pwc-archive/papers-with-abstracts huggingface dataset:
State-Space Network Topology Identification from Partial Observations
Date: 2019-06-25 00:00:00

In this work, we explore the state-space formulation of a network process to
recover, from partial observations, the underlying network topology that drives
its dynamics. To do so, we employ subspace techniques borrowed from system
identification literature and extend them to the network topology
identification problem. This approach provides a unified view of the
traditional network control theory and signal processing on graphs. In
addition, it provides theoretical guarantees for the recovery of the
topological structure of a deterministic continuous-time linear dynamical
system from input-output observations even though the input and state
interaction networks might be different. The derived mathematical analysis is
accompanied by an algorithm for identifying, from data, a network topology
consistent with the dynamics of the system and conforms to the prior
information about the underlying structure. The proposed algorithm relies on
alternating projections and is provably convergent. Numerical results
corroborate the theoretical findings and the applicability of the proposed
algorithm.
Keep soft robots soft -- a data-driven based trade-off between feed-forward and feedback control
Date: 2019-06-25 00:00:00
Tasks: regression

Tracking control for soft robots is challenging due to uncertainties in the
system model and environment. Using high feedback gains to overcome this issue
results in an increasing stiffness that clearly destroys the inherent safety
property of soft robots. However, accurate models for feed-forward control are
often difficult to obtain. In this article, we employ Gaussian Process
regression to obtain a data-driven model that is used for the feed-forward
compensation of unknown dynamics. The model fidelity is used to adapt the
feed-forward and feedback part allowing low feedback gains in regions of high
model confidence.
On Capital Allocation under Information Constraints
Date: 2020-04-21 00:00:00
Tasks: Portfolio Optimization

Attempts to allocate capital across a selection of different investments are
often hampered by the fact that investors' decisions are made under limited
information (no historical return data) and during an extremely limited
timeframe. Nevertheless, in some cases, rational investors with a certain level
of experience are able to ordinally rank investment alternatives through
relative assessments of the probabilities that investments will be successful.
However, to apply traditional portfolio optimization models, analysts must use
historical (or simulated/expected) return data as the basis for their
calculations. This paper develops an alternative portfolio optimization
framework that is able to handle this kind of information (given by an ordinal
ranking of investment alternatives) and to calculate an optimal capital
allocation based on a Cobb-Douglas function, which we call the Sorted Weighted
Portfolio (SWP). Considering risk-neutral investors, we show that the results
of this portfolio optimization model usually outperform the output generated by
the (intuitive) Equally Weighted Portfolio (EWP) of different investment
alternatives, which is the result of optimization when one is unable to
incorporate additional data (the ordinal ranking of the alternatives). To
further extend this work, we show that our model can also address risk-averse
investors to capture correlation effects.
Communication Through a Large Reflecting Surface With Phase Errors
Date: 2019-06-25 00:00:00

Assume the communication between a source and a destination is supported by a
large reflecting surface (LRS), which consists of an array of reflector
elements with adjustable reflection phases. By knowing the phase shifts induced
by the composite propagation channels through the LRS, the phases of the
reflectors can be configured such that the signals combine coherently at the
destination, which improves the communication performance. However, perfect
phase estimation or high-precision configuration of the reflection phases is
unfeasible. In this paper, we study the transmission through an LRS with phase
errors that have a generic distribution. We show that the LRS-based composite
channel is equivalent to a point-to-point Nakagami fading channel. This
equivalent representation allows for theoretical analysis of the performance
and can help the system designer study the interplay between performance, the
distribution of phase errors, and the number of reflectors. Numerical
evaluation of the error probability for a limited number of reflectors confirms
the theoretical prediction and shows that the performance is remarkably robust
against the phase errors.
Optimized energy utilization in small and large commercial loads and residential areas
Date: 2019-06-25 00:00:00
Tasks: Management, Scheduling

In smart grid, the demand side management (DSM) techniques need to be
designed to process a large number of controllable loads of several types. In
this paper, we proposed a framework to study the demand side management in
smart grid which contains a variety of loads in two service areas, one with
multiple residential households, and one bus with commercial customers.
Specifically, each household may have renewable generation as well as
interruptible and uninterruptible appliances to make individual scheduling to
optimize the electric energy cost by making the best time of the electricity
usage according to the day ahead forecast of electricity prices. A high load
bus represents a commercial area employed to demonstrate the impact of high
load at any bus on voltage profile, power loss, and load flow condition, and to
show the performance of the proposed DSM for large number of appliance. Using
the developed simulation model, we examine the performance of the proposed DSM
and study their impact on the distribution network operation and renewable
generation, overall voltage deviation, real power loss, and possible problems
such as reverse power flows, voltage rise have examined and compared, these
problems can easily be seen at the commercial load bus.
Design of distributed guidance laws for multi-UAV cooperative attacking a moving target based on reducing surrounding area
Date: 2019-06-26 00:00:00

In this paper, two cooperative guidance laws based on the area around the
target of multiattackers are designed to deal with the problem of cooperative
encirclement or simultaneous attack in the case of known target acceleration
and unknown target acceleration. Multi-attacker communication network only
needs to contain a directed spanning tree, and does not require all attackers
to observe the target information, where at least one can observe the target.
The components along the attacker-target line of sight in the novel guidance
laws can reduce the relative remaining distance between the attacker and the
target at the same speed, thus completing simultaneous attack and avoiding the
calculation of the remaining time. The components of the guidance laws
perpendicular to the attacker-target line of sight can make the normal overload
of relative motion zero, so that the trajectory will not be distorted and the
collision problem within the attacker group can be avoided. The simulation
results verify the practicability of the novel guidance laws.
Computer-aided modelling of complex physical systems with BondGraphTools
Date: 2019-06-26 00:00:00

BondGraphTools is a Python library for scripted modelling of complex
multi-physics systems. In contrast to existing modelling solutions,
BondGraphTools is based upon the well established bond graph methodology,
provides a programming interface for symbolic model composition, and is
intended to be used in conjunction with the existing scientific Python
toolchain. Here we discuss the design, implementation and use of
BondGraphTools, demonstrate how it can be used to accelerate systems modelling
with an example from optomechanics, and comment on current and future
applications in cross-domain modelling, particularly in systems biology.
End-to-End Emotional Speech Synthesis Using Style Tokens and Semi-Supervised Training
Date: 2019-06-26 00:00:00
Tasks: Emotional Speech Synthesis, Emotion Recognition, Speech Synthesis

This paper proposes an end-to-end emotional speech synthesis (ESS) method
which adopts global style tokens (GSTs) for semi-supervised training. This
model is built based on the GST-Tacotron framework. The style tokens are
defined to present emotion categories. A cross entropy loss function between
token weights and emotion labels is designed to obtain the interpretability of
style tokens utilizing the small portion of training data with emotion labels.
Emotion recognition experiments confirm that this method can achieve one-to-one
correspondence between style tokens and emotion categories effectively.
Objective and subjective evaluation results show that our model outperforms the
conventional Tacotron model for ESS when only 5\% of training data has emotion
labels. Its subjective performance is close to the Tacotron model trained using
all emotion labels.
Cross-correlated Contrast Source Inversion
Date: 2019-06-26 00:00:00

In this paper, we improved the performance of the contrast source inversion
(CSI) method by incorporating a so-called cross-correlated cost functional,
which interrelates the state error and the data error in the measurement
domain. The proposed method is referred to as the cross-correlated CSI. It
enables better robustness and higher inversion accuracy than both the classical
CSI and multiplicative regularized CSI (MR-CSI). In addition, we show how the
gradient of the modified cost functional can be calculated without
significantly increasing the computational burden. The advantages of the
proposed algorithms are demonstrated using a 2-D benchmark problem excited by a
transverse magnetic wave as well as a transverse electric wave, respectively,
in comparison to classical CSI and MR-CSI.
A Linear Method for Shape Reconstruction based on the Generalized Multiple Measurement Vectors Model
Date: 2019-06-26 00:00:00

In this paper, a novel linear method for shape reconstruction is proposed
based on the generalized multiple measurement vectors (GMMV) model. Finite
difference frequency domain (FDFD) is applied to discretized Maxwell's
equations, and the contrast sources are solved iteratively by exploiting the
joint sparsity as a regularized constraint. Cross validation (CV) technique is
used to terminate the iterations, such that the required estimation of the
noise level is circumvented. The validity is demonstrated with an excitation of
transverse magnetic (TM) experimental data, and it is observed that, in the
aspect of focusing performance, the GMMV-based linear method outperforms the
extensively used linear sampling method (LSM).
European Option Pricing of electricity under exponential functional of Lévy processes with Price-Cap principle
Authors: Martin Kegnenlezom, Patrice Takam Soh, Antoine-Marie Bogso, Yves Emvudu Wono
Date: 2019-06-26 00:00:00

We propose a new model for electricity pricing based on the price cap principle. The particularity of the model is that the asset price is an exponential functional of a jump L\'evy process. This model can capture both mean reversion and jumps which are observed in electricity market. It is shown that the value of an European option of this asset is the unique viscosity solution of a partial integro-differential equation (PIDE). A numerical approximation of this solution by the finite differences method is provided. The consistency, stability and convergence results of the scheme are given. Numerical simulations are performed under a smooth initial condition.
Probabilistic Two-Ray Model for Air-to-Air Channel in Built-Up Areas
Date: 2019-06-26 00:00:00

In this paper, we present a probabilistic two-ray (PTR) path loss model for
air-to-air (AA) propagation channel in built-up areas. Based on the statistical
model of city deployment, the PTR path loss model can be applied to suburban,
urban, dense urban, and high-rise urban. The path loss is optimally fitted as
the Weibull distribution and its fluctuation is fitted as the Normal
distribution in ray-tracing simulations. The good agreements between our model
and ray tracing indicate the proposed model can provide a useful tool for
accurate and quick prediction for aerial platforms. As an extended research of
PTR model, we extract the shadowing factor by numerous simulations and propose
the altitude-dependent shadowing model. The result shows that the proposed
shadowing model has very good consistent with the measurement-based model,
which indicates that our research performs well in the extensibility and
generality.
Dual representations for systemic risk measures based on acceptance sets
Date: 2019-10-24 00:00:00

We establish dual representations for systemic risk measures based on
acceptance sets in a general setting. We deal with systemic risk measures of
both "first allocate, then aggregate" and "first aggregate, then allocate"
type. In both cases, we provide a detailed analysis of the corresponding
systemic acceptance sets and their support functions. The same approach
delivers a simple and self-contained proof of the dual representation of
utility-based risk measures for univariate positions.
An Acoustic Emission Activity Detection Method based on Short-Term Waveform Features: Application to Metallic Components under Uniaxial Tensile Test
Date: 2019-06-26 00:00:00
Tasks: Action Detection, Activity Detection, Event Detection

The Acoustic Emission (AE) phenomenon has been used as a powerful tool with
the purpose to either detect, locate or assess damage for a wide range of
applications. Derived from its monitoring, one major current challenge on the
analysis of the acquired signal is the proper identification and separation of
each AE event. Current advanced methods for detecting events are primarily
focused on identifying with high accuracy the beginning of the AE wave;
however, the detection of the conclusion has been disregarded in the
literature. For an automatic continuous detection of events within a data
stream, this lack of accuracy for the conclusion of the events generates errors
in two critical aspects. In one hand, it deteriorates the accuracy of the
measurement of the events duration, truncating the span of the event
(undesirable in evaluation applications), and in the other hand, it causes
false detections. In this work, an accurate and computationally efficient AE
activity detector is presented, using a framework inspired by the area of
speech processing, and which provides the required indicators to accurately
detect the onset and the end of an AE event. This is achieved by means of a
threshold approach that instead of directly operates with the transduced
voltage signal it does so over the Short-Term Energy and the Short-Term
Zero-Crossing Rate measures of the signal. The STE-ZCR method is developed for
an application related to the continuous monitoring of a single AE channel
derived from the characterization of metallic components by means of a uniaxial
tensile test. Additionally, two experimental test-benches are implemented with
the aim to quantify the accuracy and the quality of event detection of the
presented method. Finally, the obtained results are compared with four
different techniques, representing the current state of the art related to AE
activity detection.
Chance-Constrained Trajectory Optimization for Non-linear Systems with Unknown Stochastic Dynamics
Date: 2019-07-31 00:00:00
Tasks: Model-based Reinforcement Learning

Iterative trajectory optimization techniques for non-linear dynamical systems
are among the most powerful and sample-efficient methods of model-based
reinforcement learning and approximate optimal control. By leveraging
time-variant local linear-quadratic approximations of system dynamics and
reward, such methods can find both a target-optimal trajectory and time-variant
optimal feedback controllers. However, the local linear-quadratic assumptions
are a major source of optimization bias that leads to catastrophic greedy
updates, raising the issue of proper regularization. Moreover, the approximate
models' disregard for any physical state-action limits of the system causes
further aggravation of the problem, as the optimization moves towards
unreachable areas of the state-action space. In this paper, we address the
issue of constrained systems in the scenario of online-fitted stochastic linear
dynamics. We propose modeling state and action physical limits as probabilistic
chance constraints linear in both state and action and introduce a new
trajectory optimization technique that integrates these probabilistic
constraints by optimizing a relaxed quadratic program. Our empirical
evaluations show a significant improvement in learning robustness, which
enables our approach to perform more effective updates and avoid premature
convergence observed in state-of-the-art algorithms.
Survey of Information Encoding Techniques for DNA
Authors: Thomas Heinis, Roman Sokolovskii, Jamie J. Alnasir
Date: 2019-06-24 00:00:00
Tasks: Information Retrieval, Retrieval, Survey, Triplet

The yearly global production of data is growing exponentially, outpacing the capacity of existing storage media, such as tape and disk, and surpassing our ability to store it. DNA storage - the representation of arbitrary information as sequences of nucleotides - offers a promising storage medium. DNA is nature's information-storage molecule of choice and has a number of key properties: it is extremely dense, offering the theoretical possibility of storing 455 EB/g; it is durable, with a half-life of approximately 520 years that can be increased to thousands of years when DNA is chilled and stored dry; and it is amenable to automated synthesis and sequencing. Furthermore, biochemical processes that act on DNA potentially enable highly parallel data manipulation. Whilst biological information is encoded in DNA via a specific mapping from triplet sequences of nucleotides to amino acids, DNA storage is not limited to a single encoding scheme, and there are many possible ways to map data to chemical sequences of nucleotides for synthesis, storage, retrieval and data manipulation. However, there are several biological, error-tolerance and information-retrieval considerations that an encoding scheme needs to address to be viable. This comprehensive review focuses on comparing existing work done in encoding arbitrary data within DNA in terms of their encoding schemes, methods to address biological constraints and measures to provide error correction. We compare encoding approaches on the overall information density and coverage they achieve, as well as the data-retrieval method they use (i.e., sequential or random access). We also discuss the background and evolution of the encoding schemes.
Mismatched Estimation of Polynomially Damped Signals
Date: 2019-06-26 00:00:00

In this work, we consider the problem of estimating the parameters of
polynomially damped sinusoidal signals, commonly encountered in, for instance,
spectroscopy. Generally, finding the parameter values of such signals
constitutes a high-dimensional problem, often further complicated by not
knowing the number of signal components or their specific signal structures. In
order to alleviate the computational burden, we herein propose a mismatched
estimation procedure using simplified, approximate signal models. Despite the
approximation, we show that such a procedure is expected to yield predictable
results, allowing for statistically and computationally efficient estimates of
the signal parameters.
Distributed Optimal Guidance Laws for Multiple Unmanned Aerial Vehicles Attacking A Moving Target
Date: 2019-06-26 00:00:00

In this paper, two cooperative guidance laws based on two-point boundary
value are designed to deal with the problem of cooperative encirclement and
simultaneous attack under condition of both known target acceleration and
unknown target acceleration. The only requirement for the multi-attacker
communication network is that it contains a directed spanning tree. The
guidance laws can function properly as long as at least one attacker can
observed the target. The acceleration components along the attacker-target line
of sight in the novel guidance laws can reduce the relative remaining distance
between each of the attackers and the target at the same speed, thus completing
simultaneous attack and avoiding the calculation of the remaining time. The
components of the guidance laws perpendicular to the attacker-target line of
sight can make the normal overload of relative motion zero, so that the
trajectory will be smooth and the collision problem within the attacker can be
avoided. Simulation results verified the practicability of the novel guidance
laws.
Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big data statistics
Date: 2019-06-15 00:00:00
Tasks: Survey, Survey Sampling

Purchase data from retail chains provide proxy measures of private household
expenditure on items that are the most troublesome to collect in the
traditional expenditure survey. Due to the sheer amount of proxy data, the bias
due to coverage and selection errors completely dominates the variance. We
develop tests for bias based on audit sampling, which makes use of available
survey data that cannot be linked to the proxy data source at the individual
level. However, audit sampling fails to yield a meaningful mean squared error
estimate, because the sampling variance is too large compared to the bias of
the big data estimate. We propose a novel accuracy measure that is applicable
in such situations. This can provide a necessary part of the statistical
argument for the uptake of big data source, in replacement of traditional
survey sampling. An application to disaggregated food price index is used to
demonstrate the proposed approach.
Content-Aware User Association and Multi-User MIMO Beamforming over Mobile Edge Caching
Authors: Susanna Mosleh, Qiang Fan, Lingjia Liu, Jonathan D. Ashdown, Erik Perrins, Kurt Turck
Date: 2019-06-26 00:00:00

Mobile edge caching (MEC) has been introduced to support ever-growing end-users' needs. To reduce the backhaul traffic demand and content delivery latency, cache-enabled edge servers at base stations (BSs) are employed to provision popular contents at the network edge. In this paper, multiple-input-multiple-output (MIMO) operation and user association policy are linked to the underlying cache placement strategy to ensure a good trade-off between load balancing and backhaul traffic taking into account the underlying wireless channel and the finite cache capacity at edge servers. Due to the coupled interference among mobile stations, the binary nature of the underlying cache placement and user association matrices, the resulting mixed-timescale mixed integer optimization problem is nonconvex and NP-hard. To solve this problem, we decompose the joint optimization problem into a long-term content placement sub-problem and a short-term content delivery sub-problem. A novel iterative algorithm is introduced by leveraging the alternating direction method of multipliers together with a stochastic parallel successive convex approximation-based algorithm. The introduced scheme enables all BSs to update their optimization variables in parallel by solving a sequence of convex subproblems. Simulation evaluation demonstrates the efficiency of our strategy.