2-Amino-1H-benzoimidazol-3-ium 4,4,4-trifluoro-1,3-dioxo-1-phenyl­butan-2-ide

In the title compound, C7H8N3 +·C10H6F3O2 −, 1H-benzoimidazol-2-amine system adopts a planar conformation with an r.m.s. deviation of 0.0174 Å. The cation and anion in the asymmetric unit are linked by N—H⋯O hydrogen bonds. There are also additional inter­molecular N—H⋯O hydrogen bonds and π–π stacking inter­actions between the phenyl rings of neighbouring anions with centroid–centroid distances of 4.0976 (13) Å

An Improved Multi-Stage Preconditioner on GPUs for Compositional Reservoir Simulation

The compositional model is often used to describe multicomponent multiphase porous media flows in the petroleum industry. The fully implicit method with strong stability and weak constraints on time-step sizes is commonly used in the mainstream commercial reservoir simulators. In this paper, we develop an efficient multi-stage preconditioner for the fully implicit compositional flow simulation. The method employs an adaptive setup phase to improve the parallel efficiency on GPUs. Furthermore, a multi-color Gauss-Seidel algorithm based on the adjacency matrix is applied in the algebraic multigrid methods for the pressure part. Numerical results demonstrate that the proposed algorithm achieves good parallel speedup while yields the same convergence behavior as the corresponding sequential version.Comment: 24 pages, 4 figures, and 8 tables. arXiv admin note: text overlap with arXiv:2201.0197

4-Phenyl-1,2,3,4-tetra­hydro­pyrimido[1,2-a]benzimidazol-2-one

In the title compound, C16H13N3O, the tetrahydropyrimidin­one ring adopts a sofa conformation. In the crystal structure, mol­ecules are linked by N—H⋯N hydrogen bonds and C—H⋯π inter­actions

Progressive amorphization of GeSbTe phase-change material under electron beam irradiation

Fast and reversible phase transitions in chalcogenide phase-change materials (PCMs), in particular, Ge-Sb-Te compounds, are not only of fundamental interests, but also make PCMs based random access memory (PRAM) a leading candidate for non-volatile memory and neuromorphic computing devices. To RESET the memory cell, crystalline Ge-Sb-Te has to undergo phase transitions firstly to a liquid state and then to an amorphous state, corresponding to an abrupt change in electrical resistance. In this work, we demonstrate a progressive amorphization process in GeSb2Te4 thin films under electron beam irradiation on transmission electron microscope (TEM). Melting is shown to be completely absent by the in situ TEM experiments. The progressive amorphization process resembles closely the cumulative crystallization process that accompanies a continuous change in electrical resistance. Our work suggests that if displacement forces can be implemented properly, it should be possible to emulate symmetric neuronal dynamics by using PCMs

6-(Trifluoro­meth­yl)pyrimidine-2,4(1H,3H)-dione monohydrate

The title compound, C5H3F3N2O2·H2O, was prepared by the reaction of ethyl 4,4,4-trifluoro-3-oxobutano­ate with urea. In the crystal, the 6-(trifluoro­meth­yl)pyrimidine-2,4(1H,3H)-dione and water mol­ecules are linked by N—H⋯O and O—H⋯O hydrogen bonds. A ring dimer structure is formed by additional inter­molecular N—H⋯O hydrogen bonds

Localization of q−q-form fields on AdSp+1AdS_{p+1} branes

In this paper, we investigate localization of a free massless $q-$form bulk field on thin and thick $AdS_{p+1}$ branes with codimension one. It is found that the zero mode of the $q-$form field with $q>(p+2)/2$ can be localized on the thin negative tension brane, which is different from the flat brane case given in [JHEP 10 (2012) 060]. For the thick $AdS_{p+1}$ branes, the $q-$form field with $q>(p+2)/2$ also has a localized zero mode under some conditions. Furthermore, we find that there are massive bound KK modes of the $q-$form field, which are localized on this type $p-$branes.Comment: 13 page

CDR: Conservative Doubly Robust Learning for Debiased Recommendation

In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a major challenge in the field of recommendation systems. Recently, Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties. However, our experimental findings indicate that existing DR methods are severely impacted by the presence of so-called Poisonous Imputation, where the imputation significantly deviates from the truth and becomes counterproductive. To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance. Theoretical analyses show that CDR offers reduced variance and improved tail bounds.In addition, our experimental investigations illustrate that CDR significantly enhances performance and can indeed reduce the frequency of poisonous imputation

Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user's preference; or adaptively infer personalized confidence weights but suffer from low efficiency. To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. The personalized data confidence weights are adaptively assigned with a parameterized neural network (function) and the network can be inferred from the data. Further, to support fast and stable learning of FAWMF, a new specific batch-based learning algorithm fBGD has been developed, which trains on all feedback data but its complexity is linear to the number of observed data. Extensive experiments on real-world datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD