Aim: The tendency for animals in warmer climates to be longer-limbed (Allen's Rule) is widely attributed to the demands of thermoregulation. The role of thermoregulation in structuring bird wings, however, has been overshadowed by the selective demands placed on wings by flight. We test whether occurrence in warmer climates is associated with longer wing bones. Location: Global. Time Period: Current. Major Taxa Studied: Aves: Passeriformes. Methods: Using computer vision, we measure wing-bone length from photographs of museum skeletal specimens for 1520 species of passerine birds. We then model the relationship between wing-bone length and temperature, accounting for allometry, the demands of flight efficiency and manoeuvrability, and a range of ecological and environmental variables. Results: Wing bones are longer in warmer climates. Our models, largely as a result of allometric effects, explain nearly all the variation in wing-bone length in our data, with a marginal R-2 = 0.80 and a conditional R-2 > 0.99. Main Conclusions: Across 1520 species of birds, higher temperatures are associated with longer wing bones, as predicted by Allen's Rule. The vascularised musculature along these bones is maximally uncovered when birds actively hold their wings away from their bodies to aid in cooling or during flight. Conversely, the musculature along the wing bones is insulated by feathering when at rest, such that wings play a minor role in heat exchange when individuals are less active and may need to retain heat. While our analyses do not directly establish the mechanistic basis underlying the pattern we recover, given the asymmetry in the role of wings in thermoregulation, we interpret the positive relationship between temperature and wing-bone length to reflect increased demand for heat dissipation in warmer climates. Our findings highlight the role of thermoregulation in shaping even the most critical features of vertebrate anatomy.

Large comparative datasets of avian functional traits have been used to address a wide range of questions in ecology and evolution. To date, this work has been constrained by the limited availability of skeletal trait datasets that include extensive inter- and intra-specific sampling. We use computer vision to identify and measure bones from photographs of museum skeletal specimens to assemble an extensive dataset of functionally important skeletal elements in birds. The dataset spans 2,057 species of birds (Aves: Passeriformes) and includes measurements of 12 skeletal elements from 14,419 individuals. In addition to the trait values directly measured from photographs, we leverage the multi-dimensional nature of our dataset and known phylogenetic relationships of the species to impute missing data under an evolutionary model. To facilitate use of the dataset, the taxonomy has been reconciled with an existing comprehensive avian phylogeny and an additional dataset of external functional traits for all birds.

We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. While recent methods pursuing 3D inference advocate learning novel-view generative models, these generations are not 3D-consistent and require a distillation process to generate a 3D output. We instead cast the task of 3D inference as directly generating mutually-consistent multiple views and build on the insight that additionally inferring depth can provide a mechanism for enforcing this consistency. Specifically, we train a denoising diffusion model to generate multi-view RGB-D images given a single RGB input image and leverage the (intermediate noisy) depth estimates to obtain reprojection-based conditioning to maintain multi-view consistency. We train our model using large-scale synthetic dataset Obajverse as well as the real-world CO3D dataset comprising of generic camera viewpoints. We demonstrate that our approach can yield more accurate synthesis compared to recent state-of-the-art, including distillation-based 3D inference and prior multi-view generation methods. We also evaluate the geometry induced by our multi-view depth prediction and find that it yields a more accurate representation than other direct 3D inference approaches.

We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent advances in neural rendering and probabilistic image generation. Existing approaches typically build on neural rendering with re-projected features but fail to generate unseen regions or handle uncertainty under large viewpoint changes. Alternate methods treat this as a (probabilistic) 2D synthesis task, and while they can generate plausible 2D images, they do not infer a consistent underlying 3D. However, we find that this trade-off between 3D consistency and probabilistic image generation does not need to exist. In fact, we show that geometric consistency and generative inference can be complementary in a mode seeking behavior. By distilling a 3D consistent scene representation from a view-conditioned latent diffusion model, we are able to recover a plausible 3D representation whose renderings are both accurate and realistic. We evaluate our approach across 51 categories in the CO3D dataset and show that it outperforms existing methods, in both distortion and perception metrics, for sparse view novel view synthesis.

The tendency for animals in warmer climates to be longer-limbed (Allen’s Rule) is widely attributed to the demands of thermoregulation. However, the underlying mechanism remains unclear, because variation in limb-length can typically be driven by selection for both efficient heat retention and increased heat dissipation capacity. Using comparative phylogenetic models, we find that occurrence in warmer climates is associated with longer wing bones for 1,520 species of passerine birds. The highly vascularized musculature along these bones is only uncovered during flight, when the wings function as the primary site of heat exchange, cooling the organism by dissipating excess heat generated by muscular activity. Conversely, the musculature along the wing bones is insulated by feathering when at rest, playing a negligible role in heat retention, even in colder climates. Given this asymmetry in thermoregulatory roles, we can identify the positive relationship between temperature and wing bone length as a phenotypic gradient shaped by increased demand for heat dissipation in warmer climates. Our findings provide a clear illustration of the mechanism by which global warming can drive spatial and temporal trends in appendage length, and also highlight the role of heat dissipation in reshaping even the most critical features of vertebrate anatomy. Significance Statement Animals tend to be longer-limbed in warmer climates, but it remains unclear whether this pattern is driven by selection for cold tolerance at low temperatures or efficient heat dissipation at high temperatures. We show that for 1,520 species of passerines, bird wing bones are relatively longer in warmer climates. The vascularized musculature along these bones primarily functions in heat exchange during flight, when the overwhelming thermoregulatory challenge is dissipating heat, suggesting longer wing-bone length is driven by heat dissipation demands. Our findings reveal the pervasive impacts of thermoregulatory demands on even the most important functional traits.

1. Increasingly, natural history museum collections are being used to generate large-scale morphological datasets to address a range of macroecological and macroevolutionary questions. One challenge to this approach is that large numbers of individuals either from a single species or from taxonomically broad sets of species may be necessary to characterize morphology at the relevant spatial, phylogenetic or temporal scales. 2. We present 'Skelevision', a method for rapidly handling, photographing and measuring skeletal specimens with a computer vision approach that uses a deep neural network to segment the photographs of specimens into individual bones, and identify and measure functional aspects of those bones. 3. We demonstrate the scale of what is feasible with Skelevision by estimating 11 functional traits from 11 different bones for 12,450 bird skeletal specimens spanning 1,882 species of passerines (similar to 32% of all passerine diversity). We quantify the accuracy of Skelevision estimates by comparing them to handmade measurements for 174 specimens from 115 species across 79 genera that span 59 families. Skelevision is precise, with a mean standard deviation of 0.86 mm for repeated independent measurements of individual bones, and is extremely accurate, with a mean RMSE of 0.89 mm across all traits when compared to handmade measurements. There is minimal phylogenetic signal in the measurement error (mean Pagel's lambda across traits = 0.13), and Skelevision estimates are robust to variation in the degree to which specimens remain articulated. 4. This approach has several important advantages over traditional methods for building large-scale morphological datasets (e.g. measurements from long-term field-based operations or handmade measurements of museum specimens). First, measuring new specimens only requires the collection of photographs, which can then be measured automatically, and effectively instantaneously, with the neural network. This is a significant departure from the time and skill required to measure skeletal specimens by hand. Second, the measurements are repeatable. Third, even as the dataset of photographed specimens expands, the amount of annotation data needed to measure new traits on all of the photographed specimens using the neural network will remain fixed and can be done without re-capturing images.

Birds have well-known distributions, phylogenetic relationships, and life histories, making them a powerful model system for understanding biotic responses to global environmental change. However, there are hundreds of thousands of museum skeletal specimens that could be analyzed to further our understanding of avian responses to climate change, but remain under-utilized due to the practical constraints of measuring elements of skeletons by hand. We introduce a dataset and system for measuring skeletal traits from museum specimens that reduces capture time by 15x for an initial effort to measure 10 traits per skeleton, allows for post hoc addition of trait data orders of magnitude faster than traditional methods, and shows high accuracy even when trained with limited data.

Conversational Intelligence requires that a person engage on informational, personal and relational levels. Advances in Natural Language Understanding have helped recent chatbots succeed at dialog on the informational level. However, current techniques still lag for conversing with humans on a personal level and fully relating to them. The University of Michigan's submission to the Alexa Prize Grand Challenge 3, Audrey, is an open-domain conversational chat-bot that aims to engage customers on these levels through interest driven conversations guided by customers' personalities and emotions. Audrey is built from socially-aware models such as Emotion Detection and a Personal Understanding Module to grasp a deeper understanding of users' interests and desires. Our architecture interacts with customers using a hybrid approach balanced between knowledge-driven response generators and context-driven neural response generators to cater to all three levels of conversations. During the semi-finals period, we achieved an average cumulative rating of 3.25 on a 1-5 Likert scale.