How do neural networks see depth in single images?

Deep neural networks have lead to a breakthrough in depth estimation from single images. Recent work often focuses on the accuracy of the depth map, where an evaluation on a publicly available test set such as the KITTI vision benchmark is often the main result of the article. While such an evaluation shows how well neural networks can estimate depth, it does not show how they do this. To the best of our knowledge, no work currently exists that analyzes what these networks have learned. In this work we take the MonoDepth network by Godard et al. and investigate what visual cues it exploits for depth estimation. We find that the network ignores the apparent size of known obstacles in favor of their vertical position in the image. Using the vertical position requires the camera pose to be known; however we find that MonoDepth only partially corrects for changes in camera pitch and roll and that these influence the estimated depth towards obstacles. We further show that MonoDepth's use of the vertical image position allows it to estimate the distance towards arbitrary obstacles, even those not appearing in the training set, but that it requires a strong edge at the ground contact point of the object to do so. In future work we will investigate whether these observations also apply to other neural networks for monocular depth estimation.Comment: Submitte

History and sensitivity comparison of two standard whole-sediment toxicity tests with crustaceans : the amphipod Hyalella azteca and the ostracod Heterocypris incongruens microbiotest

The review first details the development of the test procedures with Hyalella azteca which historically emerged as one of the recommended test species for whole-sediment assays and its gradual standardization and endorsement by national and international organizations. The sensitivity and precision of the H. azteca test for application on chemicals and on real world sediments is discussed. The review subsequently addresses the development of the whole sediment microbiotest with the ostracod crustacean Heterocypris incongruens with larvae of this test species hatched from dormant eggs (cysts), rendering this assay stock culture/maintenance free. The application of the 6-day ostracod microbiotest on sediments in Canada and in Belgium is discussed, as well as its endorsement by the ISO subsequent to an extensive international inter-laboratory ring test. The sensitivity of the amphipod and ostracod tests is compared by data from studies in which both assays were applied in parallel. A comparison of more than 1000 ostracod/amphipod data pairs of a 12-year river sediment monitoring study in Flanders/Belgium confirmed that both whole-sediment assays have a similar sensitivity and that the 6-day ostracod microbiotest is a valuable and cost-effective alternative to the 10-14 day amphipod test for evaluation of the toxic hazard of polluted sediments

VLA observations of candidate high-mass protostellar objects at 7 mm

We present radio continuum observations at 7 mm made using the Very Large Array towards three massive star forming regions thought to be in very early stages of evolution selected from the sample of Sridharan et al. (2002). Emission was detected towards all three sources (IRAS 18470-0044, IRAS 19217+1651 and IRAS 23151+5912). We find that in all cases the 7 mm emission corresponds to thermal emission from ionized gas. The regions of ionized gas associated with IRAS 19217+1651 and IRAS 23151+5912 are hypercompact with diameters of 0.009 and 0.0006 pc, and emission measures of 7.0 x 10^8 and 2.3 x 10^9 pc cm^(-6), respectively.Comment: 17 pages, 5 figures, accepted by The Astronomical Journa

Neuromorphic Control using Input-Weighted Threshold Adaptation

Neuromorphic processing promises high energy efficiency and rapid response rates, making it an ideal candidate for achieving autonomous flight of resource-constrained robots. It will be especially beneficial for complex neural networks as are involved in high-level visual perception. However, fully neuromorphic solutions will also need to tackle low-level control tasks. Remarkably, it is currently still challenging to replicate even basic low-level controllers such as proportional-integral-derivative (PID) controllers. Specifically, it is difficult to incorporate the integral and derivative parts. To address this problem, we propose a neuromorphic controller that incorporates proportional, integral, and derivative pathways during learning. Our approach includes a novel input threshold adaptation mechanism for the integral pathway. This Input-Weighted Threshold Adaptation (IWTA) introduces an additional weight per synaptic connection, which is used to adapt the threshold of the post-synaptic neuron. We tackle the derivative term by employing neurons with different time constants. We first analyze the performance and limits of the proposed mechanisms and then put our controller to the test by implementing it on a microcontroller connected to the open-source tiny Crazyflie quadrotor, replacing the innermost rate controller. We demonstrate the stability of our bio-inspired algorithm with flights in the presence of disturbances. The current work represents a substantial step towards controlling highly dynamic systems with neuromorphic algorithms, thus advancing neuromorphic processing and robotics. In addition, integration is an important part of any temporal task, so the proposed Input-Weighted Threshold Adaptation (IWTA) mechanism may have implications well beyond control tasks

Marshallian labor market pooling: evidence from Italy

This paper employs a unique Italian data source to take a comprehensive approach to labor market pooling. It jointly considers many different aspects of the agglomeration labor market relationship, including turnover, learning, matching, and hold up. It also considers labor market pooling from the perspective of both workers and firms and across a range of industries. The paper reports a general positive relationship of turnover to local population density, which is consistent with theories of agglomeration and uncertainty. The paper also finds evidence of onthejob learning that is consistent with theories of labor pooling, labor poaching, and hold up. In addition, the paper provides evidence consistent with agglomeration improving job matches. However, the labor market pooling gains that we measure are small in magnitude and seem unlikely to account for a substantial share of the agglomeration benefits accruing to worker and firms