Structure, dynamics and bifurcations of discrete solitons in trapped ion crystals

We study discrete solitons (kinks) accessible in state-of-the-art trapped ion experiments, considering zigzag crystals and quasi-3D configurations, both theoretically and experimentally. We first extend the theoretical understanding of different phenomena predicted and recently experimentally observed in the structure and dynamics of these topological excitations. Employing tools from topological degree theory, we analyze bifurcations of crystal configurations in dependence on the trapping parameters, and investigate the formation of kink configurations and the transformations of kinks between different structures. This allows us to accurately define and calculate the effective potential experienced by solitons within the Wigner crystal, and study how this (so-called Peierls-Nabarro) potential gets modified to a nonperiodic globally trapping potential in certain parameter regimes. The kinks' rest mass (energy) and spectrum of modes are computed and the dynamics of linear and nonlinear kink oscillations are analyzed. We also present novel, experimentally observed, configurations of kinks incorporating a large-mass defect realized by an embedded molecular ion, and of pairs of interacting kinks stable for long times, offering the perspective for exploring and exploiting complex collective nonlinear excitations, controllable on the quantum level.Comment: 25 pages, 10 figures, v2 corrects Fig. 2 and adds some text and reference

TraMNet - Transition Matrix Network for Efficient Action Tube Proposals

Current state-of-the-art methods solve spatiotemporal action localisation by extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate sets of temporally connected bounding boxes called \textit{action micro-tubes}. However, they fail to consider that the underlying anchor proposal hypotheses should also move (transition) from frame to frame, as the actor or the camera does. Assuming we evaluate $n$ 2D anchors in each frame, then the number of possible transitions from each 2D anchor to the next, for a sequence of $f$ consecutive frames, is in the order of $O(n^f)$, expensive even for small values of $f$. To avoid this problem, we introduce a Transition-Matrix-based Network (TraMNet) which relies on computing transition probabilities between anchor proposals while maximising their overlap with ground truth bounding boxes across frames, and enforcing sparsity via a transition threshold. As the resulting transition matrix is sparse and stochastic, this reduces the proposal hypothesis search space from $O(n^f)$ to the cardinality of the thresholded matrix. At training time, transitions are specific to cell locations of the feature maps, so that a sparse (efficient) transition matrix is used to train the network. At test time, a denser transition matrix can be obtained either by decreasing the threshold or by adding to it all the relative transitions originating from any cell location, allowing the network to handle transitions in the test data that might not have been present in the training data, and making detection translation-invariant. Finally, we show that our network can handle sparse annotations such as those available in the DALY dataset. We report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24 datasets to support our claims.Comment: 15 page

A Multi-scale Bilateral Structure Tensor Based Corner Detector

9th Asian Conference on Computer Vision, ACCV 2009, Xi'an, 23-27 September 2009In this paper, a novel multi-scale nonlinear structure tensor based corner detection algorithm is proposed to improve effectively the classical Harris corner detector. By considering both the spatial and gradient distances of neighboring pixels, a nonlinear bilateral structure tensor is constructed to examine the image local pattern. It can be seen that the linear structure tensor used in the original Harris corner detector is a special case of the proposed bilateral one by considering only the spatial distance. Moreover, a multi-scale filtering scheme is developed to tell the trivial structures from true corners based on their different characteristics in multiple scales. The comparison between the proposed approach and four representative and state-of-the-art corner detectors shows that our method has much better performance in terms of both detection rate and localization accuracy.Department of ComputingRefereed conference pape

A framework for automatic semantic video annotation

The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the ‘semantic gap’. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation

High-brightness broad-area diode lasers with enhanced self-aligned lateral structure

Broad-area diode lasers with increased brightness and efficiency are presented, which are fabricated using an enhanced self-aligned lateral structure by means of a two-step epitaxial growth process with an intermediate etching step. In this structure, current-blocking layers in the device edges ensure current confinement under the central stripe, which can limit the detrimental effects of current spreading and lateral carrier accumulation on beam quality. It also minimizes losses at stripe edges, thus lowering the lasing threshold and increasing conversion efficiency, while maintaining high polarization purity. In the first realization of this structure, the current block is integrated within an extreme-triple-asymmetric epitaxial design with a thin p-doped side, meaning that the distance between the current block and the active zone can be minimized without added process complexity. Using this configuration, enhanced self-aligned structure devices with 90 µm stripe width and 4 mm resonator length show up to 20% lower threshold current, 21% narrower beam waist, and slightly higher (1.03 ) peak efficiency in comparison to reference devices with the same dimensions, while slope, divergence angle and polarization purity remain almost unchanged. These results correspond to an increase in brightness by up to 25%, and measurement results of devices with varying stripe widths follow the same trend. © 2020 The Author(s). Published by IOP Publishing Ltd

Alternative proof for the localization of Sinai's walk

We give an alternative proof of the localization of Sinai's random walk in random environment under weaker hypothesis than the ones used by Sinai. Moreover we give estimates that are stronger than the one of Sinai on the localization neighborhood and on the probability for the random walk to stay inside this neighborhood

High-power diode lasers with in-situ-structured lateral current blocking for improved threshold, efficiency and brightness

We present high-power GaAs-based broad-area diode lasers with a novel variant of the enhanced self-aligned lateral structure ‘eSAS’, having a strongly reduced lasing threshold and improved peak conversion efficiency and beam quality in comparison to their standard gain-guided counterparts. To realize this new variant (eSAS-V2), a two-step epitaxial growth process involving in situ etching is used to integrate current-blocking layers, optimized for tunnel current suppression, within the p-Al0.8GaAs cladding layer of an extreme-triple-asymmetric epitaxial structure with a thin p-side waveguide. The blocking layers are thus in close proximity to the active zone, resulting in strong suppression of current spreading and lateral carrier accumulation. eSAS-V2 devices with 4 mm resonator length and varying stripe widths are characterized and compared to previous eSAS variant (eSAS-V1) as well as gain-guided reference devices, all having the same dimensions and epitaxial structure. Measurement results show that the new eSAS-V2 variant eliminates an estimated 89% of lateral current spreading, resulting in a strong threshold current reduction of 29% at 90 μm stripe width, while slope and series resistance are broadly unchanged. The novel eSAS-V2 devices also maintain high conversion efficiency up to high continuous-wave optical power, with an exemplary 90 μm device having 51.5% at 20 W. Near-field width is significantly narrowed in both eSAS variants, but eSAS-V2 exhibits a wider far-field angle, consistent with the presence of index guiding. Nonetheless, eSAS-V2 achieves higher beam quality and lateral brightness than gain-guided reference devices, but the index guiding in this realization prevents it from surpassing eSAS-V1. Overall, the different performance benefits of the eSAS approach are clearly demonstrated

Adaptive structure tensors and their applications

The structure tensor, also known as second moment matrix or Förstner interest operator, is a very popular tool in image processing. Its purpose is the estimation of orientation and the local analysis of structure in general. It is based on the integration of data from a local neighborhood. Normally, this neighborhood is defined by a Gaussian window function and the structure tensor is computed by the weighted sum within this window. Some recently proposed methods, however, adapt the computation of the structure tensor to the image data. There are several ways how to do that. This article wants to give an overview of the different approaches, whereas the focus lies on the methods based on robust statistics and nonlinear diffusion. Furthermore, the dataadaptive structure tensors are evaluated in some applications. Here the main focus lies on optic flow estimation, but also texture analysis and corner detection are considered