Vortex Lattice Melting of a NbSe2 single grain probed by Ultrasensitive Cantilever Magnetometry

Using dynamic cantilever magnetometry, we study the vortex lattice and its corresponding melting transition in a micrometer-size crystallite of superconducting NbSe2. Measurements of the cantilever resonance frequency as a function of magnetic field and temperature respond to the magnetization of the vortex-lattice. The cantilever dissipation depends on thermally activated vortex creep motion, whose pinning energy barrier is found to be in good agreement with transport measurements on bulk samples. This approach reveals the phase diagram of the crystallite, and is applicable to other micro- or nanometer-scale superconducting samples.Comment: 5 pages, 4 figure

A geometry for optimizing nanoscale magnetic resonance force microscopy

We implement magnetic resonance force microscopy (MRFM) in an experimental geometry, where the long axis of the cantilever is normal to both the external magnetic field and the RF microwire source. Measurements are made of the statistical polarization of $^1$H in polystyrene with negligible magnetic dissipation, gradients greater than $10^5$ T/m within 100 nm of the magnetic tip, and rotating RF magnetic fields over 12 mT at 115 MHz. This geometry could facilitate the application of nanometer-scale MRFM to nuclear species with low gyro-magnetic ratios and samples with broadened resonances, such as In spins in quantum dots.Comment: 4 pages, 5 figure

Boundary between the thermal and statistical polarization regimes in a nuclear spin ensemble

As the number of spins in an ensemble is reduced, the statistical uctuations in its polarization eventually exceed the mean thermal polarization. This transition has now been surpassed in a number of recent nuclear magnetic resonance experiments, which achieve nanometer-scale detection volumes. Here, we measure nanometer- scale ensembles of nuclear spins in a KPF6 sample using magnetic resonance force microscopy. In particular, we investigate the transition between regimes dominated by thermal and statistical nuclear polarization. The ratio between the two types of polarization provides a measure of the number of spins in the detected ensemble

Learning-Based Approach to Real Time Tracking and Analysis of Faces

This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In developing this system, we have addressed the twin issues of image representation and algorithms for learning. We have used the invariance properties of image representations based on Haar wavelets to robustly capture various facial features. Similarly, unlike previous approaches this system is entirely trained using examples and does not rely on a priori (hand-crafted) models of facial features based on optical flow or facial musculature. The system works in several stages that begin with face detection, followed by localization of facial features and estimation of mouth parameters. Each of these stages is formulated as a problem in supervised learning from examples. We apply the new and robust technique of support vector machines (SVM) for classification in the stage of skin segmentation, face detection and eye detection. Estimation of mouth parameters is modeled as a regression from a sparse subset of coefficients (basis functions) of an overcomplete dictionary of Haar wavelets

A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system

Antiferromagnetic s-d exchange coupling in GaMnAs

Measurements of coherent electron spin dynamics in Ga(1-x)Mn(x)As/Al(0.4)Ga(0.6)As quantum wells with 0.0006% < x < 0.03% show an antiferromagnetic (negative) exchange bewteen s-like conduction band electrons and electrons localized in the d-shell of the Mn2+ impurities. The magnitude of the s-d exchange parameter, N0 alpha, varies as a function of well width indicative of a large and negative contribution due to kinetic exchange. In the limit of no quantum confinement, N0 alpha extrapolates to -0.09 +/- 0.03 eV indicating that antiferromagnetic s-d exchange is a bulk property of GaMnAs. Measurements of the polarization-resolved photoluminescence show strong discrepancy from a simple model of the exchange enhanced Zeeman splitting, indicative of additional complexity in the exchange split valence band.Comment: 5 pages, 4 figures and one action figur

Force-detected nuclear double resonance between statistical spin polarizations

We demonstrate nuclear double resonance for nanometer-scale volumes of spins where random fluctuations rather than Boltzmann polarization dominate. When the Hartmann-Hahn condition is met in a cross-polarization experiment, flip-flops occur between two species of spins and their fluctuations become coupled. We use magnetic resonance force microscopy to measure this effect between 1H and 13C spins in 13C-enriched stearic acid. The development of a cross-polarization technique for statistical ensembles adds an important tool for generating chemical contrast in nanometer-scale magnetic resonance.Comment: 14 pages, 4 figure

Sparse Correlation Kernel Analysis and Reconstruction

This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing. To choose the appropriate features from this large dictionary, we use Support Vector Machine (SVM) regression and compare this to traditional Principal Component Analysis (PCA) for the tasks of signal reconstruction, superresolution, and compression. The testbed we use in this paper is a set of images of pedestrians. This paper also presents results of experiments in which we use a dictionary of multiscale basis functions and then use Basis Pursuit De-Noising to obtain a sparse, multiscale approximation of a signal. The results are analyzed and we conclude that 1) when used with a sparse representation technique, the correlation function is an effective kernel for image reconstruction and superresolution, 2) for image compression, PCA and SVM have different tradeoffs, depending on the particular metric that is used to evaluate the results, 3) in sparse representation techniques, L_1 is not a good proxy for the true measure of sparsity, L_0, and 4) the L_epsilon norm may be a better error metric for image reconstruction and compression than the L_2 norm, though the exact psychophysical metric should take into account high order structure in images