pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Computational Efficiency in Multiscale Systems Using Deep Learning of Coordinates and Flow Maps
Authors: Asif Hamid, Danish Rafiq, Shahkar Ahmad Nahvi, Mohammad Abid Bazaz
Date: 2024-04-28 00:00:00
Tasks: Computational Efficiency

Complex systems often show macroscopic coherent behavior due to the interactions of microscopic agents like molecules, cells, or individuals in a population with their environment. However, simulating such systems poses several computational challenges during simulation as the underlying dynamics vary and span wide spatiotemporal scales of interest. To capture the fast-evolving features, finer time steps are required while ensuring that the simulation time is long enough to capture the slow-scale behavior, making the analyses computationally unmanageable. This paper showcases how deep learning techniques can be used to develop a precise time-stepping approach for multiscale systems using the joint discovery of coordinates and flow maps. While the former allows us to represent the multiscale dynamics on a representative basis, the latter enables the iterative time-stepping estimation of the reduced variables. The resulting framework achieves state-of-the-art predictive accuracy while incurring lesser computational costs. We demonstrate this ability of the proposed scheme on the large-scale Fitzhugh Nagumo neuron model and the 1D Kuramoto-Sivashinsky equation in the chaotic regime.
Nonequilibrium dynamics and thermodynamics provide the underlying physical mechanism of the perceptual rivalry
Authors: Yuxuan Wu, Liufang Xu, Jin Wang
Date: 2024-06-29 00:00:00
Tasks: Decision Making, Time Series

Perceptual rivalry, where conflicting sensory information leads to alternating perceptions crucial for associated cognitive function, has attracted researcher's attention for long. Despite progresses being made, recent studies have revealed limitations and inconsistencies in our understanding across various rivalry contexts. We develop a unified physical framework, where perception undergoes a consecutive phase transition process encompassing different multi-state competitions. We reveal the underlying mechanisms of perceptual rivalry by identifying dominant switching paths among perceptual states and quantifying mean perceptual durations, switching frequencies, and proportions of different perceptions. We uncover the underlying nonequilibrium dynamics and thermodynamics by analyzing average nonequilibrium flux and entropy production rate, while associated time series irreversibility reflects the underlying nonequilibrium mechanism of perceptual rivalry and link thermodynamical results with neuro-electrophysiological experiments. Our framework provides a global and physical understanding of brain perception, which may go beyond cognitive science or psychology but embodies the connection with wider fields as decision-making.
Machine learning meets mass spectrometry: a focused perspective
Authors: Daniil A. Boiko, Valentine P. Ananikov
Date: 2024-06-27 00:00:00

Mass spectrometry is a widely used method to study molecules and processes in medicine, life sciences, chemistry, catalysis, and industrial product quality control, among many other applications. One of the main features of some mass spectrometry techniques is the extensive level of characterization (especially when coupled with chromatography and ion mobility methods, or a part of tandem mass spectrometry experiment) and a large amount of generated data per measurement. Terabyte scales can be easily reached with mass spectrometry studies. Consequently, mass spectrometry has faced the challenge of a high level of data disappearance. Researchers often neglect and then altogether lose access to the rich information mass spectrometry experiments could provide. With the development of machine learning methods, the opportunity arises to unlock the potential of these data, enabling previously inaccessible discoveries. The present perspective highlights reevaluation of mass spectrometry data analysis in the new generation of methods and describes significant challenges in the field, particularly related to problems involving the use of electrospray ionization. We argue that further applications of machine learning raise new requirements for instrumentation (increasing throughput and information density, decreasing pricing, and making more automation-friendly software), and once met, the field may experience significant transformation.
A Review of Large Language Models and Autonomous Agents in Chemistry
Authors: Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White
Date: 2024-06-26 00:00:00
Tasks: Property Prediction, scientific discovery

Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities in these domains and their potential to accelerate scientific discovery through automation. We also review LLM-based autonomous agents: LLMs with a broader set of tools to interact with their surrounding environment. These agents perform diverse tasks such as paper scraping, interfacing with automated laboratories, and synthesis planning. As agents are an emerging topic, we extend the scope of our review of agents beyond chemistry and discuss across any scientific domains. This review covers the recent history, current capabilities, and design of LLMs and autonomous agents, addressing specific challenges, opportunities, and future directions in chemistry. Key challenges include data quality and integration, model interpretability, and the need for standard benchmarks, while future directions point towards more sophisticated multi-modal agents and enhanced collaboration between agents and experimental methods. Due to the quick pace of this field, a repository has been built to keep track of the latest studies: https://github.com/ur-whitelab/LLMs-in-science.
Optimal Low-Depth Quantum Signal-Processing Phase Estimation
Authors: Yulong Dong, Jonathan A. Gross, Murphy Yuezhen Niu
Date: 2024-06-17 00:00:00
Tasks: parameter estimation

Quantum effects like entanglement and coherent amplification can be used to drastically enhance the accuracy of quantum parameter estimation beyond classical limits. However, challenges such as decoherence and time-dependent errors hinder Heisenberg-limited amplification. We introduce Quantum Signal-Processing Phase Estimation algorithms that are robust against these challenges and achieve optimal performance as dictated by the Cram\'{e}r-Rao bound. These algorithms use quantum signal transformation to decouple interdependent phase parameters into largely orthogonal ones, ensuring that time-dependent errors in one do not compromise the accuracy of learning the other. Combining provably optimal classical estimation with near-optimal quantum circuit design, our approach achieves a standard deviation accuracy of $10^{-4}$ radians for estimating unwanted swap angles in superconducting two-qubit experiments, using low-depth ($<10$) circuits. This represents up to two orders of magnitude improvement over existing methods. Theoretically and numerically, we demonstrate the optimality of our algorithm against time-dependent phase errors, observing that the variance of the time-sensitive parameter $\varphi$ scales faster than the asymptotic Heisenberg scaling in the small-depth regime. Our results are rigorously validated against the quantum Fisher information, confirming our protocol's ability to achieve unmatched precision for two-qubit gate learning.
A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression
Authors: Yufan Zhu, Zi-Yu Khoo, Jonathan Sze Choong Low, Stephane Bressan
Date: 2024-06-17 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Prompt Engineering, Symbolic Regression

Interleaved practice enhances the memory and problem-solving ability of students in undergraduate courses. We introduce a personalized learning tool built on a Large Language Model (LLM) that can provide immediate and personalized attention to students as they complete homework containing problems interleaved from undergraduate physics courses. Our tool leverages the dimensional analysis method, enhancing students' qualitative thinking and problem-solving skills for complex phenomena. Our approach combines LLMs for symbolic regression with dimensional analysis via prompt engineering and offers students a unique perspective to comprehend relationships between physics variables. This fosters a broader and more versatile understanding of physics and mathematical principles and complements a conventional undergraduate physics education that relies on interpreting and applying established equations within specific contexts. We test our personalized learning tool on the equations from Feynman's lectures on physics. Our tool can correctly identify relationships between physics variables for most equations, underscoring its value as a complementary personalized learning tool for undergraduate physics students.
A refractory density approach to a multi-scale SEIRS epidemic model
Authors: Anton Chizhov, Laurent Pujo-Menjouet, Tilo Schwalger, Mattia Sensi
Date: 2024-07-02 00:00:00
Tasks: Epidemiology

We propose a novel multi-scale modeling framework for infectious disease spreading, borrowing ideas and modeling tools from the so-called Refractory Density (RD) approach. We introduce a microscopic model that describes the probability of infection for a single individual and the evolution of the disease within their body. From the individual-level description, we then present the corresponding population-level model of epidemic spreading on the mesoscopic and macroscopic scale. We conclude with numerical illustrations taking into account either a white Gaussian noise or an escape noise to showcase the potential of our approach in producing both transient and asymptotic complex dynamics as well as finite-size fluctuations consistently across multiple scales. A comparison with the epidemiology of coronaviruses is also given to corroborate the qualitative relevance of our new approach.
Finely Stratified Rerandomization Designs
Authors: Max Cytrynbaum
Date: 2024-07-03 00:00:00

We study estimation and inference on causal parameters under finely stratified rerandomization designs, which use baseline covariates to match units into groups (e.g. matched pairs), then rerandomize within-group treatment assignments until a balance criterion is satisfied. We show that finely stratified rerandomization does partially linear regression adjustment by design, providing nonparametric control over the stratified covariates and linear control over the rerandomized covariates. We introduce several new forms of rerandomization, allowing for imbalance metrics based on nonlinear estimators, and proposing a minimax scheme that minimizes the computational cost of rerandomization subject to a bound on estimation error. While the asymptotic distribution of GMM estimators under stratified rerandomization is generically non-normal, we show how to restore asymptotic normality using ex-post linear adjustment tailored to the stratification. We derive new variance bounds that enable conservative inference on finite population causal parameters, and provide asymptotically exact inference on their superpopulation counterparts.
Wild inference for wild SVARs with application to heteroscedasticity-based IV
Authors: Bulat Gafarov, Madina Karamysheva, Andrey Polbin, Anton Skrobotov
Date: 2024-07-03 00:00:00

Structural vector autoregressions are used to compute impulse response functions (IRF) for persistent data. Existing multiple-parameter inference requires cumbersome pretesting for unit roots, cointegration, and trends with subsequent stationarization. To avoid pretesting, we propose a novel \emph{dependent wild bootstrap} procedure for simultaneous inference on IRF using local projections (LP) estimated in levels in possibly \emph{nonstationary} and \emph{heteroscedastic} SVARs. The bootstrap also allows efficient smoothing of LP estimates. We study IRF to US monetary policy identified using FOMC meetings count as an instrument for heteroscedasticity of monetary shocks. We validate our method using DSGE model simulations and alternative SVAR methods.
Conditional Forecasts in Large Bayesian VARs with Multiple Equality and Inequality Constraints
Authors: Joshua C. C. Chan, Davide Pettenuzzo, Aubrey Poon, Dan Zhu
Date: 2024-07-02 00:00:00

Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings. In spite of this, the existing algorithms used to generate conditional forecasts tend to be very computationally intensive, especially when working with large Vector Autoregressions or when multiple linear equality and inequality constraints are imposed at once. We introduce a novel precision-based sampler that is fast, scales well, and yields conditional forecasts from linear equality and inequality constraints. We show in a simulation study that the proposed method produces forecasts that are identical to those from the existing algorithms but in a fraction of the time. We then illustrate the performance of our method in a large Bayesian Vector Autoregression where we simultaneously impose a mix of linear equality and inequality constraints on the future trajectories of key US macroeconomic indicators over the 2020--2022 period.
Co-benefits of Agricultural Diversification and Technology for Food and Nutrition Security in China
Authors: Thomas Cherico Wanger, Estelle Raveloaritiana, Siyan Zeng, Haixiu Gao, Xueqing He, Yiwen Shao, Panlong Wu, Kris A. G. Wyckhuys, Wenwu Zhou, Yi Zou, Zengrong Zhu, Ling Li, Haiyan Cen, Yunhui Liu, Shenggen Fan
Date: 2024-07-01 00:00:00
Tasks: Nutrition

China is the leading crop producer and has successfully implemented sustainable development programs related to agriculture. Sustainable agriculture has been promoted to achieve national food security targets such as food self-sufficiency through the well-facilitated farmland construction (WFFC) approach. The WFFC is introduced in Chinas current national 10-year plan to consolidate farmlands into large and simplified production areas to maximise automation, and improve soil fertility and productivity. However, research suggests that diversified and smaller farms faciliate ecosystem services, can improve yield resilience, defuse human health threats, and increase farm profitability. Currently, WFFC has not considered ecological farmland improvements and it may miss long-term environmental benefits including ecosystem service preservation conducive to yields. Moreover, the nutritional status in China has changed in recent decades with undernutrition being dramatically reduced, but the prevalence of overweight, obesity, and chronic diseases being increased. While a strategic choice and management of crop and livestock species can improve nutrition, the environmental and production benefits of agricultural diversification are currently not well interlinked with Chinas food and nutrition security discussions. Lastly, the role of agricultural technology for socioeconomic benefits and the link with diversified agricultural production may provide vast benefits for food security. Here, we focus on the opportunities and co-benefits of agricultural diversification and technology innovations to advance food and nutrition security in China through ecosystem service and yield benefits. Our applied five-point research agenda can provide evidence-based opportunities to support China in reaching its ambitious food security targets through agricultural diversification with global ramifications.
AI-powered Chatbots: Effective Communication Styles for Sustainable Development Goals
Authors: Ennio Bilancini, Leonardo Boncinelli, Eugenio Vicario
Date: 2024-07-01 00:00:00

This paper presents an analysis of two pre-registered experimental studies examining the impact of `Motivational Interviewing' and `Directing Style' on discussions about Sustainable Development Goals. To evaluate the effectiveness of these communication styles in enhancing awareness and motivating action toward the Sustainable Development Goals, we measured the engagement levels of participants, along with their self-reported interest and learning outcomes. Our results indicate that `Motivational Interviewing' is more effective than `Directing Style' for engagement and interest, while no appreciable difference is found on learning.
Individuals, Crowds, and the Network Dynamics of Belief Accuracy
Authors: Charlie Pilgrim, Joshua Becker
Date: 2024-06-28 00:00:00

Does talking to others make people more accurate or less accurate on numeric estimates such as quantitative evaluations or probabilistic forecasts? Research on peer-to-peer communication suggests that discussion between people will usually improve belief accuracy, while research on social networks suggests that error can percolate through groups and reduce accuracy. One challenge to interpreting empirical literature is that some studies measure accuracy at the group level, while others measure individual accuracy. We explain how social influence impacts belief accuracy by analyzing a formal model of opinion formation to identify the relationship between individual accuracy, group accuracy, and the network dynamics of belief formation. When opinions become more similar over time, change in individual error is always strictly better than change in group error, by a value equal to the change in variance. We show that change in group error can be decomposed into the influence network centralization, the accuracy/influence correlation ("calibration"), and the averageness/influence correlation ("herding"). Because group dynamics both theoretically and empirically lead people to become more similar over time, one might intuitively expect that the same factors which reduce group accuracy will also reduce individual accuracy. Instead, we find that individuals reliably improve under nearly all conditions, even when groups get worse. We support this analysis with data from six previously published experiments.
Decarbonization analysis on residential end uses in the emerging economies
Authors: Ran Yan, Minda Ma
Date: 2024-05-17 00:00:00

This study explores the historical emission patterns and decarbonization efforts of China and India, the largest emerging emitters in residential building operations. Using a novel carbon intensity model and structural decomposition approach, it assesses the operational decarbonization progress over the past two decades. Results show significant decarbonization, with China and India collectively reducing 1498.3 and 399.7 MtCO2, respectively. Electrification notably contributed to decarbonizing space cooling and appliances in both countries.
Unveiling Patterns in European Airbnb Prices: A Comprehensive Analytical Study Using Machine Learning Techniques
Authors: Trinath Sai Subhash Reddy Pittala, Uma Maheswara R Meleti, Hemanth Vasireddy
Date: 2024-04-30 00:00:00

In the burgeoning market of short-term rentals, understanding pricing dynamics is crucial for a range of stake-holders. This study delves into the factors influencing Airbnb pricing in major European cities, employing a comprehensive dataset sourced from Kaggle. We utilize advanced regression techniques, including linear, polynomial, and random forest models, to analyze a diverse array of determinants, such as location characteristics, property types, and host-related factors. Our findings reveal nuanced insights into the variables most significantly impacting pricing, highlighting the varying roles of geographical, structural, and host-specific attributes. This research not only sheds light on the complex pricing landscape of Airbnb accommodations in Europe but also offers valuable implications for hosts seeking to optimize pricing strategies and for travelers aiming to understand pricing trends. Furthermore, the study contributes to the broader discourse on pricing mechanisms in the shared economy, suggesting avenues for future research in this rapidly evolving sector.
In the Shadow of Smith`s Invisible Hand: Risks to Economic Stability and Social Wellbeing in the Age of Intelligence
Authors: Jo-An Occhipinti, William Hynes, Ante Prodan, Harris A. Eyre, Roy Green, Sharan Burrow, Marcel Tanner, John Buchanan, Goran Ujdur, Frederic Destrebecq, Christine Song, Steven Carnevale, Ian B. Hickie, Mark Heffernan
Date: 2024-04-22 00:00:00

Work is fundamental to societal prosperity and mental health, providing financial security, identity, purpose, and social integration. The emergence of generative artificial intelligence (AI) has catalysed debate on job displacement. Some argue that many new jobs and industries will emerge to offset the displacement, while others foresee a widespread decoupling of economic productivity from human input threatening jobs on an unprecedented scale. This study explores the conditions under which both may be true and examines the potential for a self-reinforcing cycle of recessionary pressures that would necessitate sustained government intervention to maintain job security and economic stability. A system dynamics model was developed to undertake ex ante analysis of the effect of AI-capital deepening on labour underutilisation and demand in the economy. Results indicate that even a moderate increase in the AI-capital-to-labour ratio could increase labour underutilisation to double its current level, decrease per capita disposable income by 26% (95% interval, 20.6% - 31.8%), and decrease the consumption index by 21% (95% interval, 13.6% - 28.3%) by mid-2050. To prevent a reduction in per capita disposable income due to the estimated increase in underutilization, at least a 10.8-fold increase in the new job creation rate would be necessary. Results demonstrate the feasibility of an AI-capital- to-labour ratio threshold beyond which even high rates of new job creation cannot prevent declines in consumption. The precise threshold will vary across economies, emphasizing the urgent need for empirical research tailored to specific contexts. This study underscores the need for governments, civic organisations, and business to work together to ensure a smooth transition to an AI- dominated economy to safeguard the Mental Wealth of nations.
Crosswashing in Sustainable Investing: Unveiling Strategic Practices Impacting ESG Scores
Authors: Bertrand Kian Hassani, Yacoub Bahini
Date: 2024-03-21 00:00:00
Tasks: Decision Making

This paper introduces and defines a novel concept in sustainable investing, termed crosswashing, and explore its impact on ESG (Environmental, Social, and Governance) ratings through quantitative analysis using a Multi-Criteria Decision Making (MCDM) model. The study emphasises that this specific form of greenwashing is not currently considered in existing ESG assessments, potentially leading to an inflated perception of corporate ethical practices. Unlike traditional greenwashing, crosswashing involves companies strategically investing in sustainable activities to boost Environmental, Social, and Governance (ESG) scores while preserving nonsustainable core operations. By unveiling the nuances of crosswashing, the research contributes to a more nuanced understanding of sustainable investing, offering insights for improved evaluation and regulation of corporate environmental and ethical responsibilities.