2,783 research outputs found
Gravitational waves with dark matter minispikes: the combined effect
It was shown that the dark matter(DM) minihalo around an intermediate mass
black hole(IMBH) can be redistributed into a cusp, called the DM minispike. We
consider an intermediate-mass-ratio inspiral consisting of an IMBH harbored in
a DM minispike with nonannihilating DM particles and a small black hole(BH)
orbiting around it. We investigate gravitational waves(GWs) produced by this
system and analyze the waveforms with the comprehensive consideration of
gravitational pull, dynamical friction and accretion of the minispike and
calculate the time difference and phase difference caused by it. We find that
for a certain range of frequency, the inspiralling time of the system is
dramatically reduced for smaller central IMBH and large density of DM. For the
central IMBH with , the time of merger is ahead, which can be
distinguished by LISA, Taiji and Tianqin. We focus on the effect of accretion
and compare it with that of gravitational pull and friction. We find that the
accretion mass is a small quantity compared to the initial mass of the small BH
and the accretion effect is inconspicuous compared with friction. However, the
accumulated phase shift caused by accretion is large enough to be detected by
LISA, Taiji and Tianqin, which indicate that the accretion effect can not be
ignored in the detection of GWs.Comment: 10 pages, 14 figure
Product-based Neural Networks for User Response Prediction
Predicting user responses, such as clicks and conversions, is of great
importance and has found its usage in many Web applications including
recommender systems, web search and online advertising. The data in those
applications is mostly categorical and contains multiple fields; a typical
representation is to transform it into a high-dimensional sparse binary feature
representation via one-hot encoding. Facing with the extreme sparsity,
traditional models may limit their capacity of mining shallow patterns from the
data, i.e. low-order feature combinations. Deep models like deep neural
networks, on the other hand, cannot be directly applied for the
high-dimensional input because of the huge feature space. In this paper, we
propose a Product-based Neural Networks (PNN) with an embedding layer to learn
a distributed representation of the categorical data, a product layer to
capture interactive patterns between inter-field categories, and further fully
connected layers to explore high-order feature interactions. Our experimental
results on two large-scale real-world ad click datasets demonstrate that PNNs
consistently outperform the state-of-the-art models on various metrics.Comment: 6 pages, 5 figures, ICDM201
The effects of large extra dimensions on associated production at linear colliders
In the framework of the large extra dimensions (LED) model, the effects of
LED on the processes \rrtth and \eetth at future linear colliders are
investigated in both polarized and unpolarized collision modes. The results
show that the virtual Kaluza-Klein (KK) graviton exchange can significantly
modify the standard model expectations for these processes with certain
polarizations of initial states. The process \rrtth with
allows the effective scale to be probed up to 7.8 and 8.6 TeV in
the unpolarized and , J=2 polarized collision
modes, respectively. For the \eetth process with , the upper
limits of to be observed can be 6.7 and 7.0 TeV in the unpolarized
and , , polarized collision modes,
respectively. We find the \rrtth channel in J=2 polarized photon collision mode
provides a possibility to improve the sensitivity to the graviton tower
exchange.Comment: To be appeard in Physical Review
Gene cloning and characterization of a novel esterase from activated sludge metagenome
A metagenomic library was prepared using pCC2FOS vector containing about 3.0 Gbp of community DNA from the microbial assemblage of activated sludge. Screening of a part of the un-amplified library resulted in the finding of 1 unique lipolytic clone capable of hydrolyzing tributyrin, in which an esterase gene was identified. This esterase/lipase gene consists of 834 bp and encodes a polypeptide (designated EstAS) of 277 amino acid residuals with a molecular mass of 31 kDa. Sequence analysis indicated that it showed 33% and 31% amino acid identity to esterase/lipase from Gemmata obscuriglobus UQM 2246 (ZP_02733109) and Yarrowia lipolytica CLIB122 (XP_504639), respectively; and several conserved regions were identified, including the putative active site, HSMGG, a catalytic triad (Ser92, His125 and Asp216) and a LHYFRG conserved motif. The EstAS was overexpressed, purified and shown to hydrolyse p-nitrophenyl (NP) esters of fatty acids with short chain lengths (≤ C8). This EstAS had optimal temperature and pH at 35°C and 9.0, respectively, by hydrolysis of p-NP hexanoate. It also exhibited the same level of stability over wide temperature and pH ranges and in the presence of metal ions or detergents. The high level of stability of esterase EstAS with its unique substrate specificities make itself highly useful for biotechnological applications
- …