arXiv subjects

Abhinav Sharma

Publications and source records attributed to Abhinav Sharma.

At least 37 records · Page 2Linked to original sources

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↗

arXiv2025

Enhanced Efficiency in Shear-Loaded Brownian Gyrators

A Brownian gyrator is a system in which a particle experiences thermal noise from two distinct heat baths. This nonequilibrium setup inherently generates a nonzero torque, leading to gyrating motion around a potential energy minimum. As a minimal model for a heat engine, the Brownian gyrator provides valuable insights into energy conversion and nonequilibrium dynamics. Here, we investigate the effect of an externally imposed shear flow on a Brownian gyrator, treating it as a mechanical load. The shear flow introduces a tunable mechanism that allows the system to operate either as a heat engine, extracting work from the temperature gradient, or as a refrigerator, transferring heat from the colder to the hotter bath. Focusing on the heat engine regime, we analytically derive the steady-state probability distribution to compute the average torque exerted by the gyrator and quantify the mechanical power extracted from the shear. Our results show a remarkable increase in efficiency compared to the standard Brownian gyrator without shear, approaching Carnot efficiency at maximum power. Surprisingly, we also find that while the system can operate efficiently as a heat engine, it may become unstable before reaching the stall condition, highlighting a fundamental trade-off between efficiency and stability in shear-driven microscopic engines.

cond-mat.stat-mech↗

arXiv2025

An Analytical Overview Of Virtual Machine Load Balancing Scheduling Algorithms with their Comparative Case Study

Efficient virtual machine load balancing scheduling is crucial in cloud computing to optimize resource utilization and system performance. To address this issue, several load balancing scheduling algorithms have been proposed, including Particle Swarm Optimization, Multi-objective Optimization, and the Active Monitoring Algorithm. This paper provides an analytical overview of these three algorithms, discussing their key features, advantages, and limitations. It contains an analysis of VM Load Balancing Scheduling Algorithms, examining their advantages, disadvantages, and applications. As the industry shifts towards adopting Cloud Technologies, optimally load balancing client requests to servers becomes essential. It is crucial for cloud providers to adapt technologies that prevent latency issues for their customers. The algorithms most commonly used in load balancers are analytically discussed.

cs.DC↗

arXiv2024

ASSERTIFY: Utilizing Large Language Models to Generate Assertions for Production Code

Production assertions are statements embedded in the code to help developers validate their assumptions about the code. They assist developers in debugging, provide valuable documentation, and enhance code comprehension. Current research in this area primarily focuses on assertion generation for unit tests using techniques, such as static analysis and deep learning. While these techniques have shown promise, they fall short when it comes to generating production assertions, which serve a different purpose. This preprint addresses the gap by introducing Assertify, an automated end-to-end tool that leverages Large Language Models (LLMs) and prompt engineering with few-shot learning to generate production assertions. By creating context-rich prompts, the tool emulates the approach developers take when creating production assertions for their code. To evaluate our approach, we compiled a dataset of 2,810 methods by scraping 22 mature Java repositories from GitHub. Our experiments demonstrate the effectiveness of few-shot learning by producing assertions with an average ROUGE-L score of 0.526, indicating reasonably high structural similarity with the assertions written by developers. This research demonstrates the potential of LLMs in automating the generation of production assertions that resemble the original assertions.

cs.SE↗

arXiv2024

Transport of molecules via polymerization in chemical gradients

The transport of molecules for chemical reactions is critically important in various cellular biological processes. Despite thermal diffusion being prevalent in many biochemical processes, it is unreliable for any sort of directed transport or preferential accumulation of molecules. In this paper we propose a strategy for directed motion in which the molecules are transported by active carriers via polymerization. This transport is facilitated by chemical/activity gradients which generate an effective drift of the polymers. By marginalizing out the active degrees of freedom of the system, we obtain an effective Fokker-Planck equation for the Rouse modes of such active-passive hybrid polymers. In particular, we solve for the steady state distribution of the center of mass and its mean first passage time to reach an intended destination. We focus on how the arrangement of active units within the polymer affect its steady-state and dynamic behaviour and how they can be optimized to achieve high accumulation or rapid motility.

cond-mat.soft↗

arXiv2024

Self-diffusion anomalies of an odd tracer in soft-core media

Odd-diffusive systems, characterised by broken time-reversal and/or parity symmetry, have recently been shown to display counterintuitive features such as interaction-enhanced dynamics in the dilute limit. Here we we extend the investigation to the high-density limit of an odd tracer embedded in a soft-Gaussian core medium (GCM) using a field-theoretic approach based on the Dean-Kawasaki equation. Our theory reveals that interactions can enhance the dynamics of an odd tracer even in dense systems. We demonstrate that oddness results in a complete reversal of the well-known self-diffusion ($D_\mathrm{s}$) anomaly of the GCM. Ordinarily, $D_\mathrm{s}$ exhibits a non-monotonic trend with increasing density, approaching but remaining below the interaction-free diffusion, $D_0$, ($D_\mathrm{s} < D_0$) so that $D_\mathrm{s} \uparrow D_0$ at high densities. In contrast, for an odd tracer, self-diffusion is enhanced ($D_\mathrm{s}> D_0$) and the GCM anomaly is inverted, displaying $D_\mathrm{s} \downarrow D_0$ at high densities. The transition between the standard and reversed GCM anomaly is governed by the tracer's oddness, with a critical oddness value at which the tracer diffuses as a free particle ($D_\mathrm{s} \approx D_0$) across all densities. We validate our theoretical predictions with Brownian dynamics simulations, finding strong agreement between the two.

cond-mat.stat-mech↗

arXiv2024

A Lightweight Measure of Classification Difficulty from Application Dataset Characteristics

Although accuracy and computation benchmarks are widely available to help choose among neural network models, these are usually trained on datasets with many classes, and do not give a good idea of performance for few (< 10) classes. The conventional procedure to predict performance involves repeated training and testing on the different models and dataset variations. We propose an efficient cosine similarity-based classification difficulty measure S that is calculated from the number of classes and intra- and inter-class similarity metrics of the dataset. After a single stage of training and testing per model family, relative performance for different datasets and models of the same family can be predicted by comparing difficulty measures - without further training and testing. Our proposed method is verified by extensive experiments on 8 CNN and ViT models and 7 datasets. Results show that S is highly correlated to model accuracy with correlation coefficient |r| = 0.796, outperforming the baseline Euclidean distance at |r| = 0.66. We show how a practitioner can use this measure to help select an efficient model 6 to 29x faster than through repeated training and testing. We also describe using the measure for an industrial application in which options are identified to select a model 42% smaller than the baseline YOLOv5-nano model, and if class merging from 3 to 2 classes meets requirements, 85% smaller.

cs.LG↗

arXiv2024

Representation Similarity: A Better Guidance of DNN Layer Sharing for Edge Computing without Training

Edge computing has emerged as an alternative to reduce transmission and processing delay and preserve privacy of the video streams. However, the ever-increasing complexity of Deep Neural Networks (DNNs) used in video-based applications (e.g. object detection) exerts pressure on memory-constrained edge devices. Model merging is proposed to reduce the DNNs' memory footprint by keeping only one copy of merged layers' weights in memory. In existing model merging techniques, (i) only architecturally identical layers can be shared; (ii) requires computationally expensive retraining in the cloud; (iii) assumes the availability of ground truth for retraining. The re-evaluation of a merged model's performance, however, requires a validation dataset with ground truth, typically runs at the cloud. Common metrics to guide the selection of shared layers include the size or computational cost of shared layers or representation size. We propose a new model merging scheme by sharing representations (i.e., outputs of layers) at the edge, guided by representation similarity S. We show that S is extremely highly correlated with merged model's accuracy with Pearson Correlation Coefficient |r| > 0.94 than other metrics, demonstrating that representation similarity can serve as a strong validation accuracy indicator without ground truth. We present our preliminary results of the newly proposed model merging scheme with identified challenges, demonstrating a promising research future direction.

cs.CV↗

arXiv2024

WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology

Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI). Weakly supervised learning of tile-level data is typically applied for tasks where labels only exist on the patient or WSI level (e.g. patient outcomes or histological grading). In this context, there is a need for improved spatial interpretability of predictions from such models. We propose a novel method, Wsi rEgion sElection aPproach (WEEP), for model interpretation. It provides a principled yet straightforward way to establish the spatial area of WSI required for assigning a particular prediction label. We demonstrate WEEP on a binary classification task in the area of breast cancer computational pathology. WEEP is easy to implement, is directly connected to the model-based decision process, and offers information relevant to both research and diagnostic applications.

eess.IV↗

arXiv2024

VoteLab: A Modular and Adaptive Experimentation Platform for Online Collective Decision Making

Digital democracy and new forms for direct digital participation in policy making gain unprecedented momentum. This is particularly the case for preferential voting methods and decision-support systems designed to promote fairer, more inclusive and legitimate collective decision-making processes in citizens assemblies, participatory budgeting and elections. However, a systematic human experimentation with different voting methods is cumbersome and costly. This paper introduces VoteLab, an open-source and thoroughly-documented platform for modular and adaptive design of voting experiments. It supports to visually and interactively build reusable campaigns with a choice of different voting methods, while voters can easily respond to subscribed voting questions on a smartphone. A proof-of-concept with four voting methods and questions on COVID-19 in an online lab experiment have been used to study the consistency of voting outcomes. It demonstrates the capability of VoteLab to support rigorous experimentation of complex voting scenarios.

cs.CY↗

arXiv2024

The Dance of Odd-Diffusive Particles: A Fourier Approach

Odd-diffusive systems are characterized by transverse responses and exhibit unconventional behaviors in interacting systems. To address the dynamical interparticle rearrangements in a minimal system, we here exactly solve the problem of two hard disk-like interacting odd-diffusing particles. We calculate the probability density function (PDF) of the interacting particles in the Fourier-Laplace domain and find that oddness rotates all modes except the zeroth, resembling a ``mutual rolling'' of interacting odd particles. We show that only the first Fourier mode of the PDF, the polarization, enters the calculation of the force autocorrelation function (FACF) for generic systems with central-force interactions. An analysis of the polarization as a function of time reveals that the relative rotation angle between interacting particles overshoots before relaxation, thereby rationalizing the recently observed oscillating FACF in odd-diffusive systems.

cond-mat.stat-mech↗

arXiv2024

Field-Theory of Active Chiral Hard Disks: A First-Principles Approach to Steric Interactions

A first-principles approach for active chiral hard disks is presented, that explicitly accounts for steric interactions on the two-body level. We derive an effective one-body equation for the joint probability distribution of ositions and angles of the particles. By projecting onto the angular modes, we write a hierarchy for the lowest hydrodynamic modes, i.e. particle density, polarisation, and nematic tensor. Introducing dimensionless variables in the equations, we highlight the assumptions, which - though inherent - are often included implicit in typical closure schemes of the hierarchy. By considering different regimes of the P{é}clet number, the well-known models in active matter can be obtained through our consideration. Explicitly, we derive an effective diffusive description and by going to higher orders in the closure scheme, we show that this first-principles approach results in the recently introduced Active Model B +, a natural extension of the Model B for active processes. Remarkably, here we find that chirality can change the sign of the phenomenological activity parameters.

cond-mat.stat-mech↗

arXiv2024

Migration and separation of polymers in non-uniform active baths

Polymer-like structures are ubiquitous in nature and synthetic materials. Their configurational and migration properties are often affected by crowded environments leading to non-thermal fluctuations. Here, we study an ideal Rouse chain in contact with a non-homogeneous active bath, characterized by the presence of active self-propelled agents which exert time-correlated forces on the chain. By means of a coarse-graining procedure, we derive an effective evolution for the center of mass of the chain and show its tendency to migrate towards and preferentially localize in regions of high/low bath activity depending on the model parameters. In particular, we demonstrate that an active bath with non-uniform activity can be used to separate efficiently polymeric species with different lengths and/or connectivity.

cond-mat.soft↗

arXiv2023

Fair and Inclusive Participatory Budgeting: Voter Experience with Cumulative and Quadratic Voting Interfaces

Cumulative and quadratic voting are two distributional voting methods that are expressive, promoting fairness and inclusion, particularly in the realm of participatory budgeting. Despite these benefits, graphical voter interfaces for cumulative and quadratic voting are complex to implement and use effectively. As a result, such methods have not seen yet widespread adoption on digital voting platforms. This paper addresses the challenge by introducing an implementation and evaluation of cumulative and quadratic voting within a state-of-the-art voting platform: Stanford Participatory Budgeting. The findings of the study show that while voters prefer simple methods, the more expressive (and complex) cumulative voting becomes the preferred one compared to k-ranking voting that is simpler but less expressive. The implemented voting interface elements are found useful and support the observed voters' preferences for more expressive voting methods. *

cs.SE↗

arXiv2023

Strain-controlled critical slowing down in the rheology of disordered networks

Networks and dense suspensions frequently reside near a boundary between soft (or fluid-like) and rigid (or solid-like) regimes. Transitions between these regimes can be driven by changes in structure, density, or applied stress or strain. In general, near the onset or loss of rigidity in these systems, dissipation-limiting heterogeneous nonaffine rearrangements dominate the macroscopic viscoelastic response, giving rise to diverging relaxation times and power-law rheology. Here, we describe a simple quantitative relationship between nonaffinity and the excess viscosity. We test this nonaffinity-viscosity relationship computationally and demonstrate its rheological consequences in simulations of strained filament networks and dense suspensions. We also predict critical signatures in the rheology of semiflexible and stiff biopolymer networks near the strain stiffening transition.

cond-mat.soft↗

arXiv2023

Learned Lock-free Search Data Structures

Non-blocking search data structures offer scalability with a progress guarantee on high-performance multi-core architectures. In the recent past, "learned queries" have gained remarkable attention. It refers to predicting the rank of a key computed by machine learning models trained to infer the cumulative distribution function of an ordered dataset. A line of works exhibits the superiority of learned queries over classical query algorithms. Yet, to our knowledge, no existing non-blocking search data structure employs them. In this paper, we introduce \textbf{Kanva}, a framework for learned non-blocking search. Kanva has an intuitive yet non-trivial design: traverse down a shallow hierarchy of lightweight linear models to reach the "non-blocking bins," which are dynamic ordered search structures. The proposed approach significantly outperforms the current state-of-the-art -- non-blocking interpolation search trees and elimination (a,b) trees -- in many workload and data distributions. Kanva is provably linearizable.

cs.DC↗

arXiv2023

Oscillatory force autocorrelations in equilibrium odd-diffusive systems

The force autocorrelation function (FACF), a concept of fundamental interest in statistical mechanics, encodes the effect of interactions on the dynamics of a tagged particle. In equilibrium, the FACF is believed to decay monotonically in time which is a signature of slowing down of the dynamics of the tagged particle due to interactions. Here we analytically show that in odd-diffusive systems, which are characterized by a diffusion tensor with antisymmetric elements, the FACF can become negative and even exhibit temporal oscillations. We also demonstrate that, despite the isotropy, the knowledge of FACF alone is not sufficient to describe the dynamics: the full autocorrelation tensor is required and contains an antisymmetric part. These unusual properties translate into enhanced dynamics of the tagged particle quantified via the self-diffusion coefficient that, remarkably, increases due to particle interactions.

cond-mat.stat-mech↗

arXiv2023

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods.

eess.IV↗