pwc-archive/papers-with-abstracts huggingface dataset:
Reducing the Environmental Impact of Midblock Crossing
Authors: Abrar Alali, Stephan Olariu
Date: 2024-05-27 00:00:00

Accommodating pedestrians crossing midblock has been shown to have harmful environmental consequences because of increased fuel consumption and CO2 emissions. Somewhat surprisingly, no studies were devoted to mitigating the environmental impact of midblock crossing. Our main contribution is to propose schemes that mitigate the increased fuel consumption and CO2 emissions due to pedestrian midblock crossing by leveraging information about the location and expected duration of the crossing. This information is shared in a timely manner with approaching cars. We evaluated the impact of car decisions on fuel consumption and emissions by exploring potential trajectories that cars may take as a result of messages received. Our extensive simulations showed that timely dissemination of pedestrian crossing information to approaching vehicles can reduce fuel consumption and emissions by up to 16.7%.
Lichen-Mediated Self-Growing Construction Materials for Habitat Outfitting on Mars
Authors: Nisha Rokaya, Erin C. Carr, Richard A. Wilson, Congrui Jin
Date: 2024-06-04 00:00:00

As its next step in space exploration, the National Aeronautics and Space Administration (NASA) revealed plans to establish a permanent human presence on Mars. Habitat outfitting, i.e., the technology to provide the crew with the necessary equipment to perform mission tasks as well as a comfortable, safe, and livable habitable volume, has not been fully explored yet. This study proposes that, rather than shipping prefabricated outfitting elements to Mars, habitat outfitting can be realized by in-situ construction using cyanobacteria and fungi as building agents. A synthetic lichen system, composed of diazotrophic cyanobacteria and filamentous fungi, can be created to produce abundant biominerals (CaCO3) and biopolymers, which will glue Martian regolith into consolidated building blocks. These self-growing building blocks can be assembled into various structures, such as floors, walls, partitions, and furniture.
Optimizing Photometric Light Curve Analysis: Evaluating Scipy's Minimize Function for Eclipse Mapping of Cataclysmic Variables
Authors: Anoop Kumar, Madan Mohan Tito Ayyalasomayajula, Dheerendra Panwar, Yeshwanth Vasa
Date: 2024-05-30 00:00:00

With a particular focus on Scipy's minimize function the eclipse mapping method is thoroughly researched and implemented utilizing Python and essential libraries. Many optimization techniques are used, including Sequential Least Squares Programming (SLSQP), Nelder-Mead, and Conjugate Gradient (CG). However, for the purpose of examining photometric light curves these methods seek to solve the maximum entropy equation under a chi-squared constraint. Therefore, these techniques are first evaluated on two-dimensional Gaussian data without a chi-squared restriction, and then they are used to map the accretion disc and uncover the Gaussian structure of the Cataclysmic Variable KIC 201325107. Critical analysis is performed on the code structure to find possible faults and design problems. Additionally, the analysis shows how several factors impacting computing time and image quality are included including the variance in Gaussian weighting, disc image resolution, number of data points in the light curve, and degree of constraint.
Quantum Equilibrium Propagation: Gradient-Descent Training of Quantum Systems
Authors: Benjamin Scellier
Date: 2024-06-02 00:00:00

Equilibrium propagation (EP) is a training framework for energy-based systems, i.e. systems whose physics minimizes an energy function. EP has been explored in various classical physical systems such as resistor networks, elastic networks, the classical Ising model and coupled phase oscillators. A key advantage of EP is that it achieves gradient descent on a cost function using the physics of the system to extract the weight gradients, making it a candidate for the development of energy-efficient processors for machine learning. We extend EP to quantum systems, where the energy function that is minimized is the mean energy functional (expectation value of the Hamiltonian), whose minimum is the ground state of the Hamiltonian. As examples, we study the settings of the transverse-field Ising model and the quantum harmonic oscillator network -- quantum analogues of the Ising model and elastic network.
Towards a theory of how the structure of language is acquired by deep neural networks
Authors: Francesco Cagnetta, Matthieu Wyart
Date: 2024-05-28 00:00:00
Tasks: Articles, Language Modeling, Language Modelling

How much data is required to learn the structure of a language via next-token prediction? We study this question for synthetic datasets generated via a Probabilistic Context-Free Grammar (PCFG) -- a tree-like generative model that captures many of the hierarchical structures found in natural languages. We determine token-token correlations analytically in our model and show that they can be used to build a representation of the grammar's hidden variables, the longer the range the deeper the variable. In addition, a finite training set limits the resolution of correlations to an effective range, whose size grows with that of the training set. As a result, a Language Model trained with increasingly many examples can build a deeper representation of the grammar's structure, thus reaching good performance despite the high dimensionality of the problem. We conjecture that the relationship between training set size and effective range of correlations holds beyond our synthetic datasets. In particular, our conjecture predicts how the scaling law for the test loss behaviour with training set size depends on the length of the context window, which we confirm empirically in Shakespeare's plays and Wikipedia articles.
Quantum consistent neural/tensor networks for photonic circuits with strongly/weakly entangled states
Authors: Nicolas Allegra
Date: 2024-06-03 00:00:00
Tasks: parameter estimation, Tensor Networks

Modern quantum optical systems such as photonic quantum computers and quantum imaging devices require great precision in their designs and implementations in the hope to realistically exploit entanglement and reach a real quantum advantage. The theoretical and experimental explorations and validations of these systems are greatly dependent on the precision of our classical simulations. However, as Hilbert spaces increases, traditional computational methods used to design and optimize these systems encounter hard limitations due to the quantum curse of dimensionally. To address this challenge, we propose an approach based on neural and tensor networks to approximate the exact unitary evolution of closed entangled systems in a precise, efficient and quantum consistent manner. By training the networks with a reasonably small number of examples of quantum dynamics, we enable efficient parameter estimation in larger Hilbert spaces, offering an interesting solution for a great deal of quantum metrology problems.
Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning
Authors: Yinan Wang, M. Giselle Fernández-Godino, Nipun Gunawardena, Donald D. Lucas, Xiaowei Yue
Date: 2024-05-30 00:00:00
Tasks: Deep Learning, Temporal Sequences

Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas. Computer models are typically used to predict the transport of toxic plumes by solving fluid dynamical equations. However, these models can be computationally expensive due to the need for many grid cells to simulate turbulent flow and resolve individual buildings and streets. In emergency response situations, alternative methods are needed that can run quickly and adequately capture important spatiotemporal features. Here, we present a novel deep learning model called ST-GasNet that was inspired by the mathematical equations that govern the behavior of plumes as they disperse through the atmosphere. ST-GasNet learns the spatiotemporal dependencies from a limited set of temporal sequences of ground-level toxic urban plumes generated by a high-resolution large eddy simulation model. On independent sequences, ST-GasNet accurately predicts the late-time spatiotemporal evolution, given the early-time behavior as an input, even for cases when a building splits a large plume into smaller plumes. By incorporating large-scale wind boundary condition information, ST-GasNet achieves a prediction accuracy of at least 90% on test data for the entire prediction period.
Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks
Authors: Bumju Kwak, Jeonghee Jo
Date: 2024-06-01 00:00:00

Recent SO(3)-equivariant models embedded a molecule as a set of single atoms fixed in the three-dimensional space, which is analogous to a ball-and-stick view. This perspective provides a concise view of atom arrangements, however, the surrounding electron density cannot be represented and its polarization effects may be underestimated. To overcome this limitation, we propose \textit{Neural Polarization}, a novel method extending equivariant network by embedding each atom as a pair of fixed and moving points. Motivated by density functional theory, Neural Polarization represents molecules as a space-filling view which includes an electron density, in contrast with a ball-and-stick view. Neural Polarization can flexibly be applied to most type of existing equivariant models. We showed that Neural Polarization can improve prediction performances of existing models over a wide range of targets. Finally, we verified that our method can improve the expressiveness and equivariance in terms of mathematical aspects.
Predicting solvation free energies with an implicit solvent machine learning potential
Authors: Sebastien Röcken, Anton F. Burnet, Julija Zavadlav
Date: 2024-05-31 00:00:00
Tasks: Drug Design, Graph Neural Network

Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network architectures are still too expensive for applications requiring extensive sampling, such as free energy computations. Implicit solvent models could provide the necessary speed-up due to reduced degrees of freedom and faster dynamics. Here, we introduce a Solvation Free Energy Path Reweighting (ReSolv) framework to parametrize an implicit solvent ML potential for small organic molecules that accurately predicts the hydration free energy, an essential parameter in drug design and pollutant modeling. With a combination of top-down (experimental hydration free energy data) and bottom-up (ab initio data of molecules in a vacuum) learning, ReSolv bypasses the need for intractable ab initio data of molecules in explicit bulk solvent and does not have to resort to less accurate data-generating models. On the FreeSolv dataset, ReSolv achieves a mean absolute error close to average experimental uncertainty, significantly outperforming standard explicit solvent force fields. Compared to the explicit solvent ML potential, ReSolv offers a computational speedup of four orders of magnitude and attains closer agreement with experiments. The presented framework paves the way toward deep molecular models that are more accurate yet computationally cheaper than classical atomistic models.
A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics problems
Authors: Victor Matray, Faisal Amlani, Frédéric Feyel, David Néron
Date: 2024-06-03 00:00:00

This work introduces a new approach for accelerating the numerical analysis of time-domain partial differential equations (PDEs) governing complex physical systems. The methodology is based on a combination of a classical reduced-order modeling (ROM) framework and recently-introduced Graph Neural Networks (GNNs), where the latter is trained on highly heterogeneous databases of varying numerical discretization sizes. The proposed techniques are shown to be particularly suitable for non-parametric geometries, ultimately enabling the treatment of a diverse range of geometries and topologies. Performance studies are presented in an application context related to the design of aircraft seats and their corresponding mechanical responses to shocks, where the main motivation is to reduce the computational burden and enable the rapid design iteration for such problems that entail non-parametric geometries. The methods proposed here are straightforwardly applicable to other scientific or engineering problems requiring a large number of finite element-based numerical simulations, with the potential to significantly enhance efficiency while maintaining reasonable accuracy.
Uncovering dynamical equations of stochastic decision models using data-driven SINDy algorithm
Authors: Brendan Lenfesty, Saugat Bhattacharyya, KongFatt Wong-Lin
Date: 2024-06-03 00:00:00
Tasks: Decision Making

Decision formation in perceptual decision-making involves sensory evidence accumulation instantiated by the temporal integration of an internal decision variable towards some decision criterion or threshold, as described by sequential sampling theoretical models. The decision variable can be represented in the form of experimentally observable neural activities. Hence, elucidating the appropriate theoretical model becomes crucial to understanding the mechanisms underlying perceptual decision formation. Existing computational methods are limited to either fitting of choice behavioural data or linear model estimation from neural activity data. In this work, we made use of sparse identification of nonlinear dynamics (SINDy), a data-driven approach, to elucidate the deterministic linear and nonlinear components of often-used stochastic decision models within reaction time task paradigms. Based on the simulated decision variable activities of the models, SINDy, enhanced with approaches using multiple trials, could readily estimate the dynamical equations, choice accuracy and decision time of the models across a range of signal-to-noise ratio values. In particular, SINDy performed the best using the more memory intensive multi-trial approach while trial-averaging of parameters performed more moderately. The single-trial approach, although expectedly not performing as well, may be useful for real-time modelling. Taken together, our work offers alternative approaches for SINDy to uncover the dynamics in perceptual decision-making, and more generally, for first-passage time problems.
Markov Chain Monte Carlo with Gaussian Process Emulation for a 1D Hemodynamics Model of CTEPH
Authors: Amirreza Kachabi, Mitchel J. Colebank, Sofia Altieri Correa, Naomi C. Chesler
Date: 2024-04-25 00:00:00
Tasks: Gaussian Processes

Microvascular disease is a contributor to persistent pulmonary hypertension in those with chronic thromboembolic pulmonary hypertension (CTEPH). The heterogenous nature of the micro and macrovascular defects motivates the use of personalized computational models, which can predict flow dynamics within multiple generations of the arterial tree and into the microvasculature. Our study uses computational hemodynamics models and Gaussian processes for rapid, subject-specific calibration using retrospective data from a large animal model of CTEPH. Our subject-specific predictions shed light on microvascular dysfunction and arterial wall shear stress changes in CTEPH.
Extrapolability Improvement of Machine Learning-Based Evapotranspiration Models via Domain-Adversarial Neural Networks
Authors: Haiyang Shi
Date: 2024-06-02 00:00:00
Tasks: Domain Adaptation

Machine learning-based hydrological prediction models, despite their high accuracy, face limitations in extrapolation capabilities when applied globally due to uneven data distribution. This study integrates Domain-Adversarial Neural Networks (DANN) to improve the geographical adaptability of evapotranspiration (ET) models. By employing DANN, we aim to mitigate distributional discrepancies between different sites, significantly enhancing the model's extrapolation capabilities. Our results show that DANN improves ET prediction accuracy with an average increase in the Kling-Gupta Efficiency (KGE) of 0.2 to 0.3 compared to the traditional Leave-One-Out (LOO) method. DANN is particularly effective for isolated sites and transition zones between biomes, reducing data distribution discrepancies and avoiding low-accuracy predictions. By leveraging information from data-rich areas, DANN enhances the reliability of global-scale ET products, especially in ungauged regions. This study highlights the potential of domain adaptation techniques to improve the extrapolation and generalization capabilities of machine learning models in hydrological studies.
A time-to-digital converter with steady calibration through single-photon detection
Authors: Matías Rubén Bolaños Wagner, Daniele Vogrig, Paolo Villoresi, Giuseppe Vallone, Andrea Stanco
Date: 2024-06-03 00:00:00

Time-to-Digital Converters (TDCs) are a crucial tool in a wide array of fields, in particular for quantum communication, where time taggers performance can severely affect the quality of the entire application. Nowadays, FPGA-based TDCs present a viable alternative to ASIC ones, once the nonlinear behaviour due to the intrinsic nature of the device is properly mitigated. To compensate said nonlinearities, a calibration procedure is required, usually based on an interpolation methods. Here we present the design and the demonstration of a TDC that is FPGA-based and showing a residual jitter of 27 ps, that is scalable for multichannel operation. The application in Quantum Key Distribution (QKD) is discussed with a unique calibration method based on the exploitation of single-photon detection that does not require to stop the data acquisition or to use any interpolation methods, thus increasing accuracy and removing data loss. The calibration was tested in a relevant environment, investigating the device behaviour between 5{\deg}C and 80{\deg}C. Moreover, our design is capable of continuously streaming up to 12 Mevents/s for up to ~1 week without the TDC overflowing.
Insect Diversity Estimation in Polarimetric Lidar
Authors: Dolores Bernenko, Meng Li, Hampus Månefjord, Samuel Jansson, Anna Runemark, Carsten Kirkeby, Mikkel Brydegaard
Date: 2024-06-03 00:00:00
Tasks: Diagnostic, Diversity, Specificity

Identification of insects in flight is a particular challenge for ecologists in several settings with no other method able to count and classify insects at the pace of entomological lidar. Thus, it can play a unique role as a non-intrusive diagnostic tool to assess insect biodiversity, inform planning, and evaluate mitigation efforts aimed at tackling declines in insect abundance and diversity. While species richness of co-existing insects could reach tens of thousands, to date, photonic sensors and lidars can differentiate roughly one hundred signal types. This taxonomic specificity or number of discernible signal types is currently limited by instrumentation and algorithm sophistication. In this study we report 32,533 observations of wild flying insects along a 500-meter transect. We report the benefits of lidar polarization bands for differentiating species and compare the performance of two unsupervised clustering algorithms, namely Hierarchical Cluster Analysis and Gaussian Mixture Model. We demonstrate that polarimetric properties could be partially predicted even with unpolarized light, thus polarimetric lidar bands provide only a minor improvement in specificity. Finally, we use physical properties of the clustered observation, such as wing beat frequency, daily activity patterns, and spatial distribution, to establish a lower bound for the number of species represented by the differentiated signal types.
Ghost imaging-based Non-contact Heart Rate Detection
Authors: Jianming Yu, Yuchen He, Bin Li, Hui Chen, Huaibin Zheng, Jianbin Liu, Zhuo Xu
Date: 2024-06-04 00:00:00

Remote heart rate measurement is an increasingly concerned research field, usually using remote photoplethysmography (rPPG) to collect heart rate information through video data collection. However, in certain specific scenarios (such as low light conditions, intense lighting, and non-line-of-sight situations), traditional imaging methods fail to capture image information effectively, that may lead to difficulty or inability in measuring heart rate. To address these limitations, this study proposes using ghost imaging as a substitute for traditional imaging in the aforementioned scenarios. The mean absolute error between experimental measurements and reference true values is 4.24 bpm.Additionally, the bucket signals obtained by the ghost imaging system can be directly processed using digital signal processing techniques, thereby enhancing personal privacy protection.
Reinforcement learning-based architecture search for quantum machine learning
Authors: Frederic Rapp, David A. Kreplin, Marco F. Huber, Marco Roth
Date: 2024-06-04 00:00:00
Tasks: Model-based Reinforcement Learning, Quantum Machine Learning, reinforcement-learning, Reinforcement Learning

Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influences core properties of the resulting model, they are often chosen heuristically. In this work, we present a novel approach using reinforcement learning techniques to generate problem-specific encoding circuits to improve the performance of quantum machine learning models. By specifically using a model-based reinforcement learning algorithm, we reduce the number of necessary circuit evaluations during the search, providing a sample-efficient framework. In contrast to previous search algorithms, our method uses a layered circuit structure that significantly reduces the search space. Additionally, our approach can account for multiple objectives such as solution quality, hardware restrictions and circuit depth. We benchmark our tailored circuits against various reference models, including models with problem-agnostic circuits and classical models. Our results highlight the effectiveness of problem-specific encoding circuits in enhancing QML model performance.
Diffusion-Inspired Quantum Noise Mitigation in Parameterized Quantum Circuits
Authors: Hoang-Quan Nguyen, Xuan Bac Nguyen, Samuel Yen-Chi Chen, Hugh Churchill, Nicholas Borys, Samee U. Khan, Khoa Luu
Date: 2024-06-02 00:00:00
Tasks: Combinatorial Optimization

Parameterized Quantum Circuits (PQCs) have been acknowledged as a leading strategy to utilize near-term quantum advantages in multiple problems, including machine learning and combinatorial optimization. When applied to specific tasks, the parameters in the quantum circuits are trained to minimize the target function. Although there have been comprehensive studies to improve the performance of the PQCs on practical tasks, the errors caused by the quantum noise downgrade the performance when running on real quantum computers. In particular, when the quantum state is transformed through multiple quantum circuit layers, the effect of the quantum noise happens cumulatively and becomes closer to the maximally mixed state or complete noise. This paper studies the relationship between the quantum noise and the diffusion model. Then, we propose a novel diffusion-inspired learning approach to mitigate the quantum noise in the PQCs and reduce the error for specific tasks. Through our experiments, we illustrate the efficiency of the learning strategy and achieve state-of-the-art performance on classification tasks in the quantum noise scenarios.