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Arnab Bhattacharjee

Publications and source records attributed to Arnab Bhattacharjee.

13 recordsLinked to original sources
arXiv2026

Multi-filament coordination rescues active transport from inertia-induced spinning arrest

Active filaments driven by tangential forces can become trapped in a spinning state when attached to a heavy head, where activity and inertia drive persistent rotation rather than directed transport. Using three-dimensional Langevin dynamics of tangentially driven bead-spring chains anchored to a common heavy head, we demonstrate that increasing the filament number $\Nf$ systematically \emph{rescues} directed transport by sterically preventing the coiled conformations that underlie spinning. The rescue is established through three independent diagnostics: (i)~the mean-square displacement recovers monotonic growth (transport rescue), (ii)~the spatial tangent autocorrelation loses its negative dip signaling helical coiling (conformational rescue), and (iii)~the tangent time autocorrelation ceases crossing zero (orientational rescue). At high bending stiffness ($\kb = 1000$), coiling is fully eliminated at a critical filament number $\Nf^* \approx 3$. At moderate stiffness ($\kb = 100$), residual coiling persists ($\min C_s \approx -0.13$) yet transport is still rescued -- demonstrating that the destruction of spinning \emph{coherence}, not coiling elimination, is the essential mechanism. The multi-filament architecture achieves up to five orders of magnitude transport enhancement. Two physically distinct rescue pathways emerge: at high stiffness, steric constraints force filaments into a coordinated bundle sustaining directed propulsion; at low stiffness, steric interactions destroy orientational coherence, producing enhanced active diffusion. These results demonstrate a purely mechanical, density-independent route to overcome inertia-induced motility arrest, with implications for synthetic microswimmer design, motor-driven filament assays, and multi-filament organization in biological systems.

cond-mat.soft↗
arXiv2026

Cyclic adjoint modules and their embeddings in quantized enveloping algebras

We study cyclic adjoint modules arising from the relative locally finite part of the adjoint action of a quantum Levi subalgebra on a quantized enveloping algebra. We classify embeddings of finite-dimensional irreducible modules inside of quantized enveloping algebra via cyclic generators and show that such realizations are in general non-unique, exhibiting infinite families in the cominuscule case. We also introduce a partial order on cyclic adjoint modules, characterize its minimal elements, and prove finite generation by irreducible submodules.

math.QA↗
arXiv2026

Differential calculus on Hopf--Galois extension via the Durdevi\'c braiding

We introduce a class of right $H$--covariant first--order differential calculi on principal comodule algebras generated by the Durdevi\'c braiding $\sigma$ and a chosen vertical ideal. Starting from the universal calculus, a strong connection, and a right $H$--colinear splitting map, we construct $\sigma$--generated differential calculi and prove their existence for arbitrary principal comodule algebras. We show that, in this framework, universal vertical maps and connection $1$--forms descend naturally to the quotient calculus under suitable compatibility conditions. We further develop a functorial formulation of $\sigma$--generated calculi and establish a universal factorization property for the associated quotient calculi. Finally, we present explicit examples arising from quantum projective spaces and quantum lens spaces.

math.QA↗
arXiv2026

Hopf images of coactions and effective symmetry of quantum principal bundles

We introduce Hopf images of coactions of Hopf algebras and develop their role in the geometry of quantum principal bundles. Assuming cosemisimplicity of the structure Hopf algebra, we show that every quantum principal bundle equipped with a right-covariant first-order differential calculus admits a canonical and functorial reduction to one with inner-faithful quantum symmetry. This yields a classification of quantum principal bundles up to effective quantum symmetry and a rigidity result identifying the minimal effective symmetry acting on the reduced total space. Examples from quantum groups are discussed.

math.QA↗
arXiv2025

Uncertainty Discounting in Deterministic Black Box Price Predictions for Energy Arbitrage

This study examines the economic impact of post-hoc uncertainty discounting in predictive energy management, specifically in battery energy arbitrage. A 2.2 MWh, 1.1 MW Tesla battery, emulating operations at the University of Queensland's St. Lucia campus, is used as a test system. Traditionally, Model Predictive Control (MPC) frameworks rely on deterministic spot price forecasts from the Australian Energy Market Operator (AEMO) to optimize battery scheduling. However, these forecasts lack uncertainty awareness, making arbitrage strategies vulnerable to extreme price volatility. To address this, we propose simple heuristic uncertainty discounting methods, which require no access to the predictive model's architecture or inputs. By integrating these strategies into existing MPC frameworks, we demonstrate a more than 20% improvement in economic returns under identical operational constraints. This approach enhances decision-making in energy arbitrage while remaining practical, scalable, and independent of specific forecasting models

eess.SY↗
arXiv2025

A Receding Horizon Reinforcement Learning Framework for Campus Chiller Energy Management - A case study from an Australian University

This work presents a case study of optimal energy management of a large Heating Ventilation and Cooling (HVAC) system within a university campus in Australia using Reinforcement Learning (RL). The HVAC system supplies to nine university buildings with an annual average electricity consumption of $\sim2$ GWh. Updated chiller Coefficient of Performance (COP) curves are identified, and a predictive building cooling demand model is developed using historical data from the HVAC system. Based on these inputs, a Proximal Policy Optimization based RL model is trained to optimally schedule the chillers in a receding horizon control framework with a priority reward function for constraint satisfaction. Compared to the traditional way of controlling the HVAC system based on a reactive rule-based method, the proposed controller saves up to 28\% of the electricity consumed by simply controlling the mass flow rates of the chiller banks and with minimal constraint violations.

eess.SY↗
arXiv2024

Momentum Informed Inflation-at-Risk

Growth-at-Risk has recently become a key measure of macroeconomic tail-risk, which has seen it be researched extensively. Surprisingly, the same cannot be said for Inflation-at-Risk where both tails, deflation and high inflation, are of key concern to policymakers, which has seen comparatively much less research. This paper will tackle this gap and provide estimates for Inflation-at-Risk. The key insight of the paper is that inflation is best characterised by a combination of two types of nonlinearities: quantile variation, and conditioning on the momentum of inflation.

econ.EM↗
arXiv2024

MIDAS-QR with 2-Dimensional Structure

Mixed frequency data has been shown to improve the performance of growth-at-risk models in the literature. Most of the research has focused on imposing structure on the high-frequency lags when estimating MIDAS-QR models akin to what is done in mean models. However, only imposing structure on the lag-dimension can potentially induce quantile variation that would otherwise not be there. In this paper we extend the framework by introducing structure on both the lag dimension and the quantile dimension. In this way we are able to shrink unnecessary quantile variation in the high-frequency variables. This leads to more gradual lag profiles in both dimensions compared to the MIDAS-QR and UMIDAS-QR. We show that this proposed method leads to further gains in nowcasting and forecasting on a pseudo-out-of-sample exercise on US data.

econ.EM↗
arXiv2024

Fused LASSO as Non-Crossing Quantile Regression

Growth-at-Risk is vital for empirical macroeconomics but is often suspect to quantile crossing due to data limitations. While existing literature addresses this through post-processing of the fitted quantiles, these methods do not correct the estimated coefficients. We advocate for imposing non-crossing constraints during estimation and demonstrate their equivalence to fused LASSO with quantile-specific shrinkage parameters. By re-examining Growth-at-Risk through an interquantile shrinkage lens, we achieve improved left-tail forecasts and better identification of variables that drive quantile variation. We show that these improvements have ramifications for policy tools such as Expected Shortfall and Quantile Local Projections.

econ.EM↗
arXiv2023

DeeBBAA: A benchmark Deep Black Box Adversarial Attack against Cyber-Physical Power Systems

An increased energy demand, and environmental pressure to accommodate higher levels of renewable energy and flexible loads like electric vehicles have led to numerous smart transformations in the modern power systems. These transformations make the cyber-physical power system highly susceptible to cyber-adversaries targeting its numerous operations. In this work, a novel black box adversarial attack strategy is proposed targeting the AC state estimation operation of an unknown power system using historical data. Specifically, false data is injected into the measurements obtained from a small subset of the power system components which leads to significant deviations in the state estimates. Experiments carried out on the IEEE 39 bus and 118 bus test systems make it evident that the proposed strategy, called DeeBBAA, can evade numerous conventional and state-of-the-art attack detection mechanisms with very high probability.

cs.CR↗
arXiv2022

Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model

This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. Specifically, we allow shrinking state variances towards zero to avoid overfitting, extend the SSVS (spike and slab variable selection) prior to the more flexible normal-inverse-gamma prior which stays agnostic about the underlying model size, as well as adapt the horseshoe prior to the BSTS. The application to nowcasting GDP growth as well as a simulation study demonstrate that the horseshoe prior BSTS improves markedly upon the SSVS and the original BSTS model with the largest gains in dense data-generating-processes. Our application also shows that a large dimensional set of search terms is able to improve nowcasts early in a specific quarter before other macroeconomic data become available. Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects.

econ.EM↗
arXiv2021

Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning

In this paper we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. Moreover, a transfer learning mechanism is proposed in order to make the developed algorithm generalize better and estimate with an acceptable accuracy when a battery with different chemical characteristics than the one used for training the model, is used. It has been observed that using transfer learning, the model can learn sufficiently well with significantly less amount of battery data. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability.

eess.SP↗