pwc-archive/papers-with-abstracts huggingface dataset:
Estimating cross-border mobility from the difference in peak-timing: A case study in Poland-Germany border regions
Authors: Abhishek Senapati, Adam Mertel, Weronika Schlechte-Welnicz, Justin M. Calabrese
Date: 2022-11-10 00:00:00

Human mobility contributes to the fast spatio-temporal propagation of infectious diseases. During an outbreak, monitoring the infection situation on either side of an international border is very crucial as there is always a higher risk of disease importation associated with cross-border migration. Mechanistic models are effective tools to investigate the consequences of cross-border mobility on disease dynamics and help in designing effective control strategies. However, in practice, due to the unavailability of cross-border mobility data, it becomes difficult to propose reliable, model-based strategies. In this study, we propose a method for estimating cross-border mobility flux between any pair of regions that share an international border from the observed difference in the timing of the infection peak in each region. Assuming the underlying disease dynamics is governed by a Susceptible-Infected-Recovered (SIR) model, we employ stochastic simulations to obtain the maximum likelihood cross-border mobility estimate for any pair of regions where the difference in peak time can be measured. We then investigate how the estimate of cross-border mobility flux varies depending on the disease transmission rate, which is a key epidemiological parameter. We further show that the uncertainty in mobility flux estimates decreases for higher disease transmission rates and larger observed differences in peak timing. Finally, as a case study, we apply the method to some selected regions along the Poland-Germany border which are directly connected through multiple modes of transportation and quantify the cross-border fluxes from the COVID-19 cases data during the period $20^{\rm th}$ February $2021$ to $20^{\rm th}$ June $2021$.
Regional Disparities and Economic Growth in Ukraine
Authors: Khrystyna Huk, Ayaz Zeynalov
Date: 2022-11-10 00:00:00

This research is devoted to assessing regional economic disparities in Ukraine, where regional economic inequality is a crucial issue the country faces in its medium and long-term development, recently, even in the short term. We analyze the determinants of regional economic growth, mainly industrial and agricultural productions, population, human capital, fertility, migration, and regional government expenditures. Using panel data estimations from 2004 to 2020 for 27 regions of Ukraine, our results show that the gaps between regions in Ukraine have widened last two decades. Natural resource distribution, agricultural and industrial productions, government spending, and migration can explain the disparities. We show that regional government spending is highly concentrated in Kyiv, and the potential of the other regions, especially the Western ones, needs to be used sufficiently. Moreover, despite its historical and economic opportunity, the East region performed little development during the last two decades. The inefficient and inconsistent regional policies played a crucial role in these disparities.
Poverty, Unemployment and Displacement in Ukraine: three months into the war
Authors: Maksym Obrizan
Date: 2022-10-30 00:00:00

This paper identifies the causal effects of full-scale kremlin aggression on socio-economic outcomes in Ukraine three months into the full-scale war. First, forced migration after February 24th, 2022 is associated with an elevated risk of becoming unemployed by 7.5% points. Second, difference-in-difference regressions show that in regions with fighting on the ground females without a higher education face a 9.6-9.9% points higher risk of not having enough money for food. Finally, in the regions subject to ground attack females with and without a higher education, as well as males without a higher education are more likely to become unemployed by 6.1-6.9%, 4.2-4.7% and 6.5-6.6% points correspondingly. This persistent gender gap in poverty and unemployment, when even higher education is not protective for females, calls for policy action. While more accurate results may obtain with more comprehensive surveys, this paper provides a remarkably robust initial estimate of the war's effects on poverty, unemployment and internal migration.
Nash implementation by stochastic mechanisms: a simple full characterization
Authors: Siyang Xiong
Date: 2022-11-10 00:00:00

We study Nash implementation by stochastic mechanisms, and provide a surprisingly simple full characterization, which is in sharp contrast to the classical, albeit complicated, full characterization in Moore and Repullo (1990).
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data
Authors: Jiaxin Xiao, Zihan Li, Berkin Bilgic, Jonathan R. Polimeni, Susie Huang, Qiyuan Tian
Date: 2022-11-10 00:00:00
Tasks: Denoising, Super-Resolution

Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a barrier to feasible and accessible implementation. We propose to train NNs for Super-Resolution using Noisy Reference data (SRNR), leveraging the mechanism of the classic NN-based denoising method Noise2Noise. We systematically demonstrate that results from NNs trained using noisy and high-SNR references are similar for both simulated and empirical data. SRNR suggests a smaller number of repetitions of high-resolution reference data can be used to simplify the training data preparation for super-resolution MRI.
Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization
Authors: Jonathan P. Epperlein, Niall P. Hardy, Pol Mac Aonghusa, Ronan A. Cahill
Date: 2022-11-09 00:00:00

Intraoperative assessment of tissue can be guided through fluorescence imaging which involves systemic dosing with a fluorophore and subsequent examination of the tissue region of interest with a near-infrared camera. This typically involves administering indocyanine green (ICG) hours or even days before surgery and intraoperative visualization at the time predicted for steady-state signal-to-background status. Here, we describe our efforts to capture and utilize the information contained in the first few minutes after ICG administration from the perspective of both signal processing and surgical practice. We prove a method for characterization of cancerous versus benign rectal lesions now undergoing further development and validation via multicenter clinical phase studies.
Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights
Authors: Md Manjurul Ahsan, Tareque Abu Abdullah, Md Shahin Ali, Fatematuj Jahora, Md Khairul Islam, Amin G. Alhashim, Kishor Datta Gupta
Date: 2022-11-01 00:00:00
Tasks: Disease Prediction, Transfer Learning

The recent development of Monkeypox disease among various nations poses a global pandemic threat when the world is still fighting Coronavirus Disease-2019 (COVID-19). At its dawn, the slow and steady transmission of Monkeypox disease among individuals needs to be addressed seriously. Over the years, Deep learning (DL) based disease prediction has demonstrated true potential by providing early, cheap, and affordable diagnosis facilities. Considering this opportunity, we have conducted two studies where we modified and tested six distinct deep learning models-VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, and VGG19-using transfer learning approaches. Our preliminary computational results show that the proposed modified InceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracy ranging from 93% to 99%. Our findings are reinforced by recent academic work that demonstrates improved performance in constructing multiple disease diagnosis models using transfer learning approaches. Lastly, we further explain our model prediction using Local Interpretable Model-Agnostic Explanations (LIME), which play an essential role in identifying important features that characterize the onset of Monkeypox disease.
Non-contact, real-time eye blink detection with capacitive sensing
Authors: Mengxi Liu, Sizhen Bian, Paul Lukowicz
Date: 2022-11-10 00:00:00

This work described a novel non-contact, wearable, real-time eye blink detection solution based on capacitive sensing technology. A low-cost and low-power consumption capacitive sensing prototype was developed and deployed on a pair of standard glasses with a copper electrode attached to the glass frame. The eye blink action will cause the capacitance variation between the electrode and the eyelid. Thus by monitoring the capacitance variation caused oscillating frequency shift signal, the eye blink can be abstracted by a simple comparison of the raw frequency signal with a customized threshold. The feasibility and robustness of the proposed solution were demonstrated in five scenarios performed by eight volunteers with an average precision of 92\% and recall of 94\%.
Joint Receiver Design for Integrated Sensing and Communications
Authors: Yuxiang Dong, Fan Liu, Yifeng Xiong
Date: 2022-11-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

In this letter, we investigate the joint receiver design for integrated sensing and communication (ISAC) systems, where the communication signal and the target echo signal are simultaneously received and processed to achieve a balanced performance between both functionalities. In particular, we proposed two design schemes to solve the joint sensing and communication problem of receive signal processing. {\color{black} The first is based on maximum likelihood (ML) detection, minimum mean squared error (MMSE) estimation and interference cancellation (IC), and the other formulates a tailored MMSE estimator for target estimation that is independent to the detector, which form a Non-IC design. We show that with the structural information of the communication signal taken into account, the Non-IC approach outperforms the IC method and achieves optimal performance.} Numerical results are provided to validate the effectiveness of the proposed optimal designs.
Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals
Authors: Mert Kalfa, Sadik Yagiz Yetim, Arda Atalik, Mehmetcan Gok, Yiqun Ge, Rong Li, Wen Tong, Tolga Mete Duman, Orhan Arikan
Date: 2022-11-10 00:00:00
Tasks: Scheduling

Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.
Goal-Oriented Communications for the IoT and Application to Data Compression
Authors: Chao Zhang, Hang Zou, Samson Lasaulce, Walid Saad, Marios Kountouris, Mehdi Bennis
Date: 2022-11-10 00:00:00
Tasks: Data Compression

Internet of Things (IoT) devices will play an important role in emerging applications, since their sensing, actuation, processing, and wireless communication capabilities stimulate data collection, transmission and decision processes of smart applications. However, new challenges arise from the widespread popularity of IoT devices, including the need for processing more complicated data structures and high dimensional data/signals. The unprecedented volume, heterogeneity, and velocity of IoT data calls for a communication paradigm shift from a search for accuracy or fidelity to semantics extraction and goal accomplishment. In this paper, we provide a partial but insightful overview of recent research efforts in this newly formed area of goal-oriented (GO) and semantic communications, focusing on the problem of GO data compression for IoT applications.
TreeFlow: probabilistic programming and automatic differentiation for phylogenetics
Authors: Christiaan Swanepoel, Mathieu Fourment, Xiang Ji, Hassan Nasif, Marc A Suchard, Frederick A Matsen IV, Alexei Drummond
Date: 2022-11-09 00:00:00
Tasks: Probabilistic Programming

Probabilistic programming frameworks are powerful tools for statistical modelling and inference. They are not immediately generalisable to phylogenetic problems due to the particular computational properties of the phylogenetic tree object. TreeFlow is a software library for probabilistic programming and automatic differentiation with phylogenetic trees. It implements inference algorithms for phylogenetic tree times and model parameters given a tree topology. We demonstrate how TreeFlow can be used to quickly implement and assess new models. We also show that it provides reasonable performance for gradient-based inference algorithms compared to specialized computational libraries for phylogenetics.
Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling
Authors: Yao Lei Xu, Kriton Konstantinidis, Danilo P. Mandic
Date: 2022-10-26 00:00:00
Tasks: regression, tensor algebra, Tensor Decomposition

Analytics of financial data is inherently a Big Data paradigm, as such data are collected over many assets, asset classes, countries, and time periods. This represents a challenge for modern machine learning models, as the number of model parameters needed to process such data grows exponentially with the data dimensions; an effect known as the Curse-of-Dimensionality. Recently, Tensor Decomposition (TD) techniques have shown promising results in reducing the computational costs associated with large-dimensional financial models while achieving comparable performance. However, tensor models are often unable to incorporate the underlying economic domain knowledge. To this end, we develop a novel Graph-Regularized Tensor Regression (GRTR) framework, whereby knowledge about cross-asset relations is incorporated into the model in the form of a graph Laplacian matrix. This is then used as a regularization tool to promote an economically meaningful structure within the model parameters. By virtue of tensor algebra, the proposed framework is shown to be fully interpretable, both coefficient-wise and dimension-wise. The GRTR model is validated in a multi-way financial forecasting setting and compared against competing models, and is shown to achieve improved performance at reduced computational costs. Detailed visualizations are provided to help the reader gain an intuitive understanding of the employed tensor operations.
Relative growth rate optimization under behavioral criterion
Authors: Jing Peng, Pengyu Wei, Zuo Quan Xu
Date: 2022-11-10 00:00:00

This paper studies a continuous-time optimal portfolio selection problem in the complete market for a behavioral investor whose preference is of the prospect type with probability distortion. The investor concerns about the terminal relative growth rate (log-return) instead of absolute capital value. This model can be regarded as an extension of the classical growth optimal problem to the behavioral framework. It leads to a new type of M-shaped utility maximization problem under nonlinear Choquet expectation. Due to the presence of probability distortion, the classical stochastic control methods are not applicable. By the martingale method, concavification and quantile optimization techniques, we derive the closed-form optimal growth rate. We find that the benchmark growth rate has a significant impact on investment behaviors. Compared to Zhang et al where the same preference measure is applied to the terminal relative wealth, we find a new phenomenon when the investor's risk tolerance level is high and the market states are bad. In addition, our optimal wealth in every scenario is less sensitive to the pricing kernel and thus more stable than theirs.
Optimal investment and consumption under logarithmic utility and uncertainty model
Authors: Wahid Faidi
Date: 2022-11-10 00:00:00

We study a robust utility maximization problem in the case of an incomplete market and logarithmic utility with general stochastic constraints, not necessarily convex. Our problem is equivalent to maximizing of nonlinear expected logarithmic utility. We characterize the optimal solution using quadratic BSDE.
Optimal growth strategies for a representative agent in a continuous-time asset market
Authors: Mikhail Zhitlukhin
Date: 2022-11-10 00:00:00

We propose a multi-agent model of an asset market and study conditions that guarantee that the strategy of an individual agent cannot outperform the market. The model assumes a mean-field approximation of the market by considering an infinite number of infinitesimal agents who use the same strategy and another infinitesimal agent with a different strategy who tries to outperform the market. We show that the optimal strategy for the market agents is to split their investment budgets among the assets proportionally to their discounted expected relative dividend intensities.
Variance of entropy for testing time-varying regimes with an application to meme stocks
Authors: Andrey Shternshis, Piero Mazzarisi
Date: 2022-11-10 00:00:00
Tasks: Time Series, Time Series Analysis

Shannon entropy is the most common metric to measure the degree of randomness of time series in many fields, ranging from physics and finance to medicine and biology. Real-world systems may be in general non stationary, with an entropy value that is not constant in time. The goal of this paper is to propose a hypothesis testing procedure to test the null hypothesis of constant Shannon entropy for time series, against the alternative of a significant variation of the entropy between two subsequent periods. To this end, we find an unbiased approximation of the variance of the Shannon entropy's estimator, up to the order O(n^(-4)) with n the sample size. In order to characterize the variance of the estimator, we first obtain the explicit formulas of the central moments for both the binomial and the multinomial distributions, which describe the distribution of the Shannon entropy. Second, we find the optimal length of the rolling window used for estimating the time-varying Shannon entropy by optimizing a novel self-consistent criterion based on the counting of significant variations of entropy within a time window. We corroborate our findings by using the novel methodology to test for time-varying regimes of entropy for stock price dynamics, in particular considering the case of meme stocks in 2020 and 2021. We empirically show the existence of periods of market inefficiency for meme stocks. In particular, sharp increases of prices and trading volumes correspond to statistically significant drops of Shannon entropy.
Partial Differential Equations Meet Deep Neural Networks: A Survey
Authors: Shudong Huang, Wentao Feng, Chenwei Tang, Jiancheng Lv
Date: 2022-10-27 00:00:00
Tasks: Survey

Many problems in science and engineering can be represented by a set of partial differential equations (PDEs) through mathematical modeling. Mechanism-based computation following PDEs has long been an essential paradigm for studying topics such as computational fluid dynamics, multiphysics simulation, molecular dynamics, or even dynamical systems. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. At the same time, solving PDEs efficiently has been a long-standing challenge. Generally, except for a few differential equations for which analytical solutions are directly available, many more equations must rely on numerical approaches such as the finite difference method, finite element method, finite volume method, and boundary element method to be solved approximately. These numerical methods usually divide a continuous problem domain into discrete points and then concentrate on solving the system at each of those points. Though the effectiveness of these traditional numerical methods, the vast number of iterative operations accompanying each step forward significantly reduces the efficiency. Recently, another equally important paradigm, data-based computation represented by deep learning, has emerged as an effective means of solving PDEs. Surprisingly, a comprehensive review for this interesting subfield is still lacking. This survey aims to categorize and review the current progress on Deep Neural Networks (DNNs) for PDEs. We discuss the literature published in this subfield over the past decades and present them in a common taxonomy, followed by an overview and classification of applications of these related methods in scientific research and engineering scenarios. The origin, developing history, character, sort, as well as the future trends in each potential direction of this subfield are also introduced.
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques
Authors: Akhil Pandey Akella
Date: 2022-10-13 00:00:00
Tasks: Dimensionality Reduction, Graph Representation Learning, Philosophy, Representation Learning, Survey

Dimensionality reduction techniques map data represented on higher dimensions onto lower dimensions with varying degrees of information loss. Graph dimensionality reduction techniques adopt the same principle of providing latent representations of the graph structure with minor adaptations to the output representations along with the input data. There exist several cutting edge techniques that are efficient at generating embeddings from graph data and projecting them onto low dimensional latent spaces. Due to variations in the operational philosophy, the benefits of a particular graph dimensionality reduction technique might not prove advantageous to every scenario or rather every dataset. As a result, some techniques are efficient at representing the relationship between nodes at lower dimensions, while others are good at encapsulating the entire graph structure on low dimensional space. We present this survey to outline the benefits as well as problems associated with the existing graph dimensionality reduction techniques. We also attempted to connect the dots regarding the potential improvements to some of the techniques. This survey could be helpful for upcoming researchers interested in exploring the usage of graph representation learning to effectively produce low-dimensional graph embeddings with varying degrees of granularity.