Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization

Nowadays, several datasets are demonstrated by multi-view, which usually include shared and complementary information. Multi-view clustering methods integrate the information of multi-view to obtain better clustering results. Nonnegative matrix factorization has become an essential and popular tool in clustering methods because of its interpretation. However, existing nonnegative matrix factorization based multi-view clustering algorithms do not consider the disagreement between views and neglects the fact that different views will have different contributions to the data distribution. In this paper, we propose a new multi-view clustering method, named adaptive multi-view clustering based on nonnegative matrix factorization and pairwise co-regularization. The proposed algorithm can obtain the parts-based representation of multi-view data by nonnegative matrix factorization. Then, pairwise co-regularization is used to measure the disagreement between views. There is only one parameter to auto learning the weight values according to the contribution of each view to data distribution. Experimental results show that the proposed algorithm outperforms several state-of-the-arts algorithms for multi-view clustering.
A Self-Adaptive Object Detection Network for Aerial Images Based on Feature Enhancement

Object detection in aerial images has received increasing attention for its wide applications. However, it is still a challenging task when dealing with difficult cases, such as the large variations of object sizes, the complex backgrounds in the large-scale views, as well as small targets packed in dense. In this regard, we propose a self-adaptive object detection network for aerial images based on feature enhancement, including the region crop module with soft-attention (RCP), feature enhancement module (FET), self-adaptive feature extraction module (SAE). First, RCP module with soft-attention is explored to roughly crop the dense subregion and sparse subregion into patches according to the variance of feature maps for the following feature extraction and detection. Second, the FET module is proposed to acquire more semantic details by feature enhancement, which makes up the information loss during downsampling, especially for small objects in dense regions. Finally, th SAE module is explored to effectively identify multiple target regions and single target regions in the dense patches. The similarity of adjacent dense areas in dense patches is calculated, and the search range is gradually narrowed by continuously merging the areas with the largest similarity. The teacher-network and student-network are used to extract features for multiple target regions and single target regions, respectively. The proposed design improves the accuracy of real-time detection under the drone’s perspective. A large number of experiments and comprehensive evaluations on the VisDrone2019-DET dataset have shown the effectiveness and adaptability of the proposed method. Our source codes have been available at https://github.com/zhaokai152.
Selecting thresholding and shrinking parameters with generalized SURE for low rank matrix estimation

To estimate a low rank matrix from noisy observations, truncated singular value decomposition has been extensively used and studied: empirical singular values are hard thresholded and empirical singular vectors remain untouched. Recent estimators not only truncate but also shrink the singular values. In the same vein, we propose a continuum of thresholding and shrinking functions that encompasses hard and soft thresholding. To avoid an unstable and costly cross-validation search of their thresholding and shrinking parameters, we propose new rules to select these two regularization parameters from the data. In particular we propose a generalized Stein unbiased risk estimation criterion that does not require knowledge of the variance of the noise and that is computationally fast. In addition, it automatically selects the rank of the matrix. A Monte Carlo simulation reveals that our estimator outperforms the tested methods in terms of mean squared error and rank estimation.
Validity and Version of the Vision-related Quality of Life Questionnaire

Background and Objectives: The vision-related quality of life (VRQoL) questionnaire has already been developed. However, some items of these instruments are not suitable for Iranian samples. This study aims to develop the Persian version of this questionaire to evaluate the functional vision of visually impaired Iranian people. Methods: The VRQoL questionnaire was evaluated and 43 items were chosen as an initial questionnaire. Ten visually impaired people were interviewed to express their idea about the position and manner of expressing questions in the questionnaire. Some items were excluded and the remained items were given to 30 patients as well as some visual impairment specialists. Their opinions about the face validity of the questionnaire were used. The content validity of this instrument called the Iranian low vision questionnaire (ILVQ) was evaluated using Lawshe’s content validity ratio. To construct validity, factor analysis was used. Internal reliability was evaluated using Cronbach’s alpha coefficient. Results: The reduction of psychometric items showed that three items should be eliminated and a 32-item questionnaire was produced as an Iranian low vision questionnaire (ILVQ). This local instrument has performance-based (13 items) and self-reported (19 items) sections with a five-point response scale. The psychometric evaluation showed an internal reliability of 96% by Cronbach’s alpha coefficient and an overall reliability and repeatability of 0.96 (P<0.001) by intraclass correlation coefficient (ICC). The test-retest reliability was obtained at 0.94 (P<0.001) to indicate the external reliability and stability of the instrument. Conclusion: This instrument has high validity and reliability, which is a standard tool to assess the quality of life (QoL) among Iranian visually impaired people. Existing questionnaires around the world should be modified to add or remove some items due to advanced technologies.
Dietary Acid Load Is Not Associated with Serum Testosterone in Men: Insights from the NHANES

The dietary acid load (DAL) is a novel marker of overall diet quality, which has been associated with overweight, type 2 diabetes and altered glucocorticoid secretion. A potential association with sex hormones is thus not inconceivable. We investigated whether DAL was associated with serum total testosterone concentrations of men in the National Health and Nutrition Examination Survey. The DAL scores, including the potential renal acid load (PRAL) and net endogenous acid production (NEAP), were estimated and compared between participants with low and normal testosterone levels. The investigated sample encompassed n = 377 males with a mean age of 49.50 years. Approximately 73% of the sample were of Non-Hispanic White origin. None of the examined DAL scores showed significant associations with serum testosterone levels. We observed no significant differences in the crude DAL scores between individuals with low testosterone levels and individuals with normal testosterone levels. Multivariate regression models adjusting for covariates confirmed the lack of associations between the PRAL and serum testosterone. Our results are of particular importance for those individuals who wish to lower their DAL in light of the presumable health effects of a more alkaline diet. Our data suggest that diet modifications toward a lower intake of animal protein and refined grains (which consecutively translates into a lower DAL) may not negatively affect men’s testosterone levels.
Utilization of jojoba oil and salicylic acid as postharvest treatment on storability and fruit quality of ‘Late Swelling’ peach cultivar

The ‘Late-Swelling’ peach is an important cultivar due to its attributes of fruit quality, such as handling ability and late harvest season. Peaches rapidly deteriorate during storage, which shortens their shelf-life. The preservation of quality traits of ‘Late-Swelling’ peach during handling and storage investigated. Fruits harvested in the early ripe stage (last week of June) were either dipped in jojoba oil (JO) 500, 1000, and 1500 ppm and salicylic acid (SA) 100, 200, and 300 ppm, then stored at 5+1 °C and 85% RH for 35 days besides 7 days as shelf-life the fruits were examined every 7 days until the end of the experiment. Positive influences were observed for JO and SA on the different chemical and physical characteristics of the peach fruits under study especially those related to storability and fruit quality. All fruits treated with JO 1500 and 1000 ppm, and SA 300 ppm maintained the fruit firmness, hº, total acidity, and ascorbic acid over the whole storage period, in addition to the lowest decay and weight loss, as well as maintaining the fruit sensory quality and decreased peroxidase [PPO] activity while increasing polyphenol oxidase [POD], catalase [CAT], and ascorbate peroxidase [APX] activities compared with the control at the end of shelf-life period. In conclusion, our results suggest using JO and SA to enhance and prolong the storability of peach fruits at 5+1 °C and 85% RH.
The RBF-FD and RBF-FDTD Methods for Solving Time-Domain Electrical Transient Problems in Power Systems

In this paper, the development and application of the radial basis function-finite difference (RBF-FD) method and the RBF-finite difference time domain (RBF-FDTD) method for solving electrical transient problems in power systems that are defined by the time-dependent ordinary differential equations (ODEs) and the time-dependent partial differential equations (PDEs), respectively, are presented. RBFs such as Gaussian (GA), Multiquadric (MQ), Inverse Quadric (IQ), and Inverse Multiquadric (IMQ) are used in these numerical methods to formulate the central finite difference approximations of the first- and second-order derivatives of a function. The algorithm of selecting “optimal” shape parameters for our basis functions is also applied, specifically to increase the accuracy of the suggested methods with regard to high accuracy needs. Finally, the accuracy, effectiveness, and applicability of our new approaches are evaluated through simulations of the switching transient voltages on a typical electrical circuit and a 220 kV single-phase transmission line, lightning-induced voltages on a 110 kV single-phase overhead distribution line, and transient voltages along two horizontal grounding electrodes excited by lightning impulse sources. The obtained numerical results demonstrate that our proposed RBF-based numerical approaches compare favorably to the traditional numerical methods.
Energetics of hydrogen and helium-vacancy complexes in bulk and near surfaces of tungsten: First-principles study

Understanding the interaction between hydrogen (H) and helium-vacancy (He-V) complexes in tungsten (W) is important for the development of plasma-facing materials in fusion reactors. H trapping by HexVy complexes in bulk W, as well as the H solution behavior and H trapping by HexV complexes near W(100), W(111), and W(110) surfaces, has been investigated by first-principles computer simulations using density function theory. The results show that the sequential H binding energies to HexV complexes in bulk W decrease with the increasing number of H and He. For the HexV2 complexes in bulk W, H prefers to trap at interstitial sites near the junction of the di-vacancy, where the H can minimize the isosurface of optimal charge density. The most stable interstitial sites for H below W surfaces are dependent on the surface orientation. Our calculations indicate that H atoms tend to prefer a depth of 0.3 nm below the W(100) and W(111) surfaces due to the surface reconstruction. The binding energy of H to a HeV com.
Status of the Transverse Diagnostics at FLASHForward

Density modulations in plasma caused by a high-intensity laser or a high charge density electron pulse can generate extreme acceleration fields. Acceleration of electrons in such fields may produce ultra-relativistic, quasi-monoenergetic, ultra-short electron bunches over distances orders of magnitudes shorter than in state-of-the-art radio-frequency accelerators. FLASHForward is a beam-driven plasma wakefield accelerator (PWFA) project at DESY with the goal of producing, characterizing, and utilizing such beams. Temporal characterization of the acceleration process is of crucial importance for improving the stability and control in PWFA beams. While measurement of the transient field of the femtosecond bunch in a single shot is challenging, in recent years novel techniques with great promise have been developed [1, 2]. This work discusses the plans and status of the transverse diagnostics at FLASHForward.
Inf‐sup stability of Petrov‐Galerkin immersed finite element methods for one‐dimensional elliptic interface problems

In this article, we analyze the Petrov‐Galerkin immersed finite element method (PG‐IFEM) when applied to one‐dimensional elliptic interface problems. In the PG‐IFEM (T. Hou, X. Wu and Y. Zhang, Commun. Math. Sci., 2 (2004), 185‐205, and S. Hou and X. Liu, J. Comput. Phys., 202 (2005), 411‐445), the classic immersed finite element (IFE) space was taken as the trial space while the conforming linear finite element space was taken as the test space. We first prove the inf‐sup condition of the PG‐IFEM and then show the optimal error estimate in the energy norm. We also show the optimal estimate of the condition number of the stiffness matrix. The results are extended to two dimensional problems in a special case.
Synthetic Attenuation Correction Maps for SPECT Imaging Using Deep Learning: A Study on Myocardial Perfusion Imaging

(1) Background: The CT-based attenuation correction of SPECT images is essential for obtaining accurate quantitative images in cardiovascular imaging. However, there are still many SPECT cameras without associated CT scanners throughout the world, especially in developing countries. Performing additional CT scans implies troublesome planning logistics and larger radiation doses for patients, making it a suboptimal solution. Deep learning (DL) offers a revolutionary way to generate complementary images for individual patients at a large scale. Hence, we aimed to generate linear attenuation coefficient maps from SPECT emission images reconstructed without attenuation correction using deep learning. (2) Methods: A total of 384 SPECT myocardial perfusion studies that used 99mTc-sestamibi were included. A DL model based on a 2D U-Net architecture was trained using information from 312 patients. The quality of the generated synthetic attenuation correction maps (ACMs) and reconstructed emission values were evaluated using three metrics and compared to standard-of-care data using Bland–Altman plots. Finally, a quantitative evaluation of myocardial uptake was performed, followed by a semi-quantitative evaluation of myocardial perfusion. (3) Results: In a test set of 66 test patients, the ACM quality metrics were MSSIM = 0.97 ± 0.001 and NMAE = 3.08 ± 1.26 (%), and the reconstructed emission quality metrics were MSSIM = 0.99 ± 0.003 and NMAE = 0.23 ± 0.13 (%). The 95% limits of agreement (LoAs) at the voxel level for reconstructed SPECT images were: [−9.04; 9.00]%, and for the segment level, they were [−11; 10]%. The 95% LoAs for the Summed Stress Score values between the images reconstructed were [−2.8, 3.0]. When global perfusion scores were assessed, only 2 out of 66 patients showed changes in perfusion categories. (4) Conclusion: Deep learning can generate accurate attenuation correction maps from non-attenuation-corrected cardiac SPECT images. These high-quality attenuation maps are suitable for attenuation correction in myocardial perfusion SPECT imaging and could obviate the need for additional imaging in standalone SPECT scanners.
Experimental study of instability characteristics in pump turbines

ABSTRACT Experiments in a medium specific-speed pump turbine scaled model are performed to investigate instability characteristics in pump mode, namely the head-drop phenomena. Two different head-drops are captured on a pump performance curve. This paper presents the experimental investigations of the second head-drop phenomenon, with the flowrate varying from 0.6 to 0.7 times of the design flowrate in the scaled model. The low frequency signal of 0.14 times of the impeller rotating frequency in the draft tube is captured during the generation of the second head-drop. The alteration of the cavitation near the impeller inlet domain is also observed via a high-speed camera. These findings indicate that the complex vortices in the interaction area between the impeller inlet and the draft tube play a key role in the generation of the second head-drop phenomenon.
Probabilité a posteriori : amélioration d'une mesure de confiance en reconnaissance de la parole

This paper adresses the word posterior probability used as a con(cid:28)dence measure for speech recognition system. We present a new con(cid:28)dence measure based on the behavior of the language model back-o(cid:27) used during the recognition process. Merging this new con(cid:28)dence measure with word posterior probability allows to obtain another con(cid:28)dence measure which outperfoms the word posterior probability. Our experiments have been carried out on the ESTER corpora, the french evaluation campaign on french broadcast news. Using the normalized cross entropy (NCE) as an evaluation metric, experimental results on test data show a very signi(cid:28)cant improvement : whereas the word posterior probability reaches a value of 0.187, the fusion measure obtains 0.270.
Biochemical and Computational Assessment of Acute Phase Proteins in Dairy Cows Affected with Subclinical Mastitis

Subclinical mastitis (SCM) is a predominant form of mastitis wherein major visible signs of disease are absent. The present study aimed to determine acute phase proteins (APPs) like ferritin, C-reactive protein (CRP), and microalbumin (Malb) in 135 composite milk and serum samples of healthy (n = 25) and SCM (n = 110) cows. As bovine mastitis is an inflammatory disease, the present study also aimed at finding novel anti-inflammatory compounds from natural sources by repurposing approach using computational studies. The findings of the present study revealed substantial elevation (p < 0.001) in milk SCC and an increase in ferritin, CRP, and Malb (p < 0.001) in milk and sera of the SCM group as compared to healthy animals. Receiver operating characteristics of milk SCC, milk, and serum APPs unraveled statistically substantial alteration (p < 0.001). Further, SCC was correlated with milk APPs ferritin (r = 0.26 **, p < 0.002), CRP (r = 0.19 *, p < 0.02), and Malb (r = 0.21 *, p < 0.01). Additionally, milk SCC was correlated with serum ferritin (r = 0.28 **, p < 0.001), CRP (r = 0.16, p > 0.05), and Malb (r = 0.16, p > 0.05). The findings of molecular docking revealed that Chaetoglobosin U was the most effective molecule that showed the highest binding affinity (kcal/mol) of −10.1 and −8.5 against ferritin and albumin. The present study concluded that the estimation of cow-side tests, SCC, and APPs in milk/serum is suitable to detect SCM and screening herd community. Furthermore, Chaetoglobosin U could be developed as a promising anti-inflammatory inhibitor; however, further studies are required to validate these findings.
Reconhecimento de expressões faciais utilizando estimação de movimento

Facial expressions provide information on the emotional response and play an essential role in human interaction and as a form of non-verbal communication. However, there cognition of expressions is still something considered complex for the computer. In this work, it is proposed a novel feature extractor that uses motion estimation for Facial Expression Recognition (FER). In this approach, the facial movement between two expressions is coded using an estimation of the region displacements between two images, which may be of the same face or the like. The facial expression image is compared to another more similar image in each facial expression of the training base, the best match is obtained using the Structural Similarity Index (SSIM). After identifying the most similar images in the training base, the motion vectors are calculated between the reference image and the other more similar in one of the expressions of the base. To calculate the motion vectors is proposed the MARSA (Modiﬁed Adaptive Reduction of the Search Area) algorithm. All motion vectors are compared to the coordinates with the highest occurrences of all motion vectors obtained during the training phase, from this comparison the feature vectors are generated that serve as input data for a SVM (Support Vector Machine), which will perform the classiﬁcation of facial expression. Several databases of images and videos of faces reproducing facial expressions were used for the experiments, the adopted criteria for selection of the bases was the frequency which they are used in the state of the art, then were chosen: CohnKanade (CK), Extended Cohn-Kanade (CK+), Japanese Female Facial Expression (JAFFE), MMI, and CMU Pose, Illumination, and Expression (CMU-PIE). The experimental results demonstrate that there cognition rates of facial expressions are compatible to recent literature works proving the efﬁciency of the presented method.
Fire Resistance Characteristics of Firewall Structure Associated with Impact Damage Induced by Explosion

When a fire accident accompanied by an explosion occurs, the surrounding firewalls are affected by impact and thermal loads. Damaged firewalls due to accidental loads may not fully perform their essential function. Therefore, this paper proposes an advanced methodology for evaluating the fire resistance performance of firewalls damaged by explosions. The fragments were assumed to be scattered, and fire occurred as a vehicle exploded in a large compartment of a roll-on/roll-off (RO-RO) vessel. The impact velocity of the fragments was calculated based on the TNT equivalent mass corresponding to the explosion pressure. Damage and thermal-structural response analyses of the firewall were performed using Ansys LS-DYNA code. The fire resistance reduction was analyzed in terms of the temperature difference between fire-exposed and unexposed surfaces, temperature increase rate, and reference temperature arrival time. The degree of damage and the fire resistance performance of the firewalls varied significantly depending on impact loads. When naval ships and RO-RO vessels that carry various explosive substances are designed, it is reasonable to predict that the fire resistance performance will be degraded according to the explosion characteristics of the cargo.
Assessing small fiber neuropathy and subtle cardiac involvement in Fabry disease

Fabry disease (FD) is an X-linked lysosomal storage disorder characterized by reduced or absent activity of the enzyme α-galactosidase A. Due to systemic accumulation of glycolipids, FD phenotype is diverse, and diagnosis may be challenging. Clinical manifestations include small fiber neuropathy, renal dysfunction, cardiac involvement, cerebrovascular disease, among others. In the present study, we describe biopsy proven small fiber neuropathy and subclinical cardiac involvement in two cousins diagnosed with FD secondary to a recently described pathogenic variant, highlighting the importance of diagnostic tools to document organ damage and allow early treatment.
Determination of Material Parameters from Imperfect Cyclic Plasticity Test Data

Due to small deficiencies in standard test devices and/or procedures, uniaxial cyclic plasticity test data – even of the same specimen – often show noticeable uncertainties. Hence, depending on the way of calculation, the parameter values for a material model based on these data may exhibit perceptible variability. In the present contribution this issue is addressed, and some remedies are suggested. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)