7,530 research outputs found
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
"-Gamma" function for fitting the distribution by the Cram\'{e}r-von Mises
criterion. The empirical PDFs of trading volumes at different timescales
ranging from 1 min to 240 min can be well modeled. The
applicability of the -Gamma functions for multiple trades is restricted to
the transaction numbers . We find that all the PDFs have
power-law tails for large volumes. Using careful estimation of the average tail
exponents of the distribution of trade sizes and trading volumes, we
get , well outside the L{\'e}vy regime.Comment: 7 pages, 5 figures and 4 table
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