pwc-archive/papers-with-abstracts huggingface dataset:
Characterization of the probability and information entropy of a process with an exponentially increasing sample space and its application to the Broad Money Supply
Authors: Laurence F Lacey
Date: 2021-05-14 00:00:00

There is a random variable (X) with a determined outcome (i.e., X = x0), p(x0) = 1. Consider x0 to have a discrete uniform distribution over the integer interval [1, s], where the size of the sample space (s) = 1, in the initial state, such that p(x0) = 1. What is the probability of x0 and the associated information entropy (H), as s increases exponentially? If the sample space expansion occurs at an exponential rate (rate constant = lambda) with time (t) and applying time scaling, such that T = lambda x t, gives: p(x0|T)=exp(-T) and H(T)=T. The characterization has also been extended to include exponential expansion by means of simultaneous, independent processes, as well as the more general multi-exponential case. The methodology was applied to the expansion of the broad money supply of US$ over the period 2001-2019, as a real-world example. At any given time, the information entropy is related to the rate at which the sample space is expanding. In the context of the expansion of the broad money supply, the information entropy could be considered to be related to the "velocity" of the expansion of the money supply.
An algebraic approach to revealed preferences
Authors: Mikhail Freer, Cesar Martinelli
Date: 2021-05-31 00:00:00

We propose and develop an algebraic approach to revealed preference. Our approach dispenses with non algebraic structure, such as topological assumptions. We provide algebraic axioms of revealed preference that subsume previous, classical revealed preference axioms, as well as generate new axioms for behavioral theories, and show that a data set is rationalizable if and only if it is consistent with an algebraic axiom.
Assessing Brazilian agri-food policies: what impact on family farms?
Authors: Valdemar J. Wesz Junior, Simone Piras, Catia Grisa, Stefano Ghinoi
Date: 2021-05-31 00:00:00

Since the beginning of the 1990s, Brazil has introduced different policies for increasing agricultural production among family farms, such as the National Program for Strengthening Family Farming (Pronaf), the technical assistance and rural extension programmes (ATER), and seeds distribution. Despite the importance of these policies for the development of family farming, there is a lack of empirical studies investigating their impact on commercialization of food products. By considering household-level data from the 2014 Brazilian National Household Sample Survey, we use propensity score matching techniques accounting for the interaction effects between policies to compare the commercialisation behaviour of recipients with non recipients. We find that Pronaf has a significant positive impact on family farmers propensity to engage in commercialisation, and this effect increases if farmers have also access to ATER. Receiving technical assistance alone has a positive effect, but this is mostly limited to smaller farms. In turn, seed distribution appears not to increase commercialization significantly. A well balanced policy mix could ensure that, in a country subject to the pressure of international food markets, increased commercialisation does not result in reduced food security for rural dwellers.
Determinants of budget deficits: Focus on the effects from the COVID-19 crisis
Authors: Dragan Tevdovski, Petar Jolakoski, Viktor Stojkoski
Date: 2021-05-31 00:00:00
Tasks: Position

This paper revisits the discussion on determinants of budget balances and investigates the change in their effect in light of the COVID-19 crisis by utilizing data on 43 countries and a system generalized method of moments approach. The results show that the overall impact of the global pandemic led to a disproportionate increase in the magnitude of the estimated effects of the macroeconomic determinants on the budget balance. However, we also find that more developed economies were able to undertake higher stimulus packages for the relatively same level of primary balance. We believe that one of the factors affecting this outcome is that that they exhibit a higher government debt position in domestic currency denomination.
Bioelectrical brain activity can predict prosocial behavior
Authors: Mikhail Kunavin, Tatiana Kozitsina, Mikhail Myagkov, Irina Kozhevnikova, Mikhail Pankov, Ludmila Sokolova
Date: 2021-05-30 00:00:00
Tasks: Decision Making, EEG, Electroencephalogram (EEG), Rhythm

Generally, people behave in social dilemmas such as proself and prosocial. However, inside social groups, people have a tendency to choose prosocial alternatives due to in-group favoritism. The bioelectrical activity of the human brain shows the differences between proself and prosocial exist even out of a socialized group. Moreover, a group socialization strengthens these differences. We used EEG System, "Neuron-Spectrum-4/EPM" (16 channels), to track the brain bioelectrical activity during decision making in laboratory experiments with the Prisoner's dilemma game and the short-term socialization stage. We compared the spatial distribution of the spectral density during the different experimental parts. The noncooperative decision was characterized by the increased values of spectral the beta rhythm in the orbital regions of prefrontal cortex. The cooperative choice, on the contrary, was accompanied by the theta-rhythm activation in the central cortex regions in both hemispheres and the high-frequency alpha rhythm in the medial regions of the prefrontal cortex. People who increased the cooperation level after the socialization stage was initially different from the ones who decreased the cooperation in terms of the bioelectrical activity. Well-socialized participants differed by increased values of spectral density of theta-diapason and decreased values of spectral density of beta-diapason in the middle part of frontal lobe. People who decreased the cooperation level after the socialization stage was characterized by decreased values of spectral density of alpha rhythm in the middle and posterior convex regions of both hemispheres.
Impact of Public and Private Investments on Economic Growth of Developing Countries
Authors: Faruque Ahamed
Date: 2021-05-29 00:00:00

This paper aims to study the impact of public and private investments on the economic growth of developing countries. The study uses the panel data of 39 developing countries covering the periods 1990-2019. The study was based on the neoclassical growth models or exogenous growth models state in which land, labor, capital accumulation, etc., and technology proved substantial for economic growth. The paper finds that public investment has a strong positive impact on economic growth than private investment. Gross capital formation, labor growth, and government final consumption expenditure were found significant in explaining the economic growth. Overall, both public and private investments are substantial for the economic growth and development of developing countries.
Comprehensive Analysis On Determinants Of Bank Profitability In Bangladesh
Authors: Md Saimum Hossain, Faruque Ahamed
Date: 2021-05-29 00:00:00
Tasks: Management

The study investigates the relationship between bank profitability and a comprehensive list of bank specific, industry specific and macroeconomic variables using unique panel data from 23 Bangladeshi banks with large market shares from 2005 to 2019 employing the Pooled Ordinary Least Square (POLS) Method for regression estimation. The random Effect model has been used to check for robustness. Three variables, namely, Return on Asset (ROA), Return on Equity (ROE), and Net Interest Margin (NIM), have been used as profitability proxies. Non-interest income, capital ratio, and GDP growth have been found to have a significant relationship with ROA. In addition to non-interest income, market share, bank size, and real exchange rates are significant explaining variables if profitability is measured as NIM. The only significant determinant of profitability measured by ROE is market share. The primary contribution of this study to the existing knowledge base is an extensive empirical analysis by covering the entire gamut of independent variables (bank specific, industry related, and macroeconomic) to explain the profitability of the banks in Bangladesh. It also covers an extensive and recent data set. Banking sector stakeholders may find great value from the outputs of this paper. Regulators and policymakers may find this useful in undertaking analyses in setting policy rates, banking industry stability, and impact assessment of critical policy measures before and after the enactment, etc. Investors and the bank management are to use the findings of this paper in analyzing the real drivers of profitability of the banks they are contemplating to invest and managing on a daily basis.
An Implementation Approach to Rotation Programs
Authors: Ville Korpela, Michele Lombardi, Riccardo D. Saulle
Date: 2021-05-30 00:00:00

We study rotation programs within the standard implementation frame-work under complete information. A rotation program is a myopic stableset whose states are arranged circularly, and agents can effectively moveonly between two consecutive states. We provide characterizing conditionsfor the implementation of efficient rules in rotation programs. Moreover,we show that the conditions fully characterize the class of implementablemulti-valued and efficient rules
Self-Organized Residual Blocks for Image Super-Resolution
Authors: Onur Keleş, A. Murat Tekalp, Junaid Malik, Serkan Kiranyaz
Date: 2021-05-31 00:00:00
Tasks: Image Restoration, Image Super-Resolution, Super-Resolution

It has become a standard practice to use the convolutional networks (ConvNet) with RELU non-linearity in image restoration and super-resolution (SR). Although the universal approximation theorem states that a multi-layer neural network can approximate any non-linear function with the desired precision, it does not reveal the best network architecture to do so. Recently, operational neural networks (ONNs) that choose the best non-linearity from a set of alternatives, and their "self-organized" variants (Self-ONN) that approximate any non-linearity via Taylor series have been proposed to address the well-known limitations and drawbacks of conventional ConvNets such as network homogeneity using only the McCulloch-Pitts neuron model. In this paper, we propose the concept of self-organized operational residual (SOR) blocks, and present hybrid network architectures combining regular residual and SOR blocks to strike a balance between the benefits of stronger non-linearity and the overall number of parameters. The experimental results demonstrate that the~proposed architectures yield performance improvements in both PSNR and perceptual metrics.
Effect of Camera's Focal Plane Array Fill Factor on Digital Image Correlation Measurement Accuracy
Authors: Ala Hijazi, Nathir Rawashdeh, Christian Kahler
Date: 2021-05-30 00:00:00

The camera's focal plane array (FPA) fill factor is one of the parameters for digital cameras, though it is not widely known and usually not reported in specs sheets. The fill factor of an imaging sensor is defined as the ratio of a pixel's light sensitive area to its total theoretical area. It is generally believed that the lower fill factor may reduce the accuracy of photogrammetric measurements. But nevertheless, there are no studies addressing the effect of the imaging sensor's fill factor on digital image correlation (DIC) measurement accuracy. We report on research aiming to quantify the effect of fill factor on DIC measurements accuracy in terms of displacement error and strain error. We use rigid-body-translation experiments then numerically modify the recorded images to synthesize three different types of images with 1/4 of the original resolution. Each type of the synthesized images has different value of the fill factor; namely 100%, 50% and 25%. By performing DIC analysis with the same parameters on the three different types of synthesized images, the effect of fill factor on measurement accuracy may be realized. Our results show that the FPA's fill factor can have a significant effect on the accuracy of DIC measurements. This effect is clearly dependent on the type and characteristics of the speckle pattern. The fill factor has a clear effect on measurement error for low contrast speckle patterns and for high contrast speckle patterns (black dots on white background) with small dot size (3 pixels dot diameter). However, when the dot size is large enough (about 7 pixels dot diameter), the fill factor has very minor effect on measurement error.
A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices
Authors: Srutarshi Banerjee, Henry H. Chopp, Jianping Zhang, Zihao W. Wang, Oliver Cossairt, Aggelos Katsaggelos
Date: 2021-05-29 00:00:00
Tasks: Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking

We present a novel adaptive multi-modal intensity-event algorithm to optimize an overall objective of object tracking under bit rate constraints for a host-chip architecture. The chip is a computationally resource constrained device acquiring high resolution intensity frames and events, while the host is capable of performing computationally expensive tasks. We develop a joint intensity-neuromorphic event rate-distortion compression framework with a quadtree (QT) based compression of intensity and events scheme. The data acquisition on the chip is driven by the presence of objects of interest in the scene as detected by an object detector. The most informative intensity and event data are communicated to the host under rate constraints, so that the best possible tracking performance is obtained. The detection and tracking of objects in the scene are done on the distorted data at the host. Intensity and events are jointly used in a fusion framework to enhance the quality of the distorted images, so as to improve the object detection and tracking performance. The performance assessment of the overall system is done in terms of the multiple object tracking accuracy (MOTA) score. Compared to using intensity modality only, there is an improvement in MOTA using both these modalities in different scenarios.
Kurtosis-limited Sphere Shaping for Nonlinear Interference Noise Reduction in Optical Channels
Authors: Yunus Can Gültekin, Alex Alvarado, Olga Vassilieva, Inwoong Kim, Paparao Palacharla, Chigo Okonkwo, Frans M. J. Willems
Date: 2021-05-31 00:00:00

Nonlinear interference (NLI) generated during the propagation of an optical waveform through the fiber depends on the fourth order standardized moment of the channel input distribution, also known as kurtosis. Probabilistically-shaped inputs optimized for the linear Gaussian channel have a Gaussian-like distribution with high kurtosis. For optical channels, this leads to an increase in NLI power and consequently, a decrease in effective signal-to-noise ratio (SNR). In this work, we propose kurtosis-limited enumerative sphere shaping (K-ESS) as an algorithm to generate low-kurtosis shaped inputs. Numerical simulations at a shaping blocklength of 108 amplitudes demonstrate that with K-ESS, it is possible to increase the effective SNRs by 0.4 dB in a single-span single-channel scenario at 400 Gbit/s. K-ESS offers also a twofold decrease in frame error rate with respect to Gaussian-channel-optimal sphere shaping.
Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs
Authors: David Ramírez, Antonio G. Marques, Santiago Segarra
Date: 2021-05-31 00:00:00

Key to successfully deal with complex contemporary datasets is the development of tractable models that account for the irregular structure of the information at hand. This paper provides a comprehensive and unifying view of several sampling, reconstruction, and recovery problems for signals defined on irregular domains that can be accurately represented by a graph. The workhorse assumption is that the (partially) observed signals can be modeled as the output of a graph filter to a structured (parsimonious) input graph signal. When either the input or the filter coefficients are known, this is tantamount to assuming that the signals of interest live on a subspace defined by the supporting graph. When neither is known, the model becomes bilinear. Upon imposing different priors and additional structure on either the input or the filter coefficients, a broad range of relevant problem formulations arise. The goal is then to leverage those priors, the shift operator of the supporting graph, and the samples of the signal of interest to recover: the signal at the non-sampled nodes (graph-signal interpolation), the input (deconvolution), the filter coefficients (system identification), or any combination thereof (blind deconvolution).
Signal Acquisition of Luojia-1A Low Earth Orbit Navigation Augmentation System with Software Defined Receiver
Authors: Liang Chen, Xiangchen Lu, Nan Shen, Lei Wang, Yuan Zhuang, Ye Su, Deren Li, Ruizhi Chen
Date: 2021-05-31 00:00:00

Low earth orbit (LEO) satellite navigation signal can be used as an opportunity signal in case of a Global navigation satellite system (GNSS) outage, or as an enhancement means of traditional GNSS positioning algorithms. No matter which service mode is used, signal acquisition is the prerequisite of providing enhanced LEO navigation service. Compared with the medium orbit satellite, the transit time of the LEO satellite is shorter. Thus, it is of great significance to expand the successful acquisition time range of the LEO signal. Previous studies on LEO signal acquisition are based on simulation data. However, signal acquisition research based on real data is very important. In this work, the signal characteristics of LEO satellite: power space density in free space and the Doppler shift of LEO satellite are individually studied. The unified symbol definitions of several integration algorithms based on the parallel search signal acquisition algorithm are given. To verify these algorithms for LEO signal acquisition, a software-defined receiver (SDR) is developed. The performance of those integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite. The experimental results show that the integration strategy can expand the successful acquisition time range, and it will not expand indefinitely with the integration duration.
Intelligently Wireless Batteryless RF-Powered Reconfigurable Surface
Authors: Iosif Vardakis, Georgios Kotridis, Spyridon Peppas, Konstantinos Skyvalakis, Georgios Vougioukas, Aggelos Bletsas
Date: 2021-05-30 00:00:00
Tasks: TAG

This work exploits commodity, ultra-low cost, commercial radio frequency identification tags (RFID) as the elements of a reconfigurable surface. Such batteryless tags are powered and controlled by a software-defined (SDR) reader, with properly modified software, so that a source-destination link is assisted, operating at a different carrier frequency. In terms of theory, the optimal gain and corresponding best element configuration is offered, with tractable polynomial complexity (instead of exponential) in number of elements. In terms of practice, a concrete way to design and prototype a wireless, batteryless, RF-powered, reconfigurable surface is offered and a proof-of-concept is experimentally demonstrated. It is also found that even with perfect channel estimation, the weak nature of backscattered links limits the performance gains, even for large number of surface elements. Impact of channel estimation errors is also studied. Future extensions at various carrier frequencies could be directly accommodated, through simple modifications in the antenna and matching network of each RFID tag/surface element.
Model Aided Deep Learning Based MIMO OFDM Receiver With Nonlinear Power Amplifiers
Authors: Liangyuan Xu, Feifei Gao, Wei zhang, Shaodan Ma
Date: 2021-05-30 00:00:00

Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems. However, due to the issue of high peak-to-average power ratio (PAPR), the OFDM symbols may suffer from nonlinear distortions of the power amplifier (PA) at the transmitters, which degrades the channel estimation and detection performances of the receivers. To mitigate the clipping distortions at the receivers end, we leverage deep learning (DL) and devise a DL based receiver which is aided by the traditional least square (LS) channel estimation and the zero-forcing (ZF) equalization models. Moreover, a data driven DL based receiver without explicit channel estimation is proposed and combined with the model aided DL based receiver to further improve the performance. Simulation results showcase that the proposed model aided DL based receiver has superior performance of bit error rate and has robustness over different levels of clipping distortions.
Reconfigurable Intelligent Surface-assisted Multi-UAV Networks: Efficient Resource Allocation with Deep Reinforcement Learning
Authors: Khoi Khac Nguyen, Saeed Khosravirad, Daniel Benevides da Costa, Long D. Nguyen, Trung Q. Duong
Date: 2021-05-28 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAVs) networks that can utilise both advantages of UAV's agility and RIS's reflection for enhancing the network's performance. To aim at maximising the energy efficiency (EE) of the considered networks, we jointly optimise the power allocation of the UAVs and the phase-shift matrix of the RIS. A deep reinforcement learning (DRL) approach is proposed for solving the continuous optimisation problem with time-varying channels in a centralised fashion. Moreover, a parallel learning approach is also proposed for reducing the information transmission requirement of the centralised approach. Numerical results show a significant improvement of our proposed schemes compared with the conventional approaches in terms of EE, flexibility, and processing time. Our proposed DRL methods for RIS-assisted UAV networks can be used for real-time applications due to their capability of instant decision-making and handling the time-varying channel with the dynamic environmental setting.
Optimal transport with $f$-divergence regularization and generalized Sinkhorn algorithm
Authors: Dávid Terjék, Diego González-Sánchez
Date: 2021-05-29 00:00:00

Entropic regularization provides a generalization of the original optimal transport problem. It introduces a penalty term defined by the Kullback-Leibler divergence, making the problem more tractable via the celebrated Sinkhorn algorithm. Replacing the Kullback-Leibler divergence with a general $f$-divergence leads to a natural generalization. The case of divergences defined by superlinear functions was recently studied by Di Marino and Gerolin. Using convex analysis, we extend the theory developed so far to include all $f$-divergences defined by functions of Legendre type, and prove that under some mild conditions, strong duality holds, optimums in both the primal and dual problems are attained, the generalization of the $c$-transform is well-defined, and we give sufficient conditions for the generalized Sinkhorn algorithm to converge to an optimal solution. We propose a practical algorithm for computing an approximate solution of the optimal transport problem with $f$-divergence regularization via the generalized Sinkhorn algorithm. Finally, we present experimental results on synthetic 2-dimensional data, demonstrating the effects of using different $f$-divergences for regularization, which influences convergence speed, numerical stability and sparsity of the optimal coupling.