Spin injection and perpendicular spin transport in graphite nanostructures

Organic and carbon-based materials are attractive for spintronics because their small spin-orbit coupling and low hyperfine interaction is expected to give rise to large spin-relaxation times. However, the corresponding spin-relaxation length is not necessarily large when transport is via weakly interacting molecular orbitals. Here we use graphite as a model system and study spin transport in the direction perpendicular to the weakly bonded graphene sheets. We achieve injection of highly (75%) spin-polarized electrons into graphite nanostructures of 300-500 nm across and up to 17 nm thick, and observe transport without any measurable loss of spin information. Direct visualization of local spin transport in graphite-based spin-valve sandwiches also shows spatially uniform and near-unity transmission for electrons at 1.8 eV above the Fermi level

Energy dissipation and scattering angle distribution analysis of the classical trajectory calculations of methane scattering from a Ni(111) surface

We present classical trajectory calculations of the rotational vibrational scattering of a non-rigid methane molecule from a Ni(111) surface. Energy dissipation and scattering angles have been studied as a function of the translational kinetic energy, the incidence angle, the (rotational) nozzle temperature, and the surface temperature. Scattering angles are somewhat towards the surface for the incidence angles of 30, 45, and 60 degree at a translational energy of 96 kJ/mol. Energy loss is primarily from the normal component of the translational energy. It is transfered for somewhat more than half to the surface and the rest is transfered mostly to rotational motion. The spread in the change of translational energy has a basis in the spread of the transfer to rotational energy, and can be enhanced by raising of the surface temperature through the transfer process to the surface motion.Comment: 8 pages REVTeX, 5 figures (eps

Hysteresis in the de Haas-van Alphen Effect

A hysteresis loop is observed for the first time in the de Haas-van Alphen (dHvA) effect of beryllium at low temperatures and quantizing magnetic field applied parallel to the hexagonal axis of the single crystal. The irreversible behavior of the magnetization occurs at the paramagnetic part of the dHvA period in conditions of Condon domain formation arising by strong enough dHvA amplitude. The resulting extremely nonlinear response to a very small modulation field offers the possibility to find in a simple way the Condon domain phase diagram. From a harmonic analysis, the shape and size of the hysteresis loop is constructed.Comment: 4 pages, 5 figures, submitted to PR

Direct Observation of Condon Domains in Silver by Hall Probes

Using a set of micro Hall probes for the detection of the local induction, the inhomogeneous Condon domain structure has been directly observed at the surface of a pure silver single crystal under strong Landau quantization in magnetic fields up to 10 T. The inhomogeneous induction occurs in the theoretically predicted part of the H-T Condon domain phase diagram. Information about size, shape and orientation of the domains is obtained by analyzing Hall probes placed along and across the long sample axis and by tilting the sample. On a beryllium surface the induction inhomogeneity is almost absent although the expected induction splitting here is at least ten times higher than in silver.Comment: 4 pages, 6 figures, submitted to PR

Assessing Human Error Against a Benchmark of Perfection

An increasing number of domains are providing us with detailed trace data on human decisions in settings where we can evaluate the quality of these decisions via an algorithm. Motivated by this development, an emerging line of work has begun to consider whether we can characterize and predict the kinds of decisions where people are likely to make errors. To investigate what a general framework for human error prediction might look like, we focus on a model system with a rich history in the behavioral sciences: the decisions made by chess players as they select moves in a game. We carry out our analysis at a large scale, employing datasets with several million recorded games, and using chess tablebases to acquire a form of ground truth for a subset of chess positions that have been completely solved by computers but remain challenging even for the best players in the world. We organize our analysis around three categories of features that we argue are present in most settings where the analysis of human error is applicable: the skill of the decision-maker, the time available to make the decision, and the inherent difficulty of the decision. We identify rich structure in all three of these categories of features, and find strong evidence that in our domain, features describing the inherent difficulty of an instance are significantly more powerful than features based on skill or time.Comment: KDD 2016; 10 page

Numerical stability of the AA evolution system compared to the ADM and BSSN systems

We explore the numerical stability properties of an evolution system suggested by Alekseenko and Arnold. We examine its behavior on a set of standardized testbeds, and we evolve a single black hole with different gauges. Based on a comparison with two other evolution systems with well-known properties, we discuss some of the strengths and limitations of such simple tests in predicting numerical stability in general.Comment: 16 pages, 12 figure

An optimized chiral nucleon-nucleon interaction at next-to-next-to-leading order

We optimize the nucleon-nucleon interaction from chiral effective field theory at next-to-next- to-leading order. The resulting new chiral force NNLOopt yields \chi^2 \approx 1 per degree of freedom for laboratory energies below approximately 125 MeV. In the A = 3, 4 nucleon systems, the contributions of three-nucleon forces are smaller than for previous parametrizations of chiral interactions. We use NNLOopt to study properties of key nuclei and neutron matter, and demonstrate that many aspects of nuclear structure can be understood in terms of this nucleon-nucleon interaction, without explicitly invoking three-nucleon forces.Comment: 6 pages, 4 figure

Unsupervised Learning of Semantic Audio Representations

Even in the absence of any explicit semantic annotation, vast collections of audio recordings provide valuable information for learning the categorical structure of sounds. We consider several class-agnostic semantic constraints that apply to unlabeled nonspeech audio: (i) noise and translations in time do not change the underlying sound category, (ii) a mixture of two sound events inherits the categories of the constituents, and (iii) the categories of events in close temporal proximity are likely to be the same or related. Without labels to ground them, these constraints are incompatible with classification loss functions. However, they may still be leveraged to identify geometric inequalities needed for triplet loss-based training of convolutional neural networks. The result is low-dimensional embeddings of the input spectrograms that recover 41% and 84% of the performance of their fully-supervised counterparts when applied to downstream query-by-example sound retrieval and sound event classification tasks, respectively. Moreover, in limited-supervision settings, our unsupervised embeddings double the state-of-the-art classification performance.Comment: Submitted to ICASSP 201