arXiv research

Graph neural networks improve topology control of power grids.

Survey of deep learning applications in traffic congestion detection, prediction, and alleviation.

Vehicular Ad-hoc NETworks (VANET) can efficiently detect traffic congestion, but detection is not enough because congestion can be further classified as recurrent and non-recurrent congestion (NRC). In particular, NRC in an urban network is mainly caused by incidents, workzones, special events and adverse weather. We p…

As more end devices are getting connected, the Internet will become more congested. Various congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims to mitigate the congestion on the network layer through active buffer control to avoi…

Model traffic congestion events using multi-modal data and attention-based neural networks.

The paper tackles Nash-regret minimization in congestion games with bandit feedback.

Study of congestion in negative curvature manifolds using fair-division algorithms.

New method estimates traffic congestion delays using statistical causality.

Wasserstein GANs with Gradient Penalty compute a different optimal transport problem called congested transport.

The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure. Aligned to this end, modern statistical learning tools are leveraged here for electricity market inference. Day-ahead price forecasting is cast as a…

High-level synthesis (HLS) shortens the development time of hardware designs and enables faster design space exploration at a higher abstraction level. Optimization of complex applications in HLS is challenging due to the effects of implementation issues such as routing congestion. Routing congestion estimation is abse…

Non-availability of reliable and sustainable electric power is a major problem in the developing world. Renewable energy sources like solar are not very lucrative in the current stage due to various uncertainties like weather, storage, land use among others. There also exists various other issues like mis-commitment of…

NAC-FL optimizes model updates in FL systems by adapting compression to network congestion.

New algorithm for traffic routing in congested conditions.

Study predicts traffic congestion based on population mobility data.

Paper uses DeePC to improve urban traffic lights, reducing congestion and emissions.

Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) has emerged as an alternative to automatically optimize such control str…

New framework analyzes LLM personalization trade-offs under congestion.

Predicts morning traffic congestion using social media data from the previous evening.

Decentralized algorithm reduces regret and converges to Nash equilibrium in online congestion games.

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The …

Dynamic linear models improve travel time prediction for congested freeways.

Online learning improves traffic congestion prediction over time.

This paper connects deep neural networks to game theory, revealing their congestion game properties.

Overprocuring reserves can improve network efficiency by using excess reserves for congestion management.

This paper proposes MM-DAGs for analyzing traffic congestion, learning multiple DAGs jointly.

A new framework reduces traffic congestion by 36%.

Service-induced congestion in memory-constrained LLM serving

Study on-chain peak shaving to reduce Ethereum transaction costs.

Proposes three decentralized multi-agent reinforcement learning algorithms to reduce network congestion.

Backdoor attacks on DRL-based traffic controllers cause stop-and-go waves or crashes.

This paper gives a critical account of the minority game literature. The minority game is a simple congestion game: players need to choose between two options, and those who have selected the option chosen by the minority win. The learning model proposed in this literature seems to differ markedly from the learning mod…

We present a grid diagram analogue of Carter, Rieger and Saito's smooth movie theorem. Specifically, we give definitions for grid movies, grid movie isotopies and present a definition of grid planar isotopy as a particular subset of the grid diagram moves: stabilization, destabilization and commutation. We show that gr…

In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL …

In this paper we present a Recurrent neural networks (RNN) based architecture that achieves an AUCROC of 0.9147 for predicting the onset of Congestive Heart Failure (CHF) 15 months in advance using a 12-month observation window on a large cohort of 216,394 patients. We believe this to be the largest study in CHF onset …

We propose a probabilistic graphical model realizing a minimal encoding of real variables dependencies based on possibly incomplete observation and an empirical cumulative distribution function per variable. The target application is a large scale partially observed system, like e.g. a traffic network, where a small pr…

The paper introduces triple grid diagrams to construct Lagrangian surfaces in complex projective space.

Agent-based simulation assesses tradable credit schemes for congestion reduction.

Half grid diagrams prove every link can be represented by a special type of grid diagram.

Grid homology confirms the Upsilon invariant in knot theory.

GridPyM handles grid diagrams for knot theory.

Grid homology theory for spatial graphs extends skein sequence.

Extends knot invariant to filtered grid complexes.

New method finds grid diagrams for many fibered knots.

Grid homology properties for MOY graphs studied.

Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular, has the potential to learn optimal network policies. However, RL suffers from instability and over-fitting, deficiencies which so far rend…

Grid homology invariant proved for lens space links.

New trading strategy beats traditional grid in crypto markets.