Peacockfinley3797

8 vs. 15.2%, P = 0.183) or all-cause mortality (5.7% vs. 3.9%, P = 0.242) between sexes. At 3-year follow up, there was a significantly higher rate of MACE in women (29.1% vs. 22.5%, P = 0.026), this was driven by a significantly higher all-cause mortality (13.8% vs. 6.5%, P < 0.01).

Women undergoing bifurcation PCI are older and have more comorbidities than their male counterparts. Intermediate term follow-up outcomes are similar between sexes. Poorer long-term outcomes of women are likely due to baseline higher risk profile.

Women undergoing bifurcation PCI are older and have more comorbidities than their male counterparts. Intermediate term follow-up outcomes are similar between sexes. Poorer long-term outcomes of women are likely due to baseline higher risk profile.

Multilevel lumbar spondylolistheses have been reported, but only secondary to degenerative processes. We describe a case where grade 4 anterolisthesis occurred (L3,4,5 over S1) because of multiple level traumatic pedicle avulsion rather than facetal/pars interarticularis/posterior ligamentous complex disruption in a 42-year-old man who presented with paraparesis after a fall from height. Decompression was performed at the L5 level, and pedicle screw fixation was performed at L3, L5, and S1 levels.

Although such an injury pattern seems catastrophic, it is deemed relatively stable because of the intact posterior ligamentous complex. Restoration of anatomy with stabilization allowed early mobility and satisfactory neurological recovery.

Although such an injury pattern seems catastrophic, it is deemed relatively stable because of the intact posterior ligamentous complex. Restoration of anatomy with stabilization allowed early mobility and satisfactory neurological recovery.No Abstract Available.Learning meaningful representations of free-hand sketches remains a challenging task given the signal sparsity and the high-level abstraction of sketches. Existing techniques have focused on exploiting either the static nature of sketches with convolutional neural networks (CNNs) or the temporal sequential property with recurrent neural networks (RNNs). In this work, we propose a new representation of sketches as multiple sparsely connected graphs. We design a novel graph neural network (GNN), the multigraph transformer (MGT), for learning representations of sketches from multiple graphs, which simultaneously capture global and local geometric stroke structures as well as temporal information. We report extensive numerical experiments on a sketch recognition task to demonstrate the performance of the proposed approach. Particularly, MGT applied on 414k sketches from Google QuickDraw 1) achieves a small recognition gap to the CNN-based performance upper bound (72.80% versus 74.22%) and infers faster than the CNN competitors and 2) outperforms all RNN-based models by a significant margin. To the best of our knowledge, this is the first work proposing to represent sketches as graphs and apply GNNs for sketch recognition. Code and trained models are available at https//github.com/PengBoXiangShang/multigraph_transformer.In this article, a distributed adaptive iterative learning control for a group of uncertain autonomous vehicles with a time-varying reference is presented, where the autonomous vehicles are underactuated with parametric uncertainties, the actuators are subject to faults, and the control gains are not fully known. A time-varying reference is adopted, the assumption that the trajectory of the leader is linearly parameterized with some known functions is relaxed, and the control inputs are smooth. To design distributed control scheme for each vehicle, a local compensatory variable is generated based on information collected from its neighbors. The composite energy function is used in stability analysis. It is shown that uniform convergence of consensus errors is guaranteed. An illustrative example is given to demonstrate the effectiveness of the proposed control scheme.The goal of this study is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts reaching movement task. K02288 ic50 The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.Inverse synthetic aperture radar (ISAR) imaging for the sparse aperture data is affected by considerable artifacts, because under-sampling of data produces high-level grating and side lobes. Noting the ISAR image generally exhibits strong sparsity, it is often obtained by sparse signal recovery (SSR) in case of sparse aperture. The image obtained by SSR, however, is often dominated by strong isolated scatterers, resulting in difficulty to recognize the structure of target. This paper proposes a novel approach to enhance the ISAR image obtained from the sparse aperture data. Although the scatterers of target are isolated in the ISAR image, they should be associated with the neighborhood to reflect some intrinsic structural information of the target. A convolutional reweighted l1 minimization model, therefore, is proposed to model the structural sparsity of ISAR image. Specifically, the ISAR image is reconstructed by solving a sequence of reweighted l1 problems, where the weight of each pixel used for the next iteration is calculated from the convolution of its neighbor values in the current solution.