pwc-archive/papers-with-abstracts huggingface dataset:
Mechanism Design with Limited Commitment
Authors: Laura Doval, Vasiliki Skreta
Date: 2018-11-08 00:00:00

We develop a tool akin to the revelation principle for dynamic mechanism-selection games in which the designer can only commit to short-term mechanisms. We identify a canonical class of mechanisms rich enough to replicate the outcomes of any equilibrium in a mechanism-selection game between an uninformed designer and a privately informed agent. A cornerstone of our methodology is the idea that a mechanism should encode not only the rules that determine the allocation, but also the information the designer obtains from the interaction with the agent. Therefore, how much the designer learns, which is the key tension in design with limited commitment, becomes an explicit part of the design. Our result simplifies the search for the designer-optimal outcome by reducing the agent's behavior to a series of participation, truthtelling, and Bayes' plausibility constraints the mechanisms must satisfy.
Incentivising Participation in Liquid Democracy with Breadth-First Delegation
Date: 2019-02-25 00:00:00

Liquid democracy allows members of an electorate to either directly vote over
alternatives, or delegate their voting rights to someone they trust. Most of
the liquid democracy literature and implementations allow each voter to
nominate only one delegate per election. However, if that delegate abstains,
the voting rights assigned to her are left unused. To minimise the number of
unused delegations, it has been suggested that each voter should declare a
personal ranking over voters she trusts. In this paper, we show that even if
personal rankings over voters are declared, the standard delegation method of
liquid democracy remains problematic. More specifically, we show that when
personal rankings over voters are declared, it could be undesirable to receive
delegated voting rights, which is contrary to what liquid democracy
fundamentally relies on. To solve this issue, we propose a new method to
delegate voting rights in an election, called breadth-first delegation.
Additionally, the proposed method prioritises assigning voting rights to
individuals closely connected to the voters who delegate.
A new insight into the secondary path modeling problem in active noise control
Date: 2019-01-18 00:00:00
Tasks: Acoustic echo cancellation

The close relationship between the feedforward ANC system and the stereo
acoustic echo cancellation system is revealed in this paper. Accordingly, the
convergence behavior of the ANC system can be analyzed by investigating the
joint auto-correlation matrix of the reference and the filtered reference
signal. It is proved that the straightforward secondary path modeling can be
carried out without the injection of any additive noise as long as the control
filter is of a sufficiently long length. Furthermore, by taking advantage of
the time-varying characteristic of the control filter, effective modeling of
the secondary path can be even achieved without any restriction on the control
filter length.
Risk-Neutral Pricing and Hedging of In-Play Football Bets
Date: 2018-10-29 00:00:00

A risk-neutral valuation framework is developed for pricing and hedging
in-play football bets based on modelling scores by independent Poisson
processes with constant intensities. The Fundamental Theorems of Asset Pricing
are applied to this set-up which enables us to derive novel arbitrage-free
valuation formul\ae\ for contracts currently traded in the market. We also
describe how to calibrate the model to the market and how trades can be
replicated and hedged.
A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks
Authors: Marchisio Alberto, Hanif Muhammad Abdullah, Rehman Semeen, Martina Maurizio, Shafique Muhammad
Date: 2018-10-27 00:00:00

Activation functions influence behavior and performance of DNNs. Nonlinear
activation functions, like Rectified Linear Units (ReLU), Exponential Linear
Units (ELU) and Scaled Exponential Linear Units (SELU), outperform the linear
counterparts. However, selecting an appropriate activation function is a
challenging problem, as it affects the accuracy and the complexity of the given
DNN. In this paper, we propose a novel methodology to automatically select the
best-possible activation function for each layer of a given DNN, such that the
overall DNN accuracy, compared to considering only one type of activation
function for the whole DNN, is improved. However, an associated scientific
challenge in exploring all the different configurations of activation functions
would be time and resource-consuming. Towards this, our methodology identifies
the Evaluation Points during learning to evaluate the accuracy in an
intermediate step of training and to perform early termination by checking the
accuracy gradient of the learning curve. This helps in significantly reducing
the exploration time during training. Moreover, our methodology selects, for
each layer, the dropout rate that optimizes the accuracy. Experiments show that
we are able to achieve on average 7% to 15% Relative Error Reduction on MNIST,
CIFAR-10 and CIFAR-100 benchmarks, with limited performance and power penalty
on GPUs.
SURE-fuse WFF: A Multi-resolution Windowed Fourier Analysis for Interferometric Phase Denoising
Date: 2019-02-26 00:00:00
Tasks: Denoising, LEMMA

Interferometric phase (InPhase) imaging is an important part of many
present-day coherent imaging technologies. Often in such imaging techniques,
the acquired images, known as interferograms, suffer from two major
degradations: 1) phase wrapping caused by the fact that the sensing mechanism
can only measure sinusoidal $2\pi$-periodic functions of the actual phase, and
2) noise introduced by the acquisition process or the system. This work
focusses on InPhase denoising which is a fundamental restoration step to many
posterior applications of InPhase, namely to phase unwrapping. The presence of
sharp fringes that arises from phase wrapping makes InPhase denoising a
hard-inverse problem. Motivated by the fact that the InPhase images are often
locally sparse in Fourier domain, we propose a multi-resolution windowed
Fourier filtering (WFF) analysis that fuses WFF estimates with different
resolutions, thus overcoming the WFF fixed resolution limitation. The proposed
fusion relies on an unbiased estimate of the mean square error derived using
the Stein's lemma adapted to complex-valued signals. This estimate, known as
SURE, is minimized using an optimization framework to obtain the fusion
weights. Strong experimental evidence, using synthetic and real (InSAR & MRI)
data, that the developed algorithm, termed as SURE-fuse WFF, outperforms the
best hand-tuned fixed resolution WFF as well as other state-of-the-art InPhase
denoising algorithms, is provided.
A framework for simulating systemic risk and its application to the South African banking sector
Date: 2018-11-10 00:00:00
Tasks: Position

We present a network-based framework for simulating systemic risk that
considers shock propagation in banking systems. In particular, the framework
allows the modeller to reflect a top-down framework where a shock to one bank
in the system affects the solvency and liquidity position of other banks,
through systemic market risks and consequential liquidity strains. We
illustrate the framework with an application using South African bank balance
sheet data. Spikes in simulated assessments of systemic risk agree closely with
spikes in documented subjective assessments of this risk. This indicates that
network models can be useful for monitoring systemic risk levels. The model
results are sensitive to liquidity risk and market sentiment and therefore the
related parameters are important considerations when using a network approach
to systemic risk modelling.
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition
Date: 2018-11-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robust Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

Conventional deep neural network (DNN)-based speech enhancement (SE)
approaches aim to minimize the mean square error (MSE) between enhanced speech
and clean reference. The MSE-optimized model may not directly improve the
performance of an automatic speech recognition (ASR) system. If the target is
to minimize the recognition error, the recognition results should be used to
design the objective function for optimizing the SE model. However, the
structure of an ASR system, which consists of multiple units, such as acoustic
and language models, is usually complex and not differentiable. In this study,
we proposed to adopt the reinforcement learning algorithm to optimize the SE
model based on the recognition results. We evaluated the propsoed SE system on
the Mandarin Chinese broadcast news corpus (MATBN). Experimental results
demonstrate that the proposed method can effectively improve the ASR results
with a notable 12.40% and 19.23% error rate reductions for signal to noise
ratio at 0 dB and 5 dB conditions, respectively.
A Model of Competing Narratives
Date: 2018-11-10 00:00:00
Tasks: model

We formalize the argument that political disagreements can be traced to a
"clash of narratives". Drawing on the "Bayesian Networks" literature, we model
a narrative as a causal model that maps actions into consequences, weaving a
selection of other random variables into the story. An equilibrium is defined
as a probability distribution over narrative-policy pairs that maximizes a
representative agent's anticipatory utility, capturing the idea that public
opinion favors hopeful narratives. Our equilibrium analysis sheds light on the
structure of prevailing narratives, the variables they involve, the policies
they sustain and their contribution to political polarization.
Compressive Sensing and Morphology Singular Entropy-Based Real-time Secondary Voltage Control of Multi-area Power Systems
Date: 2018-11-11 00:00:00
Tasks: Compressive Sensing

This paper presents an improved secondary voltage control (SVC) methodology
incorporating compressive sensing (CS) for a multi-area power system. SVC
minimizes the voltage deviation of the load buses while CS deals with the
problem of the limited bandwidth capacity of the communication channel by
reducing the size of massive data output from phasor measurement unit (PMU)
based monitoring system. The proposed strategy further incorporates the
application of a Morphological Median Filter (MMF) to reduce noise from the
output of the PMUs. To keep the control area secure and protected locally,
Mathematical Singular Entropy (MSE) based fault identification approach is
utilized for fast discovery of faults in the control area. Simulation results
with 27-bus and 486-bus power systems show that CS can reduce the data size up
to 1/10th while the MSE based fault identification technique can accurately
distinguish between fault and steady state conditions.
Bayesian Convolutional Neural Networks for Compressed Sensing Restoration
Date: 2019-02-24 00:00:00
Tasks: Bayesian Inference, compressed sensing

Deep Neural Networks (DNNs) have aroused great attention in Compressed
Sensing (CS) restoration. However, the working mechanism of DNNs is not
explainable, thereby it is unclear that how to design an optimal DNNs for CS
restoration. In this paper, we propose a novel statistical framework to explain
DNNs, which proves that the hidden layers of DNNs are equivalent to Gibbs
distributions and interprets DNNs as a Bayesian hierarchical model. The
framework provides a Bayesian perspective to explain the working mechanism of
DNNs, namely some hidden layers learn a prior distribution and other layers
learn a likelihood distribution. Moreover, the framework provides insights into
DNNs and reveals two inherent limitations of DNNs for CS restoration. In
contrast to most previous works designing an end-to-end DNNs for CS
restoration, we propose a novel DNNs to model a prior distribution only, which
can circumvent the limitations of DNNs. Given the prior distribution generated
from the DNNs, we design a Bayesian inference algorithm to realize CS
restoration in the framework of Bayesian Compressed Sensing. Finally, extensive
simulations validate the proposed theory of DNNs and demonstrate that the
proposed algorithm outperforms the state-of-the-art CS restoration methods.
Analysis vs Synthesis - An Investigation of (Co)sparse Signal Models on Graphs
Date: 2018-11-11 00:00:00

In this work, we present a theoretical study of signals with sparse
representations in the vertex domain of a graph, which is primarily motivated
by the discrepancy arising from respectively adopting a synthesis and analysis
view of the graph Laplacian matrix. Sparsity on graphs and, in particular, the
characterization of the subspaces of signals which are sparse with respect to
the connectivity of the graph, as induced by analysis with a suitable graph
operator, remains in general an opaque concept which we aim to elucidate. By
leveraging the theory of cosparsity, we present a novel (co)sparse graph
Laplacian-based signal model and characterize the underlying (structured)
(co)sparsity, smoothness and localization of its solution subspaces on
undirected graphs, while providing more refined statements for special cases
such as circulant graphs. Ultimately, we substantiate fundamental discrepancies
between the cosparse analysis and sparse synthesis models in this structured
setting, by demonstrating that the former constitutes a special, constrained
instance of the latter.
Capital Structure and Speed of Adjustment in U.S. Firms. A Comparative Study in Microeconomic and Macroeconomic Conditions - A Quantille Regression Approach
Date: 2018-11-11 00:00:00
Tasks: quantile regression

The major perspective of this paper is to provide more evidence regarding how
"quickly", in different macroeconomic states, companies adjust their capital
structure to their leverage targets. This study extends the empirical research
on the topic of capital structure by focusing on a quantile regression method
to investigate the behavior of firm-specific characteristics and macroeconomic
factors across all quantiles of distribution of leverage (book leverage and
market leverage). Therefore, depending on a partial adjustment model, we find
that the adjustment speed fluctuated in different stages of book versus market
leverage. Furthermore, while macroeconomic states change, we detect clear
differentiations of the contribution and the effects of the firm-specific and
the macroeconomic variables between market leverage and book leverage debt
ratios. Consequently, we deduce that across different macroeconomic states the
nature and maturity of borrowing influence the persistence and endurance of the
relation between determinants and borrowing.
Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression
Date: 2018-11-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Biomarkers of Major Depressive Disorder(MDD), its phases and forms have long
been sought. Research indicates that the complexity measures of the cortical
electrical activity (EEG) might be candidates for this role. To examine whether
the complexity of EEG activity, measured by Higuchi fractal dimension (HFD) and
sample entropy (SampEn), differs between healthy subjects, patients in
remission and episode phase of the recurrent depression and whether the changes
are differentially distributed between hemispheres and cortical regions.
Resting state EEG with eyes closed was recorded from 26 patients suffering from
recurrent depression and 20 age and sex-matched healthy control subjects.
Artefact-free EEG epochs were analyzed by in-house developed programs running
HFD and SampEn algorithms. Depressed patients had higher HFD and SampEn
complexity compared to healthy subjects. Surprisingly, the complexity was even
higher in patients who were in remission than in those in the episode. Altered
complexity was present in the frontal and centro-parietal regions when compared
to the control group. The complexity in frontal and parietal regions differed
between the two phases of depressive disorder. SampEn manifested higher
sensitivity than HFD in some cortical areas. Complexity measures of EEG
distinguish between the three groups. Further studies are needed to establish
whether these measures carry the potential to aid clinically relevant decisions
about depression.
Deep Learning Based Transmitter Identification using Power Amplifier Nonlinearity
Date: 2018-11-12 00:00:00
Tasks: Deep Learning

The imperfections in the RF frontend of different transmitters can be used to
distinguish them. This process is called transmitter identification using RF
fingerprints. The nonlinearity in the power amplifier of the RF frontend is a
significant cause of the discrepancy in RF fingerprints, which enables
transmitter identification. In this work, we use deep learning to identify
different transmitters using their nonlinear characteristics. By developing a
nonlinear model generator based on extensive measurements, we were able to
extend the evaluation of transmitter identification to include a larger number
of transmitters beyond what exists in the literature. We were also able to
study the impact of transmitter variability on identification accuracy.
Additionally, many other factors were considered including modulation type,
length of data used for identification, and type of data being transmitted
whether identical or random under a realistic channel model. Simulation results
were compared with experiments which confirmed similar trends.
Topographic maps in the brain are fundamental to processing of causality
Date: 2018-11-12 00:00:00

The ubiquity of topographic maps in the brain has long been known, and
molecular mechanisms for the formation of topographic organization of neural
systems have been revealed. Less attention has been given to the question of
why are the maps topographical and why so ubiquitous. In this study, I explore
the implications of the topographic maps for brain function, by employing the
mathematical framework of the Zeeman topology. I propose the notion about the
meaning of topographic order as generic mechanisms for the representation and
analysis of causal structure, implemented by the neural systems. This leads to
a much improved understanding of the division of labour between chemical
systems and neural systems in the formation of maps to deal with causality.
140 Gbaud On-Off Keying Links in C-Band for Short-Reach Optical Interconnects
Date: 2018-11-12 00:00:00

We demonstrate 140 Gbaud intensity modulated direct detection
dispersion-uncompensated links with Mach Zehnder modulator and distributed
feedback travelling-wave electro-absorption modulator over 5500 and 960 meters
of standard single mode fibre, respectively, enabled by compact packaged
ultra-high speed InP-based 2:1-Selector.
Different Power Adaption Methods on Fluctuating Two-Ray Fading Channels
Date: 2018-11-12 00:00:00
Tasks: valid, Vocal Bursts Valence Prediction

In this letter, we consider a typical scenario where the transmitter employs
different power adaption methods, including the optimal rate and power
algorithm, optimal rate adaption, channel inversion and truncated channel
inversion, to enhance the ergodic capacity (EC) with an average transmit power
constraint over fluctuating two-way fading channels. In particular, we derive
exact closed-form expressions for the EC under different power adaption
methods, as well as corresponding asymptotic formulas for the EC valid in the
high signal-to-noise ratio (SNR) region. Finally, we compare the performance of
the EC under different power adaption methods, and this also validates the
accuracy of our derived expressions for the exact and asymptotic EC.
Topology of the mesoscale connectome of the mouse brain
Date: 2018-12-21 00:00:00

The wiring diagram of the mouse brain has recently been mapped at a
mesoscopic scale in the Allen Mouse Brain Connectivity Atlas. Axonal
projections from brain regions were traced using green fluoresent proteins. The
resulting data were registered to a common three-dimensional reference space.
They yielded a matrix of connection strengths between 213 brain regions. Global
features such as closed loops formed by connections of similar intensity can be
inferred using tools from persistent homology. We map the wiring diagram of the
mouse brain to a simplicial complex (filtered by connection strengths). We work
out generators of the first homology group. Some regions, including nucleus
accumbens, are connected to the entire brain by loops, whereas no region has
non-zero connection strength to all brain regions. Thousands of loops go
through the isocortex, the striatum and the thalamus. On the other hand,
medulla is the only major brain compartment that contains more than 100 loops.
Reimplementation and Reinterpretation of the Copycat Project
Authors: Huang Hongyi
Date: 2018-10-26 00:00:00

We present the reinterpreted and reimplemented Copycat project, an
architecture solving letter analogy domain problems. To support a flexible
implementation change and rigor testing process, we propose a implementation
method in DrRacket by using functional abstraction, naming system,
initialization, and structural reference. Finally, benefits and limitations are
analyzed for cognitive architectures along the lines of Copycat.
Assessing biological models using topological data analysis
Date: 2018-11-12 00:00:00
Tasks: Time Series, Time Series Analysis, Topological Data Analysis

We use topological data analysis as a tool to analyze the fit of mathematical
models to experimental data. This study is built on data obtained from motion
tracking groups of aphids in [Nilsen et al., PLOS One, 2013] and two random
walk models that were proposed to describe the data. One model incorporates
social interactions between the insects, and the second model is a control
model that excludes these interactions. We compare data from each model to data
from experiment by performing statistical tests based on three different sets
of measures. First, we use time series of order parameters commonly used in
collective motion studies. These order parameters measure the overall
polarization and angular momentum of the group, and do not rely on a priori
knowledge of the models that produced the data. Second, we use order parameter
time series that do rely on a priori knowledge, namely average distance to
nearest neighbor and percentage of aphids moving. Third, we use computational
persistent homology to calculate topological signatures of the data. Analysis
of the a priori order parameters indicates that the interactive model better
describes the experimental data than the control model does. The topological
approach performs as well as these a priori order parameters and better than
the other order parameters, suggesting the utility of the topological approach
in the absence of specific knowledge of mechanisms underlying the data.
Noise- and Outlier-Resistant Tomographic Reconstruction under Unknown Viewing Parameters
Date: 2018-11-12 00:00:00
Tasks: Image Reconstruction, Object

In this paper, we present an algorithm for effectively reconstructing an
object from a set of its tomographic projections without any knowledge of the
viewing directions or any prior structural information, in the presence of
pathological amounts of noise, unknown shifts in the projections, and outliers
among the projections. The outliers are mainly in the form of a number of
projections of a completely different object, as compared to the object of
interest. We introduce a novel approach of first processing the projections,
then obtaining an initial estimate for the orientations and the shifts, and
then define a refinement procedure to obtain the final reconstruction. Even in
the presence of high noise variance (up to $50\%$ of the average value of the
(noiseless) projections) and presence of outliers, we are able to successfully
reconstruct the object. We also provide interesting empirical comparisons of
our method with the sparsity based optimization procedures that have been used
earlier for image reconstruction tasks.