Miracle-dz/UniFAR-FTUs huggingface dataset:
Determinants of the incidence and mortality rates of COVID-19 during the first six months of the pandemic; A cross-country study

COVID-19 pandemic raises an extraordinary challenge to the healthcare systems globally. The governments are taking key measures to constrain the corresponding health, social, and economic impacts, however, these measures vary depending on the nature of the crisis and country-specific circumstances. Objectives: Considering different incidence and mortality rates across different countries, we aimed at explaining variance of these variables by performing accurate and precise multivariate analysis with aid of suitable predictors, accordingly, the model would proactively guide the governmental responses to the crisis. Methods: Using linear and exponential time series analysis, this research aimed at studying the incidence and mortality rates of COVID-19 in 18 countries during the first six months of the pandemic, and further utilize multivariate techniques to explain the variance in monthly exponential growth rates of cases and deaths with aid of a set of different predictors: the recorded Google mobility trends towards six categories of places, daily average temperature, daily humidity, and key socioeconomic attributes of each country. Results: The analysis showed that changes in mobility trends were the most significant predictors of the incidence and mortality rates, temperature and humidity were also significant but to a much lesser extent, on the other hand, the socioeconomic attributes did not contribute significantly to explaining different incidence and mortality rates across countries. Conclusion: Changes in mobility trends across countries dramatically affected the incidence and mortality rates across different countries, thus, it might be used as a proxy measure of contact frequency.
Development of quantitative frailty and mortality prediction models on older patients as a palliative care needs assessment tool

Background: Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Cancer patients have mainly accessed these services, but there is growing consensus about the importance of promoting access for patients with non-malignant disease. Bad survival prognosis and patient's frailty are usual dimensions to decide PC inclusion. Objectives: The main aim of this work is to design and evaluate three quantitative models based on machine learning approaches to predict frailty and mortality on older patients in the context of supporting palliative care decision making: one-year mortality, survival regression and one-year frailty classification. Methods: The dataset used in this study is composed of 39,310 hospital admissions for 19,753 older patients (age >= 65) from January 1st, 2011 to December 30th, 2018. All prediction models were based on Gradient Boosting Machines. From the initial pool of variables at hospital admission, 20 were selected by a recursive feature elimination algorithm based on the random forest's GINI importance criterion. Besides, we run an independent grid search to find the best hyperparameters in each model. The evaluation was performed by 10-fold cross-validation and area under the receiver operating characteristic curve (AUC ROC) and mean absolute error (MAE) were reported. The Cox proportional-hazards model was used to compare our proposed approach with classical survival methods. Results: The one-year mortality model achieved an AUC ROC of 0.87 +- 0.01; the mortality regression model achieved an MAE of 329.97 +- 5.24 days. The one-year frailty classification reported an AUC ROC of 0.9 +- 0.01. The Spearman's correlation between the admission frailty index and the survival time was -0.1, while the point-biserial correlation between one-year frailty index and survival time was -0.16. Conclusions: One-year mortality model performance is at a state-of-the-art level. Frailty Index used in this study behaves coherently with other works in the literature. One-year frailty classifier demonstrated that frailty status within the year could be predicted accurately. To our knowledge, this is the first study predicting on-year frailty status based on a frailty index. We found mortality and frailty as two weakly correlated and complementary PC needs assessment criteria. Predictive models are available online at http://palliativedemo.upv. es/.
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium

Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible. However, the convergence of GAN training has still not been proved. We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. TTUR has an individual learning rate for both the discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape. For the evaluation of the performance of GANs at image generation, we introduce the "Frechet Inception Distance" (FID) which captures the similarity of generated images to real ones better than the Inception Score. In experiments, TTUR improves learning for DCGANs and Improved Wasserstein GANs (WGAN-GP) outperforming conventional GAN training on CelebA, CIFAR-10, SVHN, LSUN Bedrooms, and the One Billion Word Benchmark.
Triclosan coated vicryl versus uncoated vicryl in preventing surgical site infection: a randomized controlled trial

Global prevalence of hospital acquired infections (HAI) is approximately 1.4 million. It occurs in atleast 2% of surgical patients. Surgical site infections account for approximately 10-31% of all the hospital acquired infections. A study conducted in USA found that 21.8% of all HAIs are SSIs. Incidence of SSI is higher in developing countries as compared to developed countries. Incidence is highest for gastrointestinal surgeries.
EVALUATION OF THE REMINERALIZING EFFECT OF THEOBROMINE AND FLUORIDE USING SCANNING ELECTRON MICROSCOPE

Background: Theobromine is a natural alkaloid of plant origin; theobroma cacao. Attempts have been made to use it as a remineralizing agent. Aim of the study: Assessment of the remineralizing potential of theobromine containing toothpastes compared to those with fluoride using scanning electron microscope. Methods: 15 maxillary first bicuspids were selected. Crowns were sectioned and immersed in a demineralizing solution for 4 days. 30 specimens were divided into 3 groups (10 specimens each); group 1, specimens brushed with toothpastes containing theobromine, group 2, specimens brushed with toothpastes containing fluoride and group 3 for specimens brushed with toothpaste free of fluoride or theobromine. The brushing time was 9 minutes and 20 seconds each. Remineralization was carried out using a pH cycling model for 3 days using artificial saliva. The enamel was scanned before and after demineralization and after remineralization. Results: A statistically significant difference was found in calcium and phosphate levels between group 1and 3 and also between group 2 and 3, however no statistical significant difference between group 1and 2. Conclusion: Theobromine could be suitable alternative to fluoride for caries prevention in terms of safety and efficiency.
Alteration of the gut microbiota in post-weaned calves following recovery from bovine coronavirus-mediated diarrhea

Abstract Bovine coronavirus (BCoV) is associated with severe diarrhea, dehydration, and depression, which result in significant economic damages in the dairy and beef cattle industries worldwide. However, differences in the gut microbiota structure and their correlations with differing physiological parameters between BCoV-infected calves with diarrhea and recovered calves are not well understood. In this study, fecal specimens were collected from 10 post-weaned calves, before and after 2 months of fluid therapy, and the samples were used for microbiota analysis. Following recovery, the alpha-diversity profiles (observed operational taxonomic units [OTUs], and Chao1, Shannon, and Simpson indices) changed significantly when compared with those of calves with diarrhea. Beta-diversity analysis exhibited significant differences in gut microbiota compositions between calves with diarrhea and those in the recovered state. The abundances of eight phyla and thirteen genera in feces changed markedly after restoration of BCoV diarrhea. In addition, our correlation study clearly revealed that increased abundances of the genera Caproiciproducens, Pseudoflavonifractor, and Oscillibacter negatively correlated with serum glucose, and phosphorus levels, but positively correlated with serum chloride in calves with diarrhea, whereas increased abundances of the genera Peptostreptococcaceae;Clostridium (Clostridium cluster XI), Intestinibacter, Cellulosilyticum, Ruminococcus, Romboutsia, Paeniclostridium, Clostridiaceae;Clostridium and Turicibacter in recovered calves showed the opposite pattern. These results suggest that structural changes of the gut microbiota after recovery from BCoV infection correlate with changes in physiological parameters. In conclusion, our data provide evidence of gut microbiota-composition changes and their correlations with the physical profiles of post-weaned calves, before and after fluid therapy for BCoV-related diarrhea.
Title of Document: INTERACTION OF INTENSE SHORT LASER PULSES WITH GASES OF NANOSCALE ATOMIC AND MOLECULAR CLUSTERS.

Title of Document: INTERACTION OF INTENSE SHORT LASER PULSES WITH GASES OF NANOSCALE ATOMIC AND MOLECULAR CLUSTERS. Ayush Gupta, Ph.D., 2006 Directed By: Professor Thomas M. Antonsen, Jr., Department of Electrical and Computer Engineering We study the interaction of intense laser pulses with gases of van der Waals bound atomic aggregates called clusters in the range of laser-cluster parameters such that kinetic as well as hydrodynamic effects are active. The clustered gas absorbs the laser pulse energy efficiently producing x-rays, extreme ultraviolet radiation, energetic particles and fusion neutrons. First, we investigate the effect of pulse duration on the heating of a single cluster in a strong laser field using a 2-D electrostatic particle-in-cell (PIC) code. Heating is dominated by a collision-less resonant absorption process that involves energetic electrons transiting through the cluster. A size-dependent intensity threshold defines the onset of this resonance [Taguchi et al., Phys. Rev. Lett. , v90(20), (2004)]. It is seen that increasing the laser pulse width lowers this intensity threshold and the energetic electrons take multiple laser periods to transit the cluster instead of one laser period as previously recorded [Taguchi et al., Phys. Rev. Lett. , v90(20), (2004)]. Results of our numerical simulations showing the effect of pulse duration on the heating rate and the evolution of the electron phase space are presented in this dissertation. Our simulations show that strong electron heating is accompanied by the generation of a quasi-monoenergetic high-energy peak in the ion kinetic energy distribution function. The energy at which the peak occurs is pulse duration dependent. Calculations of fusion neutron yield from exploding deuterium clusters using the PIC model with periodic boundary conditions are also presented. We also investigate the propagation of the laser pulse through a gas of clusters that is described by an effective dielectric constant determined by the single cluster polarizability. For computational advantage, we adopt a uniform density description of the exploding clusters, modified to yield experimentally consistent single cluster polarizability, and couple it to a Gaussian description of the laser pulse. This model is then used to study self-focusing, absorption, and spectral broadening of the laser pulse. The model is further extended to allow for a fraction of the gas to be present as unclustered monomers and to include the effect of unbound electrons produced in the laser-cluster interaction. INTERACTION OF INTENSE SHORT LASER PULSES WITH GASES OF NANOSCALE ATOMIC AND MOLECULAR CLUSTERS
Modifizierte Oberflächenbeschichtungen zur Foulingminderung auf wärmeübertragenden Flächen

Die Verminderung der Warmeubertragungsleistung von Verdampfern, Kondensatoren und einphasigen Warmeubertragern durch Belagbildung auf der Warmeubertragungsflache stellt ein bedeutendes wirtschaftliches und verfahrenstechnisches Problem bei der Dimensionierung und beim Betrieb der Apparate dar. Der Foulingvorgang kann beim Kristallisationsfouling in Induktions- und Schichtwachstumsphase unterteilt werden. Die Induktionsphase wird masgeblich von den molekularen und mechanischen Wechselwirkungen an der Grenzflache Kristall/Warmeubertrager bestimmt. Hier bietet der Einsatz neuer Oberflachenbeschichtungen einen interessanten Ansatzpunkt zur Foulingverminderung.
Leveraging Expert Consistency to Improve Algorithmic Decision Support

Machine learning (ML) is increasingly being used to support high-stakes decisions, a trend owed in part to its promise of superior predictive power relative to human assessment. However, there is frequently a gap between decision objectives and what is captured in the observed outcomes used as labels to train ML models. As a result, machine learning models may fail to capture important dimensions of decision criteria, hampering their utility for decision support. In this work, we explore the use of historical expert decisions as a rich—yet imperfect—source of information that is commonly available in organizational information systems, and show that it can be leveraged to bridge the gap between decision objectives and algorithm objectives. We consider the problem of estimating expert consistency indirectly when each case in the data is assessed by a single expert, and propose influence function-based methodology as a solution to this problem. We then incorporate the estimated expert consistency into a predictive model through a training-time label amalgamation approach. This approach allows ML models to learn from experts when there is inferred expert consistency, and from observed labels otherwise. We also propose alternative ways of leveraging inferred consistency via hybrid and deferral models. In our empirical evaluation, focused on the context of child maltreatment hotline screenings, we show that (1) there are high-risk cases whose risk is considered by the experts but not wholly captured in the target labels used to train a deployed model, and (2) the proposed approach significantly improves precision for these cases.
Diffusion Asymptotics for Sequential Experiments

We propose a new diﬀusion-asymptotic analysis for sequentially randomized experiments. Rather than taking sample size n to inﬁnity while keeping the problem parameters ﬁxed, we let the mean signal level scale to the order 1 / √ n so as to preserve the diﬃculty of the learning task as n gets large. In this regime, we show that the behavior of a class of methods for sequential experimentation converges to a diffusion limit. This connection enables us to make sharp performance predictions and obtain new insights on the behavior of Thompson sampling. Our diﬀusion asymptotics also help resolve a discrepancy between the Θ(log( n )) regret predicted by the ﬁxed-parameter, large-sample asymptotics on the one hand, and the Θ( √ n ) regret from worst-case, ﬁnite-sample analysis on the other, suggesting that it is an appropriate asymptotic regime for understanding practical large-scale sequential experiments. 1
Quasiconformal extension for harmonic mappings on finitely connected domains

We prove that a harmonic quasiconformal mapping defined on a finitely connected domain in the plane, all of whose boundary components are either points or quasicircles, admits a quasiconformal extension to the whole plane if its Schwarzian derivative is small. We also make the observation that a univalence criterion for harmonic mappings holds on uniform domains. Mathematical subject classification (2010). 30C55, 30C62, 31A05. Manuscript received 4th February 2021, accepted 6th June 2021.
Montmorillonite under high H2O pressures: Stability of hydrate phases, rehydration hysteresis, and the effect of interlayer cations

Abstract Dehydration of Ca- and Mg-exchanged montmorillonite was studied along H2O isochores in the hydrothermal diamond-anvil cell by in situ X-ray diffraction using a synchrotron radiation source. At pressures between the H2O liquid-vapor (L-V) boundary and ∼ 10 kbar, the dehydration temperature for the conversion from the 19 Å hydration state to the 15 Å hydration state occurred over the temperature range 260-350 °C for Ca-ex- changed montmorillonite and 200-250 °C for Mg-exchanged montmorillonite, with a slight increase with increasing pressure. For both materials, the rehydration from the 15 Å to 19 Å states occurred at the same temperature as dehydration at pressures along the H2O L-V boundary, thus showing no hysteresis. The rehydration hysteresis increased to nearly 75 °C at 6 kbar for Ca-exchanged montmorillonite and to the same amount at 2.5 kbar for Mg-exchanged montmorillonite. Dehydration experiments on Mg-exchanged montmorillonite along the isochores of 1.024 and 0.75 g/cm3 showed conversion from the 15 Å hydrate to the 12.5 Å hydrate at 590-605 °C. The 12.5 Å hydrate only partially rehydrated after cooling to room temperature along those two isochores. In an experiment started from the 15 Å state, in which the pressure was below the H2O L-V curve, dehydration occurred at 400-500 °C and rehydration at 430-350 °C. When our previous results on Na-exchanged montmorillonite are combined with the current experimental data, systematic trends can be found in the effect of pressure and interlayer-cation species on the dehydration temperature and rehydration hysteresis.
WebSRC: A Dataset for Web-Based Structural Reading Comprehension

Web search is an essential way for humans to obtain information, but it’s still a great challenge for machines to understand the contents of web pages. In this paper, we introduce the task of web-based structural reading comprehension. Given a web page and a question about it, the task is to find an answer from the web page. This task requires a system not only to understand the semantics of texts but also the structure of the web page. Moreover, we proposed WebSRC, a novel Web-based Structural Reading Comprehension dataset. WebSRC consists of 400K question-answer pairs, which are collected from 6.4K web pages with corresponding HTML source code, screenshots, and metadata. Each question in WebSRC requires a certain structural understanding of a web page to answer, and the answer is either a text span on the web page or yes/no. We evaluate various strong baselines on our dataset to show the difficulty of our task. We also investigate the usefulness of structural information and visual features. Our dataset and baselines have been publicly available.
Dictionary-based Debiasing of Pre-trained Word Embeddings

Word embeddings trained on large corpora have shown to encode high levels of unfair discriminatory gender, racial, religious and ethnic biases. In contrast, human-written dictionaries describe the meanings of words in a concise, objective and an unbiased manner. We propose a method for debiasing pre-trained word embeddings using dictionaries, without requiring access to the original training resources or any knowledge regarding the word embedding algorithms used. Unlike prior work, our proposed method does not require the types of biases to be pre-defined in the form of word lists, and learns the constraints that must be satisfied by unbiased word embeddings automatically from dictionary definitions of the words. Specifically, we learn an encoder to generate a debiased version of an input word embedding such that it (a) retains the semantics of the pre-trained word embedding, (b) agrees with the unbiased definition of the word according to the dictionary, and (c) remains orthogonal to the vector space spanned by any biased basis vectors in the pre-trained word embedding space. Experimental results on standard benchmark datasets show that the proposed method can accurately remove unfair biases encoded in pre-trained word embeddings, while preserving useful semantics.
Continuum Modeling Perspectives of Non-Fourier Heat Conduction in Biological Systems

Abstract The thermal modeling of biological systems is increasingly important in the development of more advanced and more precise techniques such as ultrasound surgery. One of the primary barriers is the complexity of biological materials: the geometrical, structural, and material properties vary in a wide range. In the present paper, we focus on the continuum modeling of heterogeneous materials of biological origin. There are numerous examples in the literature for non-Fourier thermal models. However, as we realized, they are associated with a few common misconceptions. Therefore, we first aim to clarify the basic concepts of non-Fourier thermal models. These concepts are demonstrated by revisiting two experiments from the literature in which the Cattaneo–Vernotte and the dual phase lag models are utilized. Our investigation revealed that these non-Fourier models are based on misinterpretations of the measured data, and the seeming deviation from Fourier’s law originates from the source terms and boundary conditions.
Camera Invariant Feature Learning for Generalized Face Anti-Spoofing

There has been an increasing consensus in learning based face anti-spoofing that the divergence in terms of camera models is causing a large domain gap in real application scenarios. We describe a framework that eliminates the influence of inherent variance from acquisition cameras at the feature level, leading to the generalized face spoofing detection model that could be highly adaptive to different acquisition devices. In particular, the framework is composed of two branches. The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain. Motivated by the fact that the spoofing features exist not only in the high frequency domain, in the second branch the discrimination capability of extracted spoofing features is further boosted from the enhanced image based on the recomposition of the high-frequency and low-frequency information. Finally, the classification results of the two branches are fused together by a weighting strategy. Experiments show that the proposed method can achieve better performance in both intra-dataset and cross-dataset settings, demonstrating the high generalization capability in various application scenarios.
A Machine Learning Approach to Predicting Continuous Tie Strengths

Relationships between people constantly evolve, altering interpersonal behavior and defining social groups. Relationships between nodes in social networks can be represented by a tie strength, often empirically assessed using surveys. While this is effective for taking static snapshots of relationships, such methods are difficult to scale to dynamic networks. In this paper, we propose a system that allows for the continuous approximation of relationships as they evolve over time. We evaluate this system using the NetSense study, which provides comprehensive communication records of students at the University of Notre Dame over the course of four years. These records are complemented by semesterly ego network surveys, which provide discrete samples over time of each participant's true social tie strength with others. We develop a pair of powerful machine learning models (complemented by a suite of baselines extracted from past works) that learn from these surveys to interpret the communications records as signals. These signals represent dynamic tie strengths, accurately recording the evolution of relationships between the individuals in our social networks. With these evolving tie values, we are able to make several empirically derived observations which we compare to past works.