Spin-valley blockade in carbon nanotube double quantum dots

We present a theoretical study of the Pauli or spin-valley blockade for double quantum dots in semiconducting carbon nanotubes. In our model we take into account the following characteristic features of carbon nanotubes: (i) fourfold (spin and valley) degeneracy of the quantum dot levels, (ii) the intrinsic spin-orbit interaction which is enhanced by the tube curvature, and (iii) valley-mixing due to short-range disorder, i.e., substitutional atoms, adatoms, etc. We find that the spin-valley blockade can be lifted in the presence of short-range disorder, which induces two independent random (in magnitude and direction) valley-Zeeman-fields in the two dots, and hence acts similarly to hyperfine interaction in conventional semiconductor quantum dots. In the case of strong spin-orbit interaction, we identify a parameter regime where the current as the function of an applied axial magnetic field shows a zero-field dip with a width controlled by the interdot tunneling amplitude, in agreement with recent experiments.Comment: 15 pages, 6 figures, 2 tables; v2: published versio

Temporal characteristics of the influence of punishment on perceptual decision making in the human brain

Perceptual decision making is the process by which information from sensory systems is combined and used to influence our behavior. In addition to the sensory input, this process can be affected by other factors, such as reward and punishment for correct and incorrect responses. To investigate the temporal dynamics of how monetary punishment influences perceptual decision making in humans, we collected electroencephalography (EEG) data during a perceptual categorization task whereby the punishment level for incorrect responses was parametrically manipulated across blocks of trials. Behaviorally, we observed improved accuracy for high relative to low punishment levels. Using multivariate linear discriminant analysis of the EEG, we identified multiple punishment-induced discriminating components with spatially distinct scalp topographies. Compared with components related to sensory evidence, components discriminating punishment levels appeared later in the trial, suggesting that punishment affects primarily late postsensory, decision-related processing. Crucially, the amplitude of these punishment components across participants was predictive of the size of the behavioral improvements induced by punishment. Finally, trial-by-trial changes in prestimulus oscillatory activity in the alpha and gamma bands were good predictors of the amplitude of these components. We discuss these findings in the context of increased motivation/attention, resulting from increases in punishment, which in turn yields improved decision-related processing

MATERIALS BALANCE BASED MODELLING OF ENVIRONMENTAL EFFICIENCY

A new method for analysing environmental efficiency, based on the materials balance, is proposed. With this method, an environmental allocative efficiency measure can be defined analogously to the more commonly used economic allocative efficiency. Nutrient surplus in pig fattening, a typical balance indicator, is used to illustrate the concept in a two input one output case. The materials balance based efficiency analysis is elaborated using data envelopment analysis (DEA). Results are compared with those of more common, merely input or output oriented DEA approaches. A main conclusion is that, ignoring the balance feature of environmental issues such as nutrient surplus might be a main reason why traditional integral analyses of economic and environmental efficiency yield contradictory conclusions.Environmental Economics and Policy,

How do neural networks see depth in single images?

Deep neural networks have lead to a breakthrough in depth estimation from single images. Recent work often focuses on the accuracy of the depth map, where an evaluation on a publicly available test set such as the KITTI vision benchmark is often the main result of the article. While such an evaluation shows how well neural networks can estimate depth, it does not show how they do this. To the best of our knowledge, no work currently exists that analyzes what these networks have learned. In this work we take the MonoDepth network by Godard et al. and investigate what visual cues it exploits for depth estimation. We find that the network ignores the apparent size of known obstacles in favor of their vertical position in the image. Using the vertical position requires the camera pose to be known; however we find that MonoDepth only partially corrects for changes in camera pitch and roll and that these influence the estimated depth towards obstacles. We further show that MonoDepth's use of the vertical image position allows it to estimate the distance towards arbitrary obstacles, even those not appearing in the training set, but that it requires a strong edge at the ground contact point of the object to do so. In future work we will investigate whether these observations also apply to other neural networks for monocular depth estimation.Comment: Submitte

Geometric Modular Action, Wedge Duality and Lorentz Covariance are Equivalent for Generalized Free Fields

The Tomita-Takesaki modular groups and conjugations for the observable algebras of space-like wedges and the vacuum state are computed for translationally covariant, but possibly not Lorentz covariant, generalized free quantum fields in arbitrary space-time dimension d. It is shown that for $d\geq 4$ the condition of geometric modular action (CGMA) of Buchholz, Dreyer, Florig and Summers \cite{BDFS}, Lorentz covariance and wedge duality are all equivalent in these models. The same holds for d=3 if there is a mass gap. For massless fields in d=3, and for d=2 and arbitrary mass, CGMA does not imply Lorentz covariance of the field itself, but only of the maximal local net generated by the field

Interference of heavy holes in an Aharonov-Bohm ring

We study the coherent transport of heavy holes through a one-dimensional ring in the presence of spin-orbit coupling. Spin-orbit interaction of holes, cubic in the in-plane components of momentum, gives rise to an angular momentum dependent spin texture of the eigenstates and influences transport. We analyze the dependence of the resulting differential conductance of the ring on hole polarization of the leads and the signature of the textures in the Aharonov-Bohm oscillations when the ring is in a perpendicular magnetic field. We find that the polarization-resolved conductance reveals whether the dominant spin-orbit coupling is of Dresselhaus or Rashba type, and that the cubic spin-orbit coupling can be distinguished from the conventional linear coupling by observing the four-peak structure in the Aharonov-Bohm oscillations.Comment: 12 pages, 11 figure