arXiv subjects

Francisco Barranco

Publications and source records attributed to Francisco Barranco.

14 recordsLinked to original sources

arXiv2026

A Dynamical Model of Light Halo Nuclei

We present a review of theoretical studies of the structure and reactions of N=7 and N=8 nuclei in the vicinity of 11Li, carried out within a framework based on Nuclear Field Theory. The coupling of valence nucleons to low-lying surface vibrations of the spherical core plays a central role, giving rise to self-energy processes that renormalize single-particle states and transfer form factors, as well as to an induced pairing interaction arising from the exchange of collective vibrations, which renormalizes the bare pairing force. Excitation spectra and cross sections for one- and two-nucleon transfer reactions populating states in the quasi-continuum are calculated and compared with available experimental data. Collective excitations in the particle-particle channel are investigated, with particular emphasis on Giant Pairing Vibrations and on their damping mechanisms arising from coupling to more complex configurations and continuum states. Comparisons with other theoretical schemes are also presented. We conclude that a coherent understanding of experimental data requires the detailed consideration of particle-vibration coupling effects.

nucl-th↗

arXiv2024

Fragmentation of the Giant Pairing Vibration in 14C induced by many-body processes

We present a theoretical framework for treating the full excitation spectrum of Jπ = 0+ pair addition modes, including the well-known low-lying and bound Pairing Vibration on par with the predicted Giant Pairing Vibration lying in the continuum. Our formalism includes the coupling to low-energy collective quadrupole modes of the core, in such a way that both single-particle self-energy effects and the pairing interaction induced by phonon exchange are accounted for. The theory is applied to the case of the excitation spectrum of 14C, recently populated by two-neutron transfer reactions.

nucl-th↗

arXiv2023

Event-based Vision for Early Prediction of Manipulation Actions

Neuromorphic visual sensors are artificial retinas that output sequences of asynchronous events when brightness changes occur in the scene. These sensors offer many advantages including very high temporal resolution, no motion blur and smart data compression ideal for real-time processing. In this study, we introduce an event-based dataset on fine-grained manipulation actions and perform an experimental study on the use of transformers for action prediction with events. There is enormous interest in the fields of cognitive robotics and human-robot interaction on understanding and predicting human actions as early as possible. Early prediction allows anticipating complex stages for planning, enabling effective and real-time interaction. Our Transformer network uses events to predict manipulation actions as they occur, using online inference. The model succeeds at predicting actions early on, building up confidence over time and achieving state-of-the-art classification. Moreover, the attention-based transformer architecture allows us to study the role of the spatio-temporal patterns selected by the model. Our experiments show that the Transformer network captures action dynamic features outperforming video-based approaches and succeeding with scenarios where the differences between actions lie in very subtle cues. Finally, we release the new event dataset, which is the first in the literature for manipulation action recognition. Code will be available at https://github.com/DaniDeniz/EventVisionTransformer.

cs.CV↗

arXiv2023

Optical flow estimation from event-based cameras and spiking neural networks

Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or "spikes", when the luminance change at a given pixel since the last event surpasses a certain threshold. Thanks to their inherent qualities, such as their low power consumption, low latency and high dynamic range, they seem particularly tailored to applications with challenging temporal constraints and safety requirements. Event-based sensors are an excellent fit for Spiking Neural Networks (SNNs), since the coupling of an asynchronous sensor with neuromorphic hardware can yield real-time systems with minimal power requirements. In this work, we seek to develop one such system, using both event sensor data from the DSEC dataset and spiking neural networks to estimate optical flow for driving scenarios. We propose a U-Net-like SNN which, after supervised training, is able to make dense optical flow estimations. To do so, we encourage both minimal norm for the error vector and minimal angle between ground-truth and predicted flow, training our model with back-propagation using a surrogate gradient. In addition, the use of 3d convolutions allows us to capture the dynamic nature of the data by increasing the temporal receptive fields. Upsampling after each decoding stage ensures that each decoder's output contributes to the final estimation. Thanks to separable convolutions, we have been able to develop a light model (when compared to competitors) that can nonetheless yield reasonably accurate optical flow estimates.

cs.CV↗

arXiv2022

Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective

Introducing new technologies such as messaging platforms, and the chatbots attached to them, in higher education, is rapidly growing. This introduction entails a careful consideration of the potential opportunities and/or challenges of adopting these tools. Hence, a thorough examination of the teachers' experiences in this discipline can shed light on the effective ways of enhancing students' learning and boosting their progress. In this contribution, we have surveyed the opinions of tertiary education teachers based in Spain (mainly) and Spanish-speaking countries. The focus of these surveys is to collect teachers' feedback about their opinions regarding the introduction of the messaging platforms and chatbots in their classes, understand their needs and to gather information about the various educational use cases where these tools are valuable. In addition, an analysis of how and when teachers' opinions towards the use of these tools can vary across gender, experience, and their discipline of specialisation is presented. The key findings of this study highlight the factors that can contribute to the advancement of the adoption of messaging platforms and chatbots in higher education institutions to achieve the desired learning outcomes.

cs.CY↗

arXiv2020

Reconfigurable Cyber-Physical System for Critical Infrastructure Protection in Smart Cities via Smart Video-Surveillance

Automated surveillance is essential for the protection of Critical Infrastructures (CIs) in future Smart Cities. The dynamic environments and bandwidth requirements demand systems that adapt themselves to react when events of interest occur. We present a reconfigurable Cyber Physical System for the protection of CIs using distributed cloud-edge smart video surveillance. Our local edge nodes perform people detection via Deep Learning. Processing is embedded in high performance SoCs (System-on-Chip) achieving real-time performance ($\approx$ 100 fps - frames per second) which enables efficiently managing video streams of more cameras source at lower frame rate. Cloud server gathers results from nodes to carry out biometric facial identification, tracking, and perimeter monitoring. A Quality and Resource Management module monitors data bandwidth and triggers reconfiguration adapting the transmitted video resolution. This also enables a flexible use of the network by multiple cameras while maintaining the accuracy of biometric identification. A real-world example shows a reduction of $\approx$ 75\% bandwidth use with respect to the no-reconfiguration scenario.

cs.CV↗

arXiv2020

Reconfigurable Cyber-Physical System for Lifestyle Video-Monitoring via Deep Learning

Indoor monitoring of people at their homes has become a popular application in Smart Health. With the advances in Machine Learning and hardware for embedded devices, new distributed approaches for Cyber-Physical Systems (CPSs) are enabled. Also, changing environments and need for cost reduction motivate novel reconfigurable CPS architectures. In this work, we propose an indoor monitoring reconfigurable CPS that uses embedded local nodes (Nvidia Jetson TX2). We embed Deep Learning architectures to address Human Action Recognition. Local processing at these nodes let us tackle some common issues: reduction of data bandwidth usage and preservation of privacy (no raw images are transmitted). Also real-time processing is facilitated since optimized nodes compute only its local video feed. Regarding the reconfiguration, a remote platform monitors CPS qualities and a Quality and Resource Management (QRM) tool sends commands to the CPS core to trigger its reconfiguration. Our proposal is an energy-aware system that triggers reconfiguration based on energy consumption for battery-powered nodes. Reconfiguration reduces up to 22% the local nodes energy consumption extending the device operating time, preserving similar accuracy with respect to the alternative with no reconfiguration.

cs.CV↗

arXiv2018

Real-time clustering and multi-target tracking using event-based sensors

Clustering is crucial for many computer vision applications such as robust tracking, object detection and segmentation. This work presents a real-time clustering technique that takes advantage of the unique properties of event-based vision sensors. Since event-based sensors trigger events only when the intensity changes, the data is sparse, with low redundancy. Thus, our approach redefines the well-known mean-shift clustering method using asynchronous events instead of conventional frames. The potential of our approach is demonstrated in a multi-target tracking application using Kalman filters to smooth the trajectories. We evaluated our method on an existing dataset with patterns of different shapes and speeds, and a new dataset that we collected. The sensor was attached to the Baxter robot in an eye-in-hand setup monitoring real-world objects in an action manipulation task. Clustering accuracy achieved an F-measure of 0.95, reducing the computational cost by 88% compared to the frame-based method. The average error for tracking was 2.5 pixels and the clustering achieved a consistent number of clusters along time.

cs.RO↗

arXiv2018

Joint direct estimation of 3D geometry and 3D motion using spatio temporal gradients

Conventional image motion based structure from motion methods first compute optical flow, then solve for the 3D motion parameters based on the epipolar constraint, and finally recover the 3D geometry of the scene. However, errors in optical flow due to regularization can lead to large errors in 3D motion and structure. This paper investigates whether performance and consistency can be improved by avoiding optical flow estimation in the early stages of the structure from motion pipeline, and it proposes a new direct method based on image gradients (normal flow) only. The main idea lies in a reformulation of the positive-depth constraint, which allows the use of well-known minimization techniques to solve for 3D motion. The 3D motion estimate is then refined and structure estimated adding a regularization based on depth. Experimental comparisons on standard synthetic datasets and the real-world driving benchmark dataset KITTI using three different optic flow algorithms show that the method achieves better accuracy in all but one case. Furthermore, it outperforms existing normal flow based 3D motion estimation techniques. Finally, the recovered 3D geometry is shown to be also very accurate.

cs.CV↗

arXiv2016

Prediction of Manipulation Actions

Looking at a person's hands one often can tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor's intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded from subjects performing different manipulation actions on the same object, such as "squeezing", "flipping", "washing", "wiping" and "scratching" with a sponge. In psychophysical experiments, we evaluated human observers' skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object. We then developed a recurrent neural network based method for action prediction using as input patches around the hand. We also used the same formalism to predict the forces on the finger tips using for training synchronized video and force data streams. Evaluations on two new datasets showed that our system closely matches human performance in the recognition task, and demonstrate the ability of our algorithm to predict what and how a dexterous action is performed.

cs.CV↗

arXiv2015

Unified description of structure and reactions: implementing the Nuclear Field Theory program

The modern theory of the atomic nucleus results from the merging of the liquid drop (Niels Bohr and Fritz Kalckar) and of the shell model (Marie Goeppert Meyer and Axel Jensen), which contributed the concepts of collective excitations and of independent-particle motion respectively. The unification of these apparently contradictory views in terms of the particle-vibration (rotation) coupling (Aage Bohr and Ben Mottelson) has allowed for an ever increasingly complete, accurate and detailed description of the nuclear structure, Nuclear Field Theory (NFT, developed by the Copenhagen-Buenos Aires collaboration) providing a powerful quantal embodiment. In keeping with the fact that reactions are not only at the basis of quantum mechanics (statistical interpretation, Max Born) , but also the specific tools to probe the atomic nucleus, NFT is being extended to deal with processes which involve the continuum in an intrinsic fashion, so as to be able to treat them on an equal footing with those associated with discrete states (nuclear structure). As a result, spectroscopic studies of transfer to continuum states could eventually use at profit the NFT rules, extended to take care of recoil effects. In the present contribution we review the implementation of the NFT program of structure and reactions, setting special emphasis on open problems and outstanding predictions.

nucl-th↗

arXiv2014

Pairing interaction and two-nucleon transfer reactions

Making use of the fact that the collective modes associated with the spontaneous (static and dynamic) violation of gauge invariance in atomic nuclei (pairing rotations and pairing vibrations) are amenable to a simple, quite accurate nuclear structure description (BCS and QRPA respectively), it is possible to quantitatively test the reaction mechanism which is at the basis of two-nucleon transfer reactions, specific probe of pairing in nuclei. With the help of the static and dynamic mean field spectroscopic amplitudes, taking into account successive and simultaneous transfer channels properly corrected because of non-orthogonality effects, as well as describing the associated elastic channels in terms of experimentally determined optical potentials, one obtains absolute, two-particle transfer differential cross sections which provide an overall account of the data within experimental errors. One of the first results connected with such quantitative studies of pairing correlations in nuclei is the observation of phonon mediated pairing in the exotic halo nucleus 11Li, and the associated discovery of a new mechanism to break nuclear gauge symmetry: bootstrap, pigmy-resonance-mediated Cooper pair binding.

nucl-th↗

arXiv2012

Nuclear Field Theory predictions for 11Li and 12Be: shedding light on the origin of pairing in nuclei

Recent data resulting from studies of two-nucleon transfer reaction on 11Li, analyzed through a unified nuclear-structure-direct-reaction theory have provided strong direct as well as indirect confirmation, through the population of the first excited state of 9Li and of the observation of a strongly quenched ground state transition, of the prediction that phonon mediated pairing interaction is the main mechanism binding the neutron halo of the 8.5 ms-lived 11Li nucleus. In other words, the ground state of 11Li can be viewed as a neutron Cooper pair bound to the 9Li core, mainly through the exchange of collective vibration of the core and of the pigmy resonance arizing from the sloshing back and forth of the neutron halo against the protons of the core, the mean field leading to unbound two particle states, a situation essentially not altered by the bare nucleon-nucleon interaction acting between the halo neutrons. Two-neutron pick-up data, together with (t,p) data on 7Li, suggest the existence of a pairing vibrational band based on 9Li, whose members can be excited with the help of inverse kinematic experiments as was done in the case of 11Li(p,t)9Li reaction. The deviation from harmonicity can provide insight into the workings of medium polarization effects on Cooper pair nuclear pairing, let alone specific information concering the "rigidity" of the N=6 shell closure. Further information concerning these questions is provided by the predicted absolute differential cross sections associated with the reactions 12Be(p,t)10Be(gs) and 12Be(p,t)10Be(pv)(~10Be(p,t)8Be(gs)). In particular, concerning this last reaction, predictions of σ_{abs} can change by an order of magnitude depending on whether the halo properties associated with the d_{5/2} orbital are treated selfconsistently in calculating the ground state correlations of the (pair removal) mode, or not.

nucl-th↗

arXiv2004

Quasi-particle properties of trapped Fermi gases

We develop a consistent formalism in order to explore the effects of density and spin fluctuations on the quasi-particle properties and on the pairing critical temperature of a trapped Fermi gas on the attractive side of a Feshbach resonance. We first analyze the quasi-particle properties of a gas due to interactions far from resonance (effective mass and lifetime, quasi-particle strength and effective interaction) for the two cases of a spherically symmetric harmonic trap and of a spherically symmetric infinite potential well. We then explore the effect of each of these quantities on $T_c$ and point out the important role played by the discrete level structure.

cond-mat.stat-mech↗