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

A Comparison of current SDRAM types: SDR, DDR, and RDRAM

The ever increasing demand for bandwidth of computer-systems lead to several standards of SDRAMs. This article compares SDR, DDRI, DDRII, and RDRAM systems. Besides the overall basic innovations, differences will be discussed. Topics like architecture, interfaces, and modules are described

Video Object Detection with an Aligned Spatial-Temporal Memory

We introduce Spatial-Temporal Memory Networks for video object detection. At its core, a novel Spatial-Temporal Memory module (STMM) serves as the recurrent computation unit to model long-term temporal appearance and motion dynamics. The STMM's design enables full integration of pretrained backbone CNN weights, which we find to be critical for accurate detection. Furthermore, in order to tackle object motion in videos, we propose a novel MatchTrans module to align the spatial-temporal memory from frame to frame. Our method produces state-of-the-art results on the benchmark ImageNet VID dataset, and our ablative studies clearly demonstrate the contribution of our different design choices. We release our code and models at http://fanyix.cs.ucdavis.edu/project/stmn/project.html

Physiographic Surfaces and Weathering Near Butte

Three cycles of erosion have modified the Boulder batholith. The earliest cycle produced a peneplaination that has been largely obliterated by a partially completed intermediate cycle, and the recent cycle now in progress

A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation {\it and} multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach

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

Serum osteoprotegerin and renal function in the general population: The Tromsø Study

The following article: Vik, A., Brodin, E.E., Mathiesen, E.B., Brox, J., Jørgensen, L., Njølstad, I., ... Hansen, J.-B. (2017). Serum osteoprotegerin and renal function in the general population: The Tromsø Study. Clinical Kidney Journal, 10(1), 38-44. https://doi.org/10.1093/ckj/sfw095, has been accepted for publication in Clinical Kidney Journal Published by Oxford University Press. Source at https://doi.org/10.1093/ckj/sfw095. Published manuscript version, licensed CC BY-NC-ND 4.0.Background: Serum osteoprotegerin (OPG) is elevated in patients with chronic kidney disease (CKD) and increases with decreasing renal function. However, there are limited data regarding the association between OPG and renal function in the general population. The aim of the present study was to explore the relation between serum OPG and renal function in subjects recruited from the general population. Methods: We conducted a cross-sectional study with 6689 participants recruited from the general population in Tromsø, Norway. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration equations. OPG was modelled both as a continuous and categorical variable. General linear models and linear regression with adjustment for possible confounders were used to study the association between OPG and eGFR. Analyses were stratified by the median age, as serum OPG and age displayed a significant interaction on eGFR. Results: In participants ≤62.2 years with normal renal function (eGFR ≥90 mL/min/1.73 m2) eGFR increased by 0.35 mL/min/1.73 m2 (95% CI 0.13–0.56) per 1 standard deviation (SD) increase in serum OPG after multiple adjustment. In participants older than the median age with impaired renal function (eGFR 2), eGFR decreased by 1.54 (95% CI −2.06 to −1.01) per 1 SD increase in serum OPG. Conclusions: OPG was associated with an increased eGFR in younger subjects with normal renal function and with a decreased eGFR in older subjects with reduced renal function. Our findings imply that the association between OPG and eGFR varies with age and renal function

Occlusion and Motion Reasoning for Long-Term Tracking

International audienceObject tracking is a reoccurring problem in computer vision. Tracking-by-detection approaches, in particular Struck (Hare et al., 2011), have shown to be competitive in recent evaluations. However, such approaches fail in the presence of long-term occlusions as well as severe viewpoint changes of the object. In this paper we propose a principled way to combine occlusion and motion reasoning with a tracking-by-detection approach. Occlusion and motion reasoning is based on state-of-the-art long-term trajectories which are labeled as object or background tracks with an energy-based formulation. The overlap between labeled tracks and detected regions allows to identify occlusions. The motion changes of the object between consecutive frames can be estimated robustly from the geometric relation between object trajectories. If this geometric change is significant, an additional detector is trained. Experimental results show that our tracker obtains state-of-the-art results and handles occlusion and viewpoints changes better than competing tracking methods