The MolSSI QCArchive Project: An Open-Source Platform to Compute, Organize, and Share Quantum Chemistry Data

<div>The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry results.</div><div>The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of quantum chemistry computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface.</div><div>Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org.</div><div>The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of quantum chemistry computations for individuals or groups of researchers at any scale.</div><div>The QCArchive Infrastructure is open-source (BSD-3C), code repositories can be found at https://github.com/MolSSI, and releases can be downloaded via PyPI and Conda.</div>

The MolSSI QCArchive Project: An Open-Source Platform to Compute, Organize, and Share Quantum Chemistry Data

<div>The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry results.</div><div>The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of quantum chemistry computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface.</div><div>Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org.</div><div>The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of quantum chemistry computations for individuals or groups of researchers at any scale.</div><div>The QCArchive Infrastructure is open-source (BSD-3C), code repositories can be found at https://github.com/MolSSI, and releases can be downloaded via PyPI and Conda.</div>

A High-Efficiency FPGA-Based Accelerator for Binarized Neural Network

Although the convolutional neural network (CNN) has exhibited outstanding performance in various applications, the deployment of CNN on embedded and mobile devices is limited by the massive computations and memory footprint. To address these challenges, Courbariaux and co-workers put forward binarized neural network (BNN) which quantizes both the weights and activations to [Formula: see text]1. From the perspective of hardware, BNN can greatly simplify the computation and reduce the storage. In this work, we first present the algorithm optimizations to further binarize the first layer and the padding bits of BNN; then we propose a fully binarized CNN accelerator. With the Shuffle–Compute structure and the memory-aware computation schedule scheme, the proposed design can boost the performance for feature maps of different sizes and make full use of the memory bandwidth. To evaluate our design, we implement the accelerator on the Zynq ZC702 board, and the experiments on the SVHN and CIFAR-10 datasets show the state-of-the-art performance efficiency and resource efficiency.

Computing the Nonabelian Tensor Square of Metacyclic p–Groups of Nilpotency Class Two

In this paper, we develop appropriate programme using Groups, Algorithms and Programming (GAP) software enables performing different computations on various characteristics of all finite nonabelian metacyclic p–groups, p is prime, of nilpotency class 2. Such programme enables to compute structure of the group, order of the group, structure of the center, the number of conjugacy classes, structure of commutator subgroup, abelianization, Whitehead’s universal quadratic functor and other characteristics. In addition, structures of some other groups such as the nonabelian tensor square and various homological functors including Schur multiplier and exterior square can be computed using this programme. Furthermore, by computing the epicenter order or the exterior center order the capability can be determined. In our current article, we only compute the nonabelian tensor square of certain order groups, as an example, and give GAP codes for computing other characteristics and some subgroups.

Motion and structure for vision-based navigation

This paper aims to develop a complete algorithm to determine the robot motion and the scene structure using a monocular vision system. It is based on straight lines and significant points extracted on them. In this way, an agreement between the problems to extract or to match points and the limitations of infinite lines to compute structure and motion is established. Many geometrical relations of the lines in the scene are exploited to clear up the coupling between the rotation and the translation of the camera. Several real images have been used to validate the proposed method. The algorithm has been considered for navigation of a mobile robot moving in man-made environments.

SU(3) STRUCTURE DEPENDENCE IN π→eνγ AND K→eνγ DECAYS

We compute structure-dependent form factors for both π→eνγ and K→eνγ decays using the SU(3) linear σ-model. Meson loop contributions reduce the axial-vector form factor FA(0) by about 40% in both cases. The predicted ratio γ=FA(0)/FV(0) for π→eνγ and sum |FA(0)+FV(0)| for π→eνγ and K→eνγ decays are all in excellent agreement with data.