In contemporary society, pets are increasingly regarded as integral family members, contributing significantly to human quality of life. The growing prevalence of dog ownership has concurrently escalated the economic ...
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In modern society, the number of households raising pets is increasing. As pet ownership increases, the cost of treating companion cats is also rising, with a significant portion of these costs going toward the treatm...
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Skin diseases in companion cats can worsen if not treated promptly, and this can increase the financial burden on pet owners. To prevent this, early and accurate diagnosis is essential. This study introduces a deep le...
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Low back pain is a leading cause of disability globally, is often associated with degenerative lumbar spine conditions. Accurate diagnosis of these conditions is critical but challenging due to the subjective nature o...
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Semantic segmentation is a core task in computer vision that allows AI models to interact and understand their surrounding environment. Similarly to how humans subconsciously segment scenes, this ability is crucial fo...
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Semantic segmentation is a core task in computer vision that allows AI models to interact and understand their surrounding environment. Similarly to how humans subconsciously segment scenes, this ability is crucial for scene understanding. However, a challenge many semantic learning models face is the lack of data. Existing video datasets are limited to short, low-resolution videos that are not representative of real-world examples. Thus, one of our key contributions is a customized semantic segmentation version of the Walking Tours dataset that features hour-long, high-resolution, real-world data from tours of different cities. Additionally, we evaluate the performance of open-vocabulary, semantic model OpenSeeD on our own custom dataset and discuss future implications.
In this article, we investigate an intelligent ground penetrating radar (GPR) that facilitates root-zone soil moisture estimation, a key parameter in precision agriculture. To create an intelligent GPR, we must train ...
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As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the ...
As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the availability of sensitive demographic features such as race or sex. However, in practice, regulatory obstacles and privacy concerns protect this data from collection and use. As a result, practitioners may either need to promote fairness despite the absence of these features or turn to demographic inference tools to attempt to infer them. Given that these tools are fallible, this paper aims to further understand how errors in demographic inference impact the fairness performance of popular fair LTR strategies. In which cases would it be better to keep such demographic attributes hidden from models versus infer them? We examine a spectrum of fair LTR strategies ranging from fair LTR with and without demographic features hidden versus inferred to fairness-unaware LTR followed by fair re-ranking. We conduct a controlled empirical investigation modeling different levels of inference errors by systematically perturbing the inferred sensitive attribute. We also perform three case studies with real-world datasets and popular open-source inference methods. Our findings reveal that as inference noise grows, LTR-based methods that incorporate fairness considerations into the learning process may increase bias. In contrast, fair re-ranking strategies are more robust to inference errors. All source code, data, and experimental artifacts of our experimental study are available here: https://***/sewen007/***
Tree Editing Distance is a widely applied quantity for measuring the similarity between hierarchical data structures, particularly trees. This paper reinterprets the TED problem using group action theory, exploring th...
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ISBN:
(数字)9798331521165
ISBN:
(纸本)9798331521172
Tree Editing Distance is a widely applied quantity for measuring the similarity between hierarchical data structures, particularly trees. This paper reinterprets the TED problem using group action theory, exploring the connection between tree editing operations and permutation groups. By formulating node insertion, deletion, and relabeling as group actions, we offer a novel perspective on tree transformations. This group-theoretic and metric-based approach provides new insights into the structure of tree similarity and introduces new possibilities for TED applications in various fields.
Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution and have been hypothesized ...
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Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution and have been hypothesized to influence phylogenetic structure. For instance, they can help explain natural history, steer behavior of contemporary evolving populations, and influence the efficacy of application-oriented evolutionary optimization. Likewise, in inquiry-oriented Artificial Life systems, these drivers constitute key building blocks for open-ended evolution. Here we set out to assess (a) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure;(b) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure;and (c) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Furthermore, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest a potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics.
In this article, we revisit the well-studied problem of mean estimation under user-level Ε-differential privacy (DP). While user-level Ε-DP mechanisms for mean estimation, which typically bound (or clip) user contri...
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