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Amit Kumar

Publications and source records attributed to Amit Kumar.

At least 19 recordsLinked to original sources
arXiv2026

Sensitivity Oracles for Matroid Packing, Matroid Covering, and Matching Problems with Applications

Sensitivity oracles preprocess a graph so that queries can be answered after any $f$ edge insertions and deletions, without recomputing from scratch. For structural optimization problems the known landscape is limited: for flows and cuts, all known compact oracles handle only $f\le2$ failures; existing oracles for $s$- and global min-cut apply only to undirected graphs; and for matchings, arborescence and spanning-tree packings, and arboricity, no efficient oracle is known for $f>1$. We present a unified algebraic framework based on sensitivity oracles for matroid packing, covering, and parity of sparse linear matroids, yielding the first oracles supporting an arbitrary number $f$ of updates across all of these problems (all constructions randomized Monte-Carlo). Concretely, we obtain efficient oracles for exact $(s,t)$-max-flow/min-cut, resolving an open problem of Baswana, Bhanja, and Pandey (ICALP'22) with near-optimal space; for all-pairs $k$-bounded flow, generalizing the near-optimal reachability oracle of Brand and Saranurak (FOCS'19, the case $k=1$); the first oracles for any $f$ for directed $s$- and global min-cut; oracles for $k$-disjoint arborescences, $k$-disjoint spanning trees, colorful spanning trees, and arboricity; and oracles for the existence of an $\alpha$-factor, with perfect matching as the case $\alpha=1$. We further introduce the \emph{subset sensitivity model}, in which updates are confined to a susceptible edge set of size $\sigma$ fixed during preprocessing. Here we decouple updates from the matroid representation and eliminate the dependence on $k$ and the matroid density altogether: all of the above are supported with $\widetilde O(f^\omega)$ query time and $O(f\sigma^2)$ space. We also prove a matching $\Omega(\min\{\sigma^2,n^2\})$-bit lower bound when $f\ge2$, establishing optimality.

cs.DS↗
arXiv2026

A MOF-reinforced self-foaming sponge for mechanically robust triboelectric membranes with improved resistance to humidity

Porous triboelectric materials offer significant potential for enhancing the performance of triboelectric nanogenerators, yet their practical application is limited by structural instability and humidity-induced performance degradation. In this work, a bio-derived, sustainable polyamide containing disulfide linkages was developed to enable spontaneous formation of a porous dielectric without external templating. Hydrophilic MOF fillers comprising HKUST-1 crystals are incorporated within the porous matrix to reinforce the membrane structure and regulate moisture effects. Mechanical characterization demonstrates that HKUST-1 suppresses pore collapse and improves structural robustness under repeated deformation, while analysis of stress-strain behaviour reveals the critical role of pore stability in achieving stable triboelectric output. In addition, HKUST-1 mitigates humidity-induced charge dissipation by confining water molecules within its framework, giving enhanced triboelectric output stability and reduced performance degradation under increasing relative humidity compared to the neat porous system. This work demonstrates a strategy that integrates self-foamed porous structures, sustainable polymer design, and functional filler reinforcement to engineer mechanically robust and environmentally stable triboelectric systems.

cond-mat.mtrl-sci↗
arXiv2026

Machine Learning-Based Characterisation of the Non-Markovian Dynamics of a Nitrogen-Vacancy Centre

The interaction between a quantum system and its environment can be characterized by the spectral density function: knowing its structure is important for optimizing applications of quantum technologies such as quantum sensing protocols. In this work, we present the first experimental demonstration of a machine learning-based reconstruction of reaction-coordinate spectral density parameters from NV centre Rabi dynamics. Unlike the previous work, we recover all spectral density parameters rather than only the central frequency, and benchmark the performance of the neural network against the Cram\'er-Rao bound and maximum likelihood estimator. Our results demonstrate that the model predicted by the neural network can reliably reproduce the NV dynamics over the estimation window, and can produce estimates for some parameters with variances comparable to that of maximum likelihood.

quant-ph↗
arXiv2026

From Causal Plausibility to Causal Reliability: Evaluating LLMs as Calibrated Direct Causal-Edge Classifiers

Large language models (LLMs) are increasingly used to provide prior causal knowledge for structural causal discovery, yet whether their direct-edge judgments and confidence can be trusted remains unclear. We systematically evaluate 12 instruction-tuned open-weight models across six benchmark causal graphs, five prompting strategies, and four confidence sources: verbalized, logit-based, cross-prompt agreement, and cross-model agreement. Under our language-only pairwise protocol, our evaluation yields three key findings. (i) LLM-based causal judgments are strongly recall-dominant: models predict overly dense graphs with many false-positive edges, while prompting mainly shifts the precision-recall trade-off rather than resolving overprediction. Gains from model scale diminish on the largest graphs and do not eliminate miscalibration. (ii) LLMs often capture causal relatedness without reliably identifying directness or orientation. Relative to published reference graphs, models misclassify 40.0% of indirect and 36.0% of reversed non-edges as direct edges, versus 28.2% of other non-edges. Moreover, 80.8% and 84.6% of these false positives receive verbalized confidence of at least 80%, revealing substantial overconfidence in structurally incorrect predictions. (iii) Conventional confidence estimates are unreliable, whereas agreement offers a more promising signal. Logit-based confidence frequently collapses near 1.0 regardless of correctness, while cross-prompt and cross-model agreement achieve better mean calibration and discrimination, though their advantages are not statistically significant after Holm correction. A benchmark-familiarity audit further identifies potential familiarity in five model-dataset pairs, all involving AsiaM. Overall, our results suggest LLMs are better viewed as sources of externally validated soft causal priors than as direct evidence of causal structure.

cs.LG↗
arXiv2026

Early Near-Infrared Excess and Rapid Disk-Corona Evolution in the Tidal Disruption Event 2024aepd

We present multi-wavelength observations of the tidal disruption event (TDE) 2024aepd, spanning primarily the first $\sim$300 days after discovery. The X-ray spectrum is initially dominated by a thermal disk component accompanied by a hard excess. From $\sim$178 days onward, the spectrum becomes power-law dominated and subsequently hardens, indicating the rapid emergence and strengthening of a hot corona. A prominent near-infrared (NIR) excess is detected as early as $\sim40$ days. Its nearly flat power-law spectrum strongly deviates from the Rayleigh-Jeans tail of the UV-optical blackbody. Although a conventional dust-echo origin cannot be completely ruled out, free-free emission from a reprocessing photospheric envelope provides a more plausible explanation. Moreover, the UV-optical-to-NIR break shifts to higher frequencies as the density-profile index remains nearly constant, implying evolving reprocessing conditions within a broadly unchanged density structure. Together with AT2019azh and TDE 2025abcr, TDE 2024aepd is the third TDE reported to exhibit an early-time NIR excess. A larger sample with early-time NIR coverage is needed to determine whether such excesses are common among TDEs.

astro-ph.HE↗
arXiv2026

Quantum Destabilization of Skyrmions in Centrosymmetric Frustrated Magnets

We investigate the role of the spin quantum number $s$ on the stability of skyrmions in a $J_1-J_2-J_3$ centrosymmetric quantum Heisenberg model on a square lattice using the neural network quantum states method. Our results reveal that the skyrmion stability $Q$ is severely degraded when transitioning from the semiclassical regime to the extreme quantum limit ($s=1/2$), where it ultimately vanishes. We demonstrate that this destabilization is driven by quantum longitudinal fluctuations, with $Q$ exhibiting a power-law decay as a function of the reciprocal spin moment $1/s$. Notably, the extreme quantum limit ($s=1/2$) deviates drastically from this scaling behavior, exhibiting distinct physics compared to larger spin moments. Furthermore, we reveal the microscopic origin of this decay by establishing a quantitative correspondence between skyrmion stability, entanglement, and local spin magnitude: as the local second R\'enyi entropy (an indicator of entanglement) increases and the local spin magnitude is suppressed, the skyrmion stability vanishes linearly. This regime marks a quantum state where the skyrmion number $C$ remains as remanent geometric feature of the spin orientations, yet the skyrmion stability $Q$ vanishes due to the longitudinal suppression of the local spin magnitude. Our findings suggest that classically robust skyrmion phases in frustrated lattices are fundamentally restricted to high-spin materials, indicating that a spin moment must of at least $s = 3/2$ is required for the realization of stable, atomic-scale topological textures.

cond-mat.str-el↗
arXiv2026

Slow heat-driven flow in a gas of hard disks

We study a slow heat-driven flow in a gas of elastically colliding hard disks confined to a long channel. The initial state consists of two regions with large temperature and density contrasts but nearly equal pressures, leading to a low-Mach-number, nearly isobaric evolution. In the dilute limit, the corresponding isobaric hydrodynamic theory reduces to a previously known ideal-gas description. We extend this theory to finite densities by incorporating a non-ideal equation of state of a hard-disk fluid, and solve the resulting one-dimensional equations numerically. Finite-density effects produce appreciable deviations from the ideal-gas prediction. We then test the theory directly against event-driven molecular dynamics simulations of hard disks and find very good agreement in both the dilute and finite-density regimes. The results provide, to our knowledge, the first particle-level test of isobaric gas dynamics of a strongly inhomogeneous cooling flow.

cond-mat.stat-mech↗
arXiv2026

Failed jet breakout in the metal-poor broad-lined type Ic supernova 2026gzf

A long-standing question in the death of massive stars is the role of relativistic jets. While many gamma-ray bursts and some fast X-ray transients seem to be associated with broad-lined type Ic supernovae, the opposite is not true. The lack of observable jet emission in those Ic-BL SNe can be explained by invoking off-axis jets, choked jets that inject all their energy into the stellar envelope, baryon-loaded jets for which the prompt high-energy emission is strongly suppressed, or non-jetted SNe. The lack of exact explosion time in the majority of SNe presents an obstacle to distinguish between these scenarios. Here we report the properties of SN 2026gzf associated with the X-ray thermal Einstein Probe shock-breakout EP260321a at z=0.0343. The absence of compelling shocked cocoon and radio emission up to 54 days, combined with initial expansion velocities of ~30,000 km/s and a circumstellar shell of ~0.07 M$_\odot$, favour a scenario for SN 2026gzf in which a jet was choked in the circumstellar shell. Our high-spatial resolution images of the SN environment show that the progenitor was located between two highly star-forming regions with a metallicity lower than any previously known Ic-BL SN. As the first case of a Ic-BL SN associated with high-energy prompt emission without the signature of a jet, SN 2026gzf provides a unique perspective to understand the successful launch of relativistic jets during the deaths of massive stars.

astro-ph.HE↗
arXiv2026

Multifaceted Hero Developers and Bug-Fixing Outcomes Across Severity

Open-source projects often rely on a small group of highly active contributors known as hero developers. Prior work shows that hero developers are common in many OSS and enterprise projects, yet who qualifies as a hero depends heavily on the chosen contribution metric. Code-based metrics identify implementation-focused developers, whereas discussion-based metrics highlight coordination and communication; these metrics capture distinct facets of contribution. We conducted a measurement-sensitive study of multifaceted heroism across 77 Apache Software Foundation projects using three technical measures (commit count, distinct files touched, churn) and two social measures (issue-comment count, number of distinct issues commented on). We examined hero prevalence, overlap among hero sets, and severity-wise bug-fixing outcomes via fix and reopen rates. Results show that hero projects are common under all measures, but identified heroes differ substantially across facets. The pooled Jaccard overlap between technical and social hero sets is only 0.10. Cross-facet asymmetry is evident: 71.4% of technical heroes exhibit strong social activity, while only 24.2% of social heroes show strong technical activity. Fix-rate and reopen-rate differences are modest, yet hero-category rankings vary across severity levels and outcome measures. These findings indicate that heroism is not a single, metric-independent role. A multifaceted perspective offers a more reliable understanding of key contributors and better supports developer prioritisation and severity-aware bug assignment.

cs.SE↗
arXiv2026

Broadband impulsive stimulated Raman spectroscopy reveals electronic state-specific vibronic coupling and vibrational coherence transfer through nonadiabatic electronic coupling

Vibrational wavepacket dynamics in the ground (X) and excited (B) electronic states of iodine under impulsive-pump/broadband-probe excitation are revisited. A method for accurate chirp correction, necessary to determine the zero time for each component of spectrally dispersed data and thereby separate coherent vibrational dynamics from coherent artifacts and population kinetics, is introduced. While from these processed time-domain data the absolute Raman cross-section in the ground electronic state can be calculated using steady-state absorption, we show that the same can be done using the pump-probe data itself, and further extend this method as a benchmark to calculate the same for the excited electronic state; these cross-sections report on vibronic couplings specific to these states. Further, since the Fourier transform of the processed data yields information on vibrational modes averaged over the dephasing time, a wavelet analysis is performed to yield a joint time-frequency distribution of the vibrational modes, demonstrating how the time evolution of their frequencies can be extracted. The vibrational modes of the ground and excited electronic states are shown to exhibit distinct dispersion characteristics. Since overlapping spectral features appear at different time windows, such an analysis can disentangle spectral congestion, even from a simple one-dimensional measurement. Most interestingly, a rapid time-dependent spectral shift and decay of the B state mode, followed by the appearance and growth of the A-state mode, directly correlates with the pre-dissociation, followed by solvent caging-induced recombination. Thus, the present work reveals transfer of vibrational coherence from one electronic state (B) to another (A), mediated via nonadiabatic coupling to the intermediate dissociative state (a), underscoring the importance of electronic coherence.

physics.chem-ph↗
arXiv2026

Building and maintaining a System of Intracellular Compartments

Organelle patterning and its heritability remain central mysteries in cell biology, highlighting the fundamental tension between genetic inheritance and self-assembly. Here, we explore the nonequilibrium assembly and emdedded size control of the Golgi cisternae and endosomes, amid a continuous flux of membrane traffic, within a stochastic framework of mechanochemical fusion-fission cycles that violate detailed balance. Using a dynamical systems approach, we identify distinct, robust regimes, ranging from fixed points to limit cycles with definite phase relations between cisternae. We identify these dynamical regimes with diverse phenotypes, from stable cisternae to periodic, cell-cycle-dependent dissolution/reassembly of cisternae to cisternal progression. We analyse its dynamic response to systematic perturbations or driving protocols and make definite predictions that may be tested experimentally. Our analysis reveals that the two competing models of Golgi organization - vesicular transport and cisternal progression - are, in fact, two phases of the same underlying nonequilibrium process. We see that cisternal size homeostasis is brought about by a size-dependent embedded control system driven by fusion-fission kernels. Finally, our framework offers a strategy for controlling cisternal number and chemical identity by modulating the interplay between glycosylation enzymes and membrane fission-fusion dynamics.

cond-mat.soft↗
arXiv2026

Efficient imaging of quantum emitters using compressive sensing

Optical imaging of quantum emitters is essential for a wide range of quantum applications. Conventional confocal imaging relies on point-by-point raster scanning, which is inherently time-consuming and photon-inefficient, particularly for sparse emitter distributions and photon-limited samples. Here, we demonstrate a compressive sensing-based imaging approach, where spatially structured wide-field excitation replaces raster scanning, enabling reconstruction of sparse emitters. In our implementation, random binary patterns are used to acquire compressive measurements, from which the spatial fluorescence distribution is reconstructed using a GPSR-BB algorithm. We experimentally demonstrate this approach using nitrogen-vacancy (NV) centers in diamond as a representative platform, with high-fidelity image reconstruction achieved using only approximately $20\%$ of the measurements required for conventional raster scanning. In addition to intensity reconstruction, we extend this framework to reconstruct spatial maps of the second-order correlation function $g^{(2)}(0)$ from compressive measurements. This enables identification of single-photon emitters through antibunching signatures using significantly reduced data.

physics.optics↗
arXiv2026

Leveraging Commit Size Context and Hyper Co-Change Graph Centralities for Defect Prediction

File-level defect prediction models traditionally rely on product and process metrics. While process metrics effectively complement product metrics, they often overlook commit size the number of files changed per commit despite its strong association with software quality. Network centrality measures on dependency graphs have also proven to be valuable product level indicators. Motivated by this, we first redefine process metrics as commit size aware process metric vectors, transforming conventional scalar measures into 100 dimensional profiles that capture the distribution of changes across commit size strata. We then model change history as a hyper co change graph, where hyperedges naturally encode commit-size semantics. Vector centralities computed on these hypergraphs quantify size-aware node importance for source files. Experiments on nine long-lived Apache projects using five popular classifiers show that replacing scalar process metrics with the proposed commit size aware vectors, alongside product metrics, consistently improves predictive performance. These findings establish that commit size aware process metrics and hypergraph based vector centralities capture higher-order change semantics, leading to more discriminative, better calibrated, and statistically superior defect prediction models.

cs.SE↗
arXiv2026

Thermodynamics, Shadow, and Quasinormal Modes of AdS Ay\'on--Beato--Garc\'ia Massive Black Hole

We investigate the thermodynamics, photon sphere, and dynamical stability of an AdS Ay\'{o}n--Beato--Garc\'{i}a (ABG) massive black hole with graviton mass and magnetic charge. The Gibbs free energy exhibits distinct limiting behaviors: it reduces to that of an AdS massive black hole when magnetic charge vanishes, to that of an AdS ABG black hole when graviton mass is zero, and smoothly interpolates to the AdS massive Reissner-Nordstr\"om case in the asymptotic regime. Furthermore, the photon sphere and shadow analysis indicate that increasing the graviton mass expands their radii, while increasing the magnetic charge causes contraction, in agreement with earlier studies of black hole spacetimes. Quasinormal mode (QNM) calculations further confirm dynamical stability, as the imaginary part remains negative, ensuring decay of perturbations. Additionally, the real part of the frequency decreases with graviton mass, while the imaginary part initially grows before saturating at higher values. Together, these results provide meaningful insights into the interplay between graviton mass, magnetic charge, and stability, thereby enriching the understanding of black holes in modified gravity theories.

gr-qc↗
arXiv2026

Learning-Augmented Algorithms for $k$-median via Online Learning

The field of learning-augmented algorithms seeks to use ML techniques on past instances of a problem to inform an algorithm designed for a future instance. In this paper, we introduce a novel model for learning-augmented algorithms inspired by online learning. In this model, we are given a sequence of instances of a problem and the goal of the learning-augmented algorithm is to use prior instances to propose a solution to a future instance of the problem. The performance of the algorithm is measured by its average performance across all the instances, where the performance on a single instance is the ratio between the cost of the algorithm's solution and that of an optimal solution for that instance. We apply this framework to the classic $k$-median clustering problem, and give an efficient learning algorithm that can approximately match the average performance of the best fixed $k$-median solution in hindsight across all the instances. We also experimentally evaluate our algorithm and show that its empirical performance is close to optimal, and also that it automatically adapts the solution to a dynamically changing sequence.

cs.DS↗
arXiv2026

Improved Online Hitting Set Algorithms for Structured and Geometric Set Systems

In the online hitting set problem, sets arrive over time, and the algorithm has to maintain a subset of elements that hit all the sets seen so far. Alon, Awerbuch, Azar, Buchbinder, and Naor (SICOMP 2009) gave an algorithm with competitive ratio $O(\log n \log m)$ for the (general) online hitting set and set cover problems for $m$ sets and $n$ elements; this is known to be tight for efficient online algorithms. Given this barrier for general set systems, we ask: can we break this double-logarithmic phenomenon for online hitting set/set cover on structured and geometric set systems? We provide an $O(\log n \log\log n)$-competitive algorithm for the weighted online hitting set problem on set systems with linear shallow-cell complexity, replacing the double-logarithmic factor in the general result by effectively a single logarithmic term. As a consequence of our results we obtain the first bounds for weighted online hitting set for natural geometric set families, thereby answering open questions regarding the gap between general and geometric weighted online hitting set problems.

cs.DS↗
arXiv2026

Quantifying quasiparticle chirality in a chiral topological semimetal

Recently, the projection of the electron's spin on its crystal momentum has been proposed as a metric to quantify electronic chirality of Bloch states in crystals, which is expected to affect a wide range of physical properties, such as magnetoelectric and optical responses. However, a direct experimental quantification of this chirality metric over an entire iso-energy surface has remained elusive. Here, we have used spin- and angle-resolved photoemission spectroscopy to directly probe the electronic chirality by measuring the bulk spin texture of Kramers-Weyl and Weyl cones in RhSi, a chiral topological semimetal with strong spin-orbit coupling (SOC). After quantifying the SOC splitting of Weyl cones, we determine their spin direction along different azimuthal angles to extract energy dependent the deviations (up to ~40{\deg}) from perfect parallel spin-momentum locking. From these deviations we define an energy-dependent normalized electron chirality density (NECD), a directly accessible metric of bulk electronic chirality. In RhSi, the NECD decreases from 1 at the Kramers-Weyl point to ~0.8 at ~200 meV below it. Finally, we show that this experimentally grounded NECD provides predictive power for magneto-optical and transport responses of chiral materials, exemplified by the longitudinal Edelstein effect.

cond-mat.mes-hall↗
arXiv2026

Uber's Failover Architecture: Reconciling Reliability and Efficiency in Hyperscale Microservice Infrastructure

Operating a global, real-time platform at Uber's scale requires infrastructure that is both resilient and cost-efficient. Historically, reliability was ensured through a costly 2x capacity model--each service provisioned to handle global traffic independently across two regions--leaving half the fleet idle. We present Uber's Failover Architecture (UFA), which replaces the uniform 2x model with a differentiated architecture aligned to business criticality. Critical services retain failover guarantees, while non-critical services opportunistically use failover buffer capacity reserved for critical services during steady state. During rare "full-peak" failovers, non-critical services are selectively preempted and rapidly restored, with differentiated Service-Level Agreements (SLAs) using on-demand capacity. Automated safeguards, including dependency analysis and regression gates, ensure critical services continue to function even while non-critical services are unavailable. The quantitative impact is significant: UFA reduces steady-state provisioning from 2x to 1.3x, raising utilization from ~20% to ~30% while sustaining 99.97% availability. To date, UFA has hardened over 4,000 unsafe dependencies, eliminated over one million CPU cores from a baseline of about four million cores.

cs.DC↗