Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulus of continuity of the quantum f-entropy with respect to the trace distance

A well-known result due to Fannes is a certain upper bound on the modulus of continuity of the von Neumann entropy with respect to the trace distance between density matrices; this distance is the maximum probability of distinguishing between the corresponding quantum states. Much more recently, Audenaert obtained an exact expression of this modulus of continuity. In the present note, Audenaert’s result is extended to a broad class of entropy functions indexed by arbitrary continuous convex functions f in place of the Shannon–von Neumann function x 7→ x log2 x . The proof is based on the Schur majorization. 1. Summary and discussion Let ρ and σ be two density matrices of a finite dimension d > 2, that is, two positive-semidefinite Hermitian linear operators of trace 1 acting on a d -dimensional Hilbert space H . The trace distance between ρ and σ is T (ρ ,σ) := 1 2 tr |ρ −σ |= 1 2 d ∑ i=1 |λi(ρ −σ)|= d ∑ i=1 max (
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network With Attention and Cyclic Loss

Magnetic resonance imaging (MRI) is an important medical imaging modality, but its acquisition speed is quite slow due to the physiological limitations. Recently, super-resolution methods have shown excellent performance in accelerating MRI. In some circumstances, it is difficult to obtain high-resolution images even with prolonged scan time. Therefore, we proposed a novel super-resolution method that uses a generative adversarial network with cyclic loss and attention mechanism to generate high-resolution MR images from low-resolution MR images by upsampling factors of $2\times $ and $4\times $ . We implemented our model on pelvic images from healthy subjects as training and validation data, while those data from patients were used for testing. The MR dataset was obtained using different imaging sequences, including T2, T2W SPAIR, and mDIXON-W. Four methods, i.e., BICUBIC, SRCNN, SRGAN, and EDSR were used for comparison. Structural similarity, peak signal to noise ratio, root mean square error, and variance inflation factor were used as calculation indicators to evaluate the performances of the proposed method. Various experimental results showed that our method can better restore the details of the high-resolution MR image as compared to the other methods. In addition, the reconstructed high-resolution MR image can provide better lesion textures in the tumor patients, which is promising to be used in clinical diagnosis.
You Better Look Twice: a new perspective for designing accurate detectors with reduced computations

General object detectors use powerful backbones that uniformly extract features from images for enabling detection of a vast amount of object types. However, utilization of such backbones in object detection applications developed for specific object types can unnecessarily over-process an extensive amount of background. In addition, they are agnostic to object scales, thus redundantly process all image regions at the same resolution. In this work we introduce BLT-net, a new low-computation two-stage object detection architecture designed to process images with a significant amount of background and objects of variate scales. BLT-net reduces computations by separating objects from background using a very lite first-stage. BLT-net then efficiently merges obtained proposals to further decrease processed background and then dynamically reduces their resolution to minimize computations. Resulting image proposals are then processed in the second-stage by a highly accurate model. We demonstrate our architecture on the pedestrian detection problem, where objects are of different sizes, images are of high resolution and object detection is required to run in real-time. We show that our design reduces computations by a factor of x4-x7 on the Citypersons and Caltech datasets with respect to leading pedestrian detectors, on account of a small accuracy degradation. This method can be applied on other object detection applications in scenes with a considerable amount of background and variate object sizes to reduce computations.
Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives

During the COVID-19 pandemic, social media platforms were ideal for communicating due to social isolation and quarantine. Also, it was the primary source of misinformation dissemination on a large scale, referred to as the infodemic. Therefore, automatic debunking misinformation is a crucial problem. To tackle this problem, we present two COVID-19 related misinformation datasets on Twitter and propose a misinformation detection system comprising network-based and content-based processes based on machine learning algorithms and NLP techniques. In the network-based process, we focus on social properties, network characteristics, and users. On the other hand, we classify misinformation using the content of the tweets directly in the content-based process, which contains text classification models (paragraph-level and sentence-level) and similarity models. The evaluation results on the network-based process show the best results for the artificial neural network model with an F1 score of 88.68%. In the content-based process, our novel similarity models, which obtained an F1 score of 90.26%, show an improvement in the misinformation classification results compared to the network-based models. In addition, in the text classification models, the best result was achieved using the stacking ensemble-learning model by obtaining an F1 score of 95.18%. Furthermore, we test our content-based models on the Constraint@AAAI2021 dataset, and by getting an F1 score of 94.38%, we improve the baseline results. Finally, we develop a fact-checking website called Checkovid that uses each process to detect misinformative and informative claims in the domain of COVID-19 from different perspectives.
Fully gapped superconductivity with preserved time-reversal symmetry in noncentrosymmetric LaPdIn

H. Su, 2 Z. Y. Nie, 2 F. Du, 2 S. S. Luo, 2 A. Wang, 2 Y. J. Zhang, 3 Y. Chen, 2 P. K. Biswas, D. T. Adroja, 5 C. Cao, 6 M. Smidman, 2, ∗ and H. Q. Yuan 2, 7, 8, † Center for Correlated Matter and Department of Physics, Zhejiang University, Hangzhou 310058, China Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310058, China Institute for Advanced Materials, Hubei Normal University, Huangshi 435002, China ISIS Facility, STFC Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Oxfordshire, OX11 0QX, United Kingdom Highly Correlated Matter Research Group, Physics Department, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa Condensed Matter Group, Department of Physics, Hangzhou Normal University, Hangzhou 311121, China State Key Laboratory of Silicon Materials, Zhejiang University, Hangzhou 310058, China Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China (Dated: July 22, 2021)
Infective Endocarditis Involving Mitral and Aortic Valves With Interatrial Septal Abscess

Infectious endocarditis (IE) is an infection of the endocardial surface and frequently refers to the infection of one or more heart valves. The clinical manifestations of IE are highly variable, with fever being the most common symptom, along with other nonspecific symptoms such as chills, anorexia, malaise, and myalgias. IE is associated with various systemic complications including septic emboli, cardiac complications such as valvular vegetations and intracardiac abscess, neurologic complications, and systemic immune reactions. In this case report, we present a patient with an IE that involved both mitral and aortic valves as well as a unique pathology with an interatrial septal abscess.
Influence of Personality Traits and Learning Styles on Undergraduate Medical Students’ Academic Achievement

Purpose Learning focus has shifted from conventional teacher-centered to student-centered; therefore, methods used to support and encourage learners must be considered. Meanwhile, the individual differences between learners should be taken into consideration by medical educators. Aiming to achieve a better learning experience, the current study investigates the relationship between personality traits and learning styles and their effect on students’ academic achievement. Subjects and Methods An analytical, cross-sectional study, encompassing a sample of 333 undergraduate first-year medical students, at the Faculty of Medicine, Suez Canal University in Egypt was conducted. The Big Five personality traits test and VARK learning styles questionnaires were used to assess students’ personality traits and learning styles, respectively, in the medical education module which is a part of the Foundation II module. The former is five weeks’ duration and is followed by a summative exam at the end. Students’ academic achievement was determined from their grades in Foundation II module by the end of the semester. Results Study findings generally indicate that the highest domain of learning styles among students is the kinesthetic domain. A statistically significant difference between males and females regarding their learning style preferences was detected. Also, there was a statistically significant relationship between auditory and kinesthetic learning styles and academic achievement. Though there was a significant relationship between kinesthetic learning style with both openness traits and academic achievement, no significant statistical relationship was found between any of the personality traits and academic achievement. Conclusion This study concludes that learning style could affect students’ academic achievement. It reveals that the kinesthetic learning style is the most preferred learning style among our medical students, also the agreeableness and openness traits were the most common personality traits among them. No significant relationship was detected between personality traits and academic achievement.
Calculation on runaway process of high-energy fast electrons under nanosecond-pulse

The runaway breakdown model induced by fast electrons is promising in explaining the nanosecond-pulse breakdown. In this paper, runaway process and collision ionization of fast electrons are discussed. Based on the relations between the electron energy and effective retarding force, the evolution of injected electron energy as a function of distance away from the avalanche head was simulated. The higher applied electric filed strength is, the lower the runaway energy threshold is and the more fast electrons can runaway, and gas pressures affect the runaway process of fast electrons greatly. Moreover, the runaway breakdown process under the high-voltage nanosecond pulse is described qualitatively.
in Chief Dr Saif alZahir Signal Processing : An International

It has been already revealed that the daily Solar Irradiance Data during the time period from October, 1984 to October, 2003 obtained by Earth Radiation Budget Satellite (ERBS) exhibits an Anti-persistent trend having multi-periodic phenomena. The solar irradiance time series data being a complex non linear signal in this paper we have tried to detect the irregularity and multifractality in the signal using continuous wavelet transform modulus maxima (WTMM) algorithm. Singularity spectrum of the signal has been obtained to measure the degree of multifractality of the Solar Irradiance signal. The qualitative measure of the degree of multifractality of the Solar Irradiance signal will help us to decide the nature of the signal processing tools that can be used to extract the features of the signal in our future work. This may also give an input to the research work of researchers on the solar physics and geophysics.
Reducing SAGE Data Using Genetic Algorithms

Serial Analysis of Gene Expression is a powerful quantification technique for generating cell or tissue gene expression data. The profile of the gene expression of cell or tissue in several different states is difficult for biologists to analyze because of the large number of genes typically involved. However, feature selection in machine learning can successfully reduce this problem. The method allows reducing the features (genes) in specific SAGE data, and determines only relevant genes. In this study, we used a genetic algorithm to implement feature selection, and evaluate the classification accuracy of the selected features with the K-nearest neighbor method. In order to validate the proposed method, we used two SAGE data sets for testing. The results of this study conclusively prove that the number of features of the original SAGE data set can be significantly reduced and higher classification accuracy can be achieved. Keywords—Serial Analysis of Gene Expression, Feature selection, Genetic Algorithm, K-nearest neighbor method.
Implementation of an Effective Decentralised Programme for Detection, Treatment and Prevention of Tuberculosis in Children

Childhood tuberculosis (TB) is consistently under-detected in most high-burden countries, including Uganda, especially in young children at high risk for severe disease and mortality. TB preventive treatment (TPT) for high-risk child contacts is also poorly implemented. The centralised concentration of services for child TB at the referral level is a major challenge in the prevention, detection and treatment of TB in children. In 2015, the DETECT Child TB Project was implemented in two districts of Uganda and involved decentralisation of healthcare services for child TB from tertiary to primary healthcare facilities, along with establishing linkages to support community-based household contact screening and management. The intervention resulted in improved case finding of child and adult TB cases, improved treatment outcomes for child TB and high uptake and completion of TPT for eligible child contacts. A detailed description of the development and implementation of this project is provided, along with findings from an external evaluation. The ongoing mentorship and practical support for health workers to deliver optimal services in this context were critical to complement the use of training and training tools. A summary of the project’s outcomes is provided along with the key challenges identified and the lessons learnt.
Post-bariatric body contouring surgery: analysis of complications in 180 consecutive patients

ABSTRACT Introduction: bariatric surgery is the main treatment for cases of severe obesity and body contour surgery to correct body dysmorphia resulting from weight loss. However, these procedures are associated with a significant number of postoperative complications. Objective: this study aims to analyze complications in post-bariatric patients undergoing body contour surgeries and correlating them with the age and BMI of these patients. Methods: the current study is a retrospective study evaluating 180 consecutive patients undergoing body contour surgery after bariatric surgery within a period of three years (2014-2016). Data such as age, gender, Body Mass Index before bariatric and plastic surgeries, type of surgery performed and complications were collected, and correlated the age as well as the BMI of the patients in the pre-bariatric (PB) and pre-plastic (PP) periods with the complications presented. Results: of the 180 patients evaluated, 91.7% were females (n = 165), and the mean age was 46.3 ± 1.7 years. The most performed surgery was abdominoplasty (48.9%), followed by mammaplasty (21.1%). Some complications occurred in 26.1% of the patients with partial dehiscence (40.4%) and seroma (14.9%) being the most frequent. Patients who presented complications had a higher mean age (50.8 years) than those who presented with no complications, and major complications accounted for 2.7% of the sample. Conclusions: a statistically significant number of surgeries progressed without complications and, when they occurred, there were minor complications in most of the sample. Complications were more frequent in older patients with some of them having a BMI over 30 Kg/m2.
Incidence, risk factors, and adverse outcomes of acute kidney injury in very premature neonates: a single center experience

Background/aim Acute kidney injury (AKI) is a serious morbidity in premature neonates. The aim of this study was to determine the incidence of AKI and to evaluate its impact on morbidity and mortality in very premature infants. Materials and methods This retrospective cohort study was conducted in the neonatal intensive care unit (NICU). A total of 410 preterm infants who were born before 32 gestational weeks were screened and 318 were included in this analysis. AKI was defined according to the modified neonatal Kidney Disease: Improving Global Outcomes criteria. Results The incidence of AKI was 32.1% (102/318). Regression analyses revealed that lower gestational age, vasopressor use, and hemodynamically significant patent ductus arteriosus were significantly associated with an increased risk for AKI. After adjustment for potential confounders, those with AKI had a higher risk of death before 36 weeks of corrected gestational age (adjusted hazard ratio: 3.02, 95% confidence interval 1.47– 6.22). Additionally, the AKI group had a higher rate of bronchopulmonary dysplasia (BPD) (46% vs. 24%, p < 0.001) and longer hospital stay with a mean difference of 38 days. Conclusion AKI is common in very premature neonates and associated with higher mortality, longer hospital stay, and BPD. Identification of risk factors and preventive strategies for AKI may improve the outcomes in this vulnerable population.
Deep learning neural networks to differentiate Stafne’s bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography

This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne’s bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne’s bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.
High-reliability graphene-wrapped nanoprobes for scanning probe microscopy

The nanoprobe is a powerful tool in scanning probe microscopy (SPM) that is used to explore various fields of nanoscience. However, the tips can wear out very fast due to the low stability of conventional probes, especially after the measurement of high currents or lateral friction, which results in image distortion and test imprecision. Herein, a novel functional nanoprobe is presented using graphene sheets in a high-quality graphene solution wrapped round a plasma-treated conventional Pt-Ir coated nanoprobe, which shows highly stability and resistance to degradation, leading to a significantly increased lifetime. Furthermore, we show that the graphene-wrapped nanoprobes have the advantages of enhanced electrical conductivity and reduced tip–sample friction, compared with Pt-Ir coated nanoprobes. The simplicity and low cost of this method make it valuable to various functional graphene-wrapped nanoprobes and applications.
Salivary Gland Hypofunction and/or Xerostomia Induced by Nonsurgical Cancer Therapies: ISOO/MASCC/ASCO Guideline

PURPOSE To provide evidence-based recommendations for prevention and management of salivary gland hypofunction and xerostomia induced by nonsurgical cancer therapies. METHODS Multinational Association of Supportive Care in Cancer/International Society of Oral Oncology (MASCC/ISOO) and ASCO convened a multidisciplinary Expert Panel to evaluate the evidence and formulate recommendations. PubMed, EMBASE, and Cochrane Library were searched for randomized controlled trials published between January 2009 and June 2020. The guideline also incorporated two previous systematic reviews conducted by MASCC/ISOO, which included studies published from 1990 through 2008. RESULTS A total of 58 publications were identified: 46 addressed preventive interventions and 12 addressed therapeutic interventions. A majority of the evidence focused on the setting of radiation therapy for head and neck cancer. For the prevention of salivary gland hypofunction and/or xerostomia in patients with head and neck cancer, there is high-quality evidence for tissue-sparing radiation modalities. Evidence is weaker or insufficient for other interventions. For the management of salivary gland hypofunction and/or xerostomia, intermediate-quality evidence supports the use of topical mucosal lubricants, saliva substitutes, and agents that stimulate the salivary reflex. RECOMMENDATIONS For patients who receive radiation therapy for head and neck cancer, tissue-sparing radiation modalities should be used when possible to reduce the risk of salivary gland hypofunction and xerostomia. Other risk-reducing interventions that may be offered during radiation therapy for head and neck cancer include bethanechol and acupuncture. For patients who develop salivary gland hypofunction and/or xerostomia, interventions include topical mucosal lubricants, saliva substitutes, and sugar-free lozenges or chewing gum. For patients with head and neck cancer, oral pilocarpine and oral cevimeline, acupuncture, or transcutaneous electrostimulation may be offered after radiation therapy. Additional information can be found at www.asco. org/supportive-care-guidelines.
Reframing Single- and Dual-Process Theories as Cognitive Models: Commentary on De Neys (2021)

De Neys (this issue) argues that the debate between single- and dual-process theorists of thought has become both empirically intractable and scientifically inconsequential. I argue that this is true only under the traditional framing of the debate—when single- and dual-process theories are understood as claims about whether thought processes share the same defining properties (e.g., making mathematical judgments) or have two different defining properties (e.g., making mathematical judgments autonomously versus via access to a central working memory capacity), respectively. But if single- and dual-process theories are understood in cognitive modeling terms as claims about whether thought processes function to implement one or two broad types of algorithms, respectively, then the debate becomes scientifically consequential and, presumably, empirically tractable. So, I argue, the correct response to the current state of the debate is not to abandon it, as De Neys suggests, but to reframe it as a debate about cognitive models.