pwc-archive/papers-with-abstracts huggingface dataset:
Identification and Statistical Decision Theory
Authors: Charles F. Manski
Date: 2022-04-24 00:00:00
Tasks: Decision Making

Econometricians have usefully separated study of estimation into identification and statistical components. Identification analysis, which assumes knowledge of the probability distribution generating observable data, places an upper bound on what may be learned about population parameters of interest with finite sample data. Yet Wald's statistical decision theory studies decision making with sample data without reference to identification, indeed without reference to estimation. This paper asks if identification analysis is useful to statistical decision theory. The answer is positive, as it can yield an informative and tractable upper bound on the achievable finite sample performance of decision criteria. The reasoning is simple when the decision relevant parameter is point identified. It is more delicate when the true state is partially identified and a decision must be made under ambiguity. Then the performance of some criteria, such as minimax regret, is enhanced by randomizing choice of an action. This may be accomplished by making choice a function of sample data. I find it useful to recast choice of a statistical decision function as selection of choice probabilities for the elements of the choice set. Using sample data to randomize choice conceptually differs from and is complementary to its traditional use to estimate population parameters.
Trade Facilitation and Economic Growth Among Middle-Income Countries
Authors: Victor Ushahemba Ijirshar
Date: 2022-04-23 00:00:00

This study examined the relationship between trade facilitation and economic growth among the middle-income countries from 2010 to 2020 using 94 countries made up of 48 lower-middle-income countries and 46 upper-middle-income countries. The study utilized both difference and system Generalised Method of Moments (GMM) since the cross-sections (N) were greater than the periods (T). The study found that container port traffic, quality of trade and transport-related infrastructure have a strong influence on imports and exports of goods and national income while trade tariff hurts the growth of the countries. The study also found that most of the trade facilitation indicators indicated a weak positive influence on trade flows and economic growth. Based on these findings, the study recommends that reforms aimed at significantly lowering the costs of trading across borders among middle-income countries should be highly prioritized in policy formulations, with a focus on the export side by reducing at-the-border documentation, time, and real costs of trading across borders while the international organizations should continue to report the set of Trade Facilitation Indicators (TFIs) that identify areas for action and enable the potential impact of reforms to be assessed.
Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems
Authors: Zhen Zhang, Yuxiang Zhang, Jianhua Zhang, Feifei Gao
Date: 2022-04-25 00:00:00
Tasks: Generative Adversarial Network, Prediction

In this paper, a time-varying channel prediction method based on conditional generative adversarial network (CPcGAN) is proposed for time division duplexing/frequency division duplexing (TDD/FDD) systems. CPcGAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information (CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI. The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.
A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System
Authors: Zhimin Chen, Peng Chen, Ziyu Guo, Yudong Zhang, Xianbin Wang
Date: 2022-04-25 00:00:00
Tasks: Integrated sensing and communication, ISAC

With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle's direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram'{e}r-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://github.com/chenpengseu/PassiveDOA-ISAC-RIS.git.
Leveraging RIS-Enabled Smart Signal Propagation for Solving Infeasible Localization Problems
Authors: Kamran Keykhosravi, Benoit Denis, George C. Alexandropoulos, Zhongxia Simon He, Antonio Albanese, Vincenzo Sciancalepore, Henk Wymeersch
Date: 2022-04-25 00:00:00

Reconfigurable intelligent surfaces (RISs) have tremendous potential for both communication and localization. While communication benefits are now well-understood, the breakthrough nature of the technology may well lie in its capability to provide location estimates when conventional approaches fail, (e.g., due to insufficient available infrastructure). A limited number of example scenarios have been identified, but an overview of possible RIS-enabled localization scenarios is still missing from the literature. In this article, we present such an overview and extend localization to include even user orientation or velocity. In particular, we consider localization scenarios with various numbers of RISs, single- or multi-antenna base stations, narrowband or wideband transmissions, and near- and farfield operation. Furthermore, we provide a short description of the general RIS operation together with radio localization fundamentals, experimental validation of a localization scheme with two RISs, as well as key research directions and open challenges specific to RIS-enabled localization and sensing.
On Identifiable Polytope Characterization for Polytopic Matrix Factorization
Authors: Bariscan Bozkurt, Alper T. Erdogan
Date: 2022-04-25 00:00:00

Polytopic matrix factorization (PMF) is a recently introduced matrix decomposition method in which the data vectors are modeled as linear transformations of samples from a polytope. The successful recovery of the original factors in the generative PMF model is conditioned on the "identifiability" of the chosen polytope. In this article, we investigate the problem of determining the identifiability of a polytope. The identifiability condition requires the polytope to be permutation-and/or-sign-only invariant. We show how this problem can be efficiently solved by using a graph automorphism algorithm. In particular, we show that checking only the generating set of the linear automorphism group of a polytope, which corresponds to the automorphism group of an edge-colored complete graph, is sufficient. This property prevents checking all the elements of the permutation group, which requires factorial algorithm complexity. We demonstrate the feasibility of the proposed approach through some numerical experiments.
Brain-Computer Interfaces: Investigating the Transition from Visually Evoked to Purely Imagined Steady-State Potentials
Authors: Arturo Micheli, Davide Consoli, Adrien Merlini, Paolo Ricci, Francesco P. Andriulli
Date: 2022-04-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), SSVEP

Brain-Computer Interfaces (BCIs) based on Steady State Visually Evoked Potentials (SSVEPs) have proven effective and provide significant accuracy and information-transfer rates. This family of strategies, however, requires external devices that provide the frequency stimuli required by the technique. This limits the scenarios in which they can be applied, especially when compared to other BCI approaches. In this work, we have investigated the possibility of obtaining frequency responses in the EEG output based on the pure visual imagination of SSVEP-eliciting stimuli. Our results show that not only that EEG signals present frequency-specific peaks related to the frequency the user is focusing on, but also that promising classification accuracy can be achieved, paving the way for a robust and reliable visual imagery BCI modality.
Federated Learning with Lossy Distributed Source Coding: Analysis and Optimization
Authors: Huiyuan Yang, Tian Ding, Xiaojun Yuan
Date: 2022-04-23 00:00:00
Tasks: Federated Learning, Quantization

Recently, federated learning (FL), which replaces data sharing with model sharing, has emerged as an efficient and privacy-friendly machine learning (ML) paradigm. One of the main challenges in FL is the huge communication cost for model aggregation. Many compression/quantization schemes have been proposed to reduce the communication cost for model aggregation. However, the following question remains unanswered: What is the fundamental trade-off between the communication cost and the FL convergence performance? In this paper, we manage to answer this question. Specifically, we first put forth a general framework for model aggregation performance analysis based on the rate-distortion theory. Under the proposed analysis framework, we derive an inner bound of the rate-distortion region of model aggregation. We then conduct an FL convergence analysis to connect the aggregation distortion and the FL convergence performance. We formulate an aggregation distortion minimization problem to improve the FL convergence performance. Two algorithms are developed to solve the above problem. Numerical results on aggregation distortion, convergence performance, and communication cost demonstrate that the baseline model aggregation schemes still have great potential for further improvement.
Kyle's Model with Stochastic Liquidity
Authors: Ibrahim Ekren, Brad Mostowski, Gordan Žitković
Date: 2022-04-23 00:00:00
Tasks: model

We construct an equilibrium for the continuous time Kyle's model with stochastic liquidity, a general distribution of the fundamental price, and correlated stock and volatility dynamics. For distributions with positive support, our equilibrium allows us to study the impact of the stochastic volatility of noise trading on the volatility of the asset. In particular, when the fundamental price is log-normally distributed, informed trading forces the log-return up to maturity to be Gaussian for any choice of noise-trading volatility even though the price process itself comes with stochastic volatility. Surprisingly, we find that in equilibrium both Kyle's Lambda and its inverse (the market depth) are submartingales.
Bioinformatic analysis for structure and function of Glutamine synthetase(GS)
Authors: Jiahao Ma, Guotong Xu, Le Ao, Siqi Chen, Jingze Liu
Date: 2022-04-23 00:00:00

Objective: To predict structure and function of Glutamine synthetase (GS) from Pseudoalteromonas sp. by bioinformatics technology, and to provide a theoretical basis for further study. Methods: Open reading frame (ORF) of GS sequence from Pseudoalteromonas sp. was obtained by ORF finder and was translated into amino acid residue. The structure domain was analyzed by Blast. By the method of analysis tools: Protparam, ProtScale, SignalP-4.0, TMHMM, SOPMA, SWISS-MODEL, NCBI SMART-BLAST and MAGA 7.0, the structure and function of the protein were predicted and analyzed. Results: The results showed that the sequence was GS with 468 amino acid residues, theoretical molecular weight was 51986.64 Da. The protein has the closest evolutionary status with Shewanella oneidensis. Then it had no signal peptide site and transmembrane domain. Secondary structure of GS contained 35.04% alpha-helix, 16.67% Extended chain, 5.34% beta-turn, 42.95% RandomCoil. Conclusions: This GU was a variety of biological functions of protein that may be used as a molecular samples of microbial nitrogen metabolism in extreme environments.
The effect of global warming on Western Mediterranean seagrasses: towards an agent-based modelling approach
Authors: Eva Llabrés, Aina Blanco-Magadán, Marta Sales, Tomàs Sintes
Date: 2022-04-25 00:00:00

Seagrasses are key structural elements in coastal ecosystems, and studying how temperature affects these species is crucial to anticipate the implications of global warming. In this work, we use an empirically-based numerical model to study the combined dynamics of {\it Posidonia oceanica} and {\it Cymodocea nodosa} and their resilience to sea warming. The model is parametrised using seagrass growth rates measured at the Western Mediterranean Sea. Under favorable growth conditions, our simulations predict the emergence of a coexistence region at the front between mono-specific meadows. This region can be characterised by its width and local shoot densities, which are found to depend on the coupling parameter between {\it Posidonia oceanica} and {\it Cymodocea nodosa} species. Such regions have been empirically observed in Ses Olles de Son Saura (Balearic Islands, Western Mediterranean Sea). A comparison between the field measurements at the study site with the model predictions has been used to fit the value of the coupling parameter. Field data also relates the width of the coexistence region to the average length of {\it Posidonia oceanica} leaves at the front. Remarkably, a linear relationship is found between the coupling parameter and the leaf length. In the presence of sea warming, the model predicts an exponential decay in the population of {\it Posidonia oceanica}, which is highly sensitive to temperature. This behaviour is a direct consequence of the clonal nature of the plant and can be characterised by the model parameters. Considering a scenario of high greenhouse emissions, our model forecasts that {\it Posidonia oceanica} meadows will lose 70$\%$ of their population by the year $2050$. {\it Cymodocea nodosa}, with higher thermal resilience, acts as an opportunistic species conquering the space left by the degraded {\it Posidonia oceanica}.
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
Authors: Alexander Partin, Thomas Brettin, Yitan Zhu, James M. Dolezal, Sara Kochanny, Alexander T. Pearson, Maulik Shukla, Yvonne A. Evrard, James H. Doroshow, Rick L. Stevens
Date: 2022-04-25 00:00:00
Tasks: Data Augmentation, Drug Response Prediction, whole slide images

Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies because the in vivo environment of PDXs helps preserve tumor heterogeneity and usually better mimics drug response of patients with cancer compared to CCLs. We investigate multimodal neural network (MM-Net) and data augmentation for drug response prediction in PDXs. The MM-Net learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs) where the multi-modality refers to the tumor features. We explore whether the integration of WSIs with GE improves predictions as compared with models that use GE alone. We use two methods to address the limited number of response values: 1) homogenize drug representations which allows to combine single-drug and drug-pairs treatments into a single dataset, 2) augment drug-pair samples by switching the order of drug features which doubles the sample size of all drug-pair samples. These methods enable us to combine single-drug and drug-pair treatments, allowing us to train multimodal and unimodal neural networks (NNs) without changing architectures or the dataset. Prediction performance of three unimodal NNs which use GE are compared to assess the contribution of data augmentation methods. NN that uses the full dataset which includes the original and the augmented drug-pair treatments as well as single-drug treatments significantly outperforms NNs that ignore either the augmented drug-pairs or the single-drug treatments. In assessing the contribution of multimodal learning based on the MCC metric, MM-Net statistically significantly outperforms all the baselines. Our results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs.
In silico evaluation of Paxlovid's pharmacometrics for SARS-CoV-2: a multiscale approach
Authors: Ferenc A. Bartha, Nóra Juhász, Sadegh Marzban, Renji Han, Gergely Röst
Date: 2022-04-23 00:00:00

Paxlovid is a promising, orally bioavailable novel drug for SARS--CoV--2 with excellent safety profiles. Our main goal here is to explore the pharmacometric features of this new antiviral. To provide a detailed assessment of Paxlovid, we propose a hybrid multiscale mathematical approach. We demonstrate that the results of the present \textit{in silico} evaluation match the clinical expectations remarkably well: on the one hand, our computations successfully replicate the outcome of an actual \textit{in vitro} experiment; on the other hand we verify both the sufficiency and the necessity of Paxlovid's two main components (nirmatrelvir and ritonavir) for a simplified \textit{in vivo} case. Moreover, in the simulated context of our computational framework we visualize the importance of early interventions, and identify the time window where a unit--length delay causes the highest level of tissue damage. Finally, the results' sensitivity to the diffusion coefficient of the virus is explored in details.
CVA in fractional and rough volatility models
Authors: Elisa Alòs, Fabio Antonelli, Alessandro Ramponi, Sergio Scarlatti
Date: 2022-04-25 00:00:00

In this work we present a general representation formula for the price of a vulnerable European option, and the related CVA in stochastic (either rough or not) volatility models for the underlying's price, when admitting correlation with the default event. We specialize it for some volatility models and we provide price approximations, based on the representation formula. We study numerically their accuracy, comparing the results with Monte Carlo simulations, and we run a theoretical study of the error. We also introduce a seminal study of roughness influence on the claim's price.
Quantum Bohmian Inspired Potential to Model Non-Gaussian Events and the Application in Financial Markets
Authors: Reza Hosseini, Samin Tajik, Zahra Koohi Lai, Tayeb Jamali, Emmanuel Haven, G. Reza Jafari
Date: 2022-04-24 00:00:00
Tasks: Time Series, Time Series Analysis

We have implemented quantum modeling mainly based on Bohmian Mechanics to study time series that contain strong coupling between their events. We firstly propose how compared to normal densities, our target time series seem to be associated with a higher number of rare events, and Gaussian statistics tend to underestimate these events' frequency drastically. To this end, we suggest that by imposing Gaussian densities to the natural processes, one will seriously neglect the existence of extreme events in many circumstances. The central question of our study concerns the consideration of the effects of these rare events in the corresponding probability densities and studying their role from the point of view of quantum measurements. To model the non-Gaussian behavior of these time-series, we utilize the multifractal random walk (MRW) approach and control the non-Gaussianity parameter $\lambda$ accordingly. Using the framework of quantum mechanics, we then examine the role of $\lambda$ in quantum potentials derived for these time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for the rare events. We thus conclude that these events could generate a potential barrier that the system, lingering in a non-Gaussian high-frequency region, encounters, and their role becomes more prominent when it comes to transversing this barrier. In this study, as an example of the application of quantum potential outside of the micro-world, we compute the quantum potentials for the S\&P financial market time series to verify the presence of rare events in the non-Gaussian densities for this real data and remark the deviation from the Gaussian case.
Will claim history become a deprecated rating factor? An optimal design method for the real-time road risk model
Authors: Jiamin Yu
Date: 2022-04-25 00:00:00

With the popularity of Telematics and Self-driving, more and more rating factors, such as mileage, route, driving behavior, etc., are introduced into actuarial models. There are quite a few doubts and disputes on the rationality and accuracy of the selection of rating variables, but it does not involve the widely accepted historical claim records. Recently, Tesla Insurance released a new generation of Safety Score-based insurance, irrespective of accident history. Forward-looking experts and scholars began to discuss whether claim history will disappear in the future auto insurance rate-making system. Therefore, this paper proposes a new risk variable elimination method as well as a real-time road risk model design framework and concludes that claim history will be regarded as a "noise" factor and deprecated in the Pay-How-You-Drive model.
Debt-Financed Collateral and Stability Risks in the DeFi Ecosystem
Authors: Michael Darlin, Georgios Palaiokrassas, Leandros Tassiulas
Date: 2022-04-23 00:00:00

The rise of Decentralized Finance ("DeFi") on the Ethereum blockchain has enabled the creation of lending platforms, which serve as marketplaces to lend and borrow digital currencies. We first categorize the activity of lending platforms within a standard regulatory framework. We then employ a novel grouping and classification algorithm to calculate the percentage of fund flows into DeFi lending platforms that can be attributed to debt created elsewhere in the system ("debt-financed collateral"). Based on our results, we conclude that the wide-spread use of stablecoins as debt-financed collateral increases financial stability risks in the DeFi ecosystem.