pwc-archive/papers-with-abstracts huggingface dataset:
On the relation between Preference Reversal and Strategy-Proofness
Authors: K. P. S. Bhaskara Rao, Achille Basile, Surekha Rao
Date: 2021-04-20 00:00:00
Tasks: Relation

We analyze the relation between strategy-proofness and preference reversal in the case that agents may declare indifference. Interestingly, Berga and Moreno (2020), have recently derived preference reversal from group strategy-proofness of social choice functions on strict preferences domains if the range has no more than three elements. We extend this result and at the same time simplify it. Our analysis points out the role of individual strategy-proofness in deriving the preference reversal property, giving back to the latter its original individual nature (cfr. Eliaz, 2004). Moreover, we show that the difficulties Berga and Moreno highlighted relaxing the assumption on the cardinality of the range, disappear under a proper assumption on the domain. We introduce the concept of complete sets of preferences and show that individual strategy-proofness is sufficient to obtain the preference reversal property when the agents' feasible set of orderings is complete. This covers interesting cases like single peaked preferences, rich domains admitting regular social choice functions, and universal domains. The fact that we use individual rather than group strategy-proofness, allows to get immediately some of the known, and some new, equivalences between individual and group strategy-proofness. Finally, we show that group strategy-proofness is only really needed to obtain preference reversal if there are infinitely many voters.
Coded Divergent Waves for Fast Ultrasonic Imaging: Optimization and Comparative Performance Analysis
Authors: Yasin Kumru, Hayrettin Köymen
Date: 2021-04-21 00:00:00

In this paper, we present the optimal use of coded signals in diverging wave transmission for fast ultrasonic imaging. The performance of coded imaging with diverging waves, quantified by SNR, CNR, speckle power and target signal strength, is optimized in terms of code length, wave profile, aperture size and field of view. We obtained one frame in 200 microseconds using coded diverging wave transmission, equivalent to very high 5000 frames/s rate, where the imaging depth is 7.5 cm. The performances of coded diverging wave transmission and conventional single focused phased array imaging are compared on a single frame basis. Complementary Golay sequences with code lengths ranging from 2 to 10 bits are used to code the signal. The signal strength and SNR obtained using synthetic transmit aperture and conventional single focused phased array imaging techniques, respectively, are used as reference in the performance analysis. We present a method to determine the optimum diverging wave profile to maximize the SNR across the entire viewing sector, which matches the received signal strength distribution to that of synthetic transmit aperture imaging. Optimum diverging wave profiles are determined for different sector angles and for different array apertures. For 90{\deg} sector, the SNR of 8-bit coded signal with optimized diverging wave profile is higher than that of conventional single focused phased array imaging at all depths and regions except the focal region, where it is 2-8 dB lower.
Hybrid Constructions of Binary Sequences with Low Autocorrelation Sidelobes
Authors: Miroslav Dimitrov, Tsonka Baitcheva, Nikolay Nikolov
Date: 2021-04-21 00:00:00

In this work, a classical problem of the digital sequence design, or more precisely, finding binary sequences with optimal peak sidelobe level (PSL), is revisited. By combining some of our previous works, together with some mathematical insights, few hybrid heuristic algorithms were created. During our experiments, and by using the aforementioned algorithms, we were able to find PSL-optimal binary sequences for all those lengths, which were previously found during exhaustive searches by various papers throughout the literature. Then, by using a general-purpose computer, we further demonstrate the effectiveness of the proposed algorithms by revealing binary sequences with lengths between 106 and 300, the majority of which possess record-breaking PSL values. Then, by using some well-known algebraic constructions, we outline few strategies for finding highly competitive binary sequences, which could be efficiently optimized, in terms of PSL, by the proposed algorithms.
Wide-Beam Array Antenna Power Gain Maximization via ADMM Framework
Authors: Shiwen Lei, Jing Tian, Zhipeng Lin, Haoquan Hu, Bo Chen, Wei Yang, Pu Tang, Xiangdong Qiu
Date: 2021-04-21 00:00:00

This paper proposes two algorithms to maximize the minimum array power gain in a wide-beam mainlobe by solving the power gain pattern synthesis (PGPS) problem with and without sidelobe constraints. Firstly, the nonconvex PGPS problem is transformed into a nonconvex linear inequality optimization problem and then converted to an augmented Lagrangian problem by introducing auxiliary variables via the Alternating Direction Method of Multipliers (ADMM) framework. Next,the original intractable problem is converted into a series of nonconvex and convex subproblems. The nonconvex subproblems are solved by dividing their solution space into a finite set of smaller ones, in which the solution would be obtained pseudoanalytically. In such a way, the proposed algorithms are superior to the existing PGPS-based ones as their convergence can be theoretically guaranteed with a lower computational burden. Numerical examples with both isotropic element pattern (IEP) and active element pattern (AEP) arrays are simulated to show the effectiveness and superiority of the proposed algorithms by comparing with the related existing algorithms.
Deep Learning Based Proactive Optimization for Mobile LiFi Systems with Channel Aging
Authors: Mohamed Amine Arfaoui, Ali Ghrayeb, Chadi Assi, Marwa Qaraqe
Date: 2021-04-21 00:00:00

This paper investigates the channel aging problem of mobile light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are non-convex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often, cause a high processing delay at the physical layer. Hence, the obtained solutions are no longer optimal since the LiFi channels are evolving. In this paper, a proactive-optimization (PO) approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-term-memory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited to derive near-optimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction error and time, as well as its application in a practical LiFi optimization problem.
A Novel GCN based Indoor Localization System with Multiple Access Points
Authors: Yanzan Sun, Qinggang Xie, Guangjin Pan, Shunqing Zhang, Shugong Xu
Date: 2021-04-21 00:00:00
Tasks: Indoor Localization

With the rapid development of indoor location-based services (LBSs), the demand for accurate localization keeps growing as well. To meet this demand, we propose an indoor localization algorithm based on graph convolutional network (GCN). We first model access points (APs) and the relationships between them as a graph, and utilize received signal strength indication (RSSI) to make up fingerprints. Then the graph and the fingerprint will be put into GCN for feature extraction, and get classification by multilayer perceptron (MLP).In the end, experiments are performed under a 2D scenario and 3D scenario with floor prediction. In the 2D scenario, the mean distance error of GCN-based method is 11m, which improves by 7m and 13m compare with DNN-based and CNN-based schemes respectively. In the 3D scenario, the accuracy of predicting buildings and floors are up to 99.73% and 93.43% respectively. Moreover, in the case of predicting floors and buildings correctly, the mean distance error is 13m, which outperforms DNN-based and CNN-based schemes, whose mean distance errors are 34m and 26m respectively.
Amplifier-Coupled Tone Reservation for Minimization of OFDM Nonlinear Distortion
Authors: Pawel Kryszkiewicz
Date: 2021-04-20 00:00:00

Nonlinear distortion of an OFDM signal is a serious problem when it comes to energy-efficient Power Amplifier(PA) utilization. Typically, Peak-to-Average Power Ratio(PAPR) reduction algorithms and digital predistortion algorithms are used independently to fight the same phenomenon. This paper proposes an Amplifier-Coupled Tone Reservation (ACTR)algorithm for the reduction of nonlinear distortion power, utilizing knowledge on thep redistorted PA characteristic. The optimization problem is defined. Its convexity is proved. A computationally-efficient solution is presented. Finally, its performance is compared against two state-of-the-art TR algorithms by means of simulations and measurements. The results show the proposed solution is advantageous, both in terms of nonlinear distortion power and the required number of computations.
Broadband Radar Invisibility with Time-Dependent Metasurfaces
Authors: Vitali Kozlov, Dmytro Vovchuk, Pavel Ginzburg
Date: 2021-04-20 00:00:00

Concealing objects from interrogation has been a primary objective since the integration of radars into surveillance systems. Metamaterial-based invisibility cloaking, which was considered a promising solution, did not yet succeed in delivering reliable performance against real radar systems, mainly due to its narrow operational bandwidth. Here we propose an approach, which addresses the issue from a signal-processing standpoint and, as a result, is capable of coping with the vast majority of unclassified radar systems by exploiting vulnerabilities in their design. In particular, we demonstrate complete concealment of a 0.25 square meter moving metal plate from an investigating radar system, operating in a broad frequency range approaching 20% bandwidth around the carrier of 1.5GHz. The key element of the radar countermeasure is a temporally modulated coating. This auxiliary structure is designed to dynamically and controllably adjust the reflected phase of the impinging radar signal, which acquires a user-defined Doppler shift. A special case of interest is imposing a frequency shift that compensates for the real Doppler signatures originating from the motion of the target. In this case the radar will consider the target static, even though it is moving. As a result, the reflected echo will be discarded by the clutter removal filter, which is an inherent part of any modern radar system that is designed to operate in real conditions. This signal-processing loophole allows rendering the target invisible to the radar even though it scatters electromagnetic radiation. This application claims the benefit of patent number 273995, filed on April 16 2020.
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion
Authors: Daniel Heestermans Svendsen, Pablo Morales-Alvarez, Ana Belen Ruescas, Rafael Molina, Gustau Camps-Valls
Date: 2021-04-16 00:00:00
Tasks: Earth Observation, Gaussian Processes, Retrieval

Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have provided useful and informative solutions to such RTM inversion problems. This is in large part due to the confidence intervals they provide, and their predictive accuracy. However, RTMs are very complex, highly nonlinear, and typically hierarchical models, so that often a shallow GP model cannot capture complex feature relations for inversion. This motivates the use of deeper hierarchical architectures, while still preserving the desirable properties of GPs. This paper introduces the use of deep Gaussian Processes (DGPs) for bio-geo-physical model inversion. Unlike shallow GP models, DGPs account for complicated (modular, hierarchical) processes, provide an efficient solution that scales well to big datasets, and improve prediction accuracy over their single layer counterpart. In the experimental section, we provide empirical evidence of performance for the estimation of surface temperature and dew point temperature from infrared sounding data, as well as for the prediction of chlorophyll content, inorganic suspended matter, and coloured dissolved matter from multispectral data acquired by the Sentinel-3 OLCI sensor. The presented methodology allows for more expressive forms of GPs in remote sensing model inversion problems.
A topological selection of folding pathways from native states of knotted proteins
Authors: Agnese Barbensi, Naya Yerolemou, Oliver Vipond, Barbara I. Mahler, Pawel Dabrowski-Tumanski, Dimos Goundaroulis
Date: 2021-04-21 00:00:00
Tasks: Clustering

Understanding the biological function of knots in proteins and their folding process is an open and challenging question in biology. Recent studies classify the topology and geometry of knotted proteins by analysing the distribution of a protein's planar projections using topological objects called knotoids. We approach the analysis of proteins with the same topology by introducing a topologically inspired statistical metric between their knotoid distributions. We detect geometric differences between trefoil proteins by characterising their entanglement and we recover a clustering by sequence similarity. By looking directly at the geometry and topology of their native states, we are able to probe different folding pathways for proteins forming open-ended trefoil knots. Interestingly, our pipeline reveals that the folding pathway of shallow knotted Carbonic Anhydrases involves the creation of a double-looped structure, differently from what was previously observed for deeply knotted trefoil proteins. We validate this with Molecular Dynamics simulations.
A Note on Relative Consciousness
Authors: Kynan Eng
Date: 2021-04-16 00:00:00

This paper describes a mathematical formulation for measuring how one system can estimate the consciousness of another. This consciousness estimate is always relative to the observer. The paper shows how this formulation leads to simple resolutions of some key problems of consciousness.
Modelling the Spread of an Epidemic in Presence of Vaccination using Cellular Automata
Authors: Agniva Datta, Muktish Acharyya
Date: 2021-04-21 00:00:00

The results of Kermack-McKendrick SIR model are planned to be reproduced by cellular automata (CA) lattice model. The CA algorithms are proposed to study the model of epidemic, systematically. The basic goal is to capture the effects of spreading of infection over a scale of length. This CA model can provide the rate of growth of the infection over the space which was lacking in the mean-field like SIR model. The motion of the circular front of an infected cluster shows a linear behaviour in time. The correlation of a particular site to be infected, with respect to the central site is also studied. The outcomes of CA model are in good agreement with that obtained from SIR model. The results of vaccination have been also incorporated in the CA algorithm with a satisfactory degree of success. The advantage of the present model is that it can shed considerable amount of light to the physical properties of the spread of a typical epidemic in a simple, yet robust way.
Extinction in complex communities as driven by adaptive dynamics
Authors: Vu AT Nguyen, Dervis C Vural
Date: 2021-04-20 00:00:00

In a complex community, species continuously adapt to each other. On rare occasions, the adaptation of a species can lead to the extinction of others, and even its own. "Adaptive dynamics" is the standard mathematical framework to describe evolutionary changes in community interactions, and in particular, predict adaptation driven extinction. Unfortunately, most authors implement the equations of adaptive dynamics through computer simulations, that require assuming a large number of questionable parameters and fitness functions. In this study we present analytical solutions to adaptive dynamics equations, thereby clarifying how outcomes depend on any computational input. We develop general formulas that predict equilibrium abundances over evolutionary time scales. Additionally, we predict which species will go extinct next, and when this will happen.
Cost Effective Reproduction Number Based Strategies for Reducing Deaths from COVID-19
Authors: Christopher Thron, Vianney Mbazumutima, Luis Vargas Tamayo, Leonard Todjihounde
Date: 2021-04-19 00:00:00
Tasks: Epidemiology

In epidemiology, the effective reproduction number $R_e$ is used to characterize the growth rate of an epidemic outbreak. In this paper, we investigate properties of $R_e$ for a modified SEIR model of COVID-19 in the city of Houston, TX USA, in which the population is divided into low-risk and high-risk subpopulations. The response of $R_e$ to two types of control measures (testing and distancing) applied to the two different subpopulations is characterized. A nonlinear cost model is used for control measures, to include the effects of diminishing returns. We propose three types of heuristic strategies for mitigating COVID-19 that are targeted at reducing $R_e$, and we exhibit the tradeoffs between strategy implementation costs and number of deaths. We also consider two variants of each type of strategy: basic strategies, which consider only the effects of controls on $R_e$, without regard to subpopulation; and high-risk prioritizing strategies, which maximize control of the high-risk subpopulation. Results showed that of the three heuristic strategy types, the most cost-effective involved setting a target value for $R_e$ and applying sufficient controls to attain that target value. This heuristic led to strategies that begin with strict distancing of the entire population, later followed by increased testing. Strategies that maximize control on high-risk individuals were less cost-effective than basic strategies that emphasize reduction of the rate of spreading of the disease. The model shows that delaying the start of control measures past a certain point greatly worsens strategy outcomes. We conclude that the effective reproduction can be a valuable real-time indicator in determining cost-effective control strategies.
Three little arbitrage theorems
Authors: Mauricio Contreras G., Roberto Ortiz H
Date: 2021-04-20 00:00:00

We prove three theorems about the exact solutions of a generalized or interacting Black-Scholes equation that explicitly includes arbitrage bubbles. These arbitrage bubbles can be characterized by an arbitrage number $A_N(T)$. The first theorem states that if $A_N(T) = 0$, then the solution at the maturity of the interacting equation is identical to the solution of the free Black-Scholes equation with the same initial interest rate $r$. The second theorem states that if $A_N(T) \ne 0$, the solution can be expressed in terms of all higher derivatives of solutions to the free Black-Scholes equation with the initial interest rate $r$. The third theorem states that whatever the arbitrage number is, the solution is a solution to the free Black-Scholes equation with a variable interest rate $r(\tau) = r + (1/\tau) A_N(\tau)$. Also, we show, by using the Feynman-Kac theorem, that for the special case of a Call contract, the exact solution for a Call with strike price $K$ is equivalent to the usual Call solution to the Black-Scholes equation with strike price $\tilde{K} = K e^{-A_N(T)}$.
Backtesting Systemic Risk Forecasts using Multi-Objective Elicitability
Authors: Tobias Fissler, Yannick Hoga
Date: 2021-04-20 00:00:00

Systemic risk measures such as CoVaR, CoES and MES are widely-used in finance, macroeconomics and by regulatory bodies. Despite their importance, we show that they fail to be elicitable and identifiable. This renders forecast comparison and validation, commonly summarised as `backtesting', impossible. The novel notion of \emph{multi-objective elicitability} solves this problem. Specifically, we propose Diebold--Mariano type tests utilising two-dimensional scores equipped with the lexicographic order. We illustrate the test decisions by an easy-to-apply traffic-light approach. We apply our traffic-light approach to DAX~30 and S\&P~500 returns, and infer some recommendations for regulators.
A q-binomial extension of the CRR asset pricing model
Authors: Jean-Christophe Breton, Youssef El-Khatib, Jun Fan, Nicolas Privault
Date: 2021-04-20 00:00:00

We propose an extension of the Cox-Ross-Rubinstein (CRR) model based on $q$-binomial (or Kemp) random walks, with application to default with logistic failure rates. This model allows us to consider time-dependent switching probabilities varying according to a trend parameter on a non-self-similar binomial tree. In particular, it includes tilt and stretch parameters that control increment sizes. Option pricing formulas are written using $q$-binomial coefficients, and we study the convergence of this model to a Black-Scholes type formula in continuous time. A convergence rate of order $O(N^{-1/2})$ is obtained.
Machine Learning Meets Natural Language Processing -- The story so far
Authors: N. -I. Galanis, P. Vafiadis, K. -G. Mirzaev, G. A. Papakostas
Date: 2021-03-27 00:00:00
Tasks: BIG-bench Machine Learning

Natural Language Processing (NLP) has evolved significantly over the last decade. This paper highlights the most important milestones of this period while trying to pinpoint the contribution of each individual model and algorithm to the overall progress. Furthermore, it focuses on issues still remaining to be solved, emphasizing the groundbreaking proposals of Transformers, BERT, and all the similar attention-based models.