A strong antidiamond principle compatible with CH

A strong antidiamond principle (*c) is shown to be consistent with CH. This principle can be stated as a "P-ideal dichotomy": every P-ideal on omega-1 (i.e. an ideal that is sigma-directed under inclusion modulo finite) either has a closed unbounded subset of omega-1 locally inside of it, or else has a stationary subset of omega-1 orthogonal to it. We rely on Shelah's theory of parameterized properness for NNR iterations, and make a contribution to the theory with a method of constructing the properness parameter simultaneously with the iteration. Our handling of the application of the NNR iteration theory involves definability of forcing notions in third order arithmetic, analogous to Souslin forcing in second order arithmetic.Comment: 54 pages (Elsevier article style). To appear in Annals of Pure and Applied Logic. Homepage: http://homepage.univie.ac.at/James.Hirschorn/research/strong.antidiamond/strong.antidiamond.htm

Pose Anything: A Graph-Based Approach for Category-Agnostic Pose Estimation

Traditional 2D pose estimation models are limited by their category-specific design, making them suitable only for predefined object categories. This restriction becomes particularly challenging when dealing with novel objects due to the lack of relevant training data. To address this limitation, category-agnostic pose estimation (CAPE) was introduced. CAPE aims to enable keypoint localization for arbitrary object categories using a single model, requiring minimal support images with annotated keypoints. This approach not only enables object pose generation based on arbitrary keypoint definitions but also significantly reduces the associated costs, paving the way for versatile and adaptable pose estimation applications. We present a novel approach to CAPE that leverages the inherent geometrical relations between keypoints through a newly designed Graph Transformer Decoder. By capturing and incorporating this crucial structural information, our method enhances the accuracy of keypoint localization, marking a significant departure from conventional CAPE techniques that treat keypoints as isolated entities. We validate our approach on the MP-100 benchmark, a comprehensive dataset comprising over 20,000 images spanning more than 100 categories. Our method outperforms the prior state-of-the-art by substantial margins, achieving remarkable improvements of 2.16% and 1.82% under 1-shot and 5-shot settings, respectively. Furthermore, our method's end-to-end training demonstrates both scalability and efficiency compared to previous CAPE approaches

Normalizing Flows for Human Pose Anomaly Detection

Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more. We distill the problem to anomaly detection of human pose, thus reducing the risk of nuisance parameters such as appearance affecting the result. Focusing on pose alone also has the side benefit of reducing bias against distinct minority groups. Our model works directly on human pose graph sequences and is exceptionally lightweight ($\sim1K$ parameters), capable of running on any machine able to run the pose estimation with negligible additional resources. We leverage the highly compact pose representation in a normalizing flows framework, which we extend to tackle the unique characteristics of spatio-temporal pose data and show its advantages in this use case. Our algorithm uses normalizing flows to learn a bijective mapping between the pose data distribution and a Gaussian distribution, using spatio-temporal graph convolution blocks. The algorithm is quite general and can handle training data of only normal examples, as well as a supervised dataset that consists of labeled normal and abnormal examples. We report state-of-the-art results on two anomaly detection benchmarks - the unsupervised ShanghaiTech dataset and the recent supervised UBnormal dataset

Rotation invariant probability distributions in geodesy

Rotation invariant probability distributions in geodesy and covariance of gravity anomalies above spherical surfac

Leaving it all behind to travel: venturing uncertainty as a means to personal growth and authenticity

The potential for personal growth in cross-cultural travel has been posited by numerous psychologists; however, a “gaping hole” in empirical research has left these hypotheses unexplored. Meanwhile, rapidly increasing numbers of people are choosing to leave their careers to pursue a personal dream of extensive foreign travel. The aim of this study was to explore the motivations, psychological experiences, and outcomes from travel in this growing “career-break” demographic. Ten men and women who had exited their careers to pursue extensive culturally engaging travel participated in an individual semistructured interview that was analyzed using constructivist grounded theory. Three main themes emerged: an existential yearning to travel, “jumping off the ledge” (courage), and discovering authenticity. Personal growth occurred via adversity within the travel experience itself, but also at the pretravel stage of departure, where leaving security and venturing uncertainty was experienced with “milestone” significance and, for some, as a seismic event akin to trauma. Future research should explore the potential for a new type of intrinsically emerging trauma in posttraumatic growth and the potential for anxiety as a positive construct in authentic becoming and growth