Single-Shot Refinement Neural Network for Object Detection

For object detection, the two-stage approach (e.g., Faster R-CNN) has been achieving the highest accuracy, whereas the one-stage approach (e.g., SSD) has the advantage of high efficiency. To inherit the merits of both while overcoming their disadvantages, in this paper, we propose a novel single-shot based detector, called RefineDet, that achieves better accuracy than two-stage methods and maintains comparable efficiency of one-stage methods. RefineDet consists of two inter-connected modules, namely, the anchor refinement module and the object detection module. Specifically, the former aims to (1) filter out negative anchors to reduce search space for the classifier, and (2) coarsely adjust the locations and sizes of anchors to provide better initialization for the subsequent regressor. The latter module takes the refined anchors as the input from the former to further improve the regression and predict multi-class label. Meanwhile, we design a transfer connection block to transfer the features in the anchor refinement module to predict locations, sizes and class labels of objects in the object detection module. The multi-task loss function enables us to train the whole network in an end-to-end way. Extensive experiments on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO demonstrate that RefineDet achieves state-of-the-art detection accuracy with high efficiency. Code is available at https://github.com/sfzhang15/RefineDetComment: 14 pages, 7 figures, 7 table

Pascal-Interpolation-Based Noninteger Delay Filter and Low-Complexity Realization

This paper proposes a new method for designing the polynomial-interpolation-type noninteger-delay filter with a new structure formulation. Since the design formulation and the new realization structure are based on the discrete Pascal transform (DPT) and Pascal interpolation, we call the resulting filter Pascal noninteger-delay filter. The kth-order Pascal polynomial is used to pass through the given (k+1) data points in achieving the kth-order Pascal filter. The Pascal noninteger-delay filter is a real-time filter that consists of two sections, which can be realized into the front-section and the back-section. The front-section contains multiplication-free digital filters, and the number of multiplications in the back-section just linearly increases as order becomes high. Since the new Pascal filter has low complexity and structure can adjust non-integer delay online, it is more suited for fast delay tuning. Consequently, the polynomial-interpolation-type delay filter can be achieved by using the Pascal approach with high efficiency and low-complexity structure

VersaCross Transseptal System for Mitral Transcatheter Edge-To-Edge Repair With the PASCAL Repair Platform

BackgroundVersaCross is a novel radiofrequency transseptal solution that may improve the efficiency and workflow of transseptal puncture (TSP). The aim of this study was to compare the VersaCross transseptal system with mechanical needle systems during mitral transcatheter edge-to-edge repair (M-TEER) with the PASCAL device.MethodsThis is a single-center retrospective study of consecutive patients who underwent M-TEER with the PASCAL. Transseptal puncture was undertaken with either a mechanical needle or the VersaCross wire. The primary endpoints were success of TSP and successful delivery of the Edwards sheath on the chosen delivery wire. Secondary endpoints included number of wires used, tamponade rate, interval from femoral venous access to TSP and first PASCAL device deployment, procedural death, and stroke.ResultsThirty-three consecutive patients (10 with mechanical needle, 23 with VersaCross) who underwent M-TEER with the Edwards PASCAL device were identified. All patients had successful TSP. In the mechanical needle group, the Edwards sheath was successfully delivered on the Superstiff Amplatz wire in all cases. In the VersaCross arm, the radiofrequency wire was used successfully for delivery of the sheath in all cases. There were no cases of pericardial effusion/tamponade in either arm. Interval from femoral venous access to TSP and to deployment of the first PASCAL device was shorter with the VersaCross system. Significantly fewer wires were used with VersaCross. There were no procedural deaths or strokes in either group.ConclusionsVersaCross appears a safe and effective method of TSP and for delivery of the 22Fr sheath for M-TEER with PASCAL

Compiling vector pascal to the XeonPhi

Intel's XeonPhi is a highly parallel x86 architecture chip made by Intel. It has a number of novel features which make it a particularly challenging target for the compiler writer. This paper describes the techniques used to port the Glasgow Vector Pascal Compiler to this architecture and assess its performance by comparisons of the XeonPhi with 3 other machines running the same algorithms

SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation

We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to the efficiency of self-attention in encoding spatial information. In this paper, we show that convolutional attention is a more efficient and effective way to encode contextual information than the self-attention mechanism in transformers. By re-examining the characteristics owned by successful segmentation models, we discover several key components leading to the performance improvement of segmentation models. This motivates us to design a novel convolutional attention network that uses cheap convolutional operations. Without bells and whistles, our SegNeXt significantly improves the performance of previous state-of-the-art methods on popular benchmarks, including ADE20K, Cityscapes, COCO-Stuff, Pascal VOC, Pascal Context, and iSAID. Notably, SegNeXt outperforms EfficientNet-L2 w/ NAS-FPN and achieves 90.6% mIoU on the Pascal VOC 2012 test leaderboard using only 1/10 parameters of it. On average, SegNeXt achieves about 2.0% mIoU improvements compared to the state-of-the-art methods on the ADE20K datasets with the same or fewer computations. Code is available at https://github.com/uyzhang/JSeg (Jittor) and https://github.com/Visual-Attention-Network/SegNeXt (Pytorch).Comment: SegNeXt, a simple CNN for semantic segmentation. Code is availabl