pwc-archive/papers-with-abstracts huggingface dataset:
Watching ion-driven kinetics of ribozyme folding and misfolding caused by energetic and topological frustration one molecule at a time
Authors: Naoto Hori, D. Thirumalai
Date: 2023-03-05 00:00:00

Folding of ribozymes into well-defined tertiary structures usually requires divalent cations. How Mg$^{2+}$ ions direct the folding kinetics has been a long-standing unsolved problem because experiments cannot detect the positions and dynamics of ions. To address this problem, we used molecular simulations to dissect the folding kinetics of the Azoarcus ribozyme by monitoring the path each molecule takes to reach the folded state. We quantitatively establish that Mg$^{2+}$ binding to specific sites, coupled with counter-ion release of monovalent cations, stimulate the formation of secondary and tertiary structures, leading to diverse pathways that include direct rapid folding and trapping in misfolded structures. In some molecules, key tertiary structural elements form when Mg$^{2+}$ ions bind to specific RNA sites at the earliest stages of the folding, leading to specific collapse and rapid folding. In others, the formation of non-native base pairs, whose rearrangement is needed to reach the folded state, is the rate-limiting step. Escape from energetic traps, driven by thermal fluctuations, occurs readily. In contrast, the transition to the native state from long-lived topologically trapped native-like metastable states is extremely slow. Specific collapse and formation of energetically or topologically frustrated states occur early in the assembly process.
Neural complexity -- Statistical-mechanical approach of human electroencephalograms
Authors: Dimitri Marques Abramov, Constantino Tsallis, Henrique Santos Lima
Date: 2023-03-06 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The brain is a complex system whose understanding enables potentially deeper approaches to mental phenomena. Dynamics of wide classes of complex systems have been satisfactorily described within $q$-statistics, a current generalization of Boltzmann-Gibbs (BG) statistics. Here, we study human electroencephalograms of typical human adults (EEG), very specifically their inter-occurrence times across an arbitrarily chosen threshold of the signal (observed, for instance, at the midparietal location in scalp). The distributions of these inter-occurrence times differ from those usually emerging within BG statistical mechanics. They are instead well approached within the $q$-statistical theory, based on non-additive entropies characterized by the index $q$. The present method points towards a suitable tool for quantitatively accessing brain complexity, thus potentially opening useful studies of the properties of both typical and altered brain physiology.
Emergent competition shapes the ecological properties of multi-trophic ecosystems
Authors: Zhijie Feng, Robert Marsland III, Jason W. Rocks, Pankaj Mehta
Date: 2023-03-06 00:00:00
Tasks: Diversity

Ecosystems are commonly organized into trophic levels -- organisms that occupy the same level in a food chain (e.g., plants, herbivores, carnivores). A fundamental question in theoretical ecology is how the interplay between trophic structure, diversity, and competition shapes the properties of ecosystems. To address this problem, we analyze a generalized Consumer Resource Model with three trophic levels using the zero-temperature cavity method and numerical simulations. We find that intra-trophic diversity gives rise to ``emergent competition'' between species within a trophic level due to feedbacks mediated by other trophic levels. This emergent competition gives rise to a crossover from a regime of top-down control (populations are limited by predators) to a regime of bottom-up control (populations are limited by primary producers) and is captured by a simple order parameter related to the ratio of surviving species in different trophic levels. We show that our theoretical results agree with empirical observations, suggesting that the theoretical approach outlined here can be used to understand complex ecosystems with multiple trophic levels.
Online functional connectivity analysis of large all-to-all networks
Authors: Lorenz Esch, Jinlong Dong, Matti Hämäläinen, Daniel Baumgarten, Jens Haueisen, Johannes Vorwerk
Date: 2023-03-06 00:00:00
Tasks: All, Connectivity Estimation, EEG, Electroencephalogram (EEG), Functional Connectivity

The analysis of EEG/MEG functional connectivity has become an important tool in neural research. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, i.e., after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervalls. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity.
Estimating the parameters of epidemic spread on two-layer random graphs: a classical and a neural network approach
Authors: Ágnes Backhausz, Edit Bognár, Villő Csiszár, Damján Tárkányi, András Zempléni
Date: 2023-03-03 00:00:00
Tasks: Graph Neural Network

In this paper, we study the spread of a classical SIR process on a two-layer random network, where the first layer represents the households, while the second layer models the contacts outside the households by a random scale-free graph. We build a three-parameter graph, called polynomial model, where the new vertices are connected to the existing ones either uniformly, or preferentially, or by forming random triangles. We examine the effect of the graph's properties on the goodness of the estimation of the infection rate $\tau$, which is the most important parameter, determining the reproduction rate of the epidemic. In the classical maximum likelihood approach, to estimate $\tau$ one needs to approximate the number of SI edges between households, since the graph itself is supposed to be unobservable. Our simulation study reveals that the estimation is poorer at the beginning of the epidemic, for larger preferential attachment parameter of the graph, and for larger $\tau$. We present two heuristic improvement algorithms and establish our method to be robust to changes in average clustering of the graph model. We also extend a graph neural network (GNN) approach for estimating contagion dynamics for our two-layered graphs. We find that dense networks offer better training datasets. Moreover, GNN perfomance is measured better using the $l_2$ loss function rather than cross-entropy.
Just Ask Them Twice: Choice Probabilities and Identification of Ex ante returns and Willingness-To-Pay
Authors: Romuald Meango, Esther Mirjam Girsberger
Date: 2023-03-06 00:00:00
Tasks: Attribute, counterfactual, Survey

One of the exciting developments in the stated preference literature is the use of probabilistic stated preference experiments to estimate semi-parametric population distributions of ex ante returns and willingness-to-pay (WTP) for a choice attribute. This relies on eliciting several choices per individual, and estimating separate demand functions, at the cost of possibly long survey instruments. This paper shows that the distributions of interest can be recovered from at most two stated choices, without requiring ad-hoc parametric assumptions. Hence, it allows for significantly shorter survey instruments. The paper also shows that eliciting probabilistic stated choices allows identifying much richer objects than we have done so far, and therefore, provides better tools for ex ante policy evaluation. Finally, it showcases the feasibility and relevance of the results by studying the preference of high ability students in Cote d'Ivoire for public sector jobs exploiting a unique survey on this population. Our analysis supports the claim that public sector jobs might significantly increase the cost of hiring elite students for the private sector.
EnsembleIV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference
Authors: Gordon Burtch, Edward McFowland III, Mochen Yang, Gediminas Adomavicius
Date: 2023-03-06 00:00:00
Tasks: regression, valid

Despite increasing popularity in empirical studies, the integration of machine learning generated variables into regression models for statistical inference suffers from the measurement error problem, which can bias estimation and threaten the validity of inferences. In this paper, we develop a novel approach to alleviate associated estimation biases. Our proposed approach, EnsembleIV, creates valid and strong instrumental variables from weak learners in an ensemble model, and uses them to obtain consistent estimates that are robust against the measurement error problem. Our empirical evaluations, using both synthetic and real-world datasets, show that EnsembleIV can effectively reduce estimation biases across several common regression specifications, and can be combined with modern deep learning techniques when dealing with unstructured data.
Censored Quantile Regression with Many Controls
Authors: Seoyun Hong
Date: 2023-03-05 00:00:00
Tasks: quantile regression, regression, valid

This paper develops estimation and inference methods for censored quantile regression models with high-dimensional controls. The methods are based on the application of double/debiased machine learning (DML) framework to the censored quantile regression estimator of Buchinsky and Hahn (1998). I provide valid inference for low-dimensional parameters of interest in the presence of high-dimensional nuisance parameters when implementing machine learning estimators. The proposed estimator is shown to be consistent and asymptotically normal. The performance of the estimator with high-dimensional controls is illustrated with numerical simulation and an empirical application that examines the effect of 401(k) eligibility on savings.
Monetary Policy and Economic Growth in Developing Countries: A Literature Review
Authors: Marouane Daoui
Date: 2023-03-06 00:00:00

This article conducts a literature review on the topic of monetary policy in developing countries and focuses on the effectiveness of monetary policy in promoting economic growth and the relationship between monetary policy and economic growth. The literature review finds that the activities of central banks in developing countries are often overlooked by economic models, but recent studies have shown that there are many factors that can affect the effectiveness of monetary policy in these countries. These factors include the profitability of central banks and monetary unions, the independence of central banks in their operations, and lags, rigidities, and disequilibrium analysis. The literature review also finds that studies on the topic have produced mixed results, with some studies finding that monetary policy has a limited or non-existent impact on economic growth and others finding that it plays a crucial role. The article aims to provide a comprehensive understanding of the current state of research in this field and to identify areas for future study.
The Economic Costs of the Russia-Ukraine War: A Synthetic Control Study of (Lost) Entrepreneurship
Authors: David Audretsch, Paul P. Momtaz, Hanna Motuzenko, Silvio Vismara
Date: 2023-03-05 00:00:00
Tasks: counterfactual

This synthetic control study quantifies the economic costs of the Russo-Ukrainian war in terms of foregone entrepreneurial activity in both countries since the invasion of Crimea in 2014. Relative to its synthetic counterfactual, Ukraine's number of self-employed dropped by 675,000, corresponding to a relative loss of 20%. The number of Ukrainian SMEs temporarily dropped by 71,000 (14%) and recovered within five years of the conflict. In contrast, Russia had lost more than 1.4 million SMEs (42%) five years into the conflict. The disappearance of Russian SMEs is driven by both fewer new businesses created and more existing business closures.
Identifying the Distribution of Welfare from Discrete Choice
Authors: Bart Capéau, Liebrecht De Sadeleer, Sebastiaan Maes
Date: 2023-03-05 00:00:00

Empirical welfare analyses often impose stringent parametric assumptions on individuals' preferences and neglect unobserved preference heterogeneity. We develop a framework to conduct individual and social welfare analysis for discrete choice that does not suffer from these drawbacks. We first adapt the class of individual welfare measures introduced by Fleurbaey (2009) to settings where individual choice is discrete. Allowing for unrestricted, unobserved preference heterogeneity, these measures become random variables. We then demonstrate that their distribution can be derived from choice probabilities, which can be estimated nonparametrically from cross-sectional data. Additionally, we derive nonparametric results for the joint distribution of welfare and welfare differences, and for social welfare. The former is an important tool in determining whether the winners of a price change belong disproportionately to those groups who were initially well-off.
Combating Algorithmic Collusion: A Mechanism Design Approach
Authors: Soumen Banerjee
Date: 2023-03-05 00:00:00

Attention has recently been focused on the possibility of artificially intelligent sellers on platforms colluding to limit output and raise prices. Such arrangements (cartels), however, feature an incentive for individual sellers to deviate to a lower price (cheat) to increase their own profits. Stabilizing such cartels therefore requires credible threats of punishments, such as price wars. In this paper, I propose a mechanism to destabilize cartels by protecting any cheaters from a price war by guaranteeing a stream of profits which is unaffected by arbitrary punishments, only if such punishments actually occur. Equilibrium analysis of the induced game predicts a reversion to repeated static Nash pricing. When implemented in a reinforcement learning framework, it provides substantial reductions in prices (reducing markups by 40% or more), without affecting product variety or requiring the platform to make any payments on path. This mechanism applies to both the sale of differentiated goods on platforms, and the sale of homogeneous goods through direct sales. The mechanism operates purely off-path, thereby inducing no welfare losses in practice, and does not depend on the choice of discount factors.
Variational Inference of Structured Line Spectra Exploiting Group-Sparsity
Authors: Jakob Möderl, Franz Pernkopf, Klaus Witrisal, Erik Leitinger
Date: 2023-03-06 00:00:00
Tasks: Variational Inference

In this paper, we present a variational inference algorithm that decomposes a signal into multiple groups of related spectral lines. The spectral lines in each group are associated with a group parameter common to all spectral lines within the group. The proposed algorithm jointly estimates the group parameters, the number of spetral lines within a group, and the number of groups exploiting a Bernoulli-Gamma-Gaussian hierarchical prior model which promotes sparse solutions. Aiming to maximize the evidence lower bound (ELBO), variational inference provides analytic approximations of the posterior probability density functions (PDFs) and also gives estimates of the additional model parameters such as the measurement noise variance. While the activation variables of the groups and the associated group parameters (such as fundamental frequencies and the corresponding higher order harmonics) are estimated as point estimates, the remaining parameters such as the complex amplitudes of the spectral lines and their precision parameters are estimated as approximate posterior PDFs. We demonstrate the versatility and performance of the proposed algorithm on three different inference problems. In particular, the proposed algorithm is applied to the multi-pitch estimation problem, the radar signal-based extended object estimation problem, and variational mode decomposition (VMD) using synthetic measurements and to real multi-pitch estimation problem using the Bach-10 dataset. The results show that the proposed algorithm outperforms state-of-the-art model-based and pre-trained algorithms on all three inference problems.
Automated Ventricle Parcellation and Evan's Ratio Computation in Pre- and Post-Surgical Ventriculomegaly
Authors: Yuli Wang, Anqi Feng, Yuan Xue, Lianrui Zuo, Yihao Liu, Ari M. Blitz, Mark G. Luciano, Aaron Carass, Jerry L. Prince
Date: 2023-03-03 00:00:00
Tasks: Management

Normal pressure hydrocephalus~(NPH) is a brain disorder associated with enlarged ventricles and multiple cognitive and motor symptoms. The degree of ventricular enlargement can be measured using magnetic resonance images~(MRIs) and characterized quantitatively using the Evan's ratio (ER). Automatic computation of ER is desired to avoid the extra time and variations associated with manual measurements on MRI. Because shunt surgery is often used to treat NPH, it is necessary that this process be robust to image artifacts caused by the shunt and related implants. In this paper, we propose a 3D regions-of-interest aware (ROI-aware) network for segmenting the ventricles. The method achieves state-of-the-art performance on both pre-surgery MRIs and post-surgery MRIs with artifacts. Based on our segmentation results, we also describe an automated approach to compute ER from these results. Experimental results on multiple datasets demonstrate the potential of the proposed method to assist clinicians in the diagnosis and management of NPH.
Augmented smartphone bilirubinometer enabled by a mobile app that turns smartphone into multispectral imager
Authors: Qinghua He, Wanyu Li, Yaping Shi, Yi Yu, Yi Zhang, Wenqian Geng, Zhiyuan Sun, Ruikang K Wang
Date: 2023-03-04 00:00:00
Tasks: Hybrid Machine Learning, Prediction

We present the development of SpeCamX, a mobile application that transforms any unmodified smartphone into a powerful multispectral imager capable of capturing multispectral information. Our application includes an augmented bilirubinometer, enabling accurate prediction of blood bilirubin levels (BBL). In a clinical study involving 320 patients with liver diseases, we used SpeCamX to image the bulbar conjunctiva region, and we employed a hybrid machine learning prediction model to predict BBL. We observed a high correlation with blood test results, demonstrating the efficacy of our approach. Furthermore, we compared our method, which uses spectrally augmented learning (SAL), with traditional learning based on RGB photographs (RGBL), and our results clearly indicate that SpeCamX outperforms RGBL in terms of prediction accuracy, efficiency, and stability. This study highlights the potential of SpeCamX to improve the prediction of bio-chromophores, and its ability to transform an ordinary smartphone into a powerful medical tool without the need for additional investments or expertise. This makes it suitable for widespread use, particularly in areas where medical resources are scarce.
Zero-Shot Self-Supervised Joint Temporal Image and Sensitivity Map Reconstruction via Linear Latent Space
Authors: Molin Zhang, Junshen Xu, Yamin Arefeen, Elfar Adalsteinsson
Date: 2023-03-03 00:00:00
Tasks: Self-Supervised Learning, Sensitivity, SSIM, Time Series, Time Series Analysis, Zero-Shot Learning

Fast spin-echo (FSE) pulse sequences for Magnetic Resonance Imaging (MRI) offer important imaging contrast in clinically feasible scan times. T2-shuffling is widely used to resolve temporal signal dynamics in FSE acquisitions by exploiting temporal correlations via linear latent space and a predefined regularizer. However, predefined regularizers fail to exploit the incoherence especially for 2D acquisitions.Recent self-supervised learning methods achieve high-fidelity reconstructions by learning a regularizer from undersampled data without a standard supervised training data set. In this work, we propose a novel approach that utilizes a self supervised learning framework to learn a regularizer constrained on a linear latent space which improves time-resolved FSE images reconstruction quality. Additionally, in regimes without groundtruth sensitivity maps, we propose joint estimation of coil-sensitivity maps using an iterative reconstruction technique. Our technique functions is in a zero-shot fashion, as it only utilizes data from a single scan of highly undersampled time series images. We perform experiments on simulated and retrospective in-vivo data to evaluate the performance of the proposed zero-shot learning method for temporal FSE reconstruction. The results demonstrate the success of our proposed method where NMSE and SSIM are significantly increased and the artifacts are reduced.
Stochastic Super-Resolution For Gaussian Textures
Authors: Emile Pierret, Bruno Galerne
Date: 2023-03-03 00:00:00
Tasks: Super-Resolution

Super-resolution (SR) is an ill-posed inverse problem which consists in proposing high-resolution images consistent with a given low-resolution one. While most SR algorithms are deterministic, stochastic SR deals with designing a stochastic sampler generating any realistic SR solution. The goal of this paper is to show that stochastic SR is a well-posed and solvable problem when restricting to Gaussian stationary textures. Using Gaussian conditional sampling and exploiting the stationarity assumption, we propose an efficient algorithm based on fast Fourier transform. We also demonstrate the practical relevance of the approach for SR with a reference image. Although limited to stationary microtextures, our approach compares favorably in terms of speed and visual quality to some state of the art methods designed for a larger class of images.
Image Coding via Perceptually Inspired Graph Learning
Authors: Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega
Date: 2023-03-03 00:00:00
Tasks: Graph Learning, MS-SSIM, SSIM

Most codec designs rely on the mean squared error (MSE) as a fidelity metric in rate-distortion optimization, which allows to choose the optimal parameters in the transform domain but may fail to reflect perceptual quality. Alternative distortion metrics, such as the structural similarity index (SSIM), can be computed only pixel-wise, so they cannot be used directly for transform-domain bit allocation. Recently, the irregularity-aware graph Fourier transform (IAGFT) emerged as a means to include pixel-wise perceptual information in the transform design. This paper extends this idea by also learning a graph (and corresponding transform) for sets of blocks that share similar perceptual characteristics and are observed to differ statistically, leading to different learned graphs. We demonstrate the effectiveness of our method with both SSIM- and saliency-based criteria. We also propose a framework to derive separable transforms, including separable IAGFTs. An empirical evaluation based on the 5th CLIC dataset shows that our approach achieves improvements in terms of MS-SSIM with respect to existing methods.