pwc-archive/papers-with-abstracts huggingface dataset:
Identification of individual coherent sets associated with flow trajectories using Coherent Structure Coloring
Authors: Kristy L. Schlueter-Kuck, John O. Dabiri
Date: 2017-08-18 00:00:00
Tasks: Clustering

We present a method for identifying the coherent structures associated with
individual Lagrangian flow trajectories even where only sparse particle
trajectory data is available. The method, based on techniques in spectral graph
theory, uses the Coherent Structure Coloring vector and associated eigenvectors
to analyze the distance in higher-dimensional eigenspace between a selected
reference trajectory and other tracer trajectories in the flow. By analyzing
this distance metric in a hierarchical clustering, the coherent structure of
which the reference particle is a member can be identified. This algorithm is
proven successful in identifying coherent structures of varying complexities in
canonical unsteady flows. Additionally, the method is able to assess the
relative coherence of the associated structure in comparison to the surrounding
flow. Although the method is demonstrated here in the context of fluid flow
kinematics, the generality of the approach allows for its potential application
to other unsupervised clustering problems in dynamical systems such as neuronal
activity, gene expression, or social networks.
Software engineering and the SP Theory of Intelligence
Authors: J. Gerard Wolff
Date: 2017-08-18 00:00:00

This paper describes a novel approach to software engineering derived from
the "SP Theory of Intelligence" and its realisation in the "SP Computer Model".
Despite superficial appearances, it is shown that many of the key ideas in
software engineering have counterparts in the structure and workings of the SP
system. Potential benefits of this new approach to software engineering
include: the automation or semi-automation of software development, with
support for programming of the SP system where necessary; allowing programmers
to concentrate on 'world-oriented' parallelism, without worries about
parallelism to speed up processing; support for the long-term goal of
programming the SP system via written or spoken natural language; reducing or
eliminating the distinction between 'design' and 'implementation'; reducing or
eliminating operations like compiling or interpretation; reducing or
eliminating the need for verification of software; reducing the need for
validation of software; no formal distinction between program and database; the
potential for substantial reductions in the number of types of data file and
the number of computer languages; benefits for version control; and reducing
technical debt.
Security, Privacy and Safety Evaluation of Dynamic and Static Fleets of Drones
Authors: Raja Naeem Akram, Konstantinos Markantonakis, Keith Mayes, Oussama Habachi, Damien Sauveron, Andreas Steyven, Serge Chaumette
Date: 2017-08-18 00:00:00
Tasks: Management

Inter-connected objects, either via public or private networks are the near
future of modern societies. Such inter-connected objects are referred to as
Internet-of-Things (IoT) and/or Cyber-Physical Systems (CPS). One example of
such a system is based on Unmanned Aerial Vehicles (UAVs). The fleet of such
vehicles are prophesied to take on multiple roles involving mundane to
high-sensitive, such as, prompt pizza or shopping deliveries to your homes to
battlefield deployment for reconnaissance and combat missions. Drones, as we
refer to UAVs in this paper, either can operate individually (solo missions) or
part of a fleet (group missions), with and without constant connection with the
base station. The base station acts as the command centre to manage the
activities of the drones. However, an independent, localised and effective
fleet control is required, potentially based on swarm intelligence, for the
reasons: 1) increase in the number of drone fleets, 2) number of drones in a
fleet might be multiple of tens, 3) time-criticality in making decisions by
such fleets in the wild, 4) potential communication congestions/lag, and 5) in
some cases working in challenging terrains that hinders or mandates-limited
communication with control centre (i.e., operations spanning long period of
times or military usage of such fleets in enemy territory). This self-ware,
mission-focused and independent fleet of drones that potential utilises swarm
intelligence for a) air-traffic and/or flight control management, b) obstacle
avoidance, c) self-preservation while maintaining the mission criteria, d)
collaboration with other fleets in the wild (autonomously) and e) assuring the
security, privacy and safety of physical (drones itself) and virtual (data,
software) assets. In this paper, we investigate the challenges faced by fleet
of drones and propose a potential course of action on how to overcome them.
Neural machine translation for low-resource languages
Authors: Robert Östling, Jörg Tiedemann
Date: 2017-08-18 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Neural machine translation (NMT) approaches have improved the state of the
art in many machine translation settings over the last couple of years, but
they require large amounts of training data to produce sensible output. We
demonstrate that NMT can be used for low-resource languages as well, by
introducing more local dependencies and using word alignments to learn sentence
reordering during translation. In addition to our novel model, we also present
an empirical evaluation of low-resource phrase-based statistical machine
translation (SMT) and NMT to investigate the lower limits of the respective
technologies. We find that while SMT remains the best option for low-resource
settings, our method can produce acceptable translations with only 70000 tokens
of training data, a level where the baseline NMT system fails completely.
Cross-Lingual Dependency Parsing for Closely Related Languages - Helsinki's Submission to VarDial 2017
Authors: Jörg Tiedemann
Date: 2017-08-18 00:00:00
Tasks: Dependency Parsing, Translation

This paper describes the submission from the University of Helsinki to the
shared task on cross-lingual dependency parsing at VarDial 2017. We present
work on annotation projection and treebank translation that gave good results
for all three target languages in the test set. In particular, Slovak seems to
work well with information coming from the Czech treebank, which is in line
with related work. The attachment scores for cross-lingual models even surpass
the fully supervised models trained on the target language treebank. Croatian
is the most difficult language in the test set and the improvements over the
baseline are rather modest. Norwegian works best with information coming from
Swedish whereas Danish contributes surprisingly little.
Semantic Summarization of Egocentric Photo Stream Events
Authors: Aniol Lidon, Marc Bolaños, Mariella Dimiccoli, Petia Radeva, Maite Garolera, Xavier Giró-i-Nieto
Date: 2015-11-02 00:00:00
Tasks: Diversity, Image Retrieval, Retrieval

With the rapid increase of users of wearable cameras in recent years and of
the amount of data they produce, there is a strong need for automatic retrieval
and summarization techniques. This work addresses the problem of automatically
summarizing egocentric photo streams captured through a wearable camera by
taking an image retrieval perspective. After removing non-informative images by
a new CNN-based filter, images are ranked by relevance to ensure semantic
diversity and finally re-ranked by a novelty criterion to reduce redundancy. To
assess the results, a new evaluation metric is proposed which takes into
account the non-uniqueness of the solution. Experimental results applied on a
database of 7,110 images from 6 different subjects and evaluated by experts
gave 95.74% of experts satisfaction and a Mean Opinion Score of 4.57 out of
5.0. Source code is available at
https://github.com/imatge-upc/egocentric-2017-lta
A probabilistic approach to emission-line galaxy classification
Authors: R. S. de Souza, M. L. L. Dantas, M. V. Costa-Duarte, E. D. Feigelson, M. Killedar, P. -Y. Lablanche, R. Vilalta, A. Krone-Martins, R. Beck, F. Gieseke
Date: 2017-03-22 00:00:00
Tasks: Classification, General Classification

We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional
emission-line classification schemes of galaxy ionization sources: the
Baldwin-Phillips-Terlevich (BPT) and $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$
(WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey
Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically
define classes of galaxies in a three-dimensional space spanned by the $\log$
[OIII]/H$\beta$, $\log$ [NII]/H$\alpha$, and $\log$ EW(H${\alpha}$), optical
parameters. The best-fit GMM based on several statistical criteria suggests a
solution around four Gaussian components (GCs), which are capable to explain up
to 97 per cent of the data variance. Using elements of information theory, we
compare each GC to their respective astronomical counterpart. GC1 and GC4 are
associated with star-forming galaxies, suggesting the need to define a new
starburst subgroup. GC2 is associated with BPT's Active Galaxy Nuclei (AGN)
class and WHAN's weak AGN class. GC3 is associated with BPT's composite class
and WHAN's strong AGN class. Conversely, there is no statistical evidence --
based on four GCs -- for the existence of a Seyfert/LINER dichotomy in our
sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The
GC5 appears associated to the LINER and Passive galaxies on the BPT and WHAN
diagrams respectively. Subtleties aside, we demonstrate the potential of our
methodology to recover/unravel different objects inside the wilderness of
astronomical datasets, without lacking the ability to convey physically
interpretable results. The probabilistic classifications from the GMM analysis
are publicly available within the COINtoolbox
(https://cointoolbox.github.io/GMM\_Catalogue/).
Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting
Authors: Zhuang Ma, Zongming Ma
Date: 2017-05-05 00:00:00
Tasks: Community Detection

Latent space models are effective tools for statistical modeling and
exploration of network data. These models can effectively model real world
network characteristics such as degree heterogeneity, transitivity, homophily,
etc. Due to their close connection to generalized linear models, it is also
natural to incorporate covariate information in them. The current paper
presents two universal fitting algorithms for networks with edge covariates:
one based on nuclear norm penalization and the other based on projected
gradient descent. Both algorithms are motivated by maximizing likelihood for a
special class of inner-product models while working simultaneously for a wide
range of different latent space models, such as distance models, which allow
latent vectors to affect edge formation in flexible ways. These fitting
methods, especially the one based on projected gradient descent, are fast and
scalable to large networks. We obtain their rates of convergence for both
inner-product models and beyond. The effectiveness of the modeling approach and
fitting algorithms is demonstrated on five real world network datasets for
different statistical tasks, including community detection with and without
edge covariates, and network assisted learning.
Learning to Transfer
Authors: Ying Wei, Yu Zhang, Qiang Yang
Date: 2017-08-18 00:00:00
Tasks: Transfer Learning

Transfer learning borrows knowledge from a source domain to facilitate
learning in a target domain. Two primary issues to be addressed in transfer
learning are what and how to transfer. For a pair of domains, adopting
different transfer learning algorithms results in different knowledge
transferred between them. To discover the optimal transfer learning algorithm
that maximally improves the learning performance in the target domain,
researchers have to exhaustively explore all existing transfer learning
algorithms, which is computationally intractable. As a trade-off, a sub-optimal
algorithm is selected, which requires considerable expertise in an ad-hoc way.
Meanwhile, it is widely accepted in educational psychology that human beings
improve transfer learning skills of deciding what to transfer through
meta-cognitive reflection on inductive transfer learning practices. Motivated
by this, we propose a novel transfer learning framework known as Learning to
Transfer (L2T) to automatically determine what and how to transfer are the best
by leveraging previous transfer learning experiences. We establish the L2T
framework in two stages: 1) we first learn a reflection function encrypting
transfer learning skills from experiences; and 2) we infer what and how to
transfer for a newly arrived pair of domains by optimizing the reflection
function. Extensive experiments demonstrate the L2T's superiority over several
state-of-the-art transfer learning algorithms and its effectiveness on
discovering more transferable knowledge.
3D Pose Regression using Convolutional Neural Networks
Authors: Siddharth Mahendran, Haider Ali, Rene Vidal
Date: 2017-08-18 00:00:00
Tasks: 3D Pose Estimation, Autonomous Navigation, Data Augmentation, General Classification, Pose Estimation, regression, Scene Understanding

3D pose estimation is a key component of many important computer vision tasks
such as autonomous navigation and 3D scene understanding. Most state-of-the-art
approaches to 3D pose estimation solve this problem as a pose-classification
problem in which the pose space is discretized into bins and a CNN classifier
is used to predict a pose bin. We argue that the 3D pose space is continuous
and propose to solve the pose estimation problem in a CNN regression framework
with a suitable representation, data augmentation and loss function that
captures the geometry of the pose space. Experiments on PASCAL3D+ show that the
proposed 3D pose regression approach achieves competitive performance compared
to the state-of-the-art.
Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution
Authors: Jinchao Liu, Margarita Osadchy, Lorna Ashton, Michael Foster, Christopher J. Solomon, Stuart J. Gibson
Date: 2017-08-18 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Machine learning methods have found many applications in Raman spectroscopy,
especially for the identification of chemical species. However, almost all of
these methods require non-trivial preprocessing such as baseline correction
and/or PCA as an essential step. Here we describe our unified solution for the
identification of chemical species in which a convolutional neural network is
trained to automatically identify substances according to their Raman spectrum
without the need of ad-hoc preprocessing steps. We evaluated our approach using
the RRUFF spectral database, comprising mineral sample data. Superior
classification performance is demonstrated compared with other frequently used
machine learning algorithms including the popular support vector machine.
Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization
Authors: Viacheslav Khomenko, Oleg Shyshkov, Olga Radyvonenko, Kostiantyn Bokhan
Date: 2017-08-18 00:00:00
Tasks: GPU, Handwriting Recognition

An efficient algorithm for recurrent neural network training is presented.
The approach increases the training speed for tasks where a length of the input
sequence may vary significantly. The proposed approach is based on the optimal
batch bucketing by input sequence length and data parallelization on multiple
graphical processing units. The baseline training performance without sequence
bucketing is compared with the proposed solution for a different number of
buckets. An example is given for the online handwriting recognition task using
an LSTM recurrent neural network. The evaluation is performed in terms of the
wall clock time, number of epochs, and validation loss value.
Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization
Authors: Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2017-08-18 00:00:00
Tasks: Document Classification, Face Recognition, feature selection, General Classification, Handwritten Digit Recognition, Prognosis, Representation Learning

The success of any machine learning system depends critically on effective
representations of data. In many cases, it is desirable that a representation
scheme uncovers the parts-based, additive nature of the data. Of current
representation learning schemes, restricted Boltzmann machines (RBMs) have
proved to be highly effective in unsupervised settings. However, when it comes
to parts-based discovery, RBMs do not usually produce satisfactory results. We
enhance such capacity of RBMs by introducing nonnegativity into the model
weights, resulting in a variant called nonnegative restricted Boltzmann machine
(NRBM). The NRBM produces not only controllable decomposition of data into
interpretable parts but also offers a way to estimate the intrinsic nonlinear
dimensionality of data, and helps to stabilize linear predictive models. We
demonstrate the capacity of our model on applications such as handwritten digit
recognition, face recognition, document classification and patient readmission
prognosis. The decomposition quality on images is comparable with or better
than what produced by the nonnegative matrix factorization (NMF), and the
thematic features uncovered from text are qualitatively interpretable in a
similar manner to that of the latent Dirichlet allocation (LDA). The stability
performance of feature selection on medical data is better than RBM and
competitive with NMF. The learned features, when used for classification, are
more discriminative than those discovered by both NMF and LDA and comparable
with those by RBM.
Self-explanatory Deep Salient Object Detection
Authors: Huaxin Xiao, Jiashi Feng, Yunchao Wei, Maojun Zhang
Date: 2017-08-18 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Saliency Prediction, Salient Object Detection

Salient object detection has seen remarkable progress driven by deep learning
techniques. However, most of deep learning based salient object detection
methods are black-box in nature and lacking in interpretability. This paper
proposes the first self-explanatory saliency detection network that explicitly
exploits low- and high-level features for salient object detection. We
demonstrate that such supportive clues not only significantly enhances
performance of salient object detection but also gives better justified
detection results. More specifically, we develop a multi-stage saliency encoder
to extract multi-scale features which contain both low- and high-level saliency
context. Dense short- and long-range connections are introduced to reuse these
features iteratively. Benefiting from the direct access to low- and high-level
features, the proposed saliency encoder can not only model the object context
but also preserve the boundary. Furthermore, a self-explanatory generator is
proposed to interpret how the proposed saliency encoder or other deep saliency
models making decisions. The generator simulates the absence of interesting
features by preventing these features from contributing to the saliency
classifier and estimates the corresponding saliency prediction without these
features. A comparison function, saliency explanation, is defined to measure
the prediction changes between deep saliency models and corresponding
generator. Through visualizing the differences, we can interpret the capability
of different deep neural networks based saliency detection models and
demonstrate that our proposed model indeed uses more reasonable structure for
salient object detection. Extensive experiments on five popular benchmark
datasets and the visualized saliency explanation demonstrate that the proposed
method provides new state-of-the-art.
Statistical Latent Space Approach for Mixed Data Modelling and Applications
Authors: Tu Dinh Nguyen, Truyen Tran, Dinh Phung, Svetha Venkatesh
Date: 2017-08-18 00:00:00
Tasks: Image Retrieval, Metric Learning, Representation Learning, Retrieval

The analysis of mixed data has been raising challenges in statistics and
machine learning. One of two most prominent challenges is to develop new
statistical techniques and methodologies to effectively handle mixed data by
making the data less heterogeneous with minimum loss of information. The other
challenge is that such methods must be able to apply in large-scale tasks when
dealing with huge amount of mixed data. To tackle these challenges, we
introduce parameter sharing and balancing extensions to our recent model, the
mixed-variate restricted Boltzmann machine (MV.RBM) which can transform
heterogeneous data into homogeneous representation. We also integrate
structured sparsity and distance metric learning into RBM-based models. Our
proposed methods are applied in various applications including latent patient
profile modelling in medical data analysis and representation learning for
image retrieval. The experimental results demonstrate the models perform better
than baseline methods in medical data and outperform state-of-the-art rivals in
image dataset.
Future Word Contexts in Neural Network Language Models
Authors: Xie Chen, Xunying Liu, Anton Ragni, Yu Wang, Mark Gales
Date: 2017-08-18 00:00:00
Tasks: speech-recognition, Speech Recognition

Recently, bidirectional recurrent network language models (bi-RNNLMs) have
been shown to outperform standard, unidirectional, recurrent neural network
language models (uni-RNNLMs) on a range of speech recognition tasks. This
indicates that future word context information beyond the word history can be
useful. However, bi-RNNLMs pose a number of challenges as they make use of the
complete previous and future word context information. This impacts both
training efficiency and their use within a lattice rescoring framework. In this
paper these issues are addressed by proposing a novel neural network structure,
succeeding word RNNLMs (su-RNNLMs). Instead of using a recurrent unit to
capture the complete future word contexts, a feedforward unit is used to model
a finite number of succeeding, future, words. This model can be trained much
more efficiently than bi-RNNLMs and can also be used for lattice rescoring.
Experimental results on a meeting transcription task (AMI) show the proposed
model consistently outperformed uni-RNNLMs and yield only a slight degradation
compared to bi-RNNLMs in N-best rescoring. Additionally, performance
improvements can be obtained using lattice rescoring and subsequent confusion
network decoding.
Agree to Disagree: Improving Disagreement Detection with Dual GRUs
Authors: Sushant Hiray, Venkatesh Duppada
Date: 2017-08-18 00:00:00

This paper presents models for detecting agreement/disagreement in online
discussions. In this work we show that by using a Siamese inspired architecture
to encode the discussions, we no longer need to rely on hand-crafted features
to exploit the meta thread structure. We evaluate our model on existing online
discussion corpora - ABCD, IAC and AWTP. Experimental results on ABCD dataset
show that by fusing lexical and word embedding features, our model achieves the
state of the art performance of 0.804 average F1 score. We also show that the
model trained on ABCD dataset performs competitively on relatively smaller
annotated datasets (IAC and AWTP).