Simple approach to estimating the van der Waals interaction between carbon nanotubes

The van der Waals (vdW) interactions between carbon nanotubes (CNTs) were studied based on the continuum Lennard-Jones model. It was found that all the vdW potentials between two arbitrary CNTs fall on the same curve when plotted in terms of certain reduced parameters, the well depth, and the equilibrium vdW gap. Based on this observation, an approximate approach is developed to obtain the vdW potential between two CNTs without time-consuming computations. The vdW potential estimated by this approach is close to that obtained from complex integrations. Therefore, the developed approach can greatly simplify the calculation of vdW interactions between CNTs

Perfect crossed Andreev reflection in the proximitized graphene/superconductor/proximitized graphene junctions

We study the crossed Andreev reflection and the nonlocal transport in the proximitized graphene/supercondcutor/proximitized graphene junctions with the pseudospin staggered potential and the intrinsic spin-orbit coupling. The crossed Andreev reflection with the local Andreev reflection and the elastic cotunneling being completely eliminated can be realized for the electrons with the specific spin-valley index when the intrinsic spin-orbit couplings in the left graphene and the right graphene possess the opposite sign. The perfect crossed Andreev reflection with its probability equal to $100\%$ can be obtained in the space consisting of the incident angle and the energy of the electrons. The crossed conductance and its oscillation with the superconductor length are also investigated. The energy ranges for the crossed Andreev reflection without the local Andreev reflection and the elastic cotunneling are clarified for the different magnitudes of the pseudospin potential and the spin-orbit coupling. The spin-valley index of the electrons responsible for the perfect crossed Andreev reflection can be switched by changing the sign of the intrinsic spin-orbit coupling or exchanging the biases applied on the left graphene and the right graphene. Our results are helpful for designing the flexible and high-efficiency Cooper pair splitter based on the spin-valley degree of freedom.Comment: 9 pages,4 figure

Poly[[tetra­aqua­di-μ4-oxalato-μ2-oxalato-dineo­dymium(III)] dihydrate]

The title compound, {[Nd2(C2O4)3(H2O)4]·2H2O}n, was synthesized hydro­thermally in the presence of bis­(carb­oxy­ethyl­germanium) sesquioxide. It is isostructural with the corresponding Pr compound [Yang et al. (2009). Acta Cryst. E65, m1152–m1153]. The Nd3+ cation is nine-coordinated and its coordination polyhedron can be described as a distorted tricapped trigonal prism. Two Nd3+ ions are connected by two O atoms from two oxalate ions to give a dinuclear Nd2 unit. The unit is further linked to four others via four oxalate ions yielding a layerparallel to (0-11). The linkages between the layers by neighbouring oxalate anions lead to a three-dimensional framework with channels along the c axis. The coordinating and free water mol­ecules are located in the channels and make contact with each other and the host framework by weak O—H⋯O hydrogen bonds

SparDL: Distributed Deep Learning Training with Efficient Sparse Communication

Top-k sparsification has recently been widely used to reduce the communication volume in distributed deep learning. However, due to the Sparse Gradient Accumulation (SGA) dilemma, the performance of top-k sparsification still has limitations. Recently, a few methods have been put forward to handle the SGA dilemma. Regrettably, even the state-of-the-art method suffers from several drawbacks, e.g., it relies on an inefficient communication algorithm and requires extra transmission steps. Motivated by the limitations of existing methods, we propose a novel efficient sparse communication framework, called SparDL. Specifically, SparDL uses the Spar-Reduce-Scatter algorithm, which is based on an efficient Reduce-Scatter model, to handle the SGA dilemma without additional communication operations. Besides, to further reduce the latency cost and improve the efficiency of SparDL, we propose the Spar-All-Gather algorithm. Moreover, we propose the global residual collection algorithm to ensure fast convergence of model training. Finally, extensive experiments are conducted to validate the superiority of SparDL

Efficient Temporal Butterfly Counting and Enumeration on Temporal Bipartite Graphs

Bipartite graphs model relationships between two different sets of entities, like actor-movie, user-item, and author-paper. The butterfly, a 4-vertices 4-edges $2\times 2$ bi-clique, is the simplest cohesive motif in a bipartite graph and is the fundamental component of higher-order substructures. Counting and enumerating the butterflies offer significant benefits across various applications, including fraud detection, graph embedding, and community search. While the corresponding motif, the triangle, in the unipartite graphs has been widely studied in both static and temporal settings, the extension of butterfly to temporal bipartite graphs remains unexplored. In this paper, we investigate the temporal butterfly counting and enumeration problem: count and enumerate the butterflies whose edges establish following a certain order within a given duration. Towards efficient computation, we devise a non-trivial baseline rooted in the state-of-the-art butterfly counting algorithm on static graphs, further, explore the intrinsic property of the temporal butterfly, and develop a new optimization framework with a compact data structure and effective priority strategy. The time complexity is proved to be significantly reduced without compromising on space efficiency. In addition, we generalize our algorithms to practical streaming settings and multi-core computing architectures. Our extensive experiments on 11 large-scale real-world datasets demonstrate the efficiency and scalability of our solutions