pwc-archive/papers-with-abstracts huggingface dataset:
Fault Diagnosis and Bad Data Detection of Power Transmission Network - A Time Domain Approach
Date: 2018-10-25 00:00:00
Tasks: Fault Diagnosis

Fault analysis and bad data are often processed in separate manners. In this
paper it is proved that fault as well as bad current measurement data can be
modeled as control failure for the power transmission network and any fault on
the transmission line can be treated as multiple bad data. Subsequently a
linear observer theory is designed in order to identify the fault type and bad
data simultaneously. The state space model based observer theory allows a
particular failure mode manifest itself as residual which remains in a fixed
direction. Moreover coordinate transformation is performed to allow the
residual for each failure mode to generate specific geometry characteristic in
separate output dimensions. The design approach based on the observer theory is
presented in this paper. The design allows 1) bad data detection for current
measurement, and 2) fault location, and fault resistance estimation (as a
byproduct) where the fault location accuracy is not affected by fault
resistance. However it loses freedom in designing the eigenvalues in the
excessive subspace. While the theoretical framework is general, the analysis
and design are dedicated to transmission lines.
Note on (active-)QRAM-style data access as a quantum circuit
Authors: Carstens Tore Vincent, Theis Dirk Oliver
Date: 2018-10-25 00:00:00

We observe how an active (i.e., requring $2^n$ parallel control operations)
QRAM-like effect $$\sum_{y=0}^{N-1} |y\rangle\langle y| \otimes
U^y_{\text{result},\text{memory}_y}$$ can be realized, as a quantum circuit of
depth $O(n+\sqrt m)$ (where $m$ is the size of the result register) plus the
maximum over all~$z$ of the circuit depths of controlled-$U^z$ operations.
Expedition: A Time-Aware Exploratory Search System Designed for Scholars
Authors: Singh Jaspreet, Nejdl Wolfgang, Anand Avishek
Date: 2018-10-25 00:00:00
Tasks: Diversity, Retrieval

Archives are an important source of study for various scholars. Digitization
and the web have made archives more accessible and led to the development of
several time-aware exploratory search systems. However these systems have been
designed for more general users rather than scholars. Scholars have more
complex information needs in comparison to general users. They also require
support for corpus creation during their exploration process. In this paper we
present Expedition - a time-aware exploratory search system that addresses the
requirements and information needs of scholars. Expedition possesses a suite of
ad-hoc and diversity based retrieval models to address complex information
needs; a newspaper-style user interface to allow for larger textual previews
and comparisons; entity filters to more naturally refine a result list and an
interactive annotated timeline which can be used to better identify periods of
importance.
Non-Coherent Sensor Fusion via Entropy Regularized Optimal Mass Transport
Date: 2018-11-19 00:00:00
Tasks: Sensor Fusion

This work presents a method for information fusion in source localization
applications. The method utilizes the concept of optimal mass transport in
order to construct estimates of the spatial spectrum using a convex barycenter
formulation. We introduce an entropy regularization term to the convex
objective, which allows for low-complexity iterations of the solution algorithm
and thus makes the proposed method applicable also to higher-dimensional
problems. We illustrate the proposed method's inherent robustness to
misalignment and miscalibration of the sensor arrays using numerical examples
of localization in two dimensions.
Spanning Tests for Markowitz Stochastic Dominance
Date: 2018-10-25 00:00:00
Tasks: Management

We derive properties of the cdf of random variables defined as saddle-type
points of real valued continuous stochastic processes. This facilitates the
derivation of the first-order asymptotic properties of tests for stochastic
spanning given some stochastic dominance relation. We define the concept of
Markowitz stochastic dominance spanning, and develop an analytical
representation of the spanning property. We construct a non-parametric test for
spanning based on subsampling, and derive its asymptotic exactness and
consistency. The spanning methodology determines whether introducing new
securities or relaxing investment constraints improves the investment
opportunity set of investors driven by Markowitz stochastic dominance. In an
application to standard data sets of historical stock market returns, we reject
market portfolio Markowitz efficiency as well as two-fund separation. Hence, we
find evidence that equity management through base assets can outperform the
market, for investors with Markowitz type preferences.
Analyzing Visual Mappings of Traditional and Alternative Music Notation
Authors: Miller Matthias, Häußler Johannes, Kraus Matthias, Keim Daniel, El-Assady Mennatallah
Date: 2018-10-25 00:00:00
Tasks: Data Visualization

In this paper, we postulate that combining the domains of information
visualization and music studies paves the ground for a more structured analysis
of the design space of music notation, enabling the creation of alternative
music notations that are tailored to different users and their tasks. Hence, we
discuss the instantiation of a design and visualization pipeline for music
notation that follows a structured approach, based on the fundamental concepts
of information and data visualization. This enables practitioners and
researchers of digital humanities and information visualization, alike, to
conceptualize, create, and analyze novel music notation methods. Based on the
analysis of relevant stakeholders and their usage of music notation as a mean
of communication, we identify a set of relevant features typically encoded in
different annotations and encodings, as used by interpreters, performers, and
readers of music. We analyze the visual mappings of musical dimensions for
varying notation methods to highlight gaps and frequent usages of encodings,
visual channels, and Gestalt laws. This detailed analysis leads us to the
conclusion that such an under-researched area in information visualization
holds the potential for fundamental research. This paper discusses possible
research opportunities, open challenges, and arguments that can be pursued in
the process of analyzing, improving, or rethinking existing music notation
systems and techniques.
Snapshot fiber spectral imaging using speckle correlations and compressive sensing
Date: 2018-10-24 00:00:00
Tasks: Compressive Sensing

Snapshot spectral imaging is rapidly gaining interest for remote sensing
applications. Acquiring spatial and spectral data within one image promotes
fast measurement times, and reduces the need for stabilized scanning imaging
systems. Many current snapshot technologies, which rely on gratings or prisms
to characterize wavelength information, are difficult to reduce in size for
portable hyperspectral imaging. Here, we show that a multicore multimode fiber
can be used as a compact spectral imager with sub-nanometer resolution, by
encoding spectral information within a monochrome CMOS camera. We characterize
wavelength-dependent speckle patterns for up to 3000 fiber cores over a broad
wavelength range. A clustering algorithm is employed in combination with
l$_{1}$-minimization to limit data collection at the acquisition stage for the
reconstruction of spectral images that are sparse in the wavelength domain. We
also show that in the non-compressive regime these techniques are able to
accurately reconstruct broadband information.
The $Ca^{2+}$-activated $Cl^-$ current ensures robust and reliable signal amplification in vertebrate olfactory receptor neurons
Authors: Johannes Reisert, Jürgen Reingruber
Date: 2018-10-25 00:00:00

Activation of most primary sensory neurons results in transduction currents that are carried by cations. One notable exception is the vertebrate olfactory receptor neuron (ORN), where the transduction current is carried largely by the anion $Cl^-$. However, it remains unclear why ORNs use an anionic current for signal amplification. We have sought to provide clarification on this topic by studying the so far neglected dynamics of $Na^+$, $Ca^{2+}$, $K^+$ and $Cl^-$ in the small space of olfactory cilia during an odorant response. Using computational modeling and simulations we compared the outcomes of signal amplification based on either $Cl^-$ or $Na^+$ currents. We found that amplification produced by $Na^+$ influx instead of a $Cl^-$ efflux is problematic due to several reasons: First, the $Na^+$ current amplitude varies greatly depending on mucosal ion concentration changes. Second, a $Na^+$ current leads to a large increase in the ciliary $Na^+$ concentration during an odorant response. This increase inhibits and even reverses $Ca^{2+}$ clearance by $Na^+/Ca^{2+}/K^+$ exchange, which is essential for response termination. Finally, a $Na^+$ current increases the ciliary osmotic pressure, which could cause swelling to damage the cilia. By contrast, a transduction pathway based on $Cl^-$ efflux circumvents these problems and renders the odorant response robust and reliable.
Evaluation of algorithms for correction of transcranial magnetic stimulation induced artifacts in electroencephalograms
Date: 2018-10-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Transcranial magnetic stimulation combined with electroencephalography
(TMS-EEG) is widely used to study the reactivity and connectivity of brain
regions for clinical or research purposes. The electromagnetic pulse of the TMS
device generates at the instant of administration an artifact of large
amplitude and a duration up to tens of milliseconds that overlaps with brain
activity. Methods for TMS artifact correction have been developed to remove the
artifact and recover the underlying, immediate response of the cerebral cortex
to the magnetic stimulus. In this study, three such algorithms are evaluated.
Since there is no ground truth for the masked brain activity, pilot data formed
from the superposition of the isolated TMS artifact on the EEG brain activity
are used to evaluate the performance of the algorithms. Different scenarios of
TMS-EEG experiments are considered for the evaluation: TMS at resting state,
TMS inducing epileptiform discharges and TMS administered during epileptiform
discharges. We show that a proposed gap filling method is able to reproduce
qualitative characteristics and in many cases closely resemble the hidden EEG
signal. Finally, shortcomings of the TMS correction algorithms as well as the
pilot data approach are discussed.
Planification en temps r\'eel avec agenda de buts et sauts
Authors: Pellier Damien, Bouzy Bruno, Métivier Marc
Date: 2018-10-22 00:00:00

In the context of real-time planning, this paper investigates the
contributions of two enhancements for selecting actions. First, the
agenda-driven planning enhancement ranks relevant atomic goals and solves them
incrementally in a best-first manner. Second, the committed jump enhancement
commits a sequence of actions to be executed at the following time steps. To
assess these two enhancements, we developed a real-time planning algorithm in
which action selection can be driven by a goal-agenda, and committed jumps can
be done. Experimental results, performed on classical planning problems, show
that agenda-planning and committed jumps are clear advantages in the real-time
context. Used simultaneously, they enable the planner to be several orders of
magnitude faster and solution plans to be shorter.
MGP: Un algorithme de planification temps r\'eel prenant en compte l'\'evolution dynamique du but
Authors: Pellier Damien, Vanneufville Mickaël, Fiorino Humbert, Métivier Marc, Bouzy Bruno
Date: 2018-10-22 00:00:00

Devising intelligent robots or agents that interact with humans is a major
challenge for artificial intelligence. In such contexts, agents must constantly
adapt their decisions according to human activities and modify their goals. In
this paper, we tackle this problem by introducing a novel planning approach,
called Moving Goal Planning (MGP), to adapt plans to goal evolutions. This
planning algorithm draws inspiration from Moving Target Search (MTS)
algorithms. In order to limit the number of search iterations and to improve
its efficiency, MGP delays as much as possible triggering new searches when the
goal changes over time. To this purpose, MGP uses two strategies: Open Check
(OC) that checks if the new goal is still in the current search tree and Plan
Follow (PF) that estimates whether executing actions of the current plan brings
MGP closer to the new goal. Moreover, MGP uses a parsimonious strategy to
update incrementally the search tree at each new search that reduces the number
of calls to the heuristic function and speeds up the search. Finally, we show
evaluation results that demonstrate the effectiveness of our approach.
Une architecture cognitive et affective orient{\'e}e interaction
Authors: Pellier Damien, Adam Carole, Johal Wafa, Fiorino Humbert, Pesty Sylvie
Date: 2018-10-22 00:00:00

In this paper, we present CAIO, a Cognitive and Affective
Interaction-Oriented architecture for social human-robot interactions (HRI),
allowing robots to reason on mental states (including emotions), and to act
physically, emotionally and verbally. We also present a short scenario and
implementation on a Nao robot.
Une approche totalement instanci\'ee pour la planification HTN
Authors: Ramoul Abdeldjalil, Pellier Damien, Fiorino Humbert, Pesty Sylvie
Date: 2018-10-22 00:00:00

Many planning techniques have been developed to allow autonomous systems to
act and make decisions based on their perceptions of the environment. Among
these techniques, HTN ({\it Hierarchical Task Network}) planning is one of the
most used in practice. Unlike classical approaches of planning. HTN operates by
decomposing task into sub-tasks until each of these sub-tasks can be achieved
an action. This hierarchical representation provide a richer representation of
planning problems and allows to better guide the plan search and provides more
knowledge to the underlying algorithms. In this paper, we propose a new
approach of HTN planning in which, as in conventional planning, we instantiate
all planning operators before starting the search process. This approach has
proven its effectiveness in classical planning and is necessary for the
development of effective heuristics and encoding planning problems in other
formalism such as CSP or SAT. The instantiation is actually used by most modern
planners but has never been applied in an HTN based planning framework. We
present in this article a generic instantiation algorithm which implements many
simplification techniques to reduce the process complexity inspired from those
used in classical planning. Finally we present some results obtained from an
experimentation on a range of problems used in the international planning
competitions with a modified version of SHOP planner using fully instantiated
problems.
Critical Neuromorphic Computing based on Explosive Synchronization
Authors: Choi Jaesung, Kim Pilwon
Date: 2018-10-16 00:00:00

Synchronous oscillations in neuronal ensembles have been proposed to provide
a neural basis for the information processes in the brain. In this work, we
present a neuromorphic computing algorithm based on oscillator synchronization
in a critical regime. The algorithm uses the high dimensional transient
dynamics perturbed by an input and translates it into proper output stream. One
of the benefits of adopting coupled phase oscillators as neuromorphic elements
is that the synchrony among oscillators can be finely tuned at a critical
state. Especially near a critical state, the marginally synchronized
oscillators operate with high efficiency and maintain better computing
performances. We also show that explosive synchronization which is induced from
specific neuronal connectivity produces more improved and stable outputs. This
work provides a systematic way to encode computing in a large size coupled
oscillators, which may be useful in designing neuromorphic devices.
SilentPhone: Inferring User Unavailability based Opportune Moments to Minimize Call Interruptions
Authors: Sarker Iqbal H.
Date: 2018-10-15 00:00:00

The increasing popularity of cell phones has made them the most personal and
ubiquitous communication devices nowadays. Typically, the ringing notifications
of mobile phones are used to inform the users about the incoming calls.
However, the notifications of inappropriate incoming calls sometimes cause
interruptions not only for the users but also the surrounding people. In this
paper, we present a data-driven approach to infer the opportune moments for
such phone call interruptions based on user's unavailability, i.e., when a user
is unable to answer the incoming phone calls, by analyzing individual's past
phone log data, and to discover the corresponding phone silent mode configuring
rules for the purpose of minimizing call interruptions in an automated
intelligent system. Experiments on the real mobile phone datasets show that our
approach is able to identify the opportune moments for call interruptions and
generates corresponding silent mode configuring rules by capturing the dominant
behavior of individual users' at various times-of-the-day and days-of-the week.
Finding the best design parameters for optical nanostructures using reinforcement learning
Authors: Sajedian Iman, Badloe Trevon, Rho Junsuk
Date: 2018-10-18 00:00:00
Tasks: BIG-bench Machine Learning, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Recently, a novel machine learning model has emerged in the field of
reinforcement learning known as deep Q-learning. This model is capable of
finding the best possible solution in systems consisting of millions of
choices, without ever experiencing it before, and has been used to beat the
best human minds at complex games such as, Go and chess, which both have a huge
number of possible decisions and outcomes for each move. With a human-level
intelligence, it has been solved the problems that no other machine learning
model could do before. Here, we show the steps needed for implementing this
model on an optical problem. We investigated the colour generation by
dielectric nanostructures and show that this model can find geometrical
properties that can generate a much deeper red, green and blue colours compared
to the ones found by human researchers. This technique can easily be extended
to predict and find the best design parameters for other optical structures.
Tracking the History and Evolution of Entities: Entity-centric Temporal Analysis of Large Social Media Archives
Authors: Fafalios Pavlos, Iosifidis Vasileios, Stefanidis Kostas, Ntoutsi Eirini
Date: 2018-10-24 00:00:00
Tasks: Articles

How did the popularity of the Greek Prime Minister evolve in 2015? How did
the predominant sentiment about him vary during that period? Were there any
controversial sub-periods? What other entities were related to him during these
periods? To answer these questions, one needs to analyze archived documents and
data about the query entities, such as old news articles or social media
archives. In particular, user-generated content posted in social networks, like
Twitter and Facebook, can be seen as a comprehensive documentation of our
society, and thus meaningful analysis methods over such archived data are of
immense value for sociologists, historians and other interested parties who
want to study the history and evolution of entities and events. To this end, in
this paper we propose an entity-centric approach to analyze social media
archives and we define measures that allow studying how entities were reflected
in social media in different time periods and under different aspects, like
popularity, attitude, controversiality, and connectedness with other entities.
A case study using a large Twitter archive of four years illustrates the
insights that can be gained by such an entity-centric and multi-aspect
analysis.
Ranking Archived Documents for Structured Queries on Semantic Layers
Authors: Fafalios Pavlos, Kasturia Vaibhav, Nejdl Wolfgang
Date: 2018-10-23 00:00:00

Archived collections of documents (like newspaper and web archives) serve as
important information sources in a variety of disciplines, including Digital
Humanities, Historical Science, and Journalism. However, the absence of
efficient and meaningful exploration methods still remains a major hurdle in
the way of turning them into usable sources of information. A semantic layer is
an RDF graph that describes metadata and semantic information about a
collection of archived documents, which in turn can be queried through a
semantic query language (SPARQL). This allows running advanced queries by
combining metadata of the documents (like publication date) and content-based
semantic information (like entities mentioned in the documents). However, the
results returned by such structured queries can be numerous and moreover they
all equally match the query. In this paper, we deal with this problem and
formalize the task of "ranking archived documents for structured queries on
semantic layers". Then, we propose two ranking models for the problem at hand
which jointly consider: i) the relativeness of documents to entities, ii) the
timeliness of documents, and iii) the temporal relations among the entities.
The experimental results on a new evaluation dataset show the effectiveness of
the proposed models and allow us to understand their limitations
Towards a Ranking Model for Semantic Layers over Digital Archives
Authors: Fafalios Pavlos, Kasturia Vaibhav, Nejdl Wolfgang
Date: 2018-10-23 00:00:00

Archived collections of documents (like newspaper archives) serve as
important information sources for historians, journalists, sociologists and
other interested parties. Semantic Layers over such digital archives allow
describing and publishing metadata and semantic information about the archived
documents in a standard format (RDF), which in turn can be queried through a
structured query language (e.g., SPARQL). This enables to run advanced queries
by combining metadata of the documents (like publication date) and
content-based semantic information (like entities mentioned in the documents).
However, the results returned by structured queries can be numerous and also
they all equally match the query. Thus, there is the need to rank these results
in order to promote the most important ones. In this paper, we focus on this
problem and propose a ranking model that considers and combines: i) the
relativeness of documents to entities, ii) the timeliness of documents, and
iii) the relations among the entities.
Price Discovery and the Accuracy of Consolidated Data Feeds in the U.S. Equity Markets
Date: 2018-10-25 00:00:00

Both the scientific community and the popular press have paid much attention
to the speed of the Securities Information Processor, the data feed
consolidating all trades and quotes across the US stock market. Rather than the
speed of the Securities Information Processor, or SIP, we focus here on its
accuracy. Relying on Trade and Quote data, we provide various measures of SIP
latency relative to high-speed data feeds between exchanges, known as direct
feeds. We use first differences to highlight not only the divergence between
the direct feeds and the SIP, but also the fundamental inaccuracy of the SIP.
We find that as many as 60 percent or more of trades are reported out of
sequence for stocks with high trade volume, therefore skewing simple measures
such as returns. While not yet definitive, this analysis supports our
preliminary conclusion that the underlying infrastructure of the SIP is
currently unable to keep pace with the trading activity in today's stock
market.