown that once a switching cost is incorporated, the optimal cost-to-go
function depends on the already active subsystem, i.e., the subsystem which was
engaged in the previous time step. Afterwards, an approximate dynamic
programming based method is developed which provides an approximation of the
optimal solution to the problem in a feedback form and for different initial
conditions. Finally, the performance of the method is analyzed through
numerical examples.
Can we build a conscious machine?
Authors: Dorian Aur
Date: 2014-11-17 00:00:00

The underlying physiological mechanisms of generating conscious states are
still unknown. To make progress on the problem of consciousness, we will need
to experimentally design a system that evolves in a similar way our brains do.
Recent experimental data show that the multiscale nature of the evolving human
brain can be implemented by reprogramming human cells. A hybrid system can be
designed to include an evolving brain equipped with digital computers that
maintain homeostasis and provide the right amount of nutrients and oxygen for
the brain growth. Shaping the structure of the evolving brain will be
progressively achieved by controlling spatial organization of various types of
cells. Following a specific program, the evolving brain can be trained using
substitutional reality to learn and experience live scenes. We already know
from neuroelectrodynamics that meaningful information in the brain is
electrically (wirelessly) read out and written fast in neurons and synapses at
the molecular (protein) level during the generation of action potentials and
synaptic activities. Since with training, meaningful information accumulates
and is electrically integrated in the brain, one can predict, that this gradual
process of training will trigger a tipping point for conscious experience to
emerge in the hybrid system.