pwc-archive/papers-with-abstracts huggingface dataset:
A joint precoding framework for wideband reconfigurable intelligent surface-aided cell-free network
Authors: Zijian Zhang, Linglong Dai
Date: 2020-02-10 00:00:00

Thanks to the strong ability against the inter-cell interference, cell-free network is considered as a promising technique to improve network capacity. However, further capacity improvement requires to deploy more base stations (BSs) with high cost and power consumption. To address this issue, inspired by the recently developed reconfigurable intelligent surface (RIS) technique, we propose the concept of RIS-aided cell-free network to improve the capacity with low cost and power consumption. The key idea is to replace some of the required BSs by low-cost and energy-efficient RISs. Then, in a wideband RIS-aided cell-free network, we formulate the problem of joint precoding design at BSs and RISs to maximize the network capacity. Due to the non-convexity and high complexity of the formulated problem, we develop an alternating optimization framework to solve this challenging problem. In particular, we decouple this problem via fractional programming, and solve the subproblems alternatively. Note that most of the scenarios considered in existing works are special cases of the general scenario studied in this paper, and the proposed joint precoding framework can serve as a general solution to maximize the capacity in most existing RIS-aided scenarios. Finally, simulation results demonstrate that, compared with the conventional cell-free network, the network capacity under the proposed scheme can be improved significantly.
On CSI-free Multi-Antenna Schemes for Massive RF Wireless Energy Transfer
Authors: Onel L. A. López, Samuel Montejo-Sánchez, Richard D. Souza, Hirley Alves, Constantinos B. Papadias
Date: 2020-02-06 00:00:00

Wireless Energy Transfer (WET) is emerging as a potential green enabler for massive Internet of Things (IoT). Herein, we analyze Channel State Information (CSI)-free multi-antenna strategies for powering wirelessly a large set of single-antenna IoT devices. The CSI-free schemes are AA-SS (AA-IS), where all antennas transmit the same (independent) signal(s), and SA, where just one antenna transmits at a time such that all antennas are utilized during the coherence block. We characterize the distribution of the provided energy under correlated Rician fading for each scheme and find out that while AA-IS and SA cannot take advantage of the multiple antennas to improve the average provided energy, its dispersion can be significantly reduced. Meanwhile, AA-SS provides the greatest average energy, but also the greatest energy dispersion, and the gains depend critically on the mean phase shifts between the antenna elements. We find that consecutive antennas must be $\pi$ phase-shifted for optimum average energy performance under AA-SS. Our numerical results evidenced that correlation is beneficial under AA-SS, while a greater line of sight (LOS) and/or number of antennas is not always beneficial under such scheme. Meanwhile, both AA-IS and SA schemes benefit from small correlation, large LOS and/or large number of antennas.
First attempts to model the dynamics of the Coronavirus outbreak 2020
Date: 2020-02-10 00:00:00

Since the end of 2019 an outbreak of a new strain of coronavirus, called
2019--nCoV, is reported from China and later other parts of the world. Since
January 21, WHO reports daily data on confirmed cases and deaths from both
China and other countries. In this work we present some discrete and continuous
models to discribe the disease dynamics in China and estimate the needed
epidemiological parameters. Good agreement with the current dynamics has be
found for both a discrete transmission model and a slightly modified SIR-model.
Determining optimal input-output properties: A data-driven approach
Date: 2020-02-10 00:00:00

Due to their relevance in systems analysis and (robust) controller design, we
consider the problem of determining control-theoretic system properties of an a
priori unknown system from data only. More specifically, we introduce a
necessary and sufficient condition for a discrete-time linear time-invariant
system to satisfy a given integral quadratic constraint (IQC) over a finite
time horizon using only one input-output trajectory of finite length.
Furthermore, for certain classes of IQCs, we provide convex optimization
problems in form of semidefinite programs (SDPs) to retrieve the optimal, i.e.
the tightest, system property description that is satisfied by the unknown
system. Finally, we provide bounds on the difference between finite and
infinite horizon IQCs and illustrate the effectiveness of the proposed scheme
in a variety of simulation studies including noisy measurements and a high
dimensional system.
Dictionary-based Method for Vascular Segmentation for OCTA Images
Authors: Astrid M. E. Engberg, Vedrana A. Dahl, Anders B. Dahl
Date: 2020-02-10 00:00:00
Tasks: Segmentation

Optical coherence tomography angiography (OCTA) is an imaging technique that allows for non-invasive investigation of the microvasculature in the retina. OCTA uses laser light reflectance to measure moving blood cells. Hereby, it visualizes the blood flow in the retina and can be used for determining regions with more or less blood flow. OCTA images contain the capillary network together with larger blood vessels, and in this paper we propose a method that segments larger vessels, capillaries and background. The segmentation is obtained using a dictionary-based machine learning method that requires training data to learn the parameters of the segmentation model. Here, we give a detailed description of how the method is applied to OCTA images, and we demonstrate how it robustly labels capillaries and blood vessels and hereby provides the basis for quantifying retinal blood flow.
Knowledge Diffusion Process & Common Islamic Banking Governance Principles: Integrative Perspective (s) of Managers and Shariah Scholars
Authors: Adnan Malik, Karim Ullah, Shakir Ullah
Date: 2020-01-23 00:00:00
Tasks: Jurisprudence

Islamic banks being commercial entities strive to earn profit within shariah ambit. Therefore, they seem to be basing themselves upon two knowledge streams namely i) Islamic jurisprudence principles, and ii) banking principles. Islamic jurisprudence principles primarily aim at bringing shariah compliance while banking principles focus profitability. These principles, making two schools of thought in the discipline, however, have their unique philosophies, principles, and practices, which are now gradually diffusing into an emergent set of governance principles basing the contemporary Islamic banking theory and practice. Governance systems of Islamic banks have elements of both conventional as well as Shariah, and need to have principles having components of banking and shariah sufficiently diffused for their successful operations in a longer term. Aim of this research is to review the literature about the knowledge diffusion process of islamic banking principles which guides the governance of Islamic banks. This study review the literature using a method in which focus remain on bridging different areas which in this case are knowledge diffusion and islamic banking governance principles.
Personalized Metabolic Analysis of Diseases
Authors: Ali Cakmak, M. Hasan Celik
Date: 2020-02-10 00:00:00

The metabolic wiring of patient cells is altered drastically in many diseases, including cancer. Understanding the nature of such changes may pave the way for new therapeutic opportunities, as well as the development of personalized treatment strategies for patients. In this paper, we propose an algorithm, Metabolitics, which allows systems-level analysis of changes in the biochemical network of cells in disease states. It enables the study of a disease at both reaction- and pathway-level granularities for detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with an objective function which is dynamically built based on the biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds classification models for each disease to diagnose patients and predict their subgroups based on the computed metabolic network changes. We demonstrate the use of Metabolitics on three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully diagnose patients by f1-score of over 90% on the average. Besides, in addition to the confirmation of previously reported breast cancer associated pathways, we discovered that Butanoate metabolism experiences significantly decreased activity, while Arginine and Proline Metabolism is subject to significant increase in activity, which have not been reported before. Metabolitics is made available as a Python package in pypi.
The neonatal sepsis is diminished by cervical vagus nerve stimulation and tracked non-invasively by ECG: a preliminary report in the piglet model
Authors: Aude Castel, Patrick Burns, Colin Wakefield, Keven. J. Jean, Yael S. Frank, Mingju Cao, Andre Desrochers, Gilles Fecteau, Christophe Faure, Christophe L. Herry, Martin G. Frasch
Date: 2020-02-10 00:00:00
Tasks: Heart Rate Variability

An electrocardiogram (ECG)-derived heart rate variability (HRV) index reliably tracks the inflammatory response induced by low-dose lipopolysaccharide (LPS) in near-term sheep fetuses. We evaluated the effect of vagus nerve stimulation (VNS) on vagus nerve electroneurogram (VENG) and the systemic inflammatory response induced by a high dose of LPS in neonatal piglets to mimic late-onset neonatal sepsis. We tested if our HRV inflammatory index tracks inflammation in piglets and its relationship to VENG. Following anesthesia, electrodes were attached to the left vagal nerve; ECG and blood pressure (BP) were recorded throughout the experiment. Following baseline, the piglets were administered LPS as 2mg/kg IV bolus. In the VNS treated piglet, the vagus nerve was stimulated for 10 minutes prior to and 10 min after the injection of LPS. In both groups, every 15 min post LPS, the arterial blood sample was drawn for blood gas, metabolites, and inflammatory cytokines. At the end of the experiment, the piglets were euthanized. BP and HRV measures were calculated. The piglets developed a potent inflammatory response to the LPS injection with TNF-alpha, IL-1beta, IL-6 and IL-8 peaking between 45 and 90 min post-injection. VNS diminished the LPS-induced systemic inflammatory response varying across the measured cytokines from two to ten-fold. The HRV index tracked accurately the temporal profile of cytokines and VENG changes. This novel model allows manipulating and tracking neonatal sepsis: The HRV inflammatory index 1) applies across species pre- and postnatally and 2) performs well at different degrees of sepsis (i.e., nanogram and milligram doses of LPS); 3) the present VNS paradigm effectively suppresses LPS-induced inflammation, even at high doses of LPS. The potential of early postnatal VNS to counteract sepsis and of HRV monitoring to early detect and track it deserve further study.
Utilizing the Wavelet Transform's Structure in Compressed Sensing
Authors: Nicholas Dwork, Daniel O'Connor, Corey A. Baron, Ethan M. I. Johnson, Adam B. Kerr, John M. Pauly, Peder E. Z. Larson
Date: 2020-02-11 00:00:00
Tasks: compressed sensing, Denoising, Image Reconstruction

Compressed sensing has empowered quality image reconstruction with fewer data samples than previously though possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is a common sparsifying transformation used for this purpose. In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result. After inclusion of this affine transformation, we modify the resulting optimization problem to comply with the form of the Basis Pursuit Denoising problem. Finally, we show theoretically that this yields a lower bound on the error of the reconstruction and present results where solving this modified problem yields images of higher quality for the same sampling patterns.
Multiphase interpolating digital power amplifiers for TX beamforming
Date: 2020-02-11 00:00:00

This paper presents a 4-channel beamforming TX implemented in 65nm CMOS. Each
beamforming TX is comprised of a C-2C split-array multiphase switched-capacitor
power amplifier (SAMP-SCPA). This is the first use of multiphase interpolation
(MPI) for beam-steering. This technique is ideal for low-frequency beamforming
and MIMO, as it does not require passive or LO based phase shifters. The SCPA
is ideal to use as the core element since it can perform frequency translation,
data conversion and drive an output at high power and efficiency in a compact
die area. A prototype 4-element beamforming TX, occupying 2 mm X 2.5 mm, can
achieve peak output power of 24.4 dBm with a peak system efficiency (SE) of
24%, while achieving < 1{\deg} phase resolution and <1 dB gain error. When
transmitting a 15 MHz, 64 QAM long-term evolution (LTE) signal it outputs 18.4
dBm at 14% SE with a measured adjacent channel leakage ratio (ACLR) < -30 dBc
and error vector magnitude (EVM) of 3.27 %-rms at 1.75 GHz. A synthesized beam
pattern based on measured results from a single die achieves <0.32{\deg}-rms
beam angle error and <0.15 dB rms beam amplitude error.
Age dependence of fitness and body mass index in Korean adults
Date: 2020-02-11 00:00:00
Tasks: Sensitivity

The aim of this study was to investigate the age dependence of the fitness
and body mass index (BMI) in Korean adults and to find an effective exercise to
restore the degradation of fitness due to aging. The age dependence of the
fitness and BMI were calculated using their lump mean values (LMVs) and a
linear regression method. The fitness sensitivity percentage to age (FSPA) and
fitness sensitivity percentage to BMI (FSPB) were introduced as indicators for
the effective improvement of the fitness. The results showed that the
degradation of fitness due to aging, especially the degradation of
cardiorespiratory endurance and muscular endurance, could be improved
effectively by controlling the 20-m multi-stage shuttle run and sit-up scores
for both males and females. The results also showed that the BMIs could be
effectively controlled with enhancing the 10-m shuttle run and standing long
jump scores for both males and females. It is expected that the LMV, FSPA, and
FSPB could be used to improve fitness effectively and to establish personal
exercise aims.
On the statistics of scaling exponents and the Multiscaling Value at Risk
Authors: Giuseppe Brandi, T. Di Matteo
Date: 2020-02-11 00:00:00
Tasks: Time Series, Time Series Analysis

Scaling and multiscaling financial time series have been widely studied in the literature. The research on this topic is vast and still flourishing. One way to analyze the scaling properties of time series is through the estimation of their scaling exponents, that are recognized as being valuable measures to discriminate between random, persistent, and anti-persistent behaviors in these time series. In the literature, several methods have been proposed to study the multiscaling property. In this paper, we use the generalized Hurst exponent (GHE) tool and we propose a novel statistical procedure based on GHE which we name Relative Normalized and Standardized Generalized Hurst Exponent (RNSGHE). This method is used to robustly estimate and test the multiscaling property and, together with a combination of t-tests and F-tests, serves to discriminate between real and spurious scaling. Furthermore, we introduce a new tool to estimate the optimal aggregation time used in our methodology which we name Autocororrelation Segmented Regression. We numerically validate this procedure on simulated time series by using the Multifractal Random Walk (MRW) and we then apply it to real financial data. We present results for times series with and without anomalies and we compute the bias that such anomalies introduce in the measurement of the scaling exponents. We also show how the use of proper scaling and multiscaling can ameliorate the estimation of risk measures such as Value at Risk (VaR). Finally, we propose a methodology based on Monte Carlo simulation, which we name Multiscaling Value at Risk (MSVaR), that takes into account the statistical properties of multiscaling time series. We show that by using this statistical procedure in combination with the robustly estimated multiscaling exponents, the one year forecasted MSVaR mimics the VaR on the annual data for the majority of the stocks analyzed.
Hardware Trust and Assurance through Reverse Engineering: A Survey and Outlook from Image Analysis and Machine Learning Perspectives
Authors: Ulbert J. Botero, Ronald Wilson, Hangwei Lu, Mir Tanjidur Rahman, Mukhil A. Mallaiyan, Fatemeh Ganji, Navid Asadizanjani, Mark M. Tehranipoor, Damon L. Woodard, Domenic Forte
Date: 2020-02-11 00:00:00

In the context of hardware trust and assurance, reverse engineering has been often considered as an illegal action. Generally speaking, reverse engineering aims to retrieve information from a product, i.e., integrated circuits (ICs) and printed circuit boards (PCBs) in hardware security-related scenarios, in the hope of understanding the functionality of the device and determining its constituent components. Hence, it can raise serious issues concerning Intellectual Property (IP) infringement, the (in)effectiveness of security-related measures, and even new opportunities for injecting hardware Trojans. Ironically, reverse engineering can enable IP owners to verify and validate the design. Nevertheless, this cannot be achieved without overcoming numerous obstacles that limit successful outcomes of the reverse engineering process. This paper surveys these challenges from two complementary perspectives: image processing and machine learning. These two fields of study form a firm basis for the enhancement of efficiency and accuracy of reverse engineering processes for both PCBs and ICs. In summary, therefore, this paper presents a roadmap indicating clearly the actions to be taken to fulfill hardware trust and assurance objectives.
Quantum coupled-wave theory of price formation in financial markets: price measurement, dynamics and ergodicity
Authors: Jack Sarkissian
Date: 2020-02-11 00:00:00

We explore nature of price formation in financial markets and develop a theory of bid and ask price dynamics in which the two prices form due to quantum-chaotic interaction between buy and sell orders. In this model bid and ask prices are represented by eigenvalues of a 2x2 price operator corresponding to 'bid' and 'ask' eigenstates, while randomness of price operator results in price fluctuations that destroy oscillatory effects. We show that this theory adequately captures behavior of bid-ask spread and allows to model bid and ask price dynamics in a coordinated way. We also discuss ergodicity properties of price formation and show how directional price movement occurs due to ergodicity violation in a quantum process instead of the commonly believed forces acting on price. This theory has wide range of applications such as trade execution modeling, large order pricing and risk valuation for illiquid securities.
Timing Excess Returns A cross-universe approach to alpha
Authors: Marc Rohloff, Alexander Vogt
Date: 2020-02-11 00:00:00
Tasks: Benchmarking, Time Series, Time Series Analysis

We present a simple model that uses time series momentum in order to construct strategies that systematically outperform their benchmark. The simplicity of our model is elegant: We only require a benchmark time series and several related investable indizes, not requiring regression or other models to estimate our parameters. We find that our one size fits all approach delivers significant outperformance in both equity and bond markets while meeting the ex-ante risk requirements, nearly doubling yearly returns vs. the MSCI World and Bloomberg Barclays Euro Aggregate Corporate Bond benchmarks in a long-only backtest. We then combine both approaches into an absolute return strategy by benchmarking vs. the Eonia Total Return Index and find significant outperformance at a sharpe ratio of 1.8. Furthermore, we demonstrate that our model delivers a benefit versus a static portfolio with fixed mean weights, showing that timing of excess return momentum has a sizeable benefit vs. static allocations. This also applies to the passively investable equity factors, where we outperform a static factor exposure portfolio with statistical significance. Also, we show that our model delivers an alpha after deducting transaction costs.
Identifiability and Estimation of Possibly Non-Invertible SVARMA Models: A New Parametrisation
Authors: Bernd Funovits
Date: 2020-02-11 00:00:00

This article deals with parameterisation, identifiability, and maximum likelihood (ML) estimation of possibly non-invertible structural vector autoregressive moving average (SVARMA) models driven by independent and non-Gaussian shocks. In contrast to previous literature, the novel representation of the MA polynomial matrix using the Wiener-Hopf factorisation (WHF) focuses on the multivariate nature of the model, generates insights into its structure, and uses this structure for devising optimisation algorithms. In particular, it allows to parameterise the location of determinantal zeros inside and outside the unit circle, and it allows for MA zeros at zero, which can be interpreted as informational delays. This is highly relevant for data-driven evaluation of Dynamic Stochastic General Equilibrium (DSGE) models. Typically imposed identifying restrictions on the shock transmission matrix as well as on the determinantal root location are made testable. Furthermore, we provide low level conditions for asymptotic normality of the ML estimator and analytic expressions for the score and the information matrix. As application, we estimate the Blanchard and Quah model and show that our method provides further insights regarding non-invertibility using a standard macroeconometric model. These and further analyses are implemented in a well documented R-package.
Ramsey Optimal Policy versus Multiple Equilibria with Fiscal and Monetary Interactions
Date: 2020-02-11 00:00:00

We consider a frictionless constant endowment economy based on Leeper (1991).
In this economy, it is shown that, under an ad-hoc monetary rule and an ad-hoc
fiscal rule, there are two equilibria. One has active monetary policy and
passive fiscal policy, while the other has passive monetary policy and active
fiscal policy. We consider an extended setup in which the policy maker
minimizes a loss function under quasi-commitment, as in Schaumburg and
Tambalotti (2007). Under this formulation there exists a unique Ramsey
equilibrium, with an interest rate peg and a passive fiscal policy. We thank
John P. Conley, Luis de Araujo and one referree for their very helpful
comments.