Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel MR image recovery using patch-smoothness iterative shrinkage algorithm

Obtaining high spatial or spatiotemporal resolution along with good slice coverage is challenging in dynamic magnetic resonance imaging, MRI, due to the slow nature of the acquisition process. In recent years, there has been a rapid growth of MRI techniques that allow faster scan speed by exploiting spatial or spatiotemporal redundancy of the images. These techniques can improve the performance of imaging significantly across multiple clinical applications, including cardiac functional examinations, perfusion imaging, blood flow assessment, contrast-enhanced angiography, functional MRI, and interventional imaging, among others. The ultimate goal of this thesis is to develop novel algorithms to reconstruct heavily undersampled sparse imaging. The designed schemes aim to achieve a shorter scan duration, higher spatial resolution, increased temporal resolution, signal-to-noise ratio and coverage in multidimensional multichannel MRI. In addition to improving patients comfort and compliance while imaging under the MRI device, the newly developed schemes will allow patients with arrhythmia problems, pediatric and obese subjects to breath freely without the need for any breath-hold scans. Shortening examination periods also reduces patient’s stress, lowers the entire visit to the clinic and finally decreases the associated economic costs. Rapid imaging acquisitions will also allow for efficient extraction of quantitative information needed for the patients’ diagnosis eg. tumor characterization and veins blockages through myocardial perfusion MRI. Current applications of interests include real-time CINE MRI and contrast changing perfusion MRI.
Adoption and Effects of Software Engineering Best Practices in Machine Learning

Background. The increasing reliance on applications with machine learning (ML) components calls for mature engineering techniques that ensure these are built in a robust and future-proof manner. Aim. We aim to empirically determine the state of the art in how teams develop, deploy and maintain software with ML components. Method. We mined both academic and grey literature and identified 29 engineering best practices for ML applications. We conducted a survey among 313 practitioners to determine the degree of adoption for these practices and to validate their perceived effects. Using the survey responses, we quantified practice adoption, differentiated along demographic characteristics, such as geography or team size. We also tested correlations and investigated linear and non-linear relationships between practices and their perceived effect using various statistical models. Results. Our findings indicate, for example, that larger teams tend to adopt more practices, and that traditional software engineering practices tend to have lower adoption than ML specific practices. Also, the statistical models can accurately predict perceived effects such as agility, software quality and traceability, from the degree of adoption for specific sets of practices. Combining practice adoption rates with practice importance, as revealed by statistical models, we identify practices that are important but have low adoption, as well as practices that are widely adopted but are less important for the effects we studied. Conclusion. Overall, our survey and the analysis of responses received provide a quantitative basis for assessment and step-wise improvement of practice adoption by ML teams.
Conservative regularization of neutral fluids and plasmas

Ideal systems of equations such as Euler and MHD may develop singular structures like shocks, vortex/current sheets. Among these, vortical singularities arise due to vortex stretching which can lead to unbounded growth of enstrophy. Viscosity and resistivity provide dissipative regularizations of these singularities. In analogy with the dispersive KdV regularization of the 1D inviscid Burgers' equation, we propose a local conservative regularization of ideal 3D compressible flows, MHD and 2-fluid plasmas (with potential applications to high vorticity flows with low dissipation). The regularization involves introducing a vortical `twirl' term lambda^2 w x curl w in the velocity equation. The cut-off length lambda must be inversely proportional to square root of density to ensure the conservation of a `swirl' energy. The latter includes positive kinetic, compressional, magnetic and vortical contributions, thus leading to a priori bounds on enstrophy. The extension to 2-fluid plasmas involves additionally magnetic `twirl' terms in the ion and electron velocity equations and a solenoidal addition to the current in Ampere's law. A Hamiltonian-Poisson bracket formulation is developed using the swirl energy as Hamiltonian. We also establish a minimality property of the twirl regularization. A swirl velocity field is shown to transport vortex and magnetic flux tubes as well as w/rho and B/rho, thus generalizing the Kelvin-Helmholtz and Alfven theorems. The steady regularized equations are used to model a rotating vortex, MHD pinch and vortex sheet. Our regularization could facilitate numerical simulations and a statistical treatment of vortex and current filaments in 3D. Finally, we briefly describe a conservative regularization of shock-like singularities in compressible flow generalizing both the KdV and nonlinear Schrodinger equations to the adiabatic dynamics of a gas in 3D.
On the structure of parasitic gravity-capillary waves in the small surface tension limit

In this paper, we examine the formation of small capillary waves (parasitic ripples) on the surface of steep steadily-travelling gravity waves. Previously, authors have developed ad-hoc analytical procedures for describing the formation of such parasitic ripples in potential flows; however, it has not been clear whether the small-surface tension limit is well-posed---that is, whether it is possible for an appropriate travelling gravity-capillary wave to be continuously deformed to the classic Stokes wave in the limit of vanishing surface tension. The work of Chen & Saffman (1980) had suggested smooth continuation was not possible. In this paper, we numerically explore the low surface tension limit of the steep gravity-capillary travelling-wave problem. Our results allow for a classification of the bifurcation structure that arises, and serve to unify a number of previous numerical studies. Crucially, we demonstrate that different choices of solution amplitude can lead to subtle restrictions on the continuation procedure; the use of wave energy as an amplitude condition allows solution branches to be continuously deformed to the zero surface tension limit.
Isolation and Identification of Lactic Acid Bacteria from Raw Cow Milk, White Cheese and Rob in Sudan

The cultural, physiological and biochemical of fifty two strains of Lactic Acid Bacteria (LAB) isolated from twenty samples of Sudanese fermented milk (Rob), white cheese and raw milk were investigated. Three genera were found, Lactobacillus (69.23%), Lactococcus (19.23%) and pediococcus (11.53%). The dominate species were Lactobacillus xylosus (10 strains), Lactococcus lactic sub. cremoris (10) strain from raw cow milk. Lactobacillus delbrucckii (8 strains) from Rob and Pediococcus cereviasae (6 strains) from white cheese. Lactobacillus casei sp. Comprise 53.3% of cheese sample and 13.3% of raw milk sample (M2). Other isolates were, Lacobacillus fermentum from rob, Lactobacillus brevis from white cheese and unidentified Lactobacillus spp. from rob. This study suggests that dairy products in Sudan may be a rich source of lactic acid bacteria.
CTCV J2056-3014: An X-Ray-faint Intermediate Polar Harboring an Extremely Fast-spinning White Dwarf

We report on XMM-Newton X-ray observations that reveal CTCV J2056-3014 to be an unusual accretion-powered, intermediate polar (IP) system. It is a member of the class of X-ray-faint IPs whose space density remains unconstrained but potentially very high, with Lx,0.3–12 keV of 1.8 × 1031 erg s−1. We discovered a coherent 29.6 s pulsation in X-rays that was also revealed in our reanalysis of published optical data, showing that the system harbors the fastest-spinning, securely known white dwarf (WD) so far. There is no substantial X-ray absorption in the system. Accretion occurs at a modest rate (∼6 × 10−12 M⊙ yr−1) in a tall shock above the WD, while the star seems to be spinning in equilibrium and to have low magnetic fields. Further studies of CTCV J2056-3014 potentially have broad implications on the origin of magnetic fields in WDs, on the population and evolution of magnetic cataclysmic variables, and also on the physics of matter around rapidly rotating magnetic WDs.
Investigation of applicability of wetland biomass for producing biochar by hydrothermal carbonization (HTC)

Die Herstellung von kohlenstoffhaltigen Materialien aus Biomasse ist ein hochaktuelles Thema. Der hydrothermale Karbonisierungsprozess (HTC) ist dabei von Bedeutung, weil er HTC-Kohlen, die reich an organischem Kohlenstoff sind, aus Biomasse mit hohem Wassergehalt erzeugen kann. Diese haben das Potenzial, vielfaltig eingesetzt werden zu konnen. In diesem Beitrag werden Untersuchungen fur die Herstellung von kohlenstoffhaltigen Materialien aus vier Arten von Moorbiomasse (Schilf, Rohrkolben, Binsen und Seggen) mittels der Technik der hydrothermalen Karbonisierung vorgestellt. Neben den Biomassevarianten wurde der Einfluss der Reaktionstemperatur auf den Karbonisierungsprozess durch Variation der Temperatur im Bereich von 180 bis 230 °C gepruft. Ein Vergleich zwischen den Eigenschaften der erzeugten Kohlenvarianten wurde durchgefuhrt. Dies wurde mittels Elementaranalyse und Stickstoffadsorption realisiert. Es wurde festgestellt, dass die HTC-Kohlen je nach Biomasse ungefahr 53 bis 60 % des Kohlenstoffs der ursprunglichen Biomasse enthalten. Der Kohlenstoffanteil der HTC-Kohle steigt bei allen Varianten mit der Erhohung der Reaktionstemperatur an. Die Atomverhaltnisse H/C und O/C der Kohlen zeigten eine grosere Ahnlichkeit zu Braunkohle als zum Ausgangsmaterial. Die Studie zeigt, dass sich die Moorbiomasse fur die Herstellung von HTC-Kohle eignet.
Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems

Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes, and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-preserving approach to facilitating AI applications. However, it remains a big challenge to optimize the efficiency and effectiveness of FL when it is integrated with the MEC architecture. Moreover, the unreliable nature (e.g., stragglers and intermittent drop-out) of end devices significantly slows down the FL process and affects the global model’s quality in such circumstances. In this article, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the edge level and the cloud level) of model aggregation enacting different aggregation strategies. Moreover, in order to mitigate stragglers and end device drop-out, we introduce regional slack factors into the stage of client selection performed at the edge nodes using a probabilistic approach without identifying or probing the state of end devices (whose reliability is agnostic). We demonstrate the effectiveness of our method in modulating the proportion of clients selected and present the convergence analysis for our protocol. We have conducted extensive experiments with machine learning tasks in different scales of MEC system. The results show that HybridFL improves the FL training process significantly in terms of shortening the federated round length, speeding up the global model’s convergence (by up to 12×) and reducing end device energy consumption (by up to 58 percent).
Dust in the Wolf–Rayet nebula M 1-67

The Wolf-Rayet nebula M1-67 around WR124 is located above the Galactic plane in a region mostly empty of interstellar medium, which makes it the perfect target to study the mass-loss episodes associated with the late stages of massive star evolution. Archive photometric observations from WISE, Spitzer (MIPS) and Herschel (PACS and SPIRE) are used to construct the spectral energy distribution (SED) of the nebula in the wavelength range of 12-500$\mu$m. The infrared (photometric and spectroscopic) data and nebular optical data from the literature are modeled simultaneously using the spectral synthesis code Cloudy, where the free parameters are the gas density distribution and the dust grain size distribution. The infrared SED can be reproduced by dust grains with two size distributions: a MRN power-law distribution with grain sizes between 0.005 and 0.05$\mu$m and a population of large grains with representative size 0.9$ \mu$m. The latter points towards an eruptive origin for the formation of M1-67. The model predicts a nebular ionized gas mass of $M_\mathrm{ion} = 9.2^{+1.6}_{-1.5}~\mathrm{M}_\odot$ and the estimated mass-loss rate during the dust-formation period is $\dot{M} \approx 6 \times 10^{-4} \mathrm{M}_\odot$yr$^{-1}$. We discuss the implications of our results in the context of single and binary stellar evolution and propose that M1-67 represents the best candidate for a post-common envelope scenario in massive stars.
Effects of Temperature and Time of Harvest on the Growth and Yield of Aubergine (Solanum melongena L.)

An experimental study was conducted to define the response of aubergine to different temperatures in controlled green- houses. Set temperatures of the greenhouses were 14, 18, 22, 26 and 30˚C. In terms of vegetative growth, the plant response was the highest to the highest temperature then gradually decreasing for the lower temperatures. This was in contrast with the fruit de- velopment since as the temperature decreased from 30˚C, mean fruit weight and size increased, and the total fruit yield was highest in 22˚C which indicated an optimum temperature. However, there was no optimum for vegetative growth. This was found, by us- ing a growth model, about 32 to 34˚C for almost all plant components. The results are useful for production planning.
La-MAML: Look-ahead Meta Learning for Continual Learning

The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks. While meta-learning shows great potential for reducing interference between old and new tasks, the current training procedures tend to be either slow or offline, and sensitive to many hyper-parameters. In this work, we propose Look-ahead MAML (La-MAML), a fast optimisation-based meta-learning algorithm for online-continual learning, aided by a small episodic memory. Our proposed modulation of per-parameter learning rates in our meta-learning update allows us to draw connections to prior work on hypergradients and meta-descent. This provides a more flexible and efficient way to mitigate catastrophic forgetting compared to conventional prior-based methods. La-MAML achieves performance superior to other replay-based, prior-based and meta-learning based approaches for continual learning on real-world visual classification benchmarks. Source code can be found here: this https URL
Outcome model free causal inference with ultra-high dimensional covariates.

Causal inference has been increasingly reliant on observational studies with rich covariate information. To build tractable causal models, including the propensity score models, it is imperative to first extract important features from high dimensional data. Unlike the familiar task of variable selection for prediction modeling, our feature selection procedure aims to control for confounding while maintaining efficiency in the resulting causal effect estimate. Previous empirical studies imply that one should aim to include all predictors of the outcome, rather than the treatment, in the propensity score model. In this paper, we formalize this intuition through rigorous proofs, and propose the causal ball screening for selecting these variables from modern ultra-high dimensional data sets. A distinctive feature of our proposal is that we do not require any modeling on the outcome regression, thus providing robustness against misspecification of the functional form or violation of smoothness conditions. Our theoretical analyses show that the proposed procedure enjoys a number of oracle properties including model selection consistency, normality and efficiency. Synthetic and real data analyses show that our proposal performs favorably with existing methods in a range of realistic settings.
General framework for cosmological dark matter bounds using 
N
-body simulations

We present a general framework for obtaining robust bounds on the nature of dark matter using cosmological $N$-body simulations and Lyman-alpha forest data. We construct an emulator of hydrodynamical simulations, which is a flexible, accurate and computationally-cheap model for predicting the response of the Lyman-alpha forest flux power spectrum to different dark matter models, the state of the intergalactic medium (IGM) and the primordial power spectrum. The emulator combines a flexible parameterization for the small-scale suppression in the matter power spectrum arising in "non-cold" dark matter models, with an improved IGM model. We then demonstrate how to optimize the emulator for the case of ultra-light axion dark matter, presenting tests of convergence. We also carry out cross-validation tests of the accuracy of flux power spectrum prediction. This framework can be optimized for the analysis of many other dark matter candidates, e.g., warm or interacting dark matter. Our work demonstrates that a combination of an optimized emulator and cosmological "effective theories," where many models are described by a single set of equations, is a powerful approach for robust and computationally-efficient inference from the cosmic large-scale structure.
Neural Temporal Point Processes For Modelling Electronic Health Records

The modelling of Electronic Health Records (EHRs) has the potential to drive more efficient allocation of healthcare resources, enabling early intervention strategies and advancing personalised healthcare. However, EHRs are challenging to model due to their realisation as noisy, multi-modal data occurring at irregular time intervals. To address their temporal nature, we treat EHRs as samples generated by a Temporal Point Process (TPP), enabling us to model what happened in an event with when it happened in a principled way. We gather and propose neural network parameterisations of TPPs, collectively referred to as Neural TPPs. We perform evaluations on synthetic EHRs as well as on a set of established benchmarks. We show that TPPs significantly outperform their non-TPP counterparts on EHRs. We also show that an assumption of many Neural TPPs, that the class distribution is conditionally independent of time, reduces performance on EHRs. Finally, our proposed attention-based Neural TPP performs favourably compared to existing models, and provides insight into how it models the EHR, an important step towards a component of clinical decision support systems.
AISLAMIENTO DE HONGOS ENTOMOPATÓGENOS EN URUGUAY Y SU VIRULENCIA SOBRE Trialeurodes vaporariorum West

Se realizaron aislamientos a partir de ninfas muertas de Trialeurodes vaporariorum (West) con la sintomatologia caracteristica del ataque de hongos entomopatogenos, colectadas en los departamentos de Canelones, Montevideo y Tacuarembo, en Uruguay. Despues de obtenidos los cultivos puros se procedio a la caracterizacion morfologica, macroscopica y microscopica, realizando posteriormente la identificacion de los aislamientos, mediante las claves y descripciones de generos y especies. Se demostro que en los diferentes departamentos pueden aparecer, en forma natural, las especies Verticillium lecanii (Zimm.) Viegas, Paecilomyces fumosoroseus (Wize) Brown and Smith y el genero Beauveria, como patogenos de T. vaporariorum, sobre tomate, en invernaculos, lo cual representa el primer reporte de este tipo para el pais. Un aislamiento de p. fumosoroseus (Pf-001) y otro de V. lecanii (Vl-001) fueron utilizados para evaluar su virulencia sobre ninfas de T. vaporariorum en condiciones de laboratorio. La CL 50 calculada para el aislamiento pF-001 fue de 1.31x10 4 conidios.ml -1 , mientras que para Vl-001 fue de 4.98x0 4 , o sea, 3.8 veces superior. El TL 50 calculado para el aislamiento Pf-001 fue de 73.18h, mientras que para Vl-001 fue de 91.12h, lo que indica que el primer aislamiento necesita 17.9h menos para lograr el mismo efecto de mortalidad.
Magnetic field fluctuations in anisotropic, supersonic turbulence

The rich structure that we observe in molecular clouds is due to the interplay between strong magnetic fields and supersonic (turbulent) velocity fluctuations. The velocity fluctuations interact with the magnetic field, causing it too to fluctuate. Using numerical simulations, we explore the nature of such magnetic field fluctuations, $\vec{\delta B}$, over a wide range of turbulent Mach numbers, $\mathcal{M} = 2 - 20$ (i.e., from weak to strong compressibility), and Alfven Mach numbers, $\mathcal{M}_{\text{A}0} = 0.1 - 100$ (i.e., from strong to weak magnetic mean fields, $B_0$). We derive a compressible quasi-static fluctuation model from the magnetohydrodynamical (MHD) equations and show that velocity gradients parallel to the mean magnetic field give rise to compressible modes in sub-Alfvenic flows, which prevents the flow from becoming two-dimensional, as is the case in incompressible MHD turbulence. We then generalise an analytical model for the magnitude of the magnetic fluctuations to include $\mathcal{M}$, and find $|\vec{\delta B}| = \delta B = c_s\sqrt{\pi\rho_0}\mathcal{M}\mathcal{M}_{\text{A}0}$, where $c_s$ is the sound speed and $\rho_0$ is the mean density of gas. This new relation fits well in the strong $B$-field regime. We go on to study the anisotropy between the perpendicular ($ B_{\perp}$) and parallel ($ B_{\parallel}$) fluctuations and the mean-normalised fluctuations, which we find follow universal scaling relations, invariant of $\mathcal{M}$. We provide a detailed analysis of the morphology for the $\delta B_{\perp}$ and $\delta B_{\parallel}$ probability density functions and find that eddies aligned with $B_0$ cause parallel fluctuations that reduce $B_{\parallel}$ in the most anisotropic simulations. We discuss broadly the implications of our fluctuation models for magnetised gases in the interstellar medium.
COMET: Convolutional Dimension Interaction for Deep Matrix Factorization

Latent factor models play a dominant role among recommendation techniques. However, most of the existing latent factor models assume embedding dimensions are independent of each other, and thus regrettably ignore the interaction information across different embedding dimensions. In this paper, we propose a novel latent factor model called COMET (COnvolutional diMEnsion inTeraction), which provides the first attempt to model higher-order interaction signals among all latent dimensions in an explicit manner. To be specific, COMET stacks the embeddings of historical interactions horizontally, which results in two "embedding maps" that encode the original dimension information. In this way, users' and items' internal interactions can be exploited by convolutional neural networks with kernels of different sizes and a fully-connected multi-layer perceptron. Furthermore, the representations of users and items are enriched by the learnt interaction vectors, which can further be used to produce the final prediction. Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and the rationality of our proposed method.