arXiv subjects

Ankit Bhardwaj

Publications and source records attributed to Ankit Bhardwaj.

17 recordsLinked to original sources

arXiv2026

Observation of long-lived spin order in nanoconfined water

Liquids confined to nanometer-scale geometries exhibit behavior that departs markedly from their bulk counterparts, yet studying their dynamics under controlled conditions remains experimentally challenging. Here, we use nitrogen-vacancy (NV) center nuclear magnetic resonance (NMR) spectroscopy to probe water confined in 5.6 nm channels as a function of temperature. The system remains liquid throughout the investigated temperature range and exhibits strongly suppressed diffusivity, enabling direct detection of its 1H NMR spectrum. Occasionally, the proton resonance transforms into a doublet with a splitting of several tens of kilohertz, which we tentatively attribute to hyperfine interactions mediated by long-lived paramagnetic charge complexes, in turn seeded by solvated electrons optically injected during laser illumination. The intermittent appearance of this feature suggests a metastable state comprising a correlated population of charge-hydration complexes extending throughout the confined liquid.

cond-mat.mes-hall↗

arXiv2026

Identifiability-Aware Source Apportionment in City-Scale Advection-Diffusion Systems

Source apportionment from sparse urban air-quality sensors is an inverse problem limited by sensor placement, wind-driven transport, background variation, and noise. Known or proxy emission inventories make attribution meaningful by restricting the unknown source field to a finite set of candidate groups, but do not guarantee those groups are distinguishable from the observations. We represent time-varying source activity with a low-dimensional nonnegative temporal basis and formulate inventory-based apportionment as a wind-conditioned lagged inverse problem in which each source--basis coefficient produces a sensor-time fingerprint. After projecting out a separate low-dimensional background space, the relevant object is the projected lagged response matrix $\widetilde H_\Phi$: exact identifiability at the chosen basis resolution requires its full column rank, while noise-robust attribution is controlled by its singular values, coefficient visibility, background absorption, pairwise coherence, and ray distance. We propose an identifiability-aware apportionment (IASA) framework that estimates nonnegative source--basis coefficients, reconstructs activity trajectories, and reports uncertainty and conservative grouping recommendations for indistinguishable sources. We instantiate it on a New Delhi platform built from government PM$_{2.5}$ and wind records, regulatory sensor locations, and four proxy source groups, and define controlled and observed evaluations of recovery, ambiguity, wind diversity, background stress, transport error, inventory robustness, and residual adequacy. IASA reports the attribution resolution defensible under the declared inventories, transport, background, lag, and noise rather than the most detailed possible vector.

eess.SP↗

arXiv2026

Two-dimensional clay channels for tunable nanofluidic memristor

Dynamic reconfiguration of charge carriers in confined ion-channels under electrical stimulation produces memory effects, where the internal resistance depends on history of the electric field. Vermiculite nanofluidic devices harness this effect to store and process information within a single component. We report switching between distinct memory loops by tuning ion transport pathways, governed by asymmetrical device architecture and intrinsic surface-charge. Polarity-dependent memory switching between crossing-1 and crossing-2 loops is achieved solely by altering electrode configurations, without modifying electrolyte, channel surface chemistry or device structure: providing mechanistic insights into ionic memristors through a straightforward, experimental strategy. The memristive characteristics are demonstrated in both in-plane and out-of-plane channel configurations with channel lengths spanning from centimeters to micrometers length scales using re-stacked vermiculite membranes and further investigated for miniaturization with devices of nanometer scale channel lengths, fabricated via ultramicrotomy method. Furthermore, we demonstrate neuromorphic functionalities, including synaptic potentiation-depression and programmable memory retention, highlighting potential for bio-inspired computing systems. Cost-effective and scalable fabrication solution processed vermiculite membrane memristors pave the way for practical integration of nanofluidic memristors for neuromorphic computing applications.

cond-mat.mtrl-sci↗

arXiv2025

FieldFormer: Locality-Aware Transformers for Spatio-Temporal Modeling on Sparse Sensor Networks

Spatio-temporal sensor data in real-world systems is often sparse, noisy, and irregular, making latent field reconstruction fundamentally underconstrained. Under extreme sparsity, multiple physically plausible fields may remain consistent with the same observations, requiring models to rely on inductive biases about locality, transport, and spatial regularity. In such regimes, reliable reconstruction is concentrated around the observational support induced by the sensor network, making sensor-space modeling a more identifiable objective than unconstrained global field recovery. We introduce FieldFormer, a mesh-free transformer architecture for locality-aware sensor-space modeling in persistent sensor networks. For each query, FieldFormer aggregates local evidence using learnable velocity-scaled offsets that adapt neighborhood geometry to spatio-temporal dependencies. Neighborhoods are constructed as fixed maximal sparse contexts over nearby sensors and bounded temporal windows, enabling stable and scalable inference under extreme sparsity. A local transformer encoder integrates neighborhood information, while a coordinate-based neural field formulation supports mesh-free prediction. We evaluate FieldFormer on five synthetic and real-world benchmarks, including anisotropic heat diffusion, shallow-water dynamics, atmospheric transport, and pollution monitoring datasets. Results show that locality-aware reconstruction provides strong advantages when local domains of dependence remain observed, enabling FieldFormer to consistently outperform state-of-the-art baselines on sparse sensor-space prediction tasks.

cs.LG↗

arXiv2025

Towards Next Generation Immersive Applications in 5G Environments

The Multi-user Immersive Reality (MIR) landscape is evolving rapidly, with applications spanning virtual collaboration, entertainment, and training. However, wireless network limitations create a critical bottleneck, struggling to meet the high-bandwidth and ultra-low latency demands essential for next-generation MIR experiences. This paper presents Hera, a modular framework for next-generation immersive applications, comprising a high-level streaming and synchronization layer for AR/VR systems and a low-level delay-based QoE-aware rate control protocol optimized for dynamic wireless environments. The Hera framework integrates application-aware streaming logic with a QoE-centric rate control core, enabling adaptive video quality, multi-user fairness, and low-latency communication across challenging 5G network conditions. We demonstrate that Hera outperforms existing state-of-the-art rate control algorithms by maintaining up to 66% lower latencies with comparable throughput performance, higher visual quality with 50% average bitrate improvements in our analysis, and improved fairness. By bridging the gap between application-level responsiveness and network-level adaptability, Hera lays the foundation for more scalable, robust, and high-fidelity multi-user immersive experiences.

cs.NI↗

arXiv2025

Checkmate: Zero-Overhead Model Checkpointing via Network Gradient Replication

This paper presents Checkmate, a system that enables per-iteration checkpointing in DNN training without any training slowdown. The traditional approach to checkpointing requires a pause in training to copy model states to a separate location, allowing the state to be restored in the event of failure. This approach fundamentally has a tradeoff between the frequency of checkpoints and the cost of a failure. We avoid this tradeoff; our key insight is that in data-parallel training, all information necessary to create a checkpoint already exists in the network as gradients. Our core contribution is a new multicast abstraction that simultaneously delivers gradients to a separate CPU-based shadow cluster. The shadow maintains a checkpoint by applying those gradients to a copy of the model. Our evaluation shows that Checkmate performs per-iteration checkpointing with training throughput comparable to an ideal no-checkpoint baseline. Checkmate achieves 5 to 34.5x more frequent checkpointing compared to state-of-the-art checkpointing systems, resulting in 80% to 97.1% reduction in repeated work per failure. At the same checkpointing frequency, Checkmate delivers 1.3x to 6.5x throughput compared to other systems.

cs.DC↗

arXiv2025

Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks

Free-flow road networks, such as suburban highways, are increasingly experiencing traffic congestion due to growing commuter inflow and limited infrastructure. Traditional control mechanisms, such as traffic signals or local heuristics, are ineffective or infeasible in these high-speed, signal-free environments. We introduce self-regulating cars, a reinforcement learning-based traffic control protocol that dynamically modulates vehicle speeds to optimize throughput and prevent congestion, without requiring new physical infrastructure. Our approach integrates classical traffic flow theory, gap acceptance models, and microscopic simulation into a physics-informed RL framework. By abstracting roads into super-segments, the agent captures emergent flow dynamics and learns robust speed modulation policies from instantaneous traffic observations. Evaluated in the high-fidelity PTV Vissim simulator on a real-world highway network, our method improves total throughput by 5%, reduces average delay by 13%, and decreases total stops by 3% compared to the no-control setting. It also achieves smoother, congestion-resistant flow while generalizing across varied traffic patterns, demonstrating its potential for scalable, ML-driven traffic management.

cs.LG↗

arXiv2024

Slow water in engineered nano-channels revealed by color-center-enabled sensing

Nanoscale confinement of molecules in a fluid can result in enhanced viscosity, local fluidic order, or collective motion. Confinement also affects ion transport and/or the rate and equilibrium concentration in a chemical reaction, all of which makes it the subject of broad interest. Studying these effects, however, is notoriously difficult, mainly due to the lack of experimental methods with the required sensitivity and spatial or time resolution. Here we leverage shallow nitrogen-vacancy (NV) centers in diamond to probe the dynamics of room-temperature water molecules entrapped within ~6-nm-tall channels formed between the diamond crystal and a suspended hexagonal boron nitride (hBN) flake. NV-enabled nuclear magnetic resonance measurements of confined water protons reveal a much reduced H2O self-diffusivity, orders of magnitude lower than in bulk water. We posit the slow dynamics stem from the accumulation of photogenerated carriers at the interface and trapped fluid, a notion we support with the help of molecular dynamics modeling. Our results provide feedback for theories describing interfacial water, and lay out a route for investigating other fluids under confinement.

cond-mat.mes-hall↗

arXiv2024

Comprehensive Monitoring of Air Pollution Hotspots Using Sparse Sensor Networks

Urban air pollution hotspots pose significant health risks, yet their detection and analysis remain limited by the sparsity of public sensor networks. This paper addresses this challenge by combining predictive modeling and mechanistic approaches to comprehensively monitor pollution hotspots. We enhanced New Delhi's existing sensor network with 28 low-cost sensors, collecting PM2.5 data over 30 months from May 1, 2018, to Nov 1, 2020. Applying established definitions of hotspots to this data, we found the existence of additional 189 hidden hotspots apart from confirming 660 hotspots detected by the public network. Using predictive techniques like Space-Time Kriging, we identified hidden hotspots with 95% precision and 88% recall with 50% sensor failure rate, and with 98% precision and 95% recall with 50% missing sensors. The projected results of our predictive models were further compiled into policy recommendations for public authorities. Additionally, we developed a Gaussian Plume Dispersion Model to understand the mechanistic underpinnings of hotspot formation, incorporating an emissions inventory derived from local sources. Our mechanistic model is able to explain 65% of observed transient hotspots. Our findings underscore the importance of integrating data-driven predictive models with physics-based mechanistic models for scalable and robust air pollution management in resource-constrained settings.

cs.CY↗

arXiv2023

Fabrication of angstrom-scale two-dimensional channels for mass transport

Fluidic channels at atomic scales regulate cellular trafficking and molecular filtration across membranes and thus play crucial roles in the functioning of living systems. However, constructing synthetic channels experimentally at these scales has been a significant challenge due to the limitations in nanofabrication techniques and the surface roughness of the commonly used materials. Angstrom-scale slit-like channels address this challenge, as these can be made with precise control over their dimensions and can be used to study the fluidic properties of gases, ions and water at unprecedented scales. Here, we provide a detailed fabrication method of the two-dimensional (2D) angstrom-scale channels, which can be assembled as a single channel or up to hundreds of channels made with atomic scale precision using layered crystals. The procedure includes the fabrication of the substrate, flake, spacer layer, flake transfers, van der Waals assembly, and post-processing. We further explain how to perform molecular transport measurements with the angstrom-scale channels, for the development of methods directed at unravelling interesting and anomalous phenomena that help shed light on the physics of nanofluidic transport systems. The procedure requires a total of 1 to 2 weeks for the fabrication of the 2D channel device and is suitable for users with prior experience in clean room working environments and nanofabrication.

physics.app-ph↗

arXiv2023

Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving

In this paper, we investigate how to push the performance limits of serving Deep Neural Network (DNN) models on CPU-based servers. Specifically, we observe that while intra-operator parallelism across multiple threads is an effective way to reduce inference latency, it provides diminishing returns. Our primary insight is that instead of running a single instance of a model with all available threads on a server, running multiple instances each with smaller batch sizes and fewer threads for intra-op parallelism can provide lower inference latency. However, the right configuration is hard to determine manually since it is workload- (DNN model and batch size used by the serving system) and deployment-dependent (number of CPU cores on server). We present Packrat, a new serving system for online inference that given a model and batch size ($B$) algorithmically picks the optimal number of instances ($i$), the number of threads each should be allocated ($t$), and the batch sizes each should operate on ($b$) that minimizes latency. Packrat is built as an extension to TorchServe and supports online reconfigurations to avoid serving downtime. Averaged across a range of batch sizes, Packrat improves inference latency by 1.43$\times$ to 1.83$\times$ on a range of commonly used DNNs.

cs.DC↗

arXiv2023

Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning

Transcription factors (TFs) play a vital role in the regulation of gene expression thereby making them critical to many cellular processes. In this study, we used graph machine learning methods to create a compendium of TF cascades using data extracted from the STRING database. A TF cascade is a sequence of TFs that regulate each other, forming a directed path in the TF network. We constructed a knowledge graph of 81,488 unique TF cascades, with the longest cascade consisting of 62 TFs. Our results highlight the complex and intricate nature of TF interactions, where multiple TFs work together to regulate gene expression. We also identified 10 TFs with the highest regulatory influence based on centrality measurements, providing valuable information for researchers interested in studying specific TFs. Furthermore, our pathway enrichment analysis revealed significant enrichment of various pathways and functional categories, including those involved in cancer and other diseases, as well as those involved in development, differentiation, and cell signaling. The enriched pathways identified in this study may have potential as targets for therapeutic intervention in diseases associated with dysregulation of transcription factors. We have released the dataset, knowledge graph, and graphML methods for the TF cascades, and created a website to display the results, which can be accessed by researchers interested in using this dataset. Our study provides a valuable resource for understanding the complex network of interactions between TFs and their regulatory roles in cellular processes.

q-bio.MN↗

arXiv2023

Beyond steric selectivity of ions using angstrom-scale capillaries

Ion-selective channels play a key role in physiological processes and are used in many technologies. While biological channels can efficiently separate same-charge ions with similar hydration shells, it remains a challenge to mimic such exquisite selectivity using artificial solid-state channels. Although, there are several nanoporous membranes that show high selectivity with respect to certain ions, the underlying mechanisms are based on the hydrated ion size and/or charge. There is a need to rationalize the design of artificial channels to make them capable of selecting between similar-size same-charge ions, which in turn requires understanding of why and how such selectivity can occur. To address this issue, we study angstrom-scale artificial channels made by van der Waals assembly, which are comparable in size with typical ions and carry little residual charge on channel walls. This allows us to exclude the first-order effects of steric and Coulomb-based exclusion. We show that the studied two-dimensional angstrom-scale capillaries can distinguish between same-charge ions with similar hydrated diameters. The selectivity is attributed to different positions occupied by ions within the layered structure of nanoconfined water, which depend on the ion-core size and differ for anions and cations. The revealed mechanism points at possibilities of ion separation beyond the simple steric sieving.

cond-mat.mes-hall↗

arXiv2023

Strong electronic winds blowing under liquid flows on carbon surfaces

The interface between a liquid and a solid is the location of plethora of intrincate mechanisms at the nanoscale, at the root of their specific emerging properties in natural processes or technological applications. However, while the structural properties and chemistry of interfaces have been intensively explored, the effect of the solid-state electronic transport at the fluid interface has been broadly overlooked up to now. It has been reported that water flowing against carbon-based nanomaterials, such as carbon nanotubes or graphene sheets, does induce electronic currents, but the mechanism at stake remains controversial. Here, we unveil the molecular mechanisms underlying the hydro-electronic couplings by investigating the electronic conversion under flow at the nanoscale. We use a tuning fork-Atomic Force Microscope (AFM) to deposit and displace a micrometric droplet of both ionic and non-ionic liquids on a multilayer graphene sample, while recording the electrical current across the carbon flake. We report measurements of an oscillation-induced current which is several orders of magnitude larger than previously reported for water on carbon , and further boosted by the presence of surface wrinkles on the carbon layer. Our results point to a peculiar momentum transfer mechanism between fluid molecules and charge carriers in the carbon walls mediated by phonon excitations in the solid. Our findings pave the way for active control of fluid transfer at the nanoscale by harnessing the complex interplay between collective excitations in the solid and the molecules in the fluid.

cond-mat.mes-hall↗

arXiv2022

Weakly-Supervised Deep Learning Model for Prostate Cancer Diagnosis and Gleason Grading of Histopathology Images

Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process is time-consuming and has known interobserver variability. In the past few years, deep learning algorithms have been used to analyze histopathology images, delivering promising results for grading prostate cancer. However, most of the algorithms rely on the fully annotated datasets which are expensive to generate. In this work, we proposed a novel weakly-supervised algorithm to classify prostate cancer grades. The proposed algorithm consists of three steps: (1) extracting discriminative areas in a histopathology image by employing the Multiple Instance Learning (MIL) algorithm based on Transformers, (2) representing the image by constructing a graph using the discriminative patches, and (3) classifying the image into its Gleason grades by developing a Graph Convolutional Neural Network (GCN) based on the gated attention mechanism. We evaluated our algorithm using publicly available datasets, including TCGAPRAD, PANDA, and Gleason 2019 challenge datasets. We also cross validated the algorithm on an independent dataset. Results show that the proposed model achieved state-of-the-art performance in the Gleason grading task in terms of accuracy, F1 score, and cohen-kappa. The code is available at https://github.com/NabaviLab/Prostate-Cancer.

eess.IV↗

arXiv2022

Predicting Treatment Adherence of Tuberculosis Patients at Scale

Tuberculosis (TB), an infectious bacterial disease, is a significant cause of death, especially in low-income countries, with an estimated ten million new cases reported globally in $2020$. While TB is treatable, non-adherence to the medication regimen is a significant cause of morbidity and mortality. Thus, proactively identifying patients at risk of dropping off their medication regimen enables corrective measures to mitigate adverse outcomes. Using a proxy measure of extreme non-adherence and a dataset of nearly $700,000$ patients from four states in India, we formulate and solve the machine learning (ML) problem of early prediction of non-adherence based on a custom rank-based metric. We train ML models and evaluate against baselines, achieving a $\sim 100\%$ lift over rule-based baselines and $\sim 214\%$ over a random classifier, taking into account country-wide large-scale future deployment. We deal with various issues in the process, including data quality, high-cardinality categorical data, low target prevalence, distribution shift, variation across cohorts, algorithmic fairness, and the need for robustness and explainability. Our findings indicate that risk stratification of non-adherent patients is a viable, deployable-at-scale ML solution. As the official AI partner of India's Central TB Division, we are working on multiple city and state-level pilots with the goal of pan-India deployment.

cs.LG↗

arXiv2021

Hydrocarbon Contamination in Angstrom-scale Channels

Nonspecific molecular adsorption like airborne contamination occurs on most surfaces including those of 2D materials and alters their properties. While the surface contamination is studied using a plethora of techniques, the effect of contamination on a confined system such as nanochannels, nanopores leading to their clogging is still lacking. We report a systematic investigation of hydrocarbon adsorption in the angstrom slit channels of varied heights. Hexane is chosen to mimic the hydrocarbon contamination and the clogging of the angstrom-channels is evaluated via a Helium gas flow measurement. The level of the hexane adsorption, in other words, the degree of clogging depends on the size difference between the channels and hexane. A dynamic transition of the clogging and revival process is shown in sub-2 nm thin channels. Long-term storage and stability of our angstrom-channels is demonstrated here up to three years, alleviating the contamination and unclogging the channels using thermal treatment. This study highlights the importance of the nanochannels stability and demonstrates self-cleansing nature of sub-2 nm thin channels enabling a robust platform for molecular transport and separation studies. We provide a method to assess the cleanliness of the nanoporous membranes, which is vital for the practical applications of nanofluidics in various fields such as molecular sensing, separation and power generation.

physics.chem-ph↗