Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical and Laboratory Characteristics of Kikuchi-Fujimoto Disease According to Age

Background: Little information exists regarding the differences in the clinical and laboratory characteristics of Kikuchi-Fujimoto disease (KFD) according to age. Objective: To evaluate the clinical and laboratory characteristics of KFD according to age. Methods: The relevance of sex, age, clinical features, laboratory findings, courses, and follow-up results were retrospectively evaluated in patients diagnosed with KFD at Pusan National University Hospital between 2010 and 2020. Results: Eighty patients (46 children and 34 adults) with a mean age of 21.5 ± 11.8 years (range, 3–49 years) were included in the study. Those aged 10–19 years accounted for the largest number of patients (42.5%). Among children, the male sex ratio was higher, especially for patients aged ≤ 9 years. In adults, the female sex ratio was higher, especially for patients aged 20–29 years. Fever, tenderness in the lymph node, and skin rashes were more common in children, while myalgia and weight loss were more common in adults. In children, the recurrence rate was significantly higher among boys than among girls (15.8 vs. 0.0%, P = 0.001); lower platelet count and higher CRP levels were observed among boys than among girls. EBV and ANA positivity rates were higher in boys than in girls. In adults, the recurrence rate was significantly higher in women than in men (18.2 vs. 0.0%, P = 0.005). ANA positivity rates were higher in women than in men. Conclusion: The clinical features, laboratory findings, and recurrence of KFD may differ depending on age and sex. Clinicians should be aware of this.
The Impact of Body Mass Index and Waist Circumference on Lung Volumes in the Adult Population

BACKGROUND: This study aimed to examine the impact of Body Mass Index and Waist. The circumference on Pulmonary Function Test spirometry parameters such as FVC, FEV1, PEF and FEF25–75% in the adult population. METHODS: Twenty-five adult participants (aged between 18 and 50 years) were involved in this study. The participants completed a questionnaire about their weight, height, gender, waist circumference, and medical history. The height, weight, and waist circumferences were obtained in the pulmonary function test lab. Subjects were instructed to perform at least three acceptable forced vital capacity maneuvers by spirometry to meet the acceptability criteria for the American thoracic society (ATS) testing standards; the FVC and FEV1 maneuvers should be within 150 ml for the subject to meet the repeatability criteria. The Body mass index was calculated for all subjects. RESULTS: The result shows no significant impact of BMI on FVC, FEV1, PEFR, or FEF 25– 75% (P values = 0.056, 0.419, 0.413, 0.843, respectively and r values = 0.4, 0.2, -0.2, -0.04, respectively). Also, there was no significant impact found between WC and FVC, FEV1, PEFR, or FEF 25–75% (P values= 0.397, 0.920, 0.359, 0.727 respectively and r values= 0.2, -0.02, -0.2, -0.1, respectively). CONCLUSION: This study found no significant correlation between BMI and spirometric values (FVC, FEV1, PEFR, or FEF 25–75% ), and no correlation found between WC and spirometric values (FVC, FEV1, PEFR, or FEF25–75%). Running head: THE IMPACT OF BODY MASS INDEX IN ADULTS iii
A Hierarchical Approach for Learning Multi-Step Sequential Tasks from Video Demonstrations

—We are interested in learning the high-level policy of multi-step sequential (MSS) tasks, such as activities of daily living, from video demonstrations. Videos of MSS tasks are typically long in duration and exhibit large feature variance, especially when captured in non-engineered settings. Learning task policy from such videos using state-of-the-art end-to-end approaches is sample inefﬁcient due to a reliance on pixel-level information. Understanding the unique temporal structures of MSS tasks can make policy learning easier and sample efﬁ- cient. However, understanding this temporal structure requires analyzing the entire content of the video or a task which is a complex and under-explored area in the current literature. We propose a hierarchical solution to this problem where i) an automated feature selection process extrapolates temporally grounded task-relevant features from the video and then ii) a stochastic policy learning model learns a feature-constrained task policy. The proposed model is sample efﬁcient (trained using less than one hour of demonstration data) as a result of substituting selected temporally-grounded features for pixel- level information. We demonstrate the efﬁcacy of our proposed framework by teaching a YuMi robot a long-duration, multi- step task – tea making – from videos. We present results on sample efﬁciency and robustness against data loss. We also compare our performance to that of a state-of-the-art approach of task learning from real visual demonstrations.
ComparativeQuality Evaluation of SelectedBrands ofCefuroxime Axetil Tablets Marketed in the Greater Accra Region of Ghana

*e ever-growing commercialization of poor-quality and substandard medicines, especially anti-infectives characterized by inadequate postmarket surveillance by stakeholders remains a major global health challenge, particularly in developing countries, where antibiotic drug resistance and its repercussions on human health remain dominant. *is research sought to evaluate the pharmaceutical quality of six randomly selected brands of cefuroxime axetil tablets (250mg) marketed in the Greater Accra region of Ghana. *e selected brands were coded and subjected to both compendial and noncompendial tests. Statistical analysis and model-independent parameter (similarity factor, f2) were employed in analyzing the dissolution profiles of all the brands. All brands including the reference brand conformed to the pharmacopeial specifications for both compendial and noncompendial tests, indicating that they were of good quality. However, there were significant variations (p< 0.05) in the disintegration time amongst the various brands. All the brands had f 2 values> 50 indicating similarity of their drug release profiles with the innovator. Hence, all the sampled cefuroxime axetil brands can be considered as pharmaceutical equivalents to the innovator drug. *ese brands can, therefore, be used as a substitute for the innovator drug by physicians to patients in cases of unaffordability or unavailability of the innovator brand.
ABehavioralModelofDrivers’MeanSpeed InfluencedbyWeather Conditions, Road Geometry, and Driver Characteristics Using a Driving Simulator Study

Driving above the speed limit is one of the factors that significantly affect safety. Many studies examined the factors affecting the speed of vehicles in the simulated environment. *e present study aimed to analyze drivers’ characteristics, time and weather conditions, and geometric features’ effect on mean speed in simulated conditions simultaneously. In this regard, the simulator experiment data of 70 drivers were collected in a two-lane rural highway at six different times, and weather scenarios and their socioeconomic characteristics were collected by a questionnaire. Structural equation modeling (SEM) was used to capture the complex relationships among related variables. Eleven variables were grouped into four latent variables in the structural model. Latent variables including “Novice Drivers,” “Experienced Drivers,” “Sight Distance,” and “Geometric Design” were defined and found significant on their mean speed. *e results showed that “Novice Drivers” have a positive correlation with the mean speed. Meanwhile, “Experienced Drivers,” who drive 12% slower than the novice group, negatively affect the mean speed with a standard regression weight of −0.08. *is relation means that young and novice drivers are more inclined to choose higher speeds. Among variables, the latent variable “Sight Distance” has the most significant effect on the mean speed. *is model shows that foggy weather conditions strongly affect the speed selection behavior and reduce the mean speed by 40%. Nighttime also reduces mean speed due to poor visibility conditions. Furthermore, “Geometric design” as the latent variable indicates the presence of curves on the simulated road, and it can be concluded that the existence of a curve on the road encourages drivers to slow down, even young drivers. It is noteworthy that the parts of the simulated road with a horizontal curve act as a speed reduction tool for drivers.
OPTH_A_259011 2167..2178

1The Midwest Center for Sight, Des Plaines, IL, USA; 2Kentucky Eye Institute, Lexington, KY, USA; 3Favoris AG, Basel, Switzerland; 4Novartis Pharma AG, Basel, Switzerland; 5Ophthalmic Consultants of Long Island, Garden City, NY, USA Abstract: Presbyopia is a common age-related vision disorder characterized by a progressive inability to focus on near objects. If uncorrected or under-corrected, presbyopia can significantly impact patients’ quality of life. Presbyopia represents an area of considerable unmet need due to its rising prevalence worldwide as the population ages, the high proportion of under-treated individuals in some parts of the world, and the limitations of currently available corrective methods. Progressive or bifocal spectacles are associated with peripheral blur, a restricted visual field and impaired depth perception, which have been linked to an increased risk of falls in the elderly. Contact lens options can be difficult to maintain due to the development of age-related dry eye symptoms and reduced manual dexterity. Other corrective methods involve surgical interventions that modify the optics of the cornea, replace the crystalline lens, or attempt to restore active accommodation. While patients undergoing surgery report satisfactory outcomes post-operatively, many of them eventually require reading glasses. Non-invasive therapies with novel mechanisms of action are currently being investigated; these include miotic agents and UNR844, a lipoic acid choline ester. In this narrative review, available evidence on presbyopia prevalence, quality of life impact and risk factors are described, with a focus on observational studies in nonclinical settings. The diagnosis pathway and patient journey in presbyopia are outlined, and various treatment options are analyzed. The data reviewed herein reveals significant gaps in the provision of vision correction for this common condition, with a paucity of effective, non-invasive treatment options broadly accessible to presbyopic individuals.
Context effects on memory retrieval

MEMORY RETRIEVAL: THEORY we to a personalized storyteller for cultural heritage presentations, to as Gaze-based Personalization for Art or gpa system. First, we will review work that establishes the relation between eye gaze and interest. Next, we will introduce the gpa system, which will be evaluated in a laboratory experiment to demonstrate that adapting stories told at cultural heritage presentations to the personal interests of visitors improves their experience. We will finish with reviewing related work, and by suggesting extensions and possible further applications of our system. RETRIEVAL: APPLICATIONS APPLICATIONS
The Effect of Shareholder Scrutiny on Corporate Tax Behavior: Evidence from Shareholder Tax Litigation

This study examines the effect of shareholder scrutiny of corporate tax avoidance behavior and its related financial reporting. Specifically, we explore the factors associated with shareholder tax litigation and its effect on the future tax behavior of the sued firm and its peers. We find that sued firms have lower cash and GAAP effective tax rates (ETRs) and engage in extreme tax avoidance before litigation. After litigation, they decrease tax avoidance activities, relative to matched control firms. Peer firms in the same industry as sued firms similarly reduce their level of tax avoidance and the likelihood of extreme tax avoidance after the litigation, relative to control firms. Additional analyses suggest that sued firms change their tax avoidance behavior, rather than merely their tax financial reporting. Finally, the spillover results are strongest for peer firms with the most tax avoidance (i.e., the lowest cash ETRs) when the sued firm's alleged misconduct is revealed.This article is protected by copyright. All rights reserved.
Towards Photorealistic Colorization by Imagination

We present a novel approach to automatic image colorization by imitating the imagination process of human experts. Our imagination module is designed to generate color images that are context-correlated with black-and-white photos. Given a black-and-white image, our imagination module firstly extracts the context information, which is then used to synthesize colorful and diverse images using a conditional image synthesis network (e.g., semantic image synthesis model). We then design a colorization module to colorize the black-and-white images with the guidance of imagination for photorealistic colorization. Experimental results show that our work produces more colorful and diverse results than state-of-the-art image colorization methods. Our source codes will be publicly available.
The binary central star of the bipolar pre-planetary nebula IRAS 08005−2356 (V510 Pup)

Current models predict that binary interactions are a major ingredient in the formation of bipolar planetary nebulae (PNe) and pre-planetary nebulae (PPNe). Despite years of radial velocity (RV) monitoring, the paucity of known binaries amongst the latter systems means data are insufficient to examine this relationship in detail. In this work, we report on the discovery of a long-period (P = 2654 ± 124 d) binary at the centre of the Galactic bipolar PPN IRAS 08005−2356 (V510 Pup), determined from long-term spectroscopic and near-infrared time-series data. The spectroscopic orbit is fitted with an eccentricity of 0.36 ± 0.05, which is similar to that of other long-period post-AGB binaries. Time-resolved Hα profiles reveal high-velocity outflows (jets) with deprojected velocities up to 231$_{-27}^{+31}$ km s−1 seen at phases when the luminous primary is behind the jet. The outflow traced by Hα is likely produced via accretion on to a main-sequence companion, for which we calculate a mass of 0.63 ± 0.13 M⊙. This discovery is one of the first cases of a confirmed binary PPN and demonstrates the importance of high-resolution spectroscopic monitoring surveys using large telescopes in revealing binarity among these systems.
Augmenting Implicit Neural Shape Representations with Explicit Deformation Fields

Implicit neural representation is a recent approach to learn shape collections as zero level-sets of neural networks, where each shape is represented by a latent code. So far, the focus has been shape reconstruction, while shape generalization was mostly left to generic encoder-decoder or auto-decoder regularization. In this paper we advocate deformation-aware regularization for implicit neural representations, aiming at producing plausible deformations as latent code changes. The challenge is that implicit representations do not capture correspondences between different shapes, which makes it difficult to represent and regularize their deformations. Thus, we propose to pair the implicit representation of the shapes with an explicit, piecewise linear deformation field, learned as an auxiliary function. We demonstrate that, by regularizing these deformation fields, we can encourage the implicit neural representation to induce natural deformations in the learned shape space, such as as-rigid-as-possible deformations.
Entropic turnpike estimates for the kinetic Schrödinger problem

We investigate the kinetic Schr\"odinger problem, obtained considering Langevin dynamics instead of Brownian motion in Schr\"odinger's thought experiment. Under a quasilinearity assumption we establish exponential entropic turnpike estimates for the corresponding Schr\"odinger bridges and exponentially fast convergence of the entropic cost to the sum of the marginal entropies in the long-time regime, which provides as a corollary an entropic Talagrand inequality. In order to do so, we profit from recent advances in the understanding of classical Schr\"odinger bridges and adaptations of Bakry-\'Emery formalism to the kinetic setting. Our quantitative results are complemented by basic structural results such as dual representation of the entropic cost and the existence of Schr\"odinger potentials.
FedSkel: Efficient Federated Learning on Heterogeneous Systems with Skeleton Gradients Update

Federated learning aims to protect users' privacy while performing data analysis from different participants. However, it is challenging to guarantee the training efficiency on heterogeneous systems due to the various computational capabilities and communication bottlenecks. In this work, we propose FedSkel to enable computation-efficient and communication-efficient federated learning on edge devices by only updating the model's essential parts, named skeleton networks. FedSkel is evaluated on real edge devices with imbalanced datasets. Experimental results show that it could achieve up to 5.52x speedups for CONV layers' back-propagation, 1.82x speedups for the whole training process, and reduce 64.8% communication cost, with negligible accuracy loss.
Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches

Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. In this paper, we use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors worn while participants perform clinical assessment tasks, and use these data to estimate disease status and severity. A short period of data collection (<5 min) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson’s disease, and to provide estimates of disease severity.
Testing the Relationship between Bursty Star Formation and Size Fluctuations of Local Dwarf Galaxies

Stellar feedback in dwarf galaxies plays a critical role in regulating star formation via galaxy-scale winds. Recent hydrodynamical zoom-in simulations of dwarf galaxies predict that the periodic outward flow of gas can change the gravitational potential sufficiently to cause radial migration of stars. To test the effect of bursty star formation on stellar migration, we examine star formation observables and sizes of 86 local dwarf galaxies. We find a correlation between the R-band half-light radius (R e ) and far-UV luminosity (L FUV) for stellar masses below 108 M ⊙ and a weak correlation between the R e and Hα luminosity (L Hα ). We produce mock observations of eight low-mass galaxies from the FIRE-2 cosmological simulations and measure the similarity of the time sequences of R e and a number of star formation indicators with different timescales. Major episodes of R e time sequence align very well with the major episodes of star formation, with a delay of ∼50 Myr. This correlation decreases toward star formation rate indicators of shorter timescales such that R e is weakly correlated with L FUV (10–100 Myr timescale) and is completely uncorrelated with L Hα (a few Myr timescale), in agreement with the observations. Our findings based on FIRE-2 suggest that the R-band size of a galaxy reacts to star formation variations on a ∼50 Myr timescale. With the advent of a new generation of large space telescopes (e.g., JWST), this effect can be examined explicitly in galaxies at higher redshifts, where bursty star formation is more prominent.
Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays

In spin based quantum dot arrays, material or fabrication imprecisions affect the behaviour of the device, which must be taken into account when controlling it. This requires measuring the shape of specific convex polytopes. We present an algorithm that automatically discovers count, shape and size of the facets of a convex polytope from measurements by alternating a phase of model-fitting with a phase of querying new measurements, based on the fitted model. We evaluate the algorithm on simulated polytopes and devices, as well as a real 2 × 2 spin qubit array. Results show that we can reliably find the facets of the convex polytopes, including small facets with sizes on the order of the measurement precision.
MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is challenging due to its uncertain and multimodal nature. While generative adversarial networks can learn a distribution over future trajectories, they tend to predict out-of-distribution samples when the distribution of future trajectories is a mixture of multiple, possibly disconnected modes. To address this issue, we propose a multi-generator model for pedestrian trajectory prediction. Each generator specializes in learning a distribution over trajectories routing towards one of the primary modes in the scene, while a second network learns a categorical distribution over these generators, conditioned on the dynamics and scene input. This architecture allows us to effectively sample from specialized generators and to significantly reduce the out-of-distribution samples compared to single generator methods.
Investigation of the Assessment of Infant Vocalizations by Laypersons

The goal of this investigation was the assessment of acoustic infant vocalizations by laypersons. More specifically, the goal was to identify (1) the set of most salient classes for infant vocalizations, (2) their relationship to each other and to affective ratings, and (3) proposals for classification schemes based on these labels and relationships. The assessment behavior of laypersons has not yet been investigated, as current infant vocalization classification schemes have been aimed at professional and scientific applications. The study methodology was based on the Nijmegen protocol, in which participants rated vocalization recordings regarding acoustic class labels, and continuous affective scales valence, tense arousal and energetic arousal. We determined consensus stimuli ratings as well as stimuli similarities based on participant ratings. Our main findings are: (1) we identified 9 salient labels, (2) valence has the overall greatest association to label ratings, (3) there is a strong association between label and valence ratings in the negative valence space, but low association for neutral labels, and (4) stimuli separability is highest when grouping labels into 3 – 5 classes. We finally propose two classification schemes based on these findings.
Logarithmic, noise-induced dynamics in the Anderson insulator

We study the dynamical behavior of the Anderson insulator in the presence of a local noise. We show that the noise induces logarithmically slow energy and entanglement growth, until the system reaches an infinite-temperature state, where both quantities saturate to extensive values. The saturation value of the entanglement entropy approaches the average entanglement entropy over all possible product states. At infinite temperature, we find that a density excitation spreads logarithmically with time, without any signs of asymptotic diffusive behavior. In addition, we provide a theoretical picture which qualitatively reproduces the phenomenology of particle transport.