Miracle-dz/UniFAR-FTUs huggingface dataset:
Extreme mass-ratio inspiral of a spinning body into a Kerr black hole I: Evolution along generic trajectories

The study of spinning bodies moving in curved spacetime has relevance to binary black hole systems with large mass ratios, as well as being of formal interest. At zeroth order in a binary's mass ratio, the smaller body moves on a geodesic of the larger body's spacetime. Post-geodesic corrections describing forces driving the small body's worldline away from geodesics must be incorporated to model the system accurately. An important post-geodesic effect is the gravitational self-force, which describes the small body's interaction with its own spacetime curvature. This effect includes the backreaction due to gravitational-wave emission that leads to the inspiral of the small body into the black hole. When a spinning body orbits a black hole, its spin couples to spacetime curvature. This introduces another post-geodesic correction known as the spin-curvature force. An osculating geodesic integrator that includes both the backreaction due to gravitational waves and spin-curvature forces can be used to generate a spinning-body inspiral. In this paper, we use an osculating geodesic integrator to combine the leading backreaction of gravitational waves with the spin-curvature force. Our analysis only includes the leading orbit-averaged dissipative backreaction, and examines the spin-curvature force to leading order in the small body's spin. This is sufficient to build generic inspirals of spinning bodies, and serves as a foundation for further work examining how to include secondary spin in large-mass-ratio waveform models.
A model reduction method for large-scale linear multidimensional dynamical systems

In this work, we explore the application of multilinear algebra in reducing the order of multidimentional linear time-invariant (MLTI) systems. We use tensor Krylov subspace methods as key tools, which involve approximating the system solution within a low-dimensional subspace. We introduce the tensor extended block and global Krylov subspaces and the corresponding Arnoldi based processes. Using these methods, we develop a model reduction using projection techniques. We also show how these methods could be used to solve large-scale Lyapunov tensor equations that are needed in the balanced truncation method which is a technique for order reduction. We demonstrate how to extract approximate solutions via the Einstein product using the tensor extended block Arnoldi and the extended global Arnoldi processes.
Cold New Early Dark Energy pulls the trigger on the H 0 and S 8 tensions: a simultaneous solution to both tensions without new ingredients

In this work, we show that the Cold New Early Dark Energy (Cold NEDE) model in its original form can solve both the Hubble tension and the S 8 tension without adding any new ingredients at the fundamental level. So far, it was assumed that the trigger field in the Cold NEDE model is completely subdominant. However, relaxing this assumption and letting the trigger field contribute a mere 0.5% of the total energy density leads to a resolution of the S 8 tension while simultaneously improving it as a solution to the H 0 tension. Fitting this model to baryonic acoustic oscillations, large-scale-structure, supernovae (including a SH0ES prior), and cosmic microwave background data, we report a preferred NEDE fraction of f NEDE = 0.134+0.032 -0.025 (68% C.L.), lifting its Gaussian evidence for the first time above 5σ (up from 4σ when the trigger contribution to dark matter is negligible). At the same time, we find the new concordance values H 0 = 71.71 ± 0.88 km sec-1 Mpc-1 and S 8 = 0.793 ± 0.018. Excluding large-scale structure data and the SH0ES prior, both Gaussian tensions are reduced below the 2σ level.
Wavelet-Based Density Estimation for Persistent Homology

Persistent homology is a central methodology in topological data analysis that has been successfully implemented in many fields and is becoming increasingly popular and relevant. The output of persistent homology is a persistence diagram -- a multiset of points supported on the upper half plane -- that is often used as a statistical summary of the topological features of data. In this paper, we study the random nature of persistent homology and estimate the density of expected persistence diagrams from observations using wavelets; we show that our wavelet-based estimator is optimal. Furthermore, we propose an estimator that offers a sparse representation of the expected persistence diagram that achieves near-optimality. We demonstrate the utility of our contributions in a machine learning task in the context of dynamical systems.
Consensus and Subjectivity of Skin Tone Annotation for ML Fairness

Understanding different human attributes and how they affect model behavior may become a standard need for all model creation and usage, from traditional computer vision tasks to the newest multimodal generative AI systems. In computer vision specifically, we have relied on datasets augmented with perceived attribute signals (e.g., gender presentation, skin tone, and age) and benchmarks enabled by these datasets. Typically labels for these tasks come from human annotators. However, annotating attribute signals, especially skin tone, is a difficult and subjective task. Perceived skin tone is affected by technical factors, like lighting conditions, and social factors that shape an annotator's lived experience. This paper examines the subjectivity of skin tone annotation through a series of annotation experiments using the Monk Skin Tone (MST) scale, a small pool of professional photographers, and a much larger pool of trained crowdsourced annotators. Along with this study we release the Monk Skin Tone Examples (MST-E) dataset, containing 1515 images and 31 videos spread across the full MST scale. MST-E is designed to help train human annotators to annotate MST effectively. Our study shows that annotators can reliably annotate skin tone in a way that aligns with an expert in the MST scale, even under challenging environmental conditions. We also find evidence that annotators from different geographic regions rely on different mental models of MST categories resulting in annotations that systematically vary across regions. Given this, we advise practitioners to use a diverse set of annotators and a higher replication count for each image when annotating skin tone for fairness research.
Discrete Diffusion Probabilistic Models for Symbolic Music Generation

Denoising Diffusion Probabilistic Models (DDPMs) have made great strides in generating high-quality samples in both discrete and continuous domains. However, Discrete DDPMs (D3PMs) have yet to be applied to the domain of Symbolic Music. This work presents the direct generation of Polyphonic Symbolic Music using D3PMs. Our model exhibits state-of-the-art sample quality, according to current quantitative evaluation metrics, and allows for flexible infilling at the note level. We further show, that our models are accessible to post-hoc classifier guidance, widening the scope of possible applications. However, we also cast a critical view on quantitative evaluation of music sample quality via statistical metrics, and present a simple algorithm that can confound our metrics with completely spurious, non-musical samples.
xPQA: Cross-Lingual Product Question Answering in 12 Languages

Product Question Answering (PQA) systems are key in e-commerce applications as they provide responses to customers’ questions as they shop for products. While existing work on PQA focuses mainly on English, in practice there is need to support multiple customer languages while leveraging product information available in English. To study this practical industrial task, we present xPQA, a large-scale annotated cross-lingual PQA dataset in 12 languages, and report results in (1) candidate ranking, to select the best English candidate containing the information to answer a non-English question; and (2) answer generation, to generate a natural-sounding non-English answer based on the selected English candidate.We evaluate various approaches involving machine translation at runtime or offline, leveraging multilingual pre-trained LMs, and including or excluding xPQA training data. We find that in-domain data is essential as cross-lingual rankers trained on other domains perform poorly on the PQA task, and that translation-based approaches are most effective for candidate ranking while multilingual finetuning works best for answer generation. Still, there remains a significant performance gap between the English and the cross-lingual test sets.
Simulation-Assisted Optimization for Large-Scale Evacuation Planning with Congestion-Dependent Delays

Evacuation planning is a crucial part of disaster management. However, joint optimization of its two essential components, routing and scheduling, with objectives such as minimizing average evacuation time or evacuation completion time, is a computationally hard problem. To approach it, we present MIP-LNS, a scalable optimization method that utilizes heuristic search with mathematical optimization and can optimize a variety of objective functions. We also present the method MIP-LNS-SIM, where we combine agent-based simulation with MIP-LNS to estimate delays due to congestion, as well as, find optimized plans considering such delays. We use Harris County in Houston, Texas, as our study area. We show that, within a given time limit, MIP-LNS finds better solutions than existing methods in terms of three different metrics. However, when congestion dependent delay is considered, MIP-LNS-SIM outperforms MIP-LNS in multiple performance metrics. In addition, MIP-LNS-SIM has a significantly lower percent error in estimated evacuation completion time compared to MIP-LNS.
Implementing a Hierarchical Deep Learning Approach for Simulating multilevel Auction Data

We present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. The complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. Our methodology combines deep generative modeling (DGM) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. This approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. Our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential ofDGMas a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative AI.
GPU-parallelisation of wavelet-based grid adaptation for fast finite volume modelling: application to shallow water flows

Wavelet-based grid adaptation driven by the"multiresolution analysis"(MRA) of the Haar wavelet (HW) allows to devise an adaptive first-order finite volume (FV1) model (HWFV1) that can readily preserve the modelling fidelity of its reference uniform-grid FV1 counterpart. However, the MRA incurs a high computational cost as it involves"encoding"(coarsening),"decoding"(refining), analysing and traversing modelled data across a deep hierarchy of nested, uniform grids. GPU-parallelisation of the MRA is needed to reduce its computational cost, but its algorithmic structure (1) hinders coalesced memory access on the GPU, and (2) involves an inherently sequential tree traversal problem. This work redesigns the algorithmic structure of the MRA in order to parallelise it on the GPU, addressing (1) by applying Z-order space-filling curves and addressing (2) by adopting a parallel tree traversal algorithm. This results in a GPU-parallelised HWFV1 model (GPU-HWFV1). GPU-HWFV1 is verified against its CPU predecessor (CPU-HWFV1) and its GPU-parallelised reference uniform-grid counterpart (GPU-FV1) over five shallow water flow test cases. GPU-HWFV1 preserves the modelling fidelity of GPU-FV1 while being up to 30 times faster. Compared to CPU-HWFV1, it is up to 200 times faster, suggesting the GPU-parallelised MRA could be used to speed up other FV1 models.
Clinical Performance Evaluation of a Rapid Real-Time PCR Assay for Monkeypox Diagnosis: a Retrospective and Comparative Study

Given that a large number of mpox outbreaks have been reported worldwide since 2022 in countries in which the disease is not endemic, the main concern for clinicians and global health systems should be to develop effective, available, and easy-to-implement diagnostic strategies to interrupt mpox transmission as soon as possible. This retrospective study demonstrates the satisfactory clinical parameters of a commercially available molecular diagnostic kit for routine testing for mpox in clinical diagnostic laboratories. ABSTRACT In an increasingly globalized and interconnected world, the outbreak of an infectious disease in one country can become a worrying health emergency for the whole world. A current example is the 2022 monkeypox virus (mpox) outbreak affecting multiple areas across the world. In this context, strategies to interrupt transmission as soon as possible by identifying cases, clusters, and sources of infection should be developed around the world to prevent these crises. The aim of this retrospective and collaborative study was to perform external clinical validation of the VIASURE monkeypox virus real-time PCR detection kit (CerTest Biotec, Spain) with ready-to-use reagents designed for the rapid detection of mpox. A total of 165 samples with suspected infection were used for this analysis. The standard procedures of the clinical microbiology laboratory of the Miguel Servet University Hospital, using the RealStar Orthopoxvirus PCR kit v1.0 (Altona Diagnostics) and bidirectional Sanger sequencing (STAB VIDA, Caparica, Portugal), were considered reference techniques. Furthermore, a subset of 67 mpox-negative samples and 13 mpox-positive samples were routinely tested for clinical diagnosis of other rash/ulcerative pathologies. Accuracy testing resulted in appropriate clinical validation values, as follows: sensitivity, 1 (95% confidence interval [CI], 0.97 to 1); specificity, 1 (95% CI, 0.98 to 1); positive predictive value, 1 (95% CI, 0.93 to 1); negative predictive value, 1 (95% CI, 0.95 to 1). The strength of agreement between assays was almost perfect. The added value is the useful support for the specific diagnosis of mpox infections due to the diagnostic specificity data obtained. IMPORTANCE Given that a large number of mpox outbreaks have been reported worldwide since 2022 in countries in which the disease is not endemic, the main concern for clinicians and global health systems should be to develop effective, available, and easy-to-implement diagnostic strategies to interrupt mpox transmission as soon as possible. This retrospective study demonstrates the satisfactory clinical parameters of a commercially available molecular diagnostic kit for routine testing for mpox in clinical diagnostic laboratories.
Mindfulness-based stress reduction for autistic adults: A feasibility study in an outpatient context

Autistic adults report high stress and impaired ability to cope with stressors. Mindfulness-based stress reduction targets individual’s own resources to regulate stress responses. An open feasibility study was conducted in an outpatient context, recruiting autistic adults without intellectual disability. The intervention, guided by trained mindfulness-based stress reduction teachers, followed the contents of the standard manual while adapting the delivery to autistic adults’ needs. The total sample comprised of N = 50 participants; N = 43 (86%) attended at least one session, and N = 34 of these (79%) completed the intervention. Participants found mindfulness-based stress reduction logical, likely to lead to improvement and recommendable to autistic peers. No serious adverse events were reported. Measures of preliminary effectiveness indicated reduced symptoms of stress (p < 0.001; d = 0.51) and improved stress-coping skills (p = 0.017; d = 0.43) from pre- to post-intervention, as well as a reduction in symptoms of anxiety and depression. No changes were observed in mindful attention awareness, life satisfaction, or acceptance of the autism diagnosis. The data suggest that mindfulness-based stress reduction is a feasible and acceptable intervention for autistic adults in an outpatient setting. Further investigation in randomized clinical trials is warranted (Clinical Trials, clinicaltrials.gov, registration no. NCT05244265). Lay abstract Autistic adults report high stress levels and difficulties dealing with everyday stressors. Mindfulness-based stress reduction groups aim to help regulate stress responses. We asked 50 autistic adults, without intellectual disability, to participate in a study of mindfulness-based stress reduction. The group program was made accessible through clear group leader communication and good program predictability, as well as reduced exposure to disturbing sensory stimuli. The mindfulness and yoga based exercises from the original mindfulness-based stress reduction program were included. The participants were positive and would even recommend an autistic friend to participate in a mindfulness-based stress reduction group. They reported that mindfulness-based stress reduction could lower symptoms of stress and improved stress coping. We still need to investigate these effects further in larger studies. The findings of this work show that mindfulness-based stress reduction groups can be adapted for autistic adults and that the participants overall were positive to the intervention and the group format.
Determination of the minimal important difference for inspiratory muscle strength in people with severe and very severe COPD

Objective Inspiratory muscle training is recommended for people with chronic obstructive pulmonary disease (COPD) with inspiratory muscle weakness. Clinical interpretation of changes in inspiratory muscle strength could be helped by the determination of cut-off values. The aim of this study was to estimate the minimal important difference for inspiratory muscle strength assessed with maximal inspiratory pressure (MIP) in people with COPD. Design Post hoc analysis of a randomized controlled trial (EMI2 study) including people with severe to very severe COPD undergoing a pulmonary rehabilitation program was conducted. The determination of the minimal important difference was realized using both anchor-based and distribution-based methods. Setting The study includes patients admitted to the rehabilitation program unit of the Centre Hospitalier des Pays de Morlaix (Morlaix, France) between March 5, 2014 and September 8, 2016. Participants Seventy-three people with severe to very severe COPD (age 62.2 ± 8.0 years, forced expiratory volume in 1 s 36.4 ± 9.5% of theoretical) were analyzed. Intervention Patients followed a standardized pulmonary rehabilitation program 5 days a week for 4 weeks. The program included aerobic training, ground-based outdoor walking training, and strengthening of lower and upper limb muscles. Main measures At the end of the pulmonary rehabilitation program, MIP improved by 14.8 ± 14.9 cmH2O (p < 0.05). Regarding the anchor-based method, only the modified Medical Research Council was selected as an appropriate anchor. The receiver operating characteristic curve analysis reported a minimal important difference of 13.5 cmH2O (sensibility: 75% specificity: 67.5%). Using distribution-based methods, the estimate of minimal important difference was 7.9 cmH2O (standard error of measurement method) and 10.9 cmH2O (size effect method). Results The estimations proposed by this study ranged from 7.9 to 13.5 cmH2O. Conclusions The measurement of minimal important difference is a simple tool for assessing the changes of inspiratory muscle strength during a pulmonary rehabilitation program. We propose a minimal important difference of 13.5 cmH2O for the improvement of MIP. Further studies are needed to confirm this estimation. ClinicalTrials.gov identifier: NCT02074813
Measuring Caregiver Outcomes in Pediatric Rehabilitation Telehealth Services: A Scoping Review

Pediatric occupational therapy practitioners use telehealth to facilitate performance and participation in natural contexts. Optimal therapy outcomes occur when caregivers are integrated into telehealth sessions. This scoping review describes how caregiver outcomes are measured in the pediatric rehabilitation telehealth literature. We searched SCOPUS, MEDLINE, CINAHL, PsycINFO, and ERIC for articles on pediatric telehealth interventions published from January 2005 to June 2022. We excluded non-empirical articles and articles that measured children’s underlying deficits only. Thirty-one articles met inclusion criteria. Studies measured caregiver outcomes with study-specific questionnaires, standardized measures, electronic tracking, and interviews. Caregiver outcomes improved after treatment, and caregivers reported high acceptability and satisfaction with telehealth. Ample evidence supports measurement of caregiver outcomes in pediatric rehabilitation telehealth services (PRTS). Future PRTS work should incorporate existing sound measures that comprehensively evaluate caregiver outcomes, including caregiver engagement and its components, to demonstrate the effect of occupational therapy telehealth services.
A Modified Method for the Quantification of Immune Checkpoint Ligands on Exosomes from Human Serum using Flow Cytometry

Objectives: Exosomes are the smallest of the extracellular vesicles and can contain a variety of different cargos, including nucleic acids, lipids, and proteins. Ultracentrifugation followed by electron microscopy has historically been used for the isolation and visualization of exosomes; Western blot and ELISA have also been used, but these techniques are only semiquantitative and are unable to distinguish different exosome markers in the same sample. To resolve some of these issues, we propose a modification of a bead-based flow cytometry method. Methods: Peripheral blood serum was mixed with a commercial exosome separation reagent and incubated for 30 min at 4°, centrifuged, exosome pellet was isolated and resuspended in PBS. Exosomes were then added to magnetic beads, incubated 18 h, then incubated with exosome-specific antibodies for 1 h. The resulting bead:exosome complexes were centrifuged and then washed, then washed again using a magnetic separator, resuspended in PBS, and analyzed via flow cytometry. Results: Using commercial magnetic beads bound with anti-CD63, our protocol modifies starting conditions, washing steps, and magnetic separation and uses the FSC and SSC determination of the flow cytometer to result in increased yield and identification of the exosome populations of interest. Our modified protocol increased the yield of specific populations approximately 10-fold. Conclusion: The new protocol was used to identify exosomes positive for 2 immune checkpoint ligands in serum-derived exosomes from cervical cancer patients. We suspect that this protocol can also be used for the identification of other exosome proteins since we also quantified the exosome membrane-enriched tetraspanins CD9 and CD81. Identification of proteins rarely expressed in exosomes is complicated in this technique as serum is an inherently dirty source of exosomes, and great care must be taken in the washing and gating of the exosome:bead populations.
Pointwise and uniform convergence of multivariate kernel density estimators using random bandwidths

ABSTRACT We obtain the rates of pointwise and uniform convergence of multivariate kernel density estimators using a random bandwidth vector obtained by some data-based algorithm. We are able to obtain faster rate for pointwise convergence. The uniform convergence rate is obtained under some moment condition on the marginal distribution. The rates are obtained under i.i. d. and strongly mixing type dependence assumptions.