What exactly are the properties of scale-free and other networks?

The concept of scale-free networks has been widely applied across natural and physical sciences. Many claims are made about the properties of these networks, even though the concept of scale-free is often vaguely defined. We present tools and procedures to analyse the statistical properties of networks defined by arbitrary degree distributions and other constraints. Doing so reveals the highly likely properties, and some unrecognised richness, of scale-free networks, and casts doubt on some previously claimed properties being due to a scale-free characteristic.Comment: Preprint - submitted, 6 pages, 3 figure

Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure

Visual motion estimation is an integral and well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation, which is especially challenging in highly dynamic environments. Such environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Previous work in object tracking focuses on maintaining the integrity of object tracks but usually relies on specific appearance-based descriptors or constrained motion models. These approaches are very effective in specific applications but do not generalize to the full multimotion estimation problem. This paper presents a pipeline for estimating multiple motions, including the camera egomotion, in the presence of occlusions. This approach uses an expressive motion prior to estimate the SE (3) trajectory of every motion in the scene, even during temporary occlusions, and identify the reappearance of motions through motion closure. The performance of this occlusion-robust multimotion visual odometry (MVO) pipeline is evaluated on real-world data and the Oxford Multimotion Dataset.Comment: To appear at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). An earlier version of this work first appeared at the Long-term Human Motion Planning Workshop (ICRA 2019). 8 pages, 5 figures. Video available at https://www.youtube.com/watch?v=o_N71AA6FR

Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks

Loom (LM), a hardware inference accelerator for Convolutional Neural Networks (CNNs) is presented. In LM every bit of data precision that can be saved translates to proportional performance gains. Specifically, for convolutional layers LM's execution time scales inversely proportionally with the precisions of both weights and activations. For fully-connected layers LM's performance scales inversely proportionally with the precision of the weights. LM targets area- and bandwidth-constrained System-on-a-Chip designs such as those found on mobile devices that cannot afford the multi-megabyte buffers that would be needed to store each layer on-chip. Accordingly, given a data bandwidth budget, LM boosts energy efficiency and performance over an equivalent bit-parallel accelerator. For both weights and activations LM can exploit profile-derived perlayer precisions. However, at runtime LM further trims activation precisions at a much smaller than a layer granularity. Moreover, it can naturally exploit weight precision variability at a smaller granularity than a layer. On average, across several image classification CNNs and for a configuration that can perform the equivalent of 128 16b x 16b multiply-accumulate operations per cycle LM outperforms a state-of-the-art bit-parallel accelerator [1] by 4.38x without any loss in accuracy while being 3.54x more energy efficient. LM can trade-off accuracy for additional improvements in execution performance and energy efficiency and compares favorably to an accelerator that targeted only activation precisions. We also study 2- and 4-bit LM variants and find the the 2-bit per cycle variant is the most energy efficient

Multimotion Visual Odometry (MVO)

Visual motion estimation is a well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation in highly dynamic environments. These environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Estimating third-party motions simultaneously with the sensor egomotion is difficult because an object's observed motion consists of both its true motion and the sensor motion. Most previous works in multimotion estimation simplify this problem by relying on appearance-based object detection or application-specific motion constraints. These approaches are effective in specific applications and environments but do not generalize well to the full multimotion estimation problem (MEP). This paper presents Multimotion Visual Odometry (MVO), a multimotion estimation pipeline that estimates the full SE(3) trajectory of every motion in the scene, including the sensor egomotion, without relying on appearance-based information. MVO extends the traditional visual odometry (VO) pipeline with multimotion segmentation and tracking techniques. It uses physically founded motion priors to extrapolate motions through temporary occlusions and identify the reappearance of motions through motion closure. Evaluations on real-world data from the Oxford Multimotion Dataset (OMD) and the KITTI Vision Benchmark Suite demonstrate that MVO achieves good estimation accuracy compared to similar approaches and is applicable to a variety of multimotion estimation challenges.Comment: Under review for the International Journal of Robotics Research (IJRR), Manuscript #IJR-21-4311. 25 pages, 14 figures, 11 tables. Videos available at https://www.youtube.com/watch?v=mNj3s1nf-6A and https://www.youtube.com/playlist?list=PLbaQBz4TuPcxMIXKh5Q80s0N9ISezFcp

The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth

Datasets advance research by posing challenging new problems and providing standardized methods of algorithm comparison. High-quality datasets exist for many important problems in robotics and computer vision, including egomotion estimation and motion/scene segmentation, but not for techniques that estimate every motion in a scene. Metric evaluation of these multimotion estimation techniques requires datasets consisting of multiple, complex motions that also contain ground truth for every moving body. The Oxford Multimotion Dataset provides a number of multimotion estimation problems of varying complexity. It includes both complex problems that challenge existing algorithms as well as a number of simpler problems to support development. These include observations from both static and dynamic sensors, a varying number of moving bodies, and a variety of different 3D motions. It also provides a number of experiments designed to isolate specific challenges of the multimotion problem, including rotation about the optical axis and occlusion. In total, the Oxford Multimotion Dataset contains over 110 minutes of multimotion data consisting of stereo and RGB-D camera images, IMU data, and Vicon ground-truth trajectories. The dataset culminates in a complex toy car segment representative of many challenging real-world scenarios. This paper describes each experiment with a focus on its relevance to the multimotion estimation problem.Comment: 8 Pages. 8 Figures. Video available at https://www.youtube.com/watch?v=zXaHEdiKxdA. Dataset available at https://robotic-esp.com/datasets