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Augmented Reality assistance are increasingly popular for supporting users with tasks like assembly and cooking. However, current practice typically provide reactive responses initialized from user requests, lacking consideration of rich contextual and user-specific information. To address this limitation, we propose a novel AR assistance system, Satori, that models both user states and environmental contexts to deliver proactive guidance. Our system combines the Belief-Desire-Intention (BDI) model with a state-of-the-art multi-modal large language model (LLM) to infer contextually appropriate guidance. The design is informed by two formative studies involving twelve experts. A sixteen within-subject study find that Satori achieves performance comparable to an designer-created Wizard-of-Oz (WoZ) system without relying on manual configurations or heuristics, thereby enhancing generalizability, reusability and opening up new possibilities for AR assistance.

Modern biological research is increasingly data-intensive, leading to a growing demand for effective training in biological data science. In this article, we provide an overview of key resources and best practices available within the Bioconductor project - an open-source software community focused on omics data analysis. This guide serves as a valuable reference for both learners and educators in the field., Comment: 16 pages, 2 figures, 1 table, 1 supplemental table

Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these models tend to perform well in most instances, which could exacerbate automation bias. Therefore, detecting out-of-distribution images at inference is critical to warn the clinicians that the model likely failed. This work applied the Mahalanobis distance (MD) post hoc to the bottleneck features of four Swin UNETR and nnU-net models that segmented the liver on T1-weighted magnetic resonance imaging and computed tomography. By reducing the dimensions of the bottleneck features with either principal component analysis or uniform manifold approximation and projection, images the models failed on were detected with high performance and minimal computational load. In addition, this work explored a non-parametric alternative to the MD, a k-th nearest neighbors distance (KNN). KNN drastically improved scalability and performance over MD when both were applied to raw and average-pooled bottleneck features., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:020. Expansion of "Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation" arXiv:2308.03723. Code available at https://github.com/mckellwoodland/dimen_reduce_mahal (https://zenodo.org/records/13881989)

The concept of an intelligent augmented reality (AR) assistant has significant, wide-ranging applications, with potential uses in medicine, military, and mechanics domains. Such an assistant must be able to perceive the environment and actions, reason about the environment state in relation to a given task, and seamlessly interact with the task performer. These interactions typically involve an AR headset equipped with sensors which capture video, audio, and haptic feedback. Previous works have sought to facilitate the development of intelligent AR assistants by visualizing these sensor data streams in conjunction with the assistant's perception and reasoning model outputs. However, existing visual analytics systems do not focus on user modeling or include biometric data, and are only capable of visualizing a single task session for a single performer at a time. Moreover, they typically assume a task involves linear progression from one step to the next. We propose a visual analytics system that allows users to compare performance during multiple task sessions, focusing on non-linear tasks where different step sequences can lead to success. In particular, we design visualizations for understanding user behavior through functional near-infrared spectroscopy (fNIRS) data as a proxy for perception, attention, and memory as well as corresponding motion data (acceleration, angular velocity, and gaze). We distill these insights into embedding representations that allow users to easily select groups of sessions with similar behaviors. We provide two case studies that demonstrate how to use these visualizations to gain insights about task performance using data collected during helicopter copilot training tasks. Finally, we evaluate our approach by conducting an in-depth examination of a think-aloud experiment with five domain experts., Comment: 11 pages, 6 figures. This is the author's version of the article that has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics (TVCG)

Rotating bosonic dark matter halos are considered as potential candidates for modeling dark matter in galactic halos. These bosonic dark matter halos can be viewed as a dilute and very extended version of bosonic stars, and the methods used for the calculation and analysis of the latter objects can be directly applied. Bosonic stars, a hypothetical type of astrophysical objects, are categorized into two primary families, based on the nature of the particles composing them: Einstein-Klein-Gordon stars and Proca stars. We examine various models from both families and the rotation curves which their contribution induces in different galaxies, to identify the most plausible candidates that explain the flattening of orbital velocities observed in galactic halos. By exploring different combinations of our dark matter models with observable galactic features, we propose an interesting source to compensate for the apparent lack of matter in dwarf and spiral galaxies, providing a possible explanation for this longstanding astronomical puzzle., Comment: 18 pages, 16 figures. We added some bibliography and corrected some typos

Bosonic stars represent a hypothetical exotic type of compact stellar objects that could be observed from the gravitational signal of coalescing binaries in current and future gravitational wave detectors. There are two main families of bosonic stars, which depend on the nature that governs the particles that build them: Einstein-Klein-Gordon and Proca Stars. We study the multipolar structure for both families of rotating objects, using realistic potentials with the aim of finding possible universal relations and, thus, a method that allows us to distinguish between these and other compact objects in the gravitational wave paradigm. We also show how certain relevant observables can be obtained for these hypothetical but well-motivated astrophysical objects., Comment: 23 pages, 22 figures. We made some corrections

We introduce two novel complementary notions of the Lefschetz number for a functor from a finite acyclic category to itself and we prove a Lefschetz fixed-object theorem and a Lefschetz fixed-morphism theorem. In order to do so, we use the connection between these type of categories and simplicial structures, such as trisps or delta complexes. Through the use of a pair of functors that, when composed, form the barycentric subdivision, we are not only able to identify fixed objects but also fixed chains of morphisms., Comment: 8 pages with 3 figures

Rotating fermion-boson stars are hypothetical celestial objects that consist of both fermionic and bosonic matter interacting exclusively through gravity. Bosonic fields are believed to arise in certain models of particle physics describing dark matter and could accumulate within neutron stars, modifying some of their properties and gravitational wave emission. Fermion-boson stars have been extensively studied in the static non-rotating case, exploring their combined stability and their gravitational radiation in binary mergers. However, stationary rotating configurations were yet to be found and investigated. The presence of a bosonic component could impact the development of the bar-mode instability in differentially rotating neutron stars. Therefore, the study of rotating fermion-boson stars has important implications for astrophysics, as they could provide a new avenue for the detection of gravitational waves. In addition, these objects may shed light on the behavior of matter under extreme conditions, such as those found in the cores of neutron stars, and explain any tension in the determination of the dense-matter equation of state from multi-messenger observations. In this work we study a new consistent method of constructing uniformly rotating fermion-boson stars and we analyse some of their main properties. These objects might offer alternative explanations for current observations populating the lower black-hole mass gap, as the $2.6 M_\odot$ compact object involved in GW190814., Comment: 19 pages, 8 figures. We have added the treatment of static solutions, improved some discussions about the results, and corrected various typos

Text presented in augmented reality provides in-situ, real-time information for users. However, this content can be challenging to apprehend quickly when engaging in cognitively demanding AR tasks, especially when it is presented on a head-mounted display. We propose ARTiST, an automatic text simplification system that uses a few-shot prompt and GPT-3 models to specifically optimize the text length and semantic content for augmented reality. Developed out of a formative study that included seven users and three experts, our system combines a customized error calibration model with a few-shot prompt to integrate the syntactic, lexical, elaborative, and content simplification techniques, and generate simplified AR text for head-worn displays. Results from a 16-user empirical study showed that ARTiST lightens the cognitive load and improves performance significantly over both unmodified text and text modified via traditional methods. Our work constitutes a step towards automating the optimization of batch text data for readability and performance in augmented reality., Comment: Conditionally accepted by CHI '24

Fr\'echet Inception Distance (FID) is a widely used metric for assessing synthetic image quality. It relies on an ImageNet-based feature extractor, making its applicability to medical imaging unclear. A recent trend is to adapt FID to medical imaging through feature extractors trained on medical images. Our study challenges this practice by demonstrating that ImageNet-based extractors are more consistent and aligned with human judgment than their RadImageNet counterparts. We evaluated sixteen StyleGAN2 networks across four medical imaging modalities and four data augmentation techniques with Fr\'echet distances (FDs) computed using eleven ImageNet or RadImageNet-trained feature extractors. Comparison with human judgment via visual Turing tests revealed that ImageNet-based extractors produced rankings consistent with human judgment, with the FD derived from the ImageNet-trained SwAV extractor significantly correlating with expert evaluations. In contrast, RadImageNet-based rankings were volatile and inconsistent with human judgment. Our findings challenge prevailing assumptions, providing novel evidence that medical image-trained feature extractors do not inherently improve FDs and can even compromise their reliability. Our code is available at https://github.com/mckellwoodland/fid-med-eval., Comment: This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in LNCS vol. 15012, and is available online at https://doi.org/10.1007/978-3-031-72390-2_9

In this paper, we first report an exploratory study where three participants were instructed to use ChatGPT to implement a simple Web-based application. A key finding of this study revealed that ChatGPT does not offer a user-friendly interface for building applications, even small web systems. For example, one participant with limited experience in software development was unable to complete any of the proposed user stories. Then, and as the primary contribution of this work, we decided to design, implement, and evaluate a tool that offers a customized interface for language models like GPT, specifically targeting the implementation of small web applications without writing code. This tool, called NoCodeGPT, instruments the prompts sent to the language model with useful contextual information (e.g., the files that need to be modified when the user identifies and requests a bug fix). It also saves the files generated by the language model in the correct directories. Additionally, a simple version control feature is offered, allowing users to quickly revert to a previous version of the code when the model enters a hallucination process, generating worthless results. To evaluate our tool, we invited 14 students with limited Web development experience to implement two small web applications using only prompts and NoCodeGPT. Overall, the results of this evaluation were quite satisfactory and significantly better than those of the initial study (the one using the standard ChatGPT interface). More than half of the participants (9 out of 14) successfully completed the proposed applications, while the others completed at least half of the proposed user stories.