Miracle-dz/UniFAR-FTUs huggingface dataset:
A Crank-Nicolson leap-frog scheme for the unsteady incompressible magnetohydrodynamics equations

This paper presents a Crank-Nicolson leap-frog (CNLF) scheme for the unsteady incompressible magnetohydrodynamics (MHD) equations. The spatial discretization adopts the Galerkin finite element method (FEM), and the temporal discretization employs the CNLF method for linear terms and the semi-implicit method for nonlinear terms. The first step uses Stokes style's scheme, the second step employs the Crank-Nicolson extrapolation scheme, and others apply the CNLF scheme. We testify that the fully discrete scheme is stable and convergent when the time step is less than or equal to a positive constant. The second order $L^{2}$ error estimates can be derived by a novel negative norm technique. The numerical results are consistent with our theoretical analysis, which indicates that the method has an optimal convergence order. Therefore, the scheme is effective for different parameters.
Phase stability of Fe from first principles: Atomistic spin dynamics coupled with 
ab initio
 molecular dynamics simulations and thermodynamic integration

The calculation of free energies from ﬁrst principles in materials is a formidable task which enables the prediction of phase stability with high accuracy; these calculations are complicated in magnetic materials by the interplay of electronic, magnetic, and vibrational degrees of freedom. In this work, we show the feasibility and accuracy of the calculation of phase stability in magnetic systems with ab initio methods and thermodynamic integration by sampling the magnetic and vibrational phase space with coupled atomistic spin dynamics- ab initio molecular dynamics (ASD-AIMD) simulations [Stockem et al. , PRL 121 , 125902 (2018)], where energies and interatomic forces are calculated with density functional theory (DFT). We employ the method to calculate the phase stability of Fe at ambient pressure from 800 K up to 1800 K. The Gibbs free energy diﬀerence between fcc and bcc Fe at zero pressure as a function of temperature is calculated carrying out thermodynamic integration over temperature on the average energies at the DFT level obtained by ASD-AIMD. The thermodynamic integration over temperature needs a reference free energy diﬀerence, which we directly calculate in the paramagnetic state at temperatures much higher than the magnetic transition temperatures with thermodynamic integration over stress-strain variables using disordered local moment (DLM)- ab initio molecular dynamics simulations. For bcc Fe, we employ two diﬀerent sets of exchange interactions as a function of distance in the ASD-AIMD procedure and we show the importance of the magnetic ordering temperature on the α to γ structural transition temperature, whereas the γ to δ transition is well reproduced independently of the exchange interactions. For the best set of exchange interactions, the Gibbs free energy diﬀerence between the two structures is within 5 meV/atom from the CALPHAD estimate, and both transition temperatures are reproduced within 150 K. The present work paves the way to free energy calculations in magnetic materials from ﬁrst principles with accuracy in the order of 1 meV/atom.
Towards a non-Gaussian Generative Model of large-scale Reionization Maps

High-dimensional data sets are expected from the next generation of large-scale surveys. These data sets will carry a wealth of information about the early stages of galaxy formation and cosmic reionization. Extracting the maximum amount of information from the these data sets remains a key challenge. Current simulations of cosmic reionization are computationally too expensive to provide enough realizations to enable testing different statistical methods, such as parameter inference. We present a non-Gaussian generative model of reionization maps that is based solely on their summary statistics. We reconstruct large-scale ionization ﬁelds (bubble spatial distributions) directly from their power spectra (PS) and Wavelet Phase Harmonics (WPH) coefﬁcients. Using WPH, we show that our model is efﬁcient in generating diverse new examples of large-scale ionization maps from a single realization of a summary statistic. We compare our model with the target ionization maps using the bubble size statistics, and largely ﬁnd a good agreement. As compared to PS, our results show that WPH provide optimal summary statistics that capture most of information out of a highly non-linear ionization ﬁelds.
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach

In this article, we propose a novel pessimism-based Bayesian learning method for optimal dynamic treatment regimes in the offline setting. When the coverage condition does not hold, which is common for offline data, the existing solutions would produce sub-optimal policies. The pessimism principle addresses this issue by discouraging recommendation of actions that are less explored conditioning on the state. However, nearly all pessimism-based methods rely on a key hyper-parameter that quantifies the degree of pessimism, and the performance of the methods can be highly sensitive to the choice of this parameter. We propose to integrate the pessimism principle with Thompson sampling and Bayesian machine learning for optimizing the degree of pessimism. We derive a credible set whose boundary uniformly lower bounds the optimal Q-function, and thus we do not require additional tuning of the degree of pessimism. We develop a general Bayesian learning method that works with a range of models, from Bayesian linear basis model to Bayesian neural network model. We develop the computational algorithm based on variational inference, which is highly efficient and scalable. We establish the theoretical guarantees of the proposed method, and show empirically that it outperforms the existing state-of-the-art solutions through both simulations and a real data example.
On K-moduli of quartic threefolds

The family of smooth Fano 3-folds with Picard rank 1 and anticanonical volume 4 consists of quartic 3-folds and of double covers of the 3-dimensional quadric branched along an octic surface. They can all be parametrised as complete intersections of a quadric and a quartic in the weighted projective space $\mathbb{P}(1,1,1,1,1,2)$, denoted by $X_{2,4} \subset \mathbb{P}(1^5,2)$; all such smooth complete intersections are K-stable. With the aim of investigating the compactification of the moduli space of quartic 3-folds given by K-stability, we exhibit three phenomena: (i) there exist K-polystable complete intersection $X_{2,2,4} \subset \mathbb{P}(1^5,2^2)$ Fano 3-folds which deform to quartic 3-folds and are neither quartic 3-folds nor double covers of quadric 3-folds - in other words, the closure of the locus parametrising complete intersections $X_{2,4}\subset \mathbb{P}(1^5,2)$ in the K-moduli contains elements that are not of this type; (ii) any quasi-smooth $X_{2,2,4} \subset \mathbb{P}(1^5,2^2)$ is K-polystable; (iii) the closure in the K-moduli space of the locus parametrising complete intersections $X_{2,2,4} \subset \mathbb{P}(1^5,2^2)$ which are not complete intersections $X_{2,4} \subset \mathbb{P}(1^5,2)$ contains only points which correspond to complete intersections $X_{2,2,4} \subset \mathbb{P}(1^5,2^2)$.
Exact Manifold Gaussian Variational Bayes

We propose an optimization algorithm for Variational Inference (VI) in complex models. Our approach relies on natural gradient updates where the variational space is a Riemann manifold. We develop an efficient algorithm for Gaussian Variational Inference that implicitly satisfies the positive definite constraint on the variational covariance matrix. Our Exact manifold Gaussian Variational Bayes (EMGVB) provides exact but simple update rules and is straightforward to implement. Due to its black-box nature, EMGVB stands as a ready-to-use solution for VI in complex models. Over five datasets, we empirically validate our feasible approach on different statistical, econometric, and deep learning models, discussing its performance with respect to baseline methods.
Efficient Directed Graph Sampling via Gershgorin Disc Alignment

Graph sampling is the problem of choosing a node subset via sampling matrix $\mathbf{H} \in \{0,1\}^{K \times N}$ to collect samples $\mathbf{y} = \mathbf{H} \mathbf{x} \in \mathbb{R}^K$, $K<N$, so that the target signal $\mathbf{x} \in \mathbb{R}^N$ can be reconstructed in high fidelity. While sampling on undirected graphs is well studied, we propose the first sampling scheme tailored specifically for directed graphs, leveraging a previous undirected graph sampling method based on Gershgorin disc alignment (GDAS). Concretely, given a directed positive graph $\mathcal{G}^d$ specified by random-walk graph Laplacian matrix $\mathbf{L}_{rw}$, we first define reconstruction of a smooth signal $\mathbf{x}^*$ from samples $\mathbf{y}$ using graph shift variation (GSV) $\|\mathbf{L}_{rw} \mathbf{x}\|^2_2$ as a signal prior. To minimize worst-case reconstruction error of the linear system solution $\mathbf{x}^* = \mathbf{C}^{-1} \mathbf{H}^\top \mathbf{y}$ with symmetric coefficient matrix $\mathbf{C} = \mathbf{H}^\top \mathbf{H} + \mu \mathbf{L}_{rw}^\top \mathbf{L}_{rw}$, the sampling objective is to choose $\mathbf{H}$ to maximize the smallest eigenvalue $\lambda_{\min}(\mathbf{C})$ of $\mathbf{C}$. To circumvent eigen-decomposition entirely, we maximize instead a lower bound $\lambda^-_{\min}(\mathbf{S}\mathbf{C}\mathbf{S}^{-1})$ of $\lambda_{\min}(\mathbf{C})$ -- smallest Gershgorin disc left-end of a similarity transform of $\mathbf{C}$ -- via a variant of GDAS based on Gershgorin circle theorem (GCT). Experimental results show that our sampling method yields smaller signal reconstruction errors at a faster speed compared to competing schemes.
Experimental determination of the E2-M1 polarizability of the strontium clock transition

To operate an optical lattice clock at a fractional uncertainty below $10^{-17}$, one must typically consider not only electric-dipole (E1) interaction between an atom and the lattice light field when characterizing the resulting lattice light shift of the clock transition but also higher-order multipole contributions, such as electric-quadrupole (E2) and magnetic-dipole (M1) interactions. However, strongly incompatible values have been reported for the E2-M1 polarizability difference of the clock states $(5s5p)\,{}^{3}\mathrm{P}_{0}$ and $(5s^2)\,{}^{1}\mathrm{S}_{0}$ of strontium [Ushijima et al., Phys. Rev. Lett. 121 263202 (2018); Porsev et al., Phys. Rev. Lett. 120, 063204 (2018)]. This largely precludes operating strontium clocks with uncertainties of few $10^{-18}$, as the resulting lattice light shift corrections deviate by up to $1 \times 10^{-17}$ from each other at typical trap depths. We have measured the E2-M1 polarizability difference using our ${}^{87}\mathrm{Sr}$ lattice clock and find a value of $\Delta \alpha_{\mathrm{qm}} = -987^{+174}_{-223} \; \mathrm{\mu Hz}$. This result is in very good agreement with the value reported by Ushijima et al.
SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic Segmentation

Recent mainstream weakly supervised semantic segmentation (WSSS) approaches are mainly based on Class Activation Map (CAM) generated by a CNN (Convolutional Neural Network) based image classifier. In this paper, we propose a novel transformer-based framework, named Semantic Guided Activation Transformer (SemFormer), for WSSS. We design a transformer-based Class-Aware AutoEncoder (CAAE) to extract the class embeddings for the input image and learn class semantics for all classes of the dataset. The class embeddings and learned class semantics are then used to guide the generation of activation maps with four losses, i.e., class-foreground, class-background, activation suppression, and activation complementation loss. Experimental results show that our SemFormer achieves \textbf{74.3}\% mIoU and surpasses many recent mainstream WSSS approaches by a large margin on PASCAL VOC 2012 dataset. Code will be available at \url{https://github.com/JLChen-C/SemFormer}.
Perspectives in Quantum Hall Effects: Novel Quantum Liquids in Low-Dimensional Semiconductor Structures

Localization, metal-Insulator transitions and quantum Hall effect experimental studies of multi-component quantum Hall systems properties of the electron solid edge state transport multi-component quantum Hall systems: the sum of their parts and more Fermion Chern-Simons theory and the unquantized quantum Hall effect composite Fermions resonant inelastic light scattering from quantum Hall systems a case for the magnetic field induced "D Wigner composite Fermions in the fractional quantum Hall effect.
Spin Glass Behavior in the Dy3-xYxTaO7 System

Several x-compositions of the polycrystalline Dy3-xYxTaO7 system, crystallizing in the weberite-type structure, were synthesized and structurally characterized using Rietveld refinements based on X-ray diffraction data. In previous magnetic characterization of Dy3TaO7 (x = 0), with the same crystal structure, an antiferromagnetic transition at T = 2.3 K has been assigned to this compound. On the basis of DC and AC magnetic susceptibilities analyses, we show in this work that all compounds in the x range of 0-1.0 exhibit a spin glass behavior. The nature of the spin glass behavior in Dy3-xYxTaO7, can be attributed to the highly frustrated antiferromagnetic interaction of the Dy3+ sublattice and to the Dy3+-Dy3+ distorted tetrahedra array in the weberite-type structure of this system. By fitting AC susceptibility data, using dynamical scaling theory equations, we conclude that a cluster spin glass is present in Dy3-xYxTaO7 in the low temperature range. Depending on the x-composition, Tg ~ 2.2 - 3.2 K. In the range 15-300 K the system obeys a Curie-Weiss magnetic behavior.
Detection of Extended-Spectrum β-Lactamases (ESBLs) and AmpC in Class A and Class B Carbapenemase-Producing Enterobacterales

Antimicrobial resistance is a growing public health threat of broad concern worldwide. Timely detection of antibiotic resistance mechanisms can help to monitor and to curb the spread of resistant bacteria within the hospital setting as well as in the environment. ABSTRACT In carbapenemase-producing Enterobacterales (CPE) additional β-lactam resistance mechanisms such as extended-spectrum-β-lactamases (ESBL) and/or AmpC-β-lactamases are generally difficult to detect by phenotypical methods. Recently, a modified version of the CLSI ESBL confirmatory combination disc diffusion (CDD) test, which involves the addition of boronic acid and EDTA on discs containing ESBL and AmpC substrates ± inhibitors, has been proposed for the detection of ESBL in class A and class B CPE. Here, the performance of the modified CDD test was evaluated using 121 genotypically characterized class A and class B CPE. Also, the effectiveness of the NG-Test CTX-M-MULTI lateral flow immunoassay was evaluated for ESBL detection. For class A CPE (n = 47), the modified CDD method exhibited an equal specificity (95.7%) and a higher sensitivity (100%) compared to the standard method (91.7%). The CTX-M-MULTI test detected ESBL in all CTX-M-type ESBL producers (n = 23), whereas it was negative for all CTX-M-type ESBL-negative isolates (n = 24). For class B CPE (n = 71), the modified method significantly improved both sensitivity (95%) and specificity (100%) in detecting ESBL compared to the standard method (17.5% sensitivity and 83.9% specificity). In comparison, the CTX-M-MULTI led to identification of ESBL in all CTX-M-ESBL-producers (n = 39) and no false-positive signal was generated with the CTX-M-type-ESBL-negative isolates (n = 30). Furthermore, the modified CDD improved the robustness of the method for AmpC detection (inconclusive results were produced in 53/57 and 10/57 cases with the standard and modified method, respectively), although the sensitivity of the test was poor (23.5%). Here, we propose a practical and cost-effective approach combining the modified CDD and the CTX-M-MULTI test for detection of ESBL and/or AmpC in class A and B CPE. IMPORTANCE Antimicrobial resistance is a growing public health threat of broad concern worldwide. Timely detection of antibiotic resistance mechanisms can help to monitor and to curb the spread of resistant bacteria within the hospital setting as well as in the environment. In this work we report an accurate and affordable method to phenotypically identify difficult-to-detect resistance determinants in highly resistant (carbapenemase-producing) bacteria. This method may be implemented in any diagnostic microbiology lab and may reduce the underreporting of relevant resistance mechanisms.
Assessment of Antitumor and Antiproliferative Efficacy and Detection of Protein‐Protein Interactions in Cancer Cells from 3D Tumor Spheroids

When compared to two‐dimensional (2D) cell cultures, 3D spheroids have been considered suitable in vitro models for drug discovery research and other studies of drug activity. Based on different 3D cell culture procedures, we describe procedures we have used to obtain 3D tumor spheroids by both the hanging‐drop and ultra‐low‐attachment plate methods and to analyze the antiproliferative and antitumor efficacy of different chemotherapeutic agents, including a peptidomimetic. We have applied this method to breast and lung cancer cell lines such as BT‐474, MCF‐7, A549, and Calu‐3. We also describe a proximity ligation assay of the cells from the spheroid model to detect protein‐protein interactions of EGFR and HER2. © 2022 Wiley Periodicals LLC.
Cancellations in power series of sine type

We present a method to study the behavior of a power series of type $$f(x):=\sum_{n=0}^\infty (-1)^n c_n\frac{x^{2n+1}}{(2n+1)! }$$ when $x\to\infty$. We apply our method to study the function $$f(t):=\int_0^t\frac{dx}{x}\int_0^x\frac{dy}{y}\int_0^y\frac{dz}{z}\bigl\{ \sin x+\sin(x-y)-\sin(x-z)-\sin(x-y+z)\bigr\}. $$ We will derive various different representations of $f(t)$ by means of which it will be shown that $\lim_{t\to+\infty}f(t)=0$, disproving a conjecture by Z. Silagadze, claiming that this limit equals $-\pi^3/12$.
The Diversity of Lipopeptides in the Pseudomonas syringae Complex Parallels Phylogeny and Sheds Light on Structural Diversification during Evolutionary History

This study provides an insight into the P. syringae metabolome that emphasizes the high diversity of lipopeptides produced within the P. syringae complex. The production profiles of strains are closely related to their phylogenetic classification, indicating that structural diversification of lipopeptides parallels the phylogeny of this bacterial complex, thereby further illustrating the inherent importance of lipopeptides in the ecology of this group of bacteria throughout its evolutionary history. ABSTRACT Pseudomonas spp. colonize diverse aquatic and terrestrial habitats and produce a wide variety of secondary metabolites, including lipopeptides. However, previous studies have often examined a limited number of lipopeptide-producing strains. In this study, we performed a systematic analysis of lipopeptide production across a wide data set of strains of the Pseudomonas syringae complex (724) by using a combined bioinformatics, mass spectrometry, and phylogenetics approach. The large P. syringae complex, which is composed of 13 phylogroups, is known to produce factins (including syringafactin-like lipopeptides), mycins (including syringomycin-like lipopeptides), and peptins (such as syringopeptins). We found that 80.8% of P. syringae strains produced lipopeptides and that factins were the most frequently produced (by 96% of the producing strains). P. syringae strains were either factin monoproducers or factin, mycin, and peptin coproducers or lipopeptide nonproducers in relation to their phylogenetic group. Our analyses led to the discovery of 42 new lipopeptides, bringing the number of lipopeptides identified in the P. syringae complex to 75. We also highlighted that factins have high structural resemblance and are widely distributed among the P. syringae complex, while mycins and peptins are highly structurally diverse and patchily distributed. IMPORTANCE This study provides an insight into the P. syringae metabolome that emphasizes the high diversity of lipopeptides produced within the P. syringae complex. The production profiles of strains are closely related to their phylogenetic classification, indicating that structural diversification of lipopeptides parallels the phylogeny of this bacterial complex, thereby further illustrating the inherent importance of lipopeptides in the ecology of this group of bacteria throughout its evolutionary history. Furthermore, this overview of P. syringae lipopeptides led us to propose a refined classification that could be extended to the lipopeptides produced by other bacterial groups.
The systemic immune-inflammation index in predicting sepsis mortality

ABSTRACT Background The systemic immune-inflammation index (SII) is a novel parameter and its role in the prognosis of sepsis has never been explored previously. Methods We retrospectively assessed 267 patients with blood-culture confirmed sepsis. Clinical and laboratory data recorded at intensive care unit (ICU) admission were analyzed. Outcomes of interest included in-hospital mortality and length-of-stay (LOS) in the ICU. Sequential Organ Failure Assessment (SOFA) scores, SII, neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR) were calculated. Multivariable regression analysis was used to identify independent predictors of LOS and mortality. Area under receiver operator characteristic (AUROC) curves were used to determine optimum cutoffs, and the incremental effect of SII on the SOFA score was assessed using model discrimination and calibration properties. Results There were 76 (28.5%) non-survivors. SII, NLR, and PLR were independent predictors of sepsis mortality, with adjusted odds ratios of 1.51 (1.24–1.84), 1.67 (1.30–2.13) and 1.24 (1.11–1.39). SII and SOFA score were independent predictors of LOS. SII had an AUROC of 0.848, and the optimum cutoff was 564 with a sensitivity and specificity of 85.5% and 71.2%. The addition of SII to the model had a significant incremental effect on the predictive ability of SOFA score (Net Reclassification Index = 0.084, P = 0.025; Integrated Discrimination Index = 0.056, P = 0.001). Conclusion The SII is an inexpensive parameter that can be used in addition to clinical sepsis scores to improve the accuracy of patient assessment.
Complete Genome Sequences of Microbacterium paraoxydans Phages Cassita and Fransoyer

Phages Cassita and Fransoyer were isolated from soil in northwestern Wisconsin using Microbacterium paraoxydans as the host. The genomes of Cassita and Fransoyer are 61,868 bp and 62,277 bp, respectively, with direct terminal repeats. Both phages exhibit siphoviral morphology and are predicted to have lytic life cycles. ABSTRACT Phages Cassita and Fransoyer were isolated from soil in northwestern Wisconsin using Microbacterium paraoxydans as the host. The genomes of Cassita and Fransoyer are 61,868 bp and 62,277 bp, respectively, with direct terminal repeats. Both phages exhibit siphoviral morphology and are predicted to have lytic life cycles.