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Found 25 papers, 4 papers with code
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.
Decision Making in Non-Stationary Environments with Policy-Augmented Search
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we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment.
Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
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.
Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence for Child Vaccination Uptake in Nigeria
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.
Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction
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.
Artificial Intelligence for Emergency Response
Emergency response management (ERM) is a challenge faced by communities across the globe.
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.
Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems
To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching.
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.
Generative Anomaly Detection for Time Series Datasets
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.
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.
Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit
Vehicle routing problems (VRPs) can be divided into two major categories: offline VRPs, which consider a given set of trip requests to be served, and online VRPs, which consider requests as they arrive in real-time.
An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services
Accounting for stochastic requests while optimizing a non-myopic utility function is computationally challenging; indeed, the action space for such a problem is intractably large in practice.
Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search
We find that PC-MCTS can achieve higher cumulative rewards than the policy in isolation under several environmental shifts while converging in significantly fewer iterations than pure MCTS.
Designing Decision Support Systems for Emergency Response: Challenges and Opportunities
Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities.
Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services
First, we show how crowdsourced reports, ground-truth historical data, and other relevant determinants such as traffic and weather can be used together in a Convolutional Neural Network (CNN) architecture for early detection of emergency incidents.
Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems
These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time.
Hydrodynamic attractor and novel fixed points in superfluid Bjorken flow
Extending the quantum effective approach of Son and Nicolis and incorporating dissipation, we develop a MIS formalism for describing a superfluid out of equilibrium by including the Goldstone boson and the condensate together with the hydrodynamic modes as the effective degrees of freedom.
High Energy Physics - Phenomenology Quantum Gases High Energy Physics - Theory
Linear-in-$T$ resistivity from semiholographic non-Fermi liquid models
We construct a semiholographic effective theory in which the electron of a two-dimensional band hybridizes with a fermionic operator of a critical holographic sector, while also interacting with other bands that preserve quasiparticle characteristics.
High Energy Physics - Theory Strongly Correlated Electrons
Hierarchical Planning for Resource Allocation in Emergency Response Systems
We use the emergency response as a case study and show how a large resource allocation problem can be split into smaller problems.
Designing Emergency Response Pipelines : Lessons and Challenges
Emergency response to incidents such as accidents, crimes, and fires is a major problem faced by communities.
Uncertainty Aware Wildfire Management
A major problem in using data-driven models to combat wildfires is the lack of comprehensive data sources that relate fires with relevant covariates.
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management
In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems.
On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities
This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem.
An Online Decision-Theoretic Pipeline for Responder Dispatch
We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch.
