Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging the gap between constant step size stochastic gradient descent and Markov chains

We consider the minimization of an objective function given access to unbiased estimates of its gradient through stochastic gradient descent (SGD) with constant step-size. While the detailed analysis was only performed for quadratic functions, we provide an explicit asymptotic expansion of the moments of the averaged SGD iterates that outlines the dependence on initial conditions, the effect of noise and the step-size, as well as the lack of convergence in the general (non-quadratic) case. For this analysis, we bring tools from Markov chain theory into the analysis of stochastic gradient. We then show that Richardson-Romberg extrapolation may be used to get closer to the global optimum and we show empirical improvements of the new extrapolation scheme.
Certificate Revocation Lists or Online Mechanisms

With more and more acceptance of Digital Certificates and Public Key Infrastructures (PKI), the mechanisms to revoke a certificate in a PKI have recently received increasing attention. The revocation mechanisms are commonly classified into Certificate Revocation Lists (CRLs), trusted dictionaries and online mechanisms. The designer of a PKI should select an appropriate revocation method suiting his requirements. This turns out to be a sufficiently confusing task as different revocation solutions are good in different type of environments. We ask the question “How do we decide which revocation solution to use amongst the various categories of solutions? ” We first conduct a survey of the existing certificate revocation techniques and then analyze and compare the various classes of revocation methods for their advantages and disadvantages. This analysis can greatly help the PKI designer to select the right revocation solution.
Photo-catalytic degradation of an oil-water emulsion using the photo-fenton treatment process: Effects and statistical optimization

The application of advanced oxidation processes (AOPs) to the treatment of an effluent contaminated with hydrocarbon oils was investigated. The AOPs conducted were Fe2 +/H2O2 (Fenton's reagent), Fe2 +/H2O2/UV (Photo-Fenton's reagent) and UV-photolysis. These technologies utilize the very strong oxidizing power of hydroxyl radicals to oxidize organic compounds to harmless end products such as CO2 and H2O. A synthetic wastewater generated by emulsifying diesel oil and water was used. This wastewater might simulate, for example, a waste resulting from a hydrocarbon oil spill, onto which detergent was sprayed. The experiments utilising the Photo-Fenton treatment method with an artificial UV source, coupled with Fenton's reagent, suggest that the hydrocarbon oil is readily degradable, but that the emulsifying agent is much more resistant to degradation. The results showed that the COD (chemical oxygen demand) removal rate was affected by the Photo-Fenton parameters (Fe2+, H2O2 concentrations and the initial pH) of the aqueous solution. In addition, the applicability of the treatment method to a ‘real’ wastewater contaminated with hydrocarbon oil is demonstrated. The ‘real’ wastewater was sourced at a nearby car-wash facility located at a petroleum filling station and the experimental results demonstrate the effectiveness of the treatment method in this case. A statistical analysis of the experimental data using the Statistical Analysis System (SAS) and the response surface methodology (RSM) based on the experimental design was applied to optimize the Photo-Fenton parameters (concentrations of Fe2 +, H2O2 and initial pH) and to maximize the COD removal rate (more than 70%).
Extraction process modification to enhance properties of skin gelatin of pangas catfish (Pangasius pangasius).

This study was aimed at studying the effect of preliminary treatment variations on the pangas catfish (Pangasius pangasius) skin gelatin, particularly the gel strength. The preliminary treatment using the whiting solution gave higher gelatin property (P<0.05) than that using alum (Al3(SO4)2) and Calcium Hydroxide (Ca(OH)2). High gelatin (dry basis) was 23.37% and gel strength was 360.18 g. The high viscosity, gelling temperature, and melting temperature were 7.87 (cP), 18.67℃, and 31.67℃, respectively. The gelatin SDS-PAGE with pre-treatment of whiting (CaO) was 136.56 kDa, and the rheological test exhibited higher elastic modulus (G') and viscous modulus (G") than that of other treatments and commercial gelatin. SEM micrographs also showed that the catfish gelatin pre-treated with the whiting had thick strand with small voids and dense tissue. As conclusion, the extraction process modification using the whiting could increase the skin gelatin properties of the pangas catfish (Pangasius pangasius), and the fish skin is a prospective source of good gelatin with desired functional properties.
African American men with very low-risk prostate cancer exhibit adverse oncologic outcomes after radical prostatectomy: should active surveillance still be an option for them?

PURPOSE Active surveillance (AS) is a treatment option for men with very low-risk prostate cancer (PCa); however, favorable outcomes achieved for men in AS are based on cohorts that under-represent African American (AA) men. To explore whether race-based health disparities exist among men with very low-risk PCa, we evaluated oncologic outcomes of AA men with very low-risk PCa who were candidates for AS but elected to undergo radical prostatectomy (RP). PATIENTS AND METHODS We studied 1,801 men (256 AA, 1,473 white men, and 72 others) who met National Comprehensive Cancer Network criteria for very low-risk PCa and underwent RP. Presenting characteristics, pathologic data, and cancer recurrence were compared among the groups. Multivariable modeling was performed to assess the association of race with upgrading and adverse pathologic features. RESULTS AA men with very low-risk PCa had more adverse pathologic features at RP and poorer oncologic outcomes. AA men were more likely to experience disease upgrading at prostatectomy (27.3% v 14.4%; P < .001), positive surgical margins (9.8% v 5.9%; P = .02), and higher Cancer of the Prostate Risk Assessment Post-Surgical scoring system (CAPRA-S) scores. On multivariable analysis, AA race was an independent predictor of adverse pathologic features (odds ratio, [OR], 3.23; P = .03) and pathologic upgrading (OR, 2.26; P = .03). CONCLUSION AA men with very low-risk PCa who meet criteria for AS but undergo immediate surgery experience significantly higher rates of upgrading and adverse pathology than do white men and men of other races. AA men with very low-risk PCa should be counseled about increased oncologic risk when deciding among their disease management options.
Entropy Computations via Analytic Depoissonization

We investigate the basic question of information theory, namely, evaluation of Shannon entropy, and a more general Renyi (1961) entropy, for some discrete distributions (e.g., binomial, negative binomial, etc.). We aim at establishing analytic methods (i.e., those in which complex analysis plays a pivotal role) for such computations which often yield estimates of unparalleled precision. The main analytic tool used here is that of analytic poissonization and depoissonization. We illustrate our approach on the entropy evaluation of the binomial distribution, that is, we prove that for binomial (n, p) distribution Shannon's h/sub n/ becomes h/sub n//spl ap/ 1/2 ln n+ 1/2 +ln/spl radic/(2/spl pi/p(1-p))+/spl Sigma//sub k/spl ges/1/a/sub k/n/sup -k/ where a/sub k/ are explicitly computable constants. Moreover, we argue that analytic methods (e.g., complex asymptotics such as Rice's method and singularity analysis, Mellin transforms, poissonization, and depoissonization) can offer new tools for information theory, especially for studying second-order asymptotics (e.g., redundancy). In fact, there has been a resurgence of interest and a few successful applications of analytic methods to a variety of problems of information theory, therefore, we propose to name such investigations as analytic information theory.
Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.
Mesenchymal Stem Cell Transplantation Reverses Multi-Organ Dysfunction in Systemic Lupus Erythematosus Mice and Humans

Systemic Lupus Erythematosus (SLE) is a multisystem autoimmune disease that, despite the advances in immunosuppressive medical therapies, remains potentially fatal in some patients, especially in treatment-refractory patients. Here we reported that impairment of bone marrow mesenchymal stem cells (BMMSCs) and their associated osteoblastic niche deficiency contribute in part to the pathogenesis of SLE-like disease in MRL/lpr mice. Interestingly, allogenic BMMSC transplantation (MSCT) is capable of reconstructing the bone marrow osteoblastic niche and more effectively reverses multi-organ dysfunction as compared to medical immunosuppression with cyclophosphamide (CTX). At the cellular level, MSCT, not CTX treatment, was capable to induce osteoblastic niche reconstruction, possibly contributing to the recovery of regulatory T cells and reestablishment of the immune homeostasis. Based on the promising clinical outcomes in SLE mice, we treated 4 CTX/glucocorticoid treatment-refractory SLE patients using allogenic MSCT and showed a stable 12-18 months disease remission in all treated patients. The patients benefited an amelioration of disease activity, improvement in serologic markers and renal function. These early evidences suggest that allogenic MSCT may be a feasible and safe salvage therapy in refractory SLE patients.
Assessment of aerosol models to AOD retrieval from HJ 1

The Chinese environmental satellites HJ1 A and B can play a significant role in the aerosol retrieval due to their high spatial and temporal resolution. The current Aerosol Optical Depth (AOD) retrieval methods from HJ1-CCD are almost based on the LUT (Look-Up Table), by selecting the best fitting result to determine the AOD. However, aerosol model selection has an important impact on the retrieval results when creating the lookup table; inappropriate choice of aerosol model will significantly affect the accuracy and applicability of the method. This paper determined the local aerosol physical properties (such as complex refractive index, and size distribution) based on the observational data, thus we defined the aerosol type and retrieved the AOD of the local aerosol. Furthermore we compared the results retrieved from the measurement aerosol model with those retrieved from the inherent aerosol model in the radiative transfer model and then evaluate its effect on the aerosol type.
Heterogeneous Face Recognition by Margin-Based Cross-Modality Metric Learning

Heterogeneous face recognition deals with matching face images from different modalities or sources. The main challenge lies in cross-modal differences and variations and the goal is to make cross-modality separation among subjects. A margin-based cross-modality metric learning (MCM2L) method is proposed to address the problem. A cross-modality metric is defined in a common subspace where samples of two different modalities are mapped and measured. The objective is to learn such metrics that satisfy the following two constraints. The first minimizes pairwise, intrapersonal cross-modality distances. The second forces a margin between subject specific intrapersonal and interpersonal cross-modality distances. This is achieved by defining a hinge loss on triplet-based distance constraints for efficient optimization. It allows the proposed method to focus more on optimizing distances of those subjects whose intrapersonal and interpersonal distances are hard to separate. The proposed method is further extended to a kernelized MCM2L (KMCM2L). Both methods have been evaluated on an ID card face dataset and two other cross-modality benchmark datasets. Various feature extraction methods have also been incorporated in the study, including recent deep learned features. In extensive experiments and comparisons with the state-of-the-art methods, the MCM2L and KMCM2L methods achieved marked improvements in most cases.
Diagnosis of AF based on time and frequency features by using a hierarchical classifier

Early diagnosis of Atrial Fibrillation (AF) could be benefited from automatic analysis of a short single-lead ECG recording that can be collected easily by a portable device. Due to the limitations of both quantity and quality of the signal, it is challenging to distinguish AF from a broad taxonomy of rhythms. This paper presents a new method which classifies the recordings of single lead ECGs by combined time and time-frequency features. The time features of a recording are represented by some characteristics of its RR intervals and Poincare plot, while the time-frequency features are extracted from its representative beat waveforms by Matching Pursuits algorithm. A set of methods are adopted in the process to eliminate the effects of noise. With the features extracted, a hierarchical classifier is trained based on the CinC Challenge 2017 dataset to classify the recordings into four classes: normal sinus rhythm, AF, other rhythm and too noisy to classify. The final score of our work in the CinC Challenge 2017 is 0.78.
A Comparison of Urinary Mercury between Children with Autism Spectrum Disorders and Control Children

Background Urinary mercury concentrations are used in research exploring mercury exposure. Some theorists have proposed that autism is caused by mercury toxicity. We set out to test whether mercury concentrations in the urine of children with autism were significantly increased or decreased compared to controls or siblings. Methods Blinded cohort analyses were carried out on the urine of 56 children with autism spectrum disorders (ASD) compared to their siblings (n = 42) and a control sample of children without ASD in mainstream (n = 121) and special schools (n = 34). Results There were no statistically significant differences in creatinine levels, in uncorrected urinary mercury levels or in levels of mercury corrected for creatinine, whether or not the analysis is controlled for age, gender and amalgam fillings. Conclusions This study lends no support for the hypothesis of differences in urinary mercury excretion in children with autism compared to other groups. Some of the results, however, do suggest further research in the area may be warranted to replicate this in a larger group and with clear measurement of potential confounding factors.
Does oesophageal stenosis have any impact on survival of oesophageal cancer patients?

A best evidence topic in thoracic surgery was written according to a structured protocol. The question addressed was 'Does oesophageal stenosis have any impact on survival of oesophageal cancer patients? '. A total of 542 papers were found using the reported search, of which 8 cohort studies represented the best evidence to answer the clinical question. The authors, journal, date and country of publication, patient group studied, study type, relevant outcomes and results of these papers are tabulated. Most of these studies defined stenosis as failure to cross the tumour at endoscopic ultrasonography. Seven studies demonstrated that oesophageal cancer patients with stenosis had significantly poorer survival than those without, while 2 studies found that oesophageal stenosis was also a predictor of poor recurrence-free survival. We conclude that oesophageal stenosis is a predictor of poor prognosis in patients with oesophageal cancer.
Optimized adjustment of single action-potentials to case-specific atrial physiology: Towards clinical implementation

We present a method for modelling case-specific action potentials (APs) based on a simple model for electric wave propagation combined with an optimization procedure that searches the best combination of three model parameter values for a given action potential. The method is applied to a set of 70 APs, showing deviations around 10%. This method can be useful for prognosing possible pathological scenarios of a specific patient, based on data from electro-cardiagrams.
The prevalence of EGFR mutation in patients with non-small cell lung cancer: a systematic review and meta-analysis

Objectives Estimate the epidermal growth factor receptor (EGFR) mutation prevalence in all non-small cell lung cancer (NSCLC) patients and patient subgroups. Results A total of 456 studies were included, reporting 30,466 patients with EGFR mutation among 115,815 NSCLC patients. The overall pooled prevalence for EGFR mutations was 32.3% (95% CI 30.9% to 33.7%), ranging from 38.4% (95% CI: 36.5% to 40.3%) in China to 14.1% (95% CI: 12.7% to 15.5%) in Europe. The pooled prevalence of EGFR mutation was higher in females (females vs. males: 43.7% vs. 24.0%; OR: 2.7, 95% CI: 2.5 to 2.9), non-smokers (non-smokers vs. past or current smokers: 49.3% vs. 21.5%; OR: 3.7, 95% CI: 3.4 to 4.0), and patients with adenocarcinoma (adenocarcinoma vs. non-adenocarcinoma: 38.0% vs. 11.7%; OR: 4.1, 95% CI: 3.6 to 4.8). Materials and Methods PubMed, EMBASE, and the Cochrane Library were searched to June 2013. Eligible studies reported EGFR mutation prevalence and the association with at least one of the following factors: gender, smoking status and histology. Random-effects models were used to pool EGFR mutation prevalence data. Conclusion This study provides the exact prevalence of EGFR mutations in different countries and NSCLC patient subgroups.
Cloud-Based Programmable Sensor Data Provision

As sensor data grow towards an explosion due to the popularity of Internet of Things and mobile computing, many sensor data sharing platforms are developed to support various sensor-based applications. Although these platforms are able to provide capabilities such as collecting data from sensors and sensor data provision for applications, their capabilities are normally confined in direct retrieval of sensor data with little composition such as SQL aggregation or even no composition at all. This kind of raw sensor data provision not only increases the network traffic between platforms and applications, but also put most computation burden on the client side, which poses big challenges for applications running on resource-constrained devices such as mobile phones. In this paper, we propose cloud-based programmable sensor data provision, which moves the sensor data processing logic from client applications to cloud-based services. The key technique behind this is FilterCombine, a two-step sensor programming support framework that enables developers to specify sensor processing logic in the cloud service. By moving sensor data processing logic to the cloud, we not only reduce network traffic due to data transfer and computation on the client side, we also improve code reusability in the cloud side, as many sensor data processing logic can be shared among multiple applications. We build a prototype platform of cloud-based programming sensor data provision called MiWoT, which implements the proposed FilterCombine mechanism on the cloud side. We demonstrate the feasibility of the proposed techniques through case studies.
A computational model of crossmodal processing for conflict resolution

The brain integrates information from multiple sensory modalities to form a coherent and robust perceptual experience in complex environments. This ability is progressively acquired and fine-tuned during developmental stages in a multisensory environment. A rich set of neural mechanisms supports the integration and segregation of multimodal stimuli, providing the means to efficiently solve conflicts across modalities. Therefore, there is the motivation to develop efficient mechanisms for robotic platforms that process multisensory signals and trigger robust sensory-driven motor behavior. In this paper, we implement a computational model of crossmodal integration in a sound source localization task that accounts also for audiovisual conflict resolution. Our model consists of two layers of reciprocally connected visual and auditory neurons and a layer with crossmodal neurons that learns to integrate (or segregate) audiovisual stimuli on the basis of spatial disparity. To validate our architecture, we propose a spatial localization task in which 30 subjects had to determine the location of the sound source in a virtual scenario with four animated avatars. We measured their accuracy and reaction time under different conditions for congruent and incongruent audiovisual stimuli. We used this study as a baseline to model human-like behavioral responses with a neural network architecture exposed to the same experimental conditions.
Cornea Refractive Correction and Biomechanical Strength Following SMILE With a 110-or 160-l m Cap Thickness , Evaluated Ex Vivo by Inflation Test

Citation: Damgaard IB, Ivarsen A, Hjortdal J. Refractive correction and biomechanical strength following SMILE with a 110or 160-lm cap thickness, evaluated ex vivo by inflation test. Invest Ophthalmol Vis Sci. 2018;59:1836–1843. https://doi.org/ 10.1167/iovs.17-23675 PURPOSE. To examine the refractive correction and corneal biomechanical strength after small incision lenticule extraction (SMILE) by using a 110or 160-lm cap thickness.