Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic wetting failure and hydrodynamic assist in curtain coating

Dynamic wetting failure in curtain coating of Newtonian liquids is studied in this work. A hydrodynamic model accounting for air flow near the dynamic contact line (DCL) is developed to describe two-dimensional (2D) steady wetting and to predict the onset of wetting failure. A hybrid approach is used where air is described by a one-dimensional model and liquid by a 2D model, and the resulting hybrid formulation is solved with the Galerkin finite element method. The results reveal that the delay of wetting failure in curtain coating – often termed hydrodynamic assist – mainly arises from the hydrodynamic pressure generated by the inertia of the impinging curtain. This pressure leads to a strong capillary-stress gradient that pumps air away from the DCL and thus increases the critical substrate speed for wetting failure. Although the parameter values used in the model are different from those in experiments due to computational limitations, the model is able to capture the experimentally observed non-monotonic behaviour of the critical substrate speed as the feed flow rate increases (Blake et al., Phys. Fluids, vol. 11, 1999, p. 1995–2007). The influence of insoluble surfactants is also investigated, and the results show that Marangoni stresses tend to thin the air film and increase air-pressure gradients near the DCL, thereby promoting the onset of wetting failure. In addition, Marangoni stresses reduce the degree of hydrodynamic assist in curtain coating, suggesting a possible mechanism for experimental observations reported by Marston et al. (Exp. Fluids, vol. 46, 2009, pp. 549–558).
FVTD simulation of the acoustics of the Phonocamptic Cave in Noyon

Starting from new measurements of the acoustical pots and room geometry in the phonocamptic cave at the Cathedral of Noyon, a numerical study was undertaken to understand the acoustical effects at the boundaries, and to provide an auralization of the space. An implementation of the finite volume time domain (FVTD) method was used to model the cave, including fitting the impedance presented by the acoustical pots on certain boundaries. The individual impedances of the pots were estimated from impulse responses collected pot-by-pot and parameterized in terms of a Helmholtz resonator model. Then, using the electroacoustic analogy, the sum effect of the pots was modeled as an equivalent spatial distribution in the FVTD boundary conditions. Additionally, the space was discretized with an unstructured mesh in order to capture the complex geometry, minimize dispersion error, and to check the accuracy of the FVTD implementation.
The 2022 ReproGen Shared Task on Reproducibility of Evaluations in NLG: Overview and Results

Against a background of growing interest in reproducibility in NLP and ML, and as part of an ongoing research programme designed to develop theory and practice of reproducibility assessment in NLP, we organised the second shared task on reproducibility of evaluations in NLG, ReproGen 2022. This paper describes the shared task, summarises results from the reproduction studies submitted, and provides further comparative analysis of the results. Out of six initial team registrations, we received submissions from five teams. Meta-analysis of the five reproduction studies revealed varying degrees of reproducibility, and allowed further tentative conclusions about what types of evaluation tend to have better reproducibility.
ImbaGCD: Imbalanced Generalized Category Discovery

Generalized class discovery (GCD) aims to infer known and unknown categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising known classes. Existing research implicitly/explicitly assumes that the frequency of occurrence for each category, whether known or unknown, is approximately the same in the unlabeled data. However, in nature, we are more likely to encounter known/common classes than unknown/uncommon ones, according to the long-tailed property of visual classes. Therefore, we present a challenging and practical problem, Imbalanced Generalized Category Discovery (ImbaGCD), where the distribution of unlabeled data is imbalanced, with known classes being more frequent than unknown ones. To address these issues, we propose ImbaGCD, A novel optimal transport-based expectation maximization framework that accomplishes generalized category discovery by aligning the marginal class prior distribution. ImbaGCD also incorporates a systematic mechanism for estimating the imbalanced class prior distribution under the GCD setup. Our comprehensive experiments reveal that ImbaGCD surpasses previous state-of-the-art GCD methods by achieving an improvement of approximately 2 - 4% on CIFAR-100 and 15 - 19% on ImageNet-100, indicating its superior effectiveness in solving the Imbalanced GCD problem.
Riemannian Optimization for Averaging Positive Definite Matrices

Large data collections often need to be represented by an average value which upholds certain properties, such as reducing the noise level of repeated measurements or representing the central location of the data in case of clustering. Averaging operations are applicable for a wide variety of data types and structures. In this thesis, we focus on the set of positive definite matrices as a whole and on subsets containing all matrices of a desired structure. The geometric mean of positive numbers possesses various useful properties, which stimulated the search for a generalization of the mean towards positive definite matrices. Since several possible generalizations have been suggested during the development of the matrix geometric mean, an extensive overview of the instances is presented, along with a summary of related means and approximations. Among the multiple instances of the geometric mean, specific attention is given to the Karcher mean, which is defined as the minimizer of an optimization problem on the set of positive definite matrices. The smooth manifold structure of the set can be exploited in the theory of Riemannian optimization. We apply this rich theory to the setting of the Karcher mean and investigate a large number of firstand second-order optimization techniques. Numerical experiments indicate that the computational cost of an iteration in a second-order method nullifies the advantage of its quadratic convergence. An application in bioinformatics is considered, where we obtain an improvement over the state-of-the-art kernel fusion methods in protein fold classification. While the Karcher mean is considered as the most natural geometric mean of positive definite matrices, it does not preserve any additional structure present in the given matrices. To account for such structures, we discuss two adaptations of the Karcher mean. The first adaptation can be defined for any structure imposed on the positive definite matrices and is referred to as the structured geometric mean. We perform
Veno-venous Extracorporeal Membrane Oxygenation: Anesthetic Considerations in Clinical Practice

Context After the COVID-19 pandemic, multiple reviews have documented the success of veno-arterial extracorporeal membrane oxygenation (VA-ECMO). Patients who experience hypoxemia but have normal contractility may be switched to veno-venous-ECMO (VV-ECMO). Purpose In this review, we present three protocols for anesthesiologists. Firstly, transesophageal echocardiography (TEE) aids in cannulation and weaning off inotropes and fluids. Our main objective is to assist in patient selection for the Avalon Elite single catheter, which is inserted into the right internal jugular vein and terminates in the right atrium. Secondly, we propose appropriate anticoagulant doses. We outline day-to-day monitoring protocols to prevent heparin-induced thrombocytopenia (HIT) or resistance. Once the effects of neuromuscular paralysis subside, sedation should be reduced. Therefore, we describe techniques that may prevent delirium from progressing into permanent cognitive decline. Methods We conducted a PubMed search using the keywords VV-ECMO, TEE, Avalon Elite (Maquet, Germany), and quetiapine. We combined these findings with interviews conducted with nurses and anesthesiologists from two academic ECMO centers, focusing on anticoagulation and sedation. Results Our qualitative evidence synthesis reveals how TEE confirms cannulation while avoiding right atrial rupture or low flows. Additionally, we discovered that typically, after initial heparinization, activated partial thromboplastin time (PTT) is drawn every 1 to 2 hours or every 6 to 8 hours once stable. Daily thromboelastograms, along with platelet counts and antithrombin III levels, may detect HIT or resistance, respectively. These side effects can be prevented by discontinuing heparin on day two and initiating argatroban at a dose of 1 μg/kg/min while maintaining PTT between 61 - 80 seconds. The argatroban dose is adjusted by 10 - 20% if PTT is between 40 - 60 or 80 - 90 seconds. Perfusionists assist in establishing protocols following manufacturer guidelines. Lastly, we describe the replacement of narcotics and benzodiazepines with dexmedetomidine at a dose of 0.5 to 1 μg/kg/hour, limited by bradycardia, and the use of quetiapine starting at 25 mg per day and gradually increasing up to 200 mg twice a day, limited by prolonged QT interval. Conclusions The limitation of this review is that it necessarily covers a broad range of ECMO decisions faced by an anesthesiologist. However, its main advantage lies in the identification of straightforward argatroban protocols through interviews, as well as the discovery, via PubMed, of the usefulness of TEE in determining cannula position and contractility estimates for transitioning from VA-ECMO to VV-ECMO. Additionally, we emphasize the benefits in terms of morbidity and mortality of a seldom-discussed sedation supplement, quetiapine, to dexmedetomidine.
Towards building controllable Text to Speech systems

Text-to-speech systems convert any given text to speech. They play a vital role in making Humancomputer interaction (HCI) possible. As humans, we don’t just rely on text (language) to communicate; we use many other mechanisms like voice, gestures, expressions, etc., to communicate efficiently. In natural language processing, vocabulary and grammar tend to take center stage, but those elements of speech only tell half the story. Affective prosody of speech provides larger context and gives meaning to words, and keeps listeners engaged. Current HCI systems largely communicate in text, and they lack a lot of prosodic information, which is crucial in a conversation. To make the HCI systems communicate in speech, text to speech systems should be able to synthesize speech that is expressive and controllable. But the existing text to speech systems learn the average variation in the dataset it’s trained on, which synthesizes samples in a neutral way without many prosodic variations. To this end, we develop a textto-speech system that can synthesize the given emotion where the emotion is represented as a tuple of Arousal, Valance and Dominance (AVD) values. Text to speech systems have a lot of complexities. Training such a system requires the data to be very clear, noiseless, and collecting such data is difficult. If the data is noisy, it will reflect unnecessary artifacts in the synthesized samples. Training emotion based text to speech models is considerably more difficult and not strait forward. The fact that obtaining emotion annotated data for the desired speaker is costly and very subjective makes it a cumbersome task. Current emotion based systems can synthesize emotions with some limitations. (1) Emotion controllability comes at the cost of loss in quality, (2) Have discreet emotions which lack the finer control, and (3) cannot be generalized to new speakers without the annotated emotion data. We propose a system that overcomes the above-mentioned problems by leveraging the largely available corpus of noisy speech annotated with emotions. Even though the data is noisy, our technique trains an emotion based text to speech system that can synthesize desired emotion without any loss of quality in the output. We present a method to control the emotional prosody of Text to Speech (TTS) systems by using phoneme-level intermediate variances/features (pitch, energy, and duration) as levers. We learn how the variances change with respect to emotion. We bring the finer control in the synthesized speech by using AVD values, which can represent emotions in a 3D space. Our proposed method also doesn’t require emotion annotated data for the target speaker. Once trained on the emotion annotated data, it can be applied to any system which has the prediction of the variances as an intermediate step.
Towards Handwriting Recognition and Search in Indic & Latin Scripts

ML-powered document image analysis approaches can enable intelligent solutions in bringing handwritten information into the digital world. Two major components of handwriting understanding include handwritten text recognition(HTR) and handwritten search. The former task enables the conversion of handwritten text to digital format, whereas the latter task provides easy access to the handwritten information scattered across books, archives, manuscripts and so on. We primarily focus on both these problem statements in this thesis. Handwritten document image analysis for Indic scripts is still in its nascent stage compared to Latin scripts. For example, many commercial applications and open-source demonstrations are available for Latin scripts and there are hardly such known applications for Indic scripts. It is challenging to develop solutions for Indic scripts due to (i) variety of scripts within the Indic subcontinent, (ii) lack of huge annotated datasets and challenges in collecting data for multiple scripts, and (iii) inherent challenges in Indic scripts like inflections, joining multiple glyphs to form an akshara(equivalent to a character in Latin scripts). While challenging, it is also crucial to develop HTR and handwritten search approaches for Indic scripts. Therefore, this thesis is majorly focused on discussing approaches for Indic HTR and Indic handwritten search. In the last two decades, large digitization projects converted paper documents and ancient historical manuscripts into digital forms. However, they remain often inaccessible due to the unavailability of robust HTR solutions. Recognizing handwritten text is fundamental to any modern document analysis system. In recent years, efforts toward developing text recognition systems have advanced due to the success of deep neural networks and the availability of annotated datasets. This is especially true for Latin scripts. In this thesis, we discuss the standard text recognition pipeline that comprises various neural network modules. Then, we present a simple and effective way to improve the text recognition pipeline and training approach. We report the improvement from our approach on four benchmark datasets in Latin and Indic scripts. The existing state-of-the-art approaches for Latin HTR and Latin handwritten search are highly datadriven. Due to the lack of availability of large-scale data, developing Indic HTR and Indic handwritten search is challenging. Therefore, we release a collective Indic handwritten dataset with text images from
Impact of Workplace Violence on Sustainable Performance of Nurses with the Mediation of Social Well-being in the Pakistani Context

The current study investigated the impact of the predictors of workplace violence (harassment, physical violence, stalking, and ostracism) on sustainable work performance with mediating the role of social well-being of Pakistani nurses considering the ‘hierarchy of need’ theory. A survey method was used to conduct the current study. The data was collected from 400 respondents of four nursing teaching colleges, affiliated with hospitals of Lahore, Pakistan. The data was analyzed in Statistical Package for the Social Sciences (SPSS) and Smart Partial Least Square (PLS) software using structural equation modeling (PLS-SEM) technique. The results revealed a negative influence of workplace violence on the performance of nurses. Moreover, it tends to dissatisfy their safety and social needs, the most important human needs according to Maslow’s theory. Since Maslow’s time, researchers continued to explore how safety and social needs impact the well-being of nurses. Social well-being is related to better physical, mental, and social health and conversely, feeling isolated (unmet safety and social need) bears negative consequences for health and well-being. Eventually, it became the reason for low performance. The findings confirmed that workplace violence is the strong predictor of sustainable performance. Whereas, nurse’s social well-being mediates the relationship between the predictors (workplace violence) and the outcome variable (performance). Therefore, the study concluded that when safety and social needs are fulfilled, nurses perform their duties enthusiastically. This is the first endeavor to use the “hierarchy of need” theory in order to mediate the effect of violence, performance, and social well-being of nurses. Therefore, the current study served as a baseline for further research in this area. The findings helped the higher authorities and hospitals’ management to devise such policies which help nurses towards organizational growth and sustainable work environment.
Fibroblast growth factor 23, mineral metabolism and mortality among elderly men

Background: Fibroblast growth factor 23 (FGF23) is the earliest marker of disturbed mineral metabolism as renal function decreases. Its serum levels are associated with mortality in dialysis patients, persons with chronic kidney disease (CKD) and prevalent cardiovascular disease (CVD), and it is associated with atherosclerosis, endothelial dysfunction and left ventricular hypertrophy in the general population. The primary aim of this study is to examine the association between FGF23 and mortality, in relation to renal function in the community. A secondary aim is to examine the association between FGF23 and CVD related death. Methods: The population-based cohort of MrOS Sweden included 3014 men (age 69 – 81 years). At inclusion intact FGF23, intact parathyroid hormone (PTH), 25 hydroxyl vitamin D (25D), calcium and phosphate were measured. Mortality data were collected after an average of 4.5 years follow-up. 352 deaths occurred, 132 of CVD. Association between FGF23 and mortality was analyzed in quartiles of FGF23. Kaplan-Meier curves and Log-rank test were used to examine time to events. Cox proportional hazards regression was used to examine the association between FGF23, in quartiles and as a continuous variable, with mortality. The associations were also analyzed in the sub-cohort with estimated glomerular filtration rate (eGFR) above 60 ml/min/1.73 m 2 . Results: There was no association between FGF23 and all-cause mortality, Hazard ratio (HR) 95% confidence interval (CI): 1.02 (0.89-1.17). For CVD death the HR (95% CI) was 1.26 (0.99 - 1.59)/(1-SD) increase in log(10)FGF23 after adjustment for eGFR, and other confounders. In the sub-cohort with eGFR > 60 ml/min/1.73 m 2 the HR (95% CI) for CVD death was 55% (13 – 111)/(1-SD) increase in log(10)FGF23. Conclusions: FGF23 is not associated with mortality of all-cause in elderly community living men, but there is a weak association with CVD death, even after adjustment for eGFR and the other confounders. The association with CVD death is noticeable only in the sub-cohort with preserved renal function.
Enriching Structured and Unstructured Knowledge for Low-Resource Languages by Cross Lingual Fact Extraction and Text Generation

Natural language generation has gained tremendous popularity in recent times primarily due to the advent of large pretrained language models trained on vast amount of data. However, most of this progress has only been limited to few high resource languages like English. Almost all low resource(LR) languages still suffer from the lack of sufficient training data and hence the lack of usable generative models. Furthermore, multiple business scenarios also require an automated generation of descriptive human-readable long text from structured input data, where the source is typically a high-resource language and the target is a low or medium resource language. In this work, we present systems and approaches which can be utilised to ultimately enrich the structured and unstructured content available for low resource languages in the encyclopedic domain. In order to do so, we introduce cross lingual techniques which efficiently utilise the abundant structured data available in high resource languages. We also introduce systems to further enrich this structured data using the information present in the form of natural language text in low resource languages. Firstly we propose novel problem of cross lingual fact to text alignment in order to construct the XAlign dataset for the purpose of cross lingual fact to text generation and fact extraction. We explore several methods to automatically align English facts from Wikidata to sentences from native language Wikipedia. We experiment with approaches accounting for syntactic and semantic matches between the fact and the sentence and propose a two stage pipeline for automated alignment and evaluate it on a manually annotated high quality test set. We also experiment with distant supervision and transfer learning based techniques in order to achieve quality alignment. We use the best approach to create the XAlign dataset which consists of more than half a million aligned (sentence, facts) pairs across 12 Indian languages. Following the construction of the dataset we propose the problem of Cross Lingual Fact Extraction (CLFE). Recent approaches concentrate on automatically enriching large knowledge graphs like Wikidata and DBPedia from text. However a lot of information present in the form of natural text in low resource languages is often missed out. Furthermore, considering the potential use case of utilising structured data for generating content in various LR languages, the CLFE task aims at extracting factual information in the form of English triples from LR Indian Language text. Despite its massive potential, progress made on this task is lagging