pwc-archive/papers-with-abstracts huggingface dataset:
Quantification of Ebola virus replication kinetics in vitro
Authors: Laura E. Liao, Jonathan Carruthers, Sophie J. Smither, CL4 Virology Team, Simon A. Weller, Diane Williamson, Thomas R. Laws, Isabel Garcia-Dorival, Julian Hiscox, Benjamin P. Holder, Catherine A. A. Beauchemin, Alan S. Perelson, Martin Lopez-Garcia, Grant Lythe, John Barr, Carmen Molina-Paris
Date: 2020-10-19 00:00:00
Tasks: Bayesian Inference

Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development of antivirals and vaccines. But, thus far, these studies have not yielded a detailed specification of the infection cycle, which could provide a foundational description of the virus kinetics and thus a deeper understanding of their clinical manifestation. Here, we obtain a diverse experimental data set of the Ebola infection in vitro, and then make use of Bayesian inference methods to fully identify parameters in a mathematical model of the infection. Our results provide insights into the distribution of time an infected cell spends in the eclipse phase (the period between infection and the start of virus production), as well as the rate at which infectious virions lose infectivity. We suggest how these results can be used in future models to describe co-infection with defective interfering particles, which are an emerging alternative therapeutic.
Overlapping neural representations for the position of visible and imagined objects
Date: 2020-11-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Object, Object Tracking, Position

Humans can covertly track the position of an object, even if the object is
temporarily occluded. What are the neural mechanisms underlying our capacity to
track moving objects when there is no physical stimulus for the brain to track?
One possibility is that the brain 'fills-in' information about imagined objects
using internally generated representations similar to those generated by
feed-forward perceptual mechanisms. Alternatively, the brain might deploy a
higher order mechanism, for example using an object tracking model that
integrates visual signals and motion dynamics. In the present study, we used
EEG and time-resolved multivariate pattern analyses to investigate the spatial
processing of visible and imagined objects. Participants tracked an object that
moved in discrete steps around fixation, occupying six consecutive locations.
They were asked to imagine that the object continued on the same trajectory
after it disappeared and move their attention to the corresponding positions.
Time-resolved decoding of EEG data revealed that the location of the visible
stimuli could be decoded shortly after image onset, consistent with early
retinotopic visual processes. For processing of unseen/imagined positions, the
patterns of neural activity resembled stimulus-driven mid-level visual
processes, but were detected earlier than perceptual mechanisms, implicating an
anticipatory and more variable tracking mechanism. Encoding models revealed
that spatial representations were much weaker for imagined than visible
stimuli. Monitoring the position of imagined objects thus utilises similar
perceptual and attentional processes as monitoring objects that are actually
present, but with different temporal dynamics. These results indicate that
internally generated representations rely on top-down processes, and their
timing is influenced by the predictability of the stimulus.
Estimating and backtesting risk under heavy tails
Authors: Marcin Pitera, Thorsten Schmidt
Date: 2020-10-20 00:00:00
Tasks: Time Series, Time Series Analysis

While the {estimation} of risk is an important question in the daily business of banking and insurance, many existing plug-in estimation procedures suffer from an unnecessary bias. This often leads to the underestimation of risk and negatively impacts backtesting results, especially in small sample cases. In this article we show that the link between estimation bias and backtesting can be traced back to the dual relationship between risk measures and the corresponding performance measures, and discuss this in reference to value-at-risk, expected shortfall and expectile value-at-risk. Motivated by the consistent underestimation of risk by plug-in procedures, we propose a new algorithm for bias correction and show how to apply it for generalized Pareto distributions to the i.i.d. setting and to a GARCH(1,1) time series. In particular, we show that the application of our algorithm leads to gain in efficiency when heavy tails or heteroscedasticity exists in the data.
Metrics for Aerial, Urban LiDAR Point Clouds
Authors: Michael H. Stanley, Debra F. Laefer
Date: 2020-10-20 00:00:00

This paper introduces five new density and accuracy metrics for aerial point clouds that address the complexity and objectives of modern, dense laser scans of urban scenes. The five metrics describe (1) vertical surface density (points per area on vertical surfaces); (2) vertical density as a function of horizontal density; (3) vertical surface accuracy; and a decomposition of error into (4) within-pass and (5) cross-pass components. Specifically considered is vertical surface coverage and the practice of overlapping flight passes to reduce the occlusions and achieve the vertical density needed for twenty-first-century use cases (e.g. curb and window detection). The application of these metrics to a quartet of recent urban flyovers demonstrates their relevance by establishing (1) the efficacy of considering sensor position and wall height when predicting point density on vertical surfaces; (2) that cross-pass registration accounts for a disproportionate amount of the vertical surface error (but not horizontal) and provides a meaningful parameter to compare high-density, urban point clouds; and (3) that compared to horizontal density and accuracy, the vertical counterparts are disproportionately impacted (positively for density and negatively for accuracy) by modern, optimized flight missions.
Sampling Theory of Bandlimited Continuous-Time Graph Signals
Authors: Feng Ji, Hui Feng, Hang Sheng, Wee Peng Tay
Date: 2020-10-20 00:00:00
Tasks: Time Series, Time Series Analysis

A continuous-time graph signal can be viewed as a time series of graph signals. It generalizes both the classical continuous-time signal and ordinary graph signal. Therefore, such a signal can be considered as a function on two domains: the graph domain and the time domain. In this paper, we consider the sampling theory of bandlimited continuous-time graph signals. To formulate the sampling problem, we need to consider the interaction between the graph and time domains. We describe an explicit procedure to determine a discrete sampling set for perfect signal recovery. Moreover, in analogous to the Nyquist-Shannon sampling theorem, we give an explicit formula for the minimal sample rate.
An Efficient Algorithm for Device Detection and Channel Estimation in Asynchronous IoT Systems
Authors: Liang Liu, Ya-Feng Liu
Date: 2020-10-20 00:00:00
Tasks: Action Detection, Activity Detection

A great amount of endeavour has recently been devoted to the joint device activity detection and channel estimation problem in massive machine-type communications. This paper targets at two practical issues along this line that have not been addressed before: asynchronous transmission from uncoordinated users and efficient algorithms for real-time implementation in systems with a massive number of devices. Specifically, this paper considers a practical system where the preamble sent by each active device is delayed by some unknown number of symbols due to the lack of coordination. We manage to cast the problem of detecting the active devices and estimating their delay and channels into a group LASSO problem. Then, a block coordinate descent algorithm is proposed to solve this problem globally, where the closed-form solution is available when updating each block of variables with the other blocks of variables being fixed, thanks to the special structure of our interested problem. Our analysis shows that the overall complexity of the proposed algorithm is low, making it suitable for real-time application.
Fractional-order Modeling of the Arterial Compliance: An Alternative Surrogate Measure of the Arterial Stiffness
Date: 2020-10-20 00:00:00

Recent studies have demonstrated the advantages of fractional-order calculus
tools for probing the viscoelastic properties of collagenous tissue,
characterizing the arterial blood flow and red cell membrane mechanics, and
modeling the aortic valve cusp. In this article, we present a novel
lumped-parameter equivalent circuit models of the apparent arterial compliance
using a fractional-order capacitor (FOC). FOC, which generalizes capacitors and
resistors, displays a fractional-order behavior that can capture both elastic
and viscous properties through a power-law formulation. The proposed framework
describes the dynamic relationship between the blood pressure input and blood
volume, using linear fractional-order differential equations. The results show
that the proposed models present reasonable fit performance with in-silico data
of more than 4,000 subjects. Additionally, strong correlations have been
identified between the fractional-order parameter estimates and the central
hemodynamic determinants as well as pulse wave velocity indexes. Therefore,
fractional-order based paradigm of arterial compliance shows prominent
potential as an alternative tool in the analysis of arterial stiffness.
Identifying Crisis-Critical Intellectual Property Challenges during the Covid-19 Pandemic: A scenario analysis and conceptual extrapolation of innovation ecosystem dynamics using a visual mapping approach
Date: 2020-10-20 00:00:00
Tasks: Management

The Covid-19 pandemic exposed firms, organisations and their respective
supply chains which are directly involved in the manufacturing of products that
are critical to alleviating the effects of the health crisis, collectively
referred to as the Crisis-Critical Sector,to unprecedented challenges. Firms
from other sectors, such as automotive, luxury and home appliances, have rushed
into the Crisis-Critical Sector in order to support the effort to upscale
incumbent manufacturing capacities, thereby introducing Intellectual Property
(IP)related dynamics and challenges. We apply an innovation ecosystem
perspective on the Crisis-Critical Sector and adopt a novel visual mapping
approach to identify IP associated challenges and IP specific dynamic
developments during and potentially beyond the crisis.In this paper, we add
methodologically by devising and testing a visual approach to capturing IP
related dynamics in evolving innovation ecosystems and contribute to literature
on IP management in the open innovation context by proposing paraground IP as a
novel IP type.Finally, we also deduce managerial implications for IP management
practitioners at both incumbent firms and new entrants for navigating
innovation ecosystems subject to crisis-induced dynamic shifts.
Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations
Authors: Mehmet Ali Aygul, Mahmoud Nazzal, Huseyin Arslan
Date: 2020-10-20 00:00:00

The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be addressed to realize this technology in practice. One of the main challenges is the configuration of reflecting coefficients without the need for beam training overhead or massive channel estimation. Earlier works used estimated channel information with deep learning algorithms to design RIS reflection matrices. Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels. In this paper, different from existing works, we propose to exploit the correlation in the previously sampled channels to estimate RIS interaction more reliably. We use a deep multi-layer perceptron for this purpose. Simulation results reveal performance improvements achieved by the proposed algorithm.
Analysis of Markov Jump Processes under Terminal Constraints
Authors: Michael Backenköhler, Luca Bortolussi, Gerrit Großmann, Verena Wolf
Date: 2020-10-20 00:00:00
Tasks: Bayesian Inference

Many probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We propose a solution to this bridging problem for the widely used class of population-structured Markov jump processes. The method is based on a state-space lumping scheme that aggregates states in a grid structure. The resulting approximate bridging distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way the algorithm learns a well-justified finite-state projection yielding guaranteed lower bounds for the system behavior under endpoint constraints. We demonstrate the method's applicability to a wide range of problems such as Bayesian inference and the analysis of rare events.
Modified QPSK Partition Algorithm Based on MAP Estimation for Probabilistically-Shaped 16-QAM
Authors: Jin Hu, Zhongliang Sun, Xuekai Xu, Mengqi Guo, Xizi Tang, Yueming Lu, Yaojun Qiao
Date: 2020-10-20 00:00:00
Tasks: Noise Estimation

Probabilistic shaping (PS) is investigated as a potential technique to approach the Shannon limit. However, it has been proved that conventional carrier phase recovery (CPR) algorithm designed for uniform distribution may have extra penalty in PS systems. In this paper, we find that the performance of QPSK partition algorithm is degenerated when PS is implemented. To solve this issue, a modified QPSK partition algorithm that jointly optimizes the amplitude decision threshold and filter weight is proposed, where the optimization of decision threshold is based on maximum a posterior probability (MAP) estimation. Different from the conventional decision methods which commonly use Euclidean distance metric, the MAP-based decision introduces the statistical characteristics of the received signals to obtain an accurate amplitude partition. In addition, the filter weight is optimized for different decision thresholds to enhance the tolerance of ASE-induced phase noise. We verify the feasibility of the proposed algorithm in a 56 GBaud PS 16-ary quadrature amplitude modulation (16-QAM) system. The proposed algorithm reduces the error of phase noise estimation by nearly half. Compared with conventional QPSK partition, the proposed algorithm could narrow the gap with theoretical mutual information (MI) by more than 0.1 bit/symbol. The channel capacity is increased by 4.2%, 4.3% and 3.6% with signal-to-noise ratio (SNR) from 8 dB to 10 dB respectively. These observations show that the proposed algorithm is a promising method to relieve the penalty of QPSK partition algorithm in PS systems.
A Global View of Standards for Open Image Data Formats and Repositories
Authors: Jason R. Swedlow, Pasi Kankaanpää, Ugis Sarkans, Wojtek Goscinski, Graham Galloway, Ryan P. Sullivan, Claire M. Brown, Chris Wood, Antje Keppler, Ben Loos, Sara Zullino, Dario Livio Longo, Silvio Aime, Shuichi Onami
Date: 2020-10-20 00:00:00

Biological and biomedical imaging datasets record the constitution, architecture and dynamics of living organisms across several orders of magnitude of space and time. Imaging technologies are now used throughout the life and biomedical sciences to achieve discovery and understanding of biological mechanisms in the basic sciences as well as assessment, diagnosis and therapeutic intervention in clinical trials and animal and human medicine. The universal application and use of imaging raises an important question and opportunity: what is the value and ultimate destination of biological and medical imaging data? In the last few years, several informatics and data science technologies have matured sufficiently so that routine publication of these datasets is now possible. Participants in Global BioImaging from 15 countries and all populated continents have agreed on the need for recommendations and guidelines for the establishment of image data repositories and the formats they use for delivering data to the global scientific community. This white paper presents a shared, global view of criteria for these common, globally applicable guidelines and provisional proposals for open tools and resources that are available now and can provide a foundation for future development.
Algorithmic Reduction of Biological Networks With Multiple Time Scales
Authors: Niclas Kruff, Christoph Lüders, Ovidiu Radulescu, Thomas Sturm, Sebastian Walcher
Date: 2020-10-20 00:00:00
Tasks: Epidemiology

We present a symbolic algorithmic approach that allows to compute invariant manifolds and corresponding reduced systems for differential equations modeling biological networks which comprise chemical reaction networks for cellular biochemistry, and compartmental models for pharmacology, epidemiology and ecology. Multiple time scales of a given network are obtained by scaling, based on tropical geometry. Our reduction is mathematically justified within a singular perturbation setting. The existence of invariant manifolds is subject to hyperbolicity conditions, for which we propose an algorithmic test based on Hurwitz criteria. We finally obtain a sequence of nested invariant manifolds and respective reduced systems on those manifolds. Our theoretical results are generally accompanied by rigorous algorithmic descriptions suitable for direct implementation based on existing off-the-shelf software systems, specifically symbolic computation libraries and Satisfiability Modulo Theories solvers. We present computational examples taken from the well-known BioModels database using our own prototypical implementations.
Are Crises Predictable? A Review of the Early Warning Systems in Currency and Stock Markets
Authors: Peiwan Wang, Lu Zong
Date: 2020-10-20 00:00:00

The study efforts to explore and extend the crisis predictability by synthetically reviewing and comparing a full mixture of early warning models into two constitutions: crisis identifications and predictive models. Given empirical results on Chinese currency and stock markets, three-strata findings are concluded as (i) the SWARCH model conditional on an elastic thresholding methodology can most accurately classify crisis observations and greatly contribute to boosting the predicting precision, (ii) stylized machine learning models are preferred given higher precision in predicting and greater benefit in practicing, (iii) leading factors sign the crisis in a diversified way for different types of markets and varied prediction periods.
Opacity Enforcing Supervisory Control using Non-deterministic Supervisors
Authors: Yifan Xie, Xiang Yin, ShaoYuan Li
Date: 2020-10-20 00:00:00

In this paper, we investigate the enforcement of opacity via supervisory control in the context of discrete-event systems. A system is said to be opaque if the intruder, which is modeled as a passive observer, can never infer confidently that the system is at a secret state. The design objective is to synthesize a supervisor such that the closed-loop system is opaque even when the control policy is publicly known. In this paper, we propose a new approach for enforcing opacity using non-deterministic supervisors. A non-deterministic supervisor is a decision mechanism that provides a set of control decisions at each instant, and randomly picks a specific control decision from the decision set to actually control the plant. Compared with the standard deterministic control mechanism, such a non-deterministic control mechanism can enhance the plausible deniability of the controlled system as the online control decision is a random realization and cannot be implicitly inferred from the control policy. We provide a sound and complete algorithm for synthesizing a non-deterministic opacity-enforcing supervisor. Furthermore, we show that non-deterministic supervisors are strictly more powerful than deterministic supervisors in the sense that there may exist a non-deterministic opacity-enforcing supervisor even when deterministic supervisors cannot enforce opacity.
Identification of The Number of Wireless Channel Taps Using Deep Neural Networks
Authors: Ahmad M. Jaradat, Khaled Walid Elgammal, Mehmet Kemal Ozdemir, Huseyin Arslan
Date: 2020-10-20 00:00:00

In wireless communication systems, identifying the number of channel taps offers an enhanced estimation of the channel impulse response (CIR). In this work, efficient identification of the number of wireless channel taps has been achieved via deep neural networks (DNNs), where we modified an existing DNN and analyzed its convergence performance using only the transmitted and received signals of a wireless system. The displayed results demonstrate that the adopted DNN accomplishes superior performance in identifying the number of channel taps, as compared to an existing algorithm called Spectrum Weighted Identification of Signal Sources (SWISS).
Thermodynamics of markets
Authors: Sergey Rashkovskiy
Date: 2020-10-14 00:00:00

We consider the thermodynamic approach to the description of economic systems and processes. The first and second laws of thermodynamics as applied to economic systems are derived and analyzed. It is shown that there is a deep analogy between the parameters of thermodynamic and economic systems (markets); in particular, each thermodynamic parameter can be associated with a certain economic parameter or indicator. The economic meaning of such primordially thermodynamic concepts as pressure, volume, internal energy, heat, etc. has been established. The thermostatistics of the market is considered. It is shown that, as in conventional thermostatistics, many market parameters, such as price of goods, quantity of goods, etc., as well as their fluctuations can be calculated formally using the partition function of an economic system.
White noise jammer mathematical modelling and simulation
Authors: Theodoros Kostis
Date: 2020-10-19 00:00:00
Tasks: Descriptive

The radar equation is the fundamental mathematical modelling of the basic function of a radar system. Moreover there are many versions of the radar equation that correspond to particular radar operations, like a low PRF, a high PRF or a surveillance mode. In many cases all these expressions of the radar equation exist in their combined forms giving little information to the actual physics and signal geometry between the radar and target involved in the process. In this case study we divide the radar equation into its major steps and present a descriptive mathematical modelling of the radar and other related equations utilizing the free space loss and the target gain concepts for simulating the effect of a white noise jammer to an adversary radar. We believe that this work will be particularly beneficial to instructors of radar courses and to radar simulation engineers because of its analytical block approach of the main equations related to radar and electronic warfare fields. Finally this work falls under the field of predictive dynamics for radar systems using mathematical modelling techniques.