SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds. In particular, we wish to detect and categorize instances of interest, such as cars, pedestrians and cyclists. We formulate this problem as a point- wise classification problem, and propose an end-to-end pipeline called SqueezeSeg based on convolutional neural networks (CNN): the CNN takes a transformed LiDAR point cloud as input and directly outputs a point-wise label map, which is then refined by a conditional random field (CRF) implemented as a recurrent layer. Instance-level labels are then obtained by conventional clustering algorithms. Our CNN model is trained on LiDAR point clouds from the KITTI dataset, and our point-wise segmentation labels are derived from 3D bounding boxes from KITTI. To obtain extra training data, we built a LiDAR simulator into Grand Theft Auto V (GTA-V), a popular video game, to synthesize large amounts of realistic training data. Our experiments show that SqueezeSeg achieves high accuracy with astonishingly fast and stable runtime (8.7 ms per frame), highly desirable for autonomous driving applications. Furthermore, additionally training on synthesized data boosts validation accuracy on real-world data. Our source code and synthesized data will be open-sourced

The Effects of Filler Loading on Mechanical Properties of Coir and Bagasse Reinforced Polyethylene Biocomposites

The aim of the study is to investigate the effect of filler loading on the mechanical properties of coir and bagasse reinforced high density polyethylene (HDPE). Particulate size fibres were treated with 3 wt% of vinyltriethoxysilane to improve the adhesion between the fillers and HDPE. The fillers and matrix were compounded at filler loading of 0, 5, 10 and 15 wt% fillers using injection moulding machine to produce dog bone shapes for testing. Tensile strength of the biocomposites was observed to decrease as natural fillers were incorporated. No significant changes in tensile strength for both biocomposites were observed when filler loading was increased. Filler loading had no effect on the flexural strength of coir biocomposite as well. However, the flexural strength for bagasse biocomposite decreased gradually as filler content was increased. Scanning electron microscope micrographs at fracture surface revealed fibre pullouts, filler agglomeration, and debonding due to poor matrix/fibre adhesio

Why do people buy hybrid cars?

This article reports on the underlying dimensions used by petrol-electric hybrid and conventional car buyers when evaluating a vehicle with the intent to purchase. Buyers of conventionally fuelled vehicles reported that they considered quality and performance as the most important determinants of choice. They rated as least important, the image they derive from driving a particular car and social influence. On the other hand, petrol-electric hybrid car buyers reported that social influence and projecting a &ldquo;green&rdquo; image were most important considerations and quality and appeal were least important. These findings provide social marketers with a crucial understanding that helps in the selection of an appropriate model to promote the diffusion of eco-friendly vehicles. <br /

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadratically with respect to kernel size. In this paper, we present a parameter-free, FLOP-free "shift" operation as an alternative to spatial convolutions. We fuse shifts and point-wise convolutions to construct end-to-end trainable shift-based modules, with a hyperparameter characterizing the tradeoff between accuracy and efficiency. To demonstrate the operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based modules for improved CIFAR10 and CIFAR100 accuracy using 60% fewer parameters; we additionally demonstrate the operation's resilience to parameter reduction on ImageNet, outperforming ResNet family members. We finally show the shift operation's applicability across domains, achieving strong performance with fewer parameters on classification, face verification and style transfer.Comment: Source code will be released afterward

AutoFocusFormer: Image Segmentation off the Grid

Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small objects. Yet, the commonly used successive grid downsampling strategy in convolutional deep networks treats all areas equally. Hence, small objects are represented in very few spatial locations, leading to worse results in tasks such as segmentation. Intuitively, retaining more pixels representing small objects during downsampling helps to preserve important information. To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task. Since adaptive downsampling generates a set of pixels irregularly distributed on the image plane, we abandon the classic grid structure. Instead, we develop a novel point-based local attention block, facilitated by a balanced clustering module and a learnable neighborhood merging module, which yields representations for our point-based versions of state-of-the-art segmentation heads. Experiments show that our AutoFocusFormer (AFF) improves significantly over baseline models of similar sizes.Comment: CVPR 202

UPSCALE: Unconstrained Channel Pruning

As neural networks grow in size and complexity, inference speeds decline. To combat this, one of the most effective compression techniques -- channel pruning -- removes channels from weights. However, for multi-branch segments of a model, channel removal can introduce inference-time memory copies. In turn, these copies increase inference latency -- so much so that the pruned model can be slower than the unpruned model. As a workaround, pruners conventionally constrain certain channels to be pruned together. This fully eliminates memory copies but, as we show, significantly impairs accuracy. We now have a dilemma: Remove constraints but increase latency, or add constraints and impair accuracy. In response, our insight is to reorder channels at export time, (1) reducing latency by reducing memory copies and (2) improving accuracy by removing constraints. Using this insight, we design a generic algorithm UPSCALE to prune models with any pruning pattern. By removing constraints from existing pruners, we improve ImageNet accuracy for post-training pruned models by 2.1 points on average -- benefiting DenseNet (+16.9), EfficientNetV2 (+7.9), and ResNet (+6.2). Furthermore, by reordering channels, UPSCALE improves inference speeds by up to 2x over a baseline export.Comment: 29 pages, 26 figures, accepted to ICML 202

‘Crescendo transient ischemic attack’—an uncommon presentation of a very common disease: a case report on capsular warning syndrome

Capsular warning syndrome is a rare presentation of transient ischaemic attack, which described as recurrent episodes of motor and/or sensory deficits which typically sparring the cortical function. It has a significant risk to progress into a massive stroke with permanent disability, thus important to be recognize early. Here, we report a middle-aged gentleman with no known medical illness presented with eight episodes of transient ischaemic attack within the span of 24 h. He was treated with double anti-platelet for 21 days and was not subjected to thrombolysis at time of presentation because it was outside the window period of 4.5 h and has fully recovered after each episode. The purpose of this case report is to share the uncommon clinical presentation of transient ischaemic attack, which is still not fully understood and warrant more studies especially on the treatment that can affect the progression of the disease