Beyond Fermi pseudopotential: a modified GP equation

We present an effective potential and the corresponding modified Gross-Pitaevskii equation that account for the energy dependence of the two-body scattering amplitude through an effective-range expansion. For the ground state energy of a trapped condensate, the theory leads to what we call a shape-dependent confinement correction that improves agreements with diffusion Monte Carlo calculations. The theory illustrates, for relatively strong confinement and/or high density, how the shape dependence on atom-atom interaction can come into play in a many-atom quantum system.Comment: 8 pages, 5 figure

3-Methyl­quinoxaline-2-carb­oxy­lic acid 4-oxide monohydrate

In the crystal structure of the title compound, C10H8N2O3·H2O, mol­ecules are linked via inter­molecular O—H⋯O and O—H⋯N hydrogen bonds into a two-dimensional network

“Summer storage and winter furnace” – data center waste heat recovery and utilization system for seasonal heat storage

As Chinese society moves towards a more intelligent and connected society, there is an increasing demand for data centers. Under normal circumstances, datacenter IT equipment runs 24 hours a day, which consumes a lot of power and emits a lot of heat. Therefore, when the data center consumes power, it also needs power refrigeration and the ambient temperature to ensure the normal operation of the data center, resulting in huge resource consumption. Combined with the background of carbon neutrality and the current situation of large heating demand and long heating period in Northeast China, this project proposes to use data center waste heat resources as the heat source of building heating, reduce the power consumption required for data center cooling, improve the utilization efficiency and economic efficiency of data center waste heat, and then reduce the carbon emission of data center and building heating

Experimental evaluation of ensemble classifiers for imbalance in Big Data

Datasets are growing in size and complexity at a pace never seen before, forming ever larger datasets known as Big Data. A common problem for classification, especially in Big Data, is that the numerous examples of the different classes might not be balanced. Some decades ago, imbalanced classification was therefore introduced, to correct the tendency of classifiers that show bias in favor of the majority class and that ignore the minority one. To date, although the number of imbalanced classification methods have increased, they continue to focus on normal-sized datasets and not on the new reality of Big Data. In this paper, in-depth experimentation with ensemble classifiers is conducted in the context of imbalanced Big Data classification, using two popular ensemble families (Bagging and Boosting) and different resampling methods. All the experimentation was launched in Spark clusters, comparing ensemble performance and execution times with statistical test results, including the newest ones based on the Bayesian approach. One very interesting conclusion from the study was that simpler methods applied to unbalanced datasets in the context of Big Data provided better results than complex methods. The additional complexity of some of the sophisticated methods, which appear necessary to process and to reduce imbalance in normal-sized datasets were not effective for imbalanced Big Data.“la Caixa” Foundation, Spain, under agreement LCF/PR/PR18/51130007. This work was supported by the Junta de Castilla y León, Spain under project BU055P20 (JCyL/FEDER, UE) co-financed through European Union FEDER funds, and by the Consejería de Educación of the Junta de Castilla y León and the European Social Fund, Spain through a pre-doctoral grant (EDU/1100/2017)

Power Diagrams and Sparse Paged Grids for High Resolution Adaptive Liquids

© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Aanjaneya, M., Gao, M., Liu, H., Batty, C., & Sifakis, E. (2017). Power Diagrams and Sparse Paged Grids for High Resolution Adaptive Liquids. ACM Trans. Graph., 36(4), 140:1–140:12. https://doi.org/10.1145/3072959.3073625We present an efficient and scalable octree-inspired fluid simulation framework with the flexibility to leverage adaptivity in any part of the computational domain, even when resolution transitions reach the free surface. Our methodology ensures symmetry, definiteness and second order accuracy of the discrete Poisson operator, and eliminates numerical and visual artifacts of prior octree schemes. This is achieved by adapting the operators acting on the octree's simulation variables to reflect the structure and connectivity of a power diagram, which recovers primal-dual mesh orthogonality and eliminates problematic T-junction configurations. We show how such operators can be efficiently implemented using a pyramid of sparsely populated uniform grids, enhancing the regularity of operations and facilitating parallelization. A novel scheme is proposed for encoding the topology of the power diagram in the neighborhood of each octree cell, allowing us to locally reconstruct it on the fly via a lookup table, rather than resorting to costly explicit meshing. The pressure Poisson equation is solved via a highly efficient, matrix-free multigrid preconditioner for Conjugate Gradient, adapted to the power diagram discretization. We use another sparsely populated uniform grid for high resolution interface tracking with a narrow band level set representation. Using the recently introduced SPGrid data structure, sparse uniform grids in both the power diagram discretization and our narrow band level set can be compactly stored and efficiently updated via streaming operations. Additionally, we present enhancements to adaptive level set advection, velocity extrapolation, and the fast marching method for redistancing. Our overall framework gracefully accommodates the task of dynamically adapting the octree topology during simulation. We demonstrate end-to-end simulations of complex adaptive flows in irregularly shaped domains, with tens of millions of degrees of freedom.National Science FoundationNational Sciences and Engineering Research Council of Canad

Parallel Efficient Mesh Deformation Method Based On Support Vector Regression

Mesh deformation method is widely used in unsteady numerical simulations involving moving boundaries. This kind of method redistributes the position of grid points in accordance with the movement of the computational domain without changing their connectivity relations. In this paper, we present a parallel mesh deformation method based on the support vector machine regression (SVR). In each time step, the proposed method first trains three SVRs by the coordinates of the boundary points and their known displacements in each direction, and then predicts the displacements of the internal points using the SVRs. After deforming the mesh, the dual-time step flow solver is used to solve the governing equations. To ensure the consistency of the method running in parallel, the training part of the method is executed with all global boundary points in each decomposed domain. Therefore, each CPU needs to maintain a copy of the entire boundary points via a point-to-point communication. The internal evaluation of the method is predicted separately in each decomposed domain without any data dependency. An oscillatory and transient pitching airfoil case is simulated to demonstrate the applicability of the proposed mesh deformation method, and its parallel efficiency is over 60% with 64 cores

Energetic salt of guanidinium 3,7-Bis(dinitromethylene)-octahydro-[1,2,4]-triazino-[6,5-e][1,2,4]triazine and its crystal structure

Energetic salt of guanidinium 3,7-Bis(dinitromethylene)-octahydro-[1,2,4]-triazino-[6,5-e][1,2,4]triazine(1) was prepared through the reaction of 3,7-Bis(dinitromethylene)-octahydro-[1,2,4]-triazino-[6,5-e][1,2,4]triazine with guanidinium carbonate. The crystal structure of 1 was characterized. It is the first bicyclic energetic salt based on 3,7-Bis(dinitromethylene)-octahydro-[1,2,4]-triazino-[6,5-e][1,2,4]triazine