pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Alzheimer's Disease Using Gated Convolutional Neural Network from Audio Data
Date: 2018-03-30 00:00:00

We propose an automatic detection method of Alzheimer's diseases using a
gated convolutional neural network (GCNN) from speech data. This GCNN can be
trained with a relatively small amount of data and can capture the temporal
information in audio paralinguistic features. Since it does not utilize any
linguistic features, it can be easily applied to any languages. We evaluated
our method using Pitt Corpus. The proposed method achieved the accuracy of
73.6%, which is better than the conventional sequential minimal optimization
(SMO) by 7.6 points.
Fodis: software for protein unfolding analysis
Date: 2018-09-27 00:00:00
Tasks: Classification

The folding dynamics of proteins at the single molecule level has been
studied with single-molecule force spectroscopy (SMFS) experiments for twenty
years, but a common standardized method for the analysis of the collected data
and for the sharing among the scientific community members is still not
available. We have developed a new open source tool, Fodis, for the analysis of
the Force-distance curves obtained in SMFS experiments, providing an almost
automatic processing, analysis and classification of the obtained data. Our
method provides also a classification of the possible unfolding pathways and
structural heterogeneity, present during the unfolding of proteins.
Lateral position correction in ptychography with sub-pixel accuracy
Date: 2018-03-29 00:00:00
Tasks: Position

Ptychography, a form of Coherent Diffractive Imaging, is used with short
wavelengths (e.g. X-rays, electron beams) to achieve high-resolution image
reconstructions. One of the limiting factors for the reconstruction quality is
the accurate knowledge of the illumination probe positions. Recently, many
advances have been made to relax the requirement for the probe positions
accuracy. Here, we analyze and demonstrate a straightforward approach that can
be used to correct the probe positions with sub-pixel accuracy. Simulations and
experimental results with visible light are presented in this work.
Generating Functional Analysis of Iterative Sparse Signal Recovery Algorithms with Divergence-Free Estimators
Date: 2018-03-28 00:00:00

Approximate message passing (AMP) is an effective iterative sparse recovery
algorithm for linear system models. Its performance is characterized by the
state evolution (SE) which is a simple scalar recursion. However, depending on
a measurement matrix ensemble, AMP may face a convergence problem. To avoid
this problem, orthogonal AMP (OAMP), which uses de-correlation linear
estimation and divergence-free non-linear estimation, was proposed by Ma and
Ping. They also provide the SE analysis for OAMP. In their SE analysis, the
following two assumptions were made: (i) The estimated vector of the
de-correlation linear estimator consists of i.i.d. zero-mean Gaussian entries
independent of the vector to be estimated and (ii) the estimated vector of the
divergence-free non-linear estimator consists of i.i.d. entries independent of
the measurement matrix and the noise vector. In this paper, we derive a simple
scalar recursion to characterize iterative sparse recovery algorithms with
divergence-free estimators without such assumptions of independence of messages
by using the generating functional analysis (GFA), which allows us to study the
dynamics by an exact way in the large system limit.
Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimization
Date: 2018-09-11 00:00:00
Tasks: Portfolio Optimization, regression

The least squares Monte Carlo algorithm has become popular for solving
portfolio optimization problems. A simple approach is to approximate the value
functions on a discrete grid of portfolio weights, then use control regression
to generalize the discrete estimates. However, the classical global control
regression can be expensive and inaccurate. To overcome this difficulty, we
introduce a local control regression technique, combined with adaptive grids.
We show that choosing a coarse grid for local regression can produce
sufficiently accurate results.
Colonel Mustard in the Aviary with the Candlestick: a limit cycle attractor transitions to a stable focus via supercritical Andronov-Hopf bifurcation
Date: 2018-07-25 00:00:00

We establish the means by which Mr. Boddy came to transition from a stable
trajectory within the global phase space of Philadelphia, Pennsylvania to a
stable point on the cement floor of an aviary near the west bank of the
Schuylkill River. There exist no documented murder motives, and so the
dynamical interaction leading to the crime must be reconstructed from
circumstantial data. Our investigation proceeds in two stages. First we take an
audio stream recorded within the aviary near the time of death to identify the
local embedding dimension, thereby enumerating the suspects. Second, we
characterize Mr. Boddy's pre- and post-mortem behavior in the phase space in
terms of an attractor that undergoes an abrupt change in stability. A
supercritical Andronov-Hopf bifurcation can explain this transition. Then we
uniquely identify the murderer. Finally, we note long-term plans to construct
an underlying dynamical model capable of predicting the stability of equilibria
in different parameter regimes, in the event that Mr. Boddy is ever murdered
again.
Reactive Supervisory Control of Open Discrete-event Systems
Date: 2019-09-18 00:00:00

The conventional Wonham-Ramadge supervisory control framework of discrete
event systems enforces a closed discrete event system to generate correct
behaviors under certain environments, which can be captured by an appropriate
plant model. Nevertheless, such control methods cannot be directly applied for
many practical engineering systems nowadays since they are open systems and
their operation heavily depends on nontrivial interactions between the systems
and the external environments. These open systems should be controlled in such
a way that accomplishment of the control objective can be guaranteed for any
possible environment, which may be dynamic, uncertain and sometimes
unpredictable. In this paper, we aim at extending the conventional supervisory
control theory to open discrete event systems in a reactive manner. Starting
from a novel input-output automaton model of an open system, we consider
control objectives that characterize the desired input-output behaviors of the
system, based on which a game-theoretic approach is carried out to compute a
reactive supervisor that steers the system to fulfill the specifications
regardless of the environment behaviors. We present a necessary and sufficient
conditions for the existence of such a reactive supervisor. Furthermore,
illustrative examples are given throughout this paper to demonstrate the key
definitions and the effectiveness of the proposed reactive supervisor synthesis
framework.
Gaussian graphical models reveal inter-modal and inter-regional conditional dependencies of brain alterations in Alzheimer's disease
Date: 2020-03-27 00:00:00

Alzheimer's disease (AD) is characterized by a sequence of pathological
changes, which are commonly assessed in vivo using MRI and PET. Currently, the
most approaches to analyze statistical associations between brain regions rely
on Pearson correlation. However, these are prone to spurious correlations
arising from uninformative shared variance. Notably, there are no appropriate
multivariate statistical models available that can easily integrate dozens of
variables derived from such data, being able to use the additional information
provided from the combination of data sources. Gaussian graphical models (GGMs)
can estimate the conditional dependency from given data, which is expected to
reflect the underlying causal relationships. We applied GGMs to assess
multimodal regional brain alterations in AD. We obtained data from N=972
subjects from the Alzheimer's Disease Neuroimaging Initiative. The mean amyloid
load (AV45-PET), glucose metabolism (FDG-PET), and gray matter volume (MRI)
were calculated. GGMs were estimated using a Bayesian framework for the
combined multimodal data to obtain conditional dependency networks. Conditional
dependency matrices were much sparser (10% density) than Pearson correlation
matrices (50% density). Within modalities, conditional dependency networks
yielded clusters connecting anatomically adjacent regions. For associations
between different modalities, only few region-specific connections remained.
Graph-theoretical network statistics were significantly altered between groups,
with a biphasic u-shape trajectory. GGMs removed shared variance among
multimodal measures of regional brain alterations in MCI and AD, and yielded
sparser matrices compared to Pearson correlation networks. Therefore, GGMs may
be used as alternative to thresholding-approaches typically applied to
correlation networks to obtain the most informative relations between
variables.
Indifference pricing of pure endowments via BSDEs under partial information
Date: 2020-07-22 00:00:00

In this paper we investigate the pricing problem of a pure endowment contract
when the insurer has a limited information on the mortality intensity of the
policyholder. The payoff of this kind of policies depends on the residual life
time of the insured as well as the trend of a portfolio traded in the financial
market, where investments in a riskless asset, a risky asset and a longevity
bond are allowed. We propose a modeling framework that takes into account
mutual dependence between the financial and the insurance markets via an
observable stochastic process, which affects the risky asset and the mortality
index dynamics. Since the market is incomplete due to the presence of basis
risk, in alternative to arbitrage pricing we use expected utility maximization
under exponential preferences as evaluation approach, which leads to the
so-called indifference price. Under partial information this methodology
requires filtering techniques that can reduce the original control problem to
an equivalent problem in complete information. Using stochastic dynamics
techniques, we characterize the indifference price of the insurance derivative
via the solutions of suitable backward stochastic differential equations.
Continuous Record Laplace-based Inference about the Break Date in Structural Change Models
Date: 2020-05-11 00:00:00
Tasks: valid

Building upon the continuous record asymptotic framework recently introduced
by Casini and Perron (2018a) for inference in structural change models, we
propose a Laplace-based (Quasi-Bayes) procedure for the construction of the
estimate and confidence set for the date of a structural change. It is defined
by an integration rather than an optimization-based method. A transformation of
the least-squares criterion function is evaluated in order to derive a proper
distribution, referred to as the Quasi-posterior. For a given choice of a loss
function, the Laplace-type estimator is the minimizer of the expected risk with
the expectation taken under the Quasi-posterior. Besides providing an
alternative estimate that is more precise|lower mean absolute error (MAE) and
lower root-mean squared error (RMSE)|than the usual least-squares one, the
Quasi-posterior distribution can be used to construct asymptotically valid
inference using the concept of Highest Density Region. The resulting
Laplace-based inferential procedure is shown to have lower MAE and RMSE, and
the confidence sets strike the best balance between empirical coverage rates
and average lengths of the confidence sets relative to traditional long-span
methods, whether the break size is small or large.
Stabilizing a linear system using phone calls: when time is information
Authors: Mohammad Javad Khojasteh, Massimo Franceschetti, Gireeja Ranade
Date: 2018-04-01 00:00:00

We consider the problem of stabilizing an undisturbed, scalar, linear system over a "timing" channel, namely a channel where information is communicated through the timestamps of the transmitted symbols. Each symbol transmitted from a sensor to a controller in a closed-loop system is received subject to some to random delay. The sensor can encode messages in the waiting times between successive transmissions and the controller must decode them from the inter-reception times of successive symbols. This set-up is analogous to a telephone system where a transmitter signals a phone call to a receiver through a "ring" and, after the random delay required to establish the connection; the receiver is aware of the "ring" being received. Since there is no data payload exchange between the sensor and the controller, this set-up provides an abstraction for performing event-triggering control with zero-payload rate. We show the following requirement for stabilization: for the state of the system to converge to zero in probability, the timing capacity of the channel should be, essentially, at least as large as the entropy rate of the system. Conversely, in the case the symbol delays are exponentially distributed, we show an "almost" tight sufficient condition using a coding strategy that refines the estimate of the decoded message every time a new symbol is received. Our results generalize previous zero-payload event-triggering control strategies, revealing a fundamental limit in using timing information for stabilization, independent of any transmission strategy.
Three-dimensional alteration of neurites in schizophrenia
Date: 2019-02-16 00:00:00

This paper reports nano-CT analysis of brain tissues of schizophrenia and
control cases. The analysis revealed that: (1) neuronal structures vary between
individuals, (2) the mean curvature of distal neurites of the schizophrenia
cases was 1.5 times higher than that of the controls, and (3) dendritic spines
were categorized into two geometrically distinctive groups, though no
structural differences were observed between the disease and control cases. The
differences in the neurite curvature result in differences in the spatial
trajectory and hence alter neuronal circuits. We suggest that the structural
alteration of neurons in the schizophrenia cases should reflect psychiatric
symptoms of schizophrenia.
Predictive Network Control and Throughput Sub-Optimality of MaxWeight
Date: 2018-08-31 00:00:00
Tasks: Model Predictive Control

We present a novel control policy, called Predictive Network Control (PNC) to
control wireless communication networks (on packet level), based on paradigms
of Model Predictive Control (MPC). In contrast to common myopic policies, who
use one step ahead prediction, PNC predicts the future behavior of the system
for an extended horizon, thus facilitating performance gains. We define an
advanced system model in which we use a Markov chain in combination with a
Bernoulli trial to model the stochastic components of the network. Furthermore
we introduce the algorithm and present two detailed simulation examples, which
show general improved performance and a gain in stability region compared to
the standard policy.
Competition-induced increase of species abundance in mutualistic networks
Date: 2019-03-31 00:00:00

Nutrients from a flowering plant are shared by its pollinators, giving rise
to competition in the latter. Such exploitative competition of pollinators can
limit their abundance and affect the global organization of the mutualistic
partnership in the plant-pollinator mutualistic community. Here we investigate
the effects of the exploitative competition between pollinators on the
structure and the species abundance of the mutualistic networks which evolve by
changing mutualistic partnership towards higher abundance of species.
Simulations show different emergent network characteristics between plants and
animals; hub plants connected to many pollinators are very rare while a few
super-hub pollinators appear with the exploitative competition included, in
contrast to equally many hubs of both types without the exploitative
competition. More interestingly, the abundance of plant species increases with
increasing the exploitative competition strength. We analyze the inverse of the
generalized interaction matrix in the weak-interaction limit to identify the
leading structural factors relevant to the species abundance, which are shown
to be instrumental in optimizing the network structure to increase the
mutualistic benefit and lower the cost of exploitative competition.
Nested interaction networks represent a missing link in the study of behavioural and community ecology
Date: 2018-09-17 00:00:00

Interactions are ubiquitous across biological systems. These interactions can
be abstracted as patterns of connections among distinct units such as genes,
proteins, individual organisms, or species which form a hierarchy of biological
organisation. Connections in this hierarchy are arranged in a nested structure:
gene and protein networks shape phenotypic traits and together constitute
individuals, individuals are embedded within populations, populations within
communities, and communities within ecosystems. This pervasive "nestedness" of
networks can result in propagation of the effects from within-level
interactions at one level to units at higher or lower levels of organization.
The concept of nested biological networks is implicit in a variety of
disciplines ranging from the study of genetic circuits regulating phenotypic
trait expression (Babu et al. 2004), to the study of predator-prey interactions
influencing community composition (Poisot et al. 2016). However, studies
typically only address interactions within and among directly neighbouring
hierarchical levels, such as genotypes and phenotypic traits, or populations
and communities. Here, we formalise nested networks as having nodes that can
contain, or be embedded in, other nodes, and where edges can bridge connections
between sets of embedded nodes. We then argue that explicitly accounting for
network nestedness across levels of organization will encourage integrative
thinking on new interdisciplinary research fronts. We focus on two phenomena in
particular: (i) indirect connections among units can arise from the structure
of connections at higher or lower levels of organisation, (ii) the propagation
of effects across neighbouring hierarchical levels of organization. This
framework of nested interaction networks provides a tool for researchers across
disciplines to conceptualize their work as elements on a common scaffold.
Compression-Based ECG Biometric Identification Using a Non-fiducial Approach
Date: 2018-03-15 00:00:00

Due to its characteristics, there is a trend in biometrics to use the ECG
signal for personal identification. Recent works based on compression models
have shown that these approaches are suitable to ECG biometric identification.
However, the best results are usually achieved by the methods that, at least,
rely on one point of interest of the ECG.
  In this work, we propose a compression-based non-fiducial method, that uses a
measure of similarity, called the Normalized Relative Compression -- a measure
related to the Kolmogorov complexity of strings. Our method uses
extended-alphabet finite-context models (xaFCMs) on the quantized first-order
derivative of the signal, instead of using directly the original signal, as
other methods do.
  We were able to achieve state-of-the-art results on a database collected at
the University of Aveiro, which was used on previous works, making it a good
preliminary benchmark for the method.
Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches
Authors: Wayes Tushar, Chau Yuen, Hamed Mohsenian-Rad, Tapan Saha, H. Vincent Poor, Kristin L Wood
Date: 2018-03-19 00:00:00
Tasks: Decision Making, energy management, energy trading, Management

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.
L\'evy flight movements prevent extinctions and maximize population abundances in fragile Lotka Volterra systems
Date: 2018-03-30 00:00:00

Multiple-scale mobility is ubiquitous in nature and has become instrumental
for understanding and modeling animal foraging behavior. However, the impact of
individual movements on the long-term stability of populations remains largely
unexplored. We analyze deterministic and stochastic Lotka Volterra systems,
where mobile predators consume scarce resources (prey) confined in patches. In
fragile systems (that is, those unfavorable to species coexistence), the
predator species has a maximized abundance and is resilient to degraded prey
conditions when individual mobility is multiple scaled. Within the L\'evy
flight model, highly superdiffusive foragers rarely encounter prey patches and
go extinct, whereas normally diffusing foragers tend to proliferate within
patches, causing extinctions by overexploitation. L\'evy flights of
intermediate index allow a sustainable balance between patch exploitation and
regeneration over wide ranges of demographic rates. Our analytical and
simulated results can explain field observations and suggest that scale-free
random movements are an important mechanism by which entire populations adapt
to scarcity in fragmented ecosystems.
Mobility cost and degenerated diffusion in kinesis models
Date: 2019-02-28 00:00:00

A new critical effect is predicted in population dispersal. It is based on
the fact that a trade-off between the advantages of mobility and the cost of
mobility breaks with a significant deterioration in living conditions. The
recently developed model of purposeful kinesis (Gorban \& \c{C}abuko\v{g}lu,
Ecological Complexity 33, 2018) is based on the "let well enough alone" idea:
mobility decreases for high reproduction coefficient and, therefore, animals
stay longer in good conditions and leave quicker bad conditions. Mobility has a
cost, which should be measured in the changes of the reproduction coefficient.
Introduction of the cost of mobility into the reproduction coefficient leads to
an equation for mobility. It can be solved in a closed form using Lambert
$W$-function.
  Surprisingly, the "let well enough alone" models with the simple linear cost
of mobility have an intrinsic phase transition: when conditions worsen then the
mobility increases up to some critical value of the reproduction coefficient.
For worse conditions, there is no solution for mobility. We interpret this
critical effect as the complete loss of mobility that is degeneration of
diffusion. Qualitatively, this means that mobility increases with worsening of
conditions up to some limit, and after that, mobility is nullified.