pwc-archive/papers-with-abstracts huggingface dataset:
Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector
Authors: A. Gavrikov, V. Cerrone, A. Serafini, R. Brugnera, A. Garfagnini, M. Grassi, B. Jelmini, L. Lastrucci, S. Aiello, G. Andronico, V. Antonelli, A. Barresi, D. Basilico, M. Beretta, A. Bergnoli, M. Borghesi, A. Brigatti, R. Bruno, A. Budano, B. Caccianiga, A. Cammi, R. Caruso, D. Chiesa, C. Clementi, S. Dusini, A. Fabbri, G. Felici, F. Ferraro, M. G. Giammarchi, N. Giudice, R. M. Guizzetti, N. Guardone, C. Landini, I. Lippi, S. Loffredo, L. Loi, P. Lombardi, C. Lombardo, F. Mantovani, S. M. Mari, A. Martini, L. Miramonti, M. Montuschi, M. Nastasi, D. Orestano, F. Ortica, A. Paoloni, E. Percalli, F. Petrucci, E. Previtali, G. Ranucci, A. C. Re, M. Redchuck, B. Ricci, A. Romani, P. Saggese, G. Sava, C. Sirignano, M. Sisti, L. Stanco, E. Stanescu Farilla, V. Strati, M. D. C. Torri, A. Triossi, C. Tuvé, C. Venettacci, G. Verde, L. Votano
Date: 2024-06-09 00:00:00
Tasks: Interpretable Machine Learning

Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrounds. However, given the low cross-section of antineutrino interactions, the development of a powerful event selection algorithm becomes imperative to achieve effective discrimination between signal and backgrounds. In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. We demonstrate, using the JUNO detector as an example, that, despite the already high efficiency of a cut-based approach, the presented ML model can further improve the overall event selection efficiency. Moreover, it allows for the retention of signal events at the detector edges that would otherwise be rejected because of the overwhelming amount of background events in that region. We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches.
A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulation
Authors: Farzana Yasmin Ahmad, Vanamala Venkataswamy, Geoffrey Fox
Date: 2024-06-08 00:00:00

The pursuit of understanding fundamental particle interactions has reached unparalleled precision levels. Particle physics detectors play a crucial role in generating low-level object signatures that encode collision physics. However, simulating these particle collisions is a demanding task in terms of memory and computation which will be exasperated with larger data volumes, more complex detectors, and a higher pileup environment in the High-Luminosity LHC. The introduction of "Fast Simulation" has been pivotal in overcoming computational bottlenecks. The use of deep-generative models has sparked a surge of interest in surrogate modeling for detector simulations, generating particle showers that closely resemble the observed data. Nonetheless, there is a pressing need for a comprehensive evaluation of their performance using a standardized set of metrics. In this study, we conducted a rigorous evaluation of three generative models using standard datasets and a diverse set of metrics derived from physics, computer vision, and statistics. Furthermore, we explored the impact of using full versus mixed precision modes during inference. Our evaluation revealed that the CaloDiffusion and CaloScore generative models demonstrate the most accurate simulation of particle showers, yet there remains substantial room for improvement. Our findings identified areas where the evaluated models fell short in accurately replicating Geant4 data.
Entropy-statistical approach to phase-locking detection of pulse oscillations: application for the analysis of biosignal synchronization
Authors: Petr Boriskov, Vadim Putrolaynen, Andrei Velichko, Kristina Peltonen
Date: 2024-06-12 00:00:00
Tasks: Hippocampus, Time Series

In this study a new method for analyzing synchronization in oscillator systems is proposed using the example of modeling the dynamics of a circuit of two resistively coupled pulse oscillators. The dynamic characteristic of synchronization is fuzzy entropy (FuzzyEn) calculated a time series composed of the ratios of the number of pulse periods (subharmonic ratio, SHR) during phase-locking intervals. Low entropy values indicate strong synchronization, whereas high entropy values suggest weak synchronization between the two oscillators. This method effectively visualizes synchronized modes of the circuit using entropy maps of synchronization states. Additionally, a classification of synchronization states is proposed based on the dependencies of FuzzyEn on the length of embedding vectors of SHR time series. An extension of this method for analyzing non-relaxation (non-spike) type signals is illustrated using the example of phase-phase coupling rhythms of local field potential of rat hippocampus. The entropy-statistical approach using rational fractions and pulse signal forms makes this method promising for analyzing biosignal synchronization and implementing the algorithm in mobile digital platforms.
Towards Unlocking Insights from Logbooks Using AI
Authors: Antonin Sulc, Alex Bien, Annika Eichler, Daniel Ratner, Florian Rehm, Frank Mayet, Gregor Hartmann, Hayden Hoschouer, Henrik Tuennermann, Jan Kaiser, Jason St. John, Jennefer Maldonado, Kyle Hazelwood, Raimund Kammering, Thorsten Hellert, Tim Wilksen, Verena Kain, Wan-Lin Hu
Date: 2024-05-25 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation.
A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction
Authors: Keqiang Yan, Alexandra Saxton, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
Date: 2024-06-03 00:00:00

We consider the prediction of general tensor properties of crystalline materials, including dielectric, piezoelectric, and elastic tensors. A key challenge here is how to make the predictions satisfy the unique tensor equivariance to O(3) group and invariance to crystal space groups. To this end, we propose a General Materials Tensor Network (GMTNet), which is carefully designed to satisfy the required symmetries. To evaluate our method, we curate a dataset and establish evaluation metrics that are tailored to the intricacies of crystal tensor predictions. Experimental results show that our GMTNet not only achieves promising performance on crystal tensors of various orders but also generates predictions fully consistent with the intrinsic crystal symmetries. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).
Understanding active learning of molecular docking and its applications
Authors: JEONGHYEON KIM, Juno Nam, Seongok Ryu
Date: 2024-06-14 00:00:00
Tasks: Active Learning, Molecular Docking

With the advancing capabilities of computational methodologies and resources, ultra-large-scale virtual screening via molecular docking has emerged as a prominent strategy for in silico hit discovery. Given the exhaustive nature of ultra-large-scale virtual screening, active learning methodologies have garnered attention as a means to mitigate computational cost through iterative small-scale docking and machine learning model training. While the efficacy of active learning methodologies has been empirically validated in extant literature, a critical investigation remains in how surrogate models can predict docking score without considering three-dimensional structural features, such as receptor conformation and binding poses. In this paper, we thus investigate how active learning methodologies effectively predict docking scores using only 2D structures and under what circumstances they may work particularly well through benchmark studies encompassing six receptor targets. Our findings suggest that surrogate models tend to memorize structural patterns prevalent in high docking scored compounds obtained during acquisition steps. Despite this tendency, surrogate models demonstrate utility in virtual screening, as exemplified in the identification of actives from DUD-E dataset and high docking-scored compounds from EnamineReal library, a significantly larger set than the initial screening pool. Our comprehensive analysis underscores the reliability and potential applicability of active learning methodologies in virtual screening campaigns.
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
Authors: Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Pei Zhang, David Rogers, Jonghyun Bae, Khaled Z. Ibrahim, Ashwin M. Aji, Karl W. Schulz, Jorda Polo, Prasanna Balaprakash
Date: 2024-06-12 00:00:00
Tasks: Atomic Forces, Ensemble Learning, Graph Neural Network, Hyperparameter Optimization, Multi-Task Learning, Uncertainty Quantification

We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of graph neural network (GNN) computations in both training scale and data diversity. It abstracts over message passing algorithms, allowing both reproduction of and comparison across algorithmic innovations that define nearest-neighbor convolution in GNNs. This work discusses a series of optimizations that have allowed scaling up the GFMs training to tens of thousands of GPUs on datasets consisting of hundreds of millions of graphs. Our GFMs use multi-task learning (MTL) to simultaneously learn graph-level and node-level properties of atomistic structures, such as energy and atomic forces. Using over 154 million atomistic structures for training, we illustrate the performance of our approach along with the lessons learned on two state-of-the-art United States Department of Energy (US-DOE) supercomputers, namely the Perlmutter petascale system at the National Energy Research Scientific Computing Center and the Frontier exascale system at Oak Ridge Leadership Computing Facility. The HydraGNN architecture enables the GFM to achieve near-linear strong scaling performance using more than 2,000 GPUs on Perlmutter and 16,000 GPUs on Frontier.
Low-rank based motion correction followed by automatic frame selection in DT-CMR
Authors: Fanwen Wang, Pedro F. Ferreira, Camila Munoz, Ke Wen, Yaqing Luo, Jiahao Huang, Yinzhe Wu, Dudley J. Pennell, Andrew D. Scott, Sonia Nielles-Vallespin, Guang Yang
Date: 2024-06-19 00:00:00
Tasks: Image Registration

Motivation: Post-processing of in-vivo diffusion tensor CMR (DT-CMR) is challenging due to the low SNR and variation in contrast between frames which makes image registration difficult, and the need to manually reject frames corrupted by motion. Goals: To develop a semi-automatic post-processing pipeline for robust DT-CMR registration and automatic frame selection. Approach: We used low intrinsic rank averaged frames as the reference to register other low-ranked frames. A myocardium-guided frame selection rejected the frames with signal loss, through-plane motion and poor registration. Results: The proposed method outperformed our previous noise-robust rigid registration on helix angle data quality and reduced negative eigenvalues in healthy volunteers.
Learning thresholds lead to stable language coexistence
Authors: Mikhail V. Tamm, Els Heinsalu, Stefano Scialla, Marco Patriarca
Date: 2024-06-14 00:00:00

We introduce a language competition model that is based on the Abrams-Strogatz model and incorporates the effects of memory and learning in the language shift dynamics. On a coarse grained time scale, the effects of memory and learning can be expressed as thresholds on the speakers fractions of the competing languages. In its simplest form, the resulting model is exactly solvable. Besides the consensus on one of the two languages, the model describes additional equilibrium states that are not present in the Abrams-Strogatz model: a stable dynamical coexistence of the two languages and a frozen state coinciding with the initial state. We show numerically that these results are preserved for threshold functions of a more general shape. The comparison of the model predictions with historical datasets demonstrates that while the Abrams-Strogatz model fails to describe some relevant language competition situations, the proposed model provides a good fitting.
Estimating Time-Varying Parameters of Various Smoothness in Linear Models via Kernel Regression
Authors: Mikihito Nishi
Date: 2024-06-20 00:00:00
Tasks: regression

We consider estimating nonparametric time-varying parameters in linear models using kernel regression. Our contributions are threefold. First, we consider a broad class of time-varying parameters including deterministic smooth functions, the rescaled random walk, structural breaks, the threshold model and their mixtures. We show that those time-varying parameters can be consistently estimated by kernel regression. Our analysis exploits the smoothness of the time-varying parameter quantified by a single parameter. The second contribution is to reveal that the bandwidth used in kernel regression determines a trade-off between the rate of convergence and the size of the class of time-varying parameters that can be estimated. We demonstrate that an improper choice of the bandwidth yields biased estimation, and argue that the bandwidth should be selected according to the smoothness of the time-varying parameter. Our third contribution is to propose a data-driven procedure for bandwidth selection that is adaptive to the smoothness of the time-varying parameter.
Testing identification in mediation and dynamic treatment models
Authors: Martin Huber, Kevin Kloiber, Lukas Laffers
Date: 2024-06-19 00:00:00

We propose a test for the identification of causal effects in mediation and dynamic treatment models that is based on two sets of observed variables, namely covariates to be controlled for and suspected instruments, building on the test by Huber and Kueck (2022) for single treatment models. We consider models with a sequential assignment of a treatment and a mediator to assess the direct treatment effect (net of the mediator), the indirect treatment effect (via the mediator), or the joint effect of both treatment and mediator. We establish testable conditions for identifying such effects in observational data. These conditions jointly imply (1) the exogeneity of the treatment and the mediator conditional on covariates and (2) the validity of distinct instruments for the treatment and the mediator, meaning that the instruments do not directly affect the outcome (other than through the treatment or mediator) and are unconfounded given the covariates. Our framework extends to post-treatment sample selection or attrition problems when replacing the mediator by a selection indicator for observing the outcome, enabling joint testing of the selectivity of treatment and attrition. We propose a machine learning-based test to control for covariates in a data-driven manner and analyze its finite sample performance in a simulation study. Additionally, we apply our method to Slovak labor market data and find that our testable implications are not rejected for a sequence of training programs typically considered in dynamic treatment evaluations.
Bayesian Inference for Multidimensional Welfare Comparisons
Authors: David Gunawan, William Griffiths, Duangkamon Chotikapanich
Date: 2024-06-19 00:00:00
Tasks: Attribute, Bayesian Inference

Using both single-index measures and stochastic dominance concepts, we show how Bayesian inference can be used to make multivariate welfare comparisons. A four-dimensional distribution for the well-being attributes income, mental health, education, and happiness are estimated via Bayesian Markov chain Monte Carlo using unit-record data taken from the Household, Income and Labour Dynamics in Australia survey. Marginal distributions of beta and gamma mixtures and discrete ordinal distributions are combined using a copula. Improvements in both well-being generally and poverty magnitude are assessed using posterior means of single-index measures and posterior probabilities of stochastic dominance. The conditions for stochastic dominance depend on the class of utility functions that is assumed to define a social welfare function and the number of attributes in the utility function. Three classes of utility functions are considered, and posterior probabilities of dominance are computed for one, two, and four-attribute utility functions for three time intervals within the period 2001 to 2019.
Testing for Underpowered Literatures
Authors: Stefan Faridani
Date: 2024-06-19 00:00:00

How many experimental studies would have come to different conclusions had they been run on larger samples? I show how to estimate the expected number of statistically significant results that a set of experiments would have reported had their sample sizes all been counterfactually increased. The proposed deconvolution estimator is asymptotically normal and adjusts for publication bias. Unlike related methods, this approach requires no assumptions of any kind about the distribution of true intervention treatment effects. An application to randomized trials (RCTs) published in economics journals finds that doubling every sample would increase the power of t-tests by 7.2 percentage points on average. This effect is smaller than for non-RCTs and comparable to systematic replications in laboratory psychology where previous studies enabled more accurate power calculations. This suggests that RCTs are on average relatively insensitive to sample size increases. Funders should generally consider sponsoring more experiments rather than fewer, larger ones.
Identification of fiscal SVAR-IVs in small open economies
Authors: Henri Keränen, Sakari Lähdemäki
Date: 2024-06-20 00:00:00

We identify fiscal SVAR-IVs by utilizing unexpected variation in the output of trading partner economies, measured by professional forecast errors, to account for the systematic component of fiscal policy. Our identification builds on the small open economy assumption that these forecast errors correlate with output but are exogenous to domestic fiscal policy. In applying our approach to Canada and euro area small open economies we show that the instrument is relevant and find suggestive evidence for its exogeneity. Our baseline estimates for the two-year cumulative spending multiplier are around 1 for Canada and 0.5 for euro area small open economies.
The Economics of Coal Phaseouts: Auctions as a Novel Policy Instrument for the Energy Transition
Authors: Sugandha Srivastav, Michael Zaehringer
Date: 2024-06-20 00:00:00

The combustion of coal, the most polluting form of energy, must be significantly curtailed to limit global average temperature increase to well below 2 degrees C. The effectiveness of carbon pricing is frequently undermined by sub-optimally low prices and rigid market structures. Consequently, alternative approaches such as compensation for the early closure of coal-fired power plants are being considered. While bilateral negotiations can lead to excessive compensation due to asymmetric information, a competitive auction can discover the true cost of closure and help allocate funds more efficiently and transparently. Since Germany is the only country till date to have implemented a coal phaseout auction, we use it to analyse the merits and demerits of the policy, drawing comparisons with other countries that have phased out coal through other means. The German experience with coal phaseout auctions illustrates the necessity of considering additionality and interaction with existing climate policies, managing dynamic incentives, and evaluating impacts on security of supply. While theoretically auctions have attractive properties, in practice, their design must address these concerns to unlock the full benefits. Where auctions are not appropriate due to a concentration in coal plant ownership, alternative strategies include enhanced incentives for scrappage and repurposing of coal assets.
Serotonin as a Creativity Pump
Authors: Tariq Khan
Date: 2024-06-19 00:00:00

The location of Western Civilization defined nations in Europe and in the United States within the largest global pollen environments on the planet is proposed as a key factor leading to their success. Environments with dense pollen concentrations will cause large up and down changes in serum histamine directly causing reductions and increases in brain serotonin i.e., a larger serotonin slope, linked to higher levels of creativity. The pollen ecosystem in northern latitude nations is thus considered the hidden driver of the success of these populations as the biochemical interaction between histamine and serotonin leads to a creativity pump that is proposed as the fundamental driver of intelligence in micro and macro human populations.
Redistribution Through Market Segmentation
Authors: Victor Augias, Alexis Ghersengorin, Daniel M. A. Barreto
Date: 2024-06-20 00:00:00
Tasks: Segmentation

We study how to optimally segment monopolistic markets with a redistributive objective. We characterize optimal redistributive segmentations and show that they (i) induce the seller to price progressively, i.e., richer consumers pay higher prices than poorer ones, and (ii) may not maximize consumer surplus, instead granting extra profits to the monopolist. We further show that optimal redistributive segmentations are implementable via price-based regulation.