pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying cell cycle regulation by tissue crowding
Authors: Carles Falcó, Daniel J. Cohen, José A. Carrillo, Ruth E. Baker
Date: 2024-01-16 00:00:00
Tasks: Bayesian Inference

The spatiotemporal coordination and regulation of cell proliferation is fundamental in many aspects of development and tissue maintenance. Cells have the ability to adapt their division rates in response to mechanical constraints, yet we do not fully understand how cell proliferation regulation impacts cell migration phenomena. Here, we present a minimal continuum model of cell migration with cell cycle dynamics, which includes density-dependent effects and hence can account for cell proliferation regulation. By combining minimal mathematical modelling, Bayesian inference, and recent experimental data, we quantify the impact of tissue crowding across different cell cycle stages in epithelial tissue expansion experiments. Our model suggests that cells sense local density and adapt cell cycle progression in response, during G1 and the combined S/G2/M phases, providing an explicit relationship between each cell cycle stage duration and local tissue density, which is consistent with several experimental observations. Finally, we compare our mathematical model predictions to different experiments studying cell cycle regulation and present a quantitative analysis on the impact of density-dependent regulation on cell migration patterns. Our work presents a systematic approach for investigating and analysing cell cycle data, providing mechanistic insights into how individual cells regulate proliferation, based on population-based experimental measurements.
Equity Premium in Efficient Markets
Authors: B. N. Kausik
Date: 2024-01-17 00:00:00

Equity premium, the surplus returns of stocks over bonds, has been an enduring puzzle. While numerous prior works approach the problem assuming the utility of money is invariant across contexts, our approach implies that in efficient markets the utility of money is polymorphic, with risk aversion dependent on the information available in each context, i.e. the discount on each future cash flow depends on all information available on that cash flow. Specifically, we prove that in efficient markets, informed investors maximize return on volatility by being risk-neutral with riskless bonds, and risk-averse with equities, thereby resolving the puzzle. We validate our results on historical data with surprising consistency. JEL Classification: C58, G00, G12, G17
Airline delays, congestion internalization and non-price spillover effects of low cost carrier entry
Authors: William E. Bendinelli, Humberto F. A. J. Bettini, Alessandro V. M. Oliveira
Date: 2024-01-17 00:00:00

This paper develops an econometric model of flight delays to investigate the influence of competition and dominance on the incentives of carriers to maintain on-time performance. We consider both the route and the airport levels to inspect the local and global effects of competition, with a unifying framework to test the hypotheses of 1. airport congestion internalization and 2. the market competition-quality relationship in a single econometric model. In particular, we examine the impacts of the entry of low cost carriers (LCC) on the flight delays of incumbent full service carriers in the Brazilian airline industry. The main results indicate a highly significant effect of airport congestion self-internalization in parallel with route-level quality competition. Additionally, the potential competition caused by LCC presence provokes a global effect that suggests the existence of non-price spillovers of the LCC entry to non-entered routes.
AI Thrust: Ranking Emerging Powers for Tech Startup Investment in Latin America
Authors: Abraham Ramos Torres, Laura N Montoya
Date: 2024-01-17 00:00:00

Artificial intelligence (AI) is rapidly transforming the global economy, and Latin America is no exception. In recent years, there has been a growing interest in AI development and implementation in the region. This paper presents a ranking of Latin American (LATAM) countries based on their potential to become emerging powers in AI. The ranking is based on three pillars: infrastructure, education, and finance. Infrastructure is measured by the availability of electricity, high-speed internet, the quality of telecommunications networks, and the availability of supercomputers. Education is measured by the quality of education and the research status. Finance is measured by the cost of investments, history of investments, economic metrics, and current implementation of AI. While Brazil, Chile, and Mexico have established themselves as major players in the AI industry in Latin America, our ranking demonstrates the new emerging powers in the region. According to the results, Argentina, Colombia, Uruguay, Costa Rica, and Ecuador are leading as new emerging powers in AI in Latin America. These countries have strong education systems, well-developed infrastructure, and growing financial resources. The ranking provides a useful tool for policymakers, investors, and businesses interested in AI development in Latin America. It can help to identify emerging LATAM countries with the greatest potential for AI growth and success.
Automated Design Appraisal: Estimating Real Estate Price Growth and Value at Risk due to Local Development
Authors: Adam R. Swietek
Date: 2023-12-17 00:00:00

Financial criteria in architectural design evaluation are limited to cost performance. Here, I introduce a method, Automated Design Appraisal (ADA), to predict the market price of a generated building design concept within a local urban context. Integrating ADA with 3D building performance simulations enables financial impact assessment that exceeds the spatial resolution of previous work. Within an integrated impact assessment, ADA measures the direct and localized effect of urban development. To demonstrate its practical utility, I study local devaluation risk due to nearby development associated with changes to visual landscape quality. The results shed light on the relationship between amenities and property value, identifying clusters of properties physically exposed or financially sensitive to local land-use change. Beyond its application as a financial sensitivity tool, ADA serves as a blueprint for architectural design optimization procedures, in which economic performance is evaluated based on learned preferences derived from financial market data.
Non-Banking Sector development effect on Economic Growth. A Nighttime light data approach
Authors: Leonard Mushunje, Maxwell Mashasha
Date: 2023-11-19 00:00:00

This paper uses nighttime light(NTL) data to measure the nexus of the non-banking sector, particularly insurance, and economic growth in South Africa. We hypothesize that insurance sector growth positively propels economic growth due to its economic growth-supportive traits like investment protection and optimal risk mitigation. We also claim that Nighttime light data is a good economic measure than Gross domestic product (GDP). We used weighted regressions to measure the relationships between nighttime light data, GDP, and insurance sector development. We used time series South African GDP data collected from the World Bank for the period running from 2000 to 2018, and the nighttime lights data from the National Geophysical Data Centre (NGDC) in partnership with the National Oceanic and Atmospheric Administration (NOAA). From the models fitted and the reported BIC, AIC, and likelihood ratios, the insurance sector proved to have more predictive power on economic development in South Africa, and radiance light explained economic growth better than GDP and GDP/Capita. We concluded that nighttime data is a good proxy for economic growth than GDP/Capita in emerging economies like South Africa, where secondary data needs to be more robust and sometimes inflated. The findings will guide researchers and policymakers on what drives economic development and what policies to put in place. It would be interesting to extend the current study to other sectors such as micro-finances, mutual and hedge funds.
How do we measure trade elasticity for services?
Authors: Satoshi Nakano, Kazuhiko Nishimura
Date: 2023-11-18 00:00:00

This paper is about our attempt of identifying trade elasticities through the variations in the exchange rate, for possible applications to the case of services whose physical transactions are veiled in the trade statistics. The regression analysis to estimate the elasticity entails a situation where the explanatory variable is leaked into the error term through the latent supply equation, causing an endogeneity problem for which an instrumental variable cannot be found. Our identification strategy is to utilize the normalizing condition, which enables the supply parameter to be identified, along with the reduced-form equation of the system of demand and supply equations. We evaluate the performances of the method proposed by applying to several different tangible goods, whose benchmark trade elasticities are estimable by utilizing the information on their physical transactions.
On conditioning and consistency for nonlinear functionals
Authors: Edoardo Berton, Alessandro Doldi, Marco Maggis
Date: 2024-01-17 00:00:00

We consider a family of conditional nonlinear expectations defined on the space of bounded random variables and indexed by the class of all the sub-sigma-algebras of a given underlying sigma-algebra. We show that if this family satisfies a natural consistency property, then it collapses to a conditional certainty equivalent defined in terms of a state-dependent utility function. This result is obtained by embedding our problem in a decision theoretical framework and providing a new characterization of the Sure-Thing Principle. In particular we prove that this principle characterizes those preference relations which admit consistent backward conditional projections. We build our analysis on state-dependent preferences for a general state space as in Wakker and Zank (1999) and show that their numerical representation admits a continuous version of the state-dependent utility. In this way, we also answer positively to a conjecture posed in the aforementioned paper.
Strategic formation of production networks
Authors: Antoine Mandel, Van-Quy Nguyen, Bach Dong-Xuan
Date: 2024-01-17 00:00:00

We provide a strategic model of the formation of production networks that subsumes the standard general equilibrium approach. The objective of firms in our setting is to choose their supply relationships so as to maximize their profit at the general equilibrium that unfolds. We show that this objective is equivalent to the maximization by the firms of their eigenvector centrality in the production network. As is common in network formation games based on centrality, there are multiple Nash equilibria in our setting. We have investigated the characteristics and the social efficiency of these equilibria in a stylized version of our model representing international trade networks. We show that the impact of network structure on social welfare is firstly determined by a trade-off between costs of increasing process complexity and positive spillovers on productivity induced by the diversification of the input mix. We further analyze a variant of our model that accounts for the risks of disruption of supply relationships. In this setting, we characterize how social welfare depends on the structure of the production network, the spatial distribution of risks, and the process of shock aggregation in supply chains. We finally show that simple trade policies characterized by sets of links that are either prevented or catalyzed can be a powerful equilibrium selection device.
On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement
Authors: Iván López-Espejo, Aditya Joglekar, Antonio M. Peinado, Jesper Jensen
Date: 2024-01-17 00:00:00
Tasks: Automatic Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

Pre-emphasis filtering, compensating for the natural energy decay of speech at higher frequencies, has been considered as a common pre-processing step in a number of speech processing tasks over the years. In this work, we demonstrate, for the first time, that pre-emphasis filtering may also be used as a simple and computationally-inexpensive way to leverage deep neural network-based speech enhancement performance. Particularly, we look into pre-emphasizing the estimated and actual clean speech prior to loss calculation so that different speech frequency components better mirror their perceptual importance during the training phase. Experimental results on a noisy version of the TIMIT dataset show that integrating the pre-emphasis-based methodology at hand yields relative estimated speech quality improvements of up to 4.6% and 3.4% for noise types seen and unseen, respectively, during the training phase. Similar to the case of pre-emphasis being considered as a default pre-processing step in classical automatic speech recognition and speech coding systems, the pre-emphasis-based methodology analyzed in this article may potentially become a default add-on for modern speech enhancement.
Microphone Subset Selection for the Weighted Prediction Error Algorithm using a Group Sparsity Penalty
Authors: Anselm Lohmann, Toon van Waterschoot, Joerg Bitzer, Simon Doclo
Date: 2024-01-16 00:00:00

Reverberation can severely degrade the quality of speech signals recorded using microphones in an enclosure. In acoustic sensor networks with spatially distributed microphones, a similar dereverberation performance may be achieved using only a subset of all available microphones. Using the popular convex relaxation method, in this paper we propose to perform microphone subset selection for the weighted prediction error (WPE) multi-channel dereverberation algorithm by introducing a group sparsity penalty on the prediction filter coefficients. The resulting problem is shown to be solved efficiently using the accelerated proximal gradient algorithm. Experimental evaluation using measured impulse responses shows that the performance of the proposed method is close to the optimal performance obtained by exhaustive search, both for frequency-dependent as well as frequency-independent microphone subset selection. Furthermore, the performance using only a few microphones for frequency-independent microphone subset selection is only marginally worse than using all available microphones.
A Novel Approach to Fair Power Allocation for NOMA in Visible Light Communication
Authors: Serkan Vela, Gokce Hacioglu
Date: 2024-01-17 00:00:00
Tasks: Fairness

This paper addresses the growing demand for high-bandwidth wireless data transmission by exploring Visible Light Communication (VLC) as an alternative to Radio Frequency (RF) communication. In indoor scenarios, VLC systems utilize existing lighting infrastructure for high-speed data transmission. To meet the data rate demands of 5G and beyond, the paper proposes Non-Orthogonal Multiple Access (NOMA) and introduces Empirical Fair Optical Power Allocation (EFOPA) to simplify resource allocation in NOMA. EFOPA integrates NOMA with VLC, utilizing the Artificial Bee Colony (ABC) optimization algorithm for offline resource allocation planning. The approach then derives a simplified power allocation equation from ABC outcomes, ensuring fair resource distribution among users. EFOPA is compared against existing power allocation methods, demonstrating superior fairness and reduced computational complexity. Numerical evaluations reveal EFOPA consistently outperforms other methods across various channel conditions, making it a robust and efficient solution for fair power allocation in NOMA-VLC systems.
Maximally Concentrated Sequences after Half-sample Shifts
Authors: Karim A. Said, Lingjia Liu, A. A., Beex
Date: 2024-01-17 00:00:00

It is well known that index (discrete-time)-limited sampled sequences leak outside the support set when a band-limiting operation is applied. Similarly, a fractional shift causes an index-limited sequence to be infinite in extent due to the inherent band-limiting. Index-limited versions of discrete prolate spheroidal sequences (DPSS) are known to experience minimum leakage after band-limiting. In this work, we consider the effect of a half-sample shift and provide upper bounds on the resulting leakage energy for arbitrary sequences. Furthermore, we find an orthonormal basis derived from DPSS with members ordered according to energy concentration after half sample shifts; the primary (first) member being the global optimum.
Super-Directive Antenna Arrays: How Many Elements Do We Need?
Authors: Ihsan Kanbaz, Okan Yurduseven, Michail Matthaiou
Date: 2024-01-17 00:00:00

Super-directive antenna arrays have faced challenges in achieving high realized gains ever since their introduction in the academic literature. The primary challenges are high impedance mismatches and resistive losses, which become increasingly more dominant as the number of elements increases. Consequently, a critical limitation arises in determining the maximum number of elements that should be utilized to achieve super-directivity, particularly within dense array configurations. This paper addresses precisely this issue through an optimization study to design a super-directive antenna array with a maximum number of elements. An iterative approach is employed to increase the array of elements while sustaining a satisfactory realized gain using the differential evolution (DE) algorithm. Thus, it is observed that super-directivity can be obtained in an array with a maximum of five elements. Our results indicate that the obtained unit array has a $67.20\%$ higher realized gain than a uniform linear array with conventional excitation. For these reasons, these results make the proposed architecture a strong candidate for applications that require densely packed arrays, particularly in the context of massive multiple-input multiple-output (MIMO).
Performance Analysis and Optimisation of FFR-Aided OFDMA Networks using Channel-Aware Scheduling
Authors: Jan García Morales, Guillem Femenias, Felip Riera Palou
Date: 2024-01-17 00:00:00
Tasks: Fairness, Scheduling

Modern cellular standards typically incorporate interference coordination schemes allowing near universal frequency reuse while preserving reasonably high spectral efficiencies over the whole coverage area. In particular, fractional frequency reuse (FFR) and its variants are deemed to play a fundamental role in the next generation of cellular deployments (B4G/5G systems). This paper presents an analytical framework allowing the downlink performance evaluation of FFR-aided OFDMA networks when using channel-aware scheduling policies. Remarkably, the framework contemplates the use of different rate allocation strategies, thus allowing to assess the network behaviour under ideal (capacity-based) or realistic (throughput-based) conditions. Analytical performance results are used to optimise the FFR parameters as a function of, for instance, the resource block scheduling policy or the density of UEs per cell. Furthermore, different optimisation designs of the FFR component are proposed that allow a tradeoff between throughput performance and fairness by suitably dimensioning the FFR-defined cell-centre and cell-edge areas and the corresponding frequency allocation to each region. Numerical results serve to confirm the accuracy of the proposed analytical model while providing insight on how the different parameters and designs affect network performance.
Novel Entropy-Based Metrics for Long-Term Atrial Fibrillation Recurrence Prediction Following Surgical Ablation: Insights from Preoperative Electrocardiographic Analysis
Authors: P. Escribano, J. Ródenas, M. García, F. Hornero, J. M. Gracia-Baena, R. Alcaraz, J. J. Rieta
Date: 2024-01-17 00:00:00
Tasks: Specificity

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia often treated concomitantly with other cardiac interventions through the Cox Maze procedure. This highly invasive intervention is still linked to a long-term recurrence rate of approximately 35% in permanent AF patients. The aim of this study is to preoperatively predict long-term AF recurrence post-surgery through the analysis of atrial activity (AA) organization from non-invasive electrocardiographic (ECG) recordings. A dataset comprising ECGs from 53 patients with permanent AF who had undergone Cox Maze concomitant surgery was analyzed. The AA was extracted from the lead V1 of these recordings and then characterized using novel predictors, such as the mean and standard deviation of the relative wavelet energy (RWEm and RWEs) across different scales, and an entropy-based metric that computes the stationary wavelet entropy variability (SWEnV). The individual predictors exhibited limited predictive capabilities to anticipate the outcome of the procedure, with the SWEnV yielding a classification accuracy (Acc) of 68.07%. However, the assessment of the RWEs for the seventh scale (RWEs7), which encompassed frequencies associated with the AA, stood out as the most promising individual predictor, with sensitivity (Se) and specificity (Sp) values of 80.83% and 67.09%, respectively, and an Acc of almost 75%. Diverse multivariate decision tree-based models were constructed for prediction, giving priority to simplicity in the interpretation of the forecasting methodology. In fact, the combination of the SWEnV and RWEs7 consistently outperformed the individual predictors and excelled in predicting post-surgery outcomes one year after the Cox Maze procedure, with Se, Sp, and Acc values of approximately 80%, thus surpassing the results of previous studies based on anatomical predictors associated with atrial function or clinical data.
Interference analysis of Positioning Reference Signals in 5G NTN
Authors: Alejandro Gonzalez-Garrido, Jorge Querol, Henk Wymeersch, Symeon Chatzinotas
Date: 2024-01-17 00:00:00
Tasks: Management

Accurate asset localization holds paramount importance across various industries, ranging from transportation management to search and rescue operations. In scenarios where traditional positioning equations cannot be adequately solved due to limited measurements obtained by the receiver, the utilization of Non-Terrestrial Networks (NTN) based on Low Earth Orbit (LEO) satellites can prove pivotal for precise positioning. The decision to employ NTN in lieu of conventional Global Navigation Satellite Systems (GNSS) is rooted in two key factors. Firstly, GNSS systems are susceptible to jamming and spoofing attacks, thereby compromising their reliability, where LEO satellites link budgets can benefit from a closer distances and the new mega constellations could offer more satellites in view than GNSS. Secondly, 5G service providers seek to reduce dependence on third-party services. Presently, the NTN operation necessitates a GNSS receiver within the User Equipment (UE), placing the service provider at the mercy of GNSS reliability. Consequently, when GNSS signals are unavailable in certain regions, NTN services are also rendered inaccessible.