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Ayan Mukhopadhyay

Publications and source records attributed to Ayan Mukhopadhyay.

At least 19 recordsLinked to original sources

Entanglement inequalities, black holes and the architecture of typical states

Using the holographic realization of the Araki--Lieb (AL) inequality, we argue that the leading entanglement plateau constrains the organization of typical pure states in a fixed-$(E,J)$ sector of a large-$c$ two-dimensional holographic CFT. On the field-theory side, its equality structure motivates an effective factorization of the regulated micro-canonical code subspace into ultraviolet (UV), intermediate and infrared factors, with part of the intermediate sector providing a purifier of the ultraviolet one. In the bulk, entanglement-wedge reconstruction realizes the ultraviolet sector as an asymptotic corona, universal to simple probes with length scale smaller than $L_{\rm UV}$. Crucially, $L_{\rm UV}$ is determined only by the conserved charges. At finite $c$, universal perturbative dressing correlates these leading factors and lifts the AL plateau at order $c^0$. We formulate a generalized ETH ansatz under which $O(e^{-S_<})$ fluctuations, with $S<=\min(S_L,S_R)$, account for the $O(c^0)$ deficit of the AL plateau, while the state dependence of matrix elements of individual simple operators smeared over lengths shorter than $L_{\rm UV}$ remains exponentially small. This provides a holographic realization and rotating generalization of the Eigenstate Thermalization Hypothesis: for zero rotation it reduces to the standard $O(e^{-S/2})$. Independently, the separation between $L_{\rm UV}$ and the scale of quantum correlations is of order $e^{-6S_</c}$ at large $S_<$.

hep-th↗

Decoding multiway gravitational junctions in AdS in terms of holographic quantum maps

It has been shown that multiway junctions gluing $n$ copies of locally AdS$_3$ spacetimes ($n\geq 2$) can be described by $n-1$ strings obeying non-linear Nambu-Goto equations coupled by Monge-Ampère like terms. Here we study how such junctions along with their stringy degrees of freedom can be interpreted in terms of an interface between $n$ identical holographic conformal theories each defined on a semi-infinite line (wire). We study the gravitational scattering problem at the multiway junction, and show that at the linearized order the dual interfaces correspond to quantum maps which factorize into a product of a scattering matrix determined only by the tension of the dual junction and relative automorphisms of the Virasoro algebra governed by the $n-1$ stringy modes. Both of these are universal in the sense that they are independent of linear modifications of the background state. These generalize earlier results for the 2-way junctions implying that the dual interface is a tunable energy transmitter. We comment on understanding the quantum map corresponding to the full non-linear gravitational problem, and study Ward identities and unitarity bounds.

hep-th↗

Junctions, strings, clocks and gravitational memory in three dimensional dS space

We show that non-trivial stringy excitations in Lorentzian three dimensional de Sitter spacetime can be created self-consistently from gravitational memory in the infinite past. In addition to demonstrating that the Nambu-Goto equations for the string emerge from the two-way gravitational junction conditions, we establish the existence of well-behaved solutions corresponding to transient fluctuations of a closed string about the equator which are both borne out of and dissolve to distinct gravitational memory in the infinite past and future, respectively. The memory at infinite past, which uniquely characterizes such a solution, is a single function giving the relative angular shift at the junction gluing two two-dimensional hemispheres. This reveals that a clock dynamically emerges in the presence of a gravitational junction without the need of any external observer. We also show that our results generalize to the $n$-way gravitational junctions with $n\geq 3$, which are captured by Nambu-Goto-Monge-Ampère equations for coupled $n-1$ strings -- these degrees of freedom exist even in the tensionless limit. Furthermore, for $n\geq 3$, $n-1$ correlated clocks dynamically emerge without the need of external observers in the tensionless limit, revealing a novel feature of pure three-dimensional gravity.

hep-th↗

On decoding the string from interfaces in 2d conformal field theories

General solutions of a gravitational junction between two copies of a three-dimensional Einstein manifold $\mathcal{M}$ correspond to the solutions of the non-linear Nambu-Goto equation for a string in $\mathcal{M}$. We show that, for the junctions in three-dimensional anti-de Sitter spacetimes constituted by tensile strings, which are dual to interfaces between thermal states in conformal field theories, the solutions of the Nambu-Goto equation describing the junction correspond to wave-packets, which are perfectly reflected at the interface to future null infinity \textit{without shape distortion} when incident from past null infinity. These wavepackets are realized by half-sided conformal transformations and affect the expectation value of the displacement operator. We further show that the entanglement entropy of an interval straddling the interface deciphers the stringy modes of the dual junction even in the tensionless limit. We also demonstrate that the strong sub-additivity of entanglement entropy is satisfied and is saturated for symmetric intervals generally.

hep-th↗

Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework

The rapid growth of electric vehicles is shifting the main constraint on transport electrification from vehicle adoption to the deployment and operation of charging infrastructure. Charging-network design requires decisions across three interdependent layers: Planning, which determines where and how much infrastructure to build; Scheduling, which governs charging dispatch, pricing, and grid interaction; and Behavior, which captures how users choose stations, charging times, and charging durations. Existing studies have advanced each layer substantially, but the literature remains fragmented, and cross-layer interactions are often treated through simplifying assumptions. This survey develops a three-layer Planning-Scheduling-Behavior (PSB) framework to organize EV charging research according to decision horizon, actor objective, and coupling structure. We further identify a fidelity-tractability tradeoff, termed the PSB trilemma: each layer is computationally difficult in isolation, and realistic integration across layers generally requires reducing the fidelity of at least one layer. Reviewing the three pairwise-coupling literatures - Planning-Scheduling, Scheduling-Behavior, and Planning-Behavior - we show that the omitted third layer is typically fixed exogenously or represented by a static aggregate surrogate. These simplifications enable tractability but impose distinct costs: they can obscure long-term investment feedback, temporal grid and emissions dynamics, or heterogeneous user response and equity outcomes. Building on this diagnosis, we identify open challenges in emerging charging technologies, behavioral incentives, equity metrics, and city-scale learning-based methods that balance fidelity, interpretability, and policy relevance.

cs.MA↗

Toward Template-Free Explainability for Monte Carlo Tree Search

Probabilistic search algorithms, such as Monte Carlo Tree Search (MCTS), have proven very effective in solving sequential decision-making tasks under uncertainty. However, interpreting asymmetric search trees that incorporate bandit-based tree traversal and simulation-based value estimation is difficult for end users based solely on raw tree statistics. While prior work requires hand-crafted formal logic constraints that must be updated when the problem changes, we present a framework that enables large language models (LLMs) to generate evidence-grounded explanations of MCTS decisions from recorded search traces in an end-to-end manner. Our framework maps natural-language questions to a structured set of intent categories, determines whether the existing tree contains sufficient evidence, triggers targeted expansion when needed, and generates explanations using tree statistics such as visit counts, value estimates, and risk information. Experimental results provide the first evidence that LLMs can serve as end-to-end explainers for probabilistic search, without requiring intermediate formal representations.

cs.HC↗

On prethermal time crystals from semi-holography

We demonstrate the existence of a pair of almost dissipationless oscillating modes at low temperatures in both the shear and sound channels of a hybrid quantum system, comprised of a weakly self-interacting perturbative sector coupled to strongly self-interacting holographic degrees of freedom described by a black hole geometry. We argue that these modes realize prethermal time-crystal behavior in semi-holographic systems without fine-tuning and can be observed by measuring operators that probe either the hard (perturbative) or the soft (holographic) sector. We also find novel {short wavelength} instabilities that lead to the formation of inhomogeneities even at higher temperatures. These results provide evidence that black holes with planar horizons and dynamical boundary conditions can develop both inhomogeneous and metastable time-crystal phases over a wide range of temperatures set by an intermediate scale given by the intersector coupling. Furthermore, they suggest that such phases can be realized without external driving in non-Abelian plasmas of asymptotically free gauge theories in the large-$N$ limit.

hep-th↗

Decoding the string in terms of holographic quantum maps

It has recently been shown that the Nambu-Goto equation for a string emerges from the junction conditions in three-dimensional gravity. Holographically, gravitational junctions are dual to interfaces in conformal field theory. We demonstrate at the level of linearized gravitational perturbations that each stringy mode of the junction corresponds to a $\mathcal{H}_{in}\rightarrow \mathcal{H}_{out}$ quantum map which can be factorized into a scattering matrix involving reflection/transmission and a relative automorphism of the Virasoro algebra, and also a $\mathcal{H}_{L}\rightarrow \mathcal{H}_{R}$ map of similar nature. These maps preserve the conformal boundary condition, are independent of the background conformal frame, as in the case of conformal interfaces studied in the literature, and realize a tunable energy transmitter.

hep-th↗

The degrees of freedom of multiway junctions in three dimensional gravity

We demonstrate that $n$-way junctions in three dimensional gravity correspond to coupled $n-1$ strings each satisfying the Nambu-Goto equation in the smoothened background, and with sources consisting of Monge-Ampère like terms which couple the strings. For $n\geq 3$, these $n-1$ degrees of freedom survive the tensionless limit implying that matter-like behavior can arise out of \textit{pure} gravity. We interpret these stringy degrees of freedom of gravitational junctions holographically in terms of wavepackets which collectively undergo perfect reflection at the multi-interface in the dual conformal field theory.

hep-th↗

MoveOD: Synthesizing Origin-Destination Commute Distribution from U.S. Census Data

High-resolution origin-destination (OD) tables are essential for a wide spectrum of transportation applications, from modeling traffic and signal timing optimization to congestion pricing and vehicle routing. However, outside a handful of data rich cities, such data is rarely available. We introduce MOVEOD, an open-source pipeline that synthesizes public data into commuter OD flows with fine-grained spatial and temporal departure times for any county in the United States. MOVEOD combines five open data sources: American Community Survey (ACS) departure time and travel time distributions, Longitudinal Employer-Household Dynamics (LODES) residence-to-workplace flows, county geometries, road network information from OpenStreetMap (OSM), and building footprints from OSM and Microsoft, into a single OD dataset. We use a constrained sampling and integer-programming method to reconcile the OD dataset with data from ACS and LODES. Our approach involves: (1) matching commuter totals per origin zone, (2) aligning workplace destinations with employment distributions, and (3) calibrating travel durations to ACS-reported commute times. This ensures the OD data accurately reflects commuting patterns. We demonstrate the framework on Hamilton County, Tennessee, where we generate roughly 150,000 synthetic trips in minutes, which we feed into a benchmark suite of classical and learning-based vehicle-routing algorithms. The MOVEOD pipeline is an end-to-end automated system, enabling users to easily apply it across the United States by giving only a county and a year; and it can be adapted to other countries with comparable census datasets. The source code and a lightweight browser interface are publicly available.

cs.CY↗

Digital Guardians: The Past and The Future of Cyber-Physical Resilience

Resilience in cyber-physical systems (CPS) is the fundamental ability to maintain safety and critical functionality despite adverse "perturbations," which includes security attacks, environmental disruptions, and hardware or software failures. This survey provides a comprehensive review of CPS resilience, framing the field through five interconnected themes that are required in an integrated whole to achieve real-world resilience. The article first posits that resilience is a system-wide property emerging from interactions between hardware, software, and human users. Second, it addresses the challenges of learning-enabled CPS, which often operate in data-scarce environments characterized by imbalanced or noisy data, requiring innovative solutions like synthetic data generation and foundation model adaptation. Third, the survey examines proactive measures for resilience, which include distinctive aspects of verification, testing, and redundancy. Fourth, it explores recovery mechanisms, moving beyond traditional fault models to design "just good enough" recovery strategies that prioritize safety-critical functions during perturbations. Finally, it highlights the central role of the human, focusing on the different levels of human intervention, the necessity of trust calibration, and the requirement for explainable AI to support human-CPS teaming. These themes are illustrated through representative application domains, primarily Connected and Autonomous Transportation Systems (CATS) and Medical CPS (MCPS). By integrating the five interconnected themes, this survey provides a systematic roadmap for achieving the resilient CPS in increasingly complex and adversarial environments.

cs.CR↗

Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requests

Transit agencies that operate on-demand transportation services have to respond to trip requests from passengers in real time, which involves solving dynamic vehicle routing problems with pick-up and drop-off constraints. Based on discussions with public transit agencies, we observe a real-world problem that is not addressed by prior work: when trips are booked in advance (e.g., trip requests arrive a few hours in advance of their requested pick-up times), the agency needs to promptly confirm whether a request can be accepted or not, and ensure that accepted requests are served as promised. State-of-the-art computational approaches either provide prompt confirmation but lack the ability to continually optimize and improve routes for accepted requests, or they provide continual optimization but cannot guarantee serving all accepted requests. To address this gap, we introduce a novel problem formulation of dynamic vehicle routing with prompt confirmation and continual optimization. We propose a novel computational approach for this vehicle routing problem, which integrates a quick insertion search for prompt confirmation with an anytime algorithm for continual optimization. To maximize the number requests served, we train a non-myopic objective function using reinforcement learning, which guides both the insertion and the anytime algorithms towards optimal, non-myopic solutions. We evaluate our computational approach on a real-world microtransit dataset from a public transit agency in the U.S., demonstrating that our proposed approach provides prompt confirmation while significantly increasing the number of requests served compared to existing approaches.

cs.AI↗

In-Context Planning with Latent Temporal Abstractions

Planning-based reinforcement learning for continuous control is bottlenecked by two practical issues: planning at primitive time scales leads to prohibitive branching and long horizons, while real environments are frequently partially observable and exhibit regime shifts that invalidate stationary, fully observed dynamics assumptions. We introduce I-TAP (In-Context Latent Temporal-Abstraction Planner), an offline RL framework that unifies in-context adaptation with online planning in a learned discrete temporal-abstraction space. From offline trajectories, I-TAP learns an observation-conditioned residual-quantization VAE that compresses each observation-macro-action segment into a coarse-to-fine stack of discrete residual tokens, and a temporal Transformer that autoregressively predicts these token stacks from a short recent history. The resulting sequence model acts simultaneously as a context-conditioned prior over abstract actions and a latent dynamics model. At test time, I-TAP performs Monte Carlo Tree Search directly in token space, using short histories for implicit adaptation without gradient update, and decodes selected token stacks into executable actions. Across deterministic MuJoCo, stochastic MuJoCo with per-episode latent dynamics regimes, and high-dimensional Adroit manipulation, including partially observable variants, I-TAP consistently matches or outperforms strong model-free and model-based offline baselines, demonstrating efficient and robust in-context planning under stochastic dynamics and partial observability.

cs.LG↗

Ensemble Prediction of Task Affinity for Efficient Multi-Task Learning

A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks is prohibitively expensive for large task sets, a crucial component of efficient and effective task grouping is predicting whether a group of tasks would benefit from learning together, measured as per-task performance gain over single-task learning. In this paper, we propose ETAP (Ensemble Task Affinity Predictor), a scalable framework that integrates principled and data-driven estimators to predict MTL performance gains. First, we consider the gradient-based updates of shared parameters in an MTL model to measure the affinity between a pair of tasks as the similarity between the parameter updates based on these tasks. This linear estimator, which we call affinity score, naturally extends to estimating affinity within a group of tasks. Second, to refine these estimates, we train predictors that apply non-linear transformations and correct residual errors, capturing complex and non-linear task relationships. We train these predictors on a limited number of task groups for which we obtain ground-truth gain values via multi-task learning for each group. We demonstrate on benchmark datasets that ETAP improves MTL gain prediction and enables more effective task grouping, outperforming state-of-the-art baselines across diverse application domains.

cs.LG↗

CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty

The growth of Electric Vehicles (EVs) creates a conflict in vehicle-to-building (V2B) settings between building operators, who face high energy costs from uncoordinated charging, and drivers, who prioritize convenience and a full charge. To resolve this, we propose a negotiation-based framework that, by design, guarantees voluntary participation, strategy-proofness, and budget feasibility. It transforms EV charging into a strategic resource by offering drivers a range of incentive-backed options for modest flexibility in their departure time or requested state of charge (SoC). Our framework is calibrated with user survey data and validated using real operational data from a commercial building and an EV manufacturer. Simulations show that our negotiation protocol creates a mutually beneficial outcome: lowering the building operator's costs by over 3.5\% compared to an optimized, non-negotiating smart charging policy, while simultaneously reducing user charging expenses by 22\% below the utility's retail energy rate. By aligning operator and EV user objectives, our framework provides a strategic bridge between energy and mobility systems, transforming EV charging from a source of operational friction into a platform for collaboration and shared savings.

cs.MA↗

Hybrid thermalization in the large $N$ limit

Semi-holography provides a formulation of dynamics in gauge theories involving both weakly self-interacting (perturbative) and strongly self-interacting (non-perturbative) degrees of freedom. These two subsectors interact via their effective metrics and sources, while the full local energy-momentum tensor is conserved in the physical background metric. In the large $N$ limit, the subsectors have their individual entropy currents, and so the full system can reach a pseudo-equilibrium state in which each subsector has a different physical temperature. We first complete the proof that the global thermal equilibrium state, where both subsectors have the \textit{same} physical temperature, can be defined in consistency with the principles of thermodynamics and statistical mechanics. Particularly, we show that the global equilibrium state is the unique state with maximum entropy in the microcanonical ensemble. Furthermore, we show that in the large $N$ limit, a \textit{typical} non-equilibrium state of the full isolated system relaxes to the global equilibrium state when the average energy density is large compared to the scale set by the inter-system coupling. We discuss quantum statistical perspectives.

hep-th↗

Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit

The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational challenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real-world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.

math.OC↗

Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems

Vehicle-to-building (V2B) systems integrate physical infrastructures, such as smart buildings and electric vehicles (EVs) connected to chargers at the building, with digital control mechanisms to manage energy use. By utilizing EVs as flexible energy reservoirs, buildings can dynamically charge and discharge them to optimize energy use and cut costs under time-variable pricing and demand charge policies. This setup leads to the V2B optimization problem, where buildings coordinate EV charging and discharging to minimize total electricity costs while meeting users' charging requirements. However, the V2B optimization problem is challenging because of: (1) fluctuating electricity pricing, which includes both energy charges ($/kWh) and demand charges ($/kW); (2) long planning horizons (typically over 30 days); (3) heterogeneous chargers with varying charging rates, controllability, and directionality (i.e., unidirectional or bidirectional); and (4) user-specific battery levels at departure to ensure user requirements are met. In contrast to existing approaches that often model this setting as a single-shot combinatorial optimization problem, we highlight critical limitations in prior work and instead model the V2B optimization problem as a Markov decision process (MDP), i.e., a stochastic control process. Solving the resulting MDP is challenging due to the large state and action spaces. To address the challenges of the large state space, we leverage online search, and we counter the action space by using domain-specific heuristics to prune unpromising actions. We validate our approach in collaboration with Nissan Advanced Technology Center - Silicon Valley. Using data from their EV testbed, we show that the proposed framework significantly outperforms state-of-the-art methods.

eess.SY↗