arXiv subjects

Ayan Mukhopadhyay

Publications and source records attributed to Ayan Mukhopadhyay.

At least 55 records · Page 3Linked to original sources

arXiv2024

Hydrodynamization in hybrid Bjorken flow attractors

Hybrid fluid models, consisting of two sectors with more weakly and more strongly self-interacting degrees of freedom coupled consistently as in the semi-holographic framework, have been shown to exhibit an attractor surface for Bjorken flow. Retaining only the simple viscid fluid descriptions of both sectors, we find that, on the attractor surface, the hydrodynamization times of both subsectors decrease with increasing total energy density at the respective point of hydrodynamization following a conformal scaling, reach their minimum values, and subsequently rise rapidly. The minimum values are obtained when the respective energy densities are of the order of the inverse of the dimensionful inter-system coupling. Restricting to attractor curves which can be matched to glasma models at a time set by the saturation scale for both $p$-$p$ and Pb-Pb collisions, we find that the more weakly coupled sector hydrodynamizes much later, and the strongly coupled sector hydrodynamizes earlier in $p$-$p$ collisions, since the total energy densities at the respective hydrodynamization times of these sectors fall inside and outside of the conformal window. This holds true also for phenomenologically relevant solutions that are significantly away from the attractor surface at the time we match to glasma models.

hep-ph↗

arXiv2024

Forecasting and Mitigating Disruptions in Public Bus Transit Services

Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disruptions lead to delays and overcrowding, which are detrimental to the passengers' experience and to the overall performance of the transit service. To proactively mitigate such events, many transit agencies station substitute (reserve) vehicles throughout their service areas, which they can dispatch to augment or replace vehicles on routes that suffer overcrowding or disruption. However, determining the optimal locations where substitute vehicles should be stationed is a challenging problem due to the inherent randomness of disruptions and due to the combinatorial nature of selecting locations across a city. In collaboration with the transit agency of Nashville, TN, we address this problem by introducing data-driven statistical and machine-learning models for forecasting disruptions and an effective randomized local-search algorithm for selecting locations where substitute vehicles are to be stationed. Our research demonstrates promising results in proactive disruption management, offering a practical and easily implementable solution for transit agencies to enhance the reliability of their services. Our results resonate beyond mere operational efficiency: by advancing proactive strategies, our approach fosters more resilient and accessible public transportation, contributing to equitable urban mobility and ultimately benefiting the communities that rely on public transportation the most.

cs.AI↗

arXiv2024

An Online Approach to Solving Public Transit Stationing and Dispatch Problem

Public bus transit systems provide critical transportation services for large sections of modern communities. On-time performance and maintaining the reliable quality of service is therefore very important. Unfortunately, disruptions caused by overcrowding, vehicular failures, and road accidents often lead to service performance degradation. Though transit agencies keep a limited number of vehicles in reserve and dispatch them to relieve the affected routes during disruptions, the procedure is often ad-hoc and has to rely on human experience and intuition to allocate resources (vehicles) to affected trips under uncertainty. In this paper, we describe a principled approach using non-myopic sequential decision procedures to solve the problem and decide (a) if it is advantageous to anticipate problems and proactively station transit buses near areas with high-likelihood of disruptions and (b) decide if and which vehicle to dispatch to a particular problem. Our approach was developed in partnership with the Metropolitan Transportation Authority for a mid-sized city in the USA and models the system as a semi-Markov decision problem (solved as a Monte-Carlo tree search procedure) and shows that it is possible to obtain an answer to these two coupled decision problems in a way that maximizes the overall reward (number of people served). We sample many possible futures from generative models, each is assigned to a tree and processed using root parallelization. We validate our approach using 3 years of data from our partner agency. Our experiments show that the proposed framework serves 2% more passengers while reducing deadhead miles by 40%.

cs.CY↗

arXiv2024

Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes

A fundamental (and largely open) challenge in sequential decision-making is dealing with non-stationary environments, where exogenous environmental conditions change over time. Such problems are traditionally modeled as non-stationary Markov decision processes (NSMDP). However, existing approaches for decision-making in NSMDPs have two major shortcomings: first, they assume that the updated environmental dynamics at the current time are known (although future dynamics can change); and second, planning is largely pessimistic, i.e., the agent acts ``safely'' to account for the non-stationary evolution of the environment. We argue that both these assumptions are invalid in practice -- updated environmental conditions are rarely known, and as the agent interacts with the environment, it can learn about the updated dynamics and avoid being pessimistic, at least in states whose dynamics it is confident about. We present a heuristic search algorithm called \textit{Adaptive Monte Carlo Tree Search (ADA-MCTS)} that addresses these challenges. We show that the agent can learn the updated dynamics of the environment over time and then act as it learns, i.e., if the agent is in a region of the state space about which it has updated knowledge, it can avoid being pessimistic. To quantify ``updated knowledge,'' we disintegrate the aleatoric and epistemic uncertainty in the agent's updated belief and show how the agent can use these estimates for decision-making. We compare the proposed approach with the multiple state-of-the-art approaches in decision-making across multiple well-established open-source problems and empirically show that our approach is faster and highly adaptive without sacrificing safety.

cs.AI↗

arXiv2024

Decision Making in Non-Stationary Environments with Policy-Augmented Search

Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (e.g., Monte Carlo tree search). While the former learns a policy by interacting with the environment (typically done before execution), the latter uses a generative model of the environment to sample promising action trajectories at decision time. Decision-making is particularly challenging in non-stationary environments, where the environment in which an agent operates can change over time. Both approaches have shortcomings in such settings -- on the one hand, policies learned before execution become stale when the environment changes and relearning takes both time and computational effort. Online search, on the other hand, can return sub-optimal actions when there are limitations on allowed runtime. In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment. We prove theoretical results showing conditions under which PA-MCTS selects the one-step optimal action and also bound the error accrued while following PA-MCTS as a policy. We compare and contrast our approach with AlphaZero, another hybrid planning approach, and Deep Q Learning on several OpenAI Gym environments. Through extensive experiments, we show that under non-stationary settings with limited time constraints, PA-MCTS outperforms these baselines.

cs.AI↗

arXiv2023

Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence for Child Vaccination Uptake in Nigeria

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in underdeveloped countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. In particular, low vaccination uptake in Nigeria is a major driver of more than 2,000 daily deaths of children under the age of five years. In this paper, we describe our collaboration with government partners in Nigeria to deploy ADVISER: AI-Driven Vaccination Intervention Optimiser. The framework, based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination, is the first successful deployment of an AI-enabled toolchain for optimizing the allocation of health interventions in Nigeria. In this paper, we provide a background of the ADVISER framework and present results, lessons, and success stories of deploying ADVISER to more than 13,000 families in the state of Oyo, Nigeria.

cs.CY↗

arXiv2023

Correlation functions of the Bjorken flow in the holographic Schwinger-Keldysh approach

One of the outstanding problems in the holographic approach to many-body physics is the explicit computation of correlation functions in nonequilibrium states. We provide a new and simple proof that the horizon cap prescription of Crossley-Glorioso-Liu for implementing the thermal Schwinger-Keldysh contour in the bulk is consistent with the Kubo-Martin-Schwinger periodicity and the ingoing boundary condition for the retarded propagator at any arbitrary frequency and momentum. The generalization to the hydrodynamic Bjorken flow is achieved by a Weyl rescaling in which the dual black hole's event horizon attains a constant surface gravity and area at late time although the directions longitudinal and transverse to the flow expands and contract respectively. The dual state's temperature and entropy density thus become constants (instead of the perfect fluid expansion) although no time-translation symmetry emerges at late time. Undoing the Weyl rescaling, the correlation functions can be computed systematically in a large proper time expansion in inverse powers of the average of the two reparametrized proper time arguments. The horizon cap has to be pinned to the nonequilibrium event horizon so that regularity and consistency conditions are satisfied. Consequently, in the limit of perfect fluid expansion, the Schwinger-Keldysh correlation functions with space-time reparametrized arguments are simply thermal at an appropriate temperature. A generalized bilocal thermal structure holds to all orders. We argue that the Stokes data (which are functions rather than constants) for the hydrodynamic correlation functions can decode the quantum fluctuations behind the horizon cap pinned to the evolving event horizon, and thus the initial data.

hep-th↗

arXiv2023

Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction

When a number of similar tasks have to be learned simultaneously, multi-task learning (MTL) models can attain significantly higher accuracy than single-task learning (STL) models. However, the advantage of MTL depends on various factors, such as the similarity of the tasks, the sizes of the datasets, and so on; in fact, some tasks might not benefit from MTL and may even incur a loss of accuracy compared to STL. Hence, the question arises: which tasks should be learned together? Domain experts can attempt to group tasks together following intuition, experience, and best practices, but manual grouping can be labor-intensive and far from optimal. In this paper, we propose a novel automated approach for task grouping. First, we study the affinity of tasks for MTL using four benchmark datasets that have been used extensively in the MTL literature, focusing on neural network-based MTL models. We identify inherent task features and STL characteristics that can help us to predict whether a group of tasks should be learned together using MTL or if they should be learned independently using STL. Building on this predictor, we introduce a randomized search algorithm, which employs the predictor to minimize the number of MTL trainings performed during the search for task groups. We demonstrate on the four benchmark datasets that our predictor-driven search approach can find better task groupings than existing baseline approaches.

cs.LG↗

arXiv2023

Black hole complementarity from microstate models: A study of information replication and the encoding in the black hole interior

We study how the black hole complementarity principle can emerge from quantum gravitational dynamics within a local semiclassical approximation. Further developing and then simplifying a microstate model based on the fragmentation instability of a near-extremal black hole, we find that the key to the replication (but not cloning) of infalling information is the decoupling of various degrees of freedom. The infalling matter decouples from the interior retaining a residual time-dependent quantum state in the hair which encodes the initial state of the matter non-isometrically. The non-linear ringdown of the interior after energy absorption and decoupling also encodes the initial state, and transfers the information to Hawking radiation. During the Hawking evaporation process, the fragmented throats decouple from each other and the hair decouples from the throats. We find that the hair mirrors infalling information after the decoupling time which scales with the logarithm of the entropy (at the time of infall) when the average mass per fragmented throat (a proxy for the temperature) is held fixed. The decoding protocol for the mirrored information does not require knowledge of the interior, and only limited information from the Hawking radiation, as can be argued to be necessitated by the complementarity principle. We discuss the scope of the model to illuminate various aspects of information processing in a black hole.

hep-th↗

arXiv2023

Fairguard: Harness Logic-based Fairness Rules in Smart Cities

Smart cities operate on computational predictive frameworks that collect, aggregate, and utilize data from large-scale sensor networks. However, these frameworks are prone to multiple sources of data and algorithmic bias, which often lead to unfair prediction results. In this work, we first demonstrate that bias persists at a micro-level both temporally and spatially by studying real city data from Chattanooga, TN. To alleviate the issue of such bias, we introduce Fairguard, a micro-level temporal logic-based approach for fair smart city policy adjustment and generation in complex temporal-spatial domains. The Fairguard framework consists of two phases: first, we develop a static generator that is able to reduce data bias based on temporal logic conditions by minimizing correlations between selected attributes. Then, to ensure fairness in predictive algorithms, we design a dynamic component to regulate prediction results and generate future fair predictions by harnessing logic rules. Evaluations show that logic-enabled static Fairguard can effectively reduce the biased correlations while dynamic Fairguard can guarantee fairness on protected groups at run-time with minimal impact on overall performance.

cs.AI↗

arXiv2023

Designing Equitable Transit Networks

Public transit is an essential infrastructure enabling access to employment, healthcare, education, and recreational facilities. While accessibility to transit is important in general, some sections of the population depend critically on transit. However, existing public transit is often not designed equitably, and often, equity is only considered as an additional objective post hoc, which hampers systemic changes. We present a formulation for transit network design that considers different notions of equity and welfare explicitly. We study the interaction between network design and various concepts of equity and present trade-offs and results based on real-world data from a large metropolitan area in the United States of America.

cs.CY↗

arXiv2023

Next-to-leading power corrections to the event shape variables

We investigate the origin of next-to-leading power corrections to the event shapes thrust and $c$-parameter, at next-to-leading order. For both event shapes we trace the origin of such terms in the exact calculation, and compare with a recent approach involving the eikonal approximation and momentum shifts that follow from the Low-Burnett-Kroll-Del Duca theorem. We assess the differences both analytically and numerically. For the $c$-parameter both exact and approximate results are expressed in terms of elliptic integrals, but near the elastic limit it exhibits patterns similar to the thrust results.

hep-ph↗

arXiv2023

Artificial Intelligence for Emergency Response

Emergency response management (ERM) is a challenge faced by communities across the globe. First responders must respond to various incidents, such as fires, traffic accidents, and medical emergencies. They must respond quickly to incidents to minimize the risk to human life. Consequently, considerable attention has been devoted to studying emergency incidents and response in the last several decades. In particular, data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch. We aim to present mathematical formulations for these problems and broad frameworks for each problem. We also share open-source (synthetic) data from a large metropolitan area in the USA for future work on data-driven emergency response.

cs.CY↗

arXiv2023

Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems

Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in achieving higher levels of autonomy. However, LEC's susceptibility to dynamic and uncertain operating conditions is a critical challenge for the safety of these systems. Redundant controller architectures have been widely adopted for safety assurance in such contexts. These architectures augment LEC "performant" controllers that are difficult to verify with "safety" controllers and the decision logic to switch between them. While these architectures ensure safety, we point out two limitations. First, they are trained offline to learn a conservative policy of always selecting a controller that maintains the system's safety, which limits the system's adaptability to dynamic and non-stationary environments. Second, they do not support reverse switching from the safety controller to the performant controller, even when the threat to safety is no longer present. To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching. We consider switching as a sequential decision-making problem and model it as a semi-Markov decision process. We leverage a combination of a myopic selector using surrogate models (for the forward switch) and a non-myopic planner (for the reverse switch) to balance safety and performance. We evaluate this approach using an autonomous vehicle case study in the CARLA simulator using different driving conditions, locations, and component failures. We show that the proposed approach results in fewer collisions and higher performance than state-of-the-art alternatives.

cs.RO↗

arXiv2022

On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data

The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utilized bus routes, improving resource utilization, and passengers will be able to adjust and plan their schedules to avoid overcrowded buses and maintain a certain level of comfort. However, accurately predicting occupancy is a non-trivial task. Various reasons such as heterogeneity, evolving ridership patterns, exogenous events like weather, and other stochastic variables, make the task much more challenging. With the progress of big data, transit authorities now have access to real-time passenger occupancy information for their vehicles. The amount of data generated is staggering. While there is no shortage in data, it must still be cleaned, processed, augmented, and merged before any useful information can be generated. In this paper, we propose the use and fusion of data from multiple sources, cleaned, processed, and merged together, for use in training machine learning models to predict transit ridership. We use data that spans a 2-year period (2020-2022) incorporating transit, weather, traffic, and calendar data. The resulting data, which equates to 17 million observations, is used to train separate models for the trip and stop level prediction. We evaluate our approach on real-world transit data provided by the public transit agency of Nashville, TN. We demonstrate that the trip level model based on Xgboost and the stop level model based on LSTM outperform the baseline statistical model across the entire transit service day.

cs.LG↗

arXiv2022

Editorial: New Frontiers in Holographic Duality -- From quantum complexity and black holes to hydrodynamics and neutron stars

Over the last twenty five years, holographic duality has revolutionised our understanding of gauge theories, quantum many-body systems and also quantum black holes. This topical issue is a collection of review articles on recent advances in fundamentals of holographic duality and its applications with special focus on a few areas where it is inter-disciplinary to a large measure. The aim is to provide a sufficient background on relevant phenomenology and other theoretical areas such as quantum information theory to researchers whose primary expertise is in quantum fields, strings and gravity, and also the necessary concepts and methods of holography to researchers in other fields, so that these recent developments could be grasped and hopefully further developed by a wider community. The topics relating to fundamental aspects include understanding of bulk spacetime reconstruction in holography in the framework of quantum error correction along with the spectacular advances in resolution of the information paradoxes of quantum black holes; quantum complexity and its fundamental role in connecting holography with quantum information theory; theoretical and experimental advances in quantum simulators for information mirroring and scrambling in quantum black holes, and teleportation via wormholes; and a pedagogical review on wormholes also. The topics related to applied holography include applications to hydrodynamic attractor and its phenomenological implications, modelling of equation of state of QCD matter in neutron stars, and finally estimating hadronic contribution to light-by-light scattering for theoretical computation of the muon's $g-2$.

hep-th↗

arXiv2022

ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. We collaborate with HelpMum, a large non-profit organization in Nigeria to design and optimize the allocation of heterogeneous health interventions under uncertainty to increase vaccination uptake, the first such collaboration in Nigeria. Our framework, ADVISER: AI-Driven Vaccination Intervention Optimiser, is based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination. Our optimization formulation is intractable in practice. We present a heuristic approach that enables us to solve the problem for real-world use-cases. We also present theoretical bounds for the heuristic method. Finally, we show that the proposed approach outperforms baseline methods in terms of vaccination uptake through experimental evaluation. HelpMum is currently planning a pilot program based on our approach to be deployed in the largest city of Nigeria, which would be the first deployment of an AI-driven vaccination uptake program in the country and hopefully, pave the way for other data-driven programs to improve health outcomes in Nigeria.

cs.AI↗

arXiv2022

Generative Anomaly Detection for Time Series Datasets

Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture the spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings.

cs.LG↗