Universal Scaling in Non-equilibrium Transport Through a Single-Channel Kondo Dot

Scaling laws and universality play an important role in our understanding of critical phenomena and the Kondo effect. Here we present measurements of non-equilibrium transport through a single-channel Kondo quantum dot at low temperature and bias. We find that the low-energy Kondo conductance is consistent with universality between temperature and bias and characterized by a quadratic scaling exponent, as expected for the spin-1/2 Kondo effect. The non-equilibrium Kondo transport measurements are well-described by a universal scaling function with two scaling parameters.Comment: v2: improved introduction and theory-experiment comparsio

Experimental Realization of a Quantum Spin Pump

We demonstrate the operation of a quantum spin pump based on cyclic radio-frequency excitation of a GaAs quantum dot, including the ability to pump pure spin without pumping charge. The device takes advantage of bidirectional mesoscopic fluctuations of pumped current, made spin-dependent by the application of an in-plane Zeeman field. Spin currents are measured by placing the pump in a focusing geometry with a spin-selective collector.Comment: related papers available at http://marcuslab.harvard.ed

Detecting Spin-Polarized Currents in Ballistic Nanostructures

We demonstrate a mesoscopic spin polarizer/analyzer system that allows the spin polarization of current from a quantum point contact in an in-plane magnetic field to be measured. A transverse focusing geometry is used to couple current from an emitter point contact into a collector point contact. At large in-plane fields, with the point contacts biased to transmit only a single spin (g < e^2/h), the voltage across the collector depends on the spin polarization of the current incident on it. Spin polarizations of greater than 80% are found for both emitter and collector at 300mK and 7T in-plane field.Comment: related papers at http://marcuslab.harvard.ed

Imaging transverse electron focusing in semiconducting heterostructures with spin-orbit coupling

Transverse electron focusing in two-dimensional electron gases (2DEGs) with strong spin-orbit coupling is revisited. The transverse focusing is related to the transmission between two contacts at the edge of a 2DEG when a perpendicular magnetic field is applied. Scanning probe microscopy imaging techniques can be used to study the electron flow in these systems. Using numerical techniques we simulate the images that could be obtained in such experiments. We show that hybrid edge states can be imaged and that the outgoing flux can be polarized if the microscope tip probe is placed in specific positions.Comment: Contribution to the Book/Proceedings of the PITP Les Houches School on "Quantum Magnetism" held on June, 2006. Final forma

Exascale Deep Learning to Accelerate Cancer Research

Deep learning, through the use of neural networks, has demonstrated remarkable ability to automate many routine tasks when presented with sufficient data for training. The neural network architecture (e.g. number of layers, types of layers, connections between layers, etc.) plays a critical role in determining what, if anything, the neural network is able to learn from the training data. The trend for neural network architectures, especially those trained on ImageNet, has been to grow ever deeper and more complex. The result has been ever increasing accuracy on benchmark datasets with the cost of increased computational demands. In this paper we demonstrate that neural network architectures can be automatically generated, tailored for a specific application, with dual objectives: accuracy of prediction and speed of prediction. Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference. The speedup in inference is necessary because of the volume and velocity of cancer pathology data; specifically, the previous state-of-the-art networks are too slow for individual researchers without access to HPC systems to keep pace with the rate of data generation. Our new model enables researchers with modest computational resources to analyze newly generated data faster than it is collected.Comment: Submitted to IEEE Big Dat