pwc-archive/papers-with-abstracts huggingface dataset:
Calculation of the Optimal Installation Angle for Seasonal Adjusting of PV Panels Based on Solar Radiation Prediction
Date: 2020-04-25 00:00:00

An important parameter that affects PV panel performance of a solar power
system is the incident solar radiation with the panel or panel's area of
exposure to the sun. The direction and tilt angle of a PV panel are two
important factors in PV system design. This paper itself presents the
calculation of the optimum installation angles for the seasonal adjusting of
solar cell panels considering apparent motion of the sun. Daily adjusting for
optimal tilt angle can increase the received energy from the sun. However,
making trade-off is required because changing the panel angle daily costs lots
of endeavour, so we designed seasonal angles for installation of panels.
Constructibility of a Causal/Impulse Free NDS Using Descriptor Form Subsystems
Date: 2020-04-26 00:00:00
Tasks: Form

Requirements are investigated in this paper for each descriptor form subsystem, with which a causal/impulse free networked dynamic system (NDS) can be constructed. For this purpose, a matrix rank based necessary and sufficient condition is at first derived for the causality/impulse freeness of an NDS, in which the associated matrix depends affinely on subsystem connections. From this result, a necessary and sufficient condition is derived for each subsystem, such that there exists a subsystem connection matrix that leads to a causal/impulse free NDS. This condition further leads to a necessary and sufficient condition for the existence of a local static output feedback that guarantees the construction of a causal/impulse free NDS. A prominent property of these conditions are that all the involved numerical computations are performed independently on each individual subsystem, which is quite attractive in reducing computation costs and improving numerical stability for large scale NDS analysis and synthesis. Situations have also been clarified in which NDS causality/impulse freeness is independent of subsystem connections. It has also been made clear that under some situations, local static output feedbacks are not helpful in constructing a causal NDS.
Joint Radar-Communication-Based Bayesian Predictive Beamforming for Vehicular Networks
Date: 2020-04-26 00:00:00
Tasks: Joint Radar-Communication

In this paper, we develop a predictive beamforming scheme based on the
dual-functional radar-communication (DFRC) technique, where the road-side units
estimates the motion parameters of vehicles exploiting the echoes of the DFRC
signals. Compared to the conventional feedback-based beam tracking approaches,
the proposed method can reduce the signaling overhead and improve the tracking
performance. A novel message passing algorithm is proposed, which yields a near
optimal performance achieved by the maximum a posteriori estimation. Simulation
results have shown the effectiveness of the proposed DFRC based scheme.
Blind Data Detection in Massive MIMO via $\ell_3$-norm Maximization over the Stiefel Manifold
Authors: Ye Xue, Yifei Shen, Vincent Lau, Jun Zhang, Khaled B. Letaief
Date: 2020-04-26 00:00:00

Massive MIMO has been regarded as a key enabling technique for 5G and beyond networks. Nevertheless, its performance is limited by the large overhead needed to obtain the high-dimensional channel information. To reduce the huge training overhead associated with conventional pilot-aided designs, we propose a novel blind data detection method by leveraging the channel sparsity and data concentration properties. Specifically, we propose a novel $\ell_3$-norm-based formulation to recover the data without channel estimation. We prove that the global optimal solution to the proposed formulation can be made arbitrarily close to the transmitted data up to a phase-permutation ambiguity. We then propose an efficient parameter-free algorithm to solve the $\ell_3$-norm problem and resolve the phase permutation ambiguity. We also derive the convergence rate in terms of key system parameters such as the number of transmitters and receivers, the channel noise power, and the channel sparsity level. Numerical experiments will show that the proposed scheme has superior performance with low computational complexity.
COVID-19 in Africa -- outbreak despite interventions?
Date: 2020-04-26 00:00:00

Few African countries have reported COVID-19 case numbers above $1\,000$ as
of April 18, 2020, with South Africa reporting $3\,034$ cases being hit hardest
in Sub-Saharan Africa. Several African countries, especially South Africa, have
already taken strong non-pharmaceutical interventions that include physical
distancing, restricted economic, educational and leisure activities and reduced
human mobility options. The required strengths and overall effectiveness of
such interventions, however, are debated because of simultaneous but opposing
interests in most African countries: strongly limited health care capacities
and testing capabilities largely conflict with pressured national economies and
socio-economic hardships on the individual level, limiting compliance to
intervention targets. Here we investigate implications of interventions on the
COVID-19 outbreak dynamics, focusing on South Africa before and after the
national lockdown enacted on March 27, 2020. Our analysis shows that initial
exponential growth of existing case numbers is consistent with doubling times
of about $2.5$ days. After lockdown, the growth remains exponential, now with
doubling times of 18 days, but still in contrast to subexponential growth
reported for Hubei/China after lockdown. Moreover, a scenario analysis of a
computational data-driven agent based mobility model for the Nelson Mandela Bay
Municipality (with $1.14$ million inhabitants) hints that keeping current
levels of intervention measures and compliance until the end of April is of
insufficient length and still too weak, too unspecific or too inconsistently
complied with to not overload local intensive care capacity. Yet, enduring,
slightly stronger, more specific interventions combined with sufficient
compliance may constitute a viable option for interventions for regions in
South Africa and potentially for large parts of the African continent.
Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity
Authors: Samuele Centorrino, María Pérez-Urdiales
Date: 2020-04-26 00:00:00

We propose and study a maximum likelihood estimator of stochastic frontier models with endogeneity in cross-section data when the composite error term may be correlated with inputs and environmental variables. Our framework is a generalization of the normal half-normal stochastic frontier model with endogeneity. We derive the likelihood function in closed form using three fundamental assumptions: the existence of control functions that fully capture the dependence between regressors and unobservables; the conditional independence of the two error components given the control functions; and the conditional distribution of the stochastic inefficiency term given the control functions being a folded normal distribution. We also provide a Battese-Coelli estimator of technical efficiency. Our estimator is computationally fast and easy to implement. We study some of its asymptotic properties, and we showcase its finite sample behavior in Monte-Carlo simulations and an empirical application to farmers in Nepal.
Extended fractional-order Jeffreys model of viscoelastic hydraulic cylinder
Date: 2020-04-26 00:00:00

A novel modeling approach for viscoelastic hydraulic cylinders, with negligible inertial forces, is proposed, based on the extended fractional-order Jeffreys model. Analysis and physical reasoning for the parameter constraints and order of the fractional derivatives are provided. Comparison between the measured and computed frequency response functions and time domain transient response argues in favor of the proposed four-parameter fractional-order model.
The Jarrow & Turnbull setting revisited
Date: 2020-04-26 00:00:00

We consider a financial market with zero-coupon bonds that are exposed to
credit and liquidity risk. We revisit the famous Jarrow & Turnbull setting in
order to account for these two intricately intertwined risk types. We utilise
the foreign exchange analogy that interprets defaultable zero-coupon bonds as a
conversion of non-defaultable foreign counterparts. The relevant exchange rate
is only partially observable in the market filtration, which leads us naturally
to an application of the concept of platonic financial markets. We provide an
example of tractable term structure models that are driven by a two-dimensional
affine jump diffusion. Furthermore, we derive explicit valuation formulae for
marketable products, e.g., for credit default swaps.
A dynamic conditional approach to portfolio weights forecasting
Authors: Fabrizio Cipollini, Giampiero M. Gallo, Alessandro Palandri
Date: 2020-04-26 00:00:00
Tasks: Time Series, Time Series Analysis

We build the time series of optimal realized portfolio weights from high-frequency data and we suggest a novel Dynamic Conditional Weights (DCW) model for their dynamics. DCW is benchmarked against popular model-based and model-free specifications in terms of weights forecasts and portfolio allocations. Next to portfolio variance, certainty equivalent and turnover, we introduce the break-even transaction costs as an additional measure that identifies the range of transaction costs for which one allocation is preferred to another. By comparing minimum-variance portfolios built on the components of the Dow Jones 30 Index, the proposed DCW overall attains the best allocations with respect to the measures considered, for any degree of risk-aversion, transaction costs and exposure.
Complete dimensional collapse in the continuum limit of a delayed SEIQR network model with separable distributed infectivity
Date: 2020-06-23 00:00:00
Tasks: Numerical Integration

We take up a recently proposed compartmental SEIQR model with delays, ignore
loss of immunity in the context of a fast pandemic, extend the model to a
network structured on infectivity, and consider the continuum limit of the same
with a simple separable interaction model for the infectivities $\beta$.
Numerical simulations show that the evolving dynamics of the network is
effectively captured by a single scalar function of time, regardless of the
distribution of $\beta$ in the population. The continuum limit of the network
model allows a simple derivation of the simpler model, which is a single scalar
delay differential equation (DDE), wherein the variation in $\beta$ appears
through an integral closely related to the moment generating function of
$u=\sqrt{\beta}$. If the first few moments of $u$ exist, the governing DDE can
be expanded in a series that shows a direct correspondence with the original
compartmental DDE with a single $\beta$. Even otherwise, the new scalar DDE can
be solved using either numerical integration over $u$ at each time step, or
with the analytical integral if available in some useful form. Our work
provides a new academic example of complete dimensional collapse, ties up an
underlying continuum model for a pandemic with a simpler-seeming compartmental
model, and will hopefully lead to new analysis of continuum models for
epidemics.
Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing
Authors: Sepideh Sadegh, Julian Matschinske, David B. Blumenthal, Gihanna Galindez, Tim Kacprowski, Markus List, Reza Nasirigerdeh, Mhaned Oubounyt, Andreas Pichlmair, Tim Daniel Rose, Marisol Salgado-Albarrán, Julian Späth, Alexey Stukalov, Nina K. Wenke, Kevin Yuan, Josch K. Pauling, Jan Baumbach
Date: 2020-04-26 00:00:00

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. It was first identified in Wuhan, China, and has since spread causing a global pandemic. Various studies have been performed to understand the molecular mechanisms of viral infection for predicting drug repurposing candidates. However, such information is spread across many publications and it is very time-consuming to access, integrate, explore, and exploit. We developed CoVex, the first interactive online platform for SARS-CoV-2 and SARS-CoV-1 host interactome exploration and drug (target) identification. CoVex integrates 1) experimentally validated virus-human protein interactions, 2) human protein-protein interactions and 3) drug-target interactions. The web interface allows user-friendly visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drugs. Thus, CoVex is an important resource, not only to understand the molecular mechanisms involved in SARS-CoV-2 and SARS-CoV-1 pathogenicity, but also in clinical research for the identification and prioritization of candidate therapeutics. We apply CoVex to investigate recent hypotheses on a systems biology level and to systematically explore the molecular mechanisms driving the virus life cycle. Furthermore, we extract and discuss drug repurposing candidates involved in these mechanisms. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms integrating virus-host-drug interactions. It is available at https://exbio.wzw.tum.de/covex/.
A knowledge transfer model for COVID-19 predicting and non-pharmaceutical intervention simulation
Date: 2020-04-26 00:00:00
Tasks: Transfer Learning

Since December 2019, A novel coronavirus (2019-nCoV) has been breaking out in China, which can cause respiratory diseases and severe pneumonia. Mathematical and empirical models relying on the epidemic situation scale for forecasting disease outbreaks have received increasing attention. Given its successful application in the evaluation of infectious diseases scale, we propose a Susceptible-Undiagnosed-Infected-Removed (SUIR) model to offer the effective prediction, prevention, and control of infectious diseases. Our model is a modified susceptible-infected-recovered (SIR) model that injects undiagnosed state and offers pre-training effective reproduction number. Our SUIR model is more precise than the traditional SIR model. Moreover, we combine domain knowledge of the epidemic to estimate effective reproduction number, which addresses the initial susceptible population of the infectious disease model approach to the ground truth. These findings have implications for the forecasting of epidemic trends in COVID-19 as these could help the growth of estimating epidemic situation.
Inference with Many Weak Instruments
Authors: Anna Mikusheva, Liyang Sun
Date: 2020-04-26 00:00:00
Tasks: valid

We develop a concept of weak identification in linear IV models in which the number of instruments can grow at the same rate or slower than the sample size. We propose a jackknifed version of the classical weak identification-robust Anderson-Rubin (AR) test statistic. Large-sample inference based on the jackknifed AR is valid under heteroscedasticity and weak identification. The feasible version of this statistic uses a novel variance estimator. The test has uniformly correct size and good power properties. We also develop a pre-test for weak identification that is related to the size property of a Wald test based on the Jackknife Instrumental Variable Estimator (JIVE). This new pre-test is valid under heteroscedasticity and with many instruments.
Ending the COVID-19 epidemic in the United Kingdom
Date: 2020-04-26 00:00:00

Social distancing and lockdown are the two main non-pharmaceutical
interventions being used by the UK government to contain and control the
COVID-19 epidemic; these are being applied uniformly across the entire country,
even though the results of the Imperial College report by Ferguson et al show
that the impact of the infection increases sharply with age. This paper
develops a variant of the workhorse SIR model for epidemics, where the
population is classified into a number of age groups. This allows us to
understand the effects of age-dependent controls on the epidemic, and explore
possible exit strategies.
Human Electromagnetic Field Exposure in 5G at 28 GHz
Date: 2020-04-26 00:00:00

The fifth-generation wireless (5G) has already started showing its capability
to achieve extremely fast data transfer, which makes itself considered to be a
promising mobile technology. However, concerns have been raised on adverse
health impacts that human users can experience in a 5G system by being exposed
to electromagnetic fields (EMFs). This article investigates the human EMF
exposure in a 5G system and compare to those measured in the
previous-generation cellular systems. It suggests a minimum separation distance
between a transmitter and a human user for keeping the EMF exposure below the
safety regulation level, which provides consumers with a general understanding
on the safe use of 5G communications.
Anomalous atmospheric circulation favored the spread of COVID-19 in Europe
Date: 2020-04-26 00:00:00

The current pandemic caused by the coronavirus SARS-CoV-2 is having negative
health, social and economic consequences worldwide. In Europe, the pandemic
started to develop strongly at the end of February and beginning of March 2020.
It has subsequently spread over the continent, with special virulence in
northern Italy and inland Spain. In this study we show that an unusual
persistent anticyclonic situation prevailing in southwestern Europe during
February 2020 (i.e. anomalously strong positive phase of the North Atlantic and
Arctic Oscillations) could have resulted in favorable conditions, in terms of
air temperature and humidity, in Italy and Spain for a quicker spread of the
virus compared with the rest of the European countries. It seems plausible that
the strong atmospheric stability and associated dry conditions that dominated
in these regions may have favored the virus's propagation, by short-range
droplet transmission as well as likely by long-range aerosol (airborne)
transmission.
Subgoal Planning Algorithm for Autonomous Vehicle Guidance
Date: 2020-04-27 00:00:00

Trained humans exhibit highly agile spatial skills, enabling them to operate
vehicles with complex dynamics in demanding tasks and conditions. Prior work
shows that humans achieve this performance by using strategies such as
satisficing, learning hierarchical task structure, and using a library of
motion primitive elements. A key aspect of efficient and versatile solutions is
the decomposition of a task into a sequence of smaller tasks, represented by
subgoals. The present work uses properties of constrained optimal control
theory to define conditions that specify candidate subgoal states and enable
this decomposition. The proposed subgoal algorithm uses graph search to
determine a subgoal sequence that links a series of unconstrained motion
guidance elements into a constrained solution trajectory. In simulation
experiments, the subgoal guidance algorithm generates paths with higher
performance and less computation time than an RRT* benchmark. Examples
illustrate the robustness and versatility of this approach in multiple
environment types.
Forecast analysis of the epidemics trend of COVID-19 in the United States by a generalized fractional-order SEIR model
Date: 2020-04-29 00:00:00

In this paper, a generalized fractional-order SEIR model is proposed, denoted
by SEIQRP model, which has a basic guiding significance for the prediction of
the possible outbreak of infectious diseases like COVID-19 and other insect
diseases in the future. Firstly, some qualitative properties of the model are
analyzed. The basic reproduction number $R_{0}$ is derived. When $R_{0}<1$, the
disease-free equilibrium point is unique and locally asymptotically stable.
When $R_{0}>1$, the endemic equilibrium point is also unique. Furthermore, some
conditions are established to ensure the local asymptotic stability of
disease-free and endemic equilibrium points. The trend of COVID-19 spread in
the United States is predicted. Considering the influence of the individual
behavior and government mitigation measurement, a modified SEIQRP model is
proposed, defined as SEIQRPD model. According to the real data of the United
States, it is found that our improved model has a better prediction ability for
the epidemic trend in the next two weeks. Hence, the epidemic trend of the
United States in the next two weeks is investigated, and the peak of isolated
cases are predicted. The modified SEIQRP model successfully capture the
development process of COVID-19, which provides an important reference for
understanding the trend of the outbreak.
SLSpy: Python-Based System-Level Controller Synthesis Framework
Date: 2020-09-29 00:00:00

Synthesizing controllers for large, complex, and distributed systems is a
challenging task. Numerous proposed methods exist in the literature, but it is
difficult for practitioners to apply them -- most proposed synthesis methods
lack ready-to-use software implementations, and existing proprietary components
are too rigid to extend to general systems. To address this gap, we develop
SLSpy, a framework for controller synthesis, comparison, and testing.
  SLSpy implements a highly extensible software framework which provides two
essential workflows: synthesis and simulation. The workflows are built from
five conceptual components that can be customized to implement a wide variety
of synthesis algorithms and disturbance tests. SLSpy comes pre-equipped with a
workflow for System Level Synthesis (SLS), which enables users to easily and
freely specify desired design objectives and constraints. We demonstrate the
effectiveness of SLSpy using two examples that have been described in the
literature but do not have ready-to-use implementations. We open-source SLSpy
to facilitate future controller synthesis research and practical usage.
Bayesian Learning of Probabilistic Dipole Inversion for Quantitative Susceptibility Mapping
Date: 2020-04-27 00:00:00
Proceeding: MIDL 2019 7

A learning-based posterior distribution estimation method, Probabilistic
Dipole Inversion (PDI), is proposed to solve quantitative susceptibility
mapping (QSM) inverse problem in MRI with uncertainty estimation. A deep
convolutional neural network (CNN) is used to represent the multivariate
Gaussian distribution as the approximated posterior distribution of
susceptibility given the input measured field. In PDI, such CNN is firstly
trained on healthy subjects dataset with labels by maximizing the posterior
Gaussian distribution loss function as used in Bayesian deep learning. When
tested on new dataset without any label, PDI updates the pre-trained network in
an unsupervised fashion by minimizing the KL divergence between the
approximated posterior distribution represented by CNN and the true posterior
distribution given the likelihood distribution from known physical model and
prior distribution. Based on our experiments, PDI provides additional
uncertainty estimation compared to the conventional MAP approach, meanwhile
addressing the potential discrepancy issue of CNN when test data deviates from
training dataset.