Polytechnic University of Catalonia

About: Polytechnic University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Finite element method & Population. The organization has 16006 authors who have published 45325 publications receiving 949306 citations. The organization is also known as: UPC - BarcelonaTECH & Technical University of Catalonia.

RouteNet: leveraging graph neural networks for network modeling and optimization in SDN

Abstract: Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE = 15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.

Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques

Abstract: An adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). The LMS algorithm is used to adjust the weights in the adaptive process. It is shown that the AICF is equivalent to exponentially weighted averaging (FWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials. >

Double degenerate mergers as progenitors of high-field magnetic white dwarfs

Abstract: High-field magnetic white dwarfs have been long suspected to be the result of stellar mergers. However, the nature of the coalescing stars and the precise mechanism that produces the magnetic field are still unknown. Here, we show that the hot, convective, differentially rotating corona present in the outer layers of the remnant of the merger of two degenerate cores can produce magnetic fields of the required strength that do not decay for long timescales. Using a state-of-the-art Monte Carlo simulator, we also show that the expected number of high-field magnetic white dwarfs produced in this way is consistent with that found in the solar neighborhood.

Short attribute-based signatures for threshold predicates

Abstract: Attribute-based cryptography is a natural solution for fine-grained access control with respect to security policies. In the case of attribute-based signatures (ABS), users obtain from an authority their secret keys as a function of the attributes they hold, with which they can later sign messages for any predicate satisfied by their attributes. A verifier will be convinced of the fact that the signer's attributes satisfy the signing predicate while remaining completely ignorant of the identity of the signer. In many scenarios where authentication and anonymity are required, like distributed access control mechanisms in ad hoc networks, the bandwidth is a crucial and sensitive concern. The signatures' size of all previous ABS schemes grows linearly in the number of attributes involved in the signing predicate. We propose the first two attribute-based signature schemes with constant size signatures. Their security is proven in the selective-predicate and adaptive-message setting, in the standard model, under chosen message attacks, with respect to some algorithmic assumptions related to bilinear groups. The described schemes are for the case of threshold predicates, but they can be extended to admit some other (more expressive) kinds of monotone predicates.