arXiv research

State-augmented algorithm optimizes wireless network resource management.

Deep learning enhances water resources management through data analysis.

Study optimizes natural resource harvesting under model uncertainty using risk measures.

This paper optimizes revenue and resource balance in network revenue management.

Optimizes wireless network resource management with state-augmented policies.

HL algorithms improve resource allocation in cloud environments.

Mathematical approach assesses human resource competences accurately.

As most natural resources, fisheries are affected by random disturbances. The evolution of such resources may be modelled by a succession of deterministic process and random perturbations on biomass and/or growth rate at random times. We analyze the impact of the characteristics of the perturbations on the management o…

Paper tackles pandemic resource allocation challenges.

Graph neural networks optimize radio resource management policies for wireless networks.

Enhances multi-project scheduling with multiple priority rules.

In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resourc…

Research develops a water quality prediction model using LSTM.

Meta-DRL improves resource allocation in O-RAN networks.

Paper assesses financial potential for enterprise development.

This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributio…

EERO optimizes resource usage for efficient classification.

ATA optimizes task allocation in distributed machine learning.

Floating Content (FC) is a communication paradigm for the local dissemination of contextualized information through D2D connectivity, in a way which minimizes the use of resources while achieving some specified performance target. Existing approaches to FC dimensioning are based on unrealistic system assumptions that m…

A deep RL framework optimizes resource allocation in wireless networks.

Optimizes data power control in cell-free networks for better spectral efficiency.

Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resources in order to maximize either the average or the worst-case performance. However, joint consideration of both metrics is often neglected as …

Solves challenges of drone communication in cellular networks.

Cost forecasting helps crowdsourcers manage growing task sets efficiently.

As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers. These platforms are often resource-constrained, especially in energy storage and power, but state-of-the-art NN architectures are de…

Simple policy outperforms complex ones in cloud auto-scaling.

Langevin algorithms enhance training of deep neural networks for stochastic control problems.

CPAS uses machine learning to plan hospital resources for COVID-19.

A DRL approach optimizes resource allocation in BFL to reduce latency and energy consumption.

Sequence-to-sequence models predict resource usage for co-scheduled jobs in data centers.

Opportunistic communications are expected to playa crucial role in enabling context-aware vehicular services. A widely investigated opportunistic communication paradigm for storing a piece of content probabilistically in a geographica larea is Floating Content (FC). A key issue in the practical deployment of FC is how …

In urban environments, supply resources have to be constantly matched to the "right" locations (where customer demand is present) so as to improve quality of life. For instance, ambulances have to be matched to base stations regularly so as to reduce response time for emergency incidents in EMS (Emergency Management Sy…

These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the countrys environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and det…

HydroNets use river structure to improve hydrologic predictions.

Paper introduces RiskEmbed, a finetuned model for financial risk management.

Optimizes query routing to LLMs under cost and resource constraints.

New framework handles online decisions with replenishable resources, improving both adversarial and stochastic performance.

Deep RL tackles fleet management and dispatching for ride-sharing platforms.

RL enhances cloud autoscaling by learning optimal resource allocation.

POCAII optimizes hyperparameters with a new approach, showing superior performance.

This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.

Improved algorithm reduces regret in NRM with unknown demand.

Graph database outperforms in filtering ESG stocks efficiently.

Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep $\mathcal{Q}$-learning, DQL) is assumed to be an appropriate algorithm to solve the demand-aware inter-slice resource management issue in network slicing by regarding the …

Model predicts COVID-19 cases with high accuracy using mobility data.

LightGBM outperforms other models in predicting pH values in Georgia, USA.

Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly controlling the agents to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agent…

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have a…