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Withdrawal suppression has been a known weakness of BGP for over a decade. It has a significant detrimental impact on both the reliability and security of inter-domain routing on the Internet. This paper presents Route Status Transparency (RoST), the first design that efficiently and securely thwarts withdrawal suppression misconfigurations and attacks. RoST allows ASes to efficiently verify whether a route has been withdrawn; it is compatible with BGP as well as with BGP security enhancements. We use simulations on the Internet’s AS-level topology to evaluate the benefits from adopting RoST. We use an extensive real-world BGP announcements dataset to show that it is efficient in terms of storage, bandwidth, and computational requirements. 

The upstream mechanobiological pathways that regulate the downstream mineralization rates in periodontal tissues are limitedly understood. Herein, we spatially colocalized and correlated compression and tension strain profiles with the expressions of mechanosensory ion channels (MS-ion) TRPV4 and PIEZO1, biometal zinc, mitochondrial function marker (MFN2), cell senescence indicator (p16), and oxygen status marker hypoxia-inducible factor-1α (HIF-1α) in rats fed hard and soft foods. The observed zinc and related cellular homeostasis in vivo were ascertained by TRPV4 and PIEZO1 agonists and antagonists on human periodontal ligament fibroblasts ex vivo. Four-week-old male Sprague-Dawley rats were fed hard (n= 3) or soft (n= 3) foods for 4 wk (in vivo). Significant changes in alveolar socket and root shapes with decreased periodontal ligament space and increased cementum volume fraction were observed in maxillae on reduced loads (soft food). Reduced loads impaired distally localized compression-stimulated PIEZO1 and mesially localized tension-stimulated TRPV4, decreased mitochondrial function (MFN2), and increased cell senescence in mesial and distal periodontal regions. The switch inHIF-1αfrom hard food–distal to soft food–mesial indicated a plausible effect of shear-regulated blood and oxygen flows in the periodontal complex. Blunting or activating TRPV4 or PIEZO1 MS-ion channels by channel-specific antagonists or agonists in human periodontal ligament fibroblast cultures (in vitro) indicated a positive correlation between zinc levels and zinc transporters but not with MS-ion channel expressions. The effects of reduced chewing loads in vivo were analogous to TRPV4 and PIEZO1 antagonists in vitro. Study results collectively illustrated that tension-induced TRPV4 and compression-induced PIEZO1 activations are necessary for cell metabolism. An increased hypoxic state with reduced functional loads can be a conducive environment for cementum growth. From a practical standpoint, dose rate–controlled loads can modulate tension and compression-specific MS-ion channel activation, cellular zinc, andHIF-1αtranscription. These mechanobiochemical events indicate the plausible catalytic role of biometal zinc in mineralization, periodontal maintenance, and dentoalveolar joint function. 

Existing building recognition methods, exemplified by BRAILS, utilize supervised learning to extract information from satellite and street-view images for classification and segmentation. However, each task module requires human-annotated data, hindering the scalability and robustness to regional variations and annotation imbalances. In response, we propose a new zero-shot workflow for building attribute extraction that utilizes large-scale vision and language models to mitigate reliance on external annotations. The proposed workflow contains two key components: image-level captioning and segment-level captioning for the building images based on the vocabularies pertinent to structural and civil engineering. These two components generate descriptive captions by computing feature representations of the image and the vocabularies, and facilitating a semantic match between the visual and textual representations. Consequently, our framework offers a promising avenue to enhance AI-driven captioning for building attribute extraction in the structural and civil engineering domains, ultimately reducing reliance on human annotations while bolstering performance and adaptability. 

Over the last two decades, there has been a growth in the applications of geographically-explicit agent-based models. One thing such models have in common is the creation of synthetic populations to initialize the artificial worlds in which the agents inhabit. One challenge such models face is that it is often difficult to create reusable geographically-explicit synthetic populations with social networks. In this paper, we introduce a Python based method that generates a reusable geographically-explicit synthetic population dataset along with its social networks. In addition, we present a pipeline for using the population datasets for model initialization. With this pipeline, multiple spatial and temporal scales of geographically-explicit agent-based models are presented focusing on Western New York. Such models not only demonstrate the utility of our synthetic population on commuting patterns but also how social networks can impact the simulation of disease spread and vaccination uptake. By doing so, this pipeline could benefit any modeler wishing to reuse synthetic populations with realistic geographic locations and social networks. 

We first introduce the notion of meta-rank for a 2-parameter persistence module, an invariant that captures the information behind images of morphisms between 1D slices of the module. We then define the meta-diagram of a 2-parameter persistence module to be the Möbius inversion of the meta-rank, resulting in a function that takes values from signed 1-parameter persistence modules. We show that the meta-rank and meta-diagram contain information equivalent to the rank invariant and the signed barcode. This equivalence leads to computational benefits, as we introduce an algorithm for computing the meta-rank and meta-diagram of a 2-parameter module M indexed by a bifiltration of n simplices in O(n^3) time. This implies an improvement upon the existing algorithm for computing the signed barcode, which has O(n^4) time complexity. This also allows us to improve the existing upper bound on the number of rectangles in the rank decomposition of M from O(n^4) to O(n^3). In addition, we define notions of erosion distance between meta-ranks and between meta-diagrams, and show that under these distances, meta-ranks and meta-diagrams are stable with respect to the interleaving distance. Lastly, the meta-diagram can be visualized in an intuitive fashion as a persistence diagram of diagrams, which generalizes the well-understood persistent diagram in the 1-parameter setting.