pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning in color: towards automated quark/gluon jet discrimination
Authors: Patrick T. Komiske, Eric M. Metodiev, Matthew D. Schwartz
Date: 2016-12-05 00:00:00
Tasks: Deep Learning

Artificial intelligence offers the potential to automate challenging
data-processing tasks in collider physics. To establish its prospects, we
explore to what extent deep learning with convolutional neural networks can
discriminate quark and gluon jets better than observables designed by
physicists. Our approach builds upon the paradigm that a jet can be treated as
an image, with intensity given by the local calorimeter deposits. We supplement
this construction by adding color to the images, with red, green and blue
intensities given by the transverse momentum in charged particles, transverse
momentum in neutral particles, and pixel-level charged particle counts.
Overall, the deep networks match or outperform traditional jet variables. We
also find that, while various simulations produce different quark and gluon
jets, the neural networks are surprisingly insensitive to these differences,
similar to traditional observables. This suggests that the networks can extract
robust physical information from imperfect simulations.
Topic Modeling the Hàn diăn Ancient Classics
Authors: Colin Allen, Hongliang Luo, Jaimie Murdock, Jianghuai Pu, XiaoHong Wang, Yanjie Zhai, Kun Zhao
Date: 2017-02-02 00:00:00
Tasks: Philosophy

Ancient Chinese texts present an area of enormous challenge and opportunity
for humanities scholars interested in exploiting computational methods to
assist in the development of new insights and interpretations of culturally
significant materials. In this paper we describe a collaborative effort between
Indiana University and Xi'an Jiaotong University to support exploration and
interpretation of a digital corpus of over 18,000 ancient Chinese documents,
which we refer to as the "Handian" ancient classics corpus (H\`an di\u{a}n
g\u{u} j\'i, i.e, the "Han canon" or "Chinese classics"). It contains classics
of ancient Chinese philosophy, documents of historical and biographical
significance, and literary works. We begin by describing the Digital Humanities
context of this joint project, and the advances in humanities computing that
made this project feasible. We describe the corpus and introduce our
application of probabilistic topic modeling to this corpus, with attention to
the particular challenges posed by modeling ancient Chinese documents. We give
a specific example of how the software we have developed can be used to aid
discovery and interpretation of themes in the corpus. We outline more advanced
forms of computer-aided interpretation that are also made possible by the
programming interface provided by our system, and the general implications of
these methods for understanding the nature of meaning in these texts.
The Value of Inferring the Internal State of Traffic Participants for Autonomous Freeway Driving
Authors: Zachary Sunberg, Christopher Ho, Mykel Kochenderfer
Date: 2017-02-02 00:00:00
Tasks: Autonomous Vehicles

Safe interaction with human drivers is one of the primary challenges for
autonomous vehicles. In order to plan driving maneuvers effectively, the
vehicle's control system must infer and predict how humans will behave based on
their latent internal state (e.g., intentions and aggressiveness). This
research uses a simple model for human behavior with unknown parameters that
make up the internal states of the traffic participants and presents a method
for quantifying the value of estimating these states and planning with their
uncertainty explicitly modeled. An upper performance bound is established by an
omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of
the internal states. A baseline lower bound is established by planning with
MCTS assuming that all drivers have the same internal state. MCTS variants are
then used to solve a partially observable Markov decision process (POMDP) that
models the internal state uncertainty to determine whether inferring the
internal state offers an advantage over the baseline. Applying this method to a
freeway lane changing scenario reveals that there is a significant performance
gap between the upper bound and baseline. POMDP planning techniques come close
to closing this gap, especially when important hidden model parameters are
correlated with measurable parameters.
Guided Signal Reconstruction Theory
Authors: Andrew Knyazev, Akshay Gadde, Hassan Mansour, Dong Tian
Date: 2017-02-02 00:00:00

An axiomatic approach to signal reconstruction is formulated, involving a
sample consistent set and a guiding set, describing desired reconstructions.
New frame-less reconstruction methods are proposed, based on a novel concept of
a reconstruction set, defined as a shortest pathway between the sample
consistent set and the guiding set. Existence and uniqueness of the
reconstruction set are investigated in a Hilbert space, where the guiding set
is a closed subspace and the sample consistent set is a closed plane, formed by
a sampling subspace. Connections to earlier known consistent, generalized, and
regularized reconstructions are clarified. New stability and reconstruction
error bounds are derived, using the largest nontrivial angle between the
sampling and guiding subspaces. Conjugate gradient iterative reconstruction
algorithms are proposed and illustrated numerically for image magnification.
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
Authors: Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik
Date: 2017-02-02 00:00:00
Tasks: Anomaly Detection, Deep Learning, GPU, Time Series, Time Series Analysis

This paper presents a model based on Deep Learning algorithms of LSTM and GRU
for facilitating an anomaly detection in Large Hadron Collider superconducting
magnets. We used high resolution data available in Post Mortem database to
train a set of models and chose the best possible set of their
hyper-parameters. Using Deep Learning approach allowed to examine a vast body
of data and extract the fragments which require further experts examination and
are regarded as anomalies. The presented method does not require tedious manual
threshold setting and operator attention at the stage of the system setup.
Instead, the automatic approach is proposed, which achieves according to our
experiments accuracy of 99%. This is reached for the largest dataset of 302 MB
and the following architecture of the network: single layer LSTM, 128 cells, 20
epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam.
All the experiments were run on GPU Nvidia Tesla K80
Two forms of minimality in ASPIC+
Authors: Zimi Li, Andrea Cohen, Simon Parsons
Date: 2017-02-02 00:00:00
Tasks: Vocal Bursts Valence Prediction

Many systems of structured argumentation explicitly require that the facts
and rules that make up the argument for a conclusion be the minimal set
required to derive the conclusion. ASPIC+ does not place such a requirement on
arguments, instead requiring that every rule and fact that are part of an
argument be used in its construction. Thus ASPIC+ arguments are minimal in the
sense that removing any element of the argument would lead to a structure that
is not an argument. In this brief note we discuss these two types of minimality
and show how the first kind of minimality can, if desired, be recovered in
ASPIC+.
Scaling Properties of Human Brain Functional Networks
Authors: Riccardo Zucca, Xerxes D. Arsiwalla, Hoang Le, Mikail Rubinov, Paul Verschure
Date: 2017-02-02 00:00:00

We investigate scaling properties of human brain functional networks in the
resting-state. Analyzing network degree distributions, we statistically test
whether their tails scale as power-law or not. Initial studies, based on
least-squares fitting, were shown to be inadequate for precise estimation of
power-law distributions. Subsequently, methods based on maximum-likelihood
estimators have been proposed and applied to address this question.
Nevertheless, no clear consensus has emerged, mainly because results have shown
substantial variability depending on the data-set used or its resolution. In
this study, we work with high-resolution data (10K nodes) from the Human
Connectome Project and take into account network weights. We test for the
power-law, exponential, log-normal and generalized Pareto distributions. Our
results show that the statistics generally do not support a power-law, but
instead these degree distributions tend towards the thin-tail limit of the
generalized Pareto model. This may have implications for the number of hubs in
human brain functional networks.
Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey
Authors: Lorenzo Ferrone, Fabio Massimo Zanzotto
Date: 2017-02-02 00:00:00

Natural language is inherently a discrete symbolic representation of human knowledge. Recent advances in machine learning (ML) and in natural language processing (NLP) seem to contradict the above intuition: discrete symbols are fading away, erased by vectors or tensors called distributed and distributional representations. However, there is a strict link between distributed/distributional representations and discrete symbols, being the first an approximation of the second. A clearer understanding of the strict link between distributed/distributional representations and symbols may certainly lead to radically new deep learning networks. In this paper we make a survey that aims to renew the link between symbolic representations and distributed/distributional representations. This is the right time to revitalize the area of interpreting how discrete symbols are represented inside neural networks.
Maritime situational awareness using adaptive multi-sensor management under hazy conditions
Authors: D. K. Prasad, C. K. Prasath, D. Rajan, L. Rachmawati, E. Rajabally, C. Quek
Date: 2017-02-02 00:00:00
Tasks: Management

This paper presents a multi-sensor architecture with an adaptive multi-sensor
management system suitable for control and navigation of autonomous maritime
vessels in hazy and poor-visibility conditions. This architecture resides in
the autonomous maritime vessels. It augments the data from on-board imaging
sensors and weather sensors with the AIS data and weather data from sensors on
other vessels and the on-shore vessel traffic surveillance system. The combined
data is analyzed using computational intelligence and data analytics to
determine suitable course of action while utilizing historically learnt
knowledge and performing live learning from the current situation. Such
framework is expected to be useful in diverse weather conditions and shall be a
useful architecture to provide autonomy to maritime vessels.
QCD-Aware Recursive Neural Networks for Jet Physics
Authors: Gilles Louppe, Kyunghyun Cho, Cyril Becot, Kyle Cranmer
Date: 2017-02-02 00:00:00
Tasks: Clustering, Sentence

Recent progress in applying machine learning for jet physics has been built
upon an analogy between calorimeters and images. In this work, we present a
novel class of recursive neural networks built instead upon an analogy between
QCD and natural languages. In the analogy, four-momenta are like words and the
clustering history of sequential recombination jet algorithms is like the
parsing of a sentence. Our approach works directly with the four-momenta of a
variable-length set of particles, and the jet-based tree structure varies on an
event-by-event basis. Our experiments highlight the flexibility of our method
for building task-specific jet embeddings and show that recursive architectures
are significantly more accurate and data efficient than previous image-based
networks. We extend the analogy from individual jets (sentences) to full events
(paragraphs), and show for the first time an event-level classifier operating
on all the stable particles produced in an LHC event.
Exploration and Exploitation of Victorian Science in Darwin's Reading Notebooks
Authors: Jaimie Murdock, Colin Allen, Simon DeDeo
Date: 2015-09-23 00:00:00
Tasks: Decision Making, Topic Models

Search in an environment with an uncertain distribution of resources involves
a trade-off between exploitation of past discoveries and further exploration.
This extends to information foraging, where a knowledge-seeker shifts between
reading in depth and studying new domains. To study this decision-making
process, we examine the reading choices made by one of the most celebrated
scientists of the modern era: Charles Darwin. From the full-text of books
listed in his chronologically-organized reading journals, we generate topic
models to quantify his local (text-to-text) and global (text-to-past) reading
decisions using Kullback-Liebler Divergence, a cognitively-validated,
information-theoretic measure of relative surprise. Rather than a pattern of
surprise-minimization, corresponding to a pure exploitation strategy, Darwin's
behavior shifts from early exploitation to later exploration, seeking unusually
high levels of cognitive surprise relative to previous eras. These shifts,
detected by an unsupervised Bayesian model, correlate with major intellectual
epochs of his career as identified both by qualitative scholarship and Darwin's
own self-commentary. Our methods allow us to compare his consumption of texts
with their publication order. We find Darwin's consumption more exploratory
than the culture's production, suggesting that underneath gradual societal
changes are the explorations of individual synthesis and discovery. Our
quantitative methods advance the study of cognitive search through a framework
for testing interactions between individual and collective behavior and between
short- and long-term consumption choices. This novel application of topic
modeling to characterize individual reading complements widespread studies of
collective scientific behavior.
Analysing Temporal Evolution of Interlingual Wikipedia Article Pairs
Authors: Simon Gottschalk, Elena Demidova
Date: 2017-02-02 00:00:00
Tasks: Articles

Wikipedia articles representing an entity or a topic in different language
editions evolve independently within the scope of the language-specific user
communities. This can lead to different points of views reflected in the
articles, as well as complementary and inconsistent information. An analysis of
how the information is propagated across the Wikipedia language editions can
provide important insights in the article evolution along the temporal and
cultural dimensions and support quality control. To facilitate such analysis,
we present MultiWiki - a novel web-based user interface that provides an
overview of the similarities and differences across the article pairs
originating from different language editions on a timeline. MultiWiki enables
users to observe the changes in the interlingual article similarity over time
and to perform a detailed visual comparison of the article snapshots at a
particular time point.
Learning a time-dependent master saliency map from eye-tracking data in videos
Authors: Antoine Coutrot, Nathalie Guyader
Date: 2017-02-02 00:00:00

To predict the most salient regions of complex natural scenes, saliency
models commonly compute several feature maps (contrast, orientation, motion...)
and linearly combine them into a master saliency map. Since feature maps have
different spatial distribution and amplitude dynamic ranges, determining their
contributions to overall saliency remains an open problem. Most
state-of-the-art models do not take time into account and give feature maps
constant weights across the stimulus duration. However, visual exploration is a
highly dynamic process shaped by many time-dependent factors. For instance,
some systematic viewing patterns such as the center bias are known to
dramatically vary across the time course of the exploration. In this paper, we
use maximum likelihood and shrinkage methods to dynamically and jointly learn
feature map and systematic viewing pattern weights directly from eye-tracking
data recorded on videos. We show that these weights systematically vary as a
function of time, and heavily depend upon the semantic visual category of the
videos being processed. Our fusion method allows taking these variations into
account, and outperforms other state-of-the-art fusion schemes using constant
weights over time. The code, videos and eye-tracking data we used for this
study are available online:
http://antoinecoutrot.magix.net/public/research.html
Multilingual and Cross-lingual Timeline Extraction
Authors: Egoitz Laparra, Rodrigo Agerri, Itziar Aldabe, German Rigau
Date: 2017-02-02 00:00:00

In this paper we present an approach to extract ordered timelines of events,
their participants, locations and times from a set of multilingual and
cross-lingual data sources. Based on the assumption that event-related
information can be recovered from different documents written in different
languages, we extend the Cross-document Event Ordering task presented at
SemEval 2015 by specifying two new tasks for, respectively, Multilingual and
Cross-lingual Timeline Extraction. We then develop three deterministic
algorithms for timeline extraction based on two main ideas. First, we address
implicit temporal relations at document level since explicit time-anchors are
too scarce to build a wide coverage timeline extraction system. Second, we
leverage several multilingual resources to obtain a single, inter-operable,
semantic representation of events across documents and across languages. The
result is a highly competitive system that strongly outperforms the current
state-of-the-art. Nonetheless, further analysis of the results reveals that
linking the event mentions with their target entities and time-anchors remains
a difficult challenge. The systems, resources and scorers are freely available
to facilitate its use and guarantee the reproducibility of results.
Character-Level Language Modeling with Hierarchical Recurrent Neural Networks
Authors: Kyuyeon Hwang, Wonyong Sung
Date: 2016-09-13 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Recurrent neural network (RNN) based character-level language models (CLMs)
are extremely useful for modeling out-of-vocabulary words by nature. However,
their performance is generally much worse than the word-level language models
(WLMs), since CLMs need to consider longer history of tokens to properly
predict the next one. We address this problem by proposing hierarchical RNN
architectures, which consist of multiple modules with different timescales.
Despite the multi-timescale structures, the input and output layers operate
with the character-level clock, which allows the existing RNN CLM training
approaches to be directly applicable without any modifications. Our CLM models
show better perplexity than Kneser-Ney (KN) 5-gram WLMs on the One Billion Word
Benchmark with only 2% of parameters. Also, we present real-time
character-level end-to-end speech recognition examples on the Wall Street
Journal (WSJ) corpus, where replacing traditional mono-clock RNN CLMs with the
proposed models results in better recognition accuracies even though the number
of parameters are reduced to 30%.
Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification
Authors: Kyuyeon Hwang, Wonyong Sung
Date: 2015-11-21 00:00:00
Tasks: General Classification, Phoneme Recognition, Rolling Shutter Correction, speech-recognition, Speech Recognition

Connectionist temporal classification (CTC) based supervised sequence
training of recurrent neural networks (RNNs) has shown great success in many
machine learning areas including end-to-end speech and handwritten character
recognition. For the CTC training, however, it is required to unroll (or
unfold) the RNN by the length of an input sequence. This unrolling requires a
lot of memory and hinders a small footprint implementation of online learning
or adaptation. Furthermore, the length of training sequences is usually not
uniform, which makes parallel training with multiple sequences inefficient on
shared memory models such as graphics processing units (GPUs). In this work, we
introduce an expectation-maximization (EM) based online CTC algorithm that
enables unidirectional RNNs to learn sequences that are longer than the amount
of unrolling. The RNNs can also be trained to process an infinitely long input
sequence without pre-segmentation or external reset. Moreover, the proposed
approach allows efficient parallel training on GPUs. For evaluation, phoneme
recognition and end-to-end speech recognition examples are presented on the
TIMIT and Wall Street Journal (WSJ) corpora, respectively. Our online model
achieves 20.7% phoneme error rate (PER) on the very long input sequence that is
generated by concatenating all 192 utterances in the TIMIT core test set. On
WSJ, a network can be trained with only 64 times of unrolling while sacrificing
4.5% relative word error rate (WER).
Template shape estimation: correcting an asymptotic bias
Authors: Nina Miolane, Susan Holmes, Xavier Pennec
Date: 2016-09-06 00:00:00

We use tools from geometric statistics to analyze the usual estimation
procedure of a template shape. This applies to shapes from landmarks, curves,
surfaces, images etc. We demonstrate the asymptotic bias of the template shape
estimation using the stratified geometry of the shape space. We give a Taylor
expansion of the bias with respect to a parameter $\sigma$ describing the
measurement error on the data. We propose two bootstrap procedures that
quantify the bias and correct it, if needed. They are applicable for any type
of shape data. We give a rule of thumb to provide intuition on whether the bias
has to be corrected. This exhibits the parameters that control the bias'
magnitude. We illustrate our results on simulated and real shape data.
Learning Criticality in an Embodied Boltzmann Machine
Authors: Miguel Aguilera, Manuel G. Bedia
Date: 2017-02-02 00:00:00

Many biological and cognitive systems do not operate deep into one or other
regime of activity. Instead, they exploit critical surfaces poised at
transitions in their parameter space. The pervasiveness of criticality in
natural systems suggests that there may be general principles inducing this
behaviour. However, there is a lack of conceptual models explaining how
embodied agents propel themselves towards these critical points. In this paper,
we present a learning model driving an embodied Boltzmann Machine towards
critical behaviour by maximizing the heat capacity of the network. We test and
corroborate the model implementing an embodied agent in the mountain car
benchmark, controlled by a Boltzmann Machine that adjust its weights according
to the model. We find that the neural controller reaches a point of
criticality, which coincides with a transition point of the behaviour of the
agent between two regimes of behaviour, maximizing the synergistic information
between its sensors and the hidden and motor neurons. Finally, we discuss the
potential of our learning model to study the contribution of criticality to the
behaviour of embodied living systems in scenarios not necessarily constrained
by biological restrictions of the examples of criticality we find in nature.
Optical Flow Requires Multiple Strategies (but only one network)
Authors: Tal Schuster, Lior Wolf, David Gadot
Date: 2016-11-17 00:00:00
Proceeding: CVPR 2017 7
Tasks: Metric Learning, Optical Flow Estimation

We show that the matching problem that underlies optical flow requires
multiple strategies, depending on the amount of image motion and other factors.
We then study the implications of this observation on training a deep neural
network for representing image patches in the context of descriptor based
optical flow. We propose a metric learning method, which selects suitable
negative samples based on the nature of the true match. This type of training
produces a network that displays multiple strategies depending on the input and
leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow
benchmarks.