Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence rate analysis of fast primal-dual methods with scalings for linearly constrained convex optimization problems ⋆

We propose a primal-dual algorithm with scaling, linked to the Nesterov’s acceleration scheme, for a linear equality constrained convex optimization problem. We also consider two variants of the algorithm: an inexact proximal primal-dual algorithm and an inexact linearized primal-dual algorithm. We prove that these algorithms enjoy fast convergence properties, even faster than O (1 /k 2 ) under suitable scaling conditions. Finally, we study an inertial primal-dual dynamic with time scaling for a better understanding of accelerated schemes of the proposed algorithms
Adaptive geometric multigrid for the mixed finite cell formulation of Stokes and Navier–Stokes equations

The unfitted finite element methods have emerged as a popular alternative to classical finite element methods for the solution of partial differential equations and allow modeling arbitrary geometries without the need for a boundary‐conforming mesh. On the other hand, the efficient solution of the resultant system is a challenging task because of the numerical ill‐conditioning that typically entails from the formulation of such methods. We use an adaptive geometric multigrid solver for the solution of the mixed finite cell formulation of saddle‐point problems and investigate its convergence in the context of the Stokes and Navier–Stokes equations. We present two smoothers for the treatment of cutcells in the finite cell method and analyze their effectiveness for the model problems using a numerical benchmark. Results indicate that the presented multigrid method is capable of solving the model problems independently of the problem size and is robust with respect to the depth of the grid hierarchy.
Fundamental Theory of the Evolution Force: Gene Engineering Utilizing Synthetic Evolution Artificial Intelligence

The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so introduce the Fundamental Theory of the Evolution Force, FTEF. Herein, we prove FTEF by proof of concept using a synthetic evolution artificial intelligence to engineer 14-3-3 {\zeta} docking proteins. Synthetic genes were engineered by transforming 14-3-3 {\zeta} sequences into time-based DNA codes that served as templates for random DNA hybridizations and genetic assembly. Application of time-based DNA codes allowed us to fast forward evolution, while damping the effect of point mutations. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 {\zeta}.
A multi-scale method for complex flows of non-Newtonian fluids

We introduce a new heterogeneous multi-scale method for the simulation of flows of non-Newtonian fluids in general geometries and present its application to paradigmatic two-dimensional flows of polymeric fluids. Our method combines micro-scale data from non-equilibrium molecular dynamics (NEMD) with macro-scale continuum equations to achieve a data-driven prediction of complex flows. At the continuum level, the method is model-free, since the Cauchy stress tensor is determined locally in space and time from NEMD data. The modelling effort is thus limited to the identification of suitable interaction potentials at the micro-scale. Compared to previous proposals, our approach takes into account the fact that the material response can depend strongly on the local flow type and we show that this is a necessary feature to correctly capture the macroscopic dynamics. In particular, we highlight the importance of extensional rheology in simulating generic flows of polymeric fluids.
Systematic Mapping Study on the Machine Learning Lifecycle

The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.
Production and signatures of multi-flavour dark matter scenarios with t-channel mediators

We investigate the phenomenology of a dark matter scenario containing two generations of the dark matter particle, differing only by their mass and their couplings to the other particles, akin to the quark and lepton sectors of the Standard Model. For concreteness, we consider the case where the two dark matter generations are Majorana fermions that couple to a right-handed lepton and a scalar mediator through Yukawa couplings. We identify different production regimes in the multi-flavor dark matter scenario and we argue that in some parts of the parameter space the heavier generation can play a pivotal role in generating the correct dark matter abundance. In these regions, the strength of the dark matter coupling to the Standard Model can be much larger than in the single-flavored dark matter scenario. Correspondingly the indirect and direct detection signals can be significantly boosted. We also comment on the signatures of the model from the decay of the heavier dark matter generation into the lighter.
Hyperbolic quadrature method of moments for the one-dimensional kinetic equation

A solution is proposed to a longstanding open problem in kinetic theory, namely, given any set of realizable velocity moments up to order 2n, a closure for the moment of order 2n+1 is constructed for which the moment system found from the free-transport term in the one-dimensional (1-D) kinetic equation is globally hyperbolic and in conservative form. In prior work, the hyperbolic quadrature method of moments (HyQMOM) was introduced to close this moment system up to fourth order (n $\le$ 2). Here, HyQMOM is reformulated and extended to arbitrary even-order moments. The HyQMOM closure is defined based on the properties of the monic orthogonal polynomials Qn that are uniquely defined by the velocity moments up to order 2n -- 1. Thus, HyQMOM is strictly a moment closure and does not rely on the reconstruction of a velocity distribution function with the same moments. On the boundary of moment space, n double roots of the characteristic polynomial P2n+1 are the roots of Qn, while in the interior, P 2n+1 and Qn share n roots. The remaining n + 1 roots of P2n+1 bound and separate the roots of Qn. An efficient algorithm, based on the Chebyshev algorithm, for computing the moment of order 2n + 1 from the moments up to order 2n is developed. The analytical solution to a 1-D Riemann problem is used to demonstrate convergence of the HyQMOM closure with increasing n.
Bayesian Imaging With Data-Driven Priors Encoded by Neural Networks: Theory, Methods, and Algorithms

This paper proposes a new methodology for performing Bayesian inference in imaging inverse problems where the prior knowledge is available in the form of training data. Following the manifold hypothesis and adopting a generative modelling approach, we construct a data-driven prior that is supported on a sub-manifold of the ambient space, which we can learn from the training data by using a variational autoencoder or a generative adversarial network. We establish the existence and well-posedness of the associated posterior distribution and posterior moments under easily verifiable conditions, providing a rigorous underpinning for Bayesian estimators and uncertainty quantification analyses. Bayesian computation is performed by using a parallel tempered version of the preconditioned Crank-Nicolson algorithm on the manifold, which is shown to be ergodic and robust to the non-convex nature of these data-driven models. In addition to point estimators and uncertainty quantification analyses, we derive a model misspecification test to automatically detect situations where the data-driven prior is unreliable, and explain how to identify the dimension of the latent space directly from the training data. The proposed approach is illustrated with a range of experiments with the MNIST dataset, where it outperforms alternative image reconstruction approaches from the state of the art. A model accuracy analysis suggests that the Bayesian probabilities reported by the data-driven models are also remarkably accurate under a frequentist definition of probability.
Best estimator for bivariate Poisson regression

INTRODUCTION: Wald's, the likelihood ratio (LR) and Rao's score tests and their corresponding confidence intervals (CIs), are the three most common estimators of parameters of Generalized Linear Models. On finite samples, these estimators are biased. The objective of this work is to analyze the coverage errors of the CI estimators in small samples for the log-Poisson model (i.e. estimation of incidence rate ratio) with innovative evaluation criteria, taking in account the overestimation/underestimation unbalance of coverage errors and the variable inclusion rate and follow-up in epidemiological studies. METHODS: Exact calculations equivalent to Monte Carlo simulations with an infinite number of simulations have been used. Underestimation errors (due to the upper bound of the CI) and overestimation coverage errors (due to the lower bound of the CI) have been split. The level of confidence has been analyzed from 0.95 to 1-10, allowing the interpretation of P-values below 10 for hypothesis tests. RESULTS: The LR bias was small (actual coverage errors less than 1.5 times the nominal errors) when the expected number of events in both groups was above 1, even when unbalanced (e.g. 10 events in one group vs 1 in the other). For 95% CI, Wald's and the Score estimators showed high bias even when the number of events was large (≥20 in both groups) when groups were unbalanced. For small P-values (<10), the LR kept acceptable bias while Wald's and the score P-values had severely inflated errors (×100). CONCLUSION: The LR test and LR CI should be used.
Lost in Space? Relativistic Interstellar Navigation using an Astrometric Star Catalog

The exploration of interstellar space will require autonomous navigation systems that do not rely on tracking from the Earth. Here I develop a method to determine the 3D position and 3D velocity of a spacecraft in deep space using a star catalog. As a spacecraft moves away from the Sun, the observed positions and velocities of the stars will change relative to those in a Earth-based catalog due to parallax and aberration. By measuring just the angular distances between pairs of stars, and comparing these to the catalog, we can infer the coordinates of the spacecraft via an iterative forward-modeling process. I perform simulations with existing star catalogs to demonstrate the method and to compute its performance. Using the 20 nearest stars and a modest angular distance measurement accuracy of 1″, the position and velocity of a spacecraft light years from the Sun moving at relativistic speeds can be determined to within 3 au and 2 kms−1 respectively. These accuracies improve linearly with the measurement accuracy, e.g., with angles measured to 1 mas the navigation accuracy is 1000 times better. Performance can also be improved using more stars, or by including onboard measurements of the stars’ radial velocities, as these too are affected by the spacecraft’s position and motion.
Authentication and Traceability of Agricultural and Food Products Using Vibrational Spectroscopy

Various labels are designed to help preserve quality food products coming from particular geographical areas and protect consumers against imitations and false information. Traceability is an essential tool to enhance trader and consumer confidence in the safety, quality, and authenticity of the food. It also helps the regulatory authorities to detect fraud and dangerous substances. Traceability with regard to authenticity issues can be interpreted as verifying the product labels, tracing the origin of food, or confirming the presence of ingredients claimed to be in that food. Scientific research in this area is focused mainly on developing analytical methods to authenticate the geographical origin of food, and also to monitor possible changes in food properties during storage, distribution, and up to the point of retail sale (i.e., degradation and aging over time). The use of vibrational spectroscopy (near-infrared, mid-infrared, and Raman) in traceability helps in authenticating the geographical origin, the variety/species origin, and the production process of food and feed products. In this chapter, several examples are discussed to illustrate the high potential of vibrational spectroscopy to tackle authenticity challenges. Some applications are also presented to interpret and translate the analytical results into specifications useful for the food processing industry and the consumer. Keywords: vibrational spectroscopy; infrared; near-infrared; Raman; traceability; authenticity; food; feed
Accurate RBS measurement of ion implant doses in silicon

We demonstrate very accurate ion implant dose measurements using Rutherford backscattering spectrometry (RBS) traceable to a certified reference material from IRMM, Geel and the Bundesanstalt für Materialforschung (BAM), Berlin. The measurements have an absolute accuracy of better than 1.4% and a precision of better than 1.25%. The certified standard sample is compared directly with recent absolute determinations of the energy loss of He in Si, and also with a sample calibrated against the Harwell Bi standard. We determine the dose in a series of three In implants and six As implants of various doses and energies. Some of the samples were amorphized to eliminate channelling effects. A double detector geometry was used, giving pairs of spectra with a common incident charge but where the solid angle and the electronic gain were determined for each detector channel independently. The statistical uncertainty is reduced to <1%. The non‐linear pile‐up background is determined carefully. The errors are determined critically. The experiments were carried out at different dates and different places, so time and space reproducibility of the results is confirmed. The IBA DataFurnace is used for analysis of the certified standard reference material and compared with a transparent manual data reduction method: the use of this code for routine data analysis at the highest accuracy is validated. Copyright © 2002 John Wiley & Sons, Ltd.
Polyhydroxybutyrate, Its Copolymers and Blends

Abstract Polyhydroxybutyrate (PHB) is an optically active polymer of D(−)3-hydroxybutyric acid. It is a naturally occurring polymer produced by a wide variety of bacteria. It serves as an intracellular storage material for carbon and energy and is accumulated as granules within the cytoplasm [1–4] of the bacteria. The chemical structure of PHB is shown in Fig. 1.
Evaluating soil reinforcement by plant roots using artificial neural networks

Soil reinforcement by plant roots and its response to influencing factors are very important for bank stability evaluation and control. Models with improved accuracy are urgently needed for evaluating soil reinforcement. Using a back‐propagation (BP) learning algorithm, an artificial neural network (ANN) model with five input variables, including the number of roots, root area ratio, root tensile strength, soil shear strength, and soil moisture content, was developed to simulate the response of soil reinforcement to these factors. A connection weight approach was used to understand the relative importance of each factor. Using a data set published in 2003 and collected in Australia, soil reinforcement of four trees, Casuarina glauca, Eucalyptus amplifolia, Eucalyptus elata and Acacia floribunda, was simulated using three models: BP‐ANN, one described by Wu et al. in 1979 and the 2005 fibre bundle model (FBM) of Pollen and Simon. Comparisons of results from these models showed that the BP‐ANN model most accurately estimated the soil reinforcement. The simulated results indicated that only the effect of soil moisture content on soil reinforcement was negative. The influence of the other four factors was positive, and the relative importance was in the order: root area ratio > root tensile strength > the number of roots > soil shear strength. This study provides a new approach to soil reinforcement estimation and improves our understanding of soil resistance and bank stability.
Real or fake? Fourier analysis of generative adversarial network fundus images

With the increasing use of deep learning methodologies in various biomedical applications, there is a need for a large number of labeled medical image datasets for training and validation purposes. However, the accumulation of labeled datasets is expensive and time consuming. Recently, generative adversarial networks (GAN) have been utilized to generate synthetic datasets. Currently, the accuracy of generative adversarial networks is calculated using a structural similarity index measure (SSIM). SSIM is not adequate for comparison of images as it underestimates the distortions near hard edges. In this paper, we compare the real DRIVE dataset to the synthetic FunSyn-Net using Fourier transform techniques and show that Fourier behavior is quite different in the two datasets, especially at high frequencies. It is observed that for real images, the amplitude of the Fourier components exponentially decreased with increasing frequency. For the synthesized images, the rate of decrease of the amplitude is much slower. If a linear function is fit to the high frequency components, the slope distributions for the two datasets are completely different with no offset. The average slope in the log scale for DRIVE dataset and FunSyn-Net were 0.0195, and 0.009 respectively. We also looked at auto correlations for the horizontal cut of the Fourier transform and again saw a statistically significant difference between the means for the two datasets. Finally, we also observed that Fourier transforms with real images have higher magnitude squared coherence as compared to the synthesized images. Fourier transform has shown great success for finding differences between real and synthesized images and can be used to improve the synthesized GAN models.
Development of a CT-compatible anthropomorphic skull phantom for surgical planning, training, and simulation

Neurosurgical training is performed on human cadavers and simulation models, such as VR platforms, which have several drawbacks. Head phantoms could solve most of the issues related to these trainings. The aim of this study was to design a realistic and CT-compatible head phantom, with a specific focus on endo-nasal skull-base surgery and brain biopsy. A head phantom was created by segmenting an image dataset from a cadaver. The skull, which includes a complete structure of the nasal cavity and detailed skull-base anatomy, is 3D printed using PLA with calcium, while the brain is produced using a PVA mixture. The radiodensity and mechanical properties of the phantom were tested and adjusted in material choice to mimic real-life conditions. Surgeons find the skull, the structures at the skull-base and the brain realistically reproduced. The head phantom can be employed for neurosurgical education, training and surgical planning, and can be successfully used for simulating surgeries.
Gamification concept for acquisition of medical image segmentation via crowdsourcing

In many fields of medical imaging, image segmentation is required as a basis for further analysis and diagnosis. Convolutional neural networks are a promising approach providing high accuracy. However, large-scale annotated datasets are necessary to train these networks. As expert annotations are costly, crowdsourcing has shown to be an adequate alternative. In previous work, we examined how the workforce of a crowd should be distributed for obtaining annotations with an optimal trade-off between quantity and quality. In this work, we present a gamification approach by transforming the tedious task of image segmentation into a game. This approach aims at motivating users by having fun but nevertheless generating annotations of adequate quality. Therefore, this work presents a gamified crowdsourcing concept for medical image segmentation. We give an overview of incentives applied in state-of-the-art literature and propose two different gamification approaches on how the image segmentation task can be realized as a game. Finally, we propose a integrated game concept that combines both approaches with the following incentives: (a) points / scoring to reward users instantly for accurate segmentations, (b) leaderboard / rankings to let users accumulate scores for long-term motivation, (c) badges / achievements to give users a visual representation of their ”strength” in segmentation, and (d) levels to visualize the learning curve of users in performing the segmentation. We give details on a first prototype implementation and describe how the game concept complies with the guidelines from our prior work.
Complete Genome Sequence of Sinorhizobium meliloti S35m, a Salt-Tolerant Isolate from Alfalfa Rhizosphere in Soil Native to the Caucasus Region

The genome of a symbiotically effective salt-tolerant strain, Sinorhizobium meliloti S35m, isolated from alfalfa rhizosphere in soil native to the Caucasian region, was sequenced. Genomic islands, prophages, and elements of a potential CRISPR/Cas I type (Cas3_0_I) system were identified in the genome. ABSTRACT The genome of a symbiotically effective salt-tolerant strain, Sinorhizobium meliloti S35m, isolated from alfalfa rhizosphere in soil native to the Caucasus region, was sequenced. Genomic islands, prophages, and elements of a potential CRISPR/Cas I type (Cas3_0_I) system were identified in the genome.
A Generalized Bulk-Degradation Model for Hydrogel Networks Formed from Multivinyl Cross-linking Molecules

A theoretical model has been developed to describe the degradation behavior of hydrogels formed from multivinyl cross-linking molecules. Kinetic information was incorporated into the model to predict the cleavage of the cross-links and structural information was included to describe the mass loss from the network. Mass loss from the networks was shown to depend on many parameters, such as the number of vinyl groups on the cross-linking molecule and the average number of cross-linking molecules reacted into each kinetic chain. Other parameters that influenced the mass loss profile are the weight fraction of the network that is contained in the cross-linking molecule, the propensity to form primary cycles, and the hydrolysis kinetic constant. Two other macroscopic properties, volumetric swelling ratio and compressive modulus, were examined as a function of the average number of cross-links per kinetic chain. Through this modeling approach, the degradation of many realistic systems can be understood and pred.