Online Resource Inference in Network Utility Maximization Problems

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Recently, an open-data initiative was announced with one of the most complete datasets to study urban congestion. The pNEUMA dataset consists of more than half a million trajectories in heterogeneous traffic. In order to utilize the dataset for macroscopic ...

Can artificial agents benefit from human conventions? Human societies manage to successfully self-organize and resolve the tragedy of the commons in common-pool resources, in spite of the bleak prediction of non-cooperative game theory. On top of that, rea ...

In this paper we study the properties of the quenched pressure of a multi-layer spin-glass model (a deep Boltzmann Machine in artificial intelligence jargon) whose pairwise interactions are allowed between spins lying in adjacent layers and not inside the ...

Signalized intersections are a fundamental part of urban networks. Their understanding is crucial to identify congestion patterns, queues, delays, and safety issues in local and network level. In this work, we analyze multimodal vehicle trajectories and pr ...

Peer-to-peer (p2p) networks are not independent of their peers, and the network efficiency depends on peers contributing resources. Because shared resources are not free, this contribution must be rewarded. Peers across the network may share computation po ...

We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not ...

Objective To investigate the utility of an automatic deep learning (DL) method for segmentation of T2 maps in patients with idiopathic inflammatory myopathy (IIM) against healthy controls, and also the association of quantitative T2 values in patients with ...

Network travel time reliability can be represented by a relationship between network space-mean travel time and the standard deviation of network travel time. The primary objective of this paper is to improve estimation of the network travel time reliabili ...

Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and ...

Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data streams through on-device active learning allows us to directly sample from the ...

Neural networks have become indispensable for a wide range of applications, but they suffer from high computational- and memory-requirements, requiring optimizations from the algorithmic description of the network to the hardware implementation. Moreover, ...

Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...

Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface s ...

Supplementary datasets accompanying the manuscript "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds" published in the Metabolic Engineering Journal (https://doi.org/10.1016/j.ymben.2022.03.013). In line w ...

Online Resource Inference in Network Utility Maximization Problems

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Linking trajectory data collected with a swarm of drones to the underlying urban network for macroscopic traffic parameter estimation

Exploiting environmental signals to enable policy correlation in large-scale decentralized systems

Annealing and Replica-Symmetry in Deep Boltzmann Machines

An automatized signal timing detection at signalized intersections with detailed vehicle trajectory data

Fair Incentivization of Bandwidth Sharing in Decentralized Storage Networks

WatchNet plus plus : efficient and accurate depth-based network for detecting people attacks and intrusion

Assessment of idiopathic inflammatory myopathy using a deep learning method for muscle T2 mapping segmentation

Estimating network travel time reliability with network partitioning

Containergy-A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

Lupulus: A Flexible Hardware Accelerator For Neural Networks

Textual Explanations and Critiques in Recommendation Systems

Vision based pixel-level bridge structural damage detection using a link ASPP network

Supplementary datasets for "ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds"