pwc-archive/papers-with-abstracts huggingface dataset:
Structured stability analysis of networked systems with uncertain links
Authors: Simone Mariano, Michael Cantoni
Date: 2024-03-22 00:00:00

An input-output approach to stability analysis is explored for networked systems with uncertain link dynamics. The main result consists of a collection of integral quadratic constraints, which together imply robust stability of the uncertain networked system, under the assumption that stability is achieved with ideal links. The conditions are decentralized inasmuch as each involves only agent and uncertainty model parameters that are local to a corresponding link. This makes the main result, which imposes no restriction on network structure, suitable for the study of large-scale systems.
Transmission Benefits and Cost Allocation under Ambiguity
Authors: Han Shu, Jacob Mays
Date: 2024-03-21 00:00:00
Tasks: counterfactual, valid

Disputes over cost allocation can present a significant barrier to investment in shared infrastructure. While it may be desirable to allocate cost in a way that corresponds to expected benefits, investments in long-lived projects are made under conditions of substantial uncertainty. In the context of electricity transmission, uncertainty combined with the inherent complexity of power systems analysis prevents the calculation of an estimated distribution of benefits that is agreeable to all participants. To analyze aspects of the cost allocation problem, we construct a model for transmission and generation expansion planning under uncertainty, enabling the identification of transmission investments as well as the calculation of benefits for users of the network. Numerical tests confirm the potential for realized benefits at the participant level to differ significantly from ex ante estimates. Based on the model and numerical tests we discuss several issues, including 1) establishing a valid counterfactual against which to measure benefits, 2) allocating cost to new and incumbent generators vs. solely allocating to loads, 3) calculating benefits at the portfolio vs. the individual project level, 4) identifying losers in a surplus-enhancing transmission expansion, and 5) quantifying the divergence between cost allocation decisions made ex ante and benefits realized ex post.
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain
Authors: Rubén Calvo, Carles Martorell, Guillermo B. Morales, Serena Di Santo, Miguel A. Muñoz
Date: 2024-03-22 00:00:00
Tasks: Dimensionality Reduction

Recent analyses combining advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons provide strong support for the hypothesis that neural dynamics operate near the edge of instability across regions in the brain. However, these analyses, as well as related studies, often fail to capture the intricate temporal structure of brain activity as they primarily rely on time-integrated measurements across neurons. In this study, we present a novel framework designed to explore signatures of criticality across diverse frequency bands and construct a much more comprehensive description of brain activity. Additionally, we introduce a method for projecting brain activity onto a basis of spatio-temporal patterns, facilitating time-dependent dimensionality reduction. Applying this framework to a magnetoencephalography dataset, we observe significant differences in both criticality signatures and spatio-temporal activity patterns between healthy subjects and individuals with Parkinson's disease.
Identifying Attention-Deficit/Hyperactivity Disorder through the electroencephalogram complexity
Authors: Dimitri Marques Abramov, Henrique Santos Lima, Vladimir Lazarev, Paulo Ricardo Galhanone, Constantino Tsallis
Date: 2024-03-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

There are reasons to suggest that a number of mental disorders may be related to alteration in the neural complexity (NC). Thus, quantitative analysis of NC could be helpful in classifying mental and understanding conditions. Here, focusing on a methodological procedure, we have worked with young individuals, typical and with attention-deficit/hyperactivity disorder (ADHD) whose NC was assessed using q-statistics applied to the electroencephalogram (EEG). The EEG was recorded while subjects performed the visual Attention Network Test (ANT) and during a short pretask period of resting state. Time intervals of the EEG amplitudes that passed a threshold were collected from task and pretask signals from each subject. The data were satisfactorily fitted with a stretched $q$-exponential including a power-law prefactor(characterized by the exponent c), thus determining the best $(c, q)$ for each subject, indicative of their individual complexity. We found larger values of $q$ and $c$ in ADHD subjects as compared with the typical subjects both at task and pretask periods, the task values for both groups being larger than at rest. The $c$ parameter was highly specific in relation to DSM diagnosis for inattention, where well-defined clusters were observed. The parameter values were organized in four well-defined clusters in $(c, q)$-space. As expected, the tasks apparently induced greater complexity in neural functional states with likely greater amount of internal information processing. The results suggest that complexity is higher in ADHD subjects than in typical pairs. The distribution of values in the $(c, q)$-space derived from $q$-statistics seems to be a promising biomarker for ADHD diagnosis.
Holography inspired self-controlled reconfigurable intelligent surface
Authors: Jieao Zhu, Ze Gu, Qian Ma, Linglong Dai, Tie Jun Cui
Date: 2024-03-24 00:00:00

Among various promising candidate technologies for the sixth-generation (6G) wireless communications, recent advances in microwave metasurfaces have sparked a new research area of reconfigurable intelligent surfaces (RISs). By controllably reprogramming the wireless propagation channel, RISs are envisioned to achieve low-cost wireless capacity boosting, coverage extension, and enhanced energy efficiency. To reprogram the channel, each meta-atom on RIS needs an external control signal, which is usually generated by base station (BS). However, BS-controlled RISs require complicated control cables, which hamper their massive deployments. Here, we eliminate the need for BS control by proposing a self-controlled RIS (SC-RIS), which is inspired by the optical holography principle. Different from the existing BS-controlled RISs, each meta-atom of SC-RIS is integrated with an additional power detector for holographic recording. By applying the classical Fourier-transform processing to the measured hologram, SC-RIS is capable of retrieving the user's channel state information required for beamforming, thus enabling autonomous RIS beamforming without control cables. Owing to this WiFi-like plug-and-play capability without the BS control, SC-RISs are expected to enable easy and massive deployments in the future 6G systems.
Metasurface-Enabled Multifunctional Single-Frequency Sensors without External Power
Authors: Masaya Tashiro, Kosuke Ide, Kosei Asano, Satoshi Ishii, Yuta Sugiura, Akira Uchiyama, Ashif A. Fathnan, Hiroki Wakatsuchi
Date: 2024-03-13 00:00:00
Tasks: Autonomous Driving

IoT sensors are crucial for visualizing multidimensional and multimodal information and enabling future IT applications/services such as cyber-physical space, digital twins, autonomous driving, smart cities, and virtual/augmented reality (VR or AR). However, IoT sensors need to be battery-free to realistically manage and maintain the growing number of available sensing devices. Here, we provide a novel sensor design approach that employs metasurfaces to enable multifunctional sensing without requiring an external power source. Importantly, unlike existing metasurface-based sensors, our metasurfaces can sense multiple physical parameters even at a fixed frequency by breaking classic harmonic oscillations in the time domain, making the proposed sensors viable for usage with limited frequency resources. Moreover, we provide a method for predicting physical parameters using the machine learning-based approach of random forest regression. The sensing performance was confirmed by estimating temperature and light intensity, and excellent determination coefficients larger than 0.96 were achieved. Our study affords new opportunities for sensing multiple physical properties without relying on an external power source or needing multiple frequencies, which markedly simplifies and facilitates the design of next-generation wireless communication systems.
Universal Cold RNA Phase Transitions
Authors: Paolo Rissone, Aurelien Severino, Isabel Pastor, Felix Ritort
Date: 2024-03-22 00:00:00
Tasks: Diversity

RNA's diversity of structures and functions impacts all life forms since primordia. We use calorimetric force spectroscopy to investigate RNA folding landscapes in previously unexplored low-temperature conditions. We find that Watson-Crick RNA hairpins, the most basic secondary structure elements, undergo a glass-like transition below $\mathbf{T_G\sim 20 ^{\circ}}$C where the heat capacity abruptly changes and the RNA folds into a diversity of misfolded structures. We hypothesize that an altered RNA biochemistry, determined by sequence-independent ribose-water interactions, outweighs sequence-dependent base pairing. The ubiquitous ribose-water interactions lead to universal RNA phase transitions below $\mathbf{T_G}$, such as maximum stability at $\mathbf{T_S\sim 5 ^{\circ}}$C where water density is maximum, and cold denaturation at $\mathbf{T_C\sim-50^{\circ}}$C. RNA cold biochemistry may have a profound impact on RNA function and evolution.
Selective advantage of aerobic glycolysis over oxidative phosphorylation
Authors: Alexei Vazquez
Date: 2024-03-21 00:00:00

The utilization of glycolysis in aerobic conditions have been a subject of debate for more than a century. A hypothesis supported by previous data is that glycolysis has a higher rate of ATP production per protein mass and per occupied volume than oxidative phosphorylation (OxPhos). However, a recent work by Shen et al14 challenges previous estimates, reporting that OxPhos has a higher rate of ATP production per protein mass than glycolysis. Here I show that Shen et al14 make a key assumption that is a subject of debate: that the proteomic cost of OxPhos is limited to proteins in enzymes of OxPhos and the TCA cycle. I argue that an intact mitochondria is required for functional OxPhos and therefore the whole mitochondrial protein content should be included for the cost estimate of OxPhos. After doing so, glycolysis is the most efficient pathway per protein mass or per volume fraction.
Unified Generative Modeling of 3D Molecules via Bayesian Flow Networks
Authors: Yuxuan Song, Jingjing Gong, Yanru Qu, Hao Zhou, Mingyue Zheng, Jingjing Liu, Wei-Ying Ma
Date: 2024-03-17 00:00:00
Tasks: 3D Molecule Generation

Advanced generative model (e.g., diffusion model) derived from simplified continuity assumptions of data distribution, though showing promising progress, has been difficult to apply directly to geometry generation applications due to the multi-modality and noise-sensitive nature of molecule geometry. This work introduces Geometric Bayesian Flow Networks (GeoBFN), which naturally fits molecule geometry by modeling diverse modalities in the differentiable parameter space of distributions. GeoBFN maintains the SE-(3) invariant density modeling property by incorporating equivariant inter-dependency modeling on parameters of distributions and unifying the probabilistic modeling of different modalities. Through optimized training and sampling techniques, we demonstrate that GeoBFN achieves state-of-the-art performance on multiple 3D molecule generation benchmarks in terms of generation quality (90.87% molecule stability in QM9 and 85.6% atom stability in GEOM-DRUG. GeoBFN can also conduct sampling with any number of steps to reach an optimal trade-off between efficiency and quality (e.g., 20-times speedup without sacrificing performance).
Application of Principal Component Analysis and Artificial Neural Networks for the Prediction of QoS in FSO Links over South Africa
Authors: S. O Adebusola, P. A Owolawi, J. S Ojo, P. S Maswikaneng, A. O Ayo
Date: 2024-03-19 00:00:00

Optical Communication in Free Space (FSO) bids more radio bandwidth, operates under a gratis license, and has a lower startup cost as compared to Radio Frequency (RF). Nonetheless, its vulnerability to variations in atmospheric meteorological circumstances is a concern. Ultimately, the purpose of this study is to use Principal Component Analysis (PCA) with Artificial Neural Networks (ANN) to design a QoS prediction model for a terrestrial FSO communication connection. To accomplish the specified goal, meteorological data such as visibility, wind speed, and altitude were collected from the Weather Services in South Africa (SAWS) archive during a ten-year duration at five different locations: George, Johannesburg, Kimberly, Bloemfontein, and Polokwane. The eigenvalues of the first Principal Component (PC1) and the second Principal Component (PC2) in the PCA across the stations Bloemfontein, Johannesburg, Kimberly, George, and Polokwane are 7.624 and 1.020, 7.234, and 0.984, 6.204 and 1.723, 7.354 and 0.876, and 7.104 and 0.865, respectively, demonstrating that, they are kept as QoS variables to train the Artificial Neural Network (ANN) model as they provide the most compelling interpretation of the original variable data. The RMSE values of every proposed model across all the study locations are 0.1437, 0.2131, 0.2329, 0.1101, and 0.1977, respectively. Based on the RMSE, the proposed performed better over George. A realistic and accurate predictive model is developed for each of the study locations. Thus, the developed model will serve as a valuable tool for maintaining good QoS in FSO network services and improving telecom businesses in South Africa.
The Informativeness of Combined Experimental and Observational Data under Dynamic Selection
Authors: Yechan Park, Yuya Sasaki
Date: 2024-03-24 00:00:00
Tasks: Informativeness

This paper addresses the challenge of estimating the Average Treatment Effect on the Treated Survivors (ATETS; Vikstrom et al., 2018) in the absence of long-term experimental data, utilizing available long-term observational data instead. We establish two theoretical results. First, it is impossible to obtain informative bounds for the ATETS with no model restriction and no auxiliary data. Second, to overturn this negative result, we explore as a promising avenue the recent econometric developments in combining experimental and observational data (e.g., Athey et al., 2020, 2019); we indeed find that exploiting short-term experimental data can be informative without imposing classical model restrictions. Furthermore, building on Chesher and Rosen (2017), we explore how to systematically derive sharp identification bounds, exploiting both the novel data-combination principles and classical model restrictions. Applying the proposed method, we explore what can be learned about the long-run effects of job training programs on employment without long-term experimental data.
Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions
Authors: Gyungbae Park
Date: 2024-03-23 00:00:00
Tasks: valid

This paper studies debiased machine learning when nuisance parameters appear in indicator functions. An important example is maximized average welfare gain under optimal treatment assignment rules. For asymptotically valid inference for a parameter of interest, the current literature on debiased machine learning relies on Gateaux differentiability of the functions inside moment conditions, which does not hold when nuisance parameters appear in indicator functions. In this paper, we propose smoothing the indicator functions, and develop an asymptotic distribution theory for this class of models. The asymptotic behavior of the proposed estimator exhibits a trade-off between bias and variance due to smoothing. We study how a parameter which controls the degree of smoothing can be chosen optimally to minimize an upper bound of the asymptotic mean squared error. A Monte Carlo simulation supports the asymptotic distribution theory, and an empirical example illustrates the implementation of the method.
Difference-in-Differences with Unpoolable Data
Authors: Sunny Karim, Matthew D. Webb, Nichole Austin, Erin Strumpf
Date: 2024-03-23 00:00:00

Difference-in-differences (DID) is commonly used to estimate treatment effects but is infeasible in settings where data are unpoolable due to privacy concerns or legal restrictions on data sharing, particularly across jurisdictions. In this study, we identify and relax the assumption of data poolability in DID estimation. We propose an innovative approach to estimate DID with unpoolable data (UN-DID) which can accommodate covariates, multiple groups, and staggered adoption. Through analytical proofs and Monte Carlo simulations, we show that UN-DID and conventional DID estimates of the average treatment effect and standard errors are equal and unbiased in settings without covariates. With covariates, both methods produce estimates that are unbiased, equivalent, and converge to the true value. The estimates differ slightly but the statistical inference and substantive conclusions remain the same. Two empirical examples with real-world data further underscore UN-DID's utility. The UN-DID method allows the estimation of cross-jurisdictional treatment effects with unpoolable data, enabling better counterfactuals to be used and new research questions to be answered.
Tests for almost stochastic dominance
Authors: Amparo Baíllo, Javier Cárcamo, Carlos Mora-Corral
Date: 2024-03-22 00:00:00

We introduce a 2-dimensional stochastic dominance (2DSD) index to characterize both strict and almost stochastic dominance. Based on this index, we derive an estimator for the minimum violation ratio (MVR), also known as the critical parameter, of the almost stochastic ordering condition between two variables. We determine the asymptotic properties of the empirical 2DSD index and MVR for the most frequently used stochastic orders. We also provide conditions under which the bootstrap estimators of these quantities are strongly consistent. As an application, we develop consistent bootstrap testing procedures for almost stochastic dominance. The performance of the tests is checked via simulations and the analysis of real data.
Modelling with Discretized Variables
Authors: Felix Chan, Laszlo Matyas, Agoston Reguly
Date: 2024-03-22 00:00:00
Tasks: regression

This paper deals with econometric models in which the dependent variable, some explanatory variables, or both are observed as censored interval data. This discretization often happens due to confidentiality of sensitive variables like income. Models using these variables cannot point identify regression parameters as the conditional moments are unknown, which led the literature to use interval estimates. Here, we propose a discretization method through which the regression parameters can be point identified while preserving data confidentiality. We demonstrate the asymptotic properties of the OLS estimator for the parameters in multivariate linear regressions for cross-sectional data. The theoretical findings are supported by Monte Carlo experiments and illustrated with an application to the Australian gender wage gap.
Fast TTC Computation
Authors: Irene Aldridge
Date: 2024-03-22 00:00:00

This paper proposes a fast Markov Matrix-based methodology for computing Top Trading Cycles (TTC) that delivers O(1) computational speed, that is speed independent of the number of agents and objects in the system. The proposed methodology is well suited for complex large-dimensional problems like housing choice. The methodology retains all the properties of TTC, namely, Pareto-efficiency, individual rationality and strategy-proofness.
Determinants of Uruguay's Real Effective Exchange Rate: A Mundell-Fleming Model Approach
Authors: Didarul Islam, Mohammad Abdullah Al Faisal
Date: 2024-03-25 00:00:00

This study examines the factors influencing the short-term real effective exchange rate (REER) in Uruguay by applying an extended Mundell-Fleming model. Analyzing the impact of the US lending rate (USLR), money supply (M2), inflation (CPI), and the world interest rate (WIR), the paper uses a linear regression model with Newey-West standard errors. Key findings reveal that an increase in the USLR, CPI, and M2 is associated with a depreciation of the REER. In contrast, WIR shows no significant impact. These findings are consistent with the theoretical expectations of the Mundell-Fleming model regarding open economies under floating exchange rates. Therefore, authorities should tighten monetary policy, control inflation, adjust fiscal strategies, and boost exports in response to Peso depreciation.
Hedge Fund Index Rules and Construction
Authors: David Xiao
Date: 2024-03-23 00:00:00

A Hedge Fund Index is very useful for tracking the performance of hedge fund investments, especially the timing of fund redemption. This paper presents a methodology for constructing a hedge fund index that is more like a quantitative fund of fund, rather than a weighted sum of a number of early replicable market indices, which are re-balanced periodically. The constructed index allows hedge funds to directly hedge their exposures to index-linked products. That is important given that hedge funds are an asset class with reduced transparency, and the returns are traditionally difficult to replicate using liquid instruments.
Measuring Gender and Racial Biases in Large Language Models
Authors: Jiafu An, Difang Huang, Chen Lin, Mingzhu Tai
Date: 2024-03-22 00:00:00
Tasks: Decision Making, Language Modelling, Large Language Model

In traditional decision making processes, social biases of human decision makers can lead to unequal economic outcomes for underrepresented social groups, such as women, racial or ethnic minorities. Recently, the increasing popularity of Large language model based artificial intelligence suggests a potential transition from human to AI based decision making. How would this impact the distributional outcomes across social groups? Here we investigate the gender and racial biases of OpenAIs GPT, a widely used LLM, in a high stakes decision making setting, specifically assessing entry level job candidates from diverse social groups. Instructing GPT to score approximately 361000 resumes with randomized social identities, we find that the LLM awards higher assessment scores for female candidates with similar work experience, education, and skills, while lower scores for black male candidates with comparable qualifications. These biases may result in a 1 or 2 percentage point difference in hiring probabilities for otherwise similar candidates at a certain threshold and are consistent across various job positions and subsamples. Meanwhile, we also find stronger pro female and weaker anti black male patterns in democratic states. Our results demonstrate that this LLM based AI system has the potential to mitigate the gender bias, but it may not necessarily cure the racial bias. Further research is needed to comprehend the root causes of these outcomes and develop strategies to minimize the remaining biases in AI systems. As AI based decision making tools are increasingly employed across diverse domains, our findings underscore the necessity of understanding and addressing the potential unequal outcomes to ensure equitable outcomes across social groups.