pwc-archive/papers-with-abstracts huggingface dataset:
Containment strategies and statistical measures for the control of Bovine Viral Diarrhea spread in livestock trade networks
Date: 2019-12-12 00:00:00
Tasks: Time Series Analysis

Assessing the risk of epidemic spread on networks and developing strategies
for its containment is of tremendous practical importance, both due to direct
effects in public health and its impact on economies. In this work we present
the numerical results of a stochastic, event-driven, hierarchical agent-based
model designed to reproduce the infectious dynamics of the cattle disease
called Bovine Viral Diarrhea (BVD), for which the corresponding network of
movements is the main route of spreading. For the farm-node dynamics, the model
takes into account a vast number of breeding, infectious and animal movement
mechanisms via a susceptible-infected-recovered (SIR) type of dynamics with an
additional permanently infectious class. The interaction between the farms is
described by a supply and demand farm manager mechanism governing the network
structure and dynamics. We discuss the disease and breeding dynamics, study
numerous mitigation strategies of present and past government regulations
taking Germany as a case-study country and perform a sensitivity analysis on
key parameters. We argue that the model, subject to calibration, has universal
predictive potential, can be extended to diseases beyond BVD and demonstrate
that appropriate measures can indeed lead to eradication regarding BVD. We
further present the results of the time-series of the model and conduct a
statistical analysis of and among the different mitigation strategies.
General Principles of Secondary Active Transporter Function
Authors: Oliver Beckstein, Fiona Naughton
Date: 2019-12-13 00:00:00

Transport of ions and small molecules across the cell membrane against electrochemical gradients is catalyzed by integral membrane proteins that use a source of free energy to drive the energetically uphill flux of the transported substrate. Secondary active transporters couple the spontaneous influx of a "driving" ion such as Na$^+$ or H$^+$ to the flux of the substrate. The thermodynamics of such cyclical non-equilibrium systems are well understood and recent work has focused on the molecular mechanism of secondary active transport. The fact that these transporters change their conformation between an inward-facing and outward-facing conformation in a cyclical fashion, called the alternating access model, is broadly recognized as the molecular framework in which to describe transporter function. High resolution structures and detailed computer simulations lead to the recognition of common molecular-level principles between disparate transporter families. Inverted repeat symmetry in secondary active transporters has shed light on how protein structures can encode a bi-stable two-state system. Three broad classes of alternating access transitions have been described as rocker-switch, rocking-bundle, and elevator mechanisms. Transporters can be understood as gated pores with at least two coupled gates that map to distinct parts of the transporter protein. Enumerating all distinct gate states naturally includes occluded states in the alternating access picture and suggests observable protein conformations. By connecting the possible conformational states and ion/substrate bound states in a kinetic model, a unified picture emerges in which symporter, antiporter, and uniporter function are extremes in a continuum of functionality. We briefly discuss how biological complexity may be integrated in quantitative kinetic models to provide a bridge from structure to function.
Predicting the real-valued distances between residue pairs for proteins
Date: 2019-12-18 00:00:00
Tasks: Generative Adversarial Network, Prediction, Protein Structure Prediction, Transfer Learning

Predicting protein structure from the amino acid sequence has been a
challenge with theoretical and practical significance in biophysics. Despite
the recent progresses elicited by improved residue-residue contact prediction,
contact-based structure prediction has gradually reached the performance
ceiling. New methods have been proposed to predict the residue-residue
distance, but unanimously by simplifying the real-valued distance prediction
into a multiclass classification problem. Here we show a regression-based
distance prediction method, which adopts the generative adversarial network to
capture the delicate geometric relationship between residue pairs and thus
could predict the continuous, real-valued residue-residue distance
satisfactorily. The predicted residue distance map allows rapid structure
modeling by the CNS suite, and the constructed models approach at least the
same level of quality as the other state-of-the-art protein structure
prediction methods when tested on available CASP13 targets. Moreover, this
method can be used directly for the structure prediction of membrane proteins
without transfer learning.
A Guide to Design Disturbance Observer
Date: 2019-12-13 00:00:00

The goal of this paper is to clarify the robustness and performance
constraints in the design of control systems based on disturbance observer
(DOB). Although the bandwidth constraints of a DOB have long been very
well-known by experiences and observations, they have not been formulated and
clearly reported yet. In this regard, the Bode and Poisson integral formulas
are utilized in the robustness analysis so that the bandwidth constraints of a
DOB are derived analytically. In this paper, it is shown that the bandwidth of
a DOB has upper and lower bounds to obtain good robustness if the plant has
non-minimum phase zero(s) and pole(s), respectively. Besides that the
performance of a system can be improved by using a higher-order disturbance
observer (HODOB); however, the robustness may deteriorate, and the bandwidth
constraints become more severe. New analysis and design methods, which provide
good robustness and predefined performance criteria, are proposed for the DOB
based robust control systems. The validity of the proposals are verified by
simulation results.
An Adaptive Reaction Force Observer Design
Date: 2019-12-13 00:00:00

In this paper, a new adaptive design method is proposed for reaction force
observer (RFOB) based robust force control systems. It is a well-known fact
that an RFOB has several superiorities over a force sensor such as higher force
control bandwidth, stability improvement, force-sensorless force control, and
so on. However, there are insufficient analysis and design methods for an RFOB
based robust force control system; therefore, its stability and performance
highly depend on designers own experiences. To overcome this issue, new
stability analysis and novel adaptive design methods are proposed for RFOB
based robust force control systems. In the proposed adaptive design method, the
design parameters of the robust force control system, i.e., the bandwidths of a
disturbance observer (DOB) and an RFOB, the nominal and identified inertias in
the design of a DOB and an RFOB, respectively, and the force control gain, are
adjusted automatically by using an adaptive control algorithm which is derived
by estimating the plant parameters and environmental impedance. The proposed
adaptive design method provides good stability and performance by considering
the design constraints of a DOB. The validity of the proposals is verified by
simulation and experimental results.
Network Data
Date: 2019-12-13 00:00:00
Tasks: Causal Inference, regression

Many economic activities are embedded in networks: sets of agents and the
(often) rivalrous relationships connecting them to one another. Input sourcing
by firms, interbank lending, scientific research, and job search are four
examples, among many, of networked economic activities. Motivated by the
premise that networks' structures are consequential, this chapter describes
econometric methods for analyzing them. I emphasize (i) dyadic regression
analysis incorporating unobserved agent-specific heterogeneity and supporting
causal inference, (ii) techniques for estimating, and conducting inference on,
summary network parameters (e.g., the degree distribution or transitivity
index); and (iii) empirical models of strategic network formation admitting
interdependencies in preferences. Current research challenges and open
questions are also discussed.
Intelligent Reflecting Surface Enhanced Resilient Design for MEC Offloading over Millimeter Wave Links
Authors: Yashuai Cao, Tiejun Lv
Date: 2019-12-13 00:00:00
Tasks: Blocking, Edge-computing

The merge of mobile edge computing (MEC) and millimeter-wave (mmWave) communications will hopefully enable the fast access for computational resources, where these two technologies can benefit from each other's potentials. However, the high susceptibility to blocking in mmWave networks imposes crucial challenges for further development of mmWave-MEC vision. In this paper, a novel intelligent reflecting surface (IRS) assisted mmWave-MEC scheme is proposed to overcome the disruptive effect caused by blockage events. In this context, we investigate new methods to minimize mobile power for a multi-user mmWave-MEC system, thus efficiently orchestrating the uplink mobile power resources for latency-constrained computation offloading. In particular, the mobile power is optimized by joint design of individual device power, multi-user detection matrix and passive beamforming. To tackle this issue, we develop an alternating optimization framework so that the joint optimization can be decomposed into tractable subproblems. First, we provide closed-form expressions for the update of powers and multi-user detection vectors in each iteration step. Then, we reformulate the passive beamforming problem. To cater for large-scale IRS scenario, we propose two efficient algorithms including complex circle manifold optimization (CCMO) method and sum-of-inverse minimization (SIMin) fraction transform based alternating direction method of multipliers (ADMM) method. Finally, numerical results corroborate the merits of our proposed IRS assisted mmWave-MEC scheme, and demonstrate the feasibility and effectiveness of our algorithms.
Evolutionary dynamics in vascularised tumours under chemotherapy
Authors: Chiara Villa, Mark A. J. Chaplain, Tommaso Lorenzi
Date: 2019-12-13 00:00:00

We consider a mathematical model for the evolutionary dynamics of tumour cells in vascularised tumours under chemotherapy. The model comprises a system of coupled partial integro-differential equations for the phenotypic distribution of tumour cells, the concentration of oxygen and the concentration of a chemotherapeutic agent. In order to disentangle the impact of different evolutionary parameters on the emergence of intra-tumour phenotypic heterogeneity and the development of resistance to chemotherapy, we construct explicit solutions to the equation for the phenotypic distribution of tumour cells and provide a detailed quantitative characterisation of the long-time asymptotic behaviour of such solutions. Analytical results are integrated with numerical simulations of a calibrated version of the model based on biologically consistent parameter values. The results obtained provide a theoretical explanation for the observation that the phenotypic properties of tumour cells in vascularised tumours vary with the distance from the blood vessels. Moreover, we demonstrate that lower oxygen levels may correlate with higher levels of phenotypic variability, which suggests that the presence of hypoxic regions supports intra-tumour phenotypic heterogeneity. Finally, the results of our analysis put on a rigorous mathematical basis the idea, previously suggested by formal asymptotic results and numerical simulations, that hypoxia favours the selection for chemoresistant phenotypic variants prior to treatment. Consequently, this facilitates the development of resistance following chemotherapy.
Evolutionary dynamics in populations with fluctuating size
Date: 2019-12-13 00:00:00

Temporal environmental variations are ubiquitous in nature, yet most of the
theoretical works in population genetics and evolution assume fixed
environment. Here we analyze the effect of variations in carrying capacity on
the fate of a mutant type. We consider a two-state Moran model, where selection
intensity at equilibrium may differ (in amplitude and in sign) from selection
during periods of sharp growth and sharp decline. Using Kimura's diffusion
approximation we present simple formulae for effective population size and
effective selection, and use it to calculate the chance of ultimate fixation,
the time to fixation and the time to absorption (either fixation or loss). Our
analysis shows perfect agreement with numerical solutions for neutral,
beneficial and deleterious mutant. The contributions of different processes to
the mean and the variance of abundance variations are additive and commutative.
As a result, when selection intensity $s$ is weak such that ${\cal O}(s^2)$
terms are negligible, periodic or stochastic environmental variations yield
identical results.
A mechanical and economical based framework to help decision-makers for natural hazards and malicious events impact on infrastructure prevention
Authors: P-J. Tisserand, M. Ragueneau
Date: 2019-12-13 00:00:00
Tasks: Decision Making

Many studies in economics deal with the non-reliability cost to assess insurance fees or investment analyses, but none takes into consideration the mechanical aspect of reliability analysis. Other studies in mechanics give some tools and methods to carry out reliability analyses and fragility study. This study developed a framework where economical and mechanical considerations for infrastructure investment decision-making. The theoretical reasoning is here developed to couple mechanical reliability analyses, which are composed of fragility curves, and economical reliability analyses, which is based on resilience cost functions. This coupling is carried out with some probabilistic considerations, giving the concept of "probable cost of failure". The strength of this framework is that it can be used to analyze all possible critical components in a network with all possible natural hazards or malicious event or other undesired events which it is possible to assess its probability of occurrence. The results of the analysis are indicators of probable cost of failure of an infrastructure, which represents the insurance fee. These indicators can be computed for railway lines, for critical components, for events. This tool enables decision-makers to prioritize safety investments and to guide strategic choices. The next step of this study will be to develop smart data analysis tools, because of this framework needs and produces a lot of data, which must be smartly analyzed and presented.
Portfolio liquidation under transient price impact -- theoretical solution and implementation with 100 NASDAQ stocks
Authors: Ying Chen, Ulrich Horst, Hoang Hai Tran
Date: 2019-12-13 00:00:00

We derive an explicit solution for deterministic market impact parameters in the Graewe and Horst (2017) portfolio liquidation model. The model allows to combine various forms of market impact, namely instantaneous, permanent and temporary. We show that the solutions to the two benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013) are obtained as special cases. We relate the different forms of market impact to the microstructure of limit order book markets and show how the impact parameters can be estimated from public market data. We investigate the numerical performance of the derived optimal trading strategy based on high frequency limit order books of 100 NASDAQ stocks that represent a range of market impact profiles. It shows the strategy achieves significant cost savings compared to the benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013).
GuardRider: Towards Sustainable Backscattering System over WiFi in the Wild
Date: 2019-12-12 00:00:00
Tasks: TAG

The WiFi backscatter communications offer ultra-low power and ubiquitous
connections for IoT systems. Caused by the intermittent-nature of the WiFi
traffics, state-of-the-art WiFi backscatter communications are not reliable for
backscatter link or simple for tag to do adaptive transmission. In order to
build sustainable (reliable and simple) WiFi backscatter communications, we
present GuardRider, a WiFi backscatter system that enables backscatter
communications riding on WiFi signals in the wild. The key contribution of
GuardRider is an optimization algorithm of designing RS codes to follow the
statistical knowledge of WiFi traffics and adjust backscatter transmission.
With GuardRider, the reliable baskscatter link is guaranteed and a backscatter
tag is able to adaptively transmit information without heavily listening the
excitation channel. We built a hardware prototype of GuardRider using a
customized tag with FPGA implementation. Both the simulations and field
experiments verify that GuardRider could achieve a notably gains in bit error
rate and frame error rate, which are hundredfold reduction in simulations and
around 99% in filed experiments.
Solution of option pricing equations using orthogonal polynomial expansion
Authors: Falko Baustian, Kateřina Filipová, Jan Pospíšil
Date: 2019-12-13 00:00:00

In this paper we study both analytic and numerical solutions of option pricing equations using systems of orthogonal polynomials. Using a Galerkin-based method, we solve the parabolic partial diferential equation for the Black-Scholes model using Hermite polynomials and for the Heston model using Hermite and Laguerre polynomials. We compare obtained solutions to existing semi-closed pricing formulas. Special attention is paid to the solution of Heston model at the boundary with vanishing volatility.
The role of low temperature waste heat recovery in achieving 2050 goals: a policy positioning paper
Date: 2019-12-13 00:00:00

Urban waste heat recovery, in which low temperature heat from urban sources
is recovered for use in a district heat network, has a great deal of potential
in helping to achieve 2050 climate goals. For example, heat from data centres,
metro systems, public sector buildings and waste water treatment plants could
be used to supply ten percent of Europe's heat demand. Despite this, at
present, urban waste heat recovery is not widespread and is an immature
technology. To help achieve greater uptake, three policy recommendations are
made. First, policy raising awareness of waste heat recovery and creating a
legal framework is suggested. Second, it is recommended that pilot projects are
promoted to help demonstrate technical and economic feasibility. Finally, a
pilot credit facility is proposed aimed at bridging the gap between potential
investors and heat recovery projects.
A Multi-timescale Two-stage Robust Grid-friendly Dispatch Model for Microgrid Operation
Date: 2019-12-13 00:00:00

Uncertainty in renewable energy generation and load consumption is a great
challenge for microgrid operation, especially in islanded mode as the microgrid
may be small in size and has limited flexible resources. In this paper, a
multi-timescale, two-stage robust unit commitment and economic dispatch model
is proposed to optimize the microgrid operation. The first stage is a
combination of day-ahead hourly and real-time sub-hourly model, which means the
day-ahead dispatch result must also satisfy the real-time condition at the same
time. The second stage is to verify the feasibility of the day-ahead dispatch
result in worst-case condition considering high-level uncertainty in renewable
energy dispatch and load consumptions. In the proposed model, battery energy
storage system (BESS) and solar PV units are integrated as a combined
solar-storage system. The BESS plays an essential role to balance the variable
output of solar PV units, which keeps the combined solar-storage system output
unchanged on an hourly basis. In this way, it largely neutralizes the impact of
solar uncertainty and makes the microgrid operation grid friendly. Furthermore,
in order to enhance the flexibility and resilience of the microgrid, both BESS
and thermal units provide regulating reserve to manage solar and load
uncertainty. The model has been tested in a controlled hardware in loop (CHIL)
environment for the Bronzeville Community Microgrid system in Chicago. The
simulation results show that the proposed model works effectively in managing
the uncertainty in solar PV and load and can provide a flexible dispatch in
both grid-connected and islanded modes.
Intracellular Energy Variability Modulates Cellular Decision-Making Capacity
Authors: Ryan Kerr, Sara Jabbari, Iain G. Johnston
Date: 2019-12-13 00:00:00
Tasks: Decision Making

Cells are able to generate phenotypic diversity both during development and in response to stressful and changing environments, aiding survival. The biologically and medically vital process of a cell assuming a functionally important fate from a range of phenotypic possibilities can be thought of as a cell decision. To make these decisions, a cell relies on energy dependent pathways of signalling and expression. However, energy availability is often overlooked as a modulator of cellular decision-making. As cells can vary dramatically in energy availability, this limits our knowledge of how this key biological axis affects cell behaviour. Here, we consider the energy dependence of a highly generalisable decision-making regulatory network, and show that energy variability changes the sets of decisions a cell can make and the ease with which they can be made. Increasing intracellular energy levels can increase the number of stable phenotypes it can generate, corresponding to increased decision-making capacity. For this decision-making architecture, a cell with intracellular energy below a threshold is limited to a singular phenotype, potentially forcing the adoption of a specific cell fate. We suggest that common energetic differences between cells may explain some of the observed variability in cellular decision-making, and demonstrate the importance of considering energy levels in several diverse biological decision-making phenomena.
Correlated Effects of Relative Size and Depth in the Perceptual Organization of Multiple Figure-Ground Configurations
Date: 2019-12-13 00:00:00

The neural networks of the human visual brain derive representations of
three-dimensional structure from specific two-dimensional image cues. Neural
models backed by psychophysical data predict how local differences in either
luminance contrast or physical size of local boundaries in 2D images may
determine the perception of 3D properties. Predictions relative to the role of
color in this process do not follow from any of the current models. To further
clarify the potential contribution of color to perceptual organization, image
configurations with multiple surface representations where the relative
physical size of local boundaries between contrast regions was held constant
were submitted to perceptual judgments of relative size and relative depth. The
only potential cues available in the images were generated by the specific
local combinations of color and luminance contrast. It is shown that response
probabilities for subjective depth and subjective size are systematically and
consistently determined by local surface colors and their immediate
backgrounds. There is a statistically significant correlation between
subjective depth and subjective size, and a color specific effect on both
dependent variables. This effect depends on the polarity of the immediate
surround of the reference surface rather than on local center-surround contrast
intensity. It is suggested that the underlying neural mechanisms selectively
exploit specific color and background cues to enable intrinsically coherent
perceptual organization of ambiguous image input.