arXiv subjects

Chintan Shah

Publications and source records attributed to Chintan Shah.

At least 19 recordsLinked to original sources

arXiv2026

Parts-per-million-accurate determination of the K${\alpha}$ photoionization resonance of Be-like oxygen with resolution of its $^{16}$O-$^{18}$O isotopic shift

We determine with high accuracy the energy of the inner-shell transition $1s^2 2s^2~{}^1\mathrm{S}_0 \rightarrow 1s~2s^2~2p_{3/2}~{}^1\mathrm{P}_1$ ${}^{16}\mathrm{O}_{K\alpha}^{4+}$ at $554.372(3)~\mathrm{eV}$ ($\lambda$ = $22.36480(12)~\unicode{x212B}$) as well as its small shift of $2.2 \pm 1.3~\mathrm{meV}$ ($\Delta \lambda$ = $0.089(52)~\mathrm{m}\unicode{x212B}$) for the ${}^{18}\mathrm{O}$ isotope. This transition blends with a $K_\alpha$ line of $\mathrm{O}^{5+}$ used in astrophysical diagnostics, potentially affecting its reliability. In contrast to our experimental uncertainty of $\pm 3~\mathrm{meV}$, advanced electronic structure predictions for this four-electron system, including quantum electrodynamic (QED) corrections on the order of $100~\mathrm{meV}$, still scatter by more than $\pm 250~\mathrm{meV}$. Ions generated and stored in an electron beam ion trap were excited at the ELETTRA synchrotron facility with monochromatic soft x rays, with photon energies corrected by an additional spectrometer. Upon resonant excitation of $\mathrm{O}^{4+}$ and subsequent autoionization, we separate the photoions of each isotope by a time-of-flight measurement. This way, we resolve soft x-ray isotopic shifts of a few meV, obtain very accurate data on an essential astrophysical ion, and test calculations down to the level of QED contributions.

physics.atom-ph↗

arXiv2026

X-ray and extreme-ultraviolet spectra from collisions of Ar$^{18+}$ and O$^{8+}$ ions with neutrals

We present measurements of K-shell x-ray emission following charge exchange of fully ionized argon with various neutral gaseous targets at small collision energies inside an electron beam ion trap. We also resolve the principal quantum number of electron capture in extreme-ultraviolet spectra from initially bare and hydrogen-like oxygen ions held in the same trap. We analyze discrepancies between these as well as previous measurements with theoretical models based on the multichannel Landau-Zener approach.

physics.atom-ph↗

arXiv2025

State-of-the-art Small Language Coder Model: Mify-Coder

We present Mify-Coder, a 2.5B-parameter code model trained on 4.2T tokens using a compute-optimal strategy built on the Mify-2.5B foundation model. Mify-Coder achieves comparable accuracy and safety while significantly outperforming much larger baseline models on standard coding and function-calling benchmarks, demonstrating that compact models can match frontier-grade models in code generation and agent-driven workflows. Our training pipeline combines high-quality curated sources with synthetic data generated through agentically designed prompts, refined iteratively using enterprise-grade evaluation datasets. LLM-based quality filtering further enhances data density, enabling frugal yet effective training. Through disciplined exploration of CPT-SFT objectives, data mixtures, and sampling dynamics, we deliver frontier-grade code intelligence within a single continuous training trajectory. Empirical evidence shows that principled data and compute discipline allow smaller models to achieve competitive accuracy, efficiency, and safety compliance. Quantized variants of Mify-Coder enable deployment on standard desktop environments without requiring specialized hardware.

cs.SE↗

arXiv2025

Enhanced One-Color-Two-Photon Resonant Ionization in Highly Charged Ions by Fine-Structure Effects

Ultraintense pulses from X-ray free-electron lasers can drive, within femtoseconds, multiple processes in the inner shells of atoms and molecules in all phases of matter. The ensuing complex ionization pathways of outer-shell electrons from the neutral to the final highly charged states make a comparison with theory enormously difficult. We resolve these pathways by preparing highly charged ions in an electron beam ion trap before exposing them to the pulsed radiation. This reveals how relativistic fine-structure effects shift electronic energies, largely compensate the core-screening potential, and enable the consecutive, resonant absorption of two quasi-monochromatic X-ray photons that would generally be unfeasible. This doubly-resonant channel enhances the efficiency of two-photon ionization by more than two orders of magnitude, dominating in this regime the nonlinear interaction of light and matter with possible application for future precision X-ray metrology.

physics.atom-ph↗

arXiv2025

PLUTO-4: Frontier Pathology Foundation Models

Foundation models trained on large-scale pathology image corpora have demonstrated strong transfer capabilities across diverse histopathology tasks. Building on this progress, we introduce PLUTO-4, our next generation of pathology foundation models that extend the Pathology-Universal Transformer (PLUTO) to frontier scale. We share two complementary Vision Transformer architectures in the PLUTO-4 family: a compact and efficient PLUTO-4S model optimized for multi-scale deployment using a FlexiViT setup with 2D-RoPE embeddings, and a frontier-scale PLUTO-4G model trained with a single patch size to maximize representation capacity and stability. Both models are pretrained using a self-supervised objective derived from DINOv2 on a large multi-institutional corpus containing 551,164 WSIs from 137,144 patients across over 50 institutions, spanning over 60 disease types and over 100 stains. Comprehensive evaluation across public and internal benchmarks demonstrates that PLUTO-4 achieves state-of-the-art performance on tasks requiring varying spatial and biological context, including tile classification, segmentation, and slide-level diagnosis. The compact PLUTO-4S provides high-throughput and robust performance for practical deployment, while PLUTO-4G establishes new performance frontiers across multiple pathology benchmarks, including an 11% improvement in dermatopathology diagnosis. These diverse improvements underscore PLUTO-4's potential to transform real-world applications as a backbone for translational research and diagnostic use cases.

cs.CV↗

arXiv2025

Laboratory Measurements of Ca XIX Dielectronic Recombination Satellites

We report measurements of the K$\alpha$ emission from the astrophysically very abundant Ca XIX (He-like ion) and its satellite lines resonantly excited by dielectronic recombination (DR). We achieve an electron-energy resolution of 8 eV in a cryogenic electron beam ion trap, and determine the energies of the exciting electrons and the emitted photons up to the KLn ($n\le 8$) manifold with $0.05\%$ and $0.1\%$ respective uncertainties. For the KLL satellites, energies agree very well with our predictions using the Flexible Atomic Code (FAC) and previous state-of-the-art calculations. Our calculations also agree with our experimental direct excitation cross-sections for K$\alpha$ within their $10\%$ uncertainty. We extract DR coefficient rates and find good agreement with values tabulated in the OPEN-ADAS database. As an application, we experimentally benchmark Ca XIX atomic data used to model high-temperature astrophysical plasmas by comparing FAC synthetic spectra with recent XRISM observations revealing the contributions of DR satellites to the Ca XIX lines.

physics.atom-ph↗

arXiv2025

Comprehensive Laboratory Benchmark of K-shell Dielectronic Satellites of Fe XXV-XXI Ions

We report on comprehensive laboratory studies of the K-shell dielectronic recombination (DR) resonances of Fe XXV - XXI ions that prominently contribute to the hard X-ray spectrum of hot astrophysical plasmas. By scanning a monoenergetic electron beam to resonantly excite trapped Fe ions in an electron beam ion trap, and achieving a high electron-ion collision energy resolution of ~7 eV, we resolve their respective KL$n$ satellites up to n'=11. By normalization to known radiative recombination cross sections we also determine their excitation cross sections and that of the continuum with uncertainties below 15%, and verify our results with an independent normalization based on previous measurements. Our experimental data excellently confirm the accuracy and suitability of distorted-wave calculations obtained with the Flexible Atomic Code (FAC) for modeling astrophysical and fusion plasmas.

physics.atom-ph↗

arXiv2025

ML-Dev-Bench: Comparative Analysis of AI Agents on ML development workflows

In this report, we present ML-Dev-Bench, a benchmark aimed at testing agentic capabilities on applied Machine Learning development tasks. While existing benchmarks focus on isolated coding tasks or Kaggle-style competitions, ML-Dev-Bench tests agents' ability to handle the full complexity of ML development workflows. The benchmark assesses performance across critical aspects including dataset handling, model training, improving existing models, debugging, and API integration with popular ML tools. We evaluate three agents - ReAct, Openhands, and AIDE - on a diverse set of 30 tasks, providing insights into their strengths and limitations in handling practical ML development challenges. We open source the benchmark for the benefit of the community at \href{https://github.com/ml-dev-bench/ml-dev-bench}{https://github.com/ml-dev-bench/ml-dev-bench}.

cs.SE↗

arXiv2024

High-accuracy Measurements of Core-excited Transitions in Light Li-like Ions

The transition energies of the two $1s$-core-excited soft X-ray lines (dubbed q and r) from $1s^2 2s ^1S_{1/2}$ to the respective upper levels $1s(^{2}S)2s2p(^{3}P) ^{2}P_{3/2}$ and $^{2}P_{1/2}$ of Li-like oxygen, fluorine and neon were measured and calibrated using several nearby transitions of He-like ions. The major remaining source of energy uncertainties in monochromators, the periodic fluctuations produced by imperfect angular encoder calibration, is addressed by a simultaneously running photoelectron spectroscopy measurement. This leads to an improved energy determination of 5 parts per million, showing fair agreement with previous theories as well as with our own, involving a complete treatment of the autoionizing states studied here. Our experimental results translate to an uncertainty of only 1.6\,km/s for the oxygen line qr-blend used to determine the outflow velocities of active galactic nuclei, ten times smaller than previously possible.

physics.atom-ph↗

arXiv2024

Hanle effect for lifetime determinations in the soft X-ray regime

By exciting a series of $1\mathrm{s}^{2}\, ^{1}\mathrm{S}_{0} \to 1\mathrm{s}n\mathrm{p}\, ^{1}\mathrm{P}_{1}$ transitions in helium-like nitrogen ions with linearly polarized monochromatic soft X-rays at the Elettra facility, we found a change in the angular distribution of the fluorescence sensitive to the principal quantum number $n$. In particular it is observed that the ratio of emission in directions parallel and perpendicular to the polarization of incident radiation increases with higher $n$. We find this $n$-dependence to be a manifestation of the Hanle effect, which served as a practical tool for lifetime determinations of optical transitions since its discovery in 1924. In contrast to traditional Hanle effect experiments, in which one varies the magnetic field and considers a particular excited state, we demonstrate a 'soft X-ray Hanle effect' which arises in a static magnetic field but for a series of excited states. By comparing experimental data with theoretical predictions, we were able to determine lifetimes ranging from hundreds of femtoseconds to tens of picoseconds of the $1\mathrm{s}n\mathrm{p}\, ^{1}\mathrm{P}_{1}$ levels, which find excellent agreement with atomic-structure calculations. We argue that dedicated soft X-ray measurements could yield lifetime data that is beyond current experimental reach and cannot yet be predicted with sufficient accuracy.

physics.atom-ph↗

arXiv2024

Measurement of differential collisional excitation cross sections for the K$α$ emission of He-like oxygen

We measure the energy-differential cross sections for collisional excitation of the soft X-ray electric-dipole K$α$ ($x+y+w$) emission from He-like oxygen (O VII), using an electron beam ion trap. Values near their excitation thresholds were extracted from the observed emissivity by rapidly cycling the energy of the exciting electron beam. This allows us to subtract time-dependent contributions of the forbidden $z$-line emission to the multiplet. We develop a time-dependent collisional-radiative model to further demonstrate the method and predict all spectral features. We then compare the extracted $x+y+w$ cross-sections with calculations based on distorted-wave and R-matrix methods from the literature and our own predictions using the Flexible Atomic Code (FAC). All R-matrix results are validated by our measurements of direct and resonant excitation, supporting the use of such state-of-the-art codes for astrophysical and plasma physics diagnostics.

physics.atom-ph↗

arXiv2024

Learning biologically relevant features in a pathology foundation model using sparse autoencoders

Pathology plays an important role in disease diagnosis, treatment decision-making and drug development. Previous works on interpretability for machine learning models on pathology images have revolved around methods such as attention value visualization and deriving human-interpretable features from model heatmaps. Mechanistic interpretability is an emerging area of model interpretability that focuses on reverse-engineering neural networks. Sparse Autoencoders (SAEs) have emerged as a promising direction in terms of extracting monosemantic features from polysemantic model activations. In this work, we trained a Sparse Autoencoder on the embeddings of a pathology pretrained foundation model. We found that Sparse Autoencoder features represent interpretable and monosemantic biological concepts. In particular, individual SAE dimensions showed strong correlations with cell type counts such as plasma cells and lymphocytes. These biological representations were unique to the pathology pretrained model and were not found in a self-supervised model pretrained on natural images. We demonstrated that such biologically-grounded monosemantic representations evolved across the model's depth, and the pathology foundation model eventually gained robustness to non-biological factors such as scanner type. The emergence of biologically relevant SAE features was generalizable to an out-of-domain dataset. Our work paves the way for further exploration around interpretable feature dimensions and their utility for medical and clinical applications.

eess.IV↗

arXiv2024

High-Precision Transition Energy Measurements of Neon-like Fe XVII Ions

We improve by a factor of 4-20 the energy accuracy of the strongest soft X-ray transitions of Fe XVII ions by resonantly exciting them in an electron beam ion trap with a monochromatic beam at the P04 beamline of the PETRA III synchrotron facility. By simultaneously tracking instantaneous photon-energy fluctuations with a high-resolution photoelectron spectrometer, we minimize systematic uncertainties down to 10-15 meV, or velocity equivalent $\pm\sim$5 km s$^{-1}$ in their rest energies, substantially improving our knowledge of this key astrophysical ion. Our large-scale configuration-interaction computations include more than four million relativistic configurations and agree with the experiment at a level without precedent for a 10-electron system. Thereby, theoretical uncertainties for interelectronic correlations become far smaller than those of quantum electrodynamics (QED) corrections. The present QED benchmark strengthens our trust in future calculations of many other complex atomic ions of interest to astrophysics, plasma physics, and for the development of optical clocks with highly charged ions.

physics.atom-ph↗

arXiv2024

Natural-linewidth measurements of the 3C and 3D soft-x-ray transitions in Ni XIX

We used the monochromatic soft-x-ray beamline P04 at the synchrotron-radiation facility PETRA III to resonantly excite the strongest $2p-3d$ transitions in neon-like Ni XIX ions, $[2p^6]_{J=0} \rightarrow [(2p^5)_{1/2}\,3d_{3/2}]_{J=1}$ and $[2p^6]_{J=0} \rightarrow [(2p^5)_{3/2}\,3d_{5/2}]_{J=1}$, respectively dubbed 3C and 3D, achieving a resolving power of 15\,000 and signal-to-background ratio of 30. We obtain their natural linewidths, with an accuracy of better than 10\%, as well as the oscillator-strength ratio $f(3C)/f(3D)$ = 2.51(11) from analysis of the resonant fluorescence spectra. These results agree with those of previous experiments, earlier predictions, and our own advanced calculations.

physics.atom-ph↗

arXiv2024

PLUTO: Pathology-Universal Transformer

Pathology is the study of microscopic inspection of tissue, and a pathology diagnosis is often the medical gold standard to diagnose disease. Pathology images provide a unique challenge for computer-vision-based analysis: a single pathology Whole Slide Image (WSI) is gigapixel-sized and often contains hundreds of thousands to millions of objects of interest across multiple resolutions. In this work, we propose PathoLogy Universal TransfOrmer (PLUTO): a light-weight pathology FM that is pre-trained on a diverse dataset of 195 million image tiles collected from multiple sites and extracts meaningful representations across multiple WSI scales that enable a large variety of downstream pathology tasks. In particular, we design task-specific adaptation heads that utilize PLUTO's output embeddings for tasks which span pathology scales ranging from subcellular to slide-scale, including instance segmentation, tile classification, and slide-level prediction. We compare PLUTO's performance to other state-of-the-art methods on a diverse set of external and internal benchmarks covering multiple biologically relevant tasks, tissue types, resolutions, stains, and scanners. We find that PLUTO matches or outperforms existing task-specific baselines and pathology-specific foundation models, some of which use orders-of-magnitude larger datasets and model sizes when compared to PLUTO. Our findings present a path towards a universal embedding to power pathology image analysis, and motivate further exploration around pathology foundation models in terms of data diversity, architectural improvements, sample efficiency, and practical deployability in real-world applications.

eess.IV↗

arXiv2024

ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology

Differences in staining and imaging procedures can cause significant color variations in histopathology images, leading to poor generalization when deploying deep-learning models trained from a different data source. Various color augmentation methods have been proposed to generate synthetic images during training to make models more robust, eliminating the need for stain normalization during test time. Many color augmentation methods leverage domain labels to generate synthetic images. This approach causes three significant challenges to scaling such a model. Firstly, incorporating data from a new domain into deep-learning models trained on existing domain labels is not straightforward. Secondly, dependency on domain labels prevents the use of pathology images without domain labels to improve model performance. Finally, implementation of these methods becomes complicated when multiple domain labels (e.g., patient identification, medical center, etc) are associated with a single image. We introduce ContriMix, a novel domain label free stain color augmentation method based on DRIT++, a style-transfer method. Contrimix leverages sample stain color variation within a training minibatch and random mixing to extract content and attribute information from pathology images. This information can be used by a trained ContriMix model to create synthetic images to improve the performance of existing classifiers. ContriMix outperforms competing methods on the Camelyon17-WILDS dataset. Its performance is consistent across different slides in the test set while being robust to the color variation from rare substances in pathology images. We make our code and trained ContriMix models available for research use. The code for ContriMix can be found at https://gitlab.com/huutan86/contrimix

eess.IV↗

arXiv2024

Laboratory Benchmark of $n\geq4$ Dielectronic Recombination Satellites of Fe XVII

We calculated cross sections for the dielectronic recombination (DR) satellite lines of Fe XVII and benchmarked our predictions with experimental cross sections of Fe XVII resonances that were mono-energetically excited in an electron beam ion trap. We extend the benchmark to all resolved DR and direct electron-impact excitation (DE) channels in the experimental dataset, specifically the $n\geq4$ DR resonances of Fe XVII, complementing earlier investigations of $n=3$ channels. Our predictions overestimate by 20-25$\%$ the DR and DE absolute cross sections for the higher $n$ complexes when using the same methods as in previous works. However, we achieve agreement within $\sim$10$\%$ of the experimental results by an approach in which we "forward fold" the predicted cross sections with the spread of the electron-beam energy and the photon-energy resolution of our experiment. We then calculated rate coefficients from the experimental and theoretical cross sections, finding departures of $10-20\%$ from the rates found in the OPEN-ADAS atomic database.

physics.atom-ph↗

arXiv2023

SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology

Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images. Machine learning problems in medical imaging often deal with rare diseases, making it important for these models to work in a label-imbalanced setting. In pathology images, there is another level of imbalance, where given a positively labeled Whole Slide Image (WSI), only a fraction of pixels within it contribute to the positive label. This compounds the severity of imbalance and makes imbalanced classification in pathology challenging. Furthermore, these imbalances can occur in out-of-distribution (OOD) datasets when the models are deployed in the real-world. We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets. To this end, we investigate the integration of supervised contrastive learning with multiple instance learning (SC-MIL). Specifically, we propose a joint-training MIL framework in the presence of label imbalance that progressively transitions from learning bag-level representations to optimal classifier learning. We perform experiments with different imbalance settings for two well-studied problems in cancer pathology: subtyping of non-small cell lung cancer and subtyping of renal cell carcinoma. SC-MIL provides large and consistent improvements over other techniques on both in-distribution (ID) and OOD held-out sets across multiple imbalanced settings.

cs.CV↗