Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration

We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework. Our approach consists of two sequential networks with a specifically designed loss function to address the challenges in 3D first trimester ultrasound. The first part learns the affine transformation and the second part learns the voxelwise nonrigid deformation between the target image and the atlas. We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challenges present in 3D first trimester ultrasound. The method was tested on a dataset of human embryonic ultrasound volumes acquired at 9 weeks gestational age, which showed alignment of the brain in some cases and gave insight in open challenges for the proposed method. We conclude that our method is a promising approach towards fully automated spatial alignment and segmentation of embryonic brains in 3D ultrasound

Vortex lattices in strong type-II superconducting two-dimensional strips

We show how to calculate semi-analytically the dense vortex state in strong type-II superconducting nanostructures. For the specific case of a strip, we find vortex lattice solutions which also incorporate surface superconductivity. We calculate the energy cost to displace individual vortex rows parallel to the surfaces and find that this energy oscillates with the magnetic field. Remarkably, we also find that, at a critical field $H^*$ below $H_{c2}$, this ''shear'' energy becomes strictly zero for the surface rows due to an unexpected mismatch with the bulk lattice.Comment: Title, abstract, and some text paragraphs have been rewritte

Mode locking of vortex matter driven through mesoscopic channels

We investigated the driven dynamics of vortices confined to mesoscopic flow channels by means of a dc-rf interference technique. The observed mode-locking steps in the $IV$-curves provide detailed information on how the number of rows and lattice structure in the channel change with magnetic field. Minima in flow stress occur when an integer number of rows is moving coherently, while maxima appear when incoherent motion of mixed $n$ and $n\pm 1$ row configurations is predominant. Simulations show that the enhanced pinning at mismatch originates from quasi-static fault zones with misoriented edge dislocations induced by disorder in the channel edges.Comment: some minor changes were made, 4 pages, 4 figures, accepted for publication in Phys. Rev. Let

Patterned Geometries and Hydrodynamics at the Vortex Bose Glass Transition

Patterned irradiation of cuprate superconductors with columnar defects allows a new generation of experiments which can probe the properties of vortex liquids by confining them to controlled geometries. Here we show that an analysis of such experiments that combines an inhomogeneous Bose glass scaling theory with the hydrodynamic description of viscous flow of vortex liquids can be used to infer the critical behavior near the Bose glass transition. The shear viscosity is predicted to diverge as $|T-T_{BG}|^{-z}$ at the Bose glass transition, with $z\simeq 6$ the dynamical critical exponent.Comment: 5 pages, 4 figure

Patterned Irradiation of YBa_2Cu_3O_(7-x) Thin Films

We present a new experiment on YBa_2Cu_3O_{7-x} (YBCO) thin films using spatially resolved heavy ion irradiation. Structures consisting of a periodic array of strong and weak pinning channels were created with the help of metal masks. The channels formed an angle of +/-45 Deg with respect to the symmetry axis of the photolithographically patterned structures. Investigations of the anisotropic transport properties of these structures were performed. We found striking resemblance to guided vortex motion as it was observed in YBCO single crystals containing an array of unidirected twin boundaries. The use of two additional test bridges allowed to determine in parallel the resistivities of the irradiated and unirradiated parts as well as the respective current-voltage characteristics. These measurements provided the input parameters for a numerical simulation of the potential distribution of the Hall patterning. In contrast to the unidirected twin boundaries in our experiment both strong and weak pinning regions are spatially extended. The interfaces between unirradiated and irradiated regions therefore form a Bose-glass contact. The experimentally observed magnetic field dependence of the transverse voltage vanishes faster than expected from the numerical simulation and we interpret this as a hydrodynamical interaction between a Bose-glass phase and a vortex liquid.Comment: 7 pages, 8 Eps figures included. Submitted to PR

Entropy and information in neural spike trains: Progress on the sampling problem

The major problem in information theoretic analysis of neural responses and other biological data is the reliable estimation of entropy--like quantities from small samples. We apply a recently introduced Bayesian entropy estimator to synthetic data inspired by experiments, and to real experimental spike trains. The estimator performs admirably even very deep in the undersampled regime, where other techniques fail. This opens new possibilities for the information theoretic analysis of experiments, and may be of general interest as an example of learning from limited data.Comment: 7 pages, 4 figures; referee suggested changes, accepted versio

Melting of two dimensional solids on disordered substrate

We study 2D solids with weak substrate disorder, using Coulomb gas renormalisation. The melting transition is found to be replaced by a sharp crossover between a high $T$ liquid with thermally induced dislocations, and a low $T$ glassy regime with disorder induced dislocations at scales larger than $\xi_{d}$ which we compute ($\xi_{d}\gg R_{c}\sim R_{a}$, the Larkin and translational correlation lengths). We discuss experimental consequences, reminiscent of melting, such as size effects in vortex flow and AC response in superconducting films.Comment: 4 pages, uses RevTeX, Amssymb, multicol,eps

Incommensuration Effects and Dynamics in Vortex Chains

We examine the motion of one-dimensional (1D) vortex matter embedded in a 2D vortex system with weak pinning using numerical simulations. We confirm the conjecture of Matsuda et al. [Science 294, 2136 (2001)] that the onset of the temperature induced motion of the chain is due to an incommensuration effect of the chain with the periodic potential created by the bulk vortices. In addition, under an applied driving force we find a two stage depinning transition, where the initial depinning of the vortex chain occurs through soliton like pulses. When an ac drive is added to the dc drive, we observe phase locking of the moving vortex chain.Comment: 4 pages, 4 postscript figure

Vortex Motion Noise in Micrometre-Sized Thin Films of the Amorphous Nb0.7Ge0.3 Weak-Pinning Superconductor

We report high-resolution measurements of voltage (V) noise in the mixed state of micrometre-sized thin films of amorphous Nb0.7Ge0.3, which is a good representative of weak-pinning superconductors. There is a remarkable difference between the noise below and above the irreversibility field Birr. Below Birr, in the presence of measurable pinning, the noise at small applied currents resembles shot noise, and in the regime of flux flow at larger currents decreases with increasing voltage due to a progressive ordering of the vortex motion. At magnetic fields B between Birr and the upper critical field Bc2 flux flow is present already at vanishingly small currents. In this regime the noise scales with (1-B/Bc2)^2 V^2 and has a frequency (f) spectrum of 1/f type. We interpret this noise in terms of the properties of strongly driven depinned vortex systems at high vortex density.Comment: 8 pages, 5 figures, version accepted for publication in PR

Representation of Time-Varying Stimuli by a Network Exhibiting Oscillations on a Faster Time Scale

Sensory processing is associated with gamma frequency oscillations (30–80 Hz) in sensory cortices. This raises the question whether gamma oscillations can be directly involved in the representation of time-varying stimuli, including stimuli whose time scale is longer than a gamma cycle. We are interested in the ability of the system to reliably distinguish different stimuli while being robust to stimulus variations such as uniform time-warp. We address this issue with a dynamical model of spiking neurons and study the response to an asymmetric sawtooth input current over a range of shape parameters. These parameters describe how fast the input current rises and falls in time. Our network consists of inhibitory and excitatory populations that are sufficient for generating oscillations in the gamma range. The oscillations period is about one-third of the stimulus duration. Embedded in this network is a subpopulation of excitatory cells that respond to the sawtooth stimulus and a subpopulation of cells that respond to an onset cue. The intrinsic gamma oscillations generate a temporally sparse code for the external stimuli. In this code, an excitatory cell may fire a single spike during a gamma cycle, depending on its tuning properties and on the temporal structure of the specific input; the identity of the stimulus is coded by the list of excitatory cells that fire during each cycle. We quantify the properties of this representation in a series of simulations and show that the sparseness of the code makes it robust to uniform warping of the time scale. We find that resetting of the oscillation phase at stimulus onset is important for a reliable representation of the stimulus and that there is a tradeoff between the resolution of the neural representation of the stimulus and robustness to time-warp. Author Summary Sensory processing of time-varying stimuli, such as speech, is associated with high-frequency oscillatory cortical activity, the functional significance of which is still unknown. One possibility is that the oscillations are part of a stimulus-encoding mechanism. Here, we investigate a computational model of such a mechanism, a spiking neuronal network whose intrinsic oscillations interact with external input (waveforms simulating short speech segments in a single acoustic frequency band) to encode stimuli that extend over a time interval longer than the oscillation's period. The network implements a temporally sparse encoding, whose robustness to time warping and neuronal noise we quantify. To our knowledge, this study is the first to demonstrate that a biophysically plausible model of oscillations occurring in the processing of auditory input may generate a representation of signals that span multiple oscillation cycles.National Science Foundation (DMS-0211505); Burroughs Wellcome Fund; U.S. Air Force Office of Scientific Researc