Determination of Dark Matter Halo Mass from Dynamics of Satellite Galaxies

We show that the mass of a dark matter halo can be inferred from the dynamical status of its satellite galaxies. Using 9 dark-matter simulations of halos like the Milky Way (MW), we find that the present-day substructures in each halo follow a characteristic distribution in the phase space of orbital binding energy and angular momentum, and that this distribution is similar from halo to halo but has an intrinsic dependence on the halo formation history. We construct this distribution directly from the simulations for a specific halo and extend the result to halos of similar formation history but different masses by scaling. The mass of an observed halo can then be estimated by maximizing the likelihood in comparing the measured kinematic parameters of its satellite galaxies with these distributions. We test the validity and accuracy of this method with mock samples taken from the simulations. Using the positions, radial velocities, and proper motions of 9 tracers and assuming observational uncertainties comparable to those of MW satellite galaxies, we find that the halo mass can be recovered to within $\sim$40%. The accuracy can be improved to within $\sim$25% if 30 tracers are used. However, the dependence of the phase-space distribution on the halo formation history sets a minimum uncertainty of $\sim$20% that cannot be reduced by using more tracers. We believe that this minimum uncertainty also applies to any mass determination for a halo when the phase space information of other kinematic tracers is used.Comment: Accepted for publication in ApJ, 18 pages, 13 figure

Deadline Constrained Cloud Computing Resources Scheduling through an Ant Colony System Approach

Cloud computing resources scheduling is essential for executing workflows in the cloud platform because it relates to both execution time and execution cost. In this paper, we adopt a model that optimizes the execution cost while meeting deadline constraints. In solving this problem, we propose an Improved Ant Colony System (IACS) approach featuring two novel strategies. Firstly, a dynamic heuristic strategy is used to calculate a heuristic value during an evolutionary process by taking the workflow topological structure into consideration. Secondly, a double search strategy is used to initialize the pheromone and calculate the heuristic value according to the execution time at the beginning and to initialize the pheromone and calculate heuristic value according to the execution cost after a feasible solution is found. Therefore, the proposed IACS is adaptive to the search environment and to different objectives. We have conducted extensive experiments based on workflows with different scales and different cloud resources. We compare the result with a particle swarm optimization (PSO) approach and a dynamic objective genetic algorithm (DOGA) approach. Experimental results show that IACS is able to find better solutions with a lower cost than both PSO and DOGA do on various scheduling scales and deadline conditions

Interpretable rumor detection in microblogs by attending to user interactions

We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level

A tetra­gonal polymorph of bis­[hydro­tris­(pyrazol-1-yl)borato]iron(II)

The title compound, [Fe(C9H10BN6)2], is a polymorph of a compound reported previously [Oliver et al. (1980 ▶). Inorg. Chem. 19, 165–168]. In the previous report, the compound crystallized in the monoclinic space group P21/c (Z = 4), whereas the crystal symmetry of the compound reported here is tetra­gonal (P42/ncm, Z = 4). The mol­ecular structure is comprised of two hydro­tris­(1-pyrazol­yl)borate ligands (Tp−) and a central FeII ion, which is coordinated by six pyrazole N atoms from two two Tp− ligands, yielding a distorted bipyramidal FeN6 geometry. The complete molecule exhibits symmetry 2/m