pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Ultrasound Beamforming using Deep Learning
Date: 2019-09-23 00:00:00
Tasks: Deep Learning

Biomedical imaging is unequivocally dependent on the ability to reconstruct
interpretable and high-quality images from acquired sensor data. This
reconstruction process is pivotal across many applications, spanning from
magnetic resonance imaging to ultrasound imaging. While advanced data-adaptive
reconstruction methods can recover much higher image quality than traditional
approaches, their implementation often poses a high computational burden. In
ultrasound imaging, this burden is significant, especially when striving for
low-cost systems, and has motivated the development of high-resolution and
high-contrast adaptive beamforming methods. Here we show that deep neural
networks that adopt the algorithmic structure and constraints of adaptive
signal processing techniques can efficiently learn to perform fast high-quality
ultrasound beamforming using very little training data. We apply our technique
to two distinct ultrasound acquisition strategies (plane wave, and synthetic
aperture), and demonstrate that high image quality can be maintained when
measuring at low data-rates, using undersampled array designs. Beyond
biomedical imaging, we expect that the proposed deep~learning based adaptive
processing framework can benefit a variety of array and signal processing
applications, in particular when data-efficiency and robustness are of
importance.
The recent advances in the mathematical modelling of human pluripotent stem cells
Authors: L E Wadkin, S Orozco-Fuentes, I Neganova, M Lako, A Shukurov, N G Parker
Date: 2019-09-23 00:00:00
Tasks: Drug Design

Human pluripotent stem cells hold great promise for developments in regenerative medicine and drug design. The mathematical modelling of stem cells and their properties is necessary to understand and quantify key behaviours and develop non-invasive prognostic modelling tools to assist in the optimisation of laboratory experiments. Here, the recent advances in the mathematical modelling of hPSCs are discussed, including cell kinematics, cell proliferation and colony formation, and pluripotency and differentiation.
Analysis on One-Stage SSHC Rectifier for Piezoelectric Vibration Energy Harvesting
Authors: Sijun Du
Date: 2019-09-17 00:00:00

Conventional SSHI (synchronized switch harvesting on inductor) has been believed to be one of the most efficient interface circuits for piezoelectric vibration energy harvesting systems. It employs an inductor and the resulting RLC loop to synchronously invert the charge across the piezoelectric material to avoid charge and energy loss due to charging its internal capacitor ($C_P$). The performance of the SSHI circuit greatly depends on the inductor and a large inductor is often needed; hence significantly increases the volume of the system. An efficient interface circuit using a synchronous charge inversion technique, named as SSHC, was proposed recently. The SSHC rectifier utilizes capacitors, instead of inductors, to flip the voltage across the harvester. For a one-stage SSHC rectifier, one single intermediate capacitor ($C_T$) is employed to temporarily store charge flowed from $C_P$ and inversely charge $C_P$ to perform the charge inversion. In previous studies, the voltage flip efficiency achieves 1/3 when $C_T = C_P$. This paper presents that the voltage flip efficiency can be further increased to approach 1/2 if $C_T$ is chosen to be much larger than $C_P$.
Dynamics of genetic code evolution: The emergence of universality
Authors: John-Antonio Argyriadis, Yang-Hui He, Vishnu Jejjala, Djordje Minic
Date: 2019-09-23 00:00:00

We study the dynamics of genetic code evolution. The model of Vetsigian et al. [1] and Vetsigian [2] uses the mechanism of horizontal gene transfer to demonstrate convergence of the genetic code to a near universal solution. We reproduce and analyze the algorithm as a dynamical system. All the parameters used in the model are varied to assess their impact on convergence and optimality score. We show that by allowing specific parameters to vary with time, the solution exhibits attractor dynamics. Finally, we study automorphisms of the genetic code arising due to this model. We use this to examine the scaling of the solutions in order to re-examine universality and find that there is a direct link to mutation rate.
Event-triggered boundary control of constant-parameter reaction-diffusion PDEs: a small-gain approach
Date: 2019-09-23 00:00:00

This paper deals with an event-triggered boundary control of
constant-parameters reaction-diffusion PDE systems. The approach relies on the
emulation of backstepping control along with a suitable triggering condition
which establishes the time instants at which the control value needs to be
sampled/updated. In this paper, it is shown that under the proposed
event-triggered boundary control, there exists a minimal dwell-time
(independent of the initial condition) between two triggering times and
furthermore the well-posedness and global exponential stability are guaranteed.
The analysis follows small-gain arguments and builds on recent papers on
sampled-data control for this kind of PDE. A simulation example is presented to
validate the theoretical results.
Spots, strips, and spiral waves in models for static and motile cells
Authors: Yue Liu, Elisabeth G. Rens, Leah Edelstein-Keshet
Date: 2019-09-23 00:00:00

The polarization and motility of eukaryotic cells depends on assembly and contraction of the actin cytoskeleton and its regulation by proteins called GTPases. The activity of GTPases causes assembly of filamentous actin (by GTPases Cdc42, Rac), resulting in protrusion of the cell edge. Mathematical models for GTPase dynamics address the spontaneous formation of patterns and nonuniform spatial distributions of such proteins in the cell. Here we revisit the wave-pinning model for GTPase-induced cell polarization, together with a number of extensions proposed in the literature. These include introduction of sources and sinks of active and inactive GTPase (by the group of A. Champneys), and negative feedback from F-actin to GTPase activity. We discuss these extensions singly and in combination, in 1D, and 2D static domains. We then show how the patterns that form (spots, waves, and spirals) interact with cell boundaries to create a variety of interesting and dynamic cell shapes and motion.
Multi-Agent Coordination for Distributed Transmit Beamforming
Date: 2019-09-23 00:00:00
Tasks: Position

This paper presents the formulation and analysis of a two time-scale
optimization algorithm for multi-agent coordination for the purpose of
distributed beamforming. Each agent is assumed to be randomly positioned with
respect to each other with random phase offsets and amplitudes. Agents are
tasked with coordinate among themselves to position themselves and adjust their
phase offset and amplitude such that they can construct a desired directed
beam. Here we propose a two time-scale optimization algorithm that consists of
a fast time-scale algorithm to solve for the amplitude and phase while a slow
time-scale algorithm to solve for the control required to re-position the
agents. The numerical results given here indicate that the proposed two
time-scale approach is able to reconstruct a desired beam pattern.
Tangent space spatial filters for interpretable and efficient Riemannian classification
Date: 2019-09-23 00:00:00
Tasks: Classification

Methods based on Riemannian geometry have proven themselves to be good models
for decoding in brain-computer interfacing (BCI). However, one major drawback
of these methods is that it is not possible to determine what aspect of the
signal the classifier is built on, leaving open the possibility that artifacts
drive classification performance. In areas where artifactual control is
problematic, specifically neurofeedback and BCIs in patient populations, this
has led people to continue to rely on spatial filters as a way of generating
features that are provably brain-related. Furthermore, these methods also
suffer from the curse of dimensionality and are almost infeasible in
high-density online BCI systems. To tackle these drawbacks, we introduce here a
method for computing spatial filters from any linear function in the Riemannian
tangent space, which allows for more efficient classification as well as the
removal of artifact sources from classifiers built on Riemannian methods. We
first prove a fundamental relationship between certain tangent spaces and
spatial filtering methods, including an explanation of common spatial patterns
within this framework, and then validate our proposed approach using an
open-access BCI analysis framework.
Whole genome sequencing identifies putative associations between genomic polymorphisms and clinical response to the antiepileptic drug levetiracetam
Date: 2019-09-23 00:00:00

In the context of pharmacogenomics, whole genome sequencing provides a
powerful approach for identifying correlations between response variability to
specific drugs and genomic polymorphisms in a population, in an unbiased
manner. In this study, we employed whole genome sequencing of DNA samples from
patients showing extreme response (n=72) and non-response (n=27) to the
antiepileptic drug levetiracetam, in order to identify genomic variants that
underlie response to the drug. Although no common SNP (MAF>5%) crossed the
conventional genome-wide significance threshold of 5e-8, we found common
polymorphisms in genes SPNS3, HDC, MDGA2, NSG1 and RASGEF1C, which collectively
predict clinical response to levetiracetam in our cohort with ~91% predictive
accuracy. Among these genes, HDC, NSG1, MDGA2 and RASGEF1C are potentially
implicated in synaptic neurotransmission, while SPNS3 is an atypical solute
carrier transporter homologous to SV2A, the known molecular target of
levetiracetam. Furthermore, we performed gene- and pathway-based statistical
analysis on sets of rare and low-frequency variants (MAF<5%) and we identified
associations between the following genes or pathways and response to
levetiracetam: a) genes PRKCB and DLG2, which are involved in glutamatergic
neurotransmission, a known target of anticonvulsants, including levetiracetam;
b) genes FILIP1 and SEMA6D, which are involved in axon guidance and modelling
of neural connections; and c) pathways with a role in synaptic
neurotransmission, such as WNT5A-dependent internalization of FZD4 and
disinhibition of SNARE formation. In summary, our approach to utilise whole
genome sequencing on subjects with extreme response phenotypes is a feasible
route to generate plausible hypotheses for investigating the genetic factors
underlying drug response variability in cases of pharmaco-resistant epilepsy.
Eco: A Hardware-Software Co-Design for In Situ Power Measurement on Low-end IoT Systems
Date: 2019-09-23 00:00:00
Tasks: energy management, Management, Scheduling

Energy-constrained sensor nodes can adaptively optimize their energy
consumption if a continuous measurement exists. This is of particular
importance in scenarios of high dynamics such as energy harvesting or adaptive
task scheduling. However, self-measuring of power consumption at reasonable
cost and complexity is unavailable as a generic system service. In this paper,
we present Eco, a hardware-software co-design enabling generic energy
management on IoT nodes. Eco is tailored to devices with limited resources and
thus targets most of the upcoming IoT scenarios. The proposed measurement
module combines commodity components with a common system interfaces to achieve
easy, flexible integration with various hardware platforms and the RIOT IoT
operating system. We thoroughly evaluate and compare accuracy and overhead. Our
findings indicate that our commodity design competes well with highly optimized
solutions, while being significantly more versatile. We employ Eco for energy
management on RIOT and validate its readiness for deployment in a five-week
field trial integrated with energy harvesting.
Structural Change Analysis of Active Cryptocurrency Market
Date: 2019-09-24 00:00:00

Structural Change Analysis of Active Cryptocurrency Market
Reconstructing an epigenetic landscape using a genetic `pulling' approach
Authors: Michael Assaf, Shay Be'er, Elijah Roberts
Date: 2019-09-24 00:00:00

Cells use genetic switches to shift between alternate stable gene expression states, e.g., to adapt to new environments or to follow a developmental pathway. Conceptually, these stable phenotypes can be considered as attractive states on an epigenetic landscape with phenotypic changes being transitions between states. Measuring these transitions is challenging because they are both very rare in the absence of appropriate signals and very fast. As such, it has proven difficult to experimentally map the epigenetic landscapes that are widely believed to underly developmental networks. Here, we introduce a new nonequilibrium perturbation method to help reconstruct a regulatory network's epigenetic landscape. We derive the mathematical theory needed and then use the method on simulated data to reconstruct the landscapes. Our results show that with a relatively small number of perturbation experiments it is possible to recover an accurate representation of the true epigenetic landscape. We propose that our theory provides a general method by which epigenetic landscapes can be studied. Finally, our theory suggests that the total perturbation impulse required to induce a switch between metastable states is a fundamental quantity in developmental dynamics.
Stability properties of Haezendonck-Goovaerts premium principles
Authors: Niushan Gao, Cosimo Munari, Foivos Xanthos
Date: 2019-09-24 00:00:00

We investigate a variety of stability properties of Haezendonck-Goovaerts premium principles on their natural domain, namely Orlicz spaces. We show that such principles always satisfy the Fatou property. This allows to establish a tractable dual representation without imposing any condition on the reference Orlicz function. In addition, we show that Haezendonck-Goovaerts principles satisfy the stronger Lebesgue property if and only if the reference Orlicz function fulfills the so-called $\Delta_2$ condition. We also discuss (semi)continuity properties with respect to $\Phi$-weak convergence of probability measures. In particular, we show that Haezendonck-Goovaerts principles, restricted to the corresponding Young class, are always lower semicontinuous with respect to the $\Phi$-weak convergence.
Optimizing Execution Cost Using Stochastic Control
Date: 2019-09-24 00:00:00

We devise an optimal allocation strategy for the execution of a predefined
number of stocks in a given time frame using the technique of discrete-time
Stochastic Control Theory for a defined market model. This market structure
allows an instant execution of the market orders and has been analyzed based on
the assumption of discretized geometric movement of the stock prices. We
consider two different cost functions where the first function involves just
the fiscal cost while the cost function of the second kind incorporates the
risks of non-strategic constrained investments along with fiscal costs.
Precisely, the strategic development of constrained execution of K stocks
within a stipulated time frame of T units is established mathematically using a
well-defined stochastic behaviour of stock prices and the same is compared with
some of the commonly-used execution strategies using the historical stock price
data.
AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids
Date: 2019-09-18 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

We demonstrate progress on the deployment of two sets of technologies to
support distribution grid operators integrating high shares of renewable energy
sources, based on a market for trading local energy flexibilities. An
artificial-intelligence (AI) grid modelling tool, based on probabilistic
graphs, predicts congestions and estimates the amount and location of energy
flexibility required to avoid such events. A scalable time-series forecasting
system delivers large numbers of short-term predictions of distributed energy
demand and generation. We discuss the deployment of the technologies at three
trial demonstration sites across Europe, in the context of a research project
carried out in a consortium with energy utilities, technology providers and
research institutions.
Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees
Date: 2019-09-19 00:00:00

Closing feedback loops fast and over long distances is key to emerging
cyber-physical applications; for example, robot motion control and swarm
coordination require update intervals of tens of milliseconds. Low-power
wireless communication technology is preferred for its low cost, small form
factor, and flexibility, especially if the devices support multi-hop
communication. Thus far, however, feedback control over multi-hop low-power
wireless networks has only been demonstrated for update intervals on the order
of seconds. To fill this gap, this paper presents a wireless embedded system
that supports dynamic mode changes and tames imperfections impairing control
performance (e.g., jitter and message loss), and a control design that exploits
the essential properties of this system to provably guarantee closed-loop
stability for physical processes with linear time-invariant dynamics in the
presence of mode changes. Using experiments on a cyber-physical testbed with 20
wireless devices and multiple cart-pole systems, we are the first to
demonstrate and evaluate feedback control and coordination with mode changes
over multi-hop networks for update intervals of 20 to 50 milliseconds.
Direct Healthy Life Expectancy Estimates from Life Tables with a Sullivan Extension. Bridging the Gap Between HALE and Eurostat Estimates
Date: 2019-09-23 00:00:00

The analytic derivation of a more general model of survival-mortality and the
estimation of a parameter bx related to the Healthy Life Years Lost (HLYL) is
followed with the formulation of a computer program providing results similar
to those of the World Health Organization for the Healthy Life Expectancy
(HALE) and the corresponding HLYL estimates. This program is an extension of
the classical life table including more columns to estimate the cumulative
mortality, the average mortality, the person life years lost and finally the
HLYL parameter bx. Evenmore, a further extension of the Excel program based on
the Sullivan method provides estimates of the Healthy Life Expectancy at every
year of the lifespan for five different types of estimates that are the Direct,
WHO, Eurostat, Equal and Other. Estimates for several countries are presented.
It is also presented a methodology and a program to bridge the gap between the
World Health Organization (HALE) and Eurostat (HLE) healthy life expectancy
estimates. The latest version of this program (SKI-6) appear in the
Demographics2020 website.
On Constant Distance Spacing Policies for Cooperative Adaptive Cruise Control
Authors: Kay Massow, Ilja Radusch, Robert Shorten
Date: 2019-09-23 00:00:00

Cooperative Adaptive Cruise Control (CACC) systems are considered as key potential enablers to improve driving safety and traffic efficiency. They allow for automated vehicle following using wireless communication in addition to onboard sensors. To achieve string stability in CACC platoons, constant time headway (CTH) spacing policies have prevailed in research; namely, vehicle interspacing grows with the speed. While constant distance headway (CDH) spacing policies provide superior potential to increase traffic capacity than CTH, a major drawback is a smaller safety margin at high velocities and string stability cannot be achieved using a one-vehicle look-ahead communication. The hypothesis of this work is to apply CDH only in few driving situations, when traffic throughput is of highest importance and safety requirements can be met due to comparably low velocities. As the most relevant situations where CDH could be applied, we identify starting platoons at signalized intersections. In this paper, we illustrate this idea. Specifically, we compare CTH with CDH regarding its potential to increase the capacity of traffic lights. Starting with the elementary situation of single traffic lights we expand our scope to whole traffic networks including several thousand vehicles in simulation. Using real world data to calibrate and validate vehicle dynamics simulation and traffic simulation, the study discusses the most relevant working parameters of CDH, CTH, and the traffic system in which both are applied.
Time-consistent decisions and rational expectation equilibrium existence in DSGE models
Date: 2020-05-18 00:00:00

Under some initial conditions, it is shown that time consistency requirements
prevent rational expectation equilibrium (REE) existence for dynamic stochastic
general equilibrium models induced by consumer heterogeneity, in contrast to
static models. However, one can consider REE-prohibiting initial conditions as
limits of other initial conditions. The REE existence issue then is overcome by
using a limit of economies. This shows that significant care must be taken of
when dealing with rational expectation equilibria.
A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series
Authors: Constandina Koki, Stefanos Leonardos, Georgios Piliouras
Date: 2019-09-24 00:00:00
Tasks: Bayesian Inference

Conventional financial models fail to explain the economic and monetary properties of cryptocurrencies due to the latter's dual nature: their usage as financial assets on the one side and their tight connection to the underlying blockchain structure on the other. In an effort to examine both components via a unified approach, we apply a recently developed Non-Homogeneous Hidden Markov (NHHM) model with an extended set of financial and blockchain specific covariates on the Bitcoin (BTC) and Ether (ETH) price data. Based on the observable series, the NHHM model offers a novel perspective on the underlying microstructure of the cryptocurrency market and provides insight on unobservable parameters such as the behavior of investors, traders and miners. The algorithm identifies two alternating periods (hidden states) of inherently different activity -- fundamental versus uninformed or noise traders -- in the Bitcoin ecosystem and unveils differences in both the short/long run dynamics and in the financial characteristics of the two states, such as significant explanatory variables, extreme events and varying series autocorrelation. In a somewhat unexpected result, the Bitcoin and Ether markets are found to be influenced by markedly distinct indicators despite their perceived correlation. The current approach backs earlier findings that cryptocurrencies are unlike any conventional financial asset and makes a first step towards understanding cryptocurrency markets via a more comprehensive lens.