pwc-archive/papers-with-abstracts huggingface dataset:
Multi-domain Dialog State Tracking using Recurrent Neural Networks
Authors: Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, Steve Young
Date: 2015-06-23 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: dialog state tracking

Dialog state tracking is a key component of many modern dialog systems, most
of which are designed with a single, well-defined domain in mind. This paper
shows that dialog data drawn from different dialog domains can be used to train
a general belief tracking model which can operate across all of these domains,
exhibiting superior performance to each of the domain-specific models. We
propose a training procedure which uses out-of-domain data to initialise belief
tracking models for entirely new domains. This procedure leads to improvements
in belief tracking performance regardless of the amount of in-domain data
available for training the model.
Segmentation of Three-dimensional Images with Parametric Active Surfaces and Topology Changes
Authors: Heike Benninghoff, Harald Garcke
Date: 2015-06-23 00:00:00
Tasks: Segmentation

In this paper, we introduce a novel parametric method for segmentation of
three-dimensional images. We consider a piecewise constant version of the
Mumford-Shah and the Chan-Vese functionals and perform a region-based
segmentation of 3D image data. An evolution law is derived from energy
minimization problems which push the surfaces to the boundaries of 3D objects
in the image. We propose a parametric scheme which describes the evolution of
parametric surfaces. An efficient finite element scheme is proposed for a
numerical approximation of the evolution equations. Since standard parametric
methods cannot handle topology changes automatically, an efficient method is
presented to detect, identify and perform changes in the topology of the
surfaces. One main focus of this paper are the algorithmic details to handle
topology changes like splitting and merging of surfaces and change of the genus
of a surface. Different artificial images are studied to demonstrate the
ability to detect the different types of topology changes. Finally, the
parametric method is applied to segmentation of medical 3D images.
Coercive functions from a topological viewpoint and properties of minimizing sets of convex functions appearing in image restoration
Authors: René Ciak
Date: 2015-06-23 00:00:00
Tasks: Image Restoration

Many tasks in image processing can be tackled by modeling an appropriate data
fidelity term $\Phi: \mathbb{R}^n \rightarrow \mathbb{R} \cup \{+\infty\}$ and
then solve one of the regularized minimization problems \begin{align*}
  &{}(P_{1,\tau}) \qquad \mathop{\rm argmin}_{x \in \mathbb R^n} \big\{ \Phi(x)
\;{\rm s.t.}\; \Psi(x) \leq \tau \big\} \\ &{}(P_{2,\lambda}) \qquad
\mathop{\rm argmin}_{x \in \mathbb R^n} \{ \Phi(x) + \lambda \Psi(x) \}, \;
\lambda > 0 \end{align*} with some function $\Psi: \mathbb{R}^n \rightarrow
\mathbb{R} \cup \{+\infty\}$ and a good choice of the parameter(s). Two tasks
arise naturally here: \begin{align*} {}& \text{1. Study the solver sets ${\rm
SOL}(P_{1,\tau})$ and
  ${\rm SOL}(P_{2,\lambda})$ of the minimization problems.} \\ {}& \text{2.
Ensure that the minimization problems have solutions.} \end{align*} This thesis
provides contributions to both tasks: Regarding the first task for a more
special setting we prove that there are intervals $(0,c)$ and $(0,d)$ such that
the setvalued curves \begin{align*}
  \tau \mapsto {}& {\rm SOL}(P_{1,\tau}), \; \tau \in (0,c) \\ {} \lambda
\mapsto {}& {\rm SOL}(P_{2,\lambda}), \; \lambda \in (0,d) \end{align*} are the
same, besides an order reversing parameter change $g: (0,c) \rightarrow (0,d)$.
Moreover we show that the solver sets are changing all the time while $\tau$
runs from $0$ to $c$ and $\lambda$ runs from $d$ to $0$.
  In the presence of lower semicontinuity the second task is done if we have
additionally coercivity. We regard lower semicontinuity and coercivity from a
topological point of view and develop a new technique for proving lower
semicontinuity plus coercivity.
  Dropping any lower semicontinuity assumption we also prove a theorem on the
coercivity of a sum of functions.
Scientific Discovery by Machine Intelligence: A New Avenue for Drug Research
Authors: Carlo A. Trugenberger
Date: 2015-06-23 00:00:00
Tasks: scientific discovery

The majority of big data is unstructured and of this majority the largest
chunk is text. While data mining techniques are well developed and standardized
for structured, numerical data, the realm of unstructured data is still largely
unexplored. The general focus lies on information extraction, which attempts to
retrieve known information from text. The Holy Grail, however is knowledge
discovery, where machines are expected to unearth entirely new facts and
relations that were not previously known by any human expert. Indeed,
understanding the meaning of text is often considered as one of the main
characteristics of human intelligence. The ultimate goal of semantic artificial
intelligence is to devise software that can understand the meaning of free
text, at least in the practical sense of providing new, actionable information
condensed out of a body of documents. As a stepping stone on the road to this
vision I will introduce a totally new approach to drug research, namely that of
identifying relevant information by employing a self-organizing semantic engine
to text mine large repositories of biomedical research papers, a technique
pioneered by Merck with the InfoCodex software. I will describe the methodology
and a first successful experiment for the discovery of new biomarkers and
phenotypes for diabetes and obesity on the basis of PubMed abstracts, public
clinical trials and Merck internal documents. The reported approach shows much
promise and has potential to impact fundamentally pharmaceutical research as a
way to shorten time-to-market of novel drugs, and for early recognition of dead
ends.
Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution
Authors: J. M. Portegies, R. H. J. Fick, G. R. Sanguinetti, S. P. L. Meesters, G. Girard, R. Duits
Date: 2015-06-23 00:00:00

We propose two strategies to improve the quality of tractography results
computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both
methods are based on the same PDE framework, defined in the coupled space of
positions and orientations, associated with a stochastic process describing the
enhancement of elongated structures while preserving crossing structures. In
the first method we use the enhancement PDE for contextual regularization of a
fiber orientation distribution (FOD) that is obtained on individual voxels from
high angular resolution diffusion imaging (HARDI) data via constrained
spherical deconvolution (CSD). Thereby we improve the FOD as input for
subsequent tractography. Secondly, we introduce the fiber to bundle coherence
(FBC), a measure for quantification of fiber alignment. The FBC is computed
from a tractography result using the same PDE framework and provides a
criterion for removing the spurious fibers. We validate the proposed
combination of CSD and enhancement on phantom data and on human data, acquired
with different scanning protocols. On the phantom data we find that PDE
enhancements improve both local metrics and global metrics of tractography
results, compared to CSD without enhancements. On the human data we show that
the enhancements allow for a better reconstruction of crossing fiber bundles
and they reduce the variability of the tractography output with respect to the
acquisition parameters. Finally, we show that both the enhancement of the FODs
and the use of the FBC measure on the tractography improve the stability with
respect to different stochastic realizations of probabilistic tractography.
This is shown in a clinical application: the reconstruction of the optic
radiation for epilepsy surgery planning.
R-CNN minus R
Authors: Karel Lenc, Andrea Vedaldi
Date: 2015-06-23 00:00:00
Tasks: object-detection, Object Detection, Region Proposal

Deep convolutional neural networks (CNNs) have had a major impact in most
areas of image understanding, including object category detection. In object
detection, methods such as R-CNN have obtained excellent results by integrating
CNNs with region proposal generation algorithms such as selective search. In
this paper, we investigate the role of proposal generation in CNN-based
detectors in order to determine whether it is a necessary modelling component,
carrying essential geometric information not contained in the CNN, or whether
it is merely a way of accelerating detection. We do so by designing and
evaluating a detector that uses a trivial region generation scheme, constant
for each image. Combined with SPP, this results in an excellent and fast
detector that does not require to process an image with algorithms other than
the CNN itself. We also streamline and simplify the training of CNN-based
detectors by integrating several learning steps in a single algorithm, as well
as by proposing a number of improvements that accelerate detection.
GEFCOM 2014 - Probabilistic Electricity Price Forecasting
Authors: Gergo Barta, Gyula Borbely, Gabor Nagy, Sandor Kazi, Tamas Henk
Date: 2015-06-23 00:00:00
Tasks: regression, Time Series, Time Series Analysis, Time Series Forecasting

Energy price forecasting is a relevant yet hard task in the field of
multi-step time series forecasting. In this paper we compare a well-known and
established method, ARMA with exogenous variables with a relatively new
technique Gradient Boosting Regression. The method was tested on data from
Global Energy Forecasting Competition 2014 with a year long rolling window
forecast. The results from the experiment reveal that a multi-model approach is
significantly better performing in terms of error metrics. Gradient Boosting
can deal with seasonality and auto-correlation out-of-the box and achieve lower
rate of normalized mean absolute error on real-world data.
Graphs in machine learning: an introduction
Authors: Pierre Latouche, Fabrice Rossi
Date: 2015-06-23 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Graph Classification, Graph Clustering

Graphs are commonly used to characterise interactions between objects of
interest. Because they are based on a straightforward formalism, they are used
in many scientific fields from computer science to historical sciences. In this
paper, we give an introduction to some methods relying on graphs for learning.
This includes both unsupervised and supervised methods. Unsupervised learning
algorithms usually aim at visualising graphs in latent spaces and/or clustering
the nodes. Both focus on extracting knowledge from graph topologies. While most
existing techniques are only applicable to static graphs, where edges do not
evolve through time, recent developments have shown that they could be extended
to deal with evolving networks. In a supervised context, one generally aims at
inferring labels or numerical values attached to nodes using both the graph
and, when they are available, node characteristics. Balancing the two sources
of information can be challenging, especially as they can disagree locally or
globally. In both contexts, supervised and un-supervised, data can be
relational (augmented with one or several global graphs) as described above, or
graph valued. In this latter case, each object of interest is given as a full
graph (possibly completed by other characteristics). In this context, natural
tasks include graph clustering (as in producing clusters of graphs rather than
clusters of nodes in a single graph), graph classification, etc. 1 Real
networks One of the first practical studies on graphs can be dated back to the
original work of Moreno [51] in the 30s. Since then, there has been a growing
interest in graph analysis associated with strong developments in the modelling
and the processing of these data. Graphs are now used in many scientific
fields. In Biology [54, 2, 7], for instance, metabolic networks can describe
pathways of biochemical reactions [41], while in social sciences networks are
used to represent relation ties between actors [66, 56, 36, 34]. Other examples
include powergrids [71] and the web [75]. Recently, networks have also been
considered in other areas such as geography [22] and history [59, 39]. In
machine learning, networks are seen as powerful tools to model problems in
order to extract information from data and for prediction purposes. This is the
object of this paper. For more complete surveys, we refer to [28, 62, 49, 45].
In this section, we introduce notations and highlight properties shared by most
real networks. In Section 2, we then consider methods aiming at extracting
information from a unique network. We will particularly focus on clustering
methods where the goal is to find clusters of vertices. Finally, in Section 3,
techniques that take a series of networks into account, where each network is
Individual Biases, Cultural Evolution, and the Statistical Nature of Language Universals: The Case of Colour Naming Systems
Authors: Andrea Baronchelli, Vittorio Loreto, Andrea Puglisi
Date: 2013-10-29 00:00:00

Language universals have long been attributed to an innate Universal Grammar.
An alternative explanation states that linguistic universals emerged
independently in every language in response to shared cognitive or perceptual
biases. A computational model has recently shown how this could be the case,
focusing on the paradigmatic example of the universal properties of colour
naming patterns, and producing results in quantitative agreement with the
experimental data. Here we investigate the role of an individual perceptual
bias in the framework of the model. We study how, and to what extent, the
structure of the bias influences the corresponding linguistic universal
patterns. We show that the cultural history of a group of speakers introduces
population-specific constraints that act against the pressure for uniformity
arising from the individual bias, and we clarify the interplay between these
two forces.
Random Forest for the Contextual Bandit Problem - extended version
Authors: Raphaël Féraud, Robin Allesiardo, Tanguy Urvoy, Fabrice Clérot
Date: 2015-04-27 00:00:00

To address the contextual bandit problem, we propose an online random forest
algorithm. The analysis of the proposed algorithm is based on the sample
complexity needed to find the optimal decision stump. Then, the decision stumps
are assembled in a random collection of decision trees, Bandit Forest. We show
that the proposed algorithm is optimal up to logarithmic factors. The
dependence of the sample complexity upon the number of contextual variables is
logarithmic. The computational cost of the proposed algorithm with respect to
the time horizon is linear. These analytical results allow the proposed
algorithm to be efficient in real applications, where the number of events to
process is huge, and where we expect that some contextual variables, chosen
from a large set, have potentially non- linear dependencies with the rewards.
In the experiments done to illustrate the theoretical analysis, Bandit Forest
obtain promising results in comparison with state-of-the-art algorithms.
Prefix-Projection Global Constraint for Sequential Pattern Mining
Authors: Amina Kemmar, Samir Loudni, Yahia Lebbah, Patrice Boizumault, Thierry Charnois
Date: 2015-04-29 00:00:00
Tasks: Sequential Pattern Mining

Sequential pattern mining under constraints is a challenging data mining
task. Many efficient ad hoc methods have been developed for mining sequential
patterns, but they are all suffering from a lack of genericity. Recent works
have investigated Constraint Programming (CP) methods, but they are not still
effective because of their encoding. In this paper, we propose a global
constraint based on the projected databases principle which remedies to this
drawback. Experiments show that our approach clearly outperforms CP approaches
and competes well with ad hoc methods on large datasets.
Differentially Private Distributed Online Learning
Authors: Chencheng Li, Pan Zhou
Date: 2015-05-25 00:00:00

Online learning has been in the spotlight from the machine learning society
for a long time. To handle massive data in Big Data era, one single learner
could never efficiently finish this heavy task. Hence, in this paper, we
propose a novel distributed online learning algorithm to solve the problem.
Comparing to typical centralized online learner, the distributed learners
optimize their own learning parameters based on local data sources and timely
communicate with neighbors. However, communication may lead to a privacy
breach. Thus, we use differential privacy to preserve the privacy of learners,
and study the influence of guaranteeing differential privacy on the utility of
the distributed online learning algorithm. Furthermore, by using the results
from Kakade and Tewari (2009), we use the regret bounds of online learning to
achieve fast convergence rates for offline learning algorithms in distributed
scenarios, which provides tighter utility performance than the existing
state-of-the-art results. In simulation, we demonstrate that the differentially
private offline learning algorithm has high variance, but we can use mini-batch
to improve the performance. Finally, the simulations show that the analytical
results of our proposed theorems are right and our private distributed online
learning algorithm is a general framework.
Person re-identification via efficient inference in fully connected CRF
Authors: Jiuqing Wan, Menglin Xing
Date: 2015-06-23 00:00:00
Tasks: Person Re-Identification

In this paper, we address the problem of person re-identification problem,
i.e., retrieving instances from gallery which are generated by the same person
as the given probe image. This is very challenging because the person's
appearance usually undergoes significant variations due to changes in
illumination, camera angle and view, background clutter, and occlusion over the
camera network. In this paper, we assume that the matched gallery images should
not only be similar to the probe, but also be similar to each other, under
suitable metric. We express this assumption with a fully connected CRF model in
which each node corresponds to a gallery and every pair of nodes are connected
by an edge. A label variable is associated with each node to indicate whether
the corresponding image is from target person. We define unary potential for
each node using existing feature calculation and matching techniques, which
reflect the similarity between probe and gallery image, and define pairwise
potential for each edge in terms of a weighed combination of Gaussian kernels,
which encode appearance similarity between pair of gallery images. The specific
form of pairwise potential allows us to exploit an efficient inference
algorithm to calculate the marginal distribution of each label variable for
this dense connected CRF. We show the superiority of our method by applying it
to public datasets and comparing with the state of the art.
New Approach to translation of Isolated Units in English-Korean Machine Translation
Authors: Kim Song Jon, An Hae Gum
Date: 2015-06-23 00:00:00
Tasks: Machine Translation, Translation

It is the most effective way for quick translation of tremendous amount of
explosively increasing science and technique information material to develop a
practicable machine translation system and introduce it into translation
practice. This essay treats problems arising from translation of isolated units
on the basis of the practical materials and experiments obtained in the
development and introduction of English-Korean machine translation system. In
other words, this essay considers establishment of information for isolated
units and their Korean equivalents and word order.
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Authors: Xavier Alameda-Pineda, Jacopo Staiano, Ramanathan Subramanian, Ligia Batrinca, Elisa Ricci, Bruno Lepri, Oswald Lanz, Nicu Sebe
Date: 2015-06-23 00:00:00

Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.
Automatic vehicle tracking and recognition from aerial image sequences
Authors: Ognjen Arandjelovic
Date: 2015-06-23 00:00:00

This paper addresses the problem of automated vehicle tracking and
recognition from aerial image sequences. Motivated by its successes in the
existing literature focus on the use of linear appearance subspaces to describe
multi-view object appearance and highlight the challenges involved in their
application as a part of a practical system. A working solution which includes
steps for data extraction and normalization is described. In experiments on
real-world data the proposed methodology achieved promising results with a high
correct recognition rate and few, meaningful errors (type II errors whereby
genuinely similar targets are sometimes being confused with one another).
Directions for future research and possible improvements of the proposed method
are discussed.
Autonomous 3D Reconstruction Using a MAV
Authors: Alexander Popov, Dimitrios Zermas, Nikolaos Papanikolopoulos
Date: 2015-06-23 00:00:00
Tasks: 3D Reconstruction

An approach is proposed for high resolution 3D reconstruction of an object
using a Micro Air Vehicle (MAV). A system is described which autonomously
captures images and performs a dense 3D reconstruction via structure from
motion with no prior knowledge of the environment. Only the MAVs own sensors,
the front facing camera and the Inertial Measurement Unit (IMU) are utilized.
Precision agriculture is considered as an example application for the system.