Miracle-dz/UniFAR-FTUs huggingface dataset:
Blind Image Quality Assessment Using Local Consistency Aware Retriever and Uncertainty Aware Evaluator

Blind image quality assessment (BIQA) aims to automatically predict the perceptual quality of a digital image without accessing its pristine reference. Previous studies mainly focus on extracting various quality-relevant image features. By contrast, the explorations on highly efficient learning model are still very limited. Motivated by the fact that it is difficult to approximate a complex and large data set via a global parametric model, we propose a novel local learning method for BIQA to improve quality prediction performance. More specifically, we search for the perceptually similar neighbors of a test image to serve as its unique training set. Unlike the widely used ${k}$ nearest neighbors principle, which only measures the similarity between the testing and training samples, the local consistency of the selected training data is also considered to generate smoother sample space. The image quality is estimated via a sparse Gaussian process. As an additional benefit, the uncertainty of the predicted score is jointly inferred, which can subsequently drive more robust perceptual image processing applications, such as deblocking investigated in this paper. Extensive experiments demonstrate that the proposed learning model leads to consistent quality prediction improvements over many state-of-the-art BIQA algorithms.
Clinical Characteristics and Outcomes of Moraxella Keratitis

Purpose: Changing trends of infective keratitis have been identified worldwide. The aim of this study was to define the clinical associations, characteristics, and outcomes of patients with culture-proven Moraxella keratitis from a large tertiary corneal unit in the United Kingdom. Methods: Patients with confirmed Moraxella isolates presenting between January 2004 and November 2016 were analyzed. Patient-related factors were examined, including patient demographics, date of presentation, clinical presentation, predisposing factors, best-corrected visual acuity (BCVA), treatment plans, and clinical outcomes. Results: Eighty-six patients were identified, of whom 61 (70.9%) had at least one recognized predisposing factor. The median BCVA at presentation was 2.60 logarithm of the minimum angle of resolution (logMAR), which improved to median = 0.60 logMAR at final visit (P < 0.001). Visual improvement of more than 2 Snellen lines was achieved in 60.7% of eyes. A significant relationship (P = 0.003) between final vision and the presence of hypopyon was found, where for a fixed BCVA at presentation those with a hypopyon achieved a better final visual acuity of 0.69 logMAR. Conclusions: Our study showed that although most patients presented with at least one predisposing factor, some of the more traditionally perceived risk factors may need to be reconsidered. We have shown that with aggressive treatment, favorable visual outcomes can be achieved in most patients. Interestingly, the presence of hypopyon at presentation was linked to a better visual outcome, likely because such patients had more to gain in visual potential.
Perceptually Guided Speech Enhancement Using Deep Neural Networks

Human listeners often have difficulties understanding speech in the presence of background noise in the real world. Recently, supervised learning based speech enhancement approaches have achieved substantial success, and show significant improvements over the conventional approaches. However, existing supervised learning based approaches often try to minimize the mean squared error between the enhanced output and the pre-defined training target (e.g., the log power spectrum of clean speech), even though the purpose of such speech enhancement is to improve speech understanding in noise. In this paper, we propose a new deep neural networks based enhancement approach by incorporating a speech perception model into the loss function. Specifically, we use the short-time objective intelligibility metric in the loss in addition to the mean squared error. Optimizing the proposed perceptually guided loss is expected to improve speech intelligibility further. Systematic evaluations show that our proposed approach is able to improve speech intelligibility in a wide range of signal-to-noise ratios and noise types while maintaining speech quality.
An Event-Triggered Average Consensus Algorithm with Performance Guarantees for Distributed Sensor Networks

This paper proposes a distributed guaranteed-performance event-triggered average consensus (GP-ETAC) algorithm for multi-agent/sensor networks. The proposed GP-ETAC approach is distributed and event-triggered in the sense that the agents selectively limit their transmissions to local neighbourhoods when certain triggering conditions are satisfied. Using the Lyapunov stability theorem, a novel cost function is optimized to compute consensus design parameters (namely, the overall control gain and local event-triggering thresholds). The proposed cost function provides a structured trade-off between the number of local transmissions and the rate of consensus convergence. The performance of the GP-ETAC approach is evaluated through Monte-Carlo simulations.
Genetic predisposition to uterine leiomyoma is determined by loci for genitourinary development and genome stability

Uterine leiomyomas (ULs) are benign tumors that are a major burden to women’s health. A genome-wide association study on 15,453 UL cases and 392,628 controls was performed, followed by replication of the genomic risk in six cohorts. Effects of the risk alleles were evaluated in view of molecular and clinical characteristics. 22 loci displayed a genome-wide significant association. The likely predisposition genes could be grouped to two biological processes. Genes involved in genome stability were represented by TERT, TERC, OBFC1 - highlighting the role of telomere maintenance - TP53 and ATM. Genes involved in genitourinary development, WNT4, WT1, SALL1, MED12, ESR1, GREB1, FOXO1, DMRT1 and uterine stem cell marker antigen CD44, formed another strong subgroup. The combined risk contributed by the 22 loci was associated with MED12 mutation-positive tumors. The findings link genes for uterine development and genetic stability to leiomyomagenesis, and in part explain the more frequent occurrence of UL in women of African origin.
Different neuroinflammatory profile in amyotrophic lateral sclerosis and frontotemporal dementia is linked to the clinical phase

Objective To investigate the role of neuroinflammation in asymptomatic and symptomatic amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) mutation carriers. Methods The neuroinflammatory markers chitotriosidase 1 (CHIT1), YKL-40 and glial fibrillary acidic protein (GFAP) were measured in cerebrospinal fluid (CSF) and blood samples from asymptomatic and symptomatic ALS/FTD mutation carriers, sporadic cases and controls by ELISA. Results CSF levels of CHIT1, YKL-40 and GFAP were unaffected in asymptomatic mutation carriers (n=16). CHIT1 and YKL-40 were increased in gALS (p<0.001, n=65) whereas GFAP was not affected. Patients with ALS carrying a CHIT1 polymorphism had lower CHIT1 concentrations in CSF (−80%) whereas this polymorphism had no influence on disease severity. In gFTD (n=23), increased YKL-40 and GFAP were observed (p<0.05), whereas CHIT1 was nearly not affected. The same profile as in gALS and gFTD was observed in sALS (n=64/70) and sFTD (n=20/26). CSF and blood concentrations correlated moderately (CHIT1, r=0.51) to weak (YKL-40, r=0.30, GFAP, r=0.39). Blood concentrations of these three markers were not significantly altered in any of the groups except CHIT1 in gALS of the Ulm cohort (p<0.05). Conclusion Our data indicate that neuroinflammation is linked to the symptomatic phase of ALS/FTD and shows a similar pattern in sporadic and genetic cases. ALS and FTD are characterised by a different neuroinflammatory profile, which might be one driver of the diverse presentations of the ALS/FTD syndrome.
Monocular Visual Odometry Scale Recovery Using Geometrical Constraint

Scale recovery is one of the essential problems for monocular visual odometry. The camera height is usually used as an absolute reference to recover the scale. In this case, the precision of scale recovery depends on the accuracy of the road region detection and road geometrical model calculation. In previous works, road detection and road geometrical model calculation are solved sequentially: the road geometrical model calculation is based on the road detection and the road region detection is based on the color information. However, the color information of a road is not stable enough. In the proposed method, the estimated road geometrical model is taken into consideration to detect the road region as a feedback. Therefore, the road region detection and road geometrical model estimation can benefit each other. Delaunay Triangulation method is used to segment an input image to many triangles with the matched feature points as vertices. Every triangle region is classified as a road region or not by comparing their geometrical model with that of the road and the road geometrical model is updated online. We evaluate our visual odometry scale recovery method on the KITTI dataset and the results show that our method is achieving the best performance among all existing monocular visual odometry scale recovery methods without additional sensors.
Thermodynamic Aspects in Non-Ideal Metal Membranes for Hydrogen Purification

In this paper, an overview on thermodynamic aspects related to hydrogen-metal systems in non-ideal conditions is provided, aiming at systematically merging and analyzing information achieved from several different studies present in the open literature. In particular, the relationships among inner morphology, dissolved hydrogen and internal stresses are discussed in detail, putting in evidence the conformation complexity and the various types of dislocations induced by the presence of H-atoms in the lattice. Specifically, it is highlighted that the octahedral sites are preferentially occupied in the FCC metals (such as palladium), whereas tetrahedral sites are more energetically favored in BCC-structured ones (such as vanadium). These characteristics are shown to lead to a different macroscopic behavior of the two classes of metals, especially in terms of solubility and mechanical failure due to the consequent induced stresses. Furthermore, starting from the expression of the chemical potential generally presented in the literature, a new convenient expression of the activity of the H-atoms dissolved into the metal lattice as a function of the H-concentration is achieved. Such an activity expression is then used in the dissolution equilibrium relationship, which is shown to be the overall result of two different phenomena: (i) dissociative adsorption of molecular hydrogen onto the surface; and (ii) atomic hydrogen dissolution from the surface to the metal bulk. In this way, the obtained expression for equilibrium allows a method to calculate the equilibrium composition in non-ideal conditions (high pressure), which are of interest for real industrial applications.
Users' Perceptions on Security of Mobile Computing for Adoption of e-Applications in South Africa

Technology development particularly in the area of mobile computing revolutionize the way business is done and the way people conduct their daily activities. However, users' perceptions can play an important role towards the adoption of these new developments. The quantitative approach was used to conduct the study by using a survey questionnaire. The model used in this study was derived from Diffusion of Innovation and Technology Adoption Model 2. The sampling is convenient. Data collected in this study were analyzed with the help of various statistical measures such as reliability using Cronbach's alpha, principal component analysis, Pearson correlations analysis and multiple linear regression. The empirical study shows that the adoption of e-applications in South Africa are influenced by the perceived usefulness, perceived ease of use and subjective on security mechanisms. Results also show that all variables were confirmed to be correlated to each other. The paper closes with some developers and manufacturers recommendations for the effective and efficient adoption of e-applications to be mindful of all the outer factors especially security if they are to benefit from the implementation.
Evaluating the Impact of a Feeding Protocol in Neonates before and after Biventricular Cardiac Surgery

Introduction: Feeding difficulties and malnutrition are important challenges when caring for newborns with critical congenital heart disease (CCHD) without clear available guidelines for providers. This study describes the utilization of a feeding protocol with the focus on standardization, feeding modality, and total parenteral nutrition (TPN) utilization postoperatively. Methods: Patients included neonates with CCHD undergoing complex biventricular repair using cardiopulmonary bypass. Data were collected in 2013 preintervention and from 2015 to 2017 postintervention. The feeding protocol outlined guidelines for and postoperative use of TPN. Adverse outcomes data included rates of central line–associated bloodstream infections, necrotizing enterocolitis, chylothorax, and vocal cord dysfunction. Balance outcomes measured were weight for age Z-score at discharge, number of abdominal radiographs obtained, readmission within 90 days, and central venous line utilization. Results: We included a total of 121 neonates: 49 in the preintervention group and 72 in the postintervention group. The protocol standardized feeding practices in CCHD neonates undergoing surgery with improved compliance from 70% early in the study period to 90% at the end of the study. Infants were fed enterally more preoperatively (86% versus 67%; P = 0.023), reached a fluid goal sooner (63 hours versus 72 hours; P = 0.035), and postoperative duration of TPN usage was significantly shorter in the postintervention period (48 hours versus 62 hours; P = 0.041) with no increase in adverse outcome events or unintended consequences. Conclusions: By implementing a feeding protocol, we reduced practice variation among providers, increased the number of patients fed enterally preoperatively and reduced postoperative use of TPN without increased complications.
The effect of curcumin on the differentiation, apoptosis and cell cycle of neural stem cells is mediated through inhibiting autophagy by the modulation of Atg7 and p62

Curcumin is an orange-yellow colored, lipophilic polyphenol substance derived from the rhizome of Curcuma longa that is widely used in many countries. Curcumin has many reported functions, including antioxidant and anti-inflammatory effects. Autophagy removes damaged organelles and protein aggregates in the cell. However, whether curcumin mediates its effects on neural stem cell (NSC) differentiation, cell cycle and apoptosis through autophagy is unknown. In the present study, the effects of curcumin and 3-methyladenine (3MA; an autophagy inhibitor, as a positive control) on the autophagy, differentiation, cell cycle progression and apoptosis of NSCs in different culture states were examined. In order to confirm the role of autophagy in these processes of NSC behavioral change, the protein expression level changes of markers of autophagy, such as autophagy-related protein 7 (Atg7), light chain (LC)3 and p62, were assessed. When NSCs were in an adherent state, 10 µM curcumin inhibited their differentiation into GFAP+ astrocytes or DCX+ immature neurons, while Atg7 and p62 protein expression were also reduced compared with the untreated control group. When NSCs were in a suspended state, 10 µM curcumin inhibited the cell cycle progression and apoptosis of NSCs as determined by western blotting, which was associated with a decreased autophagic flux and Atg7 expression. In addition, the curcumin-treated group trended in a similar direction to the 3MA-treated group. Thus, the data suggest that curcumin can inhibit differentiation, promote cell survival and inhibit cell cycle progression from G1 to S in NSCs, and that these effects are mediated through the regulation of Atg7 and p62.
Neural Learning Control of Strict-Feedback Systems Using Disturbance Observer

This paper studies the compound learning control of disturbed uncertain strict-feedback systems. The design is using the dynamic surface control equipped with a novel learning scheme. This paper integrates the recently developed online recorded data-based neural learning with the nonlinear disturbance observer (DOB) to achieve good “understanding” of the system uncertainty including unknown dynamics and time-varying disturbance. With the proposed method to show how the neural networks and DOB are cooperating with each other, one indicator is constructed and included into the update law. The closed-loop system stability analysis is rigorously presented. Different kinds of disturbances are considered in a third-order system as simulation examples and the results confirm that the proposed method achieves higher tracking accuracy while the compound estimation is much more precise. The design is applied to the flexible hypersonic flight dynamics and a better tracking performance is obtained.
Global Sensitivity Analysis for Event-driven Indicators Used in Probabilistic RAMI Analysis

In this paper a novel conceptual design of a method for performing Global Sensitivity Analysis (GSA) for discrete event-driven indicators used in standard probabilistic Reliability, Availability, Maintainability and Inspectability (RAMI) analysis for a generic industrial plant is presented. The main objective of the GSA is to assess how sensitive the operability of the facility (output) is to various failure events (inputs) in the system. This is done in four steps. Firstly, suitable output indicators of plant's operability are defined given the predefined collection of basic events, which serve as inputs to the system. Secondly, the sources of uncertainty associated with basic events are detected. Thirdly, the events that are the most detrimental to the functionality of the facility are identified. Finally, the sensitivity of output indicators to input events selected in previous step is evaluated so that the most sensitive events are identified. The global sensitivity is determined with Sobol indices, which accounts for higherorder interactions between inputs. Information derived from the proposed GSA procedure, which complements standard RAMI analysis, is meant to support design and decisionmaking processes by identifying components and failure events with the highest impact on the system's availability (also through nonlinear interactions). This knowledge is further used to improve reliability and robustness of the analyzed system with optimal allocation of resources. The functionality of the proposed GSA method is visualized on a subsystem of the International Fusion Materials Irradiation Facility (IFMIF).
Hybrid Speech Enhancement with Wiener filters and Deep LSTM Denoising Autoencoders

Over the past several decades, numerous speech enhancement techniques have been proposed to improve the performance of modern communication devices in noisy environments. Among them, there is a large range of classical algorithms (e.g. spectral subtraction, Wiener filtering and Bayesian-based enhancement), and more recently several deep neural network-based. In this paper, we propose a hybrid approach to speech enhancement which combines two stages: In the first stage, the well-known Wiener filter performs the task of enhancing noisy speech. In the second stage, a refinement is performed using a new multi-stream approach, which involves a collection of denoising autoencoders and auto-associative memories based on Long Short-term Memory (LSTM) networks. We carry out a comparative performance analysis using two objective measures, using artificial noise added at different signal-to-noise levels. Results show that this hybrid system improves the signal's enhancement significantly in comparison to the Wiener filtering and the LSTM networks separately.
Predictive value of angiogenic proteins in patients with metastatic melanoma treated with bevacizumab monotherapy

The incidence of malignant melanoma is rising worldwide and survival for metastatic disease is still poor. Recently, new treatment options have become available. Still, predictive biomarkers are needed to optimise treatment for this patient group. In this study, we investigated the predictive value of 60 angiogenic factors in patients with metastatic melanoma treated with the anti‐vascular endothelial growth factor A antibody bevacizumab. Thirty‐five patients were included in a clinical phase II trial and baseline serum samples were analysed by multiplex protein array. High‐serum concentration of Activin A was significantly associated with objective response (OR) to treatment (p = 0.014). Candidate proteins that indicated a borderline association with treatment response were further investigated by immunohistochemistry. Strong expression of Activin A, interleukin‐1β, and urokinase‐type plasminogen activator receptor in metastases was significantly associated with OR (p = 0.011, p = 0.003, and p = 0.007, respectively), as well as with markers of activated angiogenesis, such as higher number of proliferating vessels and the presence of glomeruloid microvascular proliferations. Our findings indicate that these proteins may be potential predictive markers for treatment with bevacizumab monotherapy.
A Sparse Coding Framework for Gaze Prediction in Egocentric Video

To efficiently process and understand a large amount of incoming visual information from first-person perspective (i.e. egocentric vision), predicting human gaze is important. However, even though people continuously gaze in noisy environments, most existing gaze prediction methods mainly use image saliency, which is sensitive to noise in the real-world. To address this issue, we propose a sparse coding-based saliency detection method for gaze prediction. Our model uses a cost function with the 10 norm as a sparse constraint that can control the area of visual saliency in response to the contents of egocentric vision in intuitive and consistent ways. Moreover, we use canonical correlation analysis (CCA) to combine different types of features for reducing noise and the computational complexity. We also utilize the temporal continuity of image frames when defining our saliency. Experiments using a real-world gaze dataset show that our proposed approach outperforms the state-of-the-art algorithms on gaze prediction in egocentric videos.
Joint Holistic and Partial CNN for Pedestrian Detection

In this paper, we propose a new network combining holistic and partial information for detecting crowd occluded pedestrians and irregularly posed pedestrians. It consists of two parallel sub-networks and a fusion sub-network. We perform holistic bounding box detection and parts prediction respectively in two parallel sub-networks. And then, we embed parts scores into the holistic detected box using a convolutional network. The convolutional network is constructed from a tree-structured model by mapping each step of inference algorithm into an equivalent CNN layer. Finally, we perform non-maximum suppression (NMS) using both detections and their pose to keep the close-by true positive detections. Our detector outperforms the state-of-the-art methods on both CityPersons dataset and Precarious Pedestrian dataset, which demonstrates the effectiveness of our detector, especially for crowed occluded and irregular-posed pedestrians.