pwc-archive/papers-with-abstracts huggingface dataset:
Structural Periodic Vector Autoregressions
Authors: Daniel Dzikowski, Carsten Jentsch
Date: 2024-01-25 00:00:00

While seasonality inherent to raw macroeconomic data is commonly removed by seasonal adjustment techniques before it is used for structural inference, this approach might distort valuable information contained in the data. As an alternative method to commonly used structural vector autoregressions (SVAR) for seasonally adjusted macroeconomic data, this paper offers an approach in which the periodicity of not seasonally adjusted raw data is modeled directly by structural periodic vector autoregressions (SPVAR) that are based on periodic vector autoregressions (PVAR) as the reduced form model. In comparison to a VAR, the PVAR does allow not only for periodically time-varying intercepts, but also for periodic autoregressive parameters and innovations variances, respectively. As this larger flexibility leads also to an increased number of parameters, we propose linearly constrained estimation techniques. Overall, SPVARs allow to capture seasonal effects and enable a direct and more refined analysis of seasonal patterns in macroeconomic data, which can provide useful insights into their dynamics. Moreover, based on such SPVARs, we propose a general concept for structural impulse response analyses that takes seasonal patterns directly into account. We provide asymptotic theory for estimators of periodic reduced form parameters and structural impulse responses under flexible linear restrictions. Further, for the construction of confidence intervals, we propose residual-based (seasonal) bootstrap methods that allow for general forms of seasonalities in the data and prove its bootstrap consistency. A real data application on industrial production, inflation and federal funds rate is presented, showing that useful information about the data structure can be lost when using common seasonal adjustment methods.
Sustainable Market Incentives -- Lessons from European Feebates for a ZEV Future
Authors: Aditya Ramji, Daniel Sperling, Lewis Fulton
Date: 2024-01-26 00:00:00

Strong policies with sustainable incentives are needed to accelerate the EV transition. This paper assesses various feebate designs assessing recent policy evolution in five European countries. While there are key design elements that should be considered, there is no optimal feebate design. Different policy objectives could be served by feebates influencing its design and effectiveness. Using feebates to transition to EVs has emerged a key objective. With the financial sustainability of EV incentive programs being questioned, a self financing market mechanism could be the need of the hour solution. Irrespective of the policy goals, a feebate will impact both the supply side, i.e., the automotive industry and the consumer side. Globally, feebates can be used to effect technology leapfrogging while navigating the political economy of clean transportation policy in different country contexts. This paper highlights thirteen design elements of an effective feebate policy that can serve as a foundation for policymakers.
Free public transport to the destination: A causal analysis of tourists' travel mode choice
Authors: Kevin Blättler, Hannes Wallimann, Widar von Arx
Date: 2024-01-26 00:00:00

In this paper, we assess the impact of a fare-free public transport policy for overnight guests on travel mode choice to a Swiss tourism destination. The policy directly targets domestic transport to and from a destination, the substantial contributor to the CO2 emissions of overnight trips. Based on a survey sample, we identify the effect with the help of the random element that the information on the offer from a hotelier to the guest varies in day-to-day business. We estimate a shift from private cars to public transport due to the policy of, on average, 14.8 and 11.6 percentage points, depending on the application of propensity score matching and causal forest. This knowledge is relevant for policy-makers to design future offers that include more sustainable travels to a destination. Overall, our paper exemplifies how such an effect of comparable natural experiments in the travel and tourism industry can be properly identified with a causal framework and underlying assumptions.
How to Use Data Science in Economics -- a Classroom Game Based on Cartel Detection
Authors: Hannes Wallimann, Silvio Sticher
Date: 2024-01-26 00:00:00

We present a classroom game that integrates economics and data-science competencies. In the first two parts of the game, participants assume the roles of firms in a procurement market, where they must either adopt competitive behaviors or have the option to engage in collusion. Success in these parts hinges on their comprehension of market dynamics. In the third part of the game, participants transition to the role of competition-authority members. Drawing from recent literature on machine-learning-based cartel detection, they analyze the bids for patterns indicative of collusive (cartel) behavior. In this part of the game, success depends on data-science skills. We offer a detailed discussion on implementing the game, emphasizing considerations for accommodating diverging levels of preexisting knowledge in data science.
Islamic Law, Western European Law and the Roots of Middle East's Long Divergence: a Comparative Empirical Investigation (800-1600)
Authors: Hans-Bernd Schaefer, Rok Spruk
Date: 2024-01-25 00:00:00
Tasks: counterfactual

We examine the contribution of Islamic legal institutions to the comparative economic decline of the Middle East behind Latin Europe, which can be observed since the late Middle Ages. To this end, we explore whether the sacralization of Islamic law and its focus on the Sharia as supreme, sacred and unchangeable legal text, which reached its culmination in the 13th century had an impact on economic development. We use the population size of 145 cities in Islamic countries and 648 European cities for the period 800-1800 as proxies for the level of economic development, and construct novel estimates of the number of law schools (i.e. madaris) and estimate their contribution to the pre-industrial economic development. Our triple-differences estimates show that a higher density of madrasas before the sacralization of Islamic law predicts a more vibrant urban economy characterized by higher urban growth. After the consolidation of the sharia sacralization of law in the 13th century, greater density of law schools is associated with stagnating population size. We show that the economic decline of the Middle East can be partly explained by the absence of legal innovations or substitutes of them, which paved the way for the economic rise of Latin Europe, where ground-breaking legal reforms introduced a series of legal innovations conducive for economic growth. We find that the number of learned lawyers trained in universities with law schools is highly and positively correlated with the western European city population. Our counterfactual estimates show that almost all Islamic cities under consideration would have had much larger size by the year 1700 if legal innovations comparable to those in Western Europe were introduced. By making use of a series of synthetic control and difference-in-differences estimators our findings are robust against a large number of model specification checks.
Microwave lymphedema assessment using deep learning with contour assisted backprojection
Authors: Yuyi Chang, Nithin Sugavanam, Emre Ertin
Date: 2024-01-26 00:00:00
Tasks: Deep Learning

We present a method for early detection of lymphatic fluid accumulation in lymphedema patients based on microwave imaging of the limb volume across an air gap. The proposed algorithm uses contour information of the imaged limb surface to approximate the wave propagation velocity locally to solve the eikonal equation for implementing the adjoint imaging operator. This modified backprojection algorithm results in focused imagery close to the limb surface where lymphatic fluid accumulation presents itself. Next, a deep neural network based on U-Net architecture is employed to identify the location and extent of the lymphatic fluid. Simulation studies with various upper and lower arm profiles compare the focusing performance of the proposed contour assisted backprojection imaging with the baseline imaging approach that assumes homogeneous media. The empirical results of the simulation experiments show that the proposed imaging method significantly improves the ability of the deepnet model to identify the location and the volume of the excess fluid in the limb.
Towards Robust Hyperspectral Anomaly Detection: Decomposing Background, Anomaly, and Mixed Noise via Convex Optimization
Authors: Koyo Sato, Shunsuke Ono
Date: 2024-01-26 00:00:00
Tasks: Anomaly Detection

We propose a novel hyperspectral (HS) anomaly detection method that is robust to various types of noise. Most of existing HS anomaly detection methods are designed for cases where a given HS image is noise-free or is contaminated only by small Gaussian noise. However, in real-world situations, observed HS images are often degraded by various types of noise, such as sparse noise and stripe noise, due to sensor failure or calibration errors, significantly affecting the detection performance. To address this problem, this article establishes a robust HS anomaly detection method with a mechanism that can properly remove mixed noise while separating background and anomaly parts. Specifically, we newly formulate a constrained convex optimization problem to decompose background and anomaly parts, and three types of noise from a given HS image. Then, we develop an efficient algorithm based on a preconditioned variant of a primal-dual splitting method to solve this problem. Through comparison with existing methods, including state-of-the-art ones, we illustrate that the proposed method achieves a detection accuracy comparable to state-of-the-art methods in noise-free cases and is significantly more robust than these methods in noisy cases.
User Association Optimization for IRS-aided Terahertz Networks: A Matching Theory Approach
Authors: Muddasir Rahim, Thanh Luan Nguyen, Georges Kaddoum, Tri Nhu Do
Date: 2024-01-26 00:00:00

Terahertz (THz) communication is a promising technology for future wireless communications, offering data rates of up to several terabits-per-second (Tbps). However, the range of THz band communications is often limited by high pathloss and molecular absorption. To overcome these challenges, this paper proposes intelligent reconfigurable surfaces (IRSs) to enhance THz communication systems. Specifically, we introduce an angle-based trigonometric channel model to evaluate the effectiveness of IRS-aided THz networks. Additionally, to maximize the sum rate, we formulate the source-IRS-destination matching problem, which is a mixed-integer nonlinear programming (MINLP) problem. To solve this non-deterministic polynomial-time hard (NP-hard) problem, the paper proposes a Gale-Shapley-based solution that obtains stable matches between sources and IRSs, as well as between destinations and IRSs in the first and second sub-problems, respectively.
Feature Allocation for Semantic Communication with Space-Time Importance Awareness
Authors: Kequan Zhou, Guangyi Zhang, Yunlong Cai, Qiyu Hu, Guanding Yu, A. Lee Swindlehurst
Date: 2024-01-26 00:00:00
Tasks: Attribute, Semantic Communication

In the realm of semantic communication, the significance of encoded features can vary, while wireless channels are known to exhibit fluctuations across multiple subchannels in different domains. Consequently, critical features may traverse subchannels with poor states, resulting in performance degradation. To tackle this challenge, we introduce a framework called Feature Allocation for Semantic Transmission (FAST), which offers adaptability to channel fluctuations across both spatial and temporal domains. In particular, an importance evaluator is first developed to assess the importance of various features. In the temporal domain, channel prediction is utilized to estimate future channel state information (CSI). Subsequently, feature allocation is implemented by assigning suitable transmission time slots to different features. Furthermore, we extend FAST to the space-time domain, considering two common scenarios: precoding-free and precoding-based multiple-input multiple-output (MIMO) systems. An important attribute of FAST is its versatility, requiring no intricate fine-tuning. Simulation results demonstrate that this approach significantly enhances the performance of semantic communication systems in image transmission. It retains its superiority even when faced with substantial changes in system configuration.
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability
Authors: Junbin Liu, Yuening Li, Wing-Kin Ma
Date: 2024-01-26 00:00:00
Tasks: Hyperspectral Unmixing, Variational Inference

Given a hyperspectral image, the problem of hyperspectral unmixing (HU) is to identify the endmembers (or materials) and the abundance (or endmembers' contributions on pixels) that underlie the image. HU can be seen as a matrix factorization problem with a simplex structure in the abundance matrix factor. In practice, hyperspectral images may exhibit endmember variability (EV) effects -- the endmember matrix factor varies from one pixel to another. In this paper we consider a multilayer simplex-structured matrix factorization model to account for the EV effects. Our multilayer model is based on the postulate that if we arrange the varied endmembers as an expanded endmember matrix, that matrix exhibits a low-rank structure. A variational inference-based maximum-likelihood estimation method is employed to tackle the multilayer factorization problem. Simulation results are provided to demonstrate the performance of our multilayer factorization method.
Bistatic Reflectivity and Micro-Doppler Signatures of Drones for Integrated Communication and Sensing
Authors: Heraldo Cesar Alves Costa, Saw James Myint, Carsten Andrich, Sebastian W. Giehl, Christian Schneider, Reiner S. Thomä
Date: 2024-01-25 00:00:00
Tasks: valid

The integration of wireless communication and radar sensing is gaining the interest of researchers from wireless communication and radar societies. Sensing in Integrated Communication and Sensing (ICAS) systems differs from the traditional radar system in the configuration of transmitter-target-receiver, the operating frequency bands, and the transmitting waveform. It is necessary to understand how target electromagnetic signatures behave in this context. Therefore, this paper presents measurements and analysis of two important target signatures, reflectivity and micro-Doppler, for sensing in ICAS. These target signatures are measured in the state-of-the-art measurement system, Bistatische-Radar-Messeinrichtung (BiRa).
Revisiting proximity effect using broadband signals
Authors: Laurent Millot, Mohammed Elliq, Manuel Lopes, Gérard Pelé, Dominique Lambert
Date: 2024-01-11 00:00:00
Tasks: valid

Experiments studying mainly proximity effect are presented. Pink noise and music were used as stimuli and a combo guitar amplifier as source to test several microphones: omnidirectional and directional. We plot in-axis levels and spectral balances as functions of x, the distance to the source. Proximity effect was found for omnidirectional microphones. In-axis level curves show that 1/x law seems poorly valid. Spectral balance evolutions depend on microphones and moreover on stimuli: bigger decreases of low frequencies with pink noise; larger increases of other frequencies with music. For a naked loudspeaker, we found similar in-axis level curves under and above the cut-off frequency and propose an explanation. Listening equalized music recordings will help to demonstrate proximity effect for tested microphones.Paper 7106 presented at the 122th Convention of the Audio Engineering Society, Wien, 2007
Future Impact Decomposition in Request-level Recommendations
Authors: Xiaobei Wang, Shuchang Liu, Xueliang Wang, Qingpeng Cai, Lantao Hu, Han Li, Peng Jiang, Kun Gai, Guangming Xie
Date: 2024-01-29 00:00:00
Tasks: Recommendation Systems

In recommender systems, reinforcement learning solutions have shown promising results in optimizing the interaction sequence between users and the system over the long-term performance. For practical reasons, the policy's actions are typically designed as recommending a list of items to handle users' frequent and continuous browsing requests more efficiently. In this list-wise recommendation scenario, the user state is updated upon every request in the corresponding MDP formulation. However, this request-level formulation is essentially inconsistent with the user's item-level behavior. In this study, we demonstrate that an item-level optimization approach can better utilize item characteristics and optimize the policy's performance even under the request-level MDP. We support this claim by comparing the performance of standard request-level methods with the proposed item-level actor-critic framework in both simulation and online experiments. Furthermore, we show that a reward-based future decomposition strategy can better express the item-wise future impact and improve the recommendation accuracy in the long term. To achieve a more thorough understanding of the decomposition strategy, we propose a model-based re-weighting framework with adversarial learning that further boost the performance and investigate its correlation with the reward-based strategy.
Cash non-additive risk measures: horizon risk and generalized entropy
Authors: Giulia Di Nunno, Emanuela Rosazza Gianin
Date: 2024-01-25 00:00:00

Horizon risk (see arXiv:2301.04971) is studied in the context of cash non-additive fully-dynamic risk measures induced by BSDEs. Furthermore, we introduce a risk measure based on generalized Tsallis entropy which can dynamically evaluate the riskiness of losses considering both horizon risk and interest rate uncertainty. The new q-entropic risk measure on losses can be used as a quantification of capital requirement.
Amplifiers of selection for the Moran process with both Birth-death and death-Birth updating
Authors: Jakub Svoboda, Soham Joshi, Josef Tkadlec, Krishnendu Chatterjee
Date: 2024-01-26 00:00:00

Populations evolve by accumulating advantageous mutations. Every population has some spatial structure that can be modeled by an underlying network. The network then influences the probability that new advantageous mutations fixate. Amplifiers of selection are networks that increase the fixation probability of advantageous mutants, as compared to the unstructured fully-connected network. Whether or not a network is an amplifier depends on the choice of the random process that governs the evolutionary dynamics. Two popular choices are Moran process with Birth-death updating and Moran process with death-Birth updating. %Moran process has two popular versions called Birth-death updating and death-Birth updating. Interestingly, while some networks are amplifiers under Birth-death updating and other networks are amplifiers under death-Birth updating, no network is known to function as an amplifier under both types of updating simultaneously. In this work, we identify networks that act as amplifiers of selection under both versions of the Moran process. The amplifiers are robust, modular, and increase fixation probability for any mutant fitness advantage in a range $r\in(1,1.2)$. To complement this positive result, we also prove that for certain quantities closely related to fixation probability, it is impossible to improve them simultaneously for both versions of the Moran process. Together, our results highlight how the two versions of the Moran process differ and what they have in common.
Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification
Authors: Caleb C. Berggren, David Jiang, Y. F. Jack Wang, Jake A. Bergquist, Lindsay C. Rupp, Zexin Liu, Rob S. MacLeod, Akil Narayan, Lucas H. Timmins
Date: 2024-01-26 00:00:00
Tasks: Uncertainty Quantification

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter summarizing contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, this highlights the impact of material property variation on predicted artery stresses and presents a pipeline to explore and characterize uncertainty in computational biomechanics.
An innovative in silico model of the oral mucosa reveals the impact of extracellular spaces on chemical permeation through epithelium
Authors: Sean M. Edwards, Amy L. Harding, Joseph A. Leedale, Steve D. Webb, Helen E. Colley, Craig Murdoch, Rachel N. Bearon
Date: 2024-01-26 00:00:00

In pharmaceutical therapeutic design or toxicology, accurately predicting the permeation of chemicals through human epithelial tissues is crucial, where permeation is significantly influenced by the tissue's cellular architecture. Current mathematical models for multi-layered epithelium such as the oral mucosa only use simplistic 'bricks and mortar' geometries and therefore do not account for the complex cellular architecture of these tissues at the microscale level, such as the extensive plasma membrane convolutions that define the extracellular spaces between cells. Chemicals often permeate tissues via this paracellular route, meaning that permeation is underestimated. To address this, measurements of human buccal mucosal tissue were conducted to ascertain the width and tortuosity of extracellular spaces across the epithelium. Using mechanistic mathematical modelling, we show that the convoluted geometry of extracellular spaces significantly impacts chemical permeation and that this can be approximated, provided that extracellular tortuosity is accounted for. We next developed an advanced physically-relevant in silico model of oral mucosal chemical permeation using partial differential equations, fitted to chemical permeation in vitro assays on tissue-engineered human oral mucosa. Tissue geometries were measured and captured in silico, and permeation examined and predicted for chemicals with different physicochemical properties. The effect of altering the extracellular space to mimic permeation enhancers was also assessed by perturbing the in silico model. This novel in vitro-in silico approach has the potential to expedite pharmaceutical innovation for testing oromucosal chemical permeation, providing a more accurate, physiologically-relevant model which can reduce animal testing with early screening based on chemical properties.