pwc-archive/papers-with-abstracts huggingface dataset:
Design and Development of Paper-based Spirometry Device and its Smart-phone based Lung Condition Monitoring and Analysis Software
Authors: Yatin Miglani, Muddukrishna P, Harshvardhan Pande, Manoj M. Varma, Bhushan Toley
Date: 2024-08-18 00:00:00

We describe the design, construction and characterisation of paper based devices to perform spirometry, a standard test for lung function assessment. In this device the instantaneous flowrate of the incoming breath from a person gets transformed to a specific acoustic frequency. In this manner, the time course of the persons breath profile is mapped to a time varying acoustic signal. The captured acoustic signal can be converted to standard spirometry curves and relevant parameters can be extracted as we describe in this paper. We compared our device with commercially available devices and showed that these paper based devices provide similar performance. These devices have advantage of low cost and simplicity of operation compared to currently available commercial products.
Noisy information channel mediated prevention of the tragedy of the commons
Authors: Samrat Sohel Mondal, Sagar Chakraborty
Date: 2024-08-16 00:00:00

Synergy between evolutionary dynamics of cooperation and fluctuating state of shared resource being consumed by the cooperators is essential for averting the tragedy of the commons. Not only in humans, but also in the cognitively-limited organisms, this interplay between the resource and the cooperation is ubiquitously witnessed. The strategically interacting players engaged in such game-environment feedback scenarios naturally pick strategies based on their perception of the environmental state. Such perception invariably happens through some sensory information channels that the players are endowed with. The unfortunate reality is that any sensory channel must be noisy due to various factors; consequently, the perception of the environmental state becomes faulty rendering the players incapable of adopting the strategy that they otherwise would. Intriguingly, situation is not as bad as it sounds. Here we introduce the hitherto neglected information channel between players and the environment into the paradigm of stochastic evolutionary games with a view to bringing forward the counterintuitive possibility of emergence and sustenance of cooperation on account of the noise in the channel. Our primary study is in the simplest non-trivial setting of two-state stochastically fluctuating resource harnessed by a large unstructured population of cooperators and defectors adopting either memory-1 strategies or reactive strategies while engaged in repeated two-player interactions. The effect of noisy information channel in enhancing the cooperation in reactive-strategied population is unprecedented. We find that the propensity of cooperation in the population is inversely related to the mutual information (normalized by the channel capacity) of the corresponding information channel.
Why Do Experts Favor Solar and Wind as Renewable Energies Despite their Intermittency?
Authors: Steven P. Reinhardt
Date: 2024-08-13 00:00:00

As humanity accelerates its shift to renewable energy generation, people who are not experts in renewable energy are learning about energy technologies and the energy market, which are complex. The answers to some questions will be obvious to expert practitioners but not to non-experts. One such question is Why solar and wind generation are expected to supply the bulk of future energy when they are intermittent. We learn here that once the baseline hurdles of scalability to utility scale and the underlying resources being widely available globally are satisfied, the forecasted cost of solar and wind is 2-4X lower than competing technologies, even those that are not as scalable and available. The market views intermittency as surmountable.
Deep Learning for the Estimation of Heterogeneous Parameters in Discrete Choice Models
Authors: Stephan Hetzenecker, Maximilian Osterhaus
Date: 2024-08-18 00:00:00
Tasks: Deep Learning, Discrete Choice Models

This paper studies the finite sample performance of the flexible estimation approach of Farrell, Liang, and Misra (2021a), who propose to use deep learning for the estimation of heterogeneous parameters in economic models, in the context of discrete choice models. The approach combines the structure imposed by economic models with the flexibility of deep learning, which assures the interpretebility of results on the one hand, and allows estimating flexible functional forms of observed heterogeneity on the other hand. For inference after the estimation with deep learning, Farrell et al. (2021a) derive an influence function that can be applied to many quantities of interest. We conduct a series of Monte Carlo experiments that investigate the impact of regularization on the proposed estimation and inference procedure in the context of discrete choice models. The results show that the deep learning approach generally leads to precise estimates of the true average parameters and that regular robust standard errors lead to invalid inference results, showing the need for the influence function approach for inference. Without regularization, the influence function approach can lead to substantial bias and large estimated standard errors caused by extreme outliers. Regularization reduces this property and stabilizes the estimation procedure, but at the expense of inducing an additional bias. The bias in combination with decreasing variance associated with increasing regularization leads to the construction of invalid inferential statements in our experiments. Repeated sample splitting, unlike regularization, stabilizes the estimation approach without introducing an additional bias, thereby allowing for the construction of valid inferential statements.
Externally Valid Selection of Experimental Sites via the k-Median Problem
Authors: José Luis Montiel Olea, Brenda Prallon, Chen Qiu, Jörg Stoye, Yiwei Sun
Date: 2024-08-17 00:00:00
Tasks: valid

We present a decision-theoretic justification for viewing the question of how to best choose where to experiment in order to optimize external validity as a k-median (clustering) problem, a popular problem in computer science and operations research. We present conditions under which minimizing the worst-case, welfare-based regret among all nonrandom schemes that select k sites to experiment is approximately equal - and sometimes exactly equal - to finding the k most central vectors of baseline site-level covariates. The k-median problem can be formulated as a linear integer program. Two empirical applications illustrate the theoretical and computational benefits of the suggested procedure.
Method of Moments Estimation for Affine Stochastic Volatility Models
Authors: Yan-Feng Wu, Xiangyu Yang, Jian-Qiang Hu
Date: 2024-08-17 00:00:00

We develop moment estimators for the parameters of affine stochastic volatility models. We first address the challenge of calculating moments for the models by introducing a recursive equation for deriving closed-form expressions for moments of any order. Consequently, we propose our moment estimators. We then establish a central limit theorem for our estimators and derive the explicit formulas for the asymptotic covariance matrix. Finally, we provide numerical results to validate our method.
Revisiting the Many Instruments Problem using Random Matrix Theory
Authors: Helmut Farbmacher, Rebecca Groh, Michael Mühlegger, Gabriel Vollert
Date: 2024-08-16 00:00:00
Tasks: regression

We use recent results from the theory of random matrices to improve instrumental variables estimation with many instruments. In settings where the first-stage parameters are dense, we show that Ridge lowers the implicit price of a bias adjustment. This comes along with improved (finite-sample) properties in the second stage regression. Our theoretical results nest existing results on bias approximation and bias adjustment. Moreover, it extends them to settings with more instruments than observations.
Panel Data Unit Root testing: Overview
Authors: Anton Skrobotov
Date: 2024-08-13 00:00:00

This review discusses methods of testing for a panel unit root. Modern approaches to testing in cross-sectionally correlated panels are discussed, preceding the analysis with an analysis of independent panels. In addition, methods for testing in the case of non-linearity in the data (for example, in the case of structural breaks) are presented, as well as methods for testing in short panels, when the time dimension is small and finite. In conclusion, links to existing packages that allow implementing some of the described methods are provided.
Participatory Mapping of Local Green Hydrogen Cost-Potentials in Sub-Saharan Africa
Authors: C. Winkler, H. Heinrichs, S. Ishmam, B. Bayat, A. Lahnaoui, S. Agbo, E. U. Peña Sanchez, D. Franzmann, N. Oijeabou, C. Koerner, Y. Michael, B. Oloruntoba, C. Montzka, H. Vereecken, H. Hendricks Franssen, J. Brendt, S. Brauner, W. Kuckshinrichs, S. Venghaus, D. Kone, B. Korgo, K. Ogunjobi, J. Olwoch, V. Chiteculo, Z. Getenga, J. Linßen, D. Stolten
Date: 2024-08-19 00:00:00

Green hydrogen is a promising solution within carbon free energy systems with Sub-Saharan Africa being a possibly well-suited candidate for its production. However, green hydrogen in Sub-Saharan Africa is not yet investigated in detail. This work determines the green hydrogen cost-potential for green hydrogen within this region. Therefore, a potential analysis for PV, wind and hydropower, groundwater analysis, and energy systems optimization are conducted. The results are evaluated under local socio-economic factors. Results show that hydrogen costs start at 1.6 EUR/kg in Mauritania with a total potential of ~259 TWh/a under 2 EUR/kg in 2050. Two third of the regions experience groundwater limitations and need desalination at surplus costs of ~1% of hydrogen costs. Socio-economic analysis show, that green hydrogen deployment can be hindered along the Upper Guinea Coast and the African Great Lakes, driven by limited energy access, low labor costs in West Africa, and high labor potential in other regions.
Games with Planned Actions and Scouting
Authors: Wolfgang Kuhle
Date: 2024-08-19 00:00:00

We study games in which every action requires planning and preparation. Moreover, before players act, they can revise their plans based on partially revealing information that they receive on their adversary's preparations. In turn, we examine how players' information over each others' planned actions influences winning odds in matching pennies games, and how it incentivises the use of decoys, deception, and camouflage. Across scenarios, we emphasize that the decomposition of an action into (i) a preparation to act and (ii) the execution of the action, allows to analyze one-shot simultaneous-move games, where players partially observe each others' contemporaneous actions.
Working Paper: Conflicts and the New Scramble for African Resources -- A Shift-Share Approach
Authors: Raphaël Boulat
Date: 2024-08-15 00:00:00

This paper estimates the causal effect of mineral trade on conflicts in Africa using a Shift-Share IV approach based on an exogenous price-commodity shock. The main result is that an increase in mineral trade significantly increases the number of conflicts while it has no clear effect on fatalities. Exploring heterogeneous effects, I find that a specific group of minerals, oil and fuels, drives the results on the number of conflicts. Moreover, the group of rare minerals such as coltan, precious metals or cobalt has no effect on the number of conflicts but appears to have an important impact on the number of fatalities.
Periodic Trading Activities in Financial Markets: Mean-field Liquidation Game with Major-Minor Players
Authors: Yufan Chen, Lan Wu, Renyuan Xu, Ruixun Zhang
Date: 2024-08-18 00:00:00

Motivated by recent empirical findings on the periodic phenomenon of aggregated market volumes in equity markets, we aim to understand the causes and consequences of periodic trading activities through a game-theoretic perspective, examining market interactions among different types of participants. Specifically, we introduce a new mean-field liquidation game involving major and minor traders, where the major trader evaluates her strategy against a periodic targeting strategy while a continuum of minor players trade against her. We establish the existence and uniqueness of an open-loop Nash equilibrium. In addition, we prove an O(1/sqrt N) approximation rate of the mean-field solution to the Nash equilibrium in a major-minor game with N minor players. In equilibrium, minor traders exhibit front-running behaviors in both the periodic and trend components of their strategies, reducing the major trader's profit. Such strategic interactions diminish the strength of periodicity in both overall trading volume and asset prices. Our model rationalizes observed periodic trading activities in the market and offers new insights into market dynamics.
Undominated monopoly regulation
Authors: Debasis Mishra, Sanket Patil
Date: 2024-08-18 00:00:00

We study undominated mechanisms with transfers for regulating a monopolist who privately observes the marginal cost of production. We show that in any undominated mechanism, there is a quantity floor, which depends only on the primitives, and the regulator's operation decision is stochastic only if the monopolist produces at the quantity floor. We provide a near-complete characterization of the set of undominated mechanisms and use it to (a) provide a foundation for deterministic mechanisms, (b) show that the efficient mechanism is dominated, and (c) derive a max-min optimal regulatory mechanism.
How to Make an Action Better
Authors: Marilyn Pease, Mark Whitmeyer
Date: 2024-08-17 00:00:00

For two actions in a decision problem, a and b, each of which produces a state-dependent monetary reward, we study how to robustly make action a more attractive. Action a' improves upon a in this manner if the set of beliefs at which a is preferred to b is a subset of the set of beliefs at which a' is preferred to b, irrespective of the risk-averse agent's utility function (in money). We provide a full characterization of this relation and discuss applications in politics, bilateral trade, insurance, and information acquisition.
Efficient onboard multi-task AI architecture based on self-supervised learning
Authors: Gabriele Inzerillo, Diego Valsesia, Enrico Magli
Date: 2024-08-19 00:00:00
Tasks: Self-Supervised Learning

There is growing interest towards the use of AI directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This paper presents a blueprint to the mission designer for the development of a modular and efficient deep learning payload to address multiple onboard inference tasks. In particular, we design a self-supervised lightweight backbone that provides features to efficient task-specific heads. The latter can be developed independently and with reduced data labeling requirements thanks to the frozen backbone. Experiments on three sample tasks of cloud segmentation, flood detection, and marine debris classification on a 7W embedded system show competitive results with inference quality close to high-complexity state-of-the-art models and high throughput in excess of 8 Mpx/s.
Explore Cross-Codec Quality-Rate Convex Hulls Relation for Adaptive Streaming
Authors: Masoumeh Farhadi Nia
Date: 2024-08-16 00:00:00
Tasks: Quantization, Relation

With the ongoing advancement of video technology and the emergence of new video platforms, suppliers of video contents are striving to ensure that the video quality meets the desire of consumers. Accessing a limited amount of channel bandwidth, they are often looking for a novel approach to decrease the use of data and thus the required energy and cost. This study evaluates the Quality Rate performance of H.264, H.265, and VP9 codecs across resolutions (960*544, 1920*1080, 3840*2160) to optimize video quality while minimizing bitrate, crucial for energy and cost efficiency. At this approach, original videos at native resolutions were encoded, decoded, and rescaled using FFmpeg. For each resolution, encoding and decoding were performed at various quantization levels. Quality Rate (QR) curves were generated using PSNR and VMAF metric against bitrate. Convex Hull curves were then derived and mathematically modelled for each resolution. The procedure was systematically applied to H.264, H.265, and VP9 codecs. Results indicate that increasing CRF values reduce bitrate, PSNR, and VMAF, with PSNR ranging between 20-40 dB. Logarithmic polynomial modelling of convex hulls demonstrated high accuracy, with low RMSE and high R-Squared values. These findings suggest that the convex hull of one codec can predict the performance of others, aiding future content-driven prediction methodologies and enhancing adaptive streaming efficiency. Keywords: Video Codecs, Adaptive Streaming, Compression, Bitrate, PSNR, VMAF, H.264, H.265, VP9
Oral squamous cell detection using deep learning
Authors: Samrat Kumar Dev Sharma
Date: 2024-08-16 00:00:00
Tasks: Cell Detection, Deep Learning, image-classification, Image Classification

Oral squamous cell carcinoma (OSCC) represents a significant global health concern, with increasing incidence rates and challenges in early diagnosis and treatment planning. Early detection is crucial for improving patient outcomes and survival rates. Deep learning, a subset of machine learning, has shown remarkable progress in extracting and analyzing crucial information from medical imaging data.EfficientNetB3, an advanced convolutional neural network architecture, has emerged as a leading model for image classification tasks, including medical imaging. Its superior performance, characterized by high accuracy, precision, and recall, makes it particularly promising for OSCC detection and diagnosis. EfficientNetB3 achieved an accuracy of 0.9833, precision of 0.9782, and recall of 0.9782 in our analysis. By leveraging EfficientNetB3 and other deep learning technologies, clinicians can potentially improve the accuracy and efficiency of OSCC diagnosis, leading to more timely interventions and better patient outcomes. This article also discusses the role of deep learning in advancing precision medicine for OSCC and provides insights into prospects and challenges in leveraging this technology for enhanced cancer care.
On the Optimal Radius and Subcarrier Mapping for Binary Modulation on Conjugate-Reciprocal Zeros
Authors: Parker Huggins, Alphan Sahin
Date: 2024-08-19 00:00:00
Tasks: Decoder

In this work, we investigate the radius maximizing reliability for binary modulation on conjugate-reciprocal zeros (BMOCZ) implemented with both maximum likelihood (ML) and direct zero-testing (DiZeT) decoders. We first show that the optimal radius for BMOCZ is a function of the employed decoder and that the radius maximizing the minimum distance between polynomial zeros does not maximize the minimum distance of the final code. While maximizing zero separation offers an almost optimal solution with the DiZeT decoder, simulations show that the ML decoder outperforms the DiZeT decoder in both additive white Gaussian noise (AWGN) and fading channels when the radius is chosen to maximize codeword separation. Finally, we analyze different sequence-to-subcarrier mappings for BMOCZ-based orthogonal frequency division multiplexing (OFDM). We highlight a flexible time-frequency OFDM waveform that avoids distortion introduced by a frequency-selective channel at the expense of a higher peak-to-average power ratio (PAPR).
Beyond Diagonal RIS: Passive Maximum Ratio Transmission and Interference Nulling Enabler
Authors: Hamad Yahya, Hongyu Li, Matteo Nerini, Bruno Clerckx, Merouane Debbah
Date: 2024-08-19 00:00:00

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) generalizes and goes beyond conventional diagonal reconfigurable intelligent surfaces (D-RIS) by interconnecting elements to generate beyond diagonal scattering matrices, which significantly strengthen the wireless channels. In this work, we use BD-RIS for passive multiuser beamforming in multiuser multiple-input-single-output (MU-MISO) systems. Specifically, we design the scattering matrix of BD-RIS to either maximize the sum received signal power at the users following maximum ratio transmission (MRT), or to nullify the interference at the users following zero forcing (ZF). Furthermore, we investigate uniform/optimized power allocation and ZF precoding at the base station (BS). Numerical results show that BD-RIS improves the interference nulling capability and sum rate with fewer reflecting elements (REs) compared to D-RIS. In addition, at moderate to high signal to noise ratios (SNRs), passive interference nulling reduces the complexity at the BS by relaxing the need for precoding or water-filling power allocation design. Furthermore, the passive MRT with ZF precoding achieves a tight sum rate performance to the joint design considering MU-MISO scenarios with many REs while maintaining low computational complexity and simplifying the channel estimation.
Sensing in NLOS: a Stroboscopic Approach
Authors: Davide Tornielli Bellini, Dario Tagliaferri, Marouan Mizmizi, Stefano Tebaldini, Umberto Spagnolini
Date: 2024-08-19 00:00:00

Sensing in non-line-of-sight (NLOS) is a well-known issue that limits the effective range of radar-like sensors. Existing approaches for NLOS sensing consider the usage of either metallic mirrors, that only work under specular reflection, or dynamically-reconfigurable metasurfaces that steer the signal to cover a desired area in NLOS, with the drawback of cost and control signaling. This paper proposes a novel sensing system, that allows a source to image a desired region of interest (ROI) in NLOS, using the combination of a proper beam sweeping (by the source) as well as a passive reflection plane configured as a periodic angular deflecting function (that allows illuminating the ROI). \textit{Stroboscopic sensing} is obtained by sweeping over a sufficiently large portion of the reflection plane, the source covers the ROI \textit{and} enhance the spatial resolution of the image, thanks to multiple diverse observation angles of ROI. Remarkably, the proposed system achieves a near-field imaging with a sequence of far-field acquisitions, thus limiting the implementation complexity. We detail the system design criteria and trade-offs, demonstrating the remarkable benefits of such a stroboscopic sensing system, where a possibly moving source can observe a ROI through multiple points of view as if it were static.