pwc-archive/papers-with-abstracts huggingface dataset:
An Optimal Predictive Control Strategy for COVID-19 (SARS-CoV-2) Social Distancing Policies in Brazil
Date: 2020-05-21 00:00:00
Tasks: Model Predictive Control

The global COVID-19 pandemic (SARS-CoV-2 virus) is the defining health crisis
of our century. Due to the absence of vaccines and drugs that can help to fight
it, the world solution to control the spread has been to consider public social
distance measures that avoids the saturation of the health system. In this
context, we investigate a Model Predictive Control (MPC) framework to determine
the time and duration of social distancing policies. We use Brazilian data in
the period from March to May of 2020. The available data regarding the number
of infected individuals and deaths suffers from sub-notification due to the
absence of mass tests and the relevant presence of the asymptomatic
individuals. We estimate variations of the SIR model using an
uncertainty-weighted Least-Squares criterion that considers both nominal and
inconsistent-data conditions. Moreover, we add to our versions of the SIR model
an additional dynamic state variable to mimic the response of the population to
the social distancing policies determined by the government that affects the
speed of COVID-19 transmission. Our control framework is within a mixed-logical
formalism, since the decision variable is forcefully binary (the existence or
the absence of social distance policy). A dwell-time constraint is included to
avoid harsh shifting between these two states. Finally, we present simulation
results to illustrate how such optimal control policy would operate. These
results point out that no social distancing should be relaxed before mid August
2020. If relaxations are necessary, they should not be performed before the
beginning this date and should be in small periods, no longer than 25 days.
This paradigm would proceed roughly until January/2021. The second peak of
infections, which has a forecast to the beginning of October, can be reduced if
the periods of no-isolation days are shortened.
On the potential contribution of rooftop PV to a sustainable electricity mix: the case of Spain
Date: 2020-05-21 00:00:00

This work evaluates the potential contribution of rooftop PV to the future
electricity mix. Several sustainable scenarios are considered, each comprising
different shares of centralized renewables, rooftop PV and storage. For each
generation scenario, the storage capacity that balances the net hourly demand
is determined, and the portfolio combination that minimizes the cost of
supplying electricity is obtained. The analysis is applied to mainland Spain,
using public information and detailed granular models, both in time (hourly
resolution) and space (municipal level). For the Spanish case, when the
flexibility of hydro and biomass generation is taken into account, the
least-cost portfolio involves rather modest storage capacities, in the order of
daily rather than seasonal values. This shows that a sustainable, almost
emissions-free electricity system for Spain is possible, at a cost that can be
even lower than current wholesale market prices.
Self-Interference Channel Characterization in Underwater Acoustic In-Band Full-Duplex Communications Using OFDM
Authors: Mohammad Towliat, Zheng Guo, Leonard J. Cimini, Xiang-Gen Xia, Aijun Song
Date: 2020-05-21 00:00:00

Due to the limited available bandwidth and dynamic channel, data rates are extremely limited in underwater acoustic (UWA) communications. Addressing this concern, in-band fullduplex (IBFD) has the potential to double the efficiency in a given bandwidth. In an IBFD scheme, transmission and reception are performed simultaneously in the same frequency band. However, in UWA-IBFD, because of reflections from the surface and bottom and the inhomogeneity of the water, a significant part of the transmitted signal returns back to the IBFD receiver. This signal contaminates the desired signal from the remote end and is known as the self-interference (SI). With an estimate of the self-interference channel impulse response (SCIR), a receiver can estimate and eliminate the SI. A better understanding of the statistical characteristics of the SCIR is necessary for an accurate SI cancellation. In this article, we use an orthogonal frequency division multiplexing (OFDM) signal to characterize the SCIR in a lake water experiment. To verify the results, SCIR estimation is performed using estimators in both the frequency and time domains. We show that, in our experiment, regardless of the depth of the hydrophone, the direct path of SCIR is strong, stable and easily tracked; however, the reflection paths are weaker and rapidly time-varying making SI cancellation challenging. Among the reflections, the first bounce from the water surface is the prevalent path with a short coherence time around 70 ms.
Non-Coherent and Backscatter Communications: Enabling Ultra-Massive Connectivity in 6G Wireless Networks
Authors: Syed Junaid Nawaz, Shree Krishna Sharma, Babar Mansoor, Mohmammad N. Patwary, Noor M. Khan
Date: 2020-05-21 00:00:00

With the commencement of the 5G of wireless networks, researchers around the globe have started paying their attention to the imminent challenges that may emerge in the beyond 5G (B5G) era. Various revolutionary technologies and innovative services are offered in 5G networks, which, along with many principal advantages, are anticipated to bring a boom in the number of connected wireless devices and the types of use-cases that may cause the scarcity of network resources. These challenges partly emerged with the advent of massive machine-type communications (mMTC) services, require extensive research innovations to sustain the evolution towards enhanced-mMTC (e-mMTC) with the scalable network cost in 6\textsuperscript{th} generation (6G) wireless networks. Towards delivering the anticipated massive connectivity requirements with optimal energy and spectral efficiency besides low hardware cost, this paper presents an enabling framework for 6G networks, which utilizes two emerging technologies, namely, non-coherent communications and backscatter communications (BsC). Recognizing the coherence between these technologies for their joint potential of delivering e-mMTC services in the B5G era, a comprehensive review of their state-of-the-art is conducted. The joint scope of non-coherent and BsC with other emerging 6G technologies is also identified, where the reviewed technologies include unmanned aerial vehicles (UAVs)-assisted communications, visible light communications (VLC), quantum-assisted communications, reconfigurable large intelligent surfaces (RLIS), non-orthogonal multiple access (NOMA), and machine learning-aided intelligent networks. Subsequently, the scope of these enabling technologies for different device types, service types, and optimization parameters is analyzed...
A machine learning approach to using Quality-of-Life patient scores in guiding prostate radiation therapy dosing
Authors: Zhijian Yang, Daniel Olszewski, Chujun He, Giulia Pintea, Jun Lian, Tom Chou, Ronald Chen, Blerta Shtylla
Date: 2020-05-22 00:00:00
Tasks: BIG-bench Machine Learning, Sensitivity, Transfer Learning

Thanks to advancements in diagnosis and treatment, prostate cancer patients have high long-term survival rates. Currently, an important goal is to preserve quality-of-life during and after treatment. The relationship between the radiation a patient receives and the subsequent side effects he experiences is complex and difficult to model or predict. Here, we use machine learning algorithms and statistical models to explore the connection between radiation treatment and post-treatment gastro-urinary function. Since only a limited number of patient datasets are currently available, we used image flipping and curvature-based interpolation methods to generate more data in order to leverage transfer learning. Using interpolated and augmented data, we trained a convolutional autoencoder network to obtain near-optimal starting points for the weights. A convolutional neural network then analyzed the relationship between patient-reported quality-of-life and radiation. We also used analysis of variance and logistic regression to explore organ sensitivity to radiation and develop dosage thresholds for each organ region. Our findings show no connection between the bladder and quality-of-life scores. However, we found a connection between radiation applied to posterior and anterior rectal regions to changes in quality-of-life. Finally, we estimated radiation therapy dosage thresholds for each organ. Our analysis connects machine learning methods with organ sensitivity, thus providing a framework for informing cancer patient care using patient reported quality-of-life metrics.
Pricing Barrier Options with DeepBSDEs
Authors: Narayan Ganesan, Yajie Yu, Bernhard Hientzsch
Date: 2020-05-22 00:00:00

This paper presents a novel and direct approach to price boundary and final-value problems, corresponding to barrier options, using forward deep learning to solve forward-backward stochastic differential equations (FBSDEs). Barrier instruments are instruments that expire or transform into another instrument if a barrier condition is satisfied before maturity; otherwise they perform like the instrument without the barrier condition. In the PDE formulation, this corresponds to adding boundary conditions to the final value problem. The deep BSDE methods developed so far have not addressed barrier/boundary conditions directly. We extend the forward deep BSDE to the barrier condition case by adding nodes to the computational graph to explicitly monitor the barrier conditions for each realization of the dynamics as well as nodes that preserve the time, state variables, and trading strategy value at barrier breach or at maturity otherwise. Given these additional nodes in the computational graph, the forward loss function quantifies the replication of the barrier or final payoff according to a chosen risk measure such as squared sum of differences. The proposed method can handle any barrier condition in the FBSDE set-up and any Dirichlet boundary conditions in the PDE set-up, both in low and high dimensions.
Reconfigurable Intelligent Surface Enhanced Cognitive Radio Networks
Authors: Jinglian He, Kaiqiang Yu, Yong Zhou, Yuanming Shi
Date: 2020-05-22 00:00:00

The cognitive radio (CR) network is a promising network architecture that meets the requirement of enhancing scarce radio spectrum utilization. Meanwhile, reconfigurable intelligent surfaces (RIS) is a promising solution to enhance the energy and spectrum efficiency of wireless networks by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we investigate the downlink transmit power minimization problem for the RIS-enhanced single-cell cognitive radio (CR) network coexisting with a single-cell primary radio (PR) network by jointly optimizing the transmit beamformers at the secondary user (SU) transmitter and the phase shift matrix at the RIS. The investigated problem is a highly intractable due to the coupled optimization variables and unit modulus constraint, for which an alternative minimization framework is presented. Furthermore, a novel difference-of-convex (DC) algorithm is developed to solve the resulting non-convex quadratic program by lifting it into a low-rank matrix optimization problem. We then represent non-convex rank-one constraint as a DC function by exploiting the difference between trace norm and spectral norm. The simulation results validate that our proposed algorithm outperforms the existing state-of-the-art methods.
Phase retrieval by pattern classification and circular mean for robust optical testing
Date: 2020-05-25 00:00:00
Tasks: Retrieval

For the optical testing of a large mirror with a long radius of curvature, it
is generally necessary to use a single-shot phase-shifting interferometer to
take several measurements because the influence of air turbulence on air
stratification prevention should be reduced for an accurate measurement. In
this paper, a new technique that applies hierarchical clustering, which
classifies a few clusters according to the pattern similarity of acquired
wrapped phases, is proposed. As the minority patterns of the wrapped phases are
excluded, the effects of unknown noises can be reduced. For each cluster, the
circular mean is used to calculate the denoised wrapped phases. The surface
figure is obtained from the unwrapped phases. Because the running number of the
phase-unwrapping algorithm becomes the same as the number of chosen clusters,
the proposed technique is much faster than conventional procedures. In 500
measurements using a spherical mirror with a diameter of 1.5 m, the measurement
time and standard deviation of the surface figure's root-mean-square error are
reduced by up to approximately 23% and 15%, respectively, of the results
obtained via the conventional procedure.
Insurance-Finance Arbitrage
Authors: Philippe Artzner, Karl-Theodor Eisele, Thorsten Schmidt
Date: 2020-05-22 00:00:00

Most insurance contracts are inherently linked to financial markets, be it via interest rates, or -- as hybrid products like equity-linked life insurance and variable annuities -- directly to stocks or indices. However, insurance contracts are not for trade except sometimes as surrender to the selling office. This excludes the situation of arbitrage by buying and selling insurance contracts at different prices. Furthermore, the insurer uses private information on top of the publicly available one about financial market. This paper provides a study of the consistency of insurance contracts in connection with trades in the financial market with explicit mention of the information involved. By defining strategies on an insurance portfolio and combining them with financial trading strategies, we arrive at the notion of insurance-finance arbitrage (IFA). In analogy to the classical fundamental theorem of asset pricing, we give a fundamental theorem on the absence of IFA, leading to the existence of an insurance-finance-consistent probability. In addition, we study when this probability gives the expected discounted cash-flows required by the EIOPA best estimate. The generality of our approach allows to incorporate many important aspects, like mortality risk or general levels of dependence between mortality and stock markets. Utilizing the theory of enlargements of filtrations, we construct a tractable framework for insurance-finance consistent valuation.
A Modal-Space Method for Online Power System Steady-State Stability Monitoring
Date: 2020-05-22 00:00:00

This paper proposes a novel approach to estimate the steady-state angle
stability limit (SSASL) by using the nonlinear power system dynamic model in
the modal space. Through two linear changes of coordinates and a simplification
introduced by the steady-state condition, the nonlinear power system dynamic
model is transformed into a number of single-machine-like power systems whose
power-angle curves can be derived and used for estimating the SSASL. The
proposed approach estimates the SSASL of angles at all machines and all buses
without the need for manually specifying the scenario, i.e. setting sink and
source areas, and also without the need for solving multiple nonlinear power
flows. Case studies on 9-bus and 39-bus power systems demonstrate that the
proposed approach is always able to capture the aperiodic instability in an
online environment, showing promising performance in the online monitoring of
the steady-state angle stability over the traditional power flow-based
analysis.
COVID-19: The unreasonable effectiveness of simple models
Date: 2020-05-22 00:00:00

When the novel coronavirus disease SARS-CoV2 (COVID-19) was officially
declared a pandemic by the WHO in March 2020, the scientific community had
already braced up in the effort of making sense of the fast-growing wealth of
data gathered by national authorities all over the world. However, despite the
diversity of novel theoretical approaches and the comprehensiveness of many
widely established models, the official figures that recount the course of the
outbreak still sketch a largely elusive and intimidating picture. Here we show
unambiguously that the dynamics of the COVID-19 outbreak belongs to the simple
universality class of the SIR model and extensions thereof. Our analysis
naturally leads us to establish that there exists a fundamental limitation to
any theoretical approach, namely the unpredictable non-stationarity of the
testing frames behind the reported figures. However, we show how such bias can
be quantified self-consistently and employed to mine useful and accurate
information from the data. In particular, we describe how the time evolution of
the reporting rates controls the occurrence of the apparent epidemic peak,
which typically follows the true one in countries that were not vigorous enough
in their testing at the onset of the outbreak. The importance of testing early
and resolutely appears as a natural corollary of our analysis, as countries
that tested massively at the start clearly had their true peak earlier and less
deaths overall.
State-switching control of the second-order chained form system
Date: 2020-05-22 00:00:00
Tasks: Form, Motion Planning

This paper addresses a motion planning problem of the second-order chained
form system. The author presents a novel control approach based on switching a
state. The second-order chained form system is composed of three subsystems
including two double integrators and a nonlinear system. Switching a single
state of the double integrators can modify the nature of the nonlinear system.
Such state-switching and sinusoidal control construct the basis of the proposed
control approach. The effectiveness is validated by a simulation result.
A Novel Received Signal Strength Assisted Perspective-three-Point Algorithm for Indoor Visible Light Positioning
Authors: Lin Bai, Yang Yang, Chunyan Feng, Caili Guo
Date: 2020-05-21 00:00:00
Tasks: Position

In this paper, a received signal strength assisted Perspective-three-Point positioning algorithm (R-P3P) is proposed for visible light positioning (VLP) systems. The basic idea of R-P3P is to joint visual and strength information to estimate the receiver position using 3 LEDs regardless of the LEDs' orientations. R-P3P first utilizes visual information captured by the camera to estimate the incidence angles of visible lights. Then, R-P3P calculates the candidate distances between the LEDs and the receiver based on the law of cosines and the Wu-Ritt's zero decomposition method. Based on the incidence angles, the candidate distances and the physical characteristics of the LEDs, R-P3P can select the exact distances from all the candidate distances. Finally, the linear least square (LLS) method is employed to estimate the position of the receiver. Due to the combination of visual and strength information of visible light signals, R-P3P can achieve high accuracy using 3 LEDs regardless of the LEDs' orientations. Simulation results show that R-P3P can achieve positioning accuracy within 10 cm over 70% indoor area with low complexity regardless of LEDs orientations.
Poverty levels, societal and individual heterogeneities explain the SARS-CoV-2 pandemic growth in Latin America
Date: 2020-05-22 00:00:00

Latin America is experiencing severe impacts of the SARS-CoV-2 pandemic, but
poverty and weak public health institutions hamper gathering the kind of
refined data needed to inform classical SEIR models of epidemics. We present an
alternative approach that draws on advances in statistical ecology and
conservation biology to enhance the value of sparse data in projecting and
ameliorating epidemics. Our approach, leading to what we call a Stochastic
Epidemic Gompertz model, with few parameters can flexibly incorporate
heterogeneity in transmission within populations and across time. We
demonstrate that poverty has a large impact on the course of the pandemic,
across fourteen Latin American countries, and show how our approach provides
flexible, time-varying projections of disease risk that can be used to refine
public health strategies.
Cooperation in Small Groups -- an Optimal Transport Approach
Date: 2020-05-22 00:00:00

If agents cooperate only within small groups of some bounded sizes, is there
a way to partition the population into small groups such that no collection of
agents can do better by forming a new group? This paper revisited f-core in a
transferable utility setting. By providing a new formulation to the problem, we
built up a link between f-core and the transportation theory. Such a link helps
us to establish an exact existence result, and a characterization result of
f-core for a general class of agents, as well as some improvements in computing
the f-core in the finite type case.
Virtual Screening of Plant Metabolites against Main protease, RNA-dependent RNA polymerase and Spike protein of SARS-CoV-2: Therapeutics option of COVID-19
Authors: Md Sorwer Alam Parvez, Kazi Faizul Azim, Abdus Shukur Imran, Topu Raihan, Aklima Begum, Tasfia Saiyara Shammi, Sabbir Howlader, Farhana Rumzum Bhuiyan, Mahmudul Hasan
Date: 2020-05-22 00:00:00
Tasks: Blocking, Molecular Docking

Covid-19, a serious respiratory complications caused by SARS-CoV-2 has become one of the global threat to human healthcare system. The present study evaluated the possibility of plant originated approved 117 therapeutics against the main protease protein (MPP), RNA-dependent RNA polymerase (RdRp) and spike protein (S) of SARS-CoV-2 including drug surface analysis by using molecular docking through drug repurposing approaches. The molecular interaction study revealed that Rifampin (-16.3 kcal/mol) were topmost inhibitor of MPP where Azobechalcone were found most potent plant therapeutics for blocking the RdRp (-15.9 kcal /mol) and S (-14.4 kcal/mol) protein of SARS-CoV-2. After the comparative analysis of all docking results, Azobechalcone, Rifampin, Isolophirachalcone, Tetrandrine and Fangchinoline were exhibited as the most potential inhibitory plant compounds for targeting the key proteins of SARS-CoV-2. However, amino acid positions; H41, C145, and M165 of MPP played crucial roles for the drug surface interaction where F368, L371, L372, A375, W509, L514, Y515 were pivotal for RdRP. In addition, the drug interaction surface of S proteins also showed similar patterns with all of its maximum inhibitors. ADME analysis also strengthened the possibility of screened plant therapeutics as the potent drug candidates against SARS-C with the highest drug friendliness.
Edge-effects dominate copying thermodynamics for finite-length molecular oligomers
Authors: Jenny Marie Poulton, Thomas Edward Ouldridge
Date: 2020-05-22 00:00:00

Living systems produce copies of information-carrying molecules such as DNA by assembling monomer units into finite-length oligomer (short polymer) copies. We explore the role of initiation and termination of the copy process in the thermodynamics of copying. By splitting the free-energy change of copy formation into informational and chemical terms, we show that copy accuracy plays no direct role in the overall thermodynamics. Instead, it is thermodynamically costly to produce outputs that are more similar to the oligomers in the environment than sequences obtained by randomly sampling monomers. Copy accuracy can be thermodynamically neutral, or even favoured, depending on the surroundings. Oligomer copying mechanisms can thus function as information engines that interconvert chemical and information-based free energy. Hard thermodynamic constraints on accuracy derived for infinite-length polymers instead manifest as kinetic barriers experienced while the copy is template-attached. These barriers are easily surmounted by shorter oligomers.