A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
SUPAID automates vehicle rollout decisions for fleet managers.
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace. Hand-crafting heuristic solutions that account for the dynamics in these resource allocation problems is difficult, and may be…
Bayesian model transfers knowledge across different engineering fleets.
Truckload brokerages, a $100 billion/year industry in the U.S., plays the critical role of matching shippers with carriers, often to move loads several days into the future. Brokerages not only have to find companies that will agree to move a load, the brokerage often has to find a price that both the shipper and carri…
New tool detects 'fleeting modes' causing excess risk in financial markets.
Proposes a graph neural network for efficient multi-agent routing.
Framework detects anomalies in fleet-based machine monitoring.
In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet of Things allow control devices and/or machines to connect through cloud-based architectures in order to share information about their statu…
Predicting ambulance demand accurately at a fine resolution in time and space (e.g., every hour and 1 km) is critical for staff / fleet management and dynamic deployment. There are several challenges: though the dataset is typically large-scale, demand per time period and locality is almost always zero. The demand …
Optimizes electric aircraft deployment for Canadian aviation to reduce emissions.
DeepScalper uses RL to capture intraday trading opportunities, balancing risk and profit.
FLeet improves online FL for mobile apps with better performance and privacy.
Autonomous robots often encounter challenging situations where their control policies fail and an expert human operator must briefly intervene, e.g., through teleoperation. In settings where multiple robots act in separate environments, a single human operator can manage a fleet of robots by identifying and teleoperati…
The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.
Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it is unclear how to best do this: optimal dispatching requires optimizing over several sources of unc…
Deep learning solves EV routing with time windows for EV fleets.
Training data-driven approaches for complex industrial system health monitoring is challenging. When data on faulty conditions are rare or not available, the training has to be performed in a unsupervised manner. In addition, when the observation period, used for training, is kept short, to be able to monitor the syste…
Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new ma…
In this paper, we consider same-day delivery with vehicles and drones. Customers make delivery requests over the course of the day, and the dispatcher dynamically dispatches vehicles and drones to deliver the goods to customers before their delivery deadline. Vehicles can deliver multiple packages in one route but trav…
Study improves dynamic PT fleet optimization under noisy demand predictions.
In the first section, this report analyses Nuclear Power Plants (NPPs) in the context of megaprojects, explaining why they are often delivered over budget and late. In the second section, the report discusses how Small Modular Reactors (SMRs) might address these issues. Megaprojects are extremely risky and often implem…
A new framework uses multi-agent reinforcement learning for evaluating policies in two-sided markets.
Novel framework improves wind power forecasts by bundling assets and using machine learning.
We analyse all Mini Flash Crashes (or Flash Equity Failures) in the US equity markets in the four most volatile months during 2006-2011. In contrast to previous studies, we find that Mini Flash Crashes are the result of regulation framework and market fragmentation, in particular due to the aggressive use of Intermarke…
This paper explores how RL enhances HFT strategies in volatile markets.
We propose a new structural model that can compute the electricity spot and forward prices in two coupled markets with limited interconnection and multiple fuels. We choose a structural approach in order to represent some key characteristics of electricity spot prices such as their link to fuel prices, consumption leve…
The failure of a complex and safety critical industrial asset can have extremely high consequences. Close monitoring for early detection of abnormal system conditions is therefore required. Data-driven solutions to this problem have been limited for two reasons: First, safety critical assets are designed and maintained…
This paper proposes a novel model of financial prices where: (i) prices are discrete; (ii) prices change in continuous time; (iii) a high proportion of price changes are reversed in a fraction of a second. Our model is analytically tractable and directly formulated in terms of the calendar time and price impact curve. …
Tuna-AI uses ML to predict tuna biomass from oceanography and echo-sounder data.
This paper explores portfolio management strategies to maximize alpha and minimize beta.
In this paper, the problem of road friction prediction from a fleet of connected vehicles is investigated. A framework is proposed to predict the road friction level using both historical friction data from the connected cars and data from weather stations, and comparative results from different methods are presented. …
Paper introduces a framework for managing cyber risk with insurance and cybersecurity models.
The paper aims to define a benchmark for deep learning recommendation models.
The basic financial purpose of a firm is to maximize its value. An inventory management system should also contribute to realization of this basic aim. Many current asset management models currently found in financial management literature were constructed with the assumption of book profit maximization as basic aim. H…
Study finds Indian mutual funds adjust cash holdings based on inflows, impacting stock purchases.
Framework for managing cyber risks in networks.
Research identifies risks in selecting project managers for civil engineering projects.
Deep learning improves portfolio management by optimizing asset weights.
Proposes a model for predicting events from event streams.
This research develops a dynamic risk management system for industrial companies.
This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-bas…
Study finds managers' tenure and education influence their choice between in-court and out-of-court restructuring.
The paper fits cash management models to data using stochastic and linear programming.
Decision tool helps manage biofouling risks for ships in the Baltic Sea.
Model cash management under ambiguity using maxmin preferences and diffusion.
This review classifies electricity price models for risk management.
Study improves machine learning for long-term financial portfolio management.
The paper analyzes portfolio management in the Heston model, proposing new strategies.