pwc-archive/papers-with-abstracts huggingface dataset:
Term Structure Modeling under Volatility Uncertainty
Authors: Julian Hölzermann
Date: 2019-04-05 00:00:00

In this paper, we study term structure movements in the spirit of Heath, Jarrow, and Morton [Econometrica 60(1), 77-105] under volatility uncertainty. We model the instantaneous forward rate as a diffusion process driven by a G-Brownian motion. The G-Brownian motion represents the uncertainty about the volatility. Within this framework, we derive a sufficient condition for the absence of arbitrage, known as the drift condition. In contrast to the traditional model, the drift condition consists of several equations and several market prices, termed market price of risk and market prices of uncertainty, respectively. The drift condition is still consistent with the classical one if there is no volatility uncertainty. Similar to the traditional model, the risk-neutral dynamics of the forward rate are completely determined by its diffusion term. The drift condition allows to construct arbitrage-free term structure models that are completely robust with respect to the volatility. In particular, we obtain robust versions of classical term structure models.
A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours
Authors: Fiona R Macfarlane, Mark AJ Chaplain, Tommaso Lorenzi
Date: 2019-04-05 00:00:00

Spatial interactions between cancer and immune cells, as well as the recognition of tumour antigens by cells of the immune system, play a key role in the immune response against solid tumours. The existing mathematical models generally focus only on one of these key aspects. We present here a spatial stochastic individual-based model that explicitly captures antigen expression and recognition. In our model, each cancer cell is characterised by an antigen profile which can change over time due to either epimutations or mutations. The immune response against the cancer cells is initiated by the dendritic cells that recognise the tumour antigens and present them to the cytotoxic T cells. Consequently, T cells become activated against the tumour cells expressing such antigens. Moreover, the differences in movement between inactive and active immune cells are explicitly taken into account by the model. Computational simulations of our model clarify the conditions for the emergence of tumour clearance, dormancy or escape, and allow us to assess the impact of antigenic heterogeneity of cancer cells on the efficacy of immune action. Ultimately, our results highlight the complex interplay between spatial interactions and adaptive mechanisms that underpins the immune response against solid tumours, and suggest how this may be exploited to further develop cancer immunotherapies.
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Date: 2019-09-16 00:00:00

Identifying a coupled dynamical system out of many plausible candidates, each
of which could serve as the underlying generator of some observed measurements,
is a profoundly ill posed problem that commonly arises when modelling real
world phenomena. In this review, we detail a set of statistical procedures for
inferring the structure of nonlinear coupled dynamical systems (structure
learning), which has proved useful in neuroscience research. A key focus here
is the comparison of competing models of (ie, hypotheses about) network
architectures and implicit coupling functions in terms of their Bayesian model
evidence. These methods are collectively referred to as dynamical casual
modelling (DCM). We focus on a relatively new approach that is proving
remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid
evaluation and comparison of models that differ in their network architecture.
We illustrate the usefulness of these techniques through modelling
neurovascular coupling (cellular pathways linking neuronal and vascular
systems), whose function is an active focus of research in neurobiology and the
imaging of coupled neuronal systems.
Integrating PHY Security Into NDN-IoT Networks By Exploiting MEC: Authentication Efficiency, Robustness, and Accuracy Enhancement
Date: 2019-10-29 00:00:00
Tasks: Decision Making, Edge-computing, Quantization

Recent literature has demonstrated the improved data discovery and delivery
efficiency gained through applying named data networking (NDN) to a variety of
information-centric Internet of things (IoT) applications. However, from a data
security perspective, the development of NDN-IoT raises several new
authentication challenges. In particular, NDN-IoT authentication may require
per-packet-level signatures, thus imposing intolerably high computational and
time costs on the resource-poor IoT end devices. This paper proposes an
effective solution by seamlessly integrating the lightweight and unforgeable
physical-layer identity (PHY-ID) into the existing NDN signature scheme for the
mobile edge computing (MEC)-enabled NDN-IoT networks. The PHY-ID generation
exploits the inherent signal-level device-specific radio-frequency
imperfections of IoT devices, including the in-phase/quadrature-phase
imbalance, and thereby avoids adding any implementation complexity to the
constrained IoT devices. We derive the offline maximum entropy-based
quantization rule and propose an online two-step authentication scheme to
improve the accuracy of the authentication decision making. Consequently, a
cooperative MEC device can securely execute the costly signing task on behalf
of the authenticated IoT device in an optimal manner. The evaluation results
demonstrate 1) elevated authentication time efficiency, 2) robustness to
several impersonation attacks including the replay attack and the
computation-based spoofing attack, and 3) increased differentiation rate and
correct authentication probability by applying our integration design in
MEC-enabled NDN-IoT networks.
Dynamics of a birth-death process based on combinatorial innovation
Date: 2019-08-19 00:00:00

A feature of human creativity is the ability to take a subset of existing
items (e.g. objects, ideas, or techniques) and combine them in various ways to
give rise to new items, which, in turn, fuel further growth. Occasionally, some
of these items may also disappear (extinction). We model this process by a
simple stochastic birth--death model, with non-linear combinatorial terms in
the growth coefficients to capture the propensity of subsets of items to give
rise to new items. In its simplest form, this model involves just two
parameters $(P, \alpha)$. This process exhibits a characteristic 'hockey-stick'
behaviour: a long period of relatively little growth followed by a relatively
sudden 'explosive' increase. We provide exact expressions for the mean and
variance of this time to explosion and compare the results with simulations. We
then generalise our results to allow for more general parameter assignments,
and consider possible applications to data involving human productivity and
creativity.
Mitigating Gyral Bias in Cortical Tractography via Asymmetric Fiber Orientation Distributions
Date: 2019-04-11 00:00:00

Diffusion tractography in brain connectomics often involves tracing axonal
trajectories across gray-white matter boundaries in gyral blades of complex
cortical convolutions. To date, gyral bias is observed in most tractography
algorithms with streamlines predominantly terminating at gyral crowns instead
of sulcal banks. This work demonstrates that asymmetric fiber orientation
distribution functions (AFODFs), computed via a multi-tissue global estimation
framework, can mitigate the effects of gyral bias, enabling fiber streamlines
at gyral blades to make sharper turns into the cortical gray matter. We use
ex-vivo data of an adult rhesus macaque and in-vivo data from the Human
Connectome Project (HCP) to show that the fiber streamlines given by AFODFs
bend more naturally into the cortex than the conventional symmetric FODFs in
typical gyral blades. We demonstrate that AFODF tractography improves
cortico-cortical connectivity and provides highly consistent outcomes between
two different field strengths (3T and 7T).
The Leland-Toft optimal capital structure model under Poisson observations
Authors: Zbigniew Palmowski, José Luis Pérez, Budhi Arta Surya, Kazutoshi Yamazaki
Date: 2019-04-06 00:00:00
Tasks: Sensitivity

We revisit the optimal capital structure model with endogenous bankruptcy first studied by Leland \cite{Leland94} and Leland and Toft \cite{Leland96}. Differently from the standard case, where shareholders observe continuously the asset value and bankruptcy is executed instantaneously without delay, we assume that the information of the asset value is updated only at intervals, modeled by the jump times of an independent Poisson process. Under the spectrally negative L\'evy model, we obtain the optimal bankruptcy strategy and the corresponding capital structure. A series of numerical studies are given to analyze the sensitivity of observation frequency on the optimal solutions, the optimal leverage and the credit spreads.
Channel Estimation for Ambient Backscatter Communication Systems with Massive-Antenna Reader
Date: 2019-06-30 00:00:00

Ambient backscatter, an emerging green communication technology, has aroused
great interest from both academia and industry. One open problem for ambient
backscatter communication (AmBC) systems is channel estimation for a
massive-antenna reader. In this paper, we focus on channel estimation problem
in AmBC systems with uniform linear array (ULA) at the reader which consists of
large number of antennas. We first design a two-step method to jointly estimate
channel gains and direction of arrivals (DoAs), and then refine the estimates
through angular rotation. Additionally, Cramer-Rao lower bounds (CRLBs) are
derived for both the modulus of the channel gain and the DoA estimates.
Simulations are then provided to validate the analysis, and to show the
efficiency of the proposed approach.
Decoding hand kinematics from population responses in sensorimotor cortex during grasping
Date: 2019-06-20 00:00:00

The hand, a complex effector comprising dozens of degrees of freedom of
movement, endows us with the ability to flexibly, precisely, and effortlessly
interact with objects. The neural signals associated with dexterous hand
movements in primary motor cortex (M1) and somatosensory cortex (SC) have
received comparatively less attention than have those that are associated with
proximal limb control. To fill this gap, we trained three monkeys to grasp
objects varying in size, shape and orientation while tracking their hand
postures and recording single-unit activity from M1 and SC. We then decoded
their hand kinematics across 30 joints from population activity in these areas.
We found that we could accurately decode kinematics with a small number of
neural signals and that performance was higher for decoding joint angles than
joint angular velocities, in contrast to what has been found with proximal limb
decoders. We conclude that cortical signals can be used for dexterous hand
control in brain machine interface applications and that postural
representations in SC may be exploited via intracortical stimulation to close
the sensorimotor loop.
Optimizing FIR Filter Mapping on the Morphosys Reconfigurable System
Date: 2019-04-10 00:00:00

This paper proposes an optimized mapping of the FIR filter algorithm that
enhances the rate of a reconfigurable computer over a basic mapping previously
proposed [1]. It also presents a new interconnection scheme in the
reconfigurable part of MorphoSys, a reconfigurable computing system [2].
Reconfigurable computing (RC) is introduced, followed by the MorphoSys RC
system. Two optimized FIR mappings are then presented which deliver enhanced
speed. A spreadsheet model will detail the modification and the improvement.
The speedup achieved is also explained as well as the advantages in the mapping
of the application.
Parrotron: An End-to-End Speech-to-Speech Conversion Model and its Applications to Hearing-Impaired Speech and Speech Separation
Date: 2019-10-29 00:00:00
Proceeding: Interspeech 2019 2019 7
Tasks: Speech Separation

We describe Parrotron, an end-to-end-trained speech-to-speech conversion
model that maps an input spectrogram directly to another spectrogram, without
utilizing any intermediate discrete representation. The network is composed of
an encoder, spectrogram and phoneme decoders, followed by a vocoder to
synthesize a time-domain waveform. We demonstrate that this model can be
trained to normalize speech from any speaker regardless of accent, prosody, and
background noise, into the voice of a single canonical target speaker with a
fixed accent and consistent articulation and prosody. We further show that this
normalization model can be adapted to normalize highly atypical speech from a
deaf speaker, resulting in significant improvements in intelligibility and
naturalness, measured via a speech recognizer and listening tests. Finally,
demonstrating the utility of this model on other speech tasks, we show that the
same model architecture can be trained to perform a speech separation task
Minimax-Optimal Algorithms for Detecting Changes in Statistically Periodic Random Processes
Date: 2019-08-13 00:00:00

Theory and algorithms are developed for detecting changes in the distribution
of statistically periodic random processes. The statistical periodicity is
modeled using independent and periodically identically distributed processes, a
new class of stochastic processes proposed by us. An algorithm is developed
that is minimax asymptotically optimal as the false alarm rate goes to zero.
Algorithms are also developed for the cases when the post-change distribution
is not known or when there are multiple streams of observations. The modeling
is inspired by real datasets encountered in cyber-physical systems, biology,
and medicine. The developed algorithms are applied to sequences of Instagram
counts collected around a 5K run in New York City to detect the run.
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution
Date: 2019-12-23 00:00:00
Tasks: Image Reconstruction, Super-Resolution

Convex optimization recently emerges as a compelling framework for performing
super resolution, garnering significant attention from multiple communities
spanning signal processing, applied mathematics, and optimization. This article
offers a friendly exposition to atomic norm minimization as a canonical convex
approach to solve super resolution problems. The mathematical foundations and
performances guarantees of this approach are presented, and its application in
super resolution image reconstruction for single-molecule fluorescence
microscopy are highlighted.
Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications
Date: 2019-04-20 00:00:00

This work concerns receiver design for light emitting diode (LED)
communications where the LED nonlinearity can severely degrade the performance
of communications. We propose extreme learning machine (ELM) based
non-iterative receivers and iterative receivers to effectively handle the LED
nonlinearity and memory effects. For the iterative receiver design, we also
develop a data-aided receiver, where data is used as virtual training sequence
in ELM training. It is shown that the ELM based receivers significantly
outperform conventional polynomial based receivers; iterative receivers can
achieve huge performance gain compared to non-iterative receivers; and the
data-aided receiver can reduce training overhead considerably. This work can
also be extended to radio frequency communications, e.g., to deal with the
nonlinearity of power amplifiers.
Partial Enumerative Sphere Shaping
Date: 2019-11-29 00:00:00

The dependency between the Gaussianity of the input distribution for the
additive white Gaussian noise (AWGN) channel and the gap-to-capacity is
discussed. We show that a set of particular approximations to the
Maxwell-Boltzmann (MB) distribution virtually closes most of the shaping gap.
We relate these symbol-level distributions to bit-level distributions, and
demonstrate that they correspond to keeping some of the amplitude bit-levels
uniform and independent of the others. Then we propose partial enumerative
sphere shaping (P-ESS) to realize such distributions in the probabilistic
amplitude shaping (PAS) framework. Simulations over the AWGN channel exhibit
that shaping 2 amplitude bits of 16-ASK have almost the same performance as
shaping 3 bits, which is 1.3 dB more power-efficient than uniform signaling at
a rate of 3 bit/symbol. In this way, required storage and computational
complexity of shaping are reduced by factors of 6 and 3, respectively.
Analysis of Beam Sweeping Techniques for Cell-Discovery in mm Wave Systems
Authors: Rashmi P, Manoj A, Arun Pachai Kannu
Date: 2019-04-10 00:00:00

Cell discovery is the procedure in which a user equipment (UE) finds a suitable base station (BS) to establish a communication link. When beamforming with antenna arrays is done at both transmitter and receiver, cell discovery in mm wave systems also involves finding the correct angle of arrival - angle of departure alignment between the UE and the detected BS. First, we consider the single BS scenario and present the mathematical model for the cell discovery problem. We present the details of the beam sweeping based techniques and explain the beam combining scheme to reduce the training overhead. We analytically characterize the performance of energy detector at the UE for both beam sweeping and beam combining methods under some channel assumptions. Further, we consider the multiple BS case where BSs transmit synchronization signals along with directional beamforming. We analyze the performance of the energy detector in this case as well. We finally present simulation studies with channels generated using generic mm wave channel emulators and draw inferences on the role of various parameters on the cell discovery performance.
Uterine muscle networks: Connectivity analysis of the EHG during pregnancy and Labor
Date: 2019-04-10 00:00:00

In this paper, we propose a new framework to analyze the electrical activity
of the uterus recorded by electrohysterography (EHG), from abdominal electrodes
(a grid of 4x4 electrodes) during pregnancy and labor. We evaluate the
potential use of the synchronization between EHG signals in characterizing
electrical activity of the uterus during pregnancy and labor. The complete
processing pipeline consists of i) estimating the correlation between the
different EHG signals, ii) quantifying the connectivity matrices using graph
theory-based analysis and iii) testing the clinical impact of network measures
in pregnancy monitoring and labor detection. We first compared several
connectivity methods to compute the adjacency matrix represented as a graph of
a set of nodes (electrodes) connected by edges (connectivity values). We then
evaluated the performance of different graph measures in the classification of
pregnancy and labor contractions (number of women=35). A comparison with the
already existing parameters used in the state of the art of labor detection and
preterm labor prediction was also performed. Results show higher performance of
connectivity methods when combined with network measures. Denser graphs were
observed during labor than during pregnancy. The network-based metrics showed
the highest classification rate when compared to already existing features.
This network-based approach can be used not only to characterize the
propagation of the uterine contractions, but also may have high clinical impact
in labor detection and likely in the prediction of premature labor.
Detection of Metallo-\b{eta}-Lactamases-Encoding Genes Among Clinical Isolates of Escherichia Coli in a Tertiary Care Hospital, Malaysia
Authors: Fazlul Mkk, Deepthi S, Farzana Y, Najnin A, Rashid Ma, Munira B, Srikumar C, Nazmul
Date: 2019-04-10 00:00:00
Tasks: Management

The multidrug resistant Escherichia coli strains causes multiple clinical infections and has become a rising problem globally. The metallo-\b{eta}-lactamases encoding genes are very severe in gram-negative bacteria especially E. coli. This study was aimed to evaluate the prevalence of MBLs among the clinical isolates of E. coli. A total of 65 E. coli isolates were collected from various clinical samples of Malaysian patients with bacterial infections. The conventional microbiological test was performed for isolation and identification of E. coli producing MBLs strains in this vicinity. Multidrug Resistance (MDR) of E. coli isolates were assessed using disk diffusion test. Phenotypic methods, as well as genotypic- PCR methods, were performed to detect the presence of metallo-\b{eta}-lactamase resistance genes (blaIMP, blaVIM) in imipenem resistant strains. Out of 65 E. coli isolates, 42 isolates (57.3%) were MDR. The isolates from urine (19) produced significantly more MDR (10) isolates than other sources. Additionally, 19 (29.2%) imipenem resistant E. coli isolates contained 10 MBLs gene, 7(36.8%) isolates contained blaIMP and 3(15.7%) isolates contained blaVIM genes. This study revealed the significant occurrence of MBL producing E. coli isolates in clinical specimens and its association with health risk factors indicating an alarming situation in Malaysia. It demands an appropriate concern to avoid failure of treatments and infection control management.
Local Polynomial Estimation of Time-Varying Parameters in Nonlinear Models
Authors: Dennis Kristensen, Young Jun Lee
Date: 2019-04-10 00:00:00
Tasks: Time Series, Time Series Analysis

We develop a novel asymptotic theory for local polynomial (quasi-) maximum-likelihood estimators of time-varying parameters in a broad class of nonlinear time series models. Under weak regularity conditions, we show the proposed estimators are consistent and follow normal distributions in large samples. Our conditions impose weaker smoothness and moment conditions on the data-generating process and its likelihood compared to existing theories. Furthermore, the bias terms of the estimators take a simpler form. We demonstrate the usefulness of our general results by applying our theory to local (quasi-)maximum-likelihood estimators of a time-varying VAR's, ARCH and GARCH, and Poisson autogressions. For the first three models, we are able to substantially weaken the conditions found in the existing literature. For the Poisson autogression, existing theories cannot be be applied while our novel approach allows us to analyze it.
Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods
Date: 2020-02-28 00:00:00
Tasks: Discrete Choice Models, Gaussian Processes

We propose to combine smoothing, simulations and sieve approximations to
solve for either the integrated or expected value function in a general class
of dynamic discrete choice (DDC) models. We use importance sampling to
approximate the Bellman operators defining the two functions. The random
Bellman operators, and therefore also the corresponding solutions, are
generally non-smooth which is undesirable. To circumvent this issue, we
introduce a smoothed version of the random Bellman operator and solve for the
corresponding smoothed value function using sieve methods. We show that one can
avoid using sieves by generalizing and adapting the `self-approximating' method
of Rust (1997) to our setting. We provide an asymptotic theory for the
approximate solutions and show that they converge with root-N-rate, where $N$
is number of Monte Carlo draws, towards Gaussian processes. We examine their
performance in practice through a set of numerical experiments and find that
both methods perform well with the sieve method being particularly attractive
in terms of computational speed and accuracy.