pwc-archive/papers-with-abstracts huggingface dataset:
A primer on the use of probability generating functions in infectious disease modeling
Authors: Joel C. Miller
Date: 2018-03-14 00:00:00

We explore the application of probability generating functions (PGFs) to invasive processes, focusing on infectious disease introduced into large populations. Our goal is to acquaint the reader with applications of PGFs, moreso than to derive new results. PGFs help predict a number of properties about early outbreak behavior while the population is still effectively infinite, including the probability of an epidemic, the size distribution after some number of generations, and the cumulative size distribution of non-epidemic outbreaks. We show how PGFs can be used in both discrete-time and continuous-time settings, and discuss how to use these results to infer disease parameters from observed outbreaks. In the large population limit for susceptible-infected-recovered (SIR) epidemics PGFs lead to survival-function based models that are equivalent the the usual mass-action SIR models but with fewer ODEs. We use these to explore properties such as the final size of epidemics or even the dynamics once stochastic effects are negligible. We target this tutorial to biologists and public health researchers who want to learn how to apply PGFs to invasive diseases, but it could also be used in an introductory mathematics course on PGFs. We include many exercises to help demonstrate concepts and to give practice applying the results. We summarize our main results in a few tables. Additionally we provide a small python package which performs many of the relevant calculations.
Complex activity patterns generated by short-term synaptic plasticity
Date: 2018-03-14 00:00:00

Short-term synaptic plasticity (STSP) affects the efficiency of synaptic
transmission for persistent presynaptic activities. We consider attractor
neural networks, for which the attractors are given, in the absence of STSP, by
cell assemblies of excitatory cliques. We show that STSP may transform these
attracting states into attractor relics, inducing ongoing transient-state
dynamics in terms of sequences of transiently activated cell assemblies, the
former attractors. Subsequent cell assemblies may be both disjoint or partially
overlapping. It may hence be possible to use the resulting dynamics for the
generation of motor control sequences.
When few survive to tell the tale: thymus and gonad as auditioning organs: historical overview
Date: 2018-08-15 00:00:00

Unlike other organs, the thymus and gonads generate non-uniform cell
populations, many members of which perish, and a few survive. While it is
recognized that thymic cells are 'audited' to optimize an organism's immune
repertoire, whether gametogenesis could be orchestrated similarly to favour
high quality gametes is uncertain. Ideally, such quality would be affirmed at
early stages before the commitment of extensive parental resources. A case is
here made that, along the lines of a previously proposed lymphocyte quality
control mechanism, gamete quality can be registered indirectly through
detection of incompatibilities between proteins encoded by the grandparental
DNA sequences within the parent from which haploid gametes are meiotically
derived. This 'stress test' is achieved in the same way that thymic screening
for potential immunological incompatibilities is achieved - by 'promiscuous'
expression, under the influence of the AIRE protein, of the products of genes
that are not normally specific for that organ. Consistent with this, the Aire
gene is expressed in both thymus and gonads, and AIRE deficiency impedes
function in both organs. While not excluding the subsequent emergence of hybrid
incompatibilities due to the intermixing of genomic sequences from parents
(rather than grandparents), many observations, such as the number of proteins
that are aberrantly expressed during gametogenesis, can be explained on this
basis. Indeed, promiscuous expression could have first evolved in
gamete-forming cells where incompatible proteins would be manifest as aberrant
protein aggregates that cause apoptosis. This mechanism would later have been
co-opted by thymic epithelial cells which display peptides from aggregates to
remove potentially autoreactive T cells.
Potential quality improvement of stochastic optical localization nanoscopy images obtained by frame by frame localization algorithms
Date: 2020-07-18 00:00:00

A data movie of stochastic optical localization nanoscopy contains spatial
and temporal correlations, both providing information of emitter locations. The
majority of localization algorithms in the literature estimate emitter
locations by frame-by-frame localization (FFL), which exploit only the spatial
correlation and leave the temporal correlation into the FFL nanoscopy images.
The temporal correlation contained in the FFL images, if exploited, can improve
the localization accuracy and the image quality. In this paper, we analyze the
properties of the FFL images in terms of root mean square minimum distance
(RMSMD) and root mean square error (RMSE). It is shown that RMSMD and RMSE can
be potentially reduced by a maximum fold equal to the square root of the
average number of activations per emitter. Analyzed and revealed are also
several statistical properties of RMSMD and RMSE and their relationship with
respect to a large number of data frames, bias and variance of localization
errors, small localization errors, sample drift, and the worst FFL image.
Numerical examples are taken and the results confirm the prediction of
analysis. The ideas about how to develop an algorithm to exploit the temporal
correlation of FFL images are also briefly discussed. The results suggest
development of two kinds of localization algorithms: the algorithms that can
exploit the temporal correlation of FFL images and the unbiased localization
algorithms.
Speaker Verification using Convolutional Neural Networks
Date: 2018-08-10 00:00:00
Tasks: Speaker Verification

In this paper, a novel Convolutional Neural Network architecture has been
developed for speaker verification in order to simultaneously capture and
discard speaker and non-speaker information, respectively. In training phase,
the network is trained to distinguish between different speaker identities for
creating the background model. One of the crucial parts is to create the
speaker models. Most of the previous approaches create speaker models based on
averaging the speaker representations provided by the background model. We
overturn this problem by further fine-tuning the trained model using the
Siamese framework for generating a discriminative feature space to distinguish
between same and different speakers regardless of their identity. This provides
a mechanism which simultaneously captures the speaker-related information and
create robustness to within-speaker variations. It is demonstrated that the
proposed method outperforms the traditional verification methods which create
speaker models directly from the background model.
A system-wide network reconstruction of gene regulation and metabolism in Escherichia coli
Date: 2018-03-13 00:00:00

Genome-scale metabolic models have become a fundamental tool for examining
metabolic principles. However, metabolism is not solely characterized by the
underlying biochemical reactions and catalyzing enzymes, but also affected by
regulatory events. Since the pioneering work of Covert and co-workers as well
as Shlomi and co-workers it is debated, how regulation and metabolism
synergistically characterize a coherent cellular state. The first approaches
started from metabolic models which were extended by the regulation of the
encoding genes of the catalyzing enzymes. By now, bioinformatics databases in
principle allow addressing the challenge of integrating regulation and
metabolism on a system-wide level. Collecting information from several
databases we provide a network representation of the integrated gene regulatory
and metabolic system for Escherichia coli, including major cellular processes,
from metabolic processes via protein modification to a variety of regulatory
events. Besides transcriptional regulation, we also take into account
regulation of translation, enzyme activities and reactions. Our network model
provides novel topological characterizations of system components based on
their positions in the network. We show that network characteristics suggest a
representation of the integrated system as three network domains (regulatory,
metabolic and interface networks) instead of two. This new three-domain
representation reveals the structural centrality of components with known high
functional relevance. This integrated network can serve as a platform for
understanding coherent cellular states as active subnetworks and to elucidate
crossover effects between metabolism and gene regulation.
A Technique Based on Chaos for Brain Computer Interfacing
Date: 2018-03-13 00:00:00
Tasks: Brain Computer Interface, Classification, Clustering, EEG, Electroencephalogram (EEG), Motor Imagery, Translation

A user of Brain Computer Interface (BCI) system must be able to control
external computer devices with brain activity. Although the proof-of-concept
was given decades ago, the reliable translation of user intent into device
control commands is still a major challenge. There are problems associated with
classification of different BCI tasks. In this paper we propose the use of
chaotic indices of the BCI. We use largest Lyapunov exponent, mutual
information, correlation dimension and minimum embedding dimension as the
features for the classification of EEG signals which have been released by BCI
Competition IV. A multi-layer Perceptron classifier and a KM- SVM(support
vector machine classifier based on k-means clustering) is used for
classification process, which lead us to an accuracy of 95.5%, for
discrimination between two motor imagery tasks.
ITU-T SG 16 WP 3 and ISO/IEC JTC 1/SC 29/WG 11 - JCT3V-C0032: A human visual system based 3D video quality metric
Date: 2018-03-13 00:00:00
Tasks: MS-SSIM, SSIM

This contribution proposes a full-reference Human-Visual-System based 3D
video quality metric. In this report, the presented metric is used to evaluate
the quality of compressed stereo pair formed from a decoded view and a
synthesized view. The performance of the proposed metric is verified through a
series of subjective tests and compared with that of PSNR, SSIM, MS-SSIM, VIFp,
and VQM metrics. The experimental results show that HV3D has the highest
correlation with Mean Opinion Scores (MOS) compared to other tested metrics.
ISO/IEC JTC1/SC29/WG11 MPEG2014/ m34661: Quality Assessment of High Dynamic Range (HDR) Video Content Using Existing Full-Reference Metrics
Date: 2018-03-13 00:00:00

The main focus of this document is to evaluate the performance of the
existing LDR and HDR metrics on HDR video content which in turn will allow for
a better understanding of how well each of these metrics work and if they can
be applied in capturing, compressing, transmitting process of HDR data. To this
end a series of subjective tests is performed to evaluate the quality of
DML-HDR video database [1], when several different representing types of
artifacts are present using a HDR display. Then, the correlation between the
results from the existing LDR and HDR quality metrics and those from subjective
tests is measured to determine the most effective exiting quality metric for
HDR.
Wide-Sense-Stationarity of Everyday Wireless Channels for Body-to-Body Networks
Date: 2018-03-15 00:00:00

The existence of wide-sense-stationarity (WSS) in narrowband wireless
body-to-body networks is investigated for "everyday" scenarios using many hours
of contiguous experimental data. We employ different parametric and
non-parametric hypothesis tests for evaluating mean and variance stationarity,
along with distribution consistency, of several body-to-body channels found
from different on-body sensor locations. We also estimate the variation of
power spectrum to evaluate the time independence of the auto-covariance
function. Our results show that, with 95% confidence, the assumption of WSS is
met for at most 90% of the cases with window lengths of 5 seconds for the
channels between the hubs of different BANs. Additionally, in the best-case
scenario, the hub-to-hub channel remains reasonably stationary (with more than
80% probability of satisfying the null hypothesis) for longer window lengths of
more than 10 seconds. The short time power spectral variation for body-to-body
channels is also shown to be negligible. Moreover, we show that body-to-body
channels can be considered wide-sense-stationary over significantly longer
periods than on-body channels.
Spectral radii of asymptotic mappings and the convergence speed of the standard fixed point algorithm
Authors: Renato L. G. Cavalcante, Slawomir Stanczak
Date: 2018-03-15 00:00:00

Important problems in wireless networks can often be solved by computing fixed points of standard or contractive interference mappings, and the conventional fixed point algorithm is widely used for this purpose. Knowing that the mapping used in the algorithm is not only standard but also contractive (or only contractive) is valuable information because we obtain a guarantee of geometric convergence rate, and the rate is related to a property of the mapping called modulus of contraction. To date, contractive mappings and their moduli of contraction have been identified with case-by-case approaches that can be difficult to generalize. To address this limitation of existing approaches, we show in this study that the spectral radii of asymptotic mappings can be used to identify an important subclass of contractive mappings and also to estimate their moduli of contraction. In addition, if the fixed point algorithm is applied to compute fixed points of positive concave mappings, we show that the spectral radii of asymptotic mappings provide us with simple lower bounds for the estimation error of the iterates. An immediate application of this result proves that a known algorithm for load estimation in wireless networks becomes slower with increasing traffic.
Optimal liquidity-based trading tactics
Date: 2018-03-15 00:00:00

We consider an agent who needs to buy (or sell) a relatively small amount of
asset over some fixed short time interval. We work at the highest frequency
meaning that we wish to find the optimal tactic to execute our quantity using
limit orders, market orders and cancellations. To solve the agent's control
problem, we build an order book model and optimize an expected utility function
based on our price impact. We derive the equations satisfied by the optimal
strategy and solve them numerically. Moreover, we show that our optimal tactic
enables us to outperform significantly naive execution strategies.
Outperformance and Tracking: Dynamic Asset Allocation for Active and Passive Portfolio Management
Date: 2018-07-30 00:00:00
Tasks: Management

Portfolio management problems are often divided into two types: active and
passive, where the objective is to outperform and track a preselected
benchmark, respectively. Here, we formulate and solve a dynamic asset
allocation problem that combines these two objectives in a unified framework.
We look to maximize the expected growth rate differential between the wealth of
the investor's portfolio and that of a performance benchmark while penalizing
risk-weighted deviations from a given tracking portfolio. Using stochastic
control techniques, we provide explicit closed-form expressions for the optimal
allocation and we show how the optimal strategy can be related to the growth
optimal portfolio. The admissible benchmarks encompass the class of
functionally generated portfolios (FGPs), which include the market portfolio,
as the only requirement is that they depend only on the prevailing asset
values. Finally, some numerical experiments are presented to illustrate the
risk-reward profile of the optimal allocation.
Technical Uncertainty in Real Options with Learning
Date: 2018-07-30 00:00:00

We introduce a new approach to incorporate uncertainty into the decision to
invest in a commodity reserve. The investment is an irreversible one-off
capital expenditure, after which the investor receives a stream of cashflow
from extracting the commodity and selling it on the spot market. The investor
is exposed to price uncertainty and uncertainty in the amount of available
resources in the reserves (i.e. technical uncertainty). She does, however,
learn about the reserve levels through time, which is a key determinant in the
decision to invest. To model the reserve level uncertainty and how she learns
about the estimates of the commodity in the reserve, we adopt a continuous-time
Markov chain model to value the option to invest in the reserve and investigate
the value that learning has prior to investment.
Effective Connectivity from Single Trial fMRI Data by Sampling Biologically Plausible Models
Date: 2018-03-15 00:00:00
Tasks: Connectivity Estimation

The estimation of causal network architectures in the brain is fundamental
for understanding cognitive information processes. However, access to the
dynamic processes underlying cognition is limited to indirect measurements of
the hidden neuronal activity, for instance through fMRI data. Thus, estimating
the network structure of the underlying process is challenging. In this
article, we embed an adaptive importance sampler called Adaptive Path Integral
Smoother (APIS) into the Expectation-Maximization algorithm to obtain point
estimates of causal connectivity. We demonstrate on synthetic data that this
procedure finds not only the correct network structure but also the direction
of effective connections from random initializations of the connectivity
matrix. In addition--motivated by contradictory claims in the literature--we
examine the effect of the neuronal timescale on the sensitivity of the BOLD
signal to changes in the connectivity and on the maximum likelihood solutions
of the connectivity. We conclude with two warnings: First, the connectivity
estimates under the assumption of slow dynamics can be extremely biased if the
data was generated by fast neuronal processes. Second, the faster the time
scale, the less sensitive the BOLD signal is to changes in the incoming
connections to a node. Hence, connectivity estimation using realistic neural
dynamics timescale requires extremely high-quality data and seems infeasible in
many practical data sets.
A game-theoretic mechanism for aggregation and dispersal of interacting populations
Date: 2018-03-15 00:00:00

We adapt a fitness function from evolutionary game theory as a mechanism for
aggregation and dispersal in a partial differential equation (PDE) model of two
interacting populations, described by density functions $u$ and $v$. We
consider a spatial model where individuals migrate up local fitness gradients,
seeking out locations where their given traits are more advantageous. The
resulting system of fitness gradient equations is a degenerate system having
spatially structured, smooth, steady state solutions characterized by constant
fitness throughout the domain. When populations are viewed as predator and
prey, our model captures prey aggregation behavior consistent with Hamilton's
selfish herd hypothesis. We also present weak steady state solutions in 1d that
are continuous but in general not smooth everywhere, with an associated fitness
that is discontinuous, piecewise constant. We give numerical examples of
solutions that evolve toward such weak steady states. We also give an example
of a spatial Lotka--Volterra model, where a fitness gradient flux creates
instabilities that lead to spatially structured steady states. Our results also
suggest that when fitness has some dependence on local interactions, a
fitness-based dispersal mechanism may act to create spatial variation across a
habitat.
The complexity of comparing multiply-labelled trees by extending phylogenetic-tree metrics
Date: 2018-03-15 00:00:00

A multilabeled tree (or MUL-tree) is a rooted tree in which every leaf is
labelled by an element from some set, but in which more than one leaf may be
labelled by the same element of that set. In phylogenetics, such trees are used
in biogeographical studies, to study the evolution of gene families, and also
within approaches to construct phylogenetic networks. A multilabelled tree in
which no leaf-labels are repeated is called a phylogenetic tree, and one in
which every label is the same is also known as a tree-shape. In this paper, we
consider the complexity of computing metrics on MUL-trees that are obtained by
extending metrics on phylogenetic trees. In particular, by restricting our
attention to tree shapes, we show that computing the metric extension on
MUL-trees is NP complete for two well-known metrics on phylogenetic trees,
namely, the path-difference and Robinson Foulds distances. We also show that
the extension of the Robinson Foulds distance is fixed parameter tractable with
respect to the distance parameter. The path distance complexity result allows
us to also answer an open problem concerning the complexity of solving the
quadratic assignment problem for two matrices that are a Robinson similarity
and a Robinson dissimilarity, which we show to be NP-complete. We conclude by
considering the maximum agreement subtree (MAST) distance on phylogenetic trees
to MUL-trees. Although its extension to MUL-trees can be computed in polynomial
time, we show that computing its natural generalization to more than two
MUL-trees is NP-complete, although fixed-parameter tractable in the maximum
degree when the number of given trees is bounded.
Interplay of Probabilistic Shaping and the Blind Phase Search Algorithm
Date: 2018-09-12 00:00:00

Probabilistic shaping (PS) is a promising technique to approach the Shannon
limit using typical constellation geometries. However, the impact of PS on the
chain of signal processing algorithms of a coherent receiver still needs
further investigation. In this work we study the interplay of PS and phase
recovery using the blind phase search (BPS) algorithm, which is widely used in
optical communications systems. We first investigate a supervised phase search
(SPS) algorithm as a theoretical upper bound on the BPS performance, assuming
perfect decisions. It is shown that PS influences the SPS algorithm, but its
impact can be alleviated by moderate noise rejection window sizes. On the other
hand, BPS is affected by PS even for long windows because of correlated
erroneous decisions in the phase recovery scheme. The simulation results also
show that the capacity-maximizing shaping is near to the BPS worst-case
situation for square-QAM constellations, causing potential implementation
penalties.