Miracle-dz/UniFAR-FTUs huggingface dataset:
Black bounces, wormholes, and partly phantom scalar fields

Simpson and Visser recently proposed a phenomenological way to avoid some kinds of space-time singularities by replacing a parameter whose zero value corresponds to a singularity (say, $r$) with the manifestly nonzero expression $r(u) = \sqrt{u^2 + b^2}$, where $u$ is a new coordinate, and $b =$ \const $>0$. This trick, generically leading to a regular minimum of $r$ beyond a black hole horizon (called a"black bounce"), may hopefully mimic some expected results of quantum gravity, and was previously applied to regularize the Schwarzschild, Reissner-Nordstr\"om, Kerr and some other metrics. In this paper it is applied to regularize the Fisher solution with a massless canonical scalar field in general relativity (resulting in a traversable wormhole) and a family of static, spherically symmetric dilatonic black holes (resulting in regular black holes and wormholes). These new regular metrics represent exact solutions of general relativity with a sum of stress-energy tensors of a scalar field with a nonzero self-interaction potential and a magnetic field in the framework of nonlinear electrodynamics with a Lagrangian function $L(F)$, $F = F_{\mu\nu} F^{\mu\nu}$. A novel feature in the present study is that the scalar fields involved have"trapped ghost"properties, that is, are phantom in a strong-field region and canonical outside it, with a smooth transition between the regions. It is also shown that any static, spherically symmetric metric can be obtained as an exact solution to the Einstein equations with the stress-energy tensor of the above field combination.
Convergence of Policy Improvement for Entropy-Regularized Stochastic Control Problems

For a general entropy-regularized stochastic control problem on an infinite horizon, we prove that a policy improvement algorithm (PIA) converges to an optimal relaxed control. Contrary to the standard stochastic control literature, classical H\"{o}lder estimates of value functions do not ensure the convergence of the PIA, due to the added entropy-regularizing term. To circumvent this, we carry out a delicate estimation by moving back and forth between appropriate H\"{o}lder and Sobolev spaces. This requires new Sobolev estimates designed specifically for the purpose of policy improvement and a nontrivial technique to contain the entropy growth. Ultimately, we obtain a uniform H\"{o}lder bound for the sequence of value functions generated by the PIA, thereby achieving the desired convergence result. Characterization of the optimal value function as the unique solution to an exploratory Hamilton-Jacobi-Bellman equation comes as a by-product.
Spin it as you like: The (lack of a) measurement of the spin tilt distribution with LIGO-Virgo-KAGRA binary black holes

The growing set of gravitational-wave sources is being used to measure the properties of the underlying astrophysical populations of compact objects, black holes and neutron stars. Most of the detected systems are black hole binaries. While much has been learned about black holes by analyzing the latest LIGO-Virgo-KAGRA (LVK) catalog, GWTC-3, a measurement of the astrophysical distribution of the black hole spin orientations remains elusive. This is usually probed by measuring the cosine of the tilt angle (cos τ ) between each black hole spin and the orbital angular momentum, cos τ = +1 being perfect alignment. Abbott et al. (2021e) has modeled the cos τ distribution as a mixture of an isotropic component and a Gaussian component with mean ﬁxed at +1 and width measured from the data. In this paper, we want to verify if the data require the existence of such a peak at cos τ = +1. We use various models for the astrophysical tilt distribution and ﬁnd that a) Augmenting the LVK model such that the mean µ of the Gaussian is not ﬁxed at +1 returns results that strongly depend on priors. If we allow µ > +1 then the resulting astrophysical cos τ distribution peaks at +1 and looks linear, rather than Gaussian. If we constrain − 1 ≤ µ ≤ +1 the Gaussian component peaks at µ = 0 . 47 +0 . 47 − 1 . 04 (median and 90% symmetric credible interval). Two other 2-component mixture models yield cos τ distributions that either have a broad peak centered at 0 . 20 +0 . 21 − 0 . 18 or a plateau that spans the range [ − 0 . 5 , +1], without a clear peak at +1. b) All of the models we considered agree on the fact that there is no excess of black hole tilts at around − 1. c) While yielding quite diﬀerent posteriors, the models considered in this work have Bayesian evidences that are the same within error bars.
Simulation of Atlantic Hurricane Tracks and Features: A Deep Learning Approach

The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain from input data (storm features) available in or derived from the HURDAT2 database models capable of simulating important hurricane properties such as landfall location and wind speed that are consistent with historical records. In pursuit of this objective, a trajectory model providing the storm center in terms of longitude and latitude, and intensity models providing the central pressure and maximum 1-$min$ wind speed at 10 $m$ elevation were created. The trajectory and intensity models are coupled and must be advanced together, six hours at a time, as the features that serve as inputs to the models at any given step depend on predictions at the previous time steps. Once a synthetic storm database is generated, properties of interest, such as the frequencies of large wind speeds may be extracted from any part of the simulation domain. The coupling of the trajectory and intensity models obviates the need for an intensity decay inland of the coastline. Prediction results are compared to historical data, and the efficacy of the storm simulation models is demonstrated for three examples: New Orleans, Miami and Cape Hatteras.
NU-net: An Unpretentious Nested U-net for Breast Tumor Segmentation

—Breast tumor segmentation is one of the key steps that helps us characterize and localize tumor regions. However, variable tumor morphology, blurred boundary, and similar intensity distributions bring challenges for accurate segmentation of breast tumors. Recently, many U-net variants have been proposed and widely used for breast tumors segmentation. However, these architectures suffer from two limitations: (1) Ignoring the characterize ability of the benchmark networks, and (2) Introducing extra complex operations increases the difficulty of understanding and reproducing the network. To alleviate these challenges, this paper proposes a simple yet powerful nested U-net (NU-net) for accurate segmentation of breast tumors. The key idea is to utilize U-Nets with different depths and shared weights to achieve robust characterization of breast tumors. NU-net mainly has the following advantages: (1) Improving network adaptability and robustness to breast tumors with different scales, (2) This method is easy to reproduce and execute, and (3) The extra operations increase network parameters without significantly increasing computational cost. Extensive experimental results with twelve state-of-the-art segmentation methods on three public breast ultrasound datasets demonstrate that NU-net has more competitive segmentation performance on breast tumors. Furthermore, the robustness of NU-net is further illustrated on the segmentation of renal ultrasound images. The source code is publicly available on https://github.com/CGPxy/NU-net.
Implications from the Velocity Profile of the M87 Jet: A Possibility of a Slowly Rotating Black Hole Magnetosphere

Motivated by the measured velocity profile of the M87 jet using the Korean very-long-baseline interferometry (VLBI) network (KVN) and VLBI Exploration of Radio Astrometry (VERA) Array (KaVA) by Park et al. indicating that the starting position of the jet acceleration is farther from the central engine of the jet than predicted in general relativistic magnetohydrodynamic simulations, we explore how to mitigate the apparent discrepancy between the simulations and the KaVA observation. We use a semi-analytic jet model proposed by Tomimatsu & Takahashi consistently solving the transmagnetic field structure but neglecting any dissipation effects. By comparing the jet model with the observed M87 jet velocity profile, we find that the model can reproduce the logarithmic feature of the velocity profile and can fit the observed data when choosing c/(100r g ) ≲ Ω F ≲ c/(70r g ), where r g is the gravitational radius. While a total specific energy (  ) of the jet changes the terminal bulk Lorentz factor of the jet, a slower angular velocity of the black hole magnetosphere (Ω F ) makes a light-cylinder radius (r lc) larger, and it consequently pushes out a location of a starting point of the jet acceleration. Using the estimated Ω F we further estimate the magnetic field strength on the event horizon scale in M87 by assuming Blandford–Znajek process is in action. The corresponding magnetic flux threading the event horizon of M87 is in good agreement with a magnetically arrested disk regime.
One-Shot Transfer of Affordance Regions? AffCorrs!

In this work, we tackle one-shot visual search of object parts. Given a single reference image of an object with annotated affordance regions, we segment semantically corresponding parts within a target scene. We propose AffCorrs, an unsupervised model that combines the properties of pre-trained DINO-ViT's image descriptors and cyclic correspondences. We use AffCorrs to find corresponding affordances both for intra- and inter-class one-shot part segmentation. This task is more difficult than supervised alternatives, but enables future work such as learning affordances via imitation and assisted teleoperation.
Constrained Update Projection Approach to Safe Policy Optimization

Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a novel policy optimization method based on Constrained Update Projection framework that enjoys rigorous safety guarantee. Central to our CUP development is the newly proposed surrogate functions along with the performance bound. Compared to previous safe RL methods, CUP enjoys the benefits of 1) CUP generalizes the surrogate functions to generalized advantage estimator (GAE), leading to strong empirical performance. 2) CUP unifies performance bounds, providing a better understanding and interpretability for some existing algorithms; 3) CUP provides a non-convex implementation via only first-order optimizers, which does not require any strong approximation on the convexity of the objectives. To validate our CUP method, we compared CUP against a comprehensive list of safe RL baselines on a wide range of tasks. Experiments show the effectiveness of CUP both in terms of reward and safety constraint satisfaction. We have opened the source code of CUP at this link https://github.com/zmsn-2077/ CUP-safe-rl.
Geometric continuity and compatibility conditions for 4-patch surfaces

When considering regularity of surfaces, it is its geometry that is of interest. Thus, the con- cept of geometric regularity or geometric continuity of a specific order is a relevant concept. In this paper we discuss necessary and sufficient conditions for a 4-patch surface to be geometrically continuous of order one and two or, in other words, being tangent plane continuous and curvature continuous respectively. The focus is on the regularity at the point where the four patches meet and the compatibility conditions that must appear in this case. In this article the compatibility conditions are proved to be independent of the patch parametrization, i.e., the compatibility con- ditions are universal. In the end of the paper these results are applied to a specific parametrization such as Bezier representation in order to generalize a 4-patch surface result by Sarraga.
Indigenous Foods to Address Malnutrition: An Inquiry into the Diets and Nutritional Status of Women in the Indigenous Community of Munda Tribes of Jharkhand, India

ABSTRACT Background Indigenous people globally experience poor nutrition outcomes, with women facing the greater burden. Munda, a predominant tribe in Jharkhand, India, live in a biodiverse food environment but yet have high levels of malnutrition. Objectives To assess diets and the nutritional status of Munda tribal women and explore associations with their Indigenous food consumption, dietary diversity, and socioeconomic and demographic profiles. Methods A cross-sectional study with a longitudinal component to capture seasonal dietary intake was conducted in 11 villages of the Khunti district, Jharkhand. Household surveys and FFQs, supplemented with 2-d 24-h dietary recall and anthropometric assessments on 1 randomly selected woman per household were conducted. Results Limited access to diverse foods from a natural food environment (Food Accessed Diversity Index score of 0.3 ± 0.3) was observed. More than 90% women in both seasons had usual nutrient intakes below the estimated average requirements for all nutrients except protein and vitamin C; 35.5% of women were underweight. The mean Minimum Dietary Diversity Score among women (MDDS) was low [2.6 ± 0.6 in wet monsoon; 3 ± 0.7 in winters (acceptable ≥5)]. Higher MDDS contributed to higher usual nutrient intakes (P <0.001). Indigenous food intakes in both seasons (wet monsoon and winter) were low, e.g. Indigenous green leafy vegetables [10.5 and 27.8% of the recommended dietary intake (RDI), respectively], other vegetables (5.2% and 7.8% of RDI, respectively), and fruits (5.8 and 22.8% of RDI, respectively). Despite low intakes, the Indigenous food consumption score was positively associated with usual intake of vitamin A, riboflavin, vitamin C, pyridoxine, and calcium (P < 0.05) in the wet monsoon and thiamine, riboflavin, and zinc (P < 0.001) in winters. After adjusting for covariates, Indigenous food consumption was associated with a higher usual intake of vitamin A (P < 0.001) in the wet monsoon season. Conclusion Contextual food-based interventions promoting Indigenous foods and increasing dietary diversity have the potential to address malnutrition in Munda women.
A prosthesis utilizing natural vestibular encoding strategies improves sensorimotor performance in monkeys

Sensory pathways provide complex and multifaceted information to the brain. Recent advances have created new opportunities for applying our understanding of the brain to sensory prothesis development. Yet complex sensor physiology, limited numbers of electrodes, and nonspecific stimulation have proven to be a challenge for many sensory systems. In contrast, the vestibular system is uniquely suited for prosthesis development. Its peripheral anatomy allows site-specific stimulation of 3 separate sensory organs that encode distinct directions of head motion. Accordingly, here, we investigated whether implementing natural encoding strategies improves vestibular prosthesis performance. The eye movements produced by the vestibulo-ocular reflex (VOR), which plays an essential role in maintaining visual stability, were measured to quantify performance. Overall, implementing the natural tuning dynamics of vestibular afferents produced more temporally accurate VOR eye movements. Exploration of the parameter space further revealed that more dynamic tunings were not beneficial due to saturation and unnatural phase advances. Trends were comparable for stimulation encoding virtual versus physical head rotations, with gains enhanced in the latter case. Finally, using computational methods, we found that the same simple model explained the eye movements evoked by sinusoidal and transient stimulation and that a stimulation efficacy substantially less than 100% could account for our results. Taken together, our results establish that prosthesis encodings that incorporate naturalistic afferent dynamics and account for activation efficacy are well suited for restoration of gaze stability. More generally, these results emphasize the benefits of leveraging the brain’s endogenous coding strategies in prosthesis development to improve functional outcomes.
Direct percutaneous endoscopic jejunostomy tube placement in patients post Roux-en-Y gastric bypass, a single tertiary care center experience

Background and study aims Obesity prevalence continues to rise in the United States with Roux-en-Y gastric bypass (RYGB) surgery being one of the most common bariatric procedures. With this trend, more patients with altered upper gastrointestinal (UGI) anatomy have required endoscopic intervention including direct percutaneous endoscopic jejunostomy (DPEJ) placement. We aimed to assess the safety and success rates of DPEJ in RYGB patients. Patients and methods All patients at a tertiary care referral center who underwent DPEJ during an 8-year period were queried from a prospectively maintained registry of all enteroscopy procedures. Duplicate cases and altered upper UGI anatomy subtypes other than RYGB were excluded. The final cohort consisted of two groups: RYGB vs native anatomy (NA). Demographic, procedural, readmission, follow-up, and complication data were recorded. Comparative analysis was performed. Results Seventy-two patients were included where 28 had RYGB and 44 had NA. Both groups had similar baseline and pre-procedure data. Procedure success rate was 89 % in RYGB patients and 98 % in NA patients ( P = 0.13). There were no intraprocedural complications. Early and late postprocedural complication rates were similar between the groups (both 4 % vs 7 %). Average follow-up times in the RYGB and NA groups were 12.97 ± 9.35 and 13.44 ± 9.21 months, respectively. Although readmission rates at 1 and 6 months were higher in the NA versus the RYGB group (21 % vs 7 % and 25 % vs 15 %), these differences were not significant. Conclusions DPEJ can be successful and safely placed in RYGB patients with no significant difference in procedure success, complication, or readmission rates when compared to control.
Preprocessing among the Infalling Galaxy Population of EDisCS Clusters

We present results from a low-resolution spectroscopic survey for 21 galaxy clusters at 0.4 < z < 0.8 selected from the ESO Distant Cluster Survey. We measured spectra using the low-dispersion prism in IMACS on the Magellan Baade telescope and calculate redshifts with an accuracy of σz = 0.007. We find 1763 galaxies that are brighter than R = 22.9 in the large-scale cluster environs. We identify the galaxies expected to be accreted by the clusters as they evolve to z = 0 using spherical infall models and find that ∼30%–70% of the z = 0 cluster population lies outside the virial radius at z ∼ 0.6. For analogous clusters at z = 0, we calculate that the ratio of galaxies that have fallen into the clusters since z ∼ 0.6 to those that were already in the core at that redshift is typically between ∼0.3 and 1.5. This wide range of ratios is due to intrinsic scatter and is not a function of velocity dispersion, so a variety of infall histories is to be expected for clusters with current velocity dispersions of 300 km s−1 ≲ σ ≲ 1200 km s−1. Within the infall regions of z ∼ 0.6 clusters, we find a larger red fraction of galaxies than in the field and greater clustering among red galaxies than blue. We interpret these findings as evidence of “preprocessing,” where galaxies in denser local environments have their star formation rates affected prior to their aggregation into massive clusters, although the possibility of backsplash galaxies complicates the interpretation.
Motion correction in MR image for analysis of VSRAD using generative adversarial network

Voxel-based specific region analysis systems for Alzheimer’s disease (VSRAD) are clinically used to measure the atrophied hippocampus captured by magnetic resonance imaging (MRI). However, motion artifacts during acquisition of images may distort the results of the analysis. This study aims to evaluate the usefulness of the Pix2Pix network in motion correction for the input image of VSRAD analysis. Seventy-three patients examined with MRI were distinguished into the training group (n = 51) and the test group (n = 22). To create artifact images, the k-space images were manipulated. Supervised deep learning was employed to obtain a Pix2Pix that generates motion-corrected images, with artifact images as the input data and original images as the reference data. The results of the VSRAD analysis (severity of voxel of interest (VOI) atrophy, the extent of gray matter (GM) atrophy, and extent of VOI atrophy) were recorded for artifact images and motion-corrected images, and were then compared with the original images. For comparison, the image quality of Pix2Pix generated motion-corrected image was also compared with that of U-Net. The Bland-Altman analysis showed that the mean of the limits of agreement was smaller for the motion-corrected images compared to the artifact images, suggesting successful motion correction by the Pix2Pix. The Spearman’s rank correlation coefficients between original and motion-corrected images were almost perfect for all results (severity of VOI atrophy: 0.87–0.99, extent of GM atrophy: 0.88–00.98, extent of VOI atrophy: 0.90–1.00). Pix2Pix generated motion-corrected images that showed generally improved quantitative and qualitative image qualities compared with the U-net generated motion-corrected images. Our findings suggest that motion correction using Pix2Pix is a useful method for VSRAD analysis.
Development of a Mobile App for Self-Care Against COVID-19 Using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) Model: Methodological Study

Background Mobile apps have been shown to play an important role in the management, care, and prevention of infectious diseases. Thus, skills for self-care—one of the most effective ways to prevent illness—can be improved through mobile health apps. Objective This study aimed to design, develop, and evaluate an educational mobile-based self-care app in order to help the self-prevention of COVID-19 in underdeveloped countries. We intended the app to be easy to use, quick, and inexpensive. Methods In 2020 and 2021, we conducted a methodological study. Using the ADDIE (analysis, design, development, implementation, and evaluation) educational model, we developed a self-care management mobile app. According to the ADDIE model, an effective training and performance support tool is built through the 5 phases that comprise its name. There were 27 participants who conducted 2 evaluations of the mobile app’s usability and impact using the mobile health app usability and self-care inventory scales. The study design included pre- and posttesting. Results An Android app called MyShield was developed. The results of pre- and posttests showed that on a scale from 0 to 5, MyShield scored a performance average of 4.17 in the physical health dimension and an average of 3.88 in the mental well-being dimension, thereby showing positive effects on self-care skills. MyShield scored highly on the “interface and satisfaction,” “ease of use,” and “usefulness” components. Conclusions MyShield facilitates learning self-care skills at home, even during quarantine, increasing acquisition of information. Given its low development cost and the ADDIE educational design on which it is based, the app can be helpful in underdeveloped countries. Thus, low-income countries—often lacking other tools—can use the app as an effective tool for fighting COVID-19, if it becomes a standard mobile app recommended by the government.