pwc-archive/papers-with-abstracts huggingface dataset:
Underestimated cost of targeted attacks on complex networks
Authors: Xiao-Long Ren, Niels Gleinig, Dijana Tolic, Nino Antulov-Fantulin
Date: 2017-10-10 00:00:00

The robustness of complex networks under targeted attacks is deeply connected
to the resilience of complex systems, i.e., the ability to make appropriate
responses to the attacks. In this article, we investigated the state-of-the-art
targeted node attack algorithms and demonstrate that they become very
inefficient when the cost of the attack is taken into consideration. In this
paper, we made explicit assumption that the cost of removing a node is
proportional to the number of adjacent links that are removed, i.e., higher
degree nodes have higher cost. Finally, for the case when it is possible to
attack links, we propose a simple and efficient edge removal strategy named
Hierarchical Power Iterative Normalized cut (HPI-Ncut).The results on real and
artificial networks show that the HPI-Ncut algorithm outperforms all the node
removal and link removal attack algorithms when the cost of the attack is taken
into consideration. In addition, we show that on sparse networks, the
complexity of this hierarchical power iteration edge removal algorithm is only
$O(n\log^{2+\epsilon}(n))$.
An Analysis of Dropout for Matrix Factorization
Authors: Jacopo Cavazza, Connor Lane, Benjamin D. Haeffele, Vittorio Murino, René Vidal
Date: 2017-10-10 00:00:00

Dropout is a simple yet effective algorithm for regularizing neural networks
by randomly dropping out units through Bernoulli multiplicative noise, and for
some restricted problem classes, such as linear or logistic regression, several
theoretical studies have demonstrated the equivalence between dropout and a
fully deterministic optimization problem with data-dependent Tikhonov
regularization. This work presents a theoretical analysis of dropout for matrix
factorization, where Bernoulli random variables are used to drop a factor,
thereby attempting to control the size of the factorization. While recent work
has demonstrated the empirical effectiveness of dropout for matrix
factorization, a theoretical understanding of the regularization properties of
dropout in this context remains elusive. This work demonstrates the equivalence
between dropout and a fully deterministic model for matrix factorization in
which the factors are regularized by the sum of the product of the norms of the
columns. While the resulting regularizer is closely related to a variational
form of the nuclear norm, suggesting that dropout may limit the size of the
factorization, we show that it is possible to trivially lower the objective
value by doubling the size of the factorization. We show that this problem is
caused by the use of a fixed dropout rate, which motivates the use of a rate
that increases with the size of the factorization. Synthetic experiments
validate our theoretical findings.
A Note on Nesting in Dyadic Deontic Logic
Authors: Agneau Belanyek, Davide Grossi, Wiebe van der Hoek
Date: 2017-10-10 00:00:00

The paper reports on some results concerning Aqvist's dyadic logic known as
system G, which is one of the most influential logics for reasoning with dyadic
obligations ("it ought to be the case that ... if it is the case that ...").
Although this logic has been known in the literature for a while, many of its
properties still await in-depth consideration. In this short paper we show:
that any formula in system G including nested modal operators is equivalent to
some formula with no nesting; that the universal modality introduced by Aqvist
in the first presentation of the system is definable in terms of the deontic
modality.
MoNoise: Modeling Noise Using a Modular Normalization System
Authors: Rob van der Goot, Gertjan van Noord
Date: 2017-10-10 00:00:00
Tasks: Lexical Normalization, Spelling Correction, Word Embeddings

We propose MoNoise: a normalization model focused on generalizability and
efficiency, it aims at being easily reusable and adaptable. Normalization is
the task of translating texts from a non- canonical domain to a more canonical
domain, in our case: from social media data to standard language. Our proposed
model is based on a modular candidate generation in which each module is
responsible for a different type of normalization action. The most important
generation modules are a spelling correction system and a word embeddings
module. Depending on the definition of the normalization task, a static lookup
list can be crucial for performance. We train a random forest classifier to
rank the candidates, which generalizes well to all different types of
normaliza- tion actions. Most features for the ranking originate from the
generation modules; besides these features, N-gram features prove to be an
important source of information. We show that MoNoise beats the
state-of-the-art on different normalization benchmarks for English and Dutch,
which all define the task of normalization slightly different.
DocEmul: a Toolkit to Generate Structured Historical Documents
Authors: Samuele Capobianco, Simone Marinai
Date: 2017-10-10 00:00:00
Tasks: Data Augmentation

We propose a toolkit to generate structured synthetic documents emulating the
actual document production process. Synthetic documents can be used to train
systems to perform document analysis tasks. In our case we address the record
counting task on handwritten structured collections containing a limited number
of examples. Using the DocEmul toolkit we can generate a larger dataset to
train a deep architecture to predict the number of records for each page. The
toolkit is able to generate synthetic collections and also perform data
augmentation to create a larger trainable dataset. It includes one method to
extract the page background from real pages which can be used as a substrate
where records can be written on the basis of variable structures and using
cursive fonts. Moreover, it is possible to extend the synthetic collection by
adding random noise, page rotations, and other visual variations. We performed
some experiments on two different handwritten collections using the toolkit to
generate synthetic data to train a Convolutional Neural Network able to count
the number of records in the real collections.
Learning to Generalize: Meta-Learning for Domain Generalization
Authors: Da Li, Yongxin Yang, Yi-Zhe Song, Timothy M. Hospedales
Date: 2017-10-10 00:00:00
Tasks: Domain Generalization, image-classification, Image Classification, Meta-Learning, Reinforcement Learning

Domain shift refers to the well known problem that a model trained in one
source domain performs poorly when applied to a target domain with different
statistics. {Domain Generalization} (DG) techniques attempt to alleviate this
issue by producing models which by design generalize well to novel testing
domains. We propose a novel {meta-learning} method for domain generalization.
Rather than designing a specific model that is robust to domain shift as in
most previous DG work, we propose a model agnostic training procedure for DG.
Our algorithm simulates train/test domain shift during training by synthesizing
virtual testing domains within each mini-batch. The meta-optimization objective
requires that steps to improve training domain performance should also improve
testing domain performance. This meta-learning procedure trains models with
good generalization ability to novel domains. We evaluate our method and
achieve state of the art results on a recent cross-domain image classification
benchmark, as well demonstrating its potential on two classic reinforcement
learning tasks.
Safe Semi-Supervised Learning of Sum-Product Networks
Authors: Martin Trapp, Tamas Madl, Robert Peharz, Franz Pernkopf, Robert Trappl
Date: 2017-10-10 00:00:00

In several domains obtaining class annotations is expensive while at the same
time unlabelled data are abundant. While most semi-supervised approaches
enforce restrictive assumptions on the data distribution, recent work has
managed to learn semi-supervised models in a non-restrictive regime. However,
so far such approaches have only been proposed for linear models. In this work,
we introduce semi-supervised parameter learning for Sum-Product Networks
(SPNs). SPNs are deep probabilistic models admitting inference in linear time
in number of network edges. Our approach has several advantages, as it (1)
allows generative and discriminative semi-supervised learning, (2) guarantees
that adding unlabelled data can increase, but not degrade, the performance
(safe), and (3) is computationally efficient and does not enforce restrictive
assumptions on the data distribution. We show on a variety of data sets that
safe semi-supervised learning with SPNs is competitive compared to
state-of-the-art and can lead to a better generative and discriminative
objective value than a purely supervised approach.
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
Authors: Christian F. Baumgartner, Lisa M. Koch, Marc Pollefeys, Ender Konukoglu
Date: 2017-09-13 00:00:00
Tasks: GPU, Image Segmentation, Semantic Segmentation

Accurate segmentation of the heart is an important step towards evaluating
cardiac function. In this paper, we present a fully automated framework for
segmentation of the left (LV) and right (RV) ventricular cavities and the
myocardium (Myo) on short-axis cardiac MR images. We investigate various 2D and
3D convolutional neural network architectures for this task. We investigate the
suitability of various state-of-the art 2D and 3D convolutional neural network
architectures, as well as slight modifications thereof, for this task.
Experiments were performed on the ACDC 2017 challenge training dataset
comprising cardiac MR images of 100 patients, where manual reference
segmentations were made available for end-diastolic (ED) and end-systolic (ES)
frames. We find that processing the images in a slice-by-slice fashion using 2D
networks is beneficial due to a relatively large slice thickness. However, the
exact network architecture only plays a minor role. We report mean Dice
coefficients of $0.950$ (LV), $0.893$ (RV), and $0.899$ (Myo), respectively
with an average evaluation time of 1.1 seconds per volume on a modern GPU.
Deep Semantic Abstractions of Everyday Human Activities: On Commonsense Representations of Human Interactions
Authors: Jakob Suchan, Mehul Bhatt
Date: 2017-10-10 00:00:00
Tasks: Human-Object Interaction Detection, Object, Relational Reasoning

We propose a deep semantic characterization of space and motion categorically
from the viewpoint of grounding embodied human-object interactions. Our key
focus is on an ontological model that would be adept to formalisation from the
viewpoint of commonsense knowledge representation, relational learning, and
qualitative reasoning about space and motion in cognitive robotics settings. We
demonstrate key aspects of the space & motion ontology and its formalization as
a representational framework in the backdrop of select examples from a dataset
of everyday activities. Furthermore, focussing on human-object interaction data
obtained from RGBD sensors, we also illustrate how declarative
(spatio-temporal) reasoning in the (constraint) logic programming family may be
performed with the developed deep semantic abstractions.
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition
Authors: Chun Yang, Xu-Cheng Yin, Zejun Li, Jianwei Wu, Chunchao Guo, Hongfa Wang, Lei Xiao
Date: 2017-10-10 00:00:00
Tasks: Diversity, Scene Text Recognition

Recognizing text in the wild is a really challenging task because of complex
backgrounds, various illuminations and diverse distortions, even with deep
neural networks (convolutional neural networks and recurrent neural networks).
In the end-to-end training procedure for scene text recognition, the outputs of
deep neural networks at different iterations are always demonstrated with
diversity and complementarity for the target object (text). Here, a simple but
effective deep learning method, an adaptive ensemble of deep neural networks
(AdaDNNs), is proposed to simply select and adaptively combine classifier
components at different iterations from the whole learning system. Furthermore,
the ensemble is formulated as a Bayesian framework for classifier weighting and
combination. A variety of experiments on several typical acknowledged
benchmarks, i.e., ICDAR Robust Reading Competition (Challenge 1, 2 and 4)
datasets, verify the surprised improvement from the baseline DNNs, and the
effectiveness of AdaDNNs compared with the recent state-of-the-art methods.
Network of Recurrent Neural Networks
Authors: Chao-Ming Wang
Date: 2017-10-10 00:00:00

We describe a class of systems theory based neural networks called "Network
Of Recurrent neural networks" (NOR), which introduces a new structure level to
RNN related models. In NOR, RNNs are viewed as the high-level neurons and are
used to build the high-level layers. More specifically, we propose several
methodologies to design different NOR topologies according to the theory of
system evolution. Then we carry experiments on three different tasks to
evaluate our implementations. Experimental results show our models outperform
simple RNN remarkably under the same number of parameters, and sometimes
achieve even better results than GRU and LSTM.
Are crossing dependencies really scarce?
Authors: Ramon Ferrer-i-Cancho, Carlos Gomez-Rodriguez, J. L. Esteban
Date: 2017-03-24 00:00:00
Tasks: Sentence

The syntactic structure of a sentence can be modelled as a tree, where
vertices correspond to words and edges indicate syntactic dependencies. It has
been claimed recurrently that the number of edge crossings in real sentences is
small. However, a baseline or null hypothesis has been lacking. Here we
quantify the amount of crossings of real sentences and compare it to the
predictions of a series of baselines. We conclude that crossings are really
scarce in real sentences. Their scarcity is unexpected by the hubiness of the
trees. Indeed, real sentences are close to linear trees, where the potential
number of crossings is maximized.
ACCBench: A Framework for Comparing Causality Algorithms
Authors: Simon Rehwald, Amjad Ibrahim, Kristian Beckers, Alexander Pretschner
Date: 2017-10-10 00:00:00

Modern socio-technical systems are increasingly complex. A fundamental
problem is that the borders of such systems are often not well-defined
a-priori, which among other problems can lead to unwanted behavior during
runtime. Ideally, unwanted behavior should be prevented. If this is not
possible the system shall at least be able to help determine potential cause(s)
a-posterori, identify responsible parties and make them accountable for their
behavior. Recently, several algorithms addressing these concepts have been
proposed. However, the applicability of the corresponding approaches,
specifically their effectiveness and performance, is mostly unknown. Therefore,
in this paper, we propose ACCBench, a benchmark tool that allows to compare and
evaluate causality algorithms under a consistent setting. Furthermore, we
contribute an implementation of the two causality algorithms by G\"o{\ss}ler
and Metayer and G\"o{\ss}ler and Astefanoaei as well as of a policy compliance
approach based on some concepts of Main et al. Lastly, we conduct a case study
of an Intelligent Door Control System, which exposes concrete strengths and
weaknesses of all algorithms under different aspects. In the course of this, we
show that the effectiveness of the algorithms in terms of cause detection as
well as their performance differ to some extent. In addition, our analysis
reports on some qualitative aspects that should be considered when evaluating
each algorithm. For example, the human effort needed to configure the algorithm
and model the use case is analyzed.
Multi-Kernel LS-SVM Based Bio-Clinical Data Integration: Applications to Ovarian Cancer
Authors: Jaya Thomas, Lee Sael
Date: 2017-04-10 00:00:00
Tasks: Data Integration

The medical research facilitates to acquire a diverse type of data from the
same individual for particular cancer. Recent studies show that utilizing such
diverse data results in more accurate predictions. The major challenge faced is
how to utilize such diverse data sets in an effective way. In this paper, we
introduce a multiple kernel based pipeline for integrative analysis of
high-throughput molecular data (somatic mutation, copy number alteration, DNA
methylation and mRNA) and clinical data. We apply the pipeline on Ovarian
cancer data from TCGA. After multiple kernels have been generated from the
weighted sum of individual kernels, it is used to stratify patients and predict
clinical outcomes. We examine the survival time, vital status, and neoplasm
cancer status of each subtype to verify how well they cluster. We have also
examined the power of molecular and clinical data in predicting dichotomized
overall survival data and to classify the tumor grade for the cancer samples.
It was observed that the integration of various data types yields higher
log-rank statistics value. We were also able to predict clinical status with
higher accuracy as compared to using individual data types.
Prior Knowledge based mutation prioritization towards causal variant finding in rare disease
Authors: Vasundhara Dehiya, Jaya Thomas, Lee Sael
Date: 2017-10-10 00:00:00

How do we determine the mutational effects in exome sequencing data with
little or no statistical evidence? Can protein structural information fill in
the gap of not having enough statistical evidence? In this work, we answer the
two questions with the goal towards determining pathogenic effects of rare
variants in rare disease. We take the approach of determining the importance of
point mutation loci focusing on protein structure features. The proposed
structure-based features contain information about geometric, physicochemical,
and functional information of mutation loci and those of structural neighbors
of the loci. The performance of the structure-based features trained on 80\% of
HumDiv and tested on 20\% of HumDiv and on ClinVar datasets showed high levels
of discernibility in the mutation's pathogenic or benign effects: F score of
0.71 and 0.68 respectively using multi-layer perceptron. Combining structure-
and sequence-based feature further improve the accuracy: F score of 0.86
(HumDiv) and 0.75 (ClinVar). Also, careful examination of the rare variants in
rare diseases cases showed that structure-based features are important in
discerning importance of variant loci.
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks
Authors: Julian Faraone, Nicholas Fraser, Giulio Gambardella, Michaela Blott, Philip H. W. Leong
Date: 2017-09-19 00:00:00
Tasks: L2 Regularization, Model Compression, Quantization

A low precision deep neural network training technique for producing sparse,
ternary neural networks is presented. The technique incorporates hard- ware
implementation costs during training to achieve significant model compression
for inference. Training involves three stages: network training using L2
regularization and a quantization threshold regularizer, quantization pruning,
and finally retraining. Resulting networks achieve improved accuracy, reduced
memory footprint and reduced computational complexity compared with
conventional methods, on MNIST and CIFAR10 datasets. Our networks are up to 98%
sparse and 5 & 11 times smaller than equivalent binary and ternary models,
translating to significant resource and speed benefits for hardware
implementations.
Counterfactual Causality from First Principles?
Authors: Gregor Gössler, Oleg Sokolsky, Jean-Bernard Stefani
Date: 2017-10-10 00:00:00
Tasks: counterfactual, Position

In this position paper we discuss three main shortcomings of existing
approaches to counterfactual causality from the computer science perspective,
and sketch lines of work to try and overcome these issues: (1) causality
definitions should be driven by a set of precisely specified requirements
rather than specific examples; (2) causality frameworks should support system
dynamics; (3) causality analysis should have a well-understood behavior in
presence of abstraction.
Causality and Temporal Dependencies in the Design of Fault Management Systems
Authors: Marco Bozzano
Date: 2017-10-10 00:00:00
Tasks: Fault Detection, Management

Reasoning about causes and effects naturally arises in the engineering of
safety-critical systems. A classical example is Fault Tree Analysis, a
deductive technique used for system safety assessment, whereby an undesired
state is reduced to the set of its immediate causes. The design of fault
management systems also requires reasoning on causality relationships. In
particular, a fail-operational system needs to ensure timely detection and
identification of faults, i.e. recognize the occurrence of run-time faults
through their observable effects on the system. Even more complex scenarios
arise when multiple faults are involved and may interact in subtle ways.
  In this work, we propose a formal approach to fault management for complex
systems. We first introduce the notions of fault tree and minimal cut sets. We
then present a formal framework for the specification and analysis of
diagnosability, and for the design of fault detection and identification (FDI)
components. Finally, we review recent advances in fault propagation analysis,
based on the Timed Failure Propagation Graphs (TFPG) formalism.
On Preemption and Overdetermination in Formal Theories of Causality
Authors: Sjur K Dyrkolbotn
Date: 2017-10-10 00:00:00
Tasks: counterfactual, Counterfactual Reasoning, Philosophy

One of the key challenges when looking for the causes of a complex event is
to determine the causal status of factors that are neither individually
necessary nor individually sufficient to produce that event. In order to reason
about how such factors should be taken into account, we need a vocabulary to
distinguish different cases. In philosophy, the concept of overdetermination
and the concept of preemption serve an important purpose in this regard,
although their exact meaning tends to remain elusive. In this paper, I provide
theory-neutral definitions of these concepts using structural equations in the
Halpern-Pearl tradition. While my definitions do not presuppose any particular
causal theory, they take such a theory as a variable parameter. This enables us
to specify formal constraints on theories of causality, in terms of a
pre-theoretic understanding of what preemption and overdetermination actually
mean. I demonstrate the usefulness of this by presenting and arguing for what I
call the principle of presumption. Roughly speaking, this principle states that
a possible cause can only be regarded as having been preempted if there is
independent evidence to support such an inference. I conclude by showing that
the principle of presumption is violated by the two main theories of causality
formulated in the Halpern-Pearl tradition. The paper concludes by defining the
class of empirical causal theories, characterised in terms of a fixed-point of
counterfactual reasoning about difference-making. It is argued that theories of
actual causality ought to be empirical.