Ionization Yield from Nuclear Recoils in Liquid-Xenon Dark Matter Detection

The ionization yield in the two-phase liquid xenon dark-matter detector has been studied in keV nuclear-recoil energy region. The newly-obtained nuclear quenching as well as the recently-measured average energy required to produce an electron-ion pair are used to calculate the total electric charges produced. To estimate the fraction of the electron charges collected, the Thomas-Imel model is generalized to describing the field dependence for nuclear recoils in liquid xenon. With free parameters fitted to experiment measured 56.5 keV nuclear recoils, the energy dependence of ionization yield for nuclear recoils is predicted, which increases with the decreasing of the recoiling energy and reaches the maximum value at 2~3 keV. This prediction agrees well with existing data and may help to lower the energy detection threshold for nuclear recoils to ~1 keV.Comment: 13 pages, 5 figure

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

This work introduces a number of algebraic topology approaches, such as multicomponent persistent homology, multi-level persistent homology and electrostatic persistence for the representation, characterization, and description of small molecules and biomolecular complexes. Multicomponent persistent homology retains critical chemical and biological information during the topological simplification of biomolecular geometric complexity. Multi-level persistent homology enables a tailored topological description of inter- and/or intra-molecular interactions of interest. Electrostatic persistence incorporates partial charge information into topological invariants. These topological methods are paired with Wasserstein distance to characterize similarities between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for chemical and biological problems. Extensive numerical experiments involving more than 4,000 protein-ligand complexes from the PDBBind database and near 100,000 ligands and decoys in the DUD database are performed to test respectively the scoring power and the virtual screening power of the proposed topological approaches. It is demonstrated that the present approaches outperform the modern machine learning based methods in protein-ligand binding affinity predictions and ligand-decoy discrimination

Order flow dynamics around extreme price changes on an emerging stock market

We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the bid-ask spread, and the volume imbalance increase before the extreme events and decay slowly as a power law, which forms a well-established peak. The volume of buy market orders increases faster and the corresponding peak appears earlier than for sell market orders around positive events, while the volume peak of sell market orders leads buy market orders in the magnitude and time around negative events. When orders are divided into four groups according to their aggressiveness, we find that the behaviors of order volume and order number are similar, except for buy limit orders and canceled orders that the peak of order number postpones two minutes later after the peak of order volume, implying that investors placing large orders are more informed and play a central role in large price fluctuations. We also study the relative rates of different types of orders and find differences in the dynamics of relative rates between buy orders and sell orders and between individual investors and institutional investors. There is evidence showing that institutions behave very differently from individuals and that they have more aggressive strategies. Combing these findings, we conclude that institutional investors are more informed and play a more influential role in driving large price fluctuations.Comment: 22 page

Preferred numbers and the distribution of trade sizes and trading volumes in the Chinese stock market

The distribution of trade sizes and trading volumes are investigated based on the limit order book data of 22 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. We observe that the size distribution of trades for individual stocks exhibits jumps, which is caused by the number preference of traders when placing orders. We analyze the applicability of the "$q$-Gamma" function for fitting the distribution by the Cram\'{e}r-von Mises criterion. The empirical PDFs of trading volumes at different timescales $\Delta{t}$ ranging from 1 min to 240 min can be well modeled. The applicability of the $q$-Gamma functions for multiple trades is restricted to the transaction numbers $\Delta{n}\leqslant8$. We find that all the PDFs have power-law tails for large volumes. Using careful estimation of the average tail exponents $\alpha$ of the distribution of trade sizes and trading volumes, we get $\alpha>2$, well outside the L{\'e}vy regime.Comment: 7 pages, 5 figures and 4 table