Miracle-dz/UniFAR-FTUs huggingface dataset:
Bar-driven Mass Inflow: How Bar Characteristics Affect the Inflow

If the interstellar medium (ISM) in a galaxy is driven into the nuclear region, it will change the evolution of the galaxy. Although it is generally assumed that bars are efficient at driving the ISM to smaller radii, how the characteristics of a bar affect the inflow has never been examined in detail. In this paper we investigate how various bar characteristics affect the ability of the bar to drive the ISM to smaller radii. We model the ISM using high-resolution hydrodynamic simulations and are able to show that weak bars have almost no effect on the radial distribution of the ISM. We also find that for all bar strengths, bars are only able to drive gas down to a radius where a ring forms. Inside of this ring there is very little net inflow. When a nuclear ring forms the bar becomes very efficient at driving gas down to the inner kiloparsec. Alternatively, when no nuclear ring forms, gas forms a ring at the largest nonlooping orbit whose major axis is parallel to the major axis of the bar (x1 orbit). For all but the thinnest bars, this ring is far from the nuclear region. We term this type of ring an x1 ring and show that inner rings are a subset of x1 rings and form when there are no looping x1 orbits. In this case, the gas accumulates at the largest x1 orbit inside of the looping 4 : 1 orbits. We also show that the one-dimensional measure of bar strength based on the maximum nonaxisymmetric force, Qb, is degenerate with several bar characteristics that control orbit family transitions. Because these orbit transitions determine where and whether rings form, and it is ring formation that is the ultimate result of bar-driven inflow, Qb is not a useful metric for determining the effect of a bar on the evolution of a galaxy.
Automatic Attention to Sexual Images of Men and Women in Androphilic, Ambiphilic, and Gynephilic Women

Abstract Attentional resources might be automatically attracted to highly motivational stimuli such as a possible sexual partner. We tested whether attention would be automatically attracted to images of men vs women in women with a self-reported sexual attraction to men (androphilic), women (gynephilic) or to both men and women (ambiphilic) in a dot-probe paradigm. While androphilic women showed a small bias toward the female images, both ambiphilic and gynephilic women showed a strong bias toward the female images. The results show that these early automatic processes of attention are toward women in this sample of ambiphilic women and therefore inconsistent with their self-report sexual preferences.
Incidence and clinical outcomes of bacterial superinfections in critically ill patients with COVID-19

Background Bacterial superinfection is not uncommon in critically ill patients with coronavirus disease (COVID-19) pneumonia requiring intensive care unit (ICU) treatment. However, there is still a lack of evidence related to bacterial superinfection and their clinical significance in critically ill patients with COVID-19. Therefore, we assessed the incidence of bacterial superinfections and their effects on clinical outcomes in critically ill patients with COVID-19. Materials and methods This single-center retrospective cohort study analyzed critically ill patients with COVID-19 admitted to the ICU at a tertiary academic hospital between February 2020 and December 2021. We reviewed data including patient demographics, clinical and microbiological characteristics, and outcomes. Results During the study period, 106 patients (median [IQR] age, 67 [58–75] years) were included, of which 32 (30%) were diagnosed with bacterial superinfections. Of these, 12 cases (38%) were associated with multidrug-resistant pathogens. Klebsiella aerogenes (6 cases [19%]) and Klebsiella pneumoniae (6 cases [19%]) were the most common pathogens associated with superinfections. The median time to bacterial superinfection was 13 (IQR, 9–20) days after ICU admission. Patients with bacterial superinfections had significantly fewer ventilator-free days on day 28 (0 [IQR, 0–0] days) than those without bacterial superinfections (19 [IQR, 0–22] days) (p < 0.001). Patients with bacterial superinfections had a longer ICU length of stay (32 [IQR, 9–53] days) than those without bacterial superinfections (11 [IQR, 7–18] days) (p < 0.001). Additionally, they had a longer hospital length of stay after ICU admission (39 [IQR, 18–62] days) than those without bacterial superinfections (18 [IQR, 12–37] days) (p = 0.001). There were no differences in ICU mortality or in-hospital mortality between the two groups. In the multivariable analysis, higher SAPS II score (OR, 2.697; 95% CI, 1.086–6.695) and thrombocytopenia (OR, 3.318; 95% CI, 1.355–8.123) were identified as risk factors for development of bacterial superinfection. Conclusion In critically ill patients with COVID-19, bacterial superinfections were common, and more than one-third of the bacterial superinfection cases were caused by multidrug-resistant pathogens. As patients with bacterial superinfections had worse clinical outcomes, the development of bacterial superinfections should be actively monitored.
A new model for 3D graphical rendering

One of the most important tasks of a traditional 3D Rendering engine is the projection on the image plane of geometrical structures (such as triangles or lines). This operation takes place in the middle of the rendering pipeline, between the vertex shader and the fragment shader: its aim is just that of creating fragment data from vertex data. The solution of the projection problem is necessarily bound to the solution of a great number of systems of equations, where the complexity of the equations is in general related to the properties of the geometrical structures. To make this process fast, the most adopted solution is that of using linear models, so that the systems become linear and the module gets the simplest implementation. Unfortunately, linear models have some limitations: the solution is to use approximation, but to get good models they are necessary a lot of linear structures, in particular a lot of triangles; modern 3D Rendering Engines may automate the process of converting non linear models in triangles, but this does not reduce the occupation of memory and doesn’t eliminate linear approximation. In this article I consider a non linear model (the Lembo model) for geometrical structures in a 3D rendering engine: firstly I show the properties of the model; then I show an ecient algorithm to solve the projection problem directly on the model equations.
An improved algorithm for pedestrian detection

In this paper we present a technique to detect pedestrian. Histogram of gradients (HOG) and Haar wavelets with the aid of support vector machines (SVM) and AdaBoost classifiers show good identification performance on different objects classification including pedestrians. We propose a new shape descriptor derived from the intra-relationship between gradient orientations in a way similar to the HOG. The proposed descriptor is a two 2-D grid of orientation similarities measured at different offsets. The gradient magnitudes and phases derived from a sliding window with different scales and sizes are used to construct two 2-D symmetric grids. The first grid measures the co-occurence of the phases while the other one measures the corresponding percentage of gradient magnitudes for the measured orientation similarity. Since the resultant matrices will be symmetric, the feature vector is formed by concatenating the upper diagonal grid coefficients collected in a raster way. Classification is done using SVM classifier with radial basis kernel. Experimental results show improved performance compared to the current state-of-art techniques.
An Integrated Scalable Framework for Cloud and IoT Based Green Healthcare System

Recent developments in IoT-enabled cloud computing and interactive applications have made researchers rethink how healthcare services are currently provided. The IoT-cloud-based systems facilitate remote monitoring and support for patients. However, in the existing area, much emphasis has not been given to making the healthcare systems green. So, in this paper, we present an integrated framework for green healthcare and use cutting-edge technology to make an interactive user interface. We have also ensured the system’s scalability and performance ratio. This system interface has been designed and developed for patients and doctors, where patients can send their healthcare data using wearable sensors, and doctors can receive those data in real-time. For data identification and analysis, we have adopted Hierarchical Clustering Algorithms. Finally, we have come up with a solution for how to make the interactive healthcare experience better for everyone.
ACTIVE YOU: Teacher Attributes and Attitudes Predicting Physical Activity Promotion

Background: Based on the Health Belief Model, this study examined preservice teacher attributes and attitudes toward providing physical activity opportunities for children in school. Methods: A quasi-experimental design was used to collect proof of concept and feasibility data for the ACTIVE YOU intervention as part of teacher education. Conclusions: Examination of a diverse sample of preservice teachers during their fieldwork revealed that those who engage in healthy behaviors and had positive attitudes toward physical activity in schools are more likely to take action and promote physical activity for their students.
Blood Pressure Classification by Analyzing The Behavior of Heart Rate Variability in Poincare Plot

Blood pressure is the amount of pressure exerted by the blood on the walls of the vessels. In this research, the impact of three different blood pressure levels (e.g., High, Normal, and low) on the extracted features of the Poincare plot of heart rate variability $(HRV)$ is studied. After extracting $RR$ intervals from $ECG$, Global Occurrence Matrix $(GOM)$ and Co-occurrence Matrix $(COM)$ parameters demonstrating the dynamic in the Poincare plot were obtained. First, the extracted features in three blood pressure levels were compared, and then the k-nearest neighbor classifier was used to distinguish three classes of high, normal, and low blood pressure. The results show that the $GOM$ and COM features significantly differed between different blood pressure stages, with a mean accuracy of 91%.
Analysis of Atrial Fibrillation Dynamics in Body Surface Potential Maps and Electrocardiographic Imaging

Previous studies have shown that global short- (ST) and long-term (LT) atrial fibrillation (AF) dynamics can be characterized non-invasively by Body Surface Potentials (BSPM). Also, Electrocardiographic Imaging (ECGI) may add information as it can characterize locally the atrial substrate. The objective of this study is to compare AF dynamics characterized on both BSPM and ECGI signals. Two consecutive 4-second BSPM signals from 34 AF patients (23 male, 8 paroxysmal, 63.1 ± 9.5 years) were recorded, followed by ECGI computation. ST and LT dynamics metrics were computed in both BSPM and ECGI, assessed from a multivariate autocorrelation of the signals. BSPM features of ST dynamics positively correlated with LT dynamics (0.52 and 0.78). Analogous values of correlation were obtained in ECGI. When normalized by the LT dynamics, the ST inversely correlated with the speed of propagation of AF at half AF cycle (BSPM, r = −0.28 vs. ECGI, r = −0.45), showing higher stability in the ST propagation for faster AF. BSPM and ECGI reflected similar relationships in the analysis of AF propagation dynamics. Results were consistent with previous studies and suggest that BSPM are sufficient to characterize global AF dynamics, while ECGI may become relevant when more localized information is required.
PROBABILISTIC DERIVATION OF A NONCOMMUTATIVE VERSION OF VARADHAN'S THEOREM

We give a simple probabilistic derivation of a special case of a noncommutative version of Varadhan’s theorem, flrst proved by Petz, Raggio and Verbeure. It is based on a Feynman{Kac representation combined with a standard large deviation argument. In the flnal section, this theorem is then extended to a more di‐cult situation with Bose symmetry.
The block design subtest of the Wechsler adult intelligence scale as a possible non-verbal proxy of cognitive reserve

Objectives To investigate the potential of the Block design subtest of the Wechsler Adults Intelligence Scale as a non-verbal proxy of cognitive reserve. Method A total of 391 cognitively unimpaired participants were included in this study. The association between the Block design subtest and the Information subtest (an established verbal proxy of cognitive reserve) from the WAIS, as well as the association of the two subtests with a Cognitive Reserve Questionnaire (CRQ) were tested. In addition, multiple linear regression models were conducted to investigate the association of the Block design and Information subtests with cognitive performance. The capacity of the Block design subtest to minimize the negative effect of an older age over cognitive performance was also assessed and this effect was compared with that of the Information subtest. The four cognitive domains included were: verbal memory, visual–visuospatial memory, executive-premotor functions and processing speed. Results The Block design subtest correlated positively with both the Information subtest and the CRQ. A statistically significant association was observed between the Block design subtest and all four cognitive domains. Higher scores in the Block design subtest minimized the negative effect of aging on the cognitive domains of visual–visuospatial memory and executive-premotor functions, in a similar way to the results obtained for the Information subtest. Conclusion The Block design subtest is significantly correlated with two established proxies of cognitive reserve: it correlates with cognitive performance and high scores in Block design have the capacity to minimize the negative effect of an older age on cognitive performance. Therefore, the results suggest that the corrected Block design subtest could be considered as a non-verbal proxy of cognitive reserve.
Deep Reinforcement Learning Enabled Covert Transmission With UAV

This letter considers covert communications in the context of unmanned aerial vehicle (UAV) networks, where a UAV is employed as a base station to transmit covert data to a legitimate ground user, while ensuring that the data transmission cannot be detected by a warden. Aiming at maximizing the legitimate user’s average effective covert throughput (AECT), the UAV’s trajectory and transmit power are jointly optimized. Taking advantage of deep reinforcement learning (DRL) on solving dynamic and unpredictable problems, we develop a twin-delayed deep deterministic policy gradient aided covert transmission algorithm (TD3-CT), to determine the UAV’s optimal trajectory and transmit power. Furthermore, by introducing a reward shaping mechanism, the convergence of the algorithm is guaranteed. The experiment results show that the developed TD3-CT algorithm not only enables the covert transmission but also significantly improves its performance in termed of achieving a higher AECT, compared with the benchmark schemes.
External Auditory Canal Cholesteatoma after Radiation Therapy for Nasopharyngeal Cancer: Case Series and Systematic Review

The authors performed a systematic review, in accordance with the PRISMA guidelines, across multiple databases, including all original studies published until November 2022, focusing on External auditory canal cholesteatoma (EACC) after radiation therapy (RT) for nasopharyngeal cancer (NC). Inclusion criteria were original articles reporting on secondary EACC after RT for NC. Articles were critically appraised to assess level of evidence using the Oxford Center for Evidence-Based Medicine criteria. Overall, 138 papers were identified and after duplicate removal (34 papers) and excluding papers not in English, 93 papers were assessed for eligibility; finally, only five papers were included and summarized with the three cases coming from our institution. These mainly involved the anterior and the inferior part of the EAC. The mean time of diagnosis after RT was the largest series of 6.5 years (with a range from 0.5 to 15.4 years). Patients undergoing RT for NC have 18 times a higher risk of developing EACC compared to the normal population. EACC is probably one of the most underreported side effects, because patients may present variable clinical findings, which could lead to misdiagnosis. Early diagnosis of RT related EACC is advised to enable conservative treatment.
MicroRNA162 regulates stomatal conductance in response to low night temperature stress via abscisic acid signaling pathway in tomato

MicroRNAs (miRNAs) mediate the degradation of target mRNA and inhibit mRNA translation to regulate gene expression at the transcriptional and post-transcriptional levels in response to environmental stress in plants. We characterized the post-transcriptional mechanism by deep sequencing small RNA (sRNA) to examine how miRNAs were involved in low night temperature (LNT) stress in tomato and whether the molecular mechanism depended on the abscisic acid (ABA) signaling pathway. We annotated conserved miRNAs and novel miRNAs with four sRNA libraries composed of wild-type (WT) tomato plants and ABA-deficient mutant (sit) plants under normal growth and LNT stress conditions. Reverse genetics analysis suggested that miR162 participated in LNT resistance and the ABA-dependent signaling pathway in tomato. miR162-overexpressing (pRI-miR162) and miR162-silenced (pRNAi-miR162) transgenic tomato plants were generated to evaluate miR162 functions in response to LNT stress. miR162 deficiency exhibited high photosynthetic capacity and regulated stomatal opening, suggesting negative regulation of miR162 in the ABA-dependent signaling pathway in response to LNT stress. As feedback regulation, miR162 positively regulated ABA to maintain homeostasis of tomato under diverse abiotic stresses. The mRNA of DICER-LIKE1 (DCL1) was targeted by miR162, and miR162 inhibited DCL1 cleavage in LNT response, including the regulation of miRNA160/164/171a and their targets. The DCL1-deficient mutants (dcl1) with CRISPR/Cas9 prevented stomatal opening to influence photosynthesis in the ABA signaling pathway under LNT stress. Finally, we established the regulatory mechanism of ABA-miR162-DCL1, which systematically mediated cold tolerance in tomato. This study suggests that post-transcriptional modulators acted as systemic signal responders via the stress hormone signaling pathway, and the model at the post-transcriptional level presents a new direction for research in plant abiotic stress resistance.
Deep Learning Based Heart Murmur Detection Using Frequency-time Domain Features of Heartbeat Sounds

The goal of the George B. Moody PhysioNet Challenge 2022 was to use heart sound recordings gathered from various auscultation locations to identify murmurs and clinical outcomes. Our team, CAU_UMN, proposes a deep learning-based model that automatically identifies heart murmurs from a phonocardiogram (PCG). We converted the heartbeat sound into 2D features in the frequency-time domain through feature extraction techniques such as log-mel spectrogram, Short Time Fourier Transform (STFT), and Constant Q Transform (CQT). The frequency-temporal 2D features were modeled using voice classification models such as Convolutional neural networks (CNN) and Light CNN (LCNN). The model using log-mel spectrogram and LCNN was ranked 5th for murmur detection with a weighted accuracy of 0.767 and 5th for clinical outcome detection with a cost of 11933 in the test dataset of the George B. Moody PhysioNet Challenge. We believe that our deep learning based heart murmur detection system will be a promising system for automatic heart murmur detection from PCG.
Reliability and consistency assessment of land cover products at macro and local scales in typical cities

ABSTRACT Urban areas have higher heterogeneity compared to natural areas, it is crucial to assess fine-resolution land cover products and discover how they differ in urban areas so that they can be efficiently used for various application scenarios. In this study, five typical cities in China were chosen as study areas to evaluate four commonly used 30 m land cover products: GLC_FCS30-2020, FROM-GLC30-2017, Globeland30-2020, and CLCD-2019. We analyzed the reliability of these four products using validation samples as well as by examining their area and spatial pattern consistency. Given the limitations of traditional accuracy assessments at the macro level, we added a local area evaluation to further examine the classification details in these products. The macro results indicated that four land cover products within urban areas have a similar overall accuracy, surpassing 76%, but there was a low consistency among them, ranging from 42.21% to 61.13%. The local accuracy assessment illustrated that GLC_FCS30-2020 and FROM-GLC30-2017 performed well in reflecting the intricate details of the city, however, the four products exhibited varying degrees of misclassifications and omissions. These phenomena suggest that more sophisticated algorithms are needed to consider urban particularities since fine-resolution land cover products may fail to capture complex urban details.
Abnormality Detection in Chest X-Ray via Residual-Saliency From Normal Generation

In this paper, we propose a novel disease detection framework based on translating a given chest X-ray (CXR) to a corresponding normal CXR image. To train a model for normal CXR translation, we synthesize a paired image dataset from existing public CXR datasets. More specifically, we synthesize new images that appear to be from the same patient but without abnormalities, i.e., matching-pairs, that are not present in the dataset. Here, we search for normal local regions in images contained within the dataset that have a similar appearance, and we apply a blending process to effectively erase the abnormal regions. A conditional GAN can then be trained on the synthetic matching-pair images for a normal CXR translator model that transforms the given CXR images into their corresponding normal CXRs. We incorporate this pretrained translator model into a deep learning based object detection model. Here we define the difference between the translated normal CXR and the given input as the residual-saliency map. As the residual-saliency map will be most active at diseased regions, we use this as the attention within the detection model. We also utilize the matching-pair synthesis pipeline to synthesize abnormal images for data augmentation when we train the detector. We evaluate the proposed approach on two public chest X-ray datasets: RSNA and VinBigData.
Chaos of a power system model and its control

The nonlinear dynamic characteristics of a simple three-bus power system mathematic model are studied, including phase diagrams and a bifurcation map. To control this undesirable chaos, sliding mode control law without buffeting is presented based on the LaSalle invariance principle, which considers the bounded disturbances. The theoretical analysis is in agreement with numerical simulation in that the designed controller could asymptotically stabilize the unstable power system to a typical steady-state. The research of this paper may help to maintain the power system's security operation.
Ultrasound imaging system employing phase inversion subtraction to enhance the image

A method for generating an ultrasound image that enhances regions occupied by non linear scattering media. The method utilizes first and second ultrasound pulses that are alternatively transmitted into the specimen being imaged. The first and second ultrasound pulses are amplitude modulated harmonic signals, the first ultrasound pulse differing from the second ultrasound pulse by the phase of the harmonic signal. The echo signals generated by these pulses are measured and combined. In one embodiment of the present invention, the first and second ultrasound signals differ by 180 degrees and the echo signals are combined by adding the echo signals generated by each of the ultrasound pulses.