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

Correlation length and negative phase velocity in isotropic dielectric-magnetic materials

A composite material comprising randomly distributed spherical particles of two different isotropic dielectric-magnetic materials is homogenized using the second-order strong-property-fluctuation theory in the long-wavelength approximation. Whereas neither of the two constituent materials by itself supports planewave propagation with negative phase velocity (NPV), the homogenized composite material (HCM) can. The propensity of the HCM to support NPV propagation is sensitive to the distributional statistics of the constituent material particles, as characterized by a two--point covariance function and its associated correlation length. The scope for NPV propagation diminishes as the correlation length increases

Models of dynamic extraction of lipid tethers from cell membranes

When a ligand that is bound to an integral membrane receptor is pulled, the membrane and the underlying cytoskeleton can deform before either the membrane delaminates from the cytoskeleton or the ligand detaches from the receptor. If the membrane delaminates from the cytoskeleton, it may be further extruded and form a membrane tether. We develop a phenomenological model for this processes by assuming that deformations obey Hooke's law up to a critical force at which the cell membrane locally detaches from the cytoskeleton and a membrane tether forms. We compute the probability of tether formation and show that they can be extruded only within an intermediate range of force loading rates and pulling velocities. The mean tether length that arises at the moment of ligand detachment is computed as are the force loading rates and pulling velocities that yield the longest tethers.Comment: 16 pages, 7 figure

3D Monte Carlo radiation transfer modelling of photodynamic therapy

We acknowledge the support of the UK Engineering and Physics Sciences Research Council (EPSRC) for funding through a studentship for C L Campbell as well as the Alfred Stewart Trust.The effects of ageing and skin type on Photodynamic Therapy (PDT) for different treatment methods have been theoretically investigated. A multilayered Monte Carlo Radiation Transfer model is presented where both daylight activated PDT and conventional PDT are compared. It was found that light penetrates deeper through older skin with a lighter complexion, which translates into a deeper effective treatment depth. The effect of ageing was found to be larger for darker skin types. The investigation further strengthens the usage of daylight as a potential light source for PDT where effective treatment depths of about 2 mm can be achieved.Publisher PD

Harmonic balance surrogate-based immunity modeling of a nonlinear analog circuit

A novel harmonic balance surrogate-based technique to create fast and accurate behavioral models predicting, in the early design stage, the performance of nonlinear analog devices during immunity tests is presented. The obtained immunity model hides the real netlist, reduces the simulation time, and avoids expensive and time-consuming measurements after tape-out, while still providing high accuracy. The model can easily be integrated into a circuit simulator together with additional subcircuits, e.g., board and package models, as such allowing to efficiently reproduce complete immunity test setups during the early design stage and without disclosing any intellectual property. The novel method is validated by means of application to an industrial case study, being an automotive voltage regulator, clearly showing the technique's capabilities and practical advantages

The importance of including habitat-specific behaviour in models of butterfly movement

Dispersal is a key process affecting population persistence and major factors affecting dispersal rates are the amounts, connectedness and properties of habitats in landscapes. We present new data on the butterfly Maniola jurtina in flower-rich and flower-poor habitats that demonstrates how movement and behaviour differ between sexes and habitat types, and how this effects consequent dispersal rates. Females had higher flight speeds than males but their total time in flight was four times less. The effect of habitat type was strong for both sexes, flight speeds were ~2.5x and ~1.7x faster on resource-poor habitats for males and females respectively, and flights were approximately 50% longer. With few exceptions females oviposited in the mown grass habitat, likely because growing grass offers better food for emerging caterpillars, but they foraged in the resource-rich habitat. It seems that females faced a trade-off between ovipositing without foraging in the mown grass or foraging without ovipositing where flowers were abundant. We show that taking account of habitat-dependent differences in activity, here categorised as flight or non-flight, is crucial to obtaining good fits of an individual-based model to observed movement. An important implication of this finding is that incorporating habitat-specific activity budgets is likely necessary for predicting longer-term dispersal in heterogeneous habitats as habitat-specific behaviour substantially influences the mean (>30% difference) and kurtosis (1.4x difference) of dispersal kernels. The presented IBMs provide a simple method to explicitly incorporate known activity and movement rates when predicting dispersal in changing and heterogeneous landscapes

Predicting resilience of ecosystem functioning from co‐varying species' responses to environmental change

Understanding how environmental change affects ecosystem function delivery is of primary importance for fundamental and applied ecology. Current approaches focus on single environmental driver effects on communities, mediated by individual response traits. Data limitations present constraints in scaling up this approach to predict the impacts of multivariate environmental change on ecosystem functioning. We present a more holistic approach to determine ecosystem function resilience, using long‐term monitoring data to analyze the aggregate impact of multiple historic environmental drivers on species' population dynamics. By assessing covariation in population dynamics between pairs of species, we identify which species respond most synchronously to environmental change and allocate species into “response guilds.” We then use “production functions” combining trait data to estimate the relative roles of species to ecosystem functions. We quantify the correlation between response guilds and production functions, assessing the resilience of ecosystem functioning to environmental change, with asynchronous dynamics of species in the same functional guild expected to lead to more stable ecosystem functioning. Testing this method using data for butterflies collected over four decades in the United Kingdom, we find three ecosystem functions (resource provisioning, wildflower pollination, and aesthetic cultural value) appear relatively robust, with functionally important species dispersed across response guilds, suggesting more stable ecosystem functioning. Additionally, by relating genetic distances to response guilds we assess the heritability of responses to environmental change. Our results suggest it may be feasible to infer population responses of butterflies to environmental change based on phylogeny—a useful insight for conservation management of rare species with limited population monitoring data. Our approach holds promise for overcoming the impasse in predicting the responses of ecosystem functions to environmental change. Quantifying co‐varying species' responses to multivariate environmental change should enable us to significantly advance our predictions of ecosystem function resilience and enable proactive ecosystem management