PACS numbers: 72.25. Dc, In the beginning of a previous letter [1], we have given a litteral expression for a characteristic threshold resistance r 1 of a semiconductor [2] at an interface with a magnetic tunnel junction as r 1 = (ρl sf ) W/w where ρ is the semiconductor resistivity, l sf its spin diffusion length, W the contact width and w the channel thickness. This was derived in the standard theory of spin diffusion/relaxation in the conduction band [3] taking into account a geometric renormalization when the spin transport is made along a lateral channel [4]. However, the previous expression is correct only in the limit where l sf ≫ (W, w). In the case of a spin diffusion length well shorter than the contact width W (l sf ≪ W ) but still in the limit l sf ≫ w like in the experimental situation, r 1 should instead write r 1 = ρ (l sf ) 2 /w [5]. In this limit, it results that the correct spin resistance area product writessf /w ≃ 200 Ω. µm 2 for a maximal value of γ = 1 (γ is the tunnel spin asymmetry coefficient [6]) and a maximum value of l sf = 1 µm for the range of doping used [7]. Since the expected R S .A product appears even more smaller than the one previously reported (1 kΩ. µm 2 ), it does not change the core of the present letter emphasizing on the role of the surface states (or localized states) at the direct oxide/semiconductor interfaces giving rise to a strong amplification of the spin signal at the level of 3.6 M Ω. µm 2 or beyond [8] as experimentally observed.

Imagery collected by still and video cameras is an increasingly important tool for minimal impact, repeatable observations in the marine environment. Data generated from imagery includes identification, annotation and quantification of biological subjects and environmental features within an image. To be long-lived and useful beyond their project-specific initial purpose, and to maximize their utility across studies and disciplines, marine imagery data should use a standardised vocabulary of defined terms. This would enable the compilation of regional, national and/or global data sets from multiple sources, contributing to broad-scale management studies and development of automated annotation algorithms. The classification scheme developed under the Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) project provides such a vocabulary. The CATAMI classification scheme introduces Australian-wide acknowledged, standardised terminology for annotating benthic substrates and biota in marine imagery. It combines coarse-level taxonomy and morphology, and is a flexible, hierarchical classification that bridges the gap between habitat/biotope characterisation and taxonomy, acknowledging limitations when describing biological taxa through imagery. It is fully described, documented, and maintained through curated online databases, and can be applied across benthic image collection methods, annotation platforms and scoring methods. Following release in 2013, the CATAMI classification scheme was taken up by a wide variety of users, including government, academia and industry. This rapid acceptance highlights the scheme’s utility and the potential to facilitate broad-scale multidisciplinary studies of marine ecosystems when applied globally. Here we present the CATAMI classification scheme, describe its conception and features, and discuss its utility and the opportunities as well as challenges arising from its use.

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The discovery of topological insulators in noninteracting electron systems has motivated the community to research such topological states of matter in correlated electrons both theoretically and experimentally. In this paper, we investigate a phase diagram for a topological Kondo insulating system, where an emergent "spin'-dependent Kondo effect gives rise to an inversion for heavy-fermion bands, responsible for a topological Kondo insulator. Using U(1) slave-boson mean-field analysis, we uncover an additional phase transition inside the Kondo insulating state in two dimensions, which results from the appearance of the topological Kondo insulator. On the other hand, we observe that the Kondo insulating state can be separated into three insulating phases in three dimensions, identified as the weak topological Kondo insulator, the strong topological Kondo insulator, and the normal Kondo insulator, respectively, and classified by Z 2 topological indices. We discuss the possibility of novel quantum criticality between the fractionalized Fermi liquid and the topological Kondo insulator, where the band inversion occurs with the formation of the heavy-fermion band at the same time.

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Spatially continuous predictions of seabed hardness are important baseline environmental information for sustainable management of Australia’s marine jurisdiction. Seabed hardness is often inferred from multibeam backscatter data with unknown accuracy and can be inferred from underwater video footage at limited locations. In this study, we classified the seabed into four classes based on two new seabed hardness classification schemes (i.e., hard90 and hard70). We developed optimal predictive models to predict seabed hardness using random forest (RF) based on the point data of hardness classes and spatially continuous multibeam data. Five feature selection (FS) methods that are variable importance (VI), averaged variable importance (AVI), knowledge informed AVI (KIAVI), Boruta and regularized RF (RRF) were tested based on predictive accuracy. Effects of highly correlated, important and unimportant predictors on the accuracy of RF predictive models were examined. Finally, spatial predictions generated using the most accurate models were visually examined and analysed. This study confirmed that: 1) hard90 and hard70 are effective seabed hardness classification schemes; 2) seabed hardness of four classes can be predicted with a high degree of accuracy; 3) the typical approach used to pre-select predictive variables by excluding highly correlated variables needs to be re-examined; 4) the identification of the important and unimportant predictors provides useful guidelines for further improving predictive models; 5) FS methods select the most accurate predictive model(s) instead of the most parsimonious ones, and AVI and Boruta are recommended for future studies; and 6) RF is an effective modelling method with high predictive accuracy for multi-level categorical data and can be applied to ‘small p and large n’ problems in environmental sciences. Additionally, automated computational programs for AVI need to be developed to increase its computational efficiency and caution should be taken when applying filter FS methods in selecting predictive models.

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Spin-conserving hopping transport through chains of localized states has been evidenced by taking benefit of the high degree of spin-polarization of CoFeB-MgO-CoFeB magnetic tunnel junctions. In particular, our data show that relatively thick MgO barriers doped with boron favor the activation of spin-conserving inelastic channels through a chain of three localized states and leading to reduced magnetoresistance effects. We propose an extension of the Glazman-Matveev theory to the case of ferromagnetic reservoirs to account for spin-polarized inelastic tunneling through nonmagnetic localized states embedded in an insulating barrier.