arXiv subjects

Abhinav Sharma

Publications and source records attributed to Abhinav Sharma.

At least 19 recordsLinked to original sources

arXiv2026

Pre-Lane-change Signal in Transitional Autonomous Vehicles: Results from Controlled Experiments

This paper investigates how a production transitional autonomous vehicle (tAV) develops and executes mandatory lane-change decisions. Using 150 controlled mandatory lane changes from the NC-tALC experiments, the study examines whether the eventual target gap is observable before lateral movement begins and how the tAV progresses longitudinally from that pre-lane-change state to lane-change start. Signal time (SigT) is defined as an operational pre-lane-change-start reference point. A Firth logistic regression predicts whether the tAV eventually merges in front of or behind its nearest target-lane vehicle using relative position and relative speed at SigT. Longitudinal progression from SigT to lane-change start is then examined separately for in-position and repositioning cases. The traffic state at SigT contains substantial information about eventual target-gap choice and provides meaningful lead time before lateral movement begins. The proposed formulation predicts whether the tAV remains with its current gap or repositions to a neighboring gap by moving forward or dropping back, including cases with longitudinal overlap and ambiguous current-gap geometry. The model achieves an average five-fold cross-validated accuracy of 0.89. Results also provide preliminary evidence that in-position and repositioning cases follow different longitudinal pathways from SigT to lane-change start. These findings support a two-stage conjecture of the observable lane-change process: longitudinal preparation from SigT to lane-change start, followed by lateral maneuver execution. The formulation applies to in-position, repositioning, and longitudinally overlapping cases, and can support lane-change models that distinguish target-gap choice from lateral-onset timing while representing longitudinal preparation before lateral movement begins.

cs.RO↗

arXiv2026

Some geometric properties of certain class of Le Roy type functions

The main goal of this paper is to obtain sufficient conditions so that Le Roy type functions and multivariate Le Roy type functions satisfy subordination of exponential function. Moreover conditions on parameters have been derived to claim them being exponential starlike and exponential convex for both of the functions. Starlikeness, convexity and close-to-convexity have also been studied for multivariate Le Roy type functions. Results developed in this work are presumably new and their significance is illustrated by several consequences, graphical representations and examples.

math.CV↗

arXiv2026

Controlled Experiments on Lane Changing by Transitional Autonomous Vehicle: Dataset and Behavioral Insights

This paper presents the North Carolina Transitional Autonomous Vehicle Lane-Changing (NC-tALC) dataset and uses it to characterize mandatory lane-changing behavior of transitional automated vehicles (tAVs). It quantifies the evolution of lead--lag gaps throughout the lane-change process and examines how potential collision risk develops during the maneuver. A controlled field experiment comprising 78 mandatory lane-change trials was conducted on a public roadway in Apex, North Carolina. Four instrumented vehicles created repeatable traffic conditions while varying the lane changer's initial position within the candidate target gap. High-resolution RTK-GNSS/INS trajectories were processed to identify key timestamps, calculate lead, lag, and lane-change gaps, and estimate interactions using time-gap- and speed-based surrogate safety measures. Despite substantial differences in initial conditions, lead and lag gaps consistently converged toward a relatively narrow range near lane crossing. Potential collision risk increased as the maneuver progressed, peaked near physical lane entry, and was dominated by interactions with the target-lane leader. Lane-change completion did not necessarily coincide with the disappearance of collision risk. This study provides one of the first controlled empirical characterizations of the complete mandatory lane-change process of tAVs using repeatable public-road experiments. The NC-tALC dataset supports analysis of behavioral and safety evolution throughout the maneuver rather than only at the gap-acceptance instant. The dataset and findings provide empirical benchmarks for evaluating automated lane-changing behavior, calibrating behavioral models, and validating simulation and safety assessment methods for mandatory lane-change scenarios.

cs.RO↗

arXiv2026

Pivot: Proactive and Verifiable Threshold Oblivious Pseudorandom Functions From Isogeny Group Actions

Oblivious pseudorandom functions (OPRFs) allow a client to evaluate a keyed pseudorandom function on a private input without revealing that input to the server. In a threshold OPRF, the secret key is distributed among (n) servers so that any qualified set of at least (t) servers can complete an evaluation, while fewer than (t) shares reveal no information about the key. Existing isogeny-based threshold OPRFs, however, are primarily designed for static corruption models. If the same shares remain valid throughout the lifetime of the service, a mobile adversary can compromise different servers over time, accumulate (t) shares from the same sharing state, and eventually recover the master key. We introduce PIVOT (Proactive Isogeny-based Verifiable Oblivious Threshold PRF), a dealerless threshold VOPRF framework based on effective isogeny group actions. PIVOT periodically refreshes the server shares without changing the master key, public key, or previously generated OPRF outputs. The construction combines Shamir secret sharing, additively homomorphic coefficient commitments, sequential Lagrange-weighted group actions, and joint zero-knowledge relations that link certified shares to their corresponding isogeny actions. It also supports coordinated epoch transitions, publicly verifiable blame, secure erasure, and committee resharing under a possibly different threshold. We formalize the functionality of a long-lived proactive threshold VOPRF, prove the correctness of distributed key generation, threshold evaluation, proactive refresh, and committee resharing, and provide a simulation-based security analysis under the vectorization and one-more hidden-group- action assumptions. As an application, we describe a distributed private lookup service whose encrypted database remains valid across repeated share renewals and committee migrations.

cs.CR↗

arXiv2026

Riding the Wave: Polymers in Time-dependent Nonequilibrium Baths

Directed transport is a characteristic feature of numerous biological systems in response to signals such as nutrient and chemical gradients. These signals often depend on time owing to the high complexity of interactions in these systems. In this study, we focus on the steady-state behavior of polymeric systems responding to such time-dependent signals. We model them as ideal Rouse polymers submerged in a nonequilibrium bath, which is described by a spatially and temporally varying self-propulsion wave field. Through a coarse-graining analysis, we show that these polymers display rich emergent response to the temporal stimuli as a function of their length and topology. In particular, long polymers and structures with ring and star topologies ride the wave, displaying a positive drift in the direction of the wave. Whereas, shorter polymers and fully connected structures drift against the wave signal. We confirm these analytical predictions with robust numerical simulations, showing that the response of polymeric systems to temporal stimuli can be controlled by the topology or the length of the polymer.

cond-mat.soft↗

arXiv2026

Velocity and force autocorrelations in Brownian dynamics with a Lorentz force

We derive a general relation between the velocity and force autocorrelation tensors (VACT and FACT) for a Brownian particle subject to an external magnetic field. Using time-symmetry arguments, we show that, for the full Langevin dynamics, the VACT depends only on the FACT, independently of the details of the interaction potential. Under the hypothesis of timescale separation between thermalization and interaction-driven motion, this relation simplifies considerably in the overdamped (Brownian) limit. A central feature of the overdamped result is that, unlike in the field-free case, the part of the VACT that controls the self-diffusion of the particle couples to the antisymmetric part of the FACT, with a coupling strength set by the ratio of the cyclotron frequency to the thermalization rate. We validate and illustrate the formalism on an exactly solvable model: a dimer of charged particles bound by a harmonic potential. Depending on the relative sign of the particle charges, the magnetic field is found to produce either a transient suppression of mobility and diffusion that is fully recovered at long times, or a persistent oscillatory force autocorrelation, regions of negative mobility, and a long-time suppression of self-diffusion.

cond-mat.stat-mech↗

arXiv2026

Efficient Neural Network Model Selection for Few-Class Application Datasets

While much effort has focused on developing and benchmarking high-performance neural networks, less attention has been given to how dataset properties, known to practitioners, can guide efficient model selection. Neural models are typically evaluated on datasets with thousands of classes, yet many real-world applications involve fewer than ten. To address this understudied but common setting, we develop a measure of classification difficulty based on data-side properties and show how it enables more efficient model selection for few-class datasets, where traditional approaches are less effective. We term this phenomenon "few-class distinctiveness". Our metric allows comparison of models and datasets 6 to 29$\times$ faster than repeated training and testing. Leveraging this insight, we extend scaled model families below the smallest published models, achieving greater efficiency at similar accuracy, for example models up to 42% smaller than YOLOv5-nano for a mobile robot task. Targeting resource-constrained applications, we demonstrate few-class model selection across mobile robot, drone, and IoT scenarios, highlighting practical gains in efficiency without sacrificing performance.

cs.LG↗

arXiv2026

Expert-validated STEM QA

Recent advancements in AI are helping scientists achieve breakthroughs in fields such as mathematics, medicine, and materials sciences. New evaluation datasets for AI models contribute to such advancement in AI. In the STEM domain, frontier models have consumed most of the available online data, creating the need for human-created datasets that codify the knowledge of leading experts in the domain. There are several STEM datasets available for the research community in this field. However, there are some gaps in these datasets, leaving room for improvement. Examples of gaps include (1) saturation in model performance on these datasets, leaving no head-room for meaningful evaluations, (2) skewed taxonomy distributions, (3) multiple choice question format that is misaligned with how scientists use AI in the real world, and (4) inaccurate answers and rationales partially led by a contest-based data collection and a time-bound review process. In this study, we present 'Expert-validated STEM QA', a high-quality, expert-validated STEM dataset (N=398) in Physics, Chemistry, Biology, and Mathematics, created by 241 domain experts. We (1) carefully designed a taxonomy with balanced distribution, (2) vetted question contributors with quality-driven incentive, (3) conducted multiple rounds of reviews with revisions validated by domain experts based on consensus, and (4) created the dataset in verifiable question and answer format. Our study demonstrated low performance ($<25\%$) of frontier AI models on the dataset as a benchmark. Post-training on a separate, private version of the dataset (N=2,000) increased performance of the open source model by $15\%$ relative to the baseline model (p=0.045) on the STEM subset of HLE-verified dataset, indicating potential utility of the dataset for model training. We have open-sourced a portion of our dataset for the AI research community.

cs.AI↗

arXiv2026

Self-organization and cyclic positioning of active condensates

Cohesive active assemblies are often regulated by spatially heterogeneous nonequilibrium driving, such as gradients in motility, biochemical turnover, or mechanical activity. Such heterogeneous driving can influence where condensates or cell collectives accumulate, how stable they are, and how they exchange material with their surroundings. However, the minimal physical mechanisms by which activity gradients control the positioning and turnover of cohesive active matter remain unclear. Here, we address this question using a model of attractive active Brownian particles (ABPs) in a spatially varying activity field. Using Brownian dynamics simulations, we show that these particles undergo liquid-gas phase separation, and spatially varying activity fields induce striking emergent dynamics. Attractive active droplets migrate up activity gradients, and at sufficiently high activity, they can fragment or evaporate into a dilute phase. For finite clusters, evaporated ABPs can redistribute through the simulation box, reassemble into new clusters in lower-activity regions, and migrate again toward higher activity, giving rise to cyclic positioning through repeated nucleation, migration, evaporation, and reassembly.

cond-mat.soft↗

arXiv2026

Strain-stiffening critical exponents of fiber networks under uniaxial deformation

Disordered fiber networks exhibit a floppy to rigid mechanical phase transition as a function of connectivity. Sub-isostatically connected networks can undergo this transition via straining. Critical exponents governing this transition have been estimated theoretically and by numerical simulations of various types of networks. In this study, we present improved results, achieved through a combination of refined numerical simulations, larger system sizes and incorporation of theoretical predictions for better post-simulation analysis. We also report the evolution of the critical strain and critical exponents as the network is sheared while being subjected to non-volume-preserving uniaxial deformations.

cond-mat.soft↗

arXiv2026

Test-Time Strategies for More Efficient and Accurate Agentic RAG

Retrieval-Augmented Generation (RAG) systems face challenges with complex, multihop questions, and agentic frameworks such as Search-R1 (Jin et al., 2025), which operates iteratively, have been proposed to address these complexities. However, such approaches can introduce inefficiencies, including repetitive retrieval of previously processed information and challenges in contextualizing retrieved results effectively within the current generation prompt. Such issues can lead to unnecessary retrieval turns, suboptimal reasoning, inaccurate answers, and increased token consumption. In this paper, we investigate test-time modifications to the Search-R1 pipeline to mitigate these identified shortcomings. Specifically, we explore the integration of two components and their combination: a contextualization module to better integrate relevant information from retrieved documents into reasoning, and a de-duplication module that replaces previously retrieved documents with the next most relevant ones. We evaluate our approaches using the HotpotQA (Yang et al., 2018) and the Natural Questions (Kwiatkowski et al., 2019) datasets, reporting the exact match (EM) score, an LLM-as-a-Judge assessment of answer correctness, and the average number of turns. Our best-performing variant, utilizing GPT-4.1-mini for contextualization, achieves a 5.6% increase in EM score and reduces the number of turns by 10.5% compared to the Search-R1 baseline, demonstrating improved answer accuracy and retrieval efficiency.

cs.IR↗

arXiv2026

Introducing the transitional autonomous vehicle lane-changing dataset: Empirical Experiments

Transitional autonomous vehicles (tAVs), which operate beyond SAE Level 1-2 automation but short of full autonomy, are increasingly sharing the road with human-driven vehicles (HDVs). As these systems interact during complex maneuvers such as lane changes, new patterns may emerge with implications for traffic stability and safety. Assessing these dynamics, particularly during mandatory lane changes, requires high-resolution trajectory data, yet datasets capturing tAV lane-changing behavior are scarce. This study introduces the North Carolina Transitional Autonomous Vehicle Lane-Changing (NC-tALC) Dataset, a high-fidelity trajectory dataset designed to characterize tAV interactions during lane-changing maneuvers. The dataset includes two controlled experimental series. In the first, tAV lane-changing experiments, a tAV executes lane changes in the presence of adaptive cruise control (ACC) equipped target vehicles, enabling analysis of lane-changing execution. In the second, tAV responding experiments, two tAVs act as followers and respond to cut-in maneuvers initiated by another tAV, enabling analysis of follower response dynamics. The dataset contains 152 trials (72 lane-changing and 80 responding trials) sampled at 20 Hz with centimeter-level RTK-GPS accuracy. The NC-tALC dataset provides a rigorous empirical foundation for evaluating tAV decision-making and interaction dynamics in controlled mandatory lane-changing scenarios.

cs.RO↗

arXiv2026

A mobility based approach to transport in chiral fluids

Chiral fluids, for which the mobility tensor has antisymmetric, off-diagonal components, exhibit transport phenomena absent in conventional systems, including interaction-enhanced diffusion and negative mobility. While these effects have been predicted theoretically and observed in simulations, their microscopic origin has remained unclear. Here, we address this question using a mobility-based nonequilibrium approach, analysing the steady-state drift of a tracer driven through an interacting chiral fluid. We show that, under strong chirality, the tracer generates a reversed density wake, in which regions of particle accumulation and depletion are inverted compared to the achiral case. This structural inversion of the wake provides a unified physical mechanism underlying both enhanced diffusion and negative mobility. Furthermore, we demonstrate that these phenomena are robust to changes in the interaction potential, highlighting their generality as a consequence of odd mobility.

cond-mat.stat-mech↗

arXiv2026

Optimal Placement and Sizing of PV-Based DG Units in a Distribution Network Considering Loading Capacity

This research paper proposes an efficient methodology for the allocation of multiple photovoltaic (PV)-based distributed generation (DG) units in the radial distribution network (RDN), while considering the loading capacity of the network. The proposed method is structured using a two-stage approach. In the first stage, the additional active power loading capacity of the network and each individual bus is determined using an iterative approach. This analysis quantifies the network's additional active loadability limits and identifies buses with high active power loading capacity, which are considered candidate nodes for the placement of DG units. Subsequently, in the second stage, the optimal locations and sizes of DG units are determined using the Monte Carlo method, with the objectives of minimizing voltage deviation and reducing active power losses in the network. The methodology is validated on the standard IEEE 33-bus RDN to determine the optimal locations and sizes of DG units. The results demonstrate that the optimal allocation of one, two, and three DG units, achieved from proposed method, reduces network active power losses by 50.37%, 58.62%, and 65.16%, respectively, and also significantly enhances the voltage profile across all buses. When the obtained results are compared with the results of several existing studies, it is found that the proposed method allows for larger DG capacities and maintains better voltage profiles throughout the RDN.

eess.SY↗

arXiv2025

Collective Dynamics in Active Polar Polymer Assemblies

Tangentially driven active polymers (TDAPs), model systems for motor-driven filaments, have been extensively studied in uniform activity fields. Here, we show that an activity gradient breaks fore-aft symmetry, generating net body forces that steer dimers, asters, and larger assemblies toward high-activity regions. Including temporal stochasticity softens the chains, allowing them to bend and wind around other filaments. Once several contacts are established, steric interlocking arrests relative motion and stabilizes the assembly into a hierarchically entangled cluster. These clusters persist for times far exceeding single-chain relaxation and do not appear under deterministic, temporally constant activity. Remarkably, such activity-induced gelation occurs even at polymer concentrations substantially lower than those typically required for passive chains. Our results reveal a new mechanism for activity-induced aggregation, providing new strategies for designing autonomous and reconfigurable microfluidic systems.

cond-mat.soft↗

arXiv2025

Active Transport of Cargo-Carrying and Interconnected Chiral Particles

Directed motion up a concentration gradient is crucial for the survival and maintenance of numerous biological systems, such as sperms moving towards an egg during fertilization or ciliates moving towards a food source. In these systems, chirality - manifested as a rotational torque - plays a vital role in facilitating directed motion. While systematic studies of active molecules in activity gradients exist, the effect of chirality remains little studied. In this study, we examine the simplest case of a chiral active particle connected to a passive particle in a spatially varying activity field. We demonstrate that this minimal setup can exhibit rich emergent tactic behaviors, with the chiral torque serving as the tuning parameter. Notably, when the chiral torque is sufficiently large, even a small passive particle enables the system to display the desired accumulation behavior. Our results further show that in the dilute limit, this desired accumulation behavior persists despite the presence of excluded volume effects. Additionally, interconnected chiral active particles exhibit emergent chemotaxis beyond a critical chain length, with trimers and longer chains exhibiting strong accumulation at sufficiently high chiral torques. This study provides valuable insights into the design principles of hybrid bio-molecular devices of the future.

cond-mat.stat-mech↗

arXiv2025

Confined active particles with spatially dependent Lorentz force: an odd twist to the "best Fokker-Planck approximation"

We derive a version of the so-called "best Fokker-Planck approximation" (BFPA) to describe the spatial properties of interacting active Ornstein-Uhlenbeck particles (AOUPs) in arbitrary spatial dimensions. In doing so, we also take into account the odd-diffusive contribution of the Lorentz force acting on a charged particle in a spatially dependent magnetic field, sticking to the overdamped limit. While the BFPA itself does not turn out to be widely useful, our general approach allows to deduce an appropriate generalization of the Fox approximation, which we use to characterize the stationary behavior of a single active particle in an external potential by deriving analytic expressions for configurational probability distributions (or effective potentials). In agreement with computer simulations, our theory predicts that the Lorentz force reduces the effective attraction and thus the probability to find an active particle in the vicinity of a repulsive wall. Even for an inhomogeneous magnetic field, our theoretical findings provide useful qualitative insights, specifically regarding the location of accumulation regions.

cond-mat.soft↗

arXiv2025

Reversal of tracer advection and Hall drift in an interacting chiral fluid

Chiral fluids are defined by broken mirror or time-reversal symmetry, giving rise to tensorial transport coefficients with antisymmetric components. A key example is the odd mobility tensor, which governs the response of a chiral tracer to an applied force and induces a characteristic transverse drift. While this response is well understood in the infinite dilution limit, the impact of interparticle interactions on the tracer dynamics remains largely unexplored. Here, we conduct an analytical and computational study of a chiral fluid with interparticle interactions and show that, under an external driving force, a chiral tracer can undergo a complete reversal of both its transverse Hall drift and its advection along the force. This reversal emerges from the interplay between odd mobility and interaction-mediated forces, resulting in a phenomenon reminiscent of absolute negative mobility.

cond-mat.stat-mech↗