arXiv subjects

Shantanu Singh

Publications and source records attributed to Shantanu Singh.

At least 19 recordsLinked to original sources

arXiv2026

Science sandboxes measure the scientific capability of AI agents

Scientific progress depends not only on finding solutions, but on learning the rules that explain why they work and using that understanding to design better experiments. We introduce science sandboxes, a framework for studying this capability in AI agents through repeated cycles of experimentation, feedback, and hypothesis revision. Science sandboxes invite an agent to query the natural world in different ways, ranging from "wet" physical experiments, to "damp" predictive models trained on empirical data, to "dry" invented rules. By establishing a common experimental loop and a protocol for evaluating agents within it, science sandboxes allow assessment of both quantitative performance on specific metrics and qualitative scientific reasoning, across a spectrum of empirical verifiability. Here, we instantiate this framework in two biological settings, models of regulatory genomics and protein fitness prediction, and examine the capabilities of frontier agents. Across these settings, we could see when agents successfully optimized a quantitative metric without understanding the rules underlying the system. In particular, their scientific reasoning deteriorated when they encountered systems whose rules fell outside familiar biological priors. By highlighting such failure modes, science sandboxes make the frontier of scientific capability measurable and provide a controlled setting in which to study and ultimately expand it.

q-bio.QM↗

arXiv2026

JUMP-lite: Compact, reproducible benchmarking of cell representations

Image-based profiling captures rich phenotypic signatures for drug discovery and functional genomics. Large public datasets like JUMP Cell Painting now provide millions of images for systematic study. However, JUMP alone occupies 115 TB, and fragmented evaluation practices make systematic comparisons of representation methods impractical for many researchers. Here we present Nahual, an open-source framework for reproducible model deployment, and JUMP-lite, a 92.0 GB subset of JUMP that is approximately 1,250-fold smaller, selected to cover genetic modalities and compound annotations and reduced via lossy JPEG XL compression. Using these resources, we benchmark five representation methods, including classical features (CellProfiler) and deep learning models (MorphEM, OpenPhenom, SubCell, DINOv2). Moderate compression broadly retains signal relative to uncompressed images. Standardized phenotypic activity and consistency metrics reveal meaningful performance differences across methods. Together, JUMP-lite and Nahual provide a foundation for accessible, reproducible benchmarking of image-based cell representations.

q-bio.QM↗

arXiv2026

Aclass of incrementally scattering-passive nonlinear systems

We investigate a special class of nonlinear infinite dimensional systems. These are obtained by subtracting a nonlinear maximal monotone (possibly multi-valued) operator M from the semigroup generator of a scattering passive linear system. While the linear system may have unbounded linear damping (for instance, boundary damping) which is only densely defined, the nonlinear damping operator M is assumed to be defined on the whole state space. We show that this new class of nonlinear infinite dimensional systems is well-posed and incrementally scattering passive. Our approach uses the theory of maximal monotone operators and the Crandall-Pazy theorem about nonlinear contraction semigroups, which we apply to a Lax-Phillips type nonlinear semigroup that represents the whole system.

math.OC↗

arXiv2026

Projected incrementally scattering passive systems on closed convex sets

In this article we show that the projected dynamical system obtained by restricting the state of an incrementally scattering passive system to a closed and convex subset K of the state space (a real Hilbert space), is also an incrementally scattering passive system. First we show that the projection of a maximal dissipative operator to the tangent cones of K is again maximal dissipative, hence, it determines a contraction semigroup.

math.OC↗

arXiv2026

Second order systems on Hilbert spaces with nonlinear damping

We investigate a special class of nonlinear infinite dimensional systems. These systems are obtained by modifying the second order differential equation that is part of the description of conservative linear systems out of thin air introduced by M. Tucsnak and G. Weiss in 2003. The modified differential equation contains a new nonlinear damping term, that is maximal monotone and possibly set-valued. We show that this new class of nonlinear infinite dimensional systems is incrementally scattering passive (hence well-posed). Our approach uses the theory of maximal monotone operators and the Crandall-Pazy theorem about nonlinear contraction semigroups, which we apply to a Lax-Phillips type nonlinear semigroup that represents the whole system. We illustrate our result on the n-dimensional wave equation.

math.OC↗

arXiv2026

Anti-reflection coatings for highly anisotropic materials in the mid infrared

We develop and optimize thin anti-reflection coatings (ARCs) for highly anisotropic materials in the mid infrared. Unlike conventional ARCs that assume nearly isotropic refractive indices, this work fully integrates the anisotropic nature of materials into the design process. We describe two designs of thin ARCs for highly anisotropic materials: a single form-birefringent layer, and a planar bilayer. We realized the planar bilayer ARC experimentally, demonstrating excellent mid-infrared anti-reflectance across over a broad range of angles for all polarizations.

physics.optics↗

arXiv2025

cubic: CUDA-accelerated 3D Bioimage Computing

Quantitative analysis of multidimensional biological images is useful for understanding complex cellular phenotypes and accelerating advances in biomedical research. As modern microscopy generates ever-larger 2D and 3D datasets, existing computational approaches are increasingly limited by their scalability, efficiency, and integration with modern scientific computing workflows. Existing bioimage analysis tools often lack application programmable interfaces (APIs), do not support graphics processing unit (GPU) acceleration, lack broad 3D image processing capabilities, and/or have poor interoperability for compute-heavy workflows. Here, we introduce cubic, an open-source Python library that addresses these challenges by augmenting widely used SciPy and scikit-image APIs with GPU-accelerated alternatives from CuPy and RAPIDS cuCIM. cubic's API is device-agnostic and dispatches operations to GPU when data reside on the device and otherwise executes on CPU, seamlessly accelerating a broad range of image processing routines. This approach enables GPU acceleration of existing bioimage analysis workflows, from preprocessing to segmentation and feature extraction for 2D and 3D data. We evaluate cubic both by benchmarking individual operations and by reproducing existing deconvolution and segmentation pipelines, achieving substantial speedups while maintaining algorithmic fidelity. These advances establish a robust foundation for scalable, reproducible bioimage analysis that integrates with the broader Python scientific computing ecosystem, including other GPU-accelerated methods, enabling both interactive exploration and automated high-throughput analysis workflows. cubic is openly available at https://github$.$com/alxndrkalinin/cubic

cs.CV↗

arXiv2025

Progress and new challenges in image-based profiling

For over two decades, image-based profiling has revolutionized cell phenotype analysis. Image-based profiling processes rich, high-throughput, microscopy data into thousands of unbiased measurements that reveal phenotypic patterns powerful for drug discovery, functional genomics, and cell state classification. Here, we review the evolving computational landscape of image-based profiling, detailing the bioinformatics processes involved from feature extraction to normalization and batch correction. We discuss how deep learning has fundamentally reshaped the field. We examine key methodological advancements, such as single-cell analysis, the development of robust similarity metrics, and the expansion into new modalities like optical pooled screening, temporal imaging, and 3D organoid profiling. We also highlight the growth of public benchmarks and open-source software ecosystems as a key driver for fostering reproducibility and collaboration. Despite these advances, the field still faces substantial challenges, particularly in developing methods for emerging temporal and 3D data modalities, establishing robust quality control standards and workflows, and interpreting the processed features. By focusing on the technical evolution of image-based profiling rather than the wide-ranging biological applications, our aim with this review is to provide researchers with a roadmap for navigating the progress and new challenges in this rapidly advancing domain.

q-bio.QM↗

arXiv2025

Foreground-aware Virtual Staining for Accurate 3D Cell Morphological Profiling

Microscopy enables direct observation of cellular morphology in 3D, with transmitted-light methods offering low-cost, minimally invasive imaging and fluorescence microscopy providing specificity and contrast. Virtual staining combines these strengths by using machine learning to predict fluorescence images from label-free inputs. However, training of existing methods typically relies on loss functions that treat all pixels equally, thus reproducing background noise and artifacts instead of focusing on biologically meaningful signals. We introduce Spotlight, a simple yet powerful virtual staining approach that guides the model to focus on relevant cellular structures. Spotlight uses histogram-based foreground estimation to mask pixel-wise loss and to calculate a Dice loss on soft-thresholded predictions for shape-aware learning. Applied to a 3D benchmark dataset, Spotlight improves morphological representation while preserving pixel-level accuracy, resulting in virtual stains better suited for downstream tasks such as segmentation and profiling.

cs.CV↗

arXiv2025

cp_measure: API-first feature extraction for image-based profiling workflows

Biological image analysis has traditionally focused on measuring specific visual properties of interest for cells or other entities. A complementary paradigm gaining increasing traction is image-based profiling - quantifying many distinct visual features to form comprehensive profiles which may reveal hidden patterns in cellular states, drug responses, and disease mechanisms. While current tools like CellProfiler can generate these feature sets, they pose significant barriers to automated and reproducible analyses, hindering machine learning workflows. Here we introduce cp_measure, a Python library that extracts CellProfiler's core measurement capabilities into a modular, API-first tool designed for programmatic feature extraction. We demonstrate that cp_measure features retain high fidelity with CellProfiler features while enabling seamless integration with the scientific Python ecosystem. Through applications to 3D astrocyte imaging and spatial transcriptomics, we showcase how cp_measure enables reproducible, automated image-based profiling pipelines that scale effectively for machine learning applications in computational biology.

cs.CV↗

arXiv2025

Crystal Growth of Chalcogenides and Oxy-Chalcogenides Using Chloride Exchange Reaction

Chalcogenides and oxy-chalcogenides, including complex chalcogenides and transition metal dichalcogenides, are emerging semiconductors with direct or indirect band gaps within the visible spectrum. These materials are being explored for various photonic and electronic applications, such as photodetectors, photovoltaics, and phase-change electronics. Understanding the fundamental properties of these materials is crucial for optimizing their functionalities. Therefore, the availability of large, high-quality single crystals of chalcogenides and oxy-chalcogenides is essential for a better comprehension of their structure and properties. In this study, we present a novel crystal growth method that utilizes the exchange reaction between BaS and ZrCl$_4$/ HfCl$_4$. By carefully controlling the stoichiometric ratio of the binary sulfide to the chloride, we can grow single crystals of several materials, such as ZrS$_2$, HfS$_2$, BaZrS$_3$, and ZrOS. This method results in large single crystals with a short reaction time of 24 to 48 hours. High-resolution thin film diffraction and single-crystal X-ray diffraction confirm the quality of the crystals produced through this exchange reaction. We also report the optical properties of these materials investigated using photoluminescence and Raman measurements. The chloride exchange reaction method paves the way for the synthesis of single crystals of chalcogenides and oxy-chalcogenide systems with a short reaction time but with low mosaicity and can be an alternative growth technique for single crystals of materials that are difficult to synthesize using conventional growth techniques.

cond-mat.mtrl-sci↗

arXiv2025

Towards Atomic-Scale Control over Structural Modulations in Quasi-1D Chalcogenides for Colossal Optical Anisotropy

Optically anisotropic materials are sought after for tailoring the polarization of light. Recently, colossal optical anisotropy was reported in a quasi-one-dimensional chalcogenide, Sr1.125TiS3. Compared to SrTiS3, the excess Sr in Sr1.125TiS3 leads to periodic structural modulations and introduces additional electrons that undergo charge ordering on select Ti atoms to form a highly polarizable cloud oriented along the c-axis, hence, resulting in the colossolal optical anisotropy. Here, further enhancement of the colossal optical anisotropy to 2.5 in Sr1.143TiS3 is reported through control over the periodicity of the atomic-scale modulations. The role of structural modulations in tuning the optical properties in a series of SrxTiS3 compounds has been investigated using DFT calculations. The structural modulations arise from various stacking sequences of face-sharing TiS6 octahedra and twist-distorted trigonal prisms, and are found to be thermodynamically stable for x larger than 1 but smaller than 1.5. As x increases, an indirect-to-direct band gap transition is predicted for x equal to and larger than 1.143 along with an increased occupancy of Ti-dz2 states. Together, these two factors result in a theoretically predicted maximum birefriengence of 2.5 for Sr1.143TiS3. Single crystals of Sr1.143TiS3 were grown using a molten-salt flux method. Atomic-scale observations using scanning transmission electron microscopy confirm the feasibility of synthesizing SrxTiS3 with varied modulation periodicities. Overall, these findings demonstrate compositonal tunability of optical properties in SrxTiS3 compounds, and potentially in other hexagonal perovskites having structural modulations.

cond-mat.mtrl-sci↗

arXiv2024

Electrical contacts for high performance optoelectronic devices of BaZrS3 single crystals

Chalcogenide perovskites such as BaZrS3 are promising candidates for next generation optoelectronics such as photodetectors and solar cells. Compared to widely studied polycrystalline thin films, single crystals of BaZrS3 with minimal extended and point defects, are ideal platform to study the material's intrinsic transport properties and to make first-generation optoelectronic devices. However, the surface dielectrics formed on BaZrS3 single crystals due to sulfating or oxidation have led to significant challenges to achieving high quality electrical contacts, and hence, realizing the high-performance optoelectronic devices. Here, we report the development of electrical contact fabrication processes on BaZrS3 single crystals, where various processes were employed to address the surface dielectric issue. Moreover, with optimized electrical contacts fabricated through dry etching, high-performance BaZrS3 photoconductive devices with a low dark current of 0.1 nA at 10 V bias and a fast transient photoresponse with rise and decay time of <0.2 s were demonstrated.

cond-mat.mtrl-sci↗

arXiv2024

Numerical and Lyapunov-Based Investigation of the Effect of Stenosis on Blood Transport Stability Using a Control-Theoretic PDE Model of Cardiovascular Flow

We perform various numerical tests to study the effect of (boundary) stenosis on blood flow stability, employing a detailed and accurate, second-order finite-volume scheme for numerically implementing a partial differential equation (PDE) model, using clinically realistic values for the artery's parameters and the blood inflow. The model consists of a baseline $2\times 2$ hetero-directional, nonlinear hyperbolic PDE system, in which, the stenosis' effect is described by a pressure drop at the outlet of an arterial segment considered. We then study the stability properties (observed in our numerical tests) of a reference trajectory, corresponding to a given time-varying inflow (e.g., a periodic trajectory with period equal to the time interval between two consecutive heartbeats) and stenosis severity, deriving the respective linearized system and constructing a Lyapunov functional. Due to the fact that the linearized system is time varying, with time-varying parameters depending on the reference trajectories themselves (that, in turn, depend in an implicit manner on the stenosis degree), which cannot be derived analytically, we verify the Lyapunov-based stability conditions obtained, numerically. Both the numerical tests and the Lyapunov-based stability analysis show that a reference trajectory is asymptotically stable with a decay rate that decreases as the stenosis severity deteriorates.

math.NA↗

arXiv2024

Learning Molecular Representation in a Cell

Predicting drug efficacy and safety in vivo requires information on biological responses (e.g., cell morphology and gene expression) to small molecule perturbations. However, current molecular representation learning methods do not provide a comprehensive view of cell states under these perturbations and struggle to remove noise, hindering model generalization. We introduce the Information Alignment (InfoAlign) approach to learn molecular representations through the information bottleneck method in cells. We integrate molecules and cellular response data as nodes into a context graph, connecting them with weighted edges based on chemical, biological, and computational criteria. For each molecule in a training batch, InfoAlign optimizes the encoder's latent representation with a minimality objective to discard redundant structural information. A sufficiency objective decodes the representation to align with different feature spaces from the molecule's neighborhood in the context graph. We demonstrate that the proposed sufficiency objective for alignment is tighter than existing encoder-based contrastive methods. Empirically, we validate representations from InfoAlign in two downstream applications: molecular property prediction against up to 27 baseline methods across four datasets, plus zero-shot molecule-morphology matching.

cs.LG↗

arXiv2024

Emergent Atomic Scale Polarisation Vortices in BaTiS3

Topological defects, such as vortices and skyrmions in magnetic and dipolar systems, can give rise to properties that are not observed in typical magnets and dielectrics. Here, we report the discovery of long-range ordered periodic dipole arrays of atomic-scale vortices and antivortices in the unconventional charge-density-wave (CDW) phase of BaTiS3, a quasi-1D chalcogenide. Synchrotron X-ray diffraction (XRD) reveals the presence of a multi-q ordering in BaTiS3 that confines vortex-vortex-antivortex polarisation triplets to the a-b plane with alternating handedness along the c-axis. The multi-q displacive distortions are characterised by three distinctive off-centre TiS6 configurations, whose ratios are independently confirmed by 47/49Ti solid-state nuclear magnetic resonance (SSNMR). Using first-principles calculations and phenomenological modelling, we show that the dipolar vortex unit cell in BaTiS3 arises from the coupling between multiple lattice instabilities arising from flat, soft phonon bands. This mechanism contrasts with classical dipolar textures in ferroelectric heterostructures that emerge from the competition between electrostatic and strain energies. The observation of dipolar vortices in BaTiS3 brings the ultimate scaling limit for real-space dipolar topological structures down to about a nanometre and unveils the intimate connection between crystal symmetry and real-space topology. Our work sets up zero-filling semiconducting materials with competing structural instabilities as a playground for realising and understanding quantum polarisation topologies.

cond-mat.mtrl-sci↗

arXiv2024

MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction

Drug-target interaction (DTI) prediction is crucial for identifying new therapeutics and detecting mechanisms of action. While structure-based methods accurately model physical interactions between a drug and its protein target, cell-based assays such as Cell Painting can better capture complex DTI interactions. This paper introduces MOTIVE, a Morphological cOmpound Target Interaction Graph dataset comprising Cell Painting features for 11,000 genes and 3,600 compounds, along with their relationships extracted from seven publicly available databases. We provide random, cold-source (new drugs), and cold-target (new genes) data splits to enable rigorous evaluation under realistic use cases. Our benchmark results show that graph neural networks that use Cell Painting features consistently outperform those that learn from graph structure alone, feature-based models, and topological heuristics. MOTIVE accelerates both graph ML research and drug discovery by promoting the development of more reliable DTI prediction models. MOTIVE resources are available at https://github.com/carpenter-singh-lab/motive.

cs.LG↗

arXiv2024

Bifunctional Noble Metal-free Ternary Chalcogenide Electrocatalysts for Overall Water Splitting

Hydrogen has been identified as a clean, zero carbon, sustainable, and promising energy source for the future, and electrochemical water splitting for hydrogen production is an emission-free, efficient energy conversion technology. A major limitation of this approach is the unavailability of efficient, abundant, inexpensive catalysts, which prompts the need for new catalytic materials. Here, we report the synthesis and electrocatalytic properties of a novel transition metal-based ternary chalcogenide family, LaMS$_3$ (M = Mn, Fe, Co, Ni). Powder X-ray diffraction confirms the phase purity of these materials, while composition analysis using energy dispersive spectroscopy (EDS) confirms the presence of the stoichiometric ratio of elements in these compounds. X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS) were used to study the chemical states on the surface and in bulk, respectively. These materials exhibit bifunctional catalytic activity towards the two half-reactions of the water-splitting process, with LaNiS$_3$ being the most active material for both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The LaMS$_3$ compounds show long-term stability with negligible change in the overpotential at a constant current density of 10 mA cm$^{-2}$ over 18 hours of measurements. As compared to the corresponding ternary oxides, the LaMS$_3$ materials exhibit higher activity and significantly lower Tafel slopes. The ability to catalyze both half-reactions of water electrolysis makes these materials promising candidates for bifunctional catalysts and presents a new avenue to search for high-efficiency electrocatalysts for water splitting.

physics.chem-ph↗