arXiv subjects

Rahul Soni

Publications and source records attributed to Rahul Soni.

At least 19 recordsLinked to original sources

arXiv2026

Interface Engineering of Helium Confinement in Argon-Preplated MCM-41 Nanopores

Atomic-scale modification of mesopore interfaces provides a route to tune the confinement experienced by adsorbed fluids, but how a specific interface preparation translates into the resulting microscopic confinement potential remains unclear. Here, we show that preplating MCM-41 with an argon monolayer modifies the effective pore interface by occupying strongly attractive regions of the heterogeneous silica surface and screening its atomic-scale corrugation. Grand-canonical Monte Carlo simulations of argon adsorption, low-temperature molecular dynamics, and helium test-particle insertion are combined with adsorption isotherms and neutron-scattering measurements to characterize the preplated pore at the atomic scale. Helium test-particle insertion calculations show that the modified interface shifts the helium adsorption minimum to an annular region inside the pore and produces a confinement landscape dominated by a smooth radial component. The resulting radial confinement potential can be described by a continuum cylindrical model, providing microscopic support for the effective potential used in earlier quantum Monte Carlo studies. Residual corrugation persists over multiple spatial scales and is accurately captured by a Gaussian process surrogate. These results demonstrate how atomic preplating can tailor nanopore confinement and provide an experimentally constrained microscopic potential for predictive studies of confined quantum fluids.

cond-mat.mtrl-sci↗

arXiv2026

RASPRef: Retrieval-Augmented Self-Supervised Prompt Refinement for Large Reasoning Models

Recent reasoning-focused language models such as DeepSeek R1 and OpenAI o1 have demonstrated strong performance on structured reasoning benchmarks including GSM8K, MATH, and multi-hop question answering tasks. However, their performance remains highly sensitive to prompt formulation, and designing effective prompts is typically a manual and iterative process that does not scale well across tasks or domains. To address this limitation, we introduce Retrieval-Augmented Self-Supervised Prompt Refinement (RASPRef), a framework that improves prompts without requiring human annotations or task-specific supervision. The approach retrieves relevant examples and previously generated reasoning trajectories, and leverages signals such as multi-sample consistency, verifier feedback, and model-generated critiques to iteratively refine the prompt. Unlike prior approaches that focus primarily on improving model outputs, RASPRef directly treats the prompt as the optimization target and improves it through an iterative retrieval-guided refinement process. Experiments on GSM8K-style mathematical reasoning tasks show that retrieval-guided prompting improves performance compared with a static prompting baseline. We further discuss how retrieval quality, trajectory selection, and self-supervised feedback signals may influence the effectiveness of prompt refinement. These findings suggest that prompt design remains a critical factor for reasoning-oriented language models, and that self-improving prompts offer a practical and scalable strategy for improving reasoning performance.

cs.CL↗

arXiv2026

MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging

Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are limited by the wide variety of AI implementations and architectures, inconsistent documentation, and reproducibility issues. Here, we introduce MHub$.$ai, an open-source, container-based platform that standardizes access to AI models with minimal configuration, promoting accessibility and reproducibility in medical imaging. MHub$.$ai packages models from peer-reviewed publications into standardized containers that support direct processing of DICOM and other formats, provide a unified application interface, and embed structured metadata. Each model is accompanied by publicly available reference data that can be used to confirm model operation. MHub$.$ai includes an initial set of state-of-the-art segmentation, prediction, and feature extraction models for different modalities. The modular framework enables adaptation of any model and supports community contributions. We demonstrate the utility of the platform in a clinical use case through comparative evaluation of lung segmentation models. To further strengthen transparency and reproducibility, we publicly release the generated segmentations and evaluation metrics and provide interactive dashboards that allow readers to inspect individual cases and reproduce or extend our analysis. By simplifying model use, MHub$.$ai enables side-by-side benchmarking with identical execution commands and standardized outputs, and lowers the barrier to clinical translation.

cs.AI↗

arXiv2025

Improving Graduate Outcomes by Identifying Skills Gaps and Recommending Courses Based on Career Interests

This paper aims to address the challenge of selecting relevant courses for students by proposing the design and development of a course recommendation system. The course recommendation system utilises a combination of data analytics techniques and machine learning algorithms to recommend courses that align with current industry trends and requirements. In order to provide customised suggestions, the study entails the design and implementation of an extensive algorithmic framework that combines machine learning methods, user preferences, and academic criteria. The system employs data mining and collaborative filtering techniques to examine past courses and individual career goals in order to provide course recommendations. Moreover, to improve the accessibility and usefulness of the recommendation system, special attention is given to the development of an easy-to-use front-end interface. The front-end design prioritises visual clarity, interaction, and simplicity through iterative prototyping and user input revisions, guaranteeing a smooth and captivating user experience. We refined and optimised the proposed system by incorporating user feedback, ensuring that it effectively meets the needs and preferences of its target users. The proposed course recommendation system could be a useful tool for students, instructors, and career advisers to use in promoting lifelong learning and professional progression as it fills the gap between university learning and industry expectations. We hope that the proposed course recommendation system will help university students in making data-drive and industry-informed course decisions, in turn, improving graduate outcomes for the university sector.

cs.CL↗

arXiv2025

Edge Reconstruction in a Quantum Spin Hall Insulator

We study interaction-driven edge reconstruction in a quantum spin Hall insulator described by the Bernevig-Hughes-Zhang model with Kanamori-Hubbard interactions using the real-space density matrix renormalization group method in both the grand-canonical and canonical ensembles. For a two-dimensional cylinder with a smooth edge, we identify discrete particle-number transitions that lead to a spin-polarized edge state stabilized by an emergent ferromagnetic exchange interaction. The reconstruction is orbital-selective, occurring predominantly in the $s$-orbital channel. Our results reveal a microscopic mechanism for emergent fluctuating moments at the edge that could compromise the topological protection of helical edge states by time reversal symmetry.

cond-mat.mes-hall↗

arXiv2025

Interaction-Driven Topological Transitions in Monolayer TaIrTe$_4$

Discovering materials that combine topological phenomena with correlated electron behavior is a central pursuit in quantum materials research. Monolayer TaIrTe$_4$ has recently emerged as a promising platform in this context, hosting robust quantum spin Hall insulator (QSHI) phases both within a single-particle gap and within a correlation-induced gap arising from van Hove singularities (vHSs), accessed via electrostatic doping. Its intrinsic monolayer nature offers exceptional tunability and the potential to realize a versatile array of interaction-driven topological phases. In this work, we combine theory and experiment to map the phase landscape of monolayer TaIrTe$_4$. Using Hartree-Fock calculations, we investigate the interaction-driven phase diagram near the vHSs under commensurate filling conditions. By systematically tuning the dielectric screening and strain, we uncover a rich set of ground states--including QSHI, trivial insulator, higher-order topological insulator, and metallic phase--among which are interaction-driven topological phase transitions. Experimentally, we perform both local and nonlocal transport measurements across a broad set of devices, which--due to unavoidable strain variations during fabrication-realize several phases consistent with theoretical predictions. Together, our results lay the groundwork for understanding correlation-driven topological phenomena in TaIrTe$_4$ and open new directions for engineering exotic quantum phases in low-dimensional materials beyond the limitations of moir\'e superlattices.

cond-mat.str-el↗

arXiv2024

AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons

AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations for cancer radiology images. We created high-quality, AI-annotated imaging datasets for 11 IDC collections. These datasets include images from various modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), covering the lungs, breast, brain, kidneys, prostate, and liver. The nnU-Net models were trained using open-source datasets. A portion of the AI-generated annotations was reviewed and corrected by radiologists. Both the AI and radiologist annotations were encoded in compliance with the the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. All models, images, and annotations are publicly accessible, facilitating further research and development in cancer imaging. This work supports the advancement of imaging tools and algorithms by providing comprehensive and accurate annotated datasets.

eess.IV↗

arXiv2023

Improving Lesion Segmentation in FDG-18 Whole-Body PET/CT scans using Multilabel approach: AutoPET II challenge

Automatic segmentation of lesions in FDG-18 Whole Body (WB) PET/CT scans using deep learning models is instrumental for determining treatment response, optimizing dosimetry, and advancing theranostic applications in oncology. However, the presence of organs with elevated radiotracer uptake, such as the liver, spleen, brain, and bladder, often leads to challenges, as these regions are often misidentified as lesions by deep learning models. To address this issue, we propose a novel approach of segmenting both organs and lesions, aiming to enhance the performance of automatic lesion segmentation methods. In this study, we assessed the effectiveness of our proposed method using the AutoPET II challenge dataset, which comprises 1014 subjects. We evaluated the impact of inclusion of additional labels and data in the segmentation performance of the model. In addition to the expert-annotated lesion labels, we introduced eight additional labels for organs, including the liver, kidneys, urinary bladder, spleen, lung, brain, heart, and stomach. These labels were integrated into the dataset, and a 3D UNET model was trained within the nnUNet framework. Our results demonstrate that our method achieved the top ranking in the held-out test dataset, underscoring the potential of this approach to significantly improve lesion segmentation accuracy in FDG-18 Whole-Body PET/CT scans, ultimately benefiting cancer patients and advancing clinical practice.

eess.IV↗

arXiv2023

Topological and magnetic properties of the interacting Bernevig-Hughes-Zhang model

We investigate the effects of electronic correlations on the Bernevig-Hughes-Zhang model using the real-space density matrix renormalization group (DMRG) algorithm. We introduce a method to probe topological phase transitions in systems with strong correlations using DMRG, substantiated by an unsupervised machine learning methodology that analyzes the orbital structure of the real-space edges. Including the full multi-orbital Hubbard interaction term, we construct a phase diagram as a function of a gap parameter ($m$) and the Hubbard interaction strength ($U$) via exact DMRG simulations on $N\times 4$ cylinders. Our analysis confirms that the topological phase persists in the presence of interactions, consistent with previous studies, but it also reveals an intriguing phase transition from a paramagnetic to a stripey antiferromagnetic topological insulator. The combination of the magnetic structure factor, strength of magnetic moments, and the orbitally resolved density, provides real-space information on both topology and magnetism in a strongly correlated system.

cond-mat.str-el↗

arXiv2023

AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons

The National Cancer Institute (NCI) Image Data Commons (IDC) offers publicly available cancer radiology collections for cloud computing, crucial for developing advanced imaging tools and algorithms. Despite their potential, these collections are minimally annotated; only 4% of DICOM studies in collections considered in the project had existing segmentation annotations. This project increases the quantity of segmentations in various IDC collections. We produced high-quality, AI-generated imaging annotations dataset of tissues, organs, and/or cancers for 11 distinct IDC image collections. These collections contain images from a variety of modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). The collections cover various body parts, such as the chest, breast, kidneys, prostate, and liver. A portion of the AI annotations were reviewed and corrected by a radiologist to assess the performance of the AI models. Both the AI's and the radiologist's annotations were encoded in conformance to the Digital Imaging and Communications in Medicine (DICOM) standard, allowing for seamless integration into the IDC collections as third-party analysis collections. All the models, images and annotations are publicly accessible.

eess.IV↗

arXiv2022

Majorana corner states on the dice lattice

Lattice geometry continues providing exotic topological phases in condensed matter physics. Exciting recent examples are the higher-order topological phases, manifesting via localized lower-dimensional boundary states. Moreover, flat electronic bands with a non-trivial topology arise in various lattices and can hold a finite superfluid density, bounded by the Chern number $C$. Here we consider attractive interaction in the dice lattice that hosts flat bands with $C=\pm2$ and show that the induced superconducting state exhibits a second-order topological phase with mixed singlet-triplet pairing. The second-order nature of the topological superconducting phase is revealed by the zero-energy Majorana bound states at the lattice corners. Hence, the topology of the normal state dictates the nature of the Majorana localization. These findings suggest that flat bands with a higher Chern number provide feasible platforms for inducing higher-order topological superconductivity.

cond-mat.supr-con↗

arXiv2022

Electronic structure, magnetic properties and pairing tendencies of the copper-based honeycomb lattice Na$_2$Cu$_2$TeO$_6$

Spin-$1/2$ chains with alternating antiferromagnetic and ferromagnetic couplings have attracted considerable interest due to the topological character of their spin excitations. Here, using density functional theory and density matrix renormalization group methods, we have systematically studied the dimerized chain system Na$_2$Cu$_2$TeO$_6$. Near the Fermi level, the dominant states are mainly contributed by the Cu $3d_{x^2-y^2}$ orbitals highly hybridized with the O $2p$ orbitals in the nonmagnetic phase, leading to an "effective" single-orbital low-energy model. Furthermore, the bandwidth of the Cu $3d_{x^2-y^2}$ states is small ($\sim 0.8$ eV), suggesting that electronic correlations will strongly affect this system. By introducing such electronic correlations, we found this system is a Mott insulator. Moreover, by calculating the magnetic exchange interactions ($J_1$, $J_2$ and $J_3$), we explained the size and sign of the exchange interactions in Na$_2$Cu$_2$TeO$_6$, in agreement with neutron experiments. In addition, we constructed a single-orbital Hubbard model for this dimerized chain system, where the quantum fluctuations are taken into account. Both AFM and FM coupling ($\uparrow$-$\downarrow$-$\downarrow$-$\uparrow$) along the chain were found in our DMRG and Lanczos calculations, in agreement with DFT and neutron results. We also calculated the hole pairing binding energy $\Delta E$ which becomes negative at Hubbard $U \sim 11$ eV, indicating incipient pairing tendencies. Finally, we also looked at various cases of hole doping that always exhibit tight pairs. Thus, we believe our results for Na$_2$Cu$_2$TeO$_6$ could provide guidance to experimentalists and theorists working on this dimerized chain system, such as short-range magnetic coupling, doping effects, and possible pairing tendencies.

cond-mat.str-el↗

arXiv2021

Range of biquadratic and triquadratic Heisenberg effective couplings deduced from multiorbital Hubbard models

We studied a multi-orbital Hubbard model at half-filling for two and three orbitals per site on a two-site cluster via full exact diagonalization, in a wide range for the onsite repulsion $U$, from weak to strong coupling, and multiple ratios of the Hund coupling $J_H$ to $U$. The hopping matrix elements among the orbitals were also varied extensively. At intermediate and large $U$, we mapped the results into a Heisenberg model. For two orbitals per site, the mapping is into a $S=1$ Heisenberg model where by symmetry both nearest-neighbor $(\mathbf{S}_{i}\cdot\mathbf{S}_{j})$ and $(\mathbf{S}_{i}\cdot\mathbf{S}_{j})^{2}$ are allowed, with respective couplings $J_1$ and $J_2$. For the case of three orbitals per site, the mappping is into a $S=3/2$ Heisenberg model with $(\mathbf{S}_{i}\cdot\mathbf{S}_{j})$, $(\mathbf{S}_{i}\cdot\mathbf{S}_{j})^{2}$, and $(\mathbf{S}_{i}\cdot\mathbf{S}_{j})^{3}$ terms, and respective couplings $J_1$, $J_2$, and $J_3$. The strength of these coupling constants in the Heisenberg models depend on the $U$, $J_H$, and hopping amplitudes of the underlying Hubbard model. Our study allows to establish bounds on how large the ratios $J_2/J_1$ and $J_3/J_1$ can be. We show that those ratios are severely limited and, as a crude guidance, we conclude that $J_2/J_1$ is less than 0.4 and $J_3/J_1$ is less than 0.2, establishing bounds on effective models for strongly correlated Hubbard systems.

cond-mat.str-el↗

arXiv2021

Multitude of Topological Phase Transitions in Bipartite Dice and Lieb Lattices with Interacting Electrons and Rashba Coupling

We report the results of a Hartree-Fock study applied to interacting electrons moving in two different bipartite lattices: the dice and the Lieb lattices, at half-filling. Both lattices develop ferrimagnetic order in the phase diagram $U$-$\lambda$, where $U$ is the Hubbard onsite repulsion and $\lambda$ the Rashba spin-orbit coupling strength. Our main result is the observation of an unexpected multitude of topological phases for both lattices. All these phases are ferrimagnetic, but they differ among themselves in their set of six Chern numbers (six numbers because the unit cells have three atoms). The Chern numbers $|C|$ observed in our study range from 0 to 3, showing that large Chern numbers can be obtained by the effect of electronic correlations, adding to the recently discussed methodologies to increase $|C|$ based on extending the hopping range in tight-binding models, using sudden quenches, or photonic crystals, all without including electronic interactions.

cond-mat.str-el↗

arXiv2021

Intertwined charge, spin, and pairing orders in doped iron ladders

Motivated by recent experimental progress on iron-based ladder compounds, we study the doped two-orbital Hubbard model for the two-leg ladder BaFe$_2$S$_3$. The model is constructed by using {\it ab initio} hopping parameters and the ground state properties are investigated using the density matrix renormalization group method. We show that the $(\pi,0)$ magnetic ordering at half-filling, with ferromagnetic rungs and antiferromagnetic legs, becomes incommensurate upon hole doping. Moreover, depending on the strength of the Hubbard $U$ coupling, other magnetic patterns, such as $(0,\pi)$, are also stabilized. We found that the binding energy for two holes becomes negative for intermediate Hubbard interaction strength, indicating hole pairing. Due to the crystal-field split among orbitals, the holes primarily reside in one orbital, with the other one remaining half-filled. This resembles orbital selective Mott states. The formation of tight hole pairs continues with increasing hole density, as long as the magnetic order remains antiferromagnetic in one direction. The study of pair-pair correlations indicates the dominance of the intra-orbital spin-singlet channel, as opposed to other pairing channels. Although in a range of hole doping pairing correlations decay slowly, our results can also be interpreted as corresponding to a charge-density-wave made of pairs, a precursor of eventual superconductivity after interladder couplings are included. Such scenario of intertwined orders has been extensively discussed before in the cuprates, and our results suggest a similar physics could exist in ladder iron-based superconductors. Finally, we also show that a robust Hund's coupling is needed for pairing to occur.

cond-mat.str-el↗

arXiv2020

Origin of the Magnetic and Orbital ordering in $\alpha$-Sr$_2$CrO$_4$

Motivated by recent experimental progress in transition metal oxides with the K$_2$NiF$_4$ structure, we investigate the magnetic and orbital ordering in $\alpha$-Sr$_2$CrO$_4$. Using first principles calculations, first we derive a three-orbital Hubbard model, which reproduces the {\it ab initio} band structure near the Fermi level. The unique reverse splitting of $t_{2g}$ orbitals in $\alpha$-Sr$_2$CrO$_4$, with the $3d^2$ electronic configuration for the Cr$^{4+}$ oxidation state, opens up the possibility of orbital ordering in this material. Using real-space Hartree-Fock for multi-orbital systems, we constructed the ground-state phase diagram for the two-dimensional compound $\alpha$-Sr$_2$CrO$_4$. We found stable ferromagnetic, antiferromagnetic, antiferro-orbital, and staggered orbital stripe ordering in robust regions of the phase diagram. Furthermore, using the density matrix renormalization group method for two-leg ladders with the realistic hopping parameters of $\alpha$-Sr$_2$CrO$_4$, we explore magnetic and orbital ordering for experimentally relevant interaction parameters. Again, we find a clear signature of antiferromagnetic spin ordering along with antiferro-orbital ordering at moderate to large Hubbard interaction strength. We also explore the orbital-resolved density of states with Lanczos, predicting insulating behavior for the compound $\alpha$-Sr$_2$CrO$_4$, in agreement with experiments. Finally, an intuitive understanding of the results is provided based on a hierarchy between orbitals, with $d_{xy}$ driving the spin order, while electronic repulsion and the effective one dimensionality of the movement within the $d_{xz}$ and $d_{yz}$ orbitals driving the orbital order.

cond-mat.str-el↗

arXiv2020

Prediction of exotic magnetic states in the alkali metal quasi-one-dimensional iron selenide compound Na$_2$FeSe$_2$

The magnetic and electronic phase diagram of a model for the quasi-one-dimensional alkali metal iron selenide compound Na$_2$FeSe$_2$ is presented. The novelty of this material is that the valence of iron is Fe$^{2+}$ contrary to most other iron-chain compounds with valence Fe$^{3+}$. Using first-principles techniques, we developed a three-orbital tight-binding model that reproduces the {\it ab initio} band structure near the Fermi level. Including Hubbard and Hund couplings and studying the model via the density matrix renormalization group and Lanczos methods, we constructed the ground state phase diagram. A robust region where the block state $\uparrow \uparrow \downarrow \downarrow \uparrow \uparrow \downarrow \downarrow$ is stabilized was unveiled. The analog state in iron ladders, employing 2$\times$2 ferromagnetic blocks, is by now well-established, but in chains a block magnetic order has not been observed yet in real materials. The phase diagram also contains a large region of canonical staggered spin order $\uparrow \downarrow \uparrow \downarrow \uparrow \downarrow \uparrow$ at very large Hubbard repulsion. At the block to staggered transition region, a novel phase is stabilized with a mixture of both states: an inhomogeneous orbital-selective charge density wave with the exotic spin configuration $\uparrow \uparrow \downarrow \uparrow \downarrow \downarrow \uparrow \downarrow$. Our predictions for Na$_2$FeSe$_2$ may guide crystal growers and neutron scattering experimentalists towards the realization of block states in one-dimensional iron-selenide chain materials.

cond-mat.str-el↗

arXiv2020

BCS-BEC crossover in a $(t_{2g})^4$ Excitonic Magnet

The condensation of spin-orbit-induced excitons in $(t_{2g})^4$ electronic systems is attracting considerable attention. In the large Hubbard U limit, antiferromagnetism was proposed to emerge from the Bose-Einstein Condensation (BEC) of triplons ($J_{\textrm{eff}} = 1$). In this publication, we show that even for the weak and intermediate U regimes, the spin-orbit exciton condensation is possible leading also to staggered magnetic order. The canonical electron-hole excitations (excitons) transform into local triplon excitations at large U , and this BEC strong coupling regime is smoothly connected to the intermediate U excitonic insulator region. We solved the degenerate three-orbital Hubbard model with spin-orbit coupling ($\lambda$) in one-dimensional geometry using the Density Matrix Renormalization Group, while in two-dimensional square clusters we use the Hartree-Fock approximation (HFA). Employing these techniques, we provide the full $\lambda$ vs U phase diagrams for both one- and two- dimensional lattices. Our main result is that at the intermediate Hubbard U region of our focus, increasing $\lambda$ at fixed U the system transitions from an incommensurate spin-density-wave metal to a Bardeen-Cooper-Schrieffer (BCS) excitonic insulator, with coherence length r coh of O(a) and O(10a) in 1d and 2d, respectively, with a the lattice spacing. Further increasing $\lambda$, the system eventually crosses over to the BEC limit (with r coh << a).

cond-mat.str-el↗