Search

Quantum computing harnesses the principles of quantum mechanics to solve problems that are intractable for classical computers. Quantum annealing, a specialized approach within quantum computing, is particularly effective for optimization tasks, as it leverages quantum tunneling to escape local minima and efficiently explore complex energy landscapes. In geosciences, many problems are framed as high-dimensional optimization problems, including seismic inversion, which aims to estimate subsurface impedances from seismic data for accurate geological interpretation and resource exploration. This study presents a novel application of quantum computing for seismic inversion, marking the first instance of inverting seismic data to estimate both P-wave and S-wave impedances using a quantum annealer. Building upon our prior work, which demonstrated the estimation of acoustic impedances from post-stack data using a two-step framework, we propose an enhanced workflow capable of inverting both post-stack and pre-stack seismic data in a single step. This advancement significantly reduces the number of qubits per model parameter (from 20 to 5) while improving computational speed (from 20 seconds to 6.3 seconds). The seismic inversion is implemented using the D-Wave Leap hybrid solver, achieving impedance estimation within 4-9 seconds, with the quantum processing unit (QPU) contributing just 0.043-0.085 seconds. Comparative analysis with simulated annealing reveals that quantum annealing produces impedance models closely matching true values in a single epoch, whereas simulated annealing requires 10 epochs for improved accuracy. These findings underscore the transformative potential of quantum computing for real-time, high-precision seismic inversion, marking a crucial step toward fully quantum-driven geophysical solutions.

Learning-based solutions for long-tailed recognition face difficulties in generalizing on balanced test datasets. Due to imbalanced data prior, the learned \textit{a posteriori} distribution is biased toward the most frequent (head) classes, leading to an inferior performance on the least frequent (tail) classes. In general, the performance can be improved by removing such a bias by eliminating the effect of imbalanced prior modeled using the number of class samples (frequencies). We first observe that the \textit{effective prior} on the classes, learned by the model at the end of the training, can differ from the empirical prior obtained using class frequencies. Thus, we propose a novel approach to accurately model the effective prior of a trained model using \textit{a posteriori} probabilities. We propose to correct the imbalanced prior by adjusting the predicted \textit{a posteriori} probabilities (Prior2Posterior: P2P) using the calculated prior in a post-hoc manner after the training, and show that it can result in improved model performance. We present theoretical analysis showing the optimality of our approach for models trained with naive cross-entropy loss as well as logit adjusted loss. Our experiments show that the proposed approach achieves new state-of-the-art (SOTA) on several benchmark datasets from the long-tail literature in the category of logit adjustment methods. Further, the proposed approach can be used to inspect any existing method to capture the \textit{effective prior} and remove any residual bias to improve its performance, post-hoc, without model retraining. We also show that by using the proposed post-hoc approach, the performance of many existing methods can be improved further., Comment: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025

Metal-organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and mechanical stability, remains challenging due to the large number of atoms per unit cell, making traditional Density Functional Theory (DFT) methods impractical for high-throughput screening. Recent advances in machine learning potentials have led to foundation atomistic models, such as MACE-MP-0, that accurately predict equilibrium structures but struggle with phonon properties of MOFs. In this work, we developed a workflow for computing phonons in MOFs within the quasi-harmonic approximation with a fine-tuned MACE model, MACE-MP-MOF0. The model was trained on a curated dataset of 127 representative and diverse MOFs. The fine-tuned MACE-MP-MOF0 improves the accuracy of phonon density of states and corrects the imaginary phonon modes of MACE-MP-0, enabling high-throughput phonon calculations with state-of-the-art precision. The model successfully predicts thermal expansion and bulk moduli in agreement with DFT and experimental data for several well-known MOFs. These results highlight the potential of MACE-MP-MOF0 in guiding MOF design for applications in energy storage and thermoelectrics., Comment: 17 pages, 8 Figures

The inversion of DC resistivity data is a widely employed method for near-surface characterization. Recently, deep learning-based inversion techniques have garnered significant attention due to their capability to elucidate intricate non-linear relationships between geophysical data and model parameters. Nevertheless, these methods face challenges such as limited training data availability and the generation of geologically inconsistent solutions. These concerns can be mitigated through the integration of a physics-informed approach. Moreover, the quantification of prediction uncertainty is crucial yet often overlooked in deep learning-based inversion methodologies. In this study, we utilized Convolutional Neural Networks (CNNs) based Physics-Informed Neural Networks (PINNs) to invert both synthetic and field Schlumberger sounding data while also estimating prediction uncertainty via Monte Carlo dropout. For both synthetic and field case studies, the median profile estimated by PINNs is comparable to the results from existing literature, while also providing uncertainty estimates. Therefore, PINNs demonstrate significant potential for broader applications in near-surface characterization., Comment: Preprint of the extended abstract submitted to the Near Surface Geoscience'24 Conference & Exhibition by the European Association of Geoscientists & Engineers (EAGE)

Joint inversion of geophysical datasets is instrumental in subsurface characterization and has garnered significant popularity, leveraging information from multiple geophysical methods. In this study, we implemented the joint inversion of DC resistivity with MT data using the Multi-Objective Grey Wolf Optimization (MOGWO) algorithm. As an extension of the widely-used Grey Wolf Optimization algorithm, MOGWO offers a suite of pareto optimal non-dominated solutions, eliminating the need for weighting parameters in the objective functions. This set of non-dominated predictions also facilitates the understanding of uncertainty in the predicted model parameters. Through a field case study in the region around Broken Hill in South Central Australia, the paper showcases MOGWO's capabilities in joint inversion, providing confident estimates of the model parameters (resistivity profiles), as indicated by a narrow spread in the suite of solutions. The obtained results are comparable to well established methodologies and highlight the efficacy of MOGWO as a reliable tool in geophysical exploration., Comment: Preprint of the extended abstract submitted to the Near Surface Geoscience'24 Conference & Exhibition by the European Association of Geoscientists & Engineers (EAGE)

We present a conceptual design for a fiber positioning system for multi-object high-resolution spectroscopy, designed to be compatible with the upcoming large telescopes with a wide field of view. The design incorporates multiple Atmospheric Dispersion Correctors (ADCs) and tip-tilt mirrors that receive non-telecentric input from individual targets and direct it to the ADCs. Here, we introduce a mechanical design for the fiber positioner that accommodates the optics and operates in a curved focal plane with a Radius of Curvature (R) of 3m. This mechanical design provides four degrees of freedom to access the focal volume, enhancing targeting efficiency. The proposed design and an efficient target allocation algorithm ensure a targeting efficiency of approximately 80-100% for a primary observation session. We also present a methodology for target assignment, positioning, and quantification based on sequential and Monte Carlo (MC) algorithms. This method has been tested on realistic fields with varying target densities to validate its performance.

Atomistic origin of stacking faults in non-close packed systems is a fundamentally distinct mechanism from the well-known close packed structures with ABC stacking, and represents an uncharted territory in material research. According to experimental data, stacking faults in simple hexagonal WC happen in {1-100} planes that are packed rectangularly and have ABAB stacking. This work identified the type of the defect and crystallographic behaviour by creating energetically relaxed potential atomistic models of stacking faults in WC. Experimental evidence supporting the rotation axis along stacking fault caused by variation in carbon ordering at the interstitial site has been established, in accordance with the theoretical model.

A novel, single step and environment friendly solid state approach for reduction of graphene oxide (GO) monolayers has been demonstrated, in which, arachidic acid-GO-arachidic acid (AA-GO-AA) sandwich structure obtained by Langmuir-Blodgett (LB) technique was heat treated at moderate temperatures to obtain RGO sheets. Heat treatment of AA-GO-AA sandwich structure at 200 C results in substantial reduction of GO, with concurrent removal of AA molecules. Such developed RGO sheets possess sp2-C content of 69%, O/C ratio of 0.17 and significantly reduced I(D) and I(G) ratio of ~1.1. Ultraviolet photoelectron spectroscopy (UPS) studies on RGO sheets evidenced significant increase in density of states in immediate vicinity of Fermi level and decrease in work function after reduction. Bottom gated field effect transistors fabricated with isolated RGO sheets displayed charge neutrality point at a positive gate voltage, indicating p-type nature, consistent with UPS and electrostatic force microscopy (EFM) measurement results.The RGO sheets obtained by heat treatment of AA-GO-AA sandwich structure exhibited conductivity in the range of 2-7Scm-1 and field effect mobility of 0.03-2cm2V-1s-1, which are consistent with values reported for RGO sheets obtained by various chemical or thermal reduction procedures.The extent of GO reduction is determined primarily by proximity of AA molecules and found to be unaltered with either escalation of heat treatment temperature or increase of AA content in sandwich structure. The single-step GO reduction approach demonstrated in this work is an effective way for development of RGO monolayers with high structural quality towards graphene-based electronic device applications., Comment: 38 pages, 9 figures, 4 tables, Supplementary document

One of the ways to encode many-body Hamiltonians on a quantum computer to obtain their eigen-energies through Quantum Phase Estimation is by means of the Trotter approximation. There were several ways proposed to assess the quality of this approximation based on estimating the norm of the difference between the exact and approximate evolution operators. Here, we would like to explore how these different error estimates are correlated with each other and whether they can be good predictors for the true Trotter approximation error in finding eigenvalues. For a set of small molecular systems we calculated the exact Trotter approximation errors of the first order Trotter formulas for the ground state electronic energies. Comparison of these errors with previously used upper bounds show almost no correlation over the systems and various Hamiltonian partitionings. On the other hand, building the Trotter approximation error estimation based on perturbation theory up to a second order in the time-step for eigenvalues provides estimates with very good correlations with the Trotter approximation errors. The developed perturbative estimates can be used for practical time-step and Hamiltonian partitioning selection protocols, which are paramount for an accurate assessment of resources needed for the estimation of energy eigenvalues under a target accuracy., Comment: 3 figures

A detailed study of stellar populations in Milky Way (MW) satellite galaxies remains an observational challenge due to their faintness and fewer spectroscopically confirmed member stars. We use unsupervised machine learning methods to identify new members for nine nearby MW satellite galaxies using Gaia data release-3 (Gaia DR3) astrometry and the Dark Energy Survey (DES) and the DECam Local Volume Exploration Survey (DELVE) photometry. Two density-based clustering algorithms, DBSCAN and HDBSCAN, have been used in the four-dimensional astrometric parameter space to identify member stars belonging to MW satellite galaxies. Our results indicate that we can recover more than 80% of the known spectroscopically confirmed members in most of the satellite galaxies and also reject 95-100% of spectroscopic non-members. We have also added many new members using this method. We compare our results with previous studies that also use photometric and astrometric data and discuss the suitability of density-based clustering methods for MW satellite galaxies

Highly multiplexed spectroscopic surveys have changed the astronomy landscape in recent years. However, these surveys are limited to low and medium spectral resolution. High spectral resolution spectroscopy is often photon starved and will benefit from a large telescope aperture. Multiplexed high-resolution surveys require a wide field of view and a large aperture for a suitable large number of bright targets. This requirement introduces several practical difficulties, especially for large telescopes, such as the future ELTs. Some of the challenges are the need for a wide field atmospheric dispersion corrector and to deal with the curved non-telecentric focal plane. Here, we present a concept of Multi-Object Spectroscopy (MOS) mode for TMT High-Resolution Optical Spectrograph (HROS), we have designed an atmospheric dispersion corrector for individual objects that fit inside a fiber positioner. We present the ZEMAX design and the performance of the atmospheric dispersion corrector for all elevations accessible by TMT.

Rayleigh wave dispersion curves have been widely used in near-surface studies, and are primarily inverted for the shear wave (S-wave) velocity profiles. However, the inverse problem is ill-posed, non-unique and nonlinear. Here, we introduce DispersioNET, a deep learning model based on convolution neural networks (CNN) to perform the joint inversion of Rayleigh wave fundamental and higher order mode phase velocity dispersion curves. DispersioNET is trained and tested on both noise-free and noisy dispersion curve datasets and predicts S-wave velocity profiles that match closely with the true velocities. The architecture is agnostic to variations in S-wave velocity profiles such as increasing velocity with depth and intermediate low-velocity layers, while also ensuring that the output remains independent of the number of layers., Comment: Preprint of the paper submitted to the 2nd Indian Near Surface Geophysics Conference & Exhibition by AF Academy and the European Association of Geoscientists & Engineers (EAGE)

Microbial interactions regulate their spread and survival in competitive environments. It is not clear if the physical parameters of the environment regulate the outcome of these interactions. In this work, we show that the opportunistic pathogen Pseudomonas aeruginosa occupies a larger area on the substratum in the presence of yeast such as Cryptococcus neoformans , than without it. At the microscopic level, bacterial cells show an enhanced activity in the vicinity of yeast cells. We observe this behaviour even when the live yeast cells are replaced with heat-killed cells or with spherical glass beads of similar morphology, which suggests that the observed behaviour is not specific to the biology of microbes. Upon careful investigation, we find that a fluid pool is formed around yeast cells which facilitates the swimming of the flagellated P. aeruginosa , causing their enhanced motility. Using mathematical modeling we demonstrate how this local enhancement of bacterial motility leads to the enhanced spread observed at the level of the plate. We find that the dynamics of the fluid landscape around the bacteria, mediated by the growing yeast lawn, affects the spreading. For instance, when the yeast lawn grows faster, a bacterial colony prefers a lower initial loading of yeast cells for optimum enhancement in the spread. We confirm our predictions using Candida albicans and C. neoformans, at different initial compositions. In summary, our work shows the importance of considering the dynamically changing physical environment while studying bacterial motility in complex environments., Comment: 14 pages of main text, 5 figures, 4 pages of SI added

We tackle the problem of class incremental learning (CIL) in the realm of landcover classification from optical remote sensing (RS) images in this paper. The paradigm of CIL has recently gained much prominence given the fact that data are generally obtained in a sequential manner for real-world phenomenon. However, CIL has not been extensively considered yet in the domain of RS irrespective of the fact that the satellites tend to discover new classes at different geographical locations temporally. With this motivation, we propose a novel CIL framework inspired by the recent success of replay-memory based approaches and tackling two of their shortcomings. In order to reduce the effect of catastrophic forgetting of the old classes when a new stream arrives, we learn a curriculum of the new classes based on their similarity with the old classes. This is found to limit the degree of forgetting substantially. Next while constructing the replay memory, instead of randomly selecting samples from the old streams, we propose a sample selection strategy which ensures the selection of highly confident samples so as to reduce the effects of noise. We observe a sharp improvement in the CIL performance with the proposed components. Experimental results on the benchmark NWPU-RESISC45, PatternNet, and EuroSAT datasets confirm that our method offers improved stability-plasticity trade-off than the literature.

Simulating curvature due to gravity through warped surfaces is a common visualization aid in Physics education. We reprise a recent experiment exploring orbital trajectories on a precise 3D-printed surface to mimic Newtonian gravity, and elevate this analogy past the status of a mere visualization tool. We present a general analysis approach through which this straightforward experiment can be used to create a reasonably advanced computational orbital mechanics lab at the undergraduate level, creating a convenient hands-on, computational pathway to various non-trivial nuances in this discipline, such as the mean, eccentric, and true anomalies and their computation, Laplace-Runge-Lenz vector conservation, characterization of general orbits, and the extraction of orbital parameters. We show that while the motion of a marble on such a surface does not truly represent a orbital trajectory under Newtonian gravity in a strict theoretical sense, but through a proposed projection procedure, the experimentally recorded trajectories closely resemble the Kepler orbits and approximately respect the known conservation laws for orbital motion. The latter fact is demonstrated through multiple experimentally-recorded elliptical trajectories with wide-ranging eccentricities and semi-major axes. In this first part of this two-part sequence, we lay down the formal basis of this exposition, describing the experiment, its calibration, critical assessment of the results, and the computational procedures for the transformation of raw experimental data into a form useful for orbital analysis., Comment: Article 1 of a two-part sequence. Article 2 (applications) to be released shortly. Comments are most welcome !

Tamm plasmon provides a new avenue in plasmonics of interface states in planar multilayer structures due to its strong light matter interaction. This article reviews the research and development in Tamm plasmon polariton excited at the interface of a metal and a distributed Bragg reflector. Tamm plasmon offers an easy planar solution compared to patterned surface plasmon devices with huge field enhancement at the interface and does not require of any phase matching method for its excitation. The ease of depositing multilayer thin film stacks, direct optical excitation, and high-Q modes make Tamm plasmons an attractive field of research with potential practical applications. The basic properties of the Tamm plasmon modes including its dispersion, effect of different plasmon active metals, coupling with other resonant modes and their polarization splitting, and tunability of Tamm plasmon coupled hybrid modes under externally applied stimuli have been discussed. The application of Tamm plasmon modes in lasers, hot electron photodetectors, perfect absorbers, thermal emitters, light emitting devices, and sensors have also been discussed in detail. This review covers all the major advancements in this field over the last fifteen years with special emphasis on the application part.

Petrophysical inversion is an important aspect of reservoir modeling. However due to the lack of a unique and straightforward relationship between seismic traces and rock properties, predicting petrophysical properties directly from seismic data is a complex task. Many studies have attempted to identify the direct end-to-end link using supervised machine learning techniques, but face different challenges such as a lack of large petrophysical training dataset or estimates that may not conform with physics or depositional history of the rocks. We present a rock and wave physics informed neural network (RW-PINN) model that can estimate porosity directly from seismic image traces with no or limited number of wells, with predictions that are consistent with rock physics and geologic knowledge of deposition. As an example, we use the uncemented sand rock physics model and normal-incidence wave physics to guide the learning of RW-PINN to eventually get good estimates of porosities from normal-incidence seismic traces and limited well data. Training RW-PINN with few wells (weakly supervised) helps in tackling the problem of non-uniqueness as different porosity logs can give similar seismic traces. We use weighted normalized root mean square error loss function to train the weakly supervised network and demonstrate the impact of different weights on porosity predictions. The RW-PINN estimated porosities and seismic traces are compared to predictions from a completely supervised model, which gives slightly better porosity estimates but poorly matches the seismic traces, in addition to requiring a large amount of labeled training data. In this paper, we demonstrate the complete workflow for executing petrophysical inversion of seismic data using self-supervised or weakly supervised rock physics informed neural networks.

We present the design of SCALES (Slicer Combined with Array of Lenslets for Exoplanet Spectroscopy) a new 2-5 micron coronagraphic integral field spectrograph under construction for Keck Observatory. SCALES enables low-resolution (R~50) spectroscopy, as well as medium-resolution (R~4,000) spectroscopy with the goal of discovering and characterizing cold exoplanets that are brightest in the thermal infrared. Additionally, SCALES has a 12x12" field-of-view imager that will be used for general adaptive optics science at Keck. We present SCALES's specifications, its science case, its overall design, and simulations of its expected performance. Additionally, we present progress on procuring, fabricating and testing long lead-time components., Comment: 19 pages, 12 Figures, SPIE Astronomical Telescopes + Instrumentation, (#12184-18), 2022, Montreal, Canada