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Direct proton-knockout reactions of ^{55}Sc at ∼220 MeV/nucleon were studied at the RIKEN Radioactive Isotope Beam Factory. Populated states of ^{54}Ca were investigated through γ-ray and invariant-mass spectroscopy. Level energies were calculated from the nuclear shell model employing a phenomenological internucleon interaction. Theoretical cross sections to states were calculated from distorted-wave impulse approximation estimates multiplied by the shell model spectroscopic factors, which describe the wave function overlap of the ^{55}Sc ground state with states in ^{54}Ca. Despite the calculations showing a significant amplitude of excited neutron configurations in the ground-state of ^{55}Sc, valence proton removals populated predominantly the ground state of ^{54}Ca. This counterintuitive result is attributed to pairing effects leading to a dominance of the ground-state spectroscopic factor. Owing to the ubiquity of the pairing interaction, this argument should be generally applicable to direct knockout reactions from odd-even to even-even nuclei.

We present a novel vehicle detection and tracking system that works solely on 3D LiDAR information. Our approach segments vehicles using a dual-view representation of the 3D LiDAR point cloud on two independently trained convolutional neural networks, one for each view. A bounding box growing algorithm is applied to the fused output of the networks to properly enclose the segmented vehicles. Bounding boxes are grown using a probabilistic method that takes into account also occluded areas. The final vehicle bounding boxes act as observations for a multi-hypothesis tracking system which allows to estimate the position and velocity of the observed vehicles. We thoroughly evaluate our system on the KITTI benchmarks both for detection and tracking separately and show that our dual-branch classifier consistently outperforms previous single-branch approaches, improving or directly competing to other state of the art LiDAR-based methods., This work is supported by the EU project LOGIMATIC (H2020-Galileo2015-1-687534), the Spanish State Research Agency through the Mar´ıa de Maeztu Seal of Excellence to IRI (MDM-2016-0656), the ColRobTransp project (DPI2016-78957-R AEI/FEDER EU), the EB-SLAM project (DPI2017-89564-P), and the FPU grant FPU15/04446.

Twenty-one two-proton knockout (p; 3p) cross sections from neutron-rich nuclei at ~ 250 MeV/nucleon were measured. For the first time, the angular distribution of the three emitted protons were measured in coincidence with the tracker and hydrogen target MINOS, demonstrating that the (p; 3p) kinematics is consistent with two sequential proton-proton collisions within the projectile nucleus. Ratios of (p; 3p) over (p; 2p) inclusive cross sections follow the trend of other many-proton (neutron) removal reactions, further reinforcing the sequential nature of (p; 3p) in neutron-rich nuclei.

Here we present new information on the shape evolution of the very neutron-rich ^{92,94}Se nuclei from an isomer-decay spectroscopy experiment at the Radioactive Isotope Beam Factory at RIKEN. High-resolution germanium detectors were used to identify delayed γ rays emitted following the decay of their isomers. New transitions are reported extending the previously known level schemes. The isomeric levels are interpreted as originating from high-K quasineutron states with an oblate deformation of β∼0.25, with the high-K state in ^{94}Se being metastable and K hindered. Following this, ^{94}Se is the lowest-mass neutron-rich nucleus known to date with such a substantial K hindrance. Furthermore, it is the first observation of an oblate K isomer in a deformed nucleus. This opens up the possibility for a new region of K isomers at low Z and at oblate deformation, involving the same neutron orbitals as the prolate orbitals within the classic Z∼72 deformed hafnium region. From an interpretation of the level scheme guided by theoretical calculations, an oblate deformation is also suggested for the ^{94}Se_{60} ground-state band.

Spectroscopic factors of neutron-hole and proton-hole states in 131Sn and 131In, respectively, were measured using one-nucleon removal reactions from doubly magic 132Sn at relativistic energies. For 131In, a 2910(50)-keV γ ray was observed for the first time and tentatively assigned to a decay from a 5=2− state at 3275(50) keV to the known 1=2− level at 365 keV. The spectroscopic factors determined for this new excited state and three other single-hole states provide first evidence for a strong fragmentation of singlehole strength in 131Sn and 131In. The experimental results are compared to theoretical calculations based on the relativistic particle-vibration coupling

C.M. Shand et al. -- 6 pags., 3 figs., tab. --Open Access funded by Creative Commons Atribution Licence 4.0, We report on the measurement of new low-lying states in the neutron-rich Zn nuclei via in-beam γ-ray spectroscopy. These include the 4 →2 transition in Zn, the 2 →0 and 4 →2 transitions in Zn, and low-lying states in Zn were observed for the first time. The reduced E(2 ) energies and increased E(4 )/E(2 ) ratios at N=52, 54 compared to those in Zn attest that the magicity is confined to the neutron number N=50 only. The deduced level schemes are compared to three state-of-the-art shell model calculations and a good agreement is observed with all three calculations. The newly observed 2 and 4 levels in Zn suggest the onset of deformation towards heavier Zn isotopes, which has been incorporated by taking into account the upper sdg orbitals in the Ni78-II and the PFSDG-U models., All UK authors are supported by the Science and Technology Facilities Council (STFC). The development of MINOS and the core MINOS team have been supported by the European Research Council through the ERC Grant No. MINOS-258567. A. Jungclaus was supported by the Spanish Ministerio de Economía y Competitividad under contract FPA2014-57196-C5-4-P. Authors from Cologne were supported by German BMBF Grant No. 05P15PKFNA. Authors from TU Darmstadt were supported by German BMBF Grant No. 05P12RDFN8 and 05P15RDFN1. L.X. Chung and B.D. Linh would like to acknowledge Vietnam Ministry of Science and Technology for the support, and Radioactive Isotope Physics Laboratory of the RIKEN Nishina Center for supporting their stay during the experiments.

Moving object detection is a critical task for autonomous vehicles. As dynamic objects represent higher collision risk than static ones, our own ego-trajectories have to be planned attending to the future states of the moving elements of the scene. Motion can be perceived using temporal information such as optical flow. Conventional optical flow computation is based on camera sensors only, which makes it prone to failure in conditions with low illumination. On the other hand, LiDAR sensors are independent of illumination, as they measure the time-of-flight of their own emitted lasers. In this work, we propose a robust and real-time CNN architecture for Moving Object Detection (MOD) under low-light conditions by capturing motion information from both camera and LiDAR sensors. We demonstrate the impact of our algorithm on KITTI dataset where we simulate a low-light environment creating a novel dataset "Dark KITTI". We obtain a 10.1% relative improvement on Dark-KITTI, and a 4.25% improvement on standard KITTI relative to our baselines. The proposed algorithm runs at 18 fps on a standard desktop GPU using $256\times1224$ resolution images., Accepted for Oral presentation at ICCV 2019 Workshop on Autonomous Driving. https://sites.google.com/view/fusemodnet

Exclusive cross sections and momentum distributions have been measured for quasifree one-neutron knockout reactions from a ^{54}Ca beam striking on a liquid hydrogen target at ∼200 MeV/u. A significantly larger cross section to the p_{3/2} state compared to the f_{5/2} state observed in the excitation of ^{53}Ca provides direct evidence for the nature of the N=34 shell closure. This finding corroborates the arising of a new shell closure in neutron-rich calcium isotopes. The distorted-wave impulse approximation reaction formalism with shell model calculations using the effective GXPF1Bs interaction and ab initio calculations concur our experimental findings. Obtained transverse and parallel momentum distributions demonstrate the sensitivity of quasifree one-neutron knockout in inverse kinematics on a thick liquid hydrogen target with the reaction vertex reconstructed to final state spin-parity assignments.

Fifty-five inclusive single nucleon removal cross sections from medium mass neutron-rich nuclei impinging on a hydrogen target at � 250 MeV/nucleon were measured at the RIKEN Radioactive Isotope Beam Factory. Systematically higher cross sections are found for proton removal from nuclei with an even number of protons compared to odd-proton number projectiles for a given neutron separation energy. Neutron removal cross sections display no even-odd splitting contrary to nuclear cascade model predictions. Both effects are understood through simple considerations of neutron separation energies and bound state level densities originating in pairing correlations in the daughter nuclei. These conclusions are supported by comparison with semi-microscopic model predictions,highlighting the enhanced role of low-lying level densities in nucleon removal cross sections from loosely-bound nuclei.

A dedicated spectroscopic study of the β decay of 127Cd was conducted at the IGISOL facility at the University of Jyvaskyla. Following high-resolution mass separation in a Penning trap, β-γ-γ coincidences were used to considerably extend the decay scheme of 127In. The β-decaying 3/2+ and 11/2- states in 127Cd have been identified with the 127Cd ground state and the 283-keV isomer. Their respective half-lives have been measured to 0.45(+12-8)s and 0.36(4) s. The experimentally observed β feeding to excited states of 127In and the decay scheme of 127In are discussed in conjunction with large-scale shell-model calculations.

The reduced transition probability B(E2;01+→21+) to the first excited 2+ state of the neutron-rich nucleus Te136, with two protons and two neutrons outside the doubly magic Sn132 core, was measured via Coulomb excitation at relativistic energies at the RIKEN Radioactive Isotope Beam Factory. A value of B(E2)=0.191(26) e2b2 was extracted from the measured inelastic scattering cross section on an Au target taking into account the contributions from both Coulomb and nuclear excitations. In addition, an upper limit for the transition strength to a 2+ state of mixed-symmetry character in the excitation energy range of 1.5-2.2 MeV was determined and compared to the predictions of various theoretical calculations. Because of the high statistics gathered in the present experiment the error of the deduced B(E2) value is dominated by the systematic uncertainties involved in the analysis of Coulomb excitation experiments at beam energies around 150 MeV/u. Therefore, the latter are for the first time assessed in detail in the present work.

The first γ-ray spectroscopy of ^{52}Ar, with the neutron number N=34, was measured using the ^{53}K(p,2p) one-proton removal reaction at ∼210 MeV/u at the RIBF facility. The 2_{1}^{+} excitation energy is found at 1656(18) keV, the highest among the Ar isotopes with N>20. This result is the first experimental signature of the persistence of the N=34 subshell closure beyond ^{54}Ca, i.e., below the magic proton number Z=20. Shell-model calculations with phenomenological and chiral-effective-field-theory interactions both reproduce the measured 2_{1}^{+} systematics of neutron-rich Ar isotopes, and support a N=34 subshell closure in ^{52}Ar.

7 pags., 7 figs. -- XXIII International School on Nuclear Physics, Neutron Physics and Applications 22-28 September 2019, Varna, Bulgaria, Within the SEASTAR III campaign at the Radioactive Isotope Beam Factory, at the RIKEN Nishina Center, neutron-rich isotopes in the vicinity of 53K were produced from the fragmentation of the primary 70Zn beam on a 9Be target. After nucleon knockout reactions on the secondary liquid hydrogen MINOS target the known γ rays of the neutron-rich 55Sc isotope were observed (shown in this proceedings) and γ rays from 57,59Sc isotopes have been identified for the first time. The evolution of the occupied nucleon orbitals of these nuclei in the ground and excited state is investigated under the prism of the tensor force., We are grateful to the RIKEN Nishina Center accelerator staff for their work in the primary beam delivery and the BigRIPS team for preparing the secondary beams. This work was supported by the cooperation between TU Darmstadt and the GSI Helmholtz Center for Heavy Ion Research, by the Helmholtz Graduate School for Hadron and Ion Research for FAIR and its abroad program and the BMBF under grant No. 05P19RDFN1. D.S. was supported by projects No. GINOP2.3.3-15-2016-00034 and No. K128947.

We report on the first γ-ray spectroscopy of 51,53K produced via the 52,54Ca(p,2p) reactions at ∼250 MeV/nucleon. Unambiguous final-state angular-momentum assignments were achieved for beam intensities down to few particles per second by using a new technique based on reaction vertex tracking combined with a thick liquid-hydrogen target. Through γ-ray spectroscopy and exclusive parallel momentum distribution analysis, 3/2+ ground states and 1/2+ first excited states in 51,53K were established quantifying the natural ordering of the 1 d 3 / 2 and 2 s 1 / 2 proton-hole states that are restored at N = 32 and 34. State-of-the-art ab initio calculations and shell-model calculations with improved phenomenological effective interactions reproduce the present data and predict consistently the increase of the E(1/2 1 + ) - E(3/2 1 + ) energy differences towards N = 40.

ray transitions between low-spin states of the neutron-rich 84;86;88Ge were measured by means of in-flight -ray spectroscopy at 270 MeV/u. Excited 6+ 1 , 4+ 1;2 and 2+ 1;2 states of 84;86Ge and 4+ 1 and 2+ 1;2 states of 88Ge were observed. Furthermore a candidate for a 3+ 1 state of 86Ge was identified. This state plays a key role in the discussion of ground-state triaxiality of 86Ge, along with other features of its low-energy level scheme. A new region of triaxially deformed nuclei is proposed in the Ge isotopic chain.

We report on the first γ-ray spectroscopy of low-lying states in neutron-rich 98,100Kr isotopes obtained from 99,101Rb(p,2p) reactions at ∼ 220 MeV/nucleon. A reduction of the 2+ 1 state energies beyond N = 60 demonstrates a significant increase of deformation, shifted in neutron number compared to the sharper transition observed in strontium and zirconium isotopes. State-of-theart beyond-mean-field calculations using the Gogny D1S interaction predict level energies in good agreement with experimental results. The identification of a low-lying (0+ 2 , 2+ 2 ) state in 98Kr provides the first experimental evidence of a competing configuration at low energy in neutron-rich krypton isotopes consistent with the oblate-prolate shape coexistence picture suggested by theory

Excited states in the nucleus 133Sn, with one neutron outside the doubly-magic 132Sn core, were populated following one-neutron knockout from a 134Sn beam on a carbon target at relativistic energies at the Radioactive Isotope Beam Factory at RIKEN. Besides the rays emitted in the decay of the known neutron single-particle states in 133Sn additional strength in the energy range 3.5-5.5 MeV was observed for the fi rst time. Since the neutron-separation energy of 133Sn is low, Sn=2.402(4) MeV, this observation provides direct evidence for the radiative decay of neutron- unbound states in this nucleus. The ability of electromagnetic decay to compete successfully with neutron emission at energies as high as 3 MeV above threshold is attributed to a mismatch between the wave functions of the initial and final states in the latter case. These fi ndings suggest that in the region south-east of 132Sn nuclear structure effects may play a signifi cant role in the neutron vs. competition in the decay of unbound states. As a consequence, the common neglect of such effects in the evaluation of the neutron-emission probabilities in calculations of global b -decay properties for astrophysical simulations may have to be reconsidered.

Trabajo presentado a la Third Iberian Robotics Conference, celebrada en Sevilla del 22 al 24 de noviembre de 2017., Vehicle detection and tracking in real scenarios are key com- ponents to develop assisted and autonomous driving systems. Lidar sen- sors are specially suitable for this task, as they bring robustness to harsh weather conditions while providing accurate spatial information. How- ever, the resolution provided by point cloud data is very scarce in com- parison to camera images. In this work we explore the possibilities of Deep Learning (DL) methodologies applied to low resolution 3D lidar sensors such as the Velodyne VLP-16 (PUCK), in the context of vehicle detection and tracking. For this purpose we developed a lidar-based sys- tem that uses a Convolutional Neural Network (CNN), to perform point- wise vehicle detection using PUCK data, and Multi-Hypothesis Extended Kalman Filters (MH-EKF), to estimate the actual position and veloci- ties of the detected vehicles. Comparative studies between the proposed lower resolution (VLP-16) tracking system and a high-end system, using Velodyne HDL-64, were carried out on the Kitti Tracking Benchmark dataset. Moreover, to analyze the influence of the CNN-based vehicle detection approach, comparisons were also performed with respect to the geometric-only detector. The results demonstrate that the proposed low resolution Deep Learning architecture is able to successfully accom- plish the vehicle detection task, outperforming the geometric baseline approach. Moreover, it has been observed that our system achieves a similar tracking performance to the high-end HDL-64 sensor at close range. On the other hand, at long range, detection is limited to half the distance of the higher-end sensor., This work has been supported by the Spanish Ministry of Economy and Competitiveness projects ROBINSTRUCT (TIN2014-58178-R) and COLROBTRANSP (DPI2016-78957-R), by the Spanish Ministry of Education FPU grant (FPU15/04446), the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and by the EU H2020 project LOGIMATIC (H2020-Galileo-2015-1-687534).

Trabajo presentado a la IEEE International Conference on Image Processing (ICIP), celebrada en Beijing (China) del 17 al 20 de septiembre de 2017., We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets., This work was partially supported by European AEROARMS project (H2020-ICT-2014-1-644271) and CICYT projects ColRobTransp (DPI2016-78957-R), ROBINSTRUCT (TIN2014-58178-R).

Trabajo presentado a la European Conference on Mobile Robots (ECMR), celebrada en Paris (Francia) del 6 al 8 de septiembre de 2017., Vehicle detection and tracking is a core ingredient for developing autonomous driving applications in urban scenarios. Recent image-based Deep Learning (DL) techniques are obtaining breakthrough results in these perceptive tasks. However, DL research has not yet advanced much towards processing 3D point clouds from lidar range-finders. These sensors are very common in autonomous vehicles since, despite not providing as semantically rich information as images, their performance is more robust under harsh weather conditions than vision sensors. In this paper we present a full vehicle detection and tracking system that works with 3D lidar information only. Our detection step uses a Convolutional Neural Network (CNN) that receives as input a featured representation of the 3D information provided by a Velodyne HDL-64 sensor and returns a per-point classification of whether it belongs to a vehicle or not. The classified point cloud is then geometrically processed to generate observations for a multi-object tracking system implemented via a number of Multi-Hypothesis Extended Kalman Filters (MH-EKF) that estimate the position and velocity of the surrounding vehicles. The system is thoroughly evaluated on the KITTI tracking dataset, and we show the performance boost provided by our CNN-based vehicle detector over a standard geometric approach. Our lidar-based approach uses about a 4% of the data needed for an image-based detector with similarly competitive results., This work has been supported by the Spanish Ministry of Economy and Competitiveness projects ROBINSTRUCT (TIN2014-58178-R) and COLROBTRANSP (DPI2016-78957-R), by the Spanish Ministry of Education FPU grant (FPU15/04446), the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI MDM-2016-0656) and by the EU H2020 project LOGIMATIC (H2020-Galileo-015-1-687534).

Trabajo presentado a la 2nd Iberian Robotics Conference (ROBOT-2015)., The presented paper addresses the problem of detecting and tracking moving objects for autonomous cargo handling in port terminals using a perception system which input data is a single layer laser scanner. A computationally low cost and robust Detection and Tracking Moving Objects (DATMO) algorithm is presented to be used in autonomous guided vehicles and autonomous trucks for efficient transportation of cargo in ports. The method first detects moving objects and then tracks them, taking into account that in port terminals the structure of the environment is formed by containers and that the moving objects can be trucks, AGV, cars, straddle carriers and people among others. Two approaches of the DATMO system have been tested, the first one is oriented to detect moving obstacles and focused on tracking and filtering those detections; and the second one is focused on keepking targets when no detections are provided. The system has been evaluated with real data obtained in the CTT port terminal in Hengelo, the Netherlands. Both methods have been tested in the dataset with good results in tracking moving objects., This work has been partially funded by the EU project CargoANTs FP7-SST-2013-605598 and the Spanish CICYT project DPI2013-42458-P.

Trabajo presentado a la 2nd Iberian Robotics Conference (ROBOT-2015)., This paper presents a robust and real-time method for people detection in urban and crowed environments. Unlike other conventional methods which either focus on single features or compute multiple and independent classifiers specialized in a particular feature space, the proposed approach creates a synergic combination of appearance and depth cues in a unique classifier. The core of our method is a Boosted Random Ferns classifier that selects automatically the most discriminative local binary features for both the appearance and depth image spaces. Based on this classifier, a fast and robust people detector which maintains high detection rates in spite of environmental changes is created. The proposed method has been validated in a challenging RGB-D database of people in urban scenarios and has shown that outperforms state-of-the-art approaches in spite of the difficult environment conditions. As a result, this method is of special interest for real-time robotic applications where people detection is a key matter, such as human-robot interaction or safe navigation of mobile robots for example., This work has been partially funded by the EU project CargoANTs FP7-SST-2013-605598 and the Spanish CICYT project DPI2013-42458-P.

Trabajo presentado en la 24th International Conference on Pattern Recognition (ICPR), celebrada en Beijing (China), del 20 al 24 de agosto de 2018, In this paper we propose a novel approach to estimate dense optical flow from sparse lidar data acquired on an autonomous vehicle. This is intended to be used as a drop-in replacement of any image-based optical flow system when images are not reliable due to e.g. adverse weather conditions or at night. In order to infer high resolution 2D flows from discrete range data we devise a three-block architecture of multiscale filters that combines multiple intermediate objectives, both in the lidar and image domain. To train this network we introduce a dataset with approximately 20K lidar samples of the Kitti dataset which we have augmented with a pseudo ground-truth image-based optical flow computed using FlowNet2. We demonstrate the effectiveness of our approach on Kitti, and show that despite using the low-resolution and sparse measurements of the lidar, we can regress dense optical flow maps which are at par with those estimated with image-based methods., This work has been supported by the Spanish Ministry of Economy, Industry and Competitiveness projects COLROBTRANSP (DPI2016-78957-R), HuMoUR (TIN2017-90086-R), the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence (MDM-2016- 0656), and the EU project LOGIMATIC (H2020-Galileo2015-1-687534). We also thank Nvidia for hardware donation under the GPU Grant Program.