pwc-archive/papers-with-abstracts huggingface dataset:
Optimal allocation using the Sortino ratio
Authors: Tarek Nassar, Sandro Ephrem
Date: 2020-07-13 00:00:00
Tasks: valid

In this paper we present an asset allocation strategy based on the maximization of the Sortino ratio. Unlike the Sharpe ratio, the Sortino ratio penalizes negative return variances only. The resulting allocation is valid for any time horizon unlike. The returns of a strategy based on such an allocation are empirically illustrated using historical Dow Jones data and display a significant upgrade on more traditional allocation strategies such as the Kelly criterion.
Incentivizing Narrow-Spectrum Antibiotic Development with Refunding
Date: 2020-07-13 00:00:00

The rapid rise of antibiotic resistance is a serious threat to global public
health. Without further incentives, pharmaceutical companies have little
interest in developing antibiotics, since the success probability is low and
development costs are huge. The situation is exacerbated by the "antibiotics
dilemma": Developing narrow-spectrum antibiotics against resistant bacteria is
most beneficial for society, but least attractive for companies since their
usage is more limited than for broad-spectrum drugs and thus sales are low.
Starting from a general mathematical framework for the study of
antibiotic-resistance dynamics with an arbitrary number of antibiotics, we
identify efficient treatment protocols and introduce a market-based refunding
scheme that incentivizes pharmaceutical companies to develop narrow-spectrum
antibiotics: Successful companies can claim a refund from a newly established
antibiotics fund that partially covers their development costs. The proposed
refund involves a fixed and variable part. The latter (i) increases with the
use of the new antibiotic for currently resistant strains in comparison with
other newly developed antibiotics for this purpose---the resistance
premium---and (ii) decreases with the use of this antibiotic for non-resistant
bacteria. We outline how such a refunding scheme can solve the antibiotics
dilemma and cope with various sources of uncertainty inherent in antibiotic
R\&D. Finally, connecting our refunding approach to the recently established
antimicrobial resistance (AMR) action fund, we discuss how the antibiotics fund
can be financed.
Mean-variance-utility portfolio selection with time and state dependent risk aversion
Authors: Ben-Zhang Yang, Xin-Jiang He, Song-Ping Zhu
Date: 2020-07-10 00:00:00

Under mean-variance-utility framework, we propose a new portfolio selection model, which allows wealth and time both have influences on risk aversion in the process of investment. We solved the model under a game theoretic framework and analytically derived the equilibrium investment (consumption) policy. The results conform with the facts that optimal investment strategy heavily depends on the investor's wealth and future income-consumption balance as well as the continuous optimally consumption process is highly dependent on the consumption preference of the investor.
Race and gender income inequality in the USA: black women vs. white men
Authors: Ivan Kitov
Date: 2020-06-10 00:00:00

Income inequality between different races in the U.S. is especially large. This difference is even larger when gender is involved. In a complementary study, we have developed a dynamic microeconomic model accurately describing the evolution of male and female incomes since 1930. Here, we extend our analysis and model the disparity between black and white population in the U.S., separately for males and females. Unfortunately, income microdata provided by the U.S. Census Bureau for other races and ethnic groups are not time compatible or too short for modelling purposes. We are forced to constrain our analysis to black and white population, but all principal results can be extrapolated to other races and ethnicities. Our analysis shows that black females and white males are two poles of the overall income inequality. The prediction of income distribution for two extreme cases with one model is the main challenge of this study.
The New Digital Platforms: Merger Control in Pakistan
Authors: Shahzada Aamir Mushtaq, Wang Yuhui
Date: 2020-06-22 00:00:00

The Pakistan competition policy, as in many other countries, was originally designed to regulate business conduct in traditional markets and for tangible goods and services. However, the development and proliferation of the internet has led to the emergence of digital companies which have disrupted many sectors of the economy. These platforms provide digital infrastructure for a range of services including search engines, marketplaces, and social networking sites. The digital economy poses a myriad of challenges for competition authorities worldwide, especially with regard to digital mergers and acquisitions (M&As). While some jurisdictions such as the European Union and the United States have taken significant strides in regulating technological M&As, there is an increasing need for developing countries such as Pakistan to rethink their competition policy tools. This paper investigates whether merger reviews in the Pakistan digital market are informed by the same explanatory variables as in the traditional market, by performing an empirical comparative analysis of the Competition Commission of Pakistan's (CCP's) M&A decisions between 2014 and 2019. The findings indicate the CCP applies the same decision factors in reviewing both traditional and digital M&As. As such, this paper establishes a basis for igniting the policy and economic debate of regulating the digital platform industry in Pakistan.
Near-complete protein structural modelling of the minimal genome
Authors: Joe G Greener, Nikita Desai, Shaun M Kandathil, David T Jones
Date: 2020-07-13 00:00:00

Protein tertiary structure prediction has improved dramatically in recent years. A considerable fraction of various proteomes can be modelled in the absence of structural templates. We ask whether our DMPfold method can model all the proteins without templates in the JCVI-syn3.0 minimal genome, which contains 438 proteins. We find that a useful tertiary structure annotation can be provided for all but 10 proteins. The models may help annotate function in cases where it is unknown, and provide coverage for 29 predicted protein-protein interactions which lacked monomer models. We also show that DMPfold performs well on proteins with structures released since initial publication. It is likely that the minimal genome will have complete structural coverage within a few years.
Multi-Intersection Traffic Management for Autonomous Vehicles via Distributed Mixed Integer Linear Programming
Date: 2020-07-13 00:00:00
Tasks: Autonomous Vehicles, Management, Position, Scheduling

This paper extends our previous work in [1],[2], on optimal scheduling of
autonomous vehicle arrivals at intersections, from one to a grid of
intersections. A scalable distributed Mixed Integer Linear Program (MILP) is
devised that solves the scheduling problem for a grid of intersections. A
computational control node is allocated to each intersection and regularly
receives position and velocity information from subscribed vehicles. Each node
assigns an intersection access time to every subscribed vehicle by solving a
local MILP. Neighboring intersections will coordinate with each other in
real-time by sharing their solutions for vehicles' access times with each
other. Our proposed approach is applied to a grid of nine intersections and its
positive impact on traffic flow and vehicles' fuel economy is demonstrated in
comparison to conventional intersection control scenarios.
Fleet Sizing and Charger Allocation in Electric Vehicle Sharing Systems
Date: 2020-07-13 00:00:00

In this paper, we propose a closed queueing network model for performance
analysis of electric vehicle sharing systems with a certain number of chargers
in each neighborhood. Depending on the demand distribution, we devise
algorithms to compute the optimal fleet size and number of chargers required to
maximize profit while maintaining a certain quality of service. We show that
the profit is concave with respect to the fleet size and the number of chargers
at each charging point. If more chargers are installed within the city, we show
that it can not only reduce the fleet size, but it also improves the
availability of vehicles at all the points within a city. We further show
through simulation that two slow chargers may outperform one fast charger when
the variance of charging time becomes relatively large in comparison to the
mean charging time.
Channel Parameter Estimation for Millimeter-Wave Cellular Systems with Hybrid Beamforming
Authors: Fazal-E-Asim, Felix Antreich, Charles C. Cavalcante, André L. F. de Almeida, Josef A. Nossek
Date: 2020-07-13 00:00:00
Tasks: parameter estimation

To achieve high data rates defined in 5G, the use of millimeter-waves and massive-MIMO are indispensable. To benefit from these technologies, an accurate estimation of the channel parameters is crucial. We propose a novel two-stage algorithm for channel parameters estimation. In the first stage, coarse estimation is accomplished by applying parameter estimation via interpolation based on DFT grid (PREIDG) with a fixed look-up table (LUT), while the second stage refines the estimates by means of the space-alternating generalized expectation maximization (SAGE) algorithm. The two-stage algorithm uses discrete Fourier transform beamforming vectors which are efficiently implemented by a Butler matrix in the analog domain. We found that this methodology improves the estimates compared to the auxiliary beam pair (ABP) method. The two-stage algorithm shows efficient performance in the low signal to noise ratio regime for the channel parameters i.e. angles of departure, complex path gains and delays of the multipaths. Finally, we derived the Cram\'er-Rao lower bound (CRLB) to assess the performance of our two-stage estimation algorithm.
Dynamics of B-cell repertoires and emergence of cross-reactive responses in COVID-19 patients with different disease severity
Authors: Zachary Montague, Huibin Lv, Jakub Otwinowski, William S. DeWitt, Giulio Isacchini, Garrick K. Yip, Wilson W. Ng, Owen Tak-Yin Tsang, Meng Yuan, Hejun Liu, Ian A. Wilson, J. S. Malik Peiris, Nicholas C. Wu, Armita Nourmohammad, Chris Ka Pun Mok
Date: 2020-07-14 00:00:00

COVID-19 patients show varying severity of the disease ranging from asymptomatic to requiring intensive care. Although a number of SARS-CoV-2 specific monoclonal antibodies have been identified, we still lack an understanding of the overall landscape of B-cell receptor (BCR) repertoires in COVID-19 patients. Here, we used high-throughput sequencing of bulk and plasma B-cells collected over multiple time points during infection to characterize signatures of B-cell response to SARS-CoV-2 in 19 patients. Using principled statistical approaches, we determined differential features of BCRs associated with different disease severity. We identified 38 significantly expanded clonal lineages shared among patients as candidates for specific responses to SARS-CoV-2. Using single-cell sequencing, we verified reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identified natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in a number of patients. Our results provide important insights for development of rational therapies and vaccines against COVID-19.
Securing the Insecure: A First-Line-of-Defense for Nanoscale Communication Systems Operating in THz Band
Authors: Waqas Aman, M. Mahboob Ur Rahman, Hassan T. Abbas, Muhammad Arslan Khalid, Muhammad A. Imran, Akram Alomainy, Qammer H. Abbasi
Date: 2020-07-14 00:00:00

Nanoscale communication systems operating in Ter-ahertz (THz) band are anticipated to revolutionise the healthcaresystems of the future. Global wireless data traffic is undergoinga rapid growth. However, wireless systems, due to their broad-casting nature, are vulnerable to malicious security breaches. Inaddition, advances in quantum computing poses a risk to existingcrypto-based information security. It is of the utmost importanceto make the THz systems resilient to potential active and passiveattacks which may lead to devastating consequences, especiallywhen handling sensitive patient data in healthcare systems. Newstrategies are needed to analyse these malicious attacks and topropose viable countermeasures. In this manuscript, we presenta new authentication mechanism for nanoscale communicationsystems operating in THz band at the physical layer. We assessedan impersonation attack on a THz system. We propose usingpath loss as a fingerprint to conduct authentication via two-stephypothesis testing for a transmission device. We used hiddenMarkov Model (HMM) viterbi algorithm to enhance the outputof hypothesis testing. We also conducted transmitter identificationusing maximum likelihood and Gaussian mixture model (GMM)expectation maximization algorithms. Our simulations showedthat the error probabilities are a decreasing functions of SNR. At 10 dB with 0.2 false alarm, the detection probability was almostone. We further observed that HMM out-performs hypothesistesting at low SNR regime (10% increase in accuracy is recordedat SNR =5 dB) whereas the GMM is useful when groundtruths are noisy. Our work addresses major security gaps facedby communication system either through malicious breachesor quantum computing, enabling new applications of nanoscalesystems for Industry 4.0.
On the Effective Capacity of IRS-assisted wireless communication
Date: 2020-07-14 00:00:00

We consider futuristic, intelligent reflecting surfaces (IRS)-aided communication between a base station (BS) and a user equipment (UE) for two distinct scenarios: a single-input, single-output (SISO) system whereby the BS has a single antenna, and a multi-input, single-output (MISO) system whereby the BS has multiple antennas. For the considered IRS-assisted downlink, we compute the effective capacity (EC), which is a quantitative measure of the statistical quality-of-service (QoS) offered by a communication system experiencing random fading. For our analysis, we consider the two widely-known assumptions on channel state information (CSI) -- i.e., perfect CSI and no CSI, at the BS. Thereafter, we first derive the distribution of the signal-to-noise ratio (SNR) for both SISO and MISO scenarios, and subsequently derive closed-form expressions for the EC under perfect CSI and no CSI cases, for both SISO and MISO scenarios. Furthermore, for the SISO and MISO systems with no CSI, it turns out that the EC could be maximized further by searching for an optimal transmission rate $r^*$, which is computed by exploiting the iterative gradient-descent method. We provide extensive simulation results which investigate the impact of the various system parameters, e.g., QoS exponent, power budget, number of transmit antennas at the BS, number of reflective elements at the IRS etc., on the EC of the system.
Multivariate Signal Denoising Based on Generic Multivariate Detrended Fluctuation Analysis
Authors: Khuram Naveed, Sidra Mukhtar, Naveed Ur Rehman
Date: 2020-07-14 00:00:00
Tasks: Denoising

We propose a novel multivariate signal denoising method that performs long-range correlation analysis of multiple modes in input data by considering inherent inter-channel dependencies of the data. That is achieved through a novel and generic multivariate extension of detrended fluctuation analysis (DFA) method - another contribution of this paper. Specifically, our proposed denoising method first obtains data driven multiscale signal representation using multivariate variational mode decomposition (MVMD) method. Then, the proposed generic multivariate DFA is used to reject the predominantly noisy modes based on their randomness scores. Finally, the denoised signal is reconstructed by summing the remaining modes albeit after the removal of the noise traces using the principal component analysis (PCA).
Advanced Stationary Point Concentration Technique for Leakage Mitigation and Small Drone Detection with FMCW Radar
Authors: Junhyeong Park, Jun-Sung Park, Kyung-Bin Bae, Seong-Ook Park
Date: 2020-07-14 00:00:00

As the threats of small drones have grown, developing radars to detect the small drones has become an important issue. In earlier studies, we proposed the stationary point concentration (SPC) technique for the small drone detection with frequency-modulated continuous-wave (FMCW) radar. The SPC technique is a new approach to mitigate the leakage that is an inherent problem in the FMCW radar. The SPC technique improves the signal-to-noise ratio of the small drones by reducing the noise floor and provides accurate distance and velocity information of the small drones. However, the SPC technique has shortcomings in realizing it. In this paper, we present the drawbacks of the SPC technique clearly and propose an advanced SPC (A-SPC) technique. The A-SPC technique can overcome the drawbacks of the SPC technique while taking all the good effects of the SPC technique. The experimental results verify the proposed A-SPC technique and show its robustness and usefulness.
Efficient Power-Splitting and Resource Allocation for Cellular V2X Communications
Authors: Furqan Jameel, Wali Ullah Khan, Neeraj Kumar, Riku Jantti
Date: 2020-07-14 00:00:00

The research efforts on cellular vehicle-to-everything (V2X) communications are gaining momentum with each passing year. It is considered as a paradigm-altering approach to connect a large number of vehicles with minimal cost of deployment and maintenance. This article aims to further push the state-of-the-art of cellular V2X communications by providing an optimization framework for wireless charging, power allocation, and resource block assignment. Specifically, we design a network model where roadside objects use wireless power from RF signals of electric vehicles for charging and information processing. Moreover, due to the resource-constraint nature of cellular V2X, the power allocation and resource block assignment are performed to efficiently use the resources. The proposed optimization framework shows an improvement in terms of the overall energy efficiency of the network when compared with the baseline technique. The performance gains of the proposed solution clearly demonstrate its feasibility and utility for cellular V2X communications.
Distributed Receivers for Extra-Large Scale MIMO Arrays: A Message Passing Approach
Authors: Abolfazl Amiri, Sajad Rezaie, Carles Navarro Manchon, Elisabeth de Carvalho
Date: 2020-07-14 00:00:00
Tasks: CPU

We study the design of receivers in extra-large scale MIMO (XL-MIMO) systems, i.e. systems in which the base station is equipped with an antenna array of extremely large dimensions. While XL-MIMO can significantly increase the system's spectral efficiency, they present two important challenges. One is the increased computational cost of the multi-antenna processing. The second is the variations of user energy distribution over the antenna elements and therefore spatial non-stationarities in these energy distributions. Such non-stationarities limit the performance of the system. In this paper, we propose a distributed receiver for such an XL-MIMO system that can address both challenges. Based on variational message passing (VMP), We propose a set of receiver options providing a range of complexity-performance characteristics to adapt to different requirements. Furthermore, we distribute the processing into local processing units (LPU), that can perform most of the complex processing in parallel, before sharing their outcome with a central processing unit (CPU). Our designs are specifically tailored to exploit the spatial non-stationarities and require lower computations than linear receivers such as zero-forcing. Our simulation study, performed with a channel model accounting for the special characteristics of XL-MIMO channels, confirms the superior performance of our proposals compared to the state of the art methods.
Global Minimax Approximations and Bounds for the Gaussian Q-Function by Sums of Exponentials
Authors: Islam M. Tanash, Taneli Riihonen
Date: 2020-07-14 00:00:00

This paper presents a novel systematic methodology to obtain new simple and tight approximations, lower bounds, and upper bounds for the Gaussian Q-function, and functions thereof, in the form of a weighted sum of exponential functions. They are based on minimizing the maximum absolute or relative error, resulting in globally uniform error functions with equalized extrema. In particular, we construct sets of equations that describe the behaviour of the targeted error functions and solve them numerically in order to find the optimized sets of coefficients for the sum of exponentials. This also allows for establishing a trade-off between absolute and relative error by controlling weights assigned to the error functions' extrema. We further extend the proposed procedure to derive approximations and bounds for any polynomial of the Q-function, which in turn allows approximating and bounding many functions of the Q-function that meet the Taylor series conditions, and consider the integer powers of the Q-function as a special case. In the numerical results, other known approximations of the same and different forms as well as those obtained directly from quadrature rules are compared with the proposed approximations and bounds to demonstrate that they achieve increasingly better accuracy in terms of the global error, thus requiring significantly lower number of sum terms to achieve the same level of accuracy than any reference approach of the same form.
The impact of network properties and mixing on control measures and disease-induced herd immunity in epidemic models: a mean-field model perspective
Date: 2020-07-14 00:00:00

The contact structure of a population plays an important role in transmission
of infection. Many ``structured models'' capture aspects of the contact
structure through an underlying network or a mixing matrix. An important
observation in such models, is that once a fraction $1-1/\mathcal{R}_0$ has
been infected, the residual susceptible population can no longer sustain an
epidemic. A recent observation of some structured models is that this threshold
can be crossed with a smaller fraction of infected individuals, because the
disease acts like a targeted vaccine, preferentially immunizing higher-risk
individuals who play a greater role in transmission. Therefore, a limited
``first wave'' may leave behind a residual population that cannot support a
second wave once interventions are lifted. In this paper, we systematically
analyse a number of mean-field models for networks and other structured
populations to address issues relevant to the Covid-19 pandemic. In particular,
we consider herd-immunity under several scenarios. We confirm that, in networks
with high degree heterogeneity, the first wave confers herd-immunity with
significantly fewer infections than equivalent models with lower degree
heterogeneity. However, if modelling the intervention as a change in the
contact network, then this effect might become more subtle. Indeed, modifying
the structure can shield highly connected nodes from becoming infected during
the first wave and make the second wave more substantial. We confirm this
finding by using an age-structured compartmental model parameterised with real
data and comparing lockdown periods implemented either as a global scaling of
the mixing matrix or age-specific structural changes. We find that results
regarding herd immunity levels are strongly dependent on the model, the
duration of lockdown and how lockdown is implemented.