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1. An early dog from southeast Alaska supports a coastal route for the first dog migration into the Americas

   da Silva Coelho, F.A.; Gill, S.; Tomlin, C.M.; Heaton, T.H.; Lindqvist, C. (February 2021, Proceedings)
2. A three-dimensional musculoskeletal model of the dog

   https://doi.org/10.1038/s41598-021-90058-0  

   Stark, Heiko; Fischer, Martin S.; Hunt, Alexander; Young, Fletcher; Quinn, Roger; Andrada, Emanuel (December 2021, Scientific Reports)

   null (Ed.)

   Abstract The domestic dog is interesting to investigate because of the wide range of body size, body mass, and physique in the many breeds. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint load during locomotion, as well as between joint load and degenerative diseases of the locomotor system (e.g. dysplasia), are not sufficiently understood. Collecting this data through in vivo measurements/records of joint forces and loads on deep/small muscles is complex, invasive, and sometimes unethical. The use of detailed musculoskeletal models may help fill the knowledge gap. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a walking Beagle. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We identified three muscle synergy groups by using hierarchical clustering. The activation patterns predicted from the model exhibit good agreement with experimental data for most of the forelimb muscles. We expect that our model will speed up the analysis of how body size, physique, agility, and disease influence neuronal control and joint loading in dog locomotion. 

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3. The Fréchet Distance Unleashed: Approximating a Dog with a Frog

   https://doi.org/10.4230/LIPIcs.SoCG.2025.54  

   Har-Peled, Sariel; Raichel, Benjamin; Robson, Eliot W (January 2025, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Aichholzer, Oswin; Wang, Haitao (Ed.)

   We show that a variant of the continuous Fréchet distance between polygonal curves can be computed using essentially the same algorithm used to solve the discrete version. The new variant is not necessarily monotone, but this shortcoming can be easily handled via refinement. Combined with a Dijkstra/Prim type algorithm, this leads to a realization of the Fréchet distance (i.e., a morphing) that is locally optimal (aka locally correct), that is both easy to compute, and in practice, takes near linear time on many inputs. The new morphing has the property that the leash is always as short as possible. These matchings/morphings are more natural, and are better than the ones computed by standard algorithms - in particular, they handle noise more graciously. This should make the Fréchet distance more useful for real world applications. We implemented the new algorithm, and various strategies to obtain fast practical performance. We performed extensive experiments with our new algorithm, and released publicly available (and easily installable and usable) Julia and Python packages. In particular, the Julia implementation, for computing the regular Fréchet distance, seems to be {significantly faster} than other currently available implementations. See Table 2.2. Our algorithms can be used to compute the almost-exact Fréchet distance between polygonal curves. Implementations and numerous examples are available here: https://frechet.xyz. 

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4. Statistics in the Service of Science: Don’t Let the Tail Wag the Dog

   https://doi.org/10.1007/s42113-022-00129-2  

   Singmann, Henrik; Kellen, David; Cox, Gregory E.; Chandramouli, Suyog H.; Davis-Stober, Clintin P.; Dunn, John C.; Gronau, Quentin F.; Kalish, Michael L.; McMullin, Sara D.; Navarro, Danielle J.; et al (March 2023, Computational Brain & Behavior)

   Abstract Statistical modeling is generally meant to describe patterns in data in service of the broader scientific goal of developing theories to explain those patterns. Statistical models support meaningful inferences when models are built so as to align parameters of the model with potential causal mechanisms and how they manifest in data. When statistical models are instead based on assumptions chosen by default, attempts to draw inferences can be uninformative or even paradoxical—in essence, the tail is trying to wag the dog. These issues are illustrated by van Doorn et al. (this issue) in the context of using Bayes Factors to identify effects and interactions in linear mixed models. We show that the problems identified in their applications (along with other problems identified here) can be circumvented by using priors over inherently meaningful units instead of default priors on standardized scales. This case study illustrates how researchers must directly engage with a number of substantive issues in order to support meaningful inferences, of which we highlight two: The first is the problem of coordination , which requires a researcher to specify how the theoretical constructs postulated by a model are functionally related to observable variables. The second is the problem of generalization , which requires a researcher to consider how a model may represent theoretical constructs shared across similar but non-identical situations, along with the fact that model comparison metrics like Bayes Factors do not directly address this form of generalization. For statistical modeling to serve the goals of science, models cannot be based on default assumptions, but should instead be based on an understanding of their coordination function and on how they represent causal mechanisms that may be expected to generalize to other related scenarios. 

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5. Virtual Guide Dog: Next-generation pedestrian signal for the visually impaired

   https://doi.org/10.1177/1687814019883096  

   Zhong, Zijia; Lee, Joyoung (March 2020, Advances in Mechanical Engineering)

   Accessible pedestrian signal was proposed as a mean to achieve the same level of service that is set forth by the Americans with Disabilities Act for the visually impaired. One of the major issues of existing accessible pedestrian signals is the failure to deliver adequate crossing information for the visually impaired. This article presents a mobile-based accessible pedestrian signal application, namely, Virtual Guide Dog. Integrating intersection information and onboard sensors (e.g. GPS, compass, accelerometer, and gyroscope sensor) of modern smartphones, the Virtual Guide Dog application can notify the visually impaired: (1) the close proximity of an intersection and (2) the street information for crossing. By employing a screen tapping interface, Virtual Guide Dog can remotely place a pedestrian crossing call to the controller, without the need of using a pushbutton. In addition, Virtual Guide Dog informs VIs the start of a crossing phase using text-to-speech technology. The proof-of-concept test shows that Virtual Guide Dog keeps the users informed about the remaining distance as they are approaching the intersection. It was also found that the GPS-only mode is accompanied by greater distance deviation compared to the mode jointly operating with both GPS and cellular positioning. 

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