arXiv subjects

Sayak Chatterjee

Publications and source records attributed to Sayak Chatterjee.

8 recordsLinked to original sources

arXiv2026

Transitivity in Inhomogeneous Random Tournaments

Paired-comparison data are naturally represented by tournaments, where transitivity corresponds to the existence of a global ranking consistent with all pairwise outcomes. Accordingly, the classical Kendall-Smith coefficient of consistency measures deviations from transitivity in a tournament by counting the number of circular triads (directed $3$-cycles). In this paper, we characterize the fluctuations of the number of circular triads in inhomogeneous random tournaments and develop an inferential framework for the consistency coefficient. Specifically, we consider the $W$-random tournament model, where the comparison probabilities are determined by a tournamenton $W$, the analogue of a graphon in the tournament setting. We show that, for a $W$-random tournament on $n$ vertices, the number of circular triads exhibits three different fluctuation regimes, determined by suitable notions of regularity and uniformity of $W$. We further develop a novel tournamenton multiplier bootstrap that consistently approximates the limiting distribution of the circular-triad count in the relevant asymptotic regime. Combining this with procedures for testing regularity and uniformity, we design an algorithm for constructing confidence intervals for the consistency coefficient that is asymptotically valid for all tournamentons. We also obtain structural characterizations of tournamentons for which the limiting distribution of the number of circular triads exhibits specific degeneracies. These results can also be viewed through the lens of tournament quasirandomness and may be of independent interest.

math.PR↗

arXiv2026

Long Range Outlook for Short-Range Correlations

Short range correlated (SRC) N N pairs are pairs of nucleons with high relative momentum (prel > kF where kF ~ 250 MeV/c is the Fermi momentum in medium to heavy nuclei) and lower center of mass momentum. The motivation for studying SRC pairs ranges from a desire to achieve a more comprehensive understanding of the many-body nuclear wave-function at high-resolution to searching for explicit QCD-dynamics effects within the nuclear medium, not to mention connections to many other open problems in nuclear physics. Exploring short-range correlations was one of the physics motivations for building CEBAF (now Jefferson Lab). Scientists used the high luminosity and high energy of this cutting-edge machine to find kinematics that cleanly showed the signals of short-range correlations. This paved the way in the last two decades for tremendous progress understanding these correlations. This paper reviews recent progress and highlights outstanding questions and areas that need further study.

nucl-ex↗

arXiv2025

Asymptotic Normality of Subgraph Counts in Sparse Inhomogeneous Random Graphs

In this paper, we derive the asymptotic distribution of the number of copies of a fixed graph $H$ in a random graph $G_n$ sampled from a sparse graphon model. Specifically, we provide a refined analysis that separates the contributions of edge randomness and vertex-label randomness, allowing us to identify distinct sparsity regimes in which each component dominates or both contribute jointly to the fluctuations. As a result, we establish asymptotic normality for the count of any fixed graph $H$ in $G_n$ across the entire range of sparsity (above the containment threshold for $H$ in $G_n$). These results provide a complete description of subgraph count fluctuations in sparse inhomogeneous networks, closing several gaps in the existing literature that were limited to specific motifs or suboptimal sparsity assumptions.

math.PR↗

arXiv2025

Sleepy Chauffeur Detection and Alert Techniques for Road Safety

The most startling of the contemporary problems is the sleepiness of chauffeur which causes lots of car accidents. Prevention of those impending accidents by detecting and alerting the sleepy chauffeur is vital, otherwise that would lead to loss of lives and various traumas along with severe injuries. The slumber or sleep may be caused by huge stress, pressure, relentless work load or alcoholism, for which sleep deprivation occurs and the chauffeur while driving gets drowsy. So far, considerable amount of systems has been developed to detect drowsiness of drivers, most of which mainly depend on image processing algorithms using cameras. Some of them also incorporate artificial intelligence and machine learning based algorithms. This paper presents a review of the existing systems and also proposes an easy and cheap system using sensors and Arduino, capable of detecting sleepiness and generates siren alarm and send alert message to take precautionary measures.

eess.SY↗

arXiv2024

PriME: Privacy-aware Membership profile Estimation in networks

This paper presents a novel approach to estimating community membership probabilities for network vertices generated by the Degree Corrected Mixed Membership Stochastic Block Model while preserving individual edge privacy. Operating within the $\varepsilon$-edge local differential privacy framework, we introduce an optimal private algorithm based on a symmetric edge flip mechanism and spectral clustering for accurate estimation of vertex community memberships. We conduct a comprehensive analysis of the estimation risk and establish the optimality of our procedure by providing matching lower bounds to the minimax risk under privacy constraints. To validate our approach, we demonstrate its performance through numerical simulations and its practical application to real-world data. This work represents a significant step forward in balancing accurate community membership estimation with stringent privacy preservation in network data analysis.

stat.ME↗

arXiv2022

Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis

Network-valued time series are currently a common form of network data. However, the study of the aggregate behavior of network sequences generated from network-valued stochastic processes is relatively rare. Most of the existing research focuses on the simple setup where the networks are independent (or conditionally independent) across time, and all edges are updated synchronously at each time step. In this paper, we study the concentration properties of the aggregated adjacency matrix and the corresponding Laplacian matrix associated with network sequences generated from lazy network-valued stochastic processes, where edges update asynchronously, and each edge follows a lazy stochastic process for its updates independent of the other edges. We demonstrate the usefulness of these concentration results in proving consistency of standard estimators in community estimation and changepoint estimation problems. We also conduct a simulation study to demonstrate the effect of the laziness parameter, which controls the extent of temporal correlation, on the accuracy of community and changepoint estimation.

math.ST↗

arXiv2021

An AI-Enabled Agent-Based Simulation Platform for Studying COVID-19 Pandemic

Understanding outbreak dynamics is essential for designing effective control measures. We developed an agent-based model to examine how changes in epidemiological and intervention parameters affect infection progression in a synthetic population. The model incorporates individual demographic characteristics, including age, sex, and working status, as well as the number and location of infection epicentres, diagnostic sensitivity, the proportion of asymptomatic infections, and the timing and duration of lockdowns. By tracking each individual, the simulator characterizes infection progression through a community over time. In a closed population of 10000 people, cases peaked around the sixth week and declined by approximately the fifteenth week in the absence of lockdown. When primary cases were introduced within densely populated clusters, cases peaked earlier and declined more slowly. Lockdowns delayed and reduced the infection peak, whereas lower diagnostic sensitivity increased cases and deaths. The number of cases decreased as the proportion of asymptomatic infections increased under the model's assumptions. The model produces reproducible estimates under realistic parameter settings and can accommodate factors such as infectivity period, testing yield, socioeconomic status, daily travel, awareness, population density, and social distancing. It can also be adapted to infections with similar transmission dynamics. The model is available as an open, interactive web application that enables users without programming experience to design scenarios and examine outbreak dynamics in real time. Beyond forecasting, the simulator provides a reusable in-silico environment, or digital twin, for synthetic-data generation and AI-assisted optimization of intervention policies.

q-bio.PE↗

arXiv2020

Characterisation of an RPC prototype with moderate resistivity plates using tetrafluoroethane ($C_2H_2F_4$)

Keeping in mind the requirements of high rate capable, cost effective, large area detectors to be used in future high energy physics experiments, commercially available bakelite plates having moderate bulk resistivity are used to build an RPC module. The chamber is tested with cosmic rays in the avalanche mode using 100\% Tetrafluoroethane ($C_2H_2F_4$). Standard NIM electronics are used for this study. The efficiency, noise rate and time resolution are measured. The detailed method of measurement and the first test results are presented.

physics.ins-det↗