pwc-archive/papers-with-abstracts huggingface dataset:
Political Conflict and Economic Growth in Post-Independence Venezuela
Authors: Dorothy Kronick, Francisco Rodríguez
Date: 2023-05-24 00:00:00

Venezuela has suffered three economic catastrophes since independence: one each in the nineteenth, twentieth, and twenty-first centuries. Prominent explanations for this trilogy point to the interaction of class conflict and resource dependence. We turn attention to intra-class conflict, arguing that the most destructive policy choices stemmed not from the rich defending themselves against the masses but rather from pitched battles among elites. Others posit that Venezuelan political institutions failed to sustain growth because they were insufficiently inclusive; we suggest in addition that they inadequately mediated intra-elite conflict.
Estimating causal effects of sanctions impacts: what role for country-level studies?
Authors: Francisco Rodríguez
Date: 2023-05-24 00:00:00

This article reviews recent advances in addressing empirical identification issues in cross-country and country-level studies and their implications for the identification of the effectiveness and consequences of economic sanctions. I argue that, given the difficulties in assessing causal relationships in cross-national data, country-level case studies can serve as a useful and informative complement to cross-national regression studies. However, I also warn that case studies pose a set of additional potential empirical pitfalls which can obfuscate rather than clarify the identification of causal mechanisms at work. Therefore, the most sensible way to read case study evidence is as a complement rather than as a substitute to cross-national research.
Reproducibility and Transparency versus Privacy and Confidentiality: Reflections from a Data Editor
Authors: Lars Vilhuber
Date: 2023-05-23 00:00:00

Transparency and reproducibility are often seen in opposition to privacy and confidentiality. Data that need to be kept confidential are seen as an impediment to reproducibility, and privacy would seem to inhibit transparency. I bring a more nuanced view to the discussion, and show, using examples from over 1,000 reproducibility assessments, that confidential data can very well be used in reproducible and transparent research. The key insight is that access to most confidential data, while tedious, is open to hundreds if not thousands of researchers. In cases where few researchers can consider accessing such data in the future, reproducibility services, such as those provided by some journals, can provide some evidence for effective reproducibility even when the same data may not be available for future research.
Cautious Belief and Iterated Admissibility
Authors: Emiliano Catonini, Nicodemo De Vito
Date: 2023-05-24 00:00:00

We define notions of cautiousness and cautious belief to provide epistemic conditions for iterated admissibility in finite games. We show that iterated admissibility characterizes the behavioral implications of "cautious rationality and common cautious belief in cautious rationality" in a terminal lexicographic type structure. For arbitrary type structures, the behavioral implications of these epistemic assumptions are characterized by the solution concept of self-admissible set (Brandenburger, Friedenberg and Keisler 2008). We also show that analogous conclusions hold under alternative epistemic assumptions, in particular if cautiousness is "transparent" to the players. KEYWORDS: Epistemic game theory, iterated admissibility, weak dominance, lexicographic probability systems. JEL: C72.
On the Instability of Fractional Reserve Banking
Authors: Heon Lee
Date: 2023-05-23 00:00:00

This paper develops a dynamic monetary model to study the (in)stability of the fractional reserve banking system. The model shows that the fractional reserve banking system can endanger stability in that equilibrium is more prone to exhibit endogenous cyclic, chaotic, and stochastic dynamics under lower reserve requirements, although it can increase consumption in the steady-state. Introducing endogenous unsecured credit to the baseline model does not change the main results. The calibrated exercise suggests that this channel could be another source of economic fluctuations. This paper also provides empirical evidence that is consistent with the prediction of the model.
Power Reduction Opportunities on End-User Devices in Quality-Steady Video Streaming
Authors: Christian Herglotz, Werner Robitza, Alexander Raake, Tobias Hossfeld, André Kaup
Date: 2023-05-24 00:00:00

This paper uses a crowdsourced dataset of online video streaming sessions to investigate opportunities to reduce the power consumption while considering QoE. For this, we base our work on prior studies which model both the end-user's QoE and the end-user device's power consumption with the help of high-level video features such as the bitrate, the frame rate, and the resolution. On top of existing research, which focused on reducing the power consumption at the same QoE optimizing video parameters, we investigate potential power savings by other means such as using a different playback device, a different codec, or a predefined maximum quality level. We find that based on the power consumption of the streaming sessions from the crowdsourcing dataset, devices could save more than 55% of power if all participants adhere to low-power settings.
Supervised Multi-Regional Segmentation Machine Learning Architecture for Digital Twin Applications in Coastal Regions
Authors: Mohsen Ahmadi, Ahmad Gholizadeh Lonbar, Mohammadsadegh Nouri, Amir Sharifzadeh Javidi, Ali Tarlani Beris, Abbas Sharifi, Ali Salimi-Tarazouj
Date: 2023-05-23 00:00:00
Tasks: Segmentation

This study explores the use of a digital twin model and deep learning method to build a global terrain and altitude map based on USGS information. The goal is to artistically represent various landforms while incorporating precise elevation modifications in the terrain map and encoding land height in the altitude map. A random selection of 5000 segments from the worldwide map guarantees the inclusion of significant characteristics in the subsets, with rescaling according to latitude accounting for distortions caused by map projection. The process of generating segmentation maps involves using unsupervised clustering and classification methods, segmenting the terrain into seven groups: Water, Grassland, Forest, Hills, Desert, Mountain, and Tundra. Each group is assigned a unique color, and median filtering is used to improve map characteristics. Random parameters are added to provide diversity and avoid duplication in overlapping image sets. The U-Net network is deployed for the segmentation task, with training conducted on the seven terrain classes. Cross-validation is carried out every 10 epochs to gauge the model's performance. The segmentation maps produced accurately categorize the terrain, as evidenced by the ROC curve and AUC values. The main goal of this research is to create a digital twin model of Florida's coastal area. This is achieved through the application of deep learning methods and satellite imagery from Google Earth, resulting in a detailed depiction of the coast of Florida. The digital twin acts as both a physical and a simulation model of the area, emphasizing its capability to capture and replicate real-world locations. The model effectively creates a global terrain and altitude map with precise segmentation and capture of important land features. The results confirm the effectiveness of the digital twin, especially in depicting Florida's coastline.
Enabling Joint Radar-Communication Operation in Shift Register-Based PMCW Radars
Authors: Lucas Giroto de Oliveira, Elizabeth Bekker, Axel Diewald, Benjamin Nuss, Theresa Antes, Yueheng Li, Akanksha Bhutani, Thomas Zwick
Date: 2023-05-24 00:00:00
Tasks: Joint Radar-Communication

This article introduces adaptations to the conventional frame structure in binary phase-modulated continuous wave (PMCW) radars with sequence generation via linear-feedbck shift registers and additional processing steps to enable joint radar-communication (RadCom) operation. In this context, a preamble structure based on pseudorandom binary sequences (PRBSs) that is compatible with existing synchronization algorithms is outlined, and the allocation of pilot PRBS blocks is discussed. Finally, results from proof-of-concept measurements are presented to illustrate the effects of the choice of system and signal parameters and validate the investigated PMCW-based RadCom system and synchronization strategy.
Bistatic OFDM-based Joint Radar-Communication: Synchronization, Data Communication and Sensing
Authors: Lucas Giroto de Oliveira, David Brunner, Axel Diewald, Charlotte Muth, Laurent Schmalen, Thomas Zwick, Benjamin Nuss
Date: 2023-05-24 00:00:00
Tasks: Joint Radar-Communication

This article introduces a bistatic joint radar-communication (RadCom) system based on orthogonal frequency-division multiplexing (OFDM). In this context, the adopted OFDM frame structure is described and system model encompassing time, frequency, and sampling synchronization mismatches between the transmitter and receiver of the bistatic system is outlined. Next, the signal processing approaches for synchronization and communication are discussed, and radar sensing processing approaches using either only pilots or a reconstructed OFDM frame based on the estimated receive communication data are presented. Finally, proof-of-concept measurement results are presented to validate the investigated system and a trade-off between frame size and the performance of the aforementioned processing steps is observed.
3GPP-Like GBSM THz Channel Characterization, Modeling, and Simulation Based on Experimental Observations
Authors: Zhaowei Chang, Jianhua Zhang, Pan Tang, Lei Tian, Hao Jiang, Ximan Liu, and Guangyi Liu
Date: 2023-05-24 00:00:00

Terahertz (THz) communication is envisioned as one of the possible technologies for the sixth-generation (6G) communication system due to its rich spectrum. To evaluate the performance of THz communication, it is essential to propose THz channel models within the common framework of the geometry-based stochastic model (GBSM) in the 3rd Generation Partnership Project (3GPP). This paper focuses on THz channel modeling and simulation by a 3GPP-like GBSM, based on channel measurements. We first present channel measurements at 100 GHz in an indoor office scenario and 132 GHz in an urban microcellular scenario. Subsequently, channel characteristics such as path loss, delay spread, angle spread, K-factor, cluster characteristic, cross-correlations, and correlation distances are obtained and analyzed based on channel measurement. Additionally, the channel characteristics are modeled by the statistical distribution of 3GPP channel models, which can be used to reconstruct the channel impulse response (CIR). Furthermore, these obtained distributions are studied referring to the default models in the 3GPP, revealing the channel sparsity in the THz channel. For instance, in the case of line-of-sight links in the indoor office, the mean of the measured cluster number is 4 while the default value is 15. Finally, we propose the THz channel model and its simulation framework to reconstruct CIRs based on the obtained models, which aim at characterizing the sparser THz channels. The obvious channel sparsity is characterized in both scenarios, as the Gini factors obtained by the proposed model only have the maximum deviation of 0.04 for those of the measurement. Overall, these findings are helpful in understanding and modeling the THz channel, facilitating the application of THz communication techniques for 6G.
Truncated Turbo Equalizer with SIC for OTFS
Authors: Sanoopkumar P. S., Stephen McWade, Arman Farhang
Date: 2023-05-24 00:00:00

Orthogonal time frequency space (OTFS) is a promising candidate waveform for the next generation wireless communication systems. OTFS places data in the delay-Doppler (DD) domain, which simplifies channel estimation in highmobility scenarios. However, due to the 2-D convolution effect of the time-varying channel in the DD domain, equalization is still a challenge for OTFS. Existing equalizers for OTFS are either highly complex or they do not consider intercarrier interference present in high-mobility scenarios. Hence, in this paper, we propose a novel two-stage detection technique for coded OTFS systems. Our proposed detector brings orders of magnitude computational complexity reduction compared to existing methods. At the first stage, it truncates the channel by considering only the significant coefficients along the Doppler dimension and performs turbo equalization. To reduce the computational load of the turbo equalizer, our proposed method deploys the modified LSQR (mLSQR) algorithm. At the second stage, with only two successive interference cancellation (SIC) iterations, our proposed detector removes the residual interference caused by channel truncation. To evaluate the performance of our proposed truncated turbo equalizer with SIC (TTE-SIC), we set the minimum mean squared error (MMSE) equalizer without channel truncation as a benchmark. Our simulation results show that the proposed TTE-SIC technique achieves about the same bit error rate (BER) performance as the benchmark.
Low-Complexity Joint Active and Passive Beamforming Design for IRS-Assisted MIMO
Authors: Yuri S. Ribeiro, Fazal E-Asim, André L. F de Almeida, Behrooz Makki, Gabor Fodor
Date: 2023-05-24 00:00:00

In this letter, we consider an intelligent reflecting surface (IRS)-assisted multiple input multiple output (MIMO) communication and we optimize the joint active and passive beamforming by exploiting the geometrical structure of the propagation channels. Due to the inherent Kronecker product structure of the channel matrix, the global beamforming optimization problem is split into lower dimensional horizontal and vertical sub-problems. Based on this factorization property, we propose two closed-form methods for passive and active beamforming designs, at the IRS, the base station, and user equipment, respectively. The first solution is a singular value decomposition (SVD)-based algorithm independently applied on the factorized channels, while the second method resorts to a third-order rank-one tensor approximation along each domain. Simulation results show that exploiting the channel Kronecker structures yields a significant improvement in terms of computational complexity at the expense of negligible spectral efficiency (SE) loss. We also show that under imperfect channel estimation, the tensor-based solution shows better SE than the benchmark and proposed SVD-based solutions.
Automated Market Making and Arbitrage Profits in the Presence of Fees
Authors: Jason Milionis, Ciamac C. Moallemi, Tim Roughgarden
Date: 2023-05-24 00:00:00
Tasks: Form

We consider the impact of trading fees on the profits of arbitrageurs trading against an automated marker marker (AMM) or, equivalently, on the adverse selection incurred by liquidity providers due to arbitrage. We extend the model of Milionis et al. [2022] for a general class of two asset AMMs to both introduce fees and discrete Poisson block generation times. In our setting, we are able to compute the expected instantaneous rate of arbitrage profit in closed form. When the fees are low, in the fast block asymptotic regime, the impact of fees takes a particularly simple form: fees simply scale down arbitrage profits by the fraction of time that an arriving arbitrageur finds a profitable trade.
A Mean-Field Method for Generic Conductance-Based Integrate-and-Fire Neurons with Finite Timescales
Authors: Marcelo P. Becker, Marco A. P. Idiart
Date: 2023-05-24 00:00:00

The construction of transfer functions in theoretical neuroscience plays an important role in determining the spiking rate behavior of neurons in networks. These functions can be obtained through various fitting methods, but the biological relevance of the parameters is not always clear. However, for stationary inputs, such functions can be obtained without the adjustment of free parameters by using mean-field methods. In this work, we expand current Fokker-Planck approaches to account for the concurrent influence of colored and multiplicative noise terms on generic conductance-based integrate-and-fire neurons. We reduce the resulting stochastic system from the application of the diffusion approximation to a one-dimensional Langevin equation. An effective Fokker-Planck is then constructed using Fox Theory, which is solved numerically to obtain the transfer function. The solution is capable of reproducing the transfer function behavior of simulated neurons across a wide range of parameters. The method can also be easily extended to account for different sources of noise with various multiplicative terms, and it can be used in other types of problems in principle.
Association of stroke lesion distributions with atrial fibrillation detected after stroke
Authors: Yiming Chen, Sihui Wang, Dong Xu
Date: 2023-05-24 00:00:00

Background Atrial fibrillation is often missed by traditional intermittent electrocardiogram monitoring after ischemic stroke due to its paroxysmal and asymptomatic nature. The knowledge of the unique characteristics of the population with atrial fibrillation detected after stroke (AFDAS) enables more ischemic stroke patients to benefit from more aggressive anticoagulation therapy and AF management. Method This is an observational, retrospective, MRI imaging-based single-center study. Patients with AFDAS were matched in 1:3 ratio with patients without AF (NoAF)and patients with known AF before stroke(KAF) in PSM model based on age, gender, and time from stroke onset to admission. Multivariate logistic models were used to test the association of MRI-based stroke lesion distribution, other clinical parameters and AF. A backward stepwise elimination regression was conducted to identify the most important variables. Results Compared to the NoAF group(n=103), the patients with AFDAS (n=42) had more cortical involvement(p=0.016), as well as temporal(p<0.001) and insular lobes(p=0.018) infraction. After performing a backward stepwise elimination model in regression analysis, the temporal lobe infraction(OR 0.274, 95%CI 0.090-0.838, p=0.023) remained independently associated with the detection of AF. Compared to the KAF group(n=89), LAD(OR 1.113, 95%CI 1.022-1.211, p=0.014), Number of lobes infarction(p=0.012), 3-lobes involvement(OR 0.177, 95%CI 0.056-0.559, p=0.003), and left hemisphere lobe involvement(OR 5.966, 95%CI 2.273-15.817, p<0.001)) were independently associated with AFDAS and KAF. Conclusions Ischemic stroke patients with AF detected after stroke present more temporal lobe infraction and cortical involvement. These lesion distribution characteristics with clinical characteristics together may help in stratifying patients with long-term cardiac monitoring after stroke.
Brain memory working. Optimal control behavior for improved Hopfield-like models
Authors: Franco Cardin, Alberto Lovison, Amos Maritan, Aram Megighian
Date: 2023-05-11 00:00:00

Recent works have highlighted the need for a new dynamical paradigm in the modeling of brain function and evolution. Specifically, these models should incorporate non-constant and asymmetric synaptic weights \(T_{ij}\) in the neuron-neuron interaction matrix, moving beyond the classical Hopfield framework. Krotov and Hopfield proposed a non-constant yet symmetric model, resulting in a vector field that describes gradient-type dynamics, which includes a Lyapunov-like energy function. Firstly, we will outline the general conditions for generating a Hopfield-like vector field of gradient type, recovering the Krotov-Hopfield condition as a particular case. Secondly, we address the issue of symmetry, which we abandon for two key physiological reasons: (1) actual neural connections have a distinctly directional character (axons and dendrites), and (2) the gradient structure derived from symmetry forces the dynamics towards stationary points, leading to the recognition of every pattern. We propose a novel model that incorporates a set of limited but variable controls \(|\xi_{ij}|\leq K\), which are used to adjust an initially constant interaction matrix, \(T_{ij}=A_{ij}+\xi_{ij}\). Additionally, we introduce a reasonable controlled variational functional for optimization. This allows us to simulate three potential outcomes when a pattern is submitted to the learning system: (1) if the dynamics converges to an existing stationary point without activating controls, the system has \emph{recognized} an existing pattern; (2) if a new stationary point is reached through control activation, the system has \emph{learned} a new pattern; and (3) if the dynamics \emph{wanders} without reaching any stationary point, the system is unable to recognize or learn the submitted pattern. An additional feature (4) models the processes of \emph{forgetting and restoring} memory.
Creativity as Variations on a Theme: Formalizations, Evidence, and Engineered Applications
Authors: Rohan Agarwal
Date: 2023-05-07 00:00:00

There are many philosophies and theories on what creativity is and how it works, but one popular idea is that of variations on a theme and intersection of concepts. This literature review explores philosophical proposals of how creativity emerges from variations on a theme, and how formalizations of these proposals in human subject studies and computational methods result in creativity. Specifically, the philosophical idea of intangible clouds of concepts is analyzed with empirical studies of concept representation and mental model formation, and mathematical formalizations of such ideas. Empirical findings on emergent neural activity from neural network combinations are also examined for evidence of novel, emergent ideas from the collision of existing ones. Finally, work on human-AI co-creativity is used as a lens for concept collision and the effectiveness of this model of creativity. This paper also proposes directions for further research in studying creativity as variations on a theme.
Hypergraph representation in brain network analysis
Authors: Anagha P, Selvakumar R
Date: 2023-05-05 00:00:00

For the study of functional aspects of the brain network. This paper is a study on the hypergraph representation, based on the functional regions of the brain network. A new parameter that can measure how many multifunctioning regions each function contains and thereby the correlation of other functions with each function.
Mathematical modelling of cancer invasion: Phenotypic transitioning provides insight into multifocal foci formation
Authors: Zuzanna Szymańska, Mirosław Lachowicz, Nikolaos Sfakianakis, Mark A. J. Chaplain
Date: 2023-05-22 00:00:00
Tasks: Prognosis

The transition from the epithelial to mesenchymal phenotype and its reverse (from mesenchymal to epithelial) are crucial processes necessary for the progression and spread of cancer. In this paper, we investigate how phenotypic switching at the cancer cell level impacts on behaviour at the tissue level, specifically on the emergence of isolated foci of the invading solid tumour mass leading to a multifocal tumour. To this end, we propose a new mathematical model of cancer invasion that includes the influence of cancer cell phenotype on the rate of invasion and metastasis. The implications of model are explored through numerical simulations revealing that the plasticity of tumour cell phenotypes appears to be crucial for disease progression and local invasive spread. The computational simulations show the progression of the invasive spread of a primary cancer reminiscent of in vivo multifocal breast carcinomas, where multiple, synchronous, ipsilateral neoplastic foci are frequently observed and are associated with a poorer patient prognosis.