Observation of insulator-metal transition in EuNiO3_{3} under high pressure

The charge transfer antiferromagnetic (T$_{N}$ =220 K) insulator EuNiO$_{3}$ undergoes, at ambient pressure, a temperature-induced metal insulator MI transition at T$_{MI}$=463 K. We have investigated the effect of pressure (up to p~20 GPa) on the electronic, magnetic and structural properties of EuNiO$_{3}$ using electrical resistance measurements, {151}^Eu nuclear resonance scattering of synchrotron radiation and x-ray diffraction, respectively. With increasing pressure we find at p$_{c}$ =5.8 GPa a transition from the insulating state to a metallic state, while the orthorhombic structure remains unchanged up to 20 GPa. The results are explained in terms of a gradual increase of the electronic bandwidth with increasing pressure, which results in a closing of the charge transfer gap. It is further shown that the pressure-induced metallic state exhibits magnetic order with a lowervalue of T$_{N}$ (T$_{N}$ ~120 K at 9.4 GPa) which disappears between 9.4 and 14.4 GPa.Comment: 10 pages, 3 figure

Crossover in the nature of the metallic phases in the perovskite-type RNiO_3

We have measured the photoemission spectra of Nd$_{1-x}$Sm$_{x}$NiO$_{3}$, where the metal-insulator transition and the N\'{e}el ordering occur at the same temperature for $x \lesssim 0.4$ and the metal-insulator transition temperature ($T_{MI}$) is higher than the N\'{e}el temperature for $x \gtrsim 0.4$. For $x \le 0.4$, the spectral intensity at the Fermi level is high in the metallic phase above $T_{MI}$ and gradually decreases with cooling in the insulating phase below $T_{MI}$ while for $x > 0.4$ it shows a pseudogap-like behavior above $T_{MI}$ and further diminishes below $T_{MI}$. The results clearly establish that there is a sharp change in the nature of the electronic correlations in the middle ($x \sim 0.4$) of the metallic phase of the $R$NiO$_3$ system.Comment: 4 pages, 4 figure, submitted to Phys. Rev.

Stability of the Ni sites across the pressure-induced metallization in YNiO3

The local environment of nickel atoms in Y NiO3 across the pressure- induced insulator to metal (IM) transition was studied using X-ray absorption spectroscopy (XAS) supported by ab initio calculations. The monotonic contraction of the NiO6 units under applied pressure observed up to 13 GPa, stops in a limited pressure domain around 14 GPa, before resuming above 16 GPa. In this narrow pressure range, crystallographic modifications basically occur in the medium/long range, not in the NiO6 octahedron, whereas the evolution of the near-edge XAS features can be associated to metallization. Ab initio calculations show that these features are related to medium range order, provided that the Ni-O-Ni angle enables a proper overlap of the Ni eg and O 2p orbitals. Metallization is then not directly related to modifications in the average local geometry of the NiO6 units but more likely to an inter-octahedra rearrangement. These outcomes provides evidences of the bandwidth driven nature of the IM transition.Comment: 6 pages with figure

Annihilation vs. Decay: Constraining dark matter properties from a gamma-ray detection

Most proposed dark matter candidates are stable and are produced thermally in the early Universe. However, there is also the possibility of unstable (but long-lived) dark matter, produced thermally or otherwise. We propose a strategy to distinguish between dark matter annihilation and/or decay in the case that a clear signal is detected in gamma-ray observations of Milky Way dwarf spheroidal galaxies with gamma-ray experiments. The sole measurement of the energy spectrum of an indirect signal would render the discrimination between these cases impossible. We show that by examining the dependence of the intensity and energy spectrum on the angular distribution of the emission, the origin could be identified as decay, annihilation, or both. In addition, once the type of signal is established, we show how these measurements could help to extract information about the dark matter properties, including mass, annihilation cross section, lifetime, dominant annihilation and decay channels, and the presence of substructure. Although an application of the approach presented here would likely be feasible with current experiments only for very optimistic dark matter scenarios, the improved sensitivity of upcoming experiments could enable this technique to be used to study a wider range of dark matter models.Comment: 29 pp, 8 figs; replaced to match published version (minor changes and some new references

Energy-dispersive X-ray absorption spectroscopy at LNLS: Investigation on strongly correlated metal oxides

An energy-dispersive X-ray absorption spectroscopy beamline mainly dedicated to X-ray magnetic circular dichroism (XMCD) and material science under extreme conditions has been implemented in a bending-magnet port at the Brazilian Synchrotron Light Laboratory. Here the beamline technical characteristics are described, including the most important aspects of the mechanics, optical elements and detection set-up. The beamline performance is then illustrated through two case studies on strongly correlated transition metal oxides: an XMCD insight into the modifications of the magnetic properties of Cr-doped manganites and the structural deformation in nickel perovskites under high applied pressure. © 2010 International Union of Crystallography. Printed in Singapore - all rights reserved.Fil: Cezar, Julio C.. Laboratorio Nacional de Luz Síncrotron; BrasilFil: Souza Neto, Narcizo M.. Laboratorio Nacional de Luz Síncrotron; BrasilFil: Piamonteze, Cínthia. Laboratorio Nacional de Luz Síncrotron; BrasilFil: Tamura, Edilson. Laboratorio Nacional de Luz Síncrotron; BrasilFil: Garcia, Flávio. Laboratorio Nacional de Luz Síncrotron; BrasilFil: Carvalho, Edson J.. Laboratorio Nacional de Luz Síncrotron; BrasilFil: Neueschwander, Régis T.. Laboratorio Nacional de Luz Síncrotron; BrasilFil: Ramos, Aline Y.. Centre National de la Recherche Scientifique; FranciaFil: Tolentino, Hélio C. N.. Centre National de la Recherche Scientifique; FranciaFil: Caneiro, Alberto. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Massa, Nestor Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Química Inorgánica "Dr. Pedro J. Aymonino". Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Centro de Química Inorgánica "Dr. Pedro J. Aymonino"; ArgentinaFil: Martinez Lope, Maria Jesus. Instituto de Ciencia de Materiales de Madrid; España. Consejo Superior de Investigaciones Científicas; EspañaFil: Alonso, José Antonio. Instituto de Ciencia de Materiales de Madrid; España. Consejo Superior de Investigaciones Científicas; EspañaFil: Itié, Jean Paul. L'Orme des Merisiers. Synchrotron SOLEIL; Franci

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications