pwc-archive/papers-with-abstracts huggingface dataset:
Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning
Authors: Julio J. Valdés, Alain B. Tchagang
Date: 2023-09-25 00:00:00
Tasks: Descriptive, Outlier Detection

This paper explores the internal structure of two quantum mechanics datasets (QM7b, QM9), composed of several thousands of organic molecules and described in terms of electronic properties. Understanding the structure and characteristics of this kind of data is important when predicting the atomic composition from the properties in inverse molecular designs. Intrinsic dimension analysis, clustering, and outlier detection methods were used in the study. They revealed that for both datasets the intrinsic dimensionality is several times smaller than the descriptive dimensions. The QM7b data is composed of well defined clusters related to atomic composition. The QM9 data consists of an outer region predominantly composed of outliers, and an inner core region that concentrates clustered, inliner objects. A significant relationship exists between the number of atoms in the molecule and its outlier/inner nature. Despite the structural differences, the predictability of variables of interest for inverse molecular design is high. This is exemplified with models estimating the number of atoms of the molecule from both the original properties, and from lower dimensional embedding spaces.
Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights
Authors: Hao Xu, Yuntian Chen, Zhenzhong Zeng, Nina Li, Jian Li, Dongxiao Zhang
Date: 2023-09-27 00:00:00
Tasks: Precipitation Forecasting

Despite the remarkable strides made by AI-driven models in modern precipitation forecasting, these black-box models cannot inherently deepen the comprehension of underlying mechanisms. To address this limitation, we propose an AI-driven knowledge discovery framework known as genetic algorithm-geographic weighted regression (GA-GWR). Our approach seeks to unveil the explicit equations that govern the intricate relationship between precipitation patterns and terrain characteristics in regions marked by complex terrain. Through this AI-driven knowledge discovery, we uncover previously undisclosed explicit equations that shed light on the connection between terrain features and precipitation patterns. These equations demonstrate remarkable accuracy when applied to precipitation data, outperforming conventional empirical models. Notably, our research reveals that the parameters within these equations are dynamic, adapting to evolving climate patterns. Ultimately, the unveiled equations have practical applications, particularly in fine-scale downscaling for precipitation predictions using low-resolution future climate data. This capability offers invaluable insights into the anticipated changes in precipitation patterns across diverse terrains under future climate scenarios, which enhances our ability to address the challenges posed by contemporary climate science.
Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling
Authors: Morteza Mardani, Noah Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Mohammad Amin Nabian, Tao Ge, Akshay Subramaniam, Karthik Kashinath, Jan Kautz, Mike Pritchard
Date: 2023-09-24 00:00:00

The state of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis. To address the large resolution ratio, different physics involved at different scales and prediction of channels beyond those in the input data, we employ a two-step approach where a UNet predicts the mean and a corrector diffusion (CorrDiff) model predicts the residual. CorrDiff exhibits encouraging skill in bulk MAE and CRPS scores. The predicted spectra and distributions from CorrDiff faithfully recover important power law relationships in the target data. Case studies of coherent weather phenomena show that CorrDiff can help sharpen wind and temperature gradients that co-locate with intense rainfall in cold front, and can help intensify typhoons and synthesize rain band structures. Calibration of model uncertainty remains challenging. The prospect of unifying methods like CorrDiff with coarser resolution global weather models implies a potential for global-to-regional multi-scale machine learning simulation.
Density Estimation via Measure Transport: Outlook for Applications in the Biological Sciences
Authors: Vanessa Lopez-Marrero, Patrick R. Johnstone, Gilchan Park, Xihaier Luo
Date: 2023-09-27 00:00:00
Tasks: Density Estimation

One among several advantages of measure transport methods is that they allow for a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
Authors: J. Thorben Frank, Oliver T. Unke, Klaus-Robert Müller, Stefan Chmiela
Date: 2023-09-21 00:00:00

Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the reliability of MLFFs in molecular dynamics (MD) simulations is facing growing scrutiny due to concerns about instability over extended simulation timescales. Our findings suggest a potential connection between robustness to cumulative inaccuracies and the use of equivariant representations in MLFFs, but the computational cost associated with these representations can limit this advantage in practice. To address this, we propose a transformer architecture called SO3krates that combines sparse equivariant representations (Euclidean variables) with a self-attention mechanism that separates invariant and equivariant information, eliminating the need for expensive tensor products. SO3krates achieves a unique combination of accuracy, stability, and speed that enables insightful analysis of quantum properties of matter on extended time and system size scales. To showcase this capability, we generate stable MD trajectories for flexible peptides and supra-molecular structures with hundreds of atoms. Furthermore, we investigate the PES topology for medium-sized chainlike molecules (e.g., small peptides) by exploring thousands of minima. Remarkably, SO3krates demonstrates the ability to strike a balance between the conflicting demands of stability and the emergence of new minimum-energy conformations beyond the training data, which is crucial for realistic exploration tasks in the field of biochemistry.
Neural Operators for Accelerating Scientific Simulations and Design
Authors: Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, Anima Anandkumar
Date: 2023-09-27 00:00:00
Tasks: scientific discovery, Super-Resolution, Weather Forecasting

Scientific discovery and engineering design are currently limited by the time and cost of physical experiments, selected mostly through trial-and-error and intuition that require deep domain expertise. Numerical simulations present an alternative to physical experiments but are usually infeasible for complex real-world domains due to the computational requirements of existing numerical methods. Artificial intelligence (AI) presents a potential paradigm shift by developing fast data-driven surrogate models. In particular, an AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains, e.g., spatiotemporal processes and partial differential equations (PDE). They can extrapolate and predict solutions at new locations unseen during training, i.e., perform zero-shot super-resolution. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling, while being 4-5 orders of magnitude faster. Further, Neural Operators can be integrated with physics and other domain constraints enforced at finer resolutions to obtain high-fidelity solutions and good generalization. Since Neural Operators are differentiable, they can directly optimize parameters for inverse design and other inverse problems. We believe that Neural Operators present a transformative approach to simulation and design, enabling rapid research and development.
Multiple Case Physics-Informed Neural Network for Biomedical Tube Flows
Authors: Hong Shen Wong, Wei Xuan Chan, Bing Huan Li, Choon Hwai Yap
Date: 2023-09-26 00:00:00

Fluid dynamics computations for tube-like geometries are important for biomedical evaluation of vascular and airway fluid dynamics. Physics-Informed Neural Networks (PINNs) have recently emerged as a good alternative to traditional computational fluid dynamics (CFD) methods. The vanilla PINN, however, requires much longer training time than the traditional CFD methods for each specific flow scenario and thus does not justify its mainstream use. Here, we explore the use of the multi-case PINN approach for calculating biomedical tube flows, where varied geometry cases are parameterized and pre-trained on the PINN, such that results for unseen geometries can be obtained in real time. Our objective is to identify network architecture, tube-specific, and regularization strategies that can optimize this, via experiments on a series of idealized 2D stenotic tube flows.
Sluggish news reactions: A combinatorial approach for synchronizing stock jumps
Authors: Nabil Bouamara, Kris Boudt, Sébastien Laurent, Christopher J. Neely
Date: 2023-09-27 00:00:00

Stock prices often react sluggishly to news, producing gradual jumps and jump delays. Econometricians typically treat these sluggish reactions as microstructure effects and settle for a coarse sampling grid to guard against them. Synchronizing mistimed stock returns on a fine sampling grid allows us to automatically detect noisy jumps and better approximate the true common jumps in related stock prices.
To better understand realized ecosystem services: An integrated analysis framework of supply, demand, flow and use
Authors: Shuyao Wu, Kai-Di Liu, Wentao Zhang, Yuehan Dou, Yuqing Chen, Delong Li
Date: 2023-09-27 00:00:00

Realized ecosystem services (ES) are the actual use of ES by societies, which is more directly linked to human well-being than potential ES. However, there is a lack of a general analysis framework to understand how much ES was realized. In this study, we first proposed a Supply-Demand-Flow-Use (SDFU) framework that integrates the supply, demand, flow, and use of ES and differentiates these concepts into different aspects (e.g., potential vs. actual ES demand, export and import flows of supply, etc.). Then, we applied the framework to three examples of ES that can be found in typical urban green parks (i.e., wild berry supply, pollination, and recreation). We showed how the framework could assess the actual use of ES and identify the supply-limited, demand-limited, and supply-demand-balanced types of realized ES. We also discussed the scaling features, temporal dynamics, and spatial characteristics of realized ES, as well as some critical questions for future studies. Although facing challenges, we believe that the applications of the SDFU framework can provide a systematic way to accurately assess the actual use of ES and better inform management and policy-making for sustainable use of nature's benefits. Therefore, we hope that our study will stimulate more research on realized ES and contribute to a deeper understanding of their roles in enhancing human well-being.
Enumerating the climate impact of disequilibrium in critical mineral supply
Authors: Lucas Woodley, Chung Yi See, Peter Cook, Megan Yeo, Daniel S. Palmer, Laurena Huh, Seaver Wang, Ashley Nunes
Date: 2023-09-27 00:00:00

Recently proposed tailpipe emissions standards aim to significant increases in electric vehicle (EV) sales in the United States. Our work examines whether this increase is achievable given potential constraints in EV mineral supply chains. We estimate a model that reflects international sourcing rules, heterogeneity in the mineral intensity of predominant battery chemistries, and long-run grid decarbonization efforts. Our efforts yield five key findings. First, compliance with the proposed standard necessitates replacing at least 10.21 million new ICEVs with EVs between 2027 and 2032. Second, based on economically viable and geologically available mineral reserves, manufacturing sufficient EVs is plausible across most battery chemistries and could, subject to the chemistry leveraged, reduce up to 457.3 million total tons of CO2e. Third, mineral production capacities of the US and its allies constrain battery production to a total of 5.09 million EV batteries between 2027 and 2032, well short of deployment requirements to meet EPA standards even if battery manufacturing is optimized to exclusively manufacture materials efficient NMC 811 batteries. Fourth, disequilibrium between mineral supply and demand results in at least 59.54 million tons of CO2e in total lost lifecycle emissions benefits. Fifth, limited present-day production of battery-grade graphite and to a lesser extent, cobalt, constrain US electric vehicle battery pack manufacturing under strict sourcing rules. We demonstrate that should mineral supply bottlenecks persist, hybrid electric vehicles may offer equivalent lifecycle emissions benefits as EVs while relaxing mineral production demands, though this represents a tradeoff of long-term momentum in electric vehicle deployment in favor of near-term carbon dioxide emissions reductions.
The importance of quality in austere times: University competitiveness and grant income
Authors: Ye Sun, Athen Ma, Georg von Graevenitz, Vito Latora
Date: 2023-09-26 00:00:00

After 2009 many governments implemented austerity measures, often restricting science funding. Did such restrictions further skew grant income towards elite scientists and universities? And did increased competition for funding undermine participation? UK science funding agencies significantly reduced numbers of grants and total grant funding in response to austerity, but surprisingly restrictions of science funding were relaxed after the 2015 general election. Exploiting this natural experiment, we show that conventional measures of university competitiveness are poor proxies for competitiveness. An alternative measure of university competitiveness, drawn from complexity science, captures the highly dynamical way in which universities engage in scientific subjects. Building on a data set of 43,430 UK funded grants between 2006 and 2020, we analyse rankings of UK universities and investigate the effect of research competitiveness on grant income. When austerity was relaxed in 2015 the elasticity of grant income w.r.t. research competitiveness fell, reflecting increased effort by researchers at less competitive universities. These scientists increased number and size of grant applications, increasing grant income. The study reveals how funding agencies, facing heterogeneous competitiveness in the population of scientists, affect research effort across the distribution of competitiveness.
A dynamic systems approach to harness the potential of social tipping
Authors: Sibel Eker, Charlie Wilson, Niklas Höhne, Mark S. McCaffrey, Irene Monasterolo, Leila Niamir, Caroline Zimm
Date: 2023-09-26 00:00:00
Tasks: Descriptive

Social tipping points are promising levers to achieve net-zero greenhouse gas emission targets. They describe how social, political, economic or technological systems can move rapidly into a new state if cascading positive feedback mechanisms are triggered. Analysing the potential of social tipping for rapid decarbonization requires considering the inherent complexity of social systems. Here, we identify that existing scientific literature is inclined to a narrative-based account of social tipping, lacks a broad empirical framework and a multi-systems view. We subsequently outline a dynamic systems approach that entails (i) a systems outlook involving interconnected feedback mechanisms alongside cross-system and cross-scale interactions, and including a socioeconomic and environmental injustice perspective (ii) directed data collection efforts to provide empirical evidence for and monitor social tipping dynamics, (iii) global, integrated, descriptive modelling to project future dynamics and provide ex-ante evidence for interventions. Research on social tipping must be accordingly solidified for climate policy relevance.
Nuclear Energy Acceptance in Poland: From Societal Attitudes to Effective Policy Strategies -- Network Modeling Approach
Authors: Pawel Robert Smolinski, Joseph Januszewicz, Barbara Pawlowska, Jacek Winiarski
Date: 2023-09-26 00:00:00

Poland is currently undergoing substantial transformation in its energy sector, and gaining public support is pivotal for the success of its energy policies. We conducted a study with 338 Polish participants to investigate societal attitudes towards various energy sources, including nuclear energy and renewables. Applying a novel network approach, we identified a multitude of factors influencing energy acceptance. Political ideology is the central factor in shaping public acceptance, however we also found that environmental attitudes, risk perception, safety concerns, and economic variables play substantial roles. Considering the long-term commitment associated with nuclear energy and its role in Poland's energy transformation, our findings provide a foundation for improving energy policy in Poland. Our research underscores the importance of policies that resonate with the diverse values, beliefs, and preferences of the population. While the risk-risk trade-off and technology-focused strategies are effective to a degree, we advocate for a more comprehensive approach. The framing strategy, which tailors messages to distinct societal values, shows particular promise.
Linearity of Aggregate Production Functions
Authors: Christopher P. Chambers, Alexis Akira Toda
Date: 2023-09-27 00:00:00

We prove that when individual firms employ constant-returns-to-scale production functions, the aggregate production function defined by the maximum achievable total output given total inputs is always linear on some part of the domain. Our result provides a microfoundation for the linear production function.
Challenges of building medical image datasets for development of deep learning software in stroke
Authors: Alessandro Fontanella, Wenwen Li, Grant Mair, Antreas Antoniou, Eleanor Platt, Chloe Martin, Paul Armitage, Emanuele Trucco, Joanna Wardlaw, Amos Storkey
Date: 2023-09-26 00:00:00
Tasks: Image Cropping

Despite the large amount of brain CT data generated in clinical practice, the availability of CT datasets for deep learning (DL) research is currently limited. Furthermore, the data can be insufficiently or improperly prepared for machine learning and thus lead to spurious and irreproducible analyses. This lack of access to comprehensive and diverse datasets poses a significant challenge for the development of DL algorithms. In this work, we propose a complete semi-automatic pipeline to address the challenges of preparing a clinical brain CT dataset for DL analysis and describe the process of standardising this heterogeneous dataset. Challenges include handling image sets with different orientations (axial, sagittal, coronal), different image types (to view soft tissues or bones) and dimensions, and removing redundant background. The final pipeline was able to process 5,868/10,659 (45%) CT image datasets. Reasons for rejection include non-axial data (n=1,920), bone reformats (n=687), separated skull base/vault images (n=1,226), and registration failures (n=465). Further format adjustments, including image cropping, resizing and scaling are also needed for DL processing. Of the axial scans that were not localisers, bone reformats or split brains, 5,868/6,333 (93%) were accepted, while the remaining 465 failed the registration process. Appropriate preparation of medical imaging datasets for DL is a costly and time-intensive process.
Thalamic nuclei segmentation from T$_1$-weighted MRI: unifying and benchmarking state-of-the-art methods with young and old cohorts
Authors: Brendan Williams, Dan Nguyen, Julie Vidal, Alzheimer's Disease Neuroimaging Initiative, Manojkumar Saranathan
Date: 2023-09-26 00:00:00
Tasks: Benchmarking, Segmentation, Specificity

The thalamus and its constituent nuclei are critical for a broad range of cognitive and sensorimotor processes, and implicated in many neurological and neurodegenerative conditions. However, the functional involvement and specificity of thalamic nuclei in human neuroimaging is underappreciated and not well studied due, in part, to technical challenges of accurately identifying and segmenting nuclei. This challenge is further exacerbated by a lack of common nomenclature for comparing segmentation methods. Here, we use data from healthy young (Human Connectome Project, 100 subjects) and older healthy adults, plus those with minor cognitive impairment and Alzheimer$'$s disease (Alzheimer$'$s Disease Neuroimaging Initiative, 540 subjects), to benchmark four state of the art thalamic segmentation methods for T1 MRI (FreeSurfer, HIPS-THOMAS, SCS-CNN, and T1-THOMAS) under a single segmentation framework. Segmentations were compared using overlap and dissimilarity metrics to the Morel stereotaxic atlas. We also quantified each method$'$s estimation of thalamic nuclear degeneration across Alzheimer$'$s disease progression, and how accurately early and late mild cognitive impairment, and Alzheimers disease could be distinguished from healthy controls. We show that HIPS-THOMAS produced the most effective segmentations of individual thalamic nuclei and was also most accurate in discriminating healthy controls from those with mild cognitive impairment and Alzheimer$'$s disease using individual nucleus volumes. This work is the first to systematically compare the efficacy of anatomical thalamic segmentation approaches under a unified nomenclature. We also provide recommendations of which segmentation method to use for studying the functional relevance of specific thalamic nuclei, based on their overlap and dissimilarity with the Morel atlas.