Measuring the spectral running from cosmic microwave background and primordial black holes

We constrain the spectral running by combining cosmic microwave background (CMB) data, baryon acoustic oscillation (BAO) data and the constraint from primordial black holes (PBHs). We find that the constraint from PBHs has a significant impact on the running of running of scalar spectral index, and a power-law scalar power spectrum without running is consistent with observational data once the constraint from PBHs is taken into account. In addition, from the constraints on the slow-roll parameters, the derived tensor spectral index in the single-field slow-roll inflation model is quite small, namely $|n_t|\lesssim 9.3\times 10^{-3}$ which will be very difficult to be measured by CMB data only in the future, and the absolute value of derived running of tensor spectral index is not larger than $2.1\times 10^{-4}$ at 95% confidence level.Comment: 5 pages, 3 figure

Production of heavy isotopes in transfer reactions by collisions of 238^{238}U+238^{238}U

The dynamics of transfer reactions in collisions of two very heavy nuclei $^{238}$U+$^{238}$U is studied within the dinuclear system (DNS) model. Collisions of two actinide nuclei form a super heavy composite system during a very short time, in which a large number of charge and mass transfers may take place. Such reactions have been investigated experimentally as an alternative way for the production of heavy and superheavy nuclei. The role of collision orientation in the production cross sections of heavy nuclides is analyzed systematically. Calculations show that the cross sections decrease drastically with increasing the charged numbers of heavy fragments. The transfer mechanism is favorable to synthesize heavy neutron-rich isotopes, such as nuclei around the subclosure at N=162 from No (Z=102) to Db (Z=105).Comment: 4 pages, 4 figure

Asymmetric Deep Supervised Hashing

Hashing has been widely used for large-scale approximate nearest neighbor search because of its storage and search efficiency. Recent work has found that deep supervised hashing can significantly outperform non-deep supervised hashing in many applications. However, most existing deep supervised hashing methods adopt a symmetric strategy to learn one deep hash function for both query points and database (retrieval) points. The training of these symmetric deep supervised hashing methods is typically time-consuming, which makes them hard to effectively utilize the supervised information for cases with large-scale database. In this paper, we propose a novel deep supervised hashing method, called asymmetric deep supervised hashing (ADSH), for large-scale nearest neighbor search. ADSH treats the query points and database points in an asymmetric way. More specifically, ADSH learns a deep hash function only for query points, while the hash codes for database points are directly learned. The training of ADSH is much more efficient than that of traditional symmetric deep supervised hashing methods. Experiments show that ADSH can achieve state-of-the-art performance in real applications

Spin Correlations in top quark pair production near threshold at the e−e+ e^- e^+ Linear Collider

We investigate the spin correlations in top quark pair production near threshold at the $e^- e^+$ linear collider. Comparing with the results above the threshold region, we find that near the threshold region the off-diagonal basis, the optimized decomposition of the top quark spins above the threshold region, does not exist, and the beamline basis is the optimal basis, in which there are the dominant spin components: the up-down (UD) component for $e_L^- e^+$ scattering and the down-up (DU) component for $e_R^- e^+$ scattering can make up more than 50% of the total cross section, respectively.Comment: 12 pages, 3 figures, minor modification