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The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor. Here, we focus on the problem of inferring sparse representations from streaming video using dictionaries of spatiotemporal features optimized in an unsupervised manner for sparse reconstruction. Non-spiking LCA has previously been used to achieve unsupervised learning of spatiotemporal dictionaries composed of convolutional kernels from raw, unlabeled video. We demonstrate how unsupervised dictionary learning with spiking LCA (\hbox{S-LCA}) can be efficiently implemented using accumulator neurons, which combine a conventional leaky-integrate-and-fire (\hbox{LIF}) spike generator with an additional state variable that is used to minimize the difference between the integrated input and the spiking output. We demonstrate dictionary learning across a wide range of dynamical regimes, from graded to intermittent spiking, for inferring sparse representations of both static images drawn from the CIFAR database as well as video frames captured from a DVS camera. On a classification task that requires identification of the suite from a deck of cards being rapidly flipped through as viewed by a DVS camera, we find essentially no degradation in performance as the LCA model used to infer sparse spatiotemporal representations migrates from graded to spiking. We conclude that accumulator neurons are likely to provide a powerful enabling component of future neuromorphic hardware for implementing online unsupervised learning of spatiotemporal dictionaries optimized for sparse reconstruction of streaming video from event based DVS cameras.

We report new VLA and e-MERLIN high resolution and sensitivity images of the Triple Radio continuum Source in the Serpens star forming region. These observations allowed us to perform a deep multi-frequency, multi-epoch study by exploring the innermost regions (<~100 au) of an intermediate-mass YSO for the first time, with a physical resolution of ~15 au. The kinematic analysis of knots recently ejected by the protostar indicates that the jet is undergoing episodic variations in velocity. In addition, our multi-frequency images reveal striking characteristics, e.g., a highly collimated ionized stream that would be launched at a radial distance of ~0.4 au from the protostar, and a narrow (~28 au wide) ionized cavity that would be excited by the interaction of a wide-angle component with the surrounding toroid of infalling material. In light of these results, we propose the scenario in which both a highly-collimated jet and a wide-angle wind coexist to be the most plausible to explain our observations, either launched by the X-wind or X- plus Disk-wind mechanism., Comment: 13 pages, 3 figures. Accepted for publication in APJL

A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution which can be prohibitive due to long model runtimes. On the other hand, statistical downscaling techniques present an alternative approach for learning links between the large- and local-scale climate in a more efficient way. A large number of deep neural network-based approaches for statistical downscaling have been proposed in recent years, mostly based on convolutional architectures developed for computer vision and super-resolution tasks. This paper presents DL4DS, Deep Learning for empirical DownScaling, a python library that implements a wide variety of state-of-the-art and novel algorithms for downscaling gridded Earth Science data with deep neural networks. DL4DS has been designed with the goal of providing a general framework for training convolutional neural networks with configurable architectures and learning strategies to facilitate the conduction of comparative and ablation studies in a robust way. We showcase the capabilities of DL4DS on air quality CAMS data over the western Mediterranean area. The DL4DS library can be found in this repository: https://github.com/carlos-gg/dl4ds

This paper presents a biometric recognition system based on hand geometry. We describe a database specially collected for research purposes, which consists of 50 people and 10 different acquisitions of the right hand. This database can be freely downloaded. In addition, we describe a feature extraction procedure and we obtain experimental results using different classification strategies based on Multi Layer Perceptrons (MLP). We have evaluated identification rates and Detection Cost Function (DCF) values for verification applications. Experimental results reveal up to 100% identification and 0% DCF, Comment: 8 pages published in International Conference on Biometrics ICB 2006, Advances in Biometrics pages 721 727 Hong Kong, China. arXiv admin note: substantial text overlap with arXiv:2204.07764

Planning safe motions for legged robots requires sophisticated safety verification tools. However, designing such tools for such complex systems is challenging due to the nonlinear and high-dimensional nature of these systems' dynamics. In this letter, we present a probabilistic verification framework for legged systems, which evaluates the safety of planned trajectories by learning an assessment function from trajectories collected from a closed-loop system. Our approach does not require an analytic expression of the closed-loop dynamics, thus enabling safety verification of systems with complex models and controllers. Our framework consists of an offline stage that initializes a safety assessment function by simulating a nominal model and an online stage that adapts the function to address the sim-to-real gap. The performance of the proposed approach for safety verification is demonstrated using a quadruped balancing task and a humanoid reaching task. The results demonstrate that our framework accurately predicts the systems' safety both at the planning phase to generate robust trajectories and at execution phase to detect unexpected external disturbances., Comment: 8 pages, 8 figures, submitted to RA-L with IROS option

We present unprecedented high fidelity radio images of the M87 jet. We analyzed Jansky Very Large Array (VLA) broadband, full polarization, radio data from 4 to 18 GHz. The observations were taken with the most extended configuration (A configuration), which allow the study of the emission of the jet up to kpc scales with a linear resolution $\sim$10 pc. The high sensitivity and resolution of our data allow to resolve the jet width. We confirm a double-helix morphology of the jet material between $\sim$300 pc and $\sim$1 kpc. We found a gradient of the polarization degree with a minimum at the projected axis and maxima at the jet edges, and a gradient in the Faraday depth with opposite signs at the jet edges. We also found that the behavior of the polarization properties along the wide range of frequencies is consistent with internal Faraday depolarization. All these characteristics strongly support the presence of a helical magnetic field in the M87 jet up to 1 kpc from the central black hole although the jet is most likely particle dominated at these large scales. Therefore, we propose a plausible scenario in which the helical configuration of the magnetic field has been maintained to large scales thanks to the presence of Kelvin-Helmholtz instabilities., Comment: Published on ApJL

We present VLA and ALMA observations of the close (0.3" = 90 au separation) protobinary system SVS 13. We detect two small circumstellar disks (radii $\sim$12 and $\sim$9 au in dust, and $\sim$30 au in gas) with masses of $\sim$0.004-0.009 $M_{sun}$ for VLA 4A (the western component) and $\sim$0.009-0.030 $M_{sun}$ for VLA 4B (the eastern component). A circumbinary disk with prominent spiral arms extending $\sim$500 au and a mass of $\sim$0.052 $M_{sun}$ appears to be in the earliest stages of formation. The dust emission is more compact and with a very high optical depth toward VLA 4B, while toward VLA 4A the dust column density is lower, allowing the detection of stronger molecular transitions. We infer rotational temperatures of $\sim$140 K, on scales of $\sim$30 au, across the whole source, and a rich chemistry. Molecular transitions typical of hot corinos are detected toward both protostars, being stronger toward VLA 4A, with several ethylene glycol transitions detected only toward this source. There are clear velocity gradients, that we interpret in terms of infall plus rotation of the circumbinary disk, and purely rotation of the circumstellar disk of VLA 4A. We measured orbital proper motions and determined a total stellar mass of 1 $M_{sun}$. From the molecular kinematics we infer the geometry and orientation of the system, and stellar masses of $\sim$0.26 $M_{sun}$ for VLA 4A and $\sim$0.60 $M_{sun}$ for VLA 4B., Comment: 59 pages, 28 Figures, 14 Tables, accepted by The Astrophysical Journal. Includes changes after the ApJ proof corrections

A simulation strategy based in the finite element model was developed to model the corrosion and mechanical properties of biodegradable Mg scaffolds manufactured by laser power bed fusion after immersion in simulated body fluid. Corrosion was simulated through a phenomenological, diffusion-based model which can take into account pitting. The elements in which the concentration of Mg was below a certain threshold (representative of the formation of Mg(OH)2) after the corrosion simulation were deleted for the mechanical simulations, in which Mg was assumed to behave as an isotropic, elastic-perfectly plastic solid and fracture was introduced through a ductile failure model. The parameters of the models were obtained from previous experimental results and the numerical predictions of the strength and fracture mechanisms of WE43 Mg alloy porous scaffolds in the as-printed condition and after immersion in simulated body fluid were in good agreement with the experimental results. Thus, the simulation strategy is able to assess the effect of corrosion on the mechanical behavior of biodegradable scaffolds, which is critical for design of biodegradable scaffolds for biomedical applications.

The great amount of information that can be stored in electronic media is growing up daily. Many of them is got mainly by typing, such as the huge of information obtained from web 2.0 sites; or scaned and processing by an Optical Character Recognition software, like the texts of libraries and goverment offices. Both processes introduce error in texts, so it is difficult to use the data for other purposes than just to read it, i.e. the processing of those texts by other applications like e-learning, learning of languages, electronic tutorials, data minning, information retrieval and even more specialized systems such as tiflologic software, specifically blinded people-oriented applications like automatic reading, where the text would be error free as possible in order to make easier the text to speech task, and so on. In this paper it is showed an application of automatic recognition and correction of ortographic errors in electronic texts. This task is composed of three stages: a) error detection; b) candidate corrections generation; and c) correction -selection of the best candidate. The proposal is based in part of speech text categorization, word similarity, word diccionaries, statistical measures, morphologic analisys and n-grams based language model of Spanish., Comment: 15 pages, 3 figures, 11 tables, 1 algorithm. Formerly published on Journal of Research in Computer Science - Intl Conference on Computer CORE2012

Individual business processes have been changing since the Internet was created, and they are now oriented towards a more distributed and collaborative business model, in an e-commerce environment that adapts itself to the competitive and changing market conditions. This paper presents a multi-agent system architecture for supply chain management, which explores different strategies and offers solutions in a distributed e-commerce environment. The system is designed to support different types of interfaces, which allow interoperating with other business models already developed. In order to show how the entire multi-agent system is being developed, the implementation of a collaborative agent is presented and explained., Comment: 2 figures, 2 snippets

We study the $f(R,T)$ cosmological models under the self-similarity hypothesis. We determine the exact form that each physical and geometrical quantity may take in order that the Field Equations (FE) admit exact self-similar solutions through the matter collineation approach. We study two models: the case$\ f(R,T)=f_{1}(R)+f_{2}(T)$ and the case $f(R,T)=f_{1}(R)+f_{2} (R)f_{3}(T)$. In each case, we state general theorems which determine completely the form of the unknown functions $f_{i}$ such that the field equations admit self-similar solutions. We also state some corollaries as limiting cases. These results are quite general and valid for any homogeneous self-similar metric$.$ In this way, we are able to generate new cosmological scenarios. As examples, we study two cases by finding exact solutions to these particular models., Comment: accepted to IJGMMP. Matches the published version. arXiv admin note: text overlap with arXiv:1407.2013 by other authors

One of the most important questions in the field of planet formation is how mm-cm sized dust particles overcome the radial drift and fragmentation barriers to form kilometer-sized planetesimals. ALMA observations of protoplanetary disks, in particular transition disks or disks with clear signs of substructures, can provide new constraints on theories of grain growth and planetesimal formation and therefore represent one possibility to progress on this issue. We here present ALMA band 4 (2.1 mm) observations of the transition disk system Sz 91 and combine them with previously obtained band 6 (1.3 mm) and 7 (0.9 mm) observations. Sz 91 with its well defined mm-ring, more extended gas disk, and evidence of smaller dust particles close to the star, is a clear case of dust filtering and the accumulation of mm sized particles in a gas pressure bump. We computed the spectral index (nearly constant at $\sim$3.34), optical depth (marginally optically thick), and maximum grain size ($\sim\,0.61$ mm) in the dust ring from the multi-wavelength ALMA observations and compared the results with recently published simulations of grain growth in disk substructures. Our observational results are in very good agreement with the predictions of models for grain growth in dust rings that include fragmentation and planetesimal formation through the streaming instability., Comment: Accepted for publication in the ApJ 19 pages, 13 figures

The shear strength and the corrosion resistance of the fiber/matrix interface after immersion in simulated body fluid was studied in poly-lactic acid/Mg fiber composites. The shear strength of the interface was measured by means of push-out tests in thin slices of the composite perpendicular to the fibers. It was found that the interface strength dropped from 15.2 \pm 1.4 MPa to 7.8 \pm 3.7 MPa after the composite was immersed in simulated body fluid for 148 hours. The reduction of the interface strength was associated to the fast corrosion of the fibers as water diffused to the interface through the polymer. The expansion of the fibers due to the formation of corrosion products was enough to promote radial cracks in the polymer matrix which facilitate the ingress of water and the development of corrosion pitting in the fibers. Moreover, cell culture testing on the material showed that early degradation of the Mg fibers affected the proliferation of pre-osteoblasts near the Mg fibers due to the local changes in the environment produced by the fiber corrosion. Thus, surface modification of Mg fibers to delay degradation seems to be a critical point for further development of Mg/PLA composites for biomedical applications.

Protostellar jets have a fundamental role at the earliest evolution of protostars of all masses. In the case of low-mass (<8 Msun) protostars, strong observational evidence exists that the launching and collimation is due to the X- and/or disk-wind mechanisms. In these models, it is the protostar/disk system that creates all the necessary conditions to launch and collimate the jets near the protostar via strong magnetic fields. The origin of jets from more massive protostars has been investigated much less, in part because of the difficulty of resolving the collimation zone in these more distant objects. Here we present the highest angular resolution observations of a jet powered by a massive protostar, the Cep A HW2 radio jet. We imaged the radio emission at projected distances of only ~20 au from the protostar, resolving the innermost 100 au of a massive protostellar jet for the first time. The morphology of the radio jet emission in this massive object is very different than what is usually observed in jets from low-mass protostars. We found that the outflowing material in HW2 has two components: a wide-angle wind launched from the protostar/disk system, and a highly collimated jet starting at 20-30 au from the protostar. We discuss two possible scenarios: an extension of the classical disk-wind to a massive protostar, or external collimation of a wide-angle wind. These results have important consequences for our understanding of how stars of different masses are formed., Comment: Accepted for publication in ApJ Letters

In this work, we propose \textit{MVFuseNet}, a novel end-to-end method for joint object detection and motion forecasting from a temporal sequence of LiDAR data. Most existing methods operate in a single view by projecting data in either range view (RV) or bird's eye view (BEV). In contrast, we propose a method that effectively utilizes both RV and BEV for spatio-temporal feature learning as part of a temporal fusion network as well as for multi-scale feature learning in the backbone network. Further, we propose a novel sequential fusion approach that effectively utilizes multiple views in the temporal fusion network. We show the benefits of our multi-view approach for the tasks of detection and motion forecasting on two large-scale self-driving data sets, achieving state-of-the-art results. Furthermore, we show that MVFusenet scales well to large operating ranges while maintaining real-time performance., Comment: Published at CVPR 2021 Workshop on Autonomous Driving