pwc-archive/papers-with-abstracts huggingface dataset:
Multiple Combined Constraints for Image Stitching
Authors: Kai Chen, Jingmin Tu, Binbin Xiang, Li Li, Jian Yao
Date: 2018-09-18 00:00:00
Tasks: Image Stitching

Several approaches to image stitching use different constraints to estimate
the motion model between image pairs. These constraints can be roughly divided
into two categories: geometric constraints and photometric constraints. In this
paper, geometric and photometric constraints are combined to improve the
alignment quality, which is based on the observation that these two kinds of
constraints are complementary. On the one hand, geometric constraints (e.g.,
point and line correspondences) are usually spatially biased and are
insufficient in some extreme scenes, while photometric constraints are always
evenly and densely distributed. On the other hand, photometric constraints are
sensitive to displacements and are not suitable for images with large
parallaxes, while geometric constraints are usually imposed by feature matching
and are more robust to handle parallaxes. The proposed method therefore
combines them together in an efficient mesh-based image warping framework. It
achieves better alignment quality than methods only with geometric constraints,
and can handle larger parallax than photometric-constraint-based method.
Experimental results on various images illustrate that the proposed method
outperforms representative state-of-the-art image stitching methods reported in
the literature.
Is rotation forest the best classifier for problems with continuous features?
Authors: A. Bagnall, M. Flynn, J. Large, J. Line, A. Bostrom, G. Cawley
Date: 2018-09-18 00:00:00

In short, our experiments suggest that yes, on average, rotation forest is better than the most common alternatives when all the attributes are real-valued. Rotation forest is a tree based ensemble that performs transforms on subsets of attributes prior to constructing each tree. We present an empirical comparison of classifiers for problems with only real-valued features. We evaluate classifiers from three families of algorithms: support vector machines; tree-based ensembles; and neural networks tuned with a large grid search. We compare classifiers on unseen data based on the quality of the decision rule (using classification error) the ability to rank cases (area under the receiver operating characteristic) and the probability estimates (using negative log likelihood). We conclude that, in answer to the question posed in the title, yes, rotation forest is significantly more accurate on average than competing techniques when compared on three distinct sets of datasets. Further, we assess the impact of the design features of rotation forest through an ablative study that transforms random forest into rotation forest. We identify the major limitation of rotation forest as its scalability, particularly in number of attributes. To overcome this problem we develop a model to predict the train time of the algorithm and hence propose a contract version of rotation forest where a run time cap is imposed {\em a priori}. We demonstrate that on large problems rotation forest can be made an order of magnitude faster without significant loss of accuracy. We also show that there is no real benefit (on average) from tuning rotation forest. We maintain that without any domain knowledge to indicate an algorithm preference, rotation forest should be the default algorithm of choice for problems with continuous attributes.
Adding Cues to Binary Feature Descriptors for Visual Place Recognition
Authors: Dominik Schlegel, Giorgio Grisetti
Date: 2018-09-18 00:00:00
Tasks: Image Retrieval, Retrieval, Visual Place Recognition

In this paper we propose an approach to embed continuous and selector cues in
binary feature descriptors used for visual place recognition. The embedding is
achieved by extending each feature descriptor with a binary string that encodes
a cue and supports the Hamming distance metric. Augmenting the descriptors in
such a way has the advantage of being transparent to the procedure used to
compare them. We present two concrete applications of our methodology,
demonstrating the two considered types of cues. In addition to that, we
conducted on these applications a broad quantitative and comparative evaluation
covering five benchmark datasets and several state-of-the-art image retrieval
approaches in combination with various binary descriptor types.
RumourEval 2019: Determining Rumour Veracity and Support for Rumours
Authors: Genevieve Gorrell, Kalina Bontcheva, Leon Derczynski, Elena Kochkina, Maria Liakata, Arkaitz Zubiaga
Date: 2018-09-18 00:00:00
Tasks: Rumour Detection

This is the proposal for RumourEval-2019, which will run in early 2019 as
part of that year's SemEval event. Since the first RumourEval shared task in
2017, interest in automated claim validation has greatly increased, as the
dangers of "fake news" have become a mainstream concern. Yet automated support
for rumour checking remains in its infancy. For this reason, it is important
that a shared task in this area continues to provide a focus for effort, which
is likely to increase. We therefore propose a continuation in which the
veracity of further rumours is determined, and as previously, supportive of
this goal, tweets discussing them are classified according to the stance they
take regarding the rumour. Scope is extended compared with the first
RumourEval, in that the dataset is substantially expanded to include Reddit as
well as Twitter data, and additional languages are also included.
Estimating Bayesian Optimal Treatment Regimes for Dichotomous Outcomes using Observational Data
Authors: Thomas Klausch, Peter van de Ven, Tim van de Brug, Mark A. van de Wiel, Johannes Berkhof
Date: 2018-09-18 00:00:00
Tasks: Bayesian Inference

Optimal treatment regimes (OTR) are individualised treatment assignment
strategies that identify a medical treatment as optimal given all background
information available on the individual. We discuss Bayes optimal treatment
regimes estimated using a loss function defined on the bivariate distribution
of dichotomous potential outcomes. The proposed approach allows considering
more general objectives for the OTR than maximization of an expected outcome
(e.g., survival probability) by taking into account, for example, unnecessary
treatment burden. As a motivating example we consider the case of oropharynx
cancer treatment where unnecessary burden due to chemotherapy is to be avoided
while maximizing survival chances. Assuming ignorable treatment assignment we
describe Bayesian inference about the OTR including a sensitivity analysis on
the unobserved partial association of the potential outcomes. We evaluate the
methodology by simulations that apply Bayesian parametric and more flexible
non-parametric outcome models. The proposed OTR for oropharynx cancer reduces
the frequency of the more burdensome chemotherapy assignment by approximately
75% without reducing the average survival probability. This regime thus offers
a strong increase in expected quality of life of patients.
Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction
Authors: Philipp-Immanuel Schneider, Xavier Garcia Santiago, Victor Soltwisch, Martin Hammerschmidt, Sven Burger, Carsten Rockstuhl
Date: 2018-09-18 00:00:00
Tasks: Bayesian Optimization, Benchmarking, global-optimization

Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication technologies for which a rational design is no longer feasible. Also, numerical resources are available to enable the computational photonic material design and to identify structures that meet predefined optical properties for specific applications. However, the optimization objective function is in general non-convex and its computation remains resource demanding such that the right choice for the optimization method is crucial to obtain excellent results. Here, we benchmark five global optimization methods for three typical nano-optical optimization problems: \removed{downhill simplex optimization, the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, particle swarm optimization, differential evolution, and Bayesian optimization} \added{particle swarm optimization, differential evolution, and Bayesian optimization as well as multi-start versions of downhill simplex optimization and the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm}. In the shown examples from the field of shape optimization and parameter reconstruction, Bayesian optimization, mainly known from machine learning applications, obtains significantly better results in a fraction of the run times of the other optimization methods.
Compressed Sensing Parallel MRI with Adaptive Shrinkage TV Regularization
Authors: Raji Susan Mathew, Joseph Suresh Paul
Date: 2018-09-18 00:00:00
Tasks: compressed sensing

Compressed sensing (CS) methods in magnetic resonance imaging (MRI) offer
rapid acquisition and improved image quality but require iterative
reconstruction schemes with regularization to enforce sparsity. Regardless of
the difficulty in obtaining a fast numerical solution, the total variation (TV)
regularization is a preferred choice due to its edge-preserving and structure
recovery capabilities. While many approaches have been proposed to overcome the
non-differentiability of the TV cost term, an iterative shrinkage based
formulation allows recovering an image through recursive application of linear
filtering and soft thresholding. However, providing an optimal setting for the
regularization parameter is critical due to its direct impact on the rate of
convergence as well as steady state error. In this paper, a regularizer
adaptively varying in the derivative space is proposed, that follows the
generalized discrepancy principle (GDP). The implementation proceeds by
adaptively reducing the discrepancy level expressed as the absolute difference
between TV norms of the consistency error and the sparse approximation error. A
criterion based on the absolute difference between TV norms of consistency and
sparse approximation errors is used to update the threshold. Application of the
adaptive shrinkage TV regularizer to CS recovery of parallel MRI (pMRI) and
temporal gradient adaptation in dynamic MRI are shown to result in improved
image quality with accelerated convergence. In addition, the adaptive TV-based
iterative shrinkage (ATVIS) provides a significant speed advantage over the
fast iterative shrinkage-thresholding algorithm (FISTA).
A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshes
Authors: Isaak Lim, Alexander Dielen, Marcel Campen, Leif Kobbelt
Date: 2018-09-18 00:00:00
Tasks: 3D geometry

The question of representation of 3D geometry is of vital importance when it
comes to leveraging the recent advances in the field of machine learning for
geometry processing tasks. For common unstructured surface meshes
state-of-the-art methods rely on patch-based or mapping-based techniques that
introduce resampling operations in order to encode neighborhood information in
a structured and regular manner. We investigate whether such resampling can be
avoided, and propose a simple and direct encoding approach. It does not only
increase processing efficiency due to its simplicity - its direct nature also
avoids any loss in data fidelity. To evaluate the proposed method, we perform a
number of experiments in the challenging domain of intrinsic, non-rigid shape
correspondence estimation. In comparisons to current methods we observe that
our approach is able to achieve highly competitive results.
Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths Counting in Trap Images
Authors: Mohamed Chafik Bakkay, Sylvie Chambon, Hatem A. Rashwan, Christian Lubat, Sébastien Barsotti
Date: 2018-09-18 00:00:00
Tasks: Classification, General Classification

This paper aims at developing an automatic algorithm for moth recognition
from trap images in real-world conditions. This method uses our previous work
for detection [1] and introduces an adapted classification step. More
precisely, SVM classifier is trained with a multi-scale descriptor, Histogram
Of Curviness Saliency (HCS). This descriptor is robust to illumination changes
and is able to detect and to describe the external and the internal contours of
the target insect in multi-scale. The proposed classification method can be
trained with a small set of images. Quantitative evaluations show that the
proposed method is able to classify insects with higher accuracy (rate of
95.8%) than the state-of-the art approaches.
Bidirectional Attentional Encoder-Decoder Model and Bidirectional Beam Search for Abstractive Summarization
Authors: Kamal Al-Sabahi, Zhang Zuping, Yang Kang
Date: 2018-09-18 00:00:00
Tasks: Abstractive Text Summarization, Decoder, Document Summarization

Sequence generative models with RNN variants, such as LSTM, GRU, show
promising performance on abstractive document summarization. However, they
still have some issues that limit their performance, especially while deal-ing
with long sequences. One of the issues is that, to the best of our knowledge,
all current models employ a unidirectional decoder, which reasons only about
the past and still limited to retain future context while giving a prediction.
This makes these models suffer on their own by generating unbalanced outputs.
Moreover, unidirec-tional attention-based document summarization can only
capture partial aspects of attentional regularities due to the inherited
challenges in document summarization. To this end, we propose an end-to-end
trainable bidirectional RNN model to tackle the aforementioned issues. The
model has a bidirectional encoder-decoder architecture; in which the encoder
and the decoder are bidirectional LSTMs. The forward decoder is initialized
with the last hidden state of the backward encoder while the backward decoder
is initialized with the last hidden state of the for-ward encoder. In addition,
a bidirectional beam search mechanism is proposed as an approximate inference
algo-rithm for generating the output summaries from the bidi-rectional model.
This enables the model to reason about the past and future and to generate
balanced outputs as a result. Experimental results on CNN / Daily Mail dataset
show that the proposed model outperforms the current abstractive
state-of-the-art models by a considerable mar-gin.
Attribute-aware Face Aging with Wavelet-based Generative Adversarial Networks
Authors: Yunfan Liu, Qi Li, Zhenan Sun
Date: 2018-09-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute

Since it is difficult to collect face images of the same subject over a long
range of age span, most existing face aging methods resort to unpaired datasets
to learn age mappings. However, the matching ambiguity between young and aged
face images inherent to unpaired training data may lead to unnatural changes of
facial attributes during the aging process, which could not be solved by only
enforcing identity consistency like most existing studies do. In this paper, we
propose a attribute-aware face aging model with wavelet-based Generative
Adversarial Networks (GANs) to address the above issues. To be specific, we
embed facial attribute vectors into both generator and discriminator of the
model to encourage each synthesized elderly face image to be faithful to the
attribute of its corresponding input. In addition, a wavelet packet transform
(WPT) module is incorporated to improve the visual fidelity of generated images
by capturing age-related texture details at multiple scales in the frequency
space. Qualitative results demonstrate the ability of our model to synthesize
visually plausible face images, and extensive quantitative evaluation results
show that the proposed method achieves state-of-the-art performance on existing
datasets.
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement Learning
Authors: Johannes Dornheim, Norbert Link, Peter Gumbsch
Date: 2018-09-18 00:00:00
Tasks: Model Predictive Control, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Self-Learning

A self-learning optimal control algorithm for episodic fixed-horizon
manufacturing processes with time-discrete control actions is proposed and
evaluated on a simulated deep drawing process. The control model is built
during consecutive process executions under optimal control via reinforcement
learning, using the measured product quality as reward after each process
execution. Prior model formulation, which is required by state-of-the-art
algorithms from model predictive control and approximate dynamic programming,
is therefore obsolete. This avoids several difficulties namely in system
identification, accurate modelling, and runtime complexity, that arise when
dealing with processes subject to nonlinear dynamics and stochastic influences.
Instead of using pre-created process and observation models, value
function-based reinforcement learning algorithms build functions of expected
future reward, which are used to derive optimal process control decisions. The
expectation functions are learned online, by interacting with the process. The
proposed algorithm takes stochastic variations of the process conditions into
account and is able to cope with partial observability. A Q-learning-based
method for adaptive optimal control of partially observable episodic
fixed-horizon manufacturing processes is developed and studied. The resulting
algorithm is instantiated and evaluated by applying it to a simulated
stochastic optimal control problem in metal sheet deep drawing.
Talking to myself: self-dialogues as data for conversational agents
Authors: Joachim Fainberg, Ben Krause, Mihai Dobre, Marco Damonte, Emmanuel Kahembwe, Daniel Duma, Bonnie Webber, Federico Fancellu
Date: 2018-09-18 00:00:00

Conversational agents are gaining popularity with the increasing ubiquity of
smart devices. However, training agents in a data driven manner is challenging
due to a lack of suitable corpora. This paper presents a novel method for
gathering topical, unstructured conversational data in an efficient way:
self-dialogues through crowd-sourcing. Alongside this paper, we include a
corpus of 3.6 million words across 23 topics. We argue the utility of the
corpus by comparing self-dialogues with standard two-party conversations as
well as data from other corpora.
Lung Cancer Concept Annotation from Spanish Clinical Narratives
Authors: Marjan Najafabadipour, Juan Manuel Tuñas, Alejandro Rodríguez-González, Ernestina Menasalvas
Date: 2018-09-18 00:00:00
Tasks: Management

Recent rapid increase in the generation of clinical data and rapid
development of computational science make us able to extract new insights from
massive datasets in healthcare industry. Oncological clinical notes are
creating rich databases for documenting patients history and they potentially
contain lots of patterns that could help in better management of the disease.
However, these patterns are locked within free text (unstructured) portions of
clinical documents and consequence in limiting health professionals to extract
useful information from them and to finally perform Query and Answering (QA)
process in an accurate way. The Information Extraction (IE) process requires
Natural Language Processing (NLP) techniques to assign semantics to these
patterns. Therefore, in this paper, we analyze the design of annotators for
specific lung cancer concepts that can be integrated over Apache Unstructured
Information Management Architecture (UIMA) framework. In addition, we explain
the details of generation and storage of annotation outcomes.
Towards Abstraction in ASP with an Application on Reasoning about Agent Policies
Authors: Zeynep G. Saribatur, Thomas Eiter
Date: 2018-09-18 00:00:00

ASP programs are a convenient tool for problem solving, whereas with large
problem instances the size of the state space can be prohibitive. We consider
abstraction as a means of over-approximation and introduce a method to
automatically abstract (possibly non-ground) ASP programs that preserves their
structure, while reducing the size of the problem. One particular application
case is the problem of defining declarative policies for reactive agents and
reasoning about them, which we illustrate on examples.
Comparison between Suitable Priors for Additive Bayesian Networks
Authors: Gilles Kratzer, Reinhard Furrer, Marta Pittavino
Date: 2018-09-18 00:00:00
Tasks: Model Selection

Additive Bayesian networks are types of graphical models that extend the
usual Bayesian generalized linear model to multiple dependent variables through
the factorisation of the joint probability distribution of the underlying
variables. When fitting an ABN model, the choice of the prior of the parameters
is of crucial importance. If an inadequate prior - like a too weakly
informative one - is used, data separation and data sparsity lead to issues in
the model selection process. In this work a simulation study between two weakly
and a strongly informative priors is presented. As weakly informative prior we
use a zero mean Gaussian prior with a large variance, currently implemented in
the R-package abn. The second prior belongs to the Student's t-distribution,
specifically designed for logistic regressions and, finally, the strongly
informative prior is again Gaussian with mean equal to true parameter value and
a small variance. We compare the impact of these priors on the accuracy of the
learned additive Bayesian network in function of different parameters. We
create a simulation study to illustrate Lindley's paradox based on the prior
choice. We then conclude by highlighting the good performance of the
informative Student's t-prior and the limited impact of the Lindley's paradox.
Finally, suggestions for further developments are provided.
SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions
Authors: Chengwei Zhang, Xiaohong Li, Jianye Hao, Siqi Chen, Karl Tuyls, Zhiyong Feng, Wanli Xue, Rong Chen
Date: 2018-09-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Although many reinforcement learning methods have been proposed for learning
the optimal solutions in single-agent continuous-action domains, multiagent
coordination domains with continuous actions have received relatively few
investigations. In this paper, we propose an independent learner hierarchical
method, named Sample Continuous Coordination with recursive Frequency Maximum
Q-Value (SCC-rFMQ), which divides the cooperative problem with continuous
actions into two layers. The first layer samples a finite set of actions from
the continuous action spaces by a re-sampling mechanism with variable
exploratory rates, and the second layer evaluates the actions in the sampled
action set and updates the policy using a reinforcement learning cooperative
method. By constructing cooperative mechanisms at both levels, SCC-rFMQ can
handle cooperative problems in continuous action cooperative Markov games
effectively. The effectiveness of SCC-rFMQ is experimentally demonstrated on
two well-designed games, i.e., a continuous version of the climbing game and a
cooperative version of the boat problem. Experimental results show that
SCC-rFMQ outperforms other reinforcement learning algorithms.