483 research outputs found
Problems of Double Charm Production in Annihilation at GeV
Using the nonrelativistic QCD(NRQCD) factorization formalism, we calculate
the color-singlet cross sections for exclusive production processes
~ and ~~
at the center-of-mass energy =10.6 GeV. The cross sections are
estimated to be 5.5fb, 6.7fb, 1.1fb, and 1.6fb for and , respectively. The calculated
production rate is smaller than the recent Belle data by about an order of
magnitude, which might indicate the failure of perturbative QCD calculation to
explain the double-charmonium production data. The complete
color-singlet cross section for is calculated. In addition, we also evaluate the ratio
of exclusive to inclusive production cross sections. The ratio of
production to production could be consistent
with the experimental data.Comment: 10 pages, 4 figures. A few references added, errors and typoes
correcte
Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory
Top- frequent items detection is a fundamental task in data stream mining.
Many promising solutions are proposed to improve memory efficiency while still
maintaining high accuracy for detecting the Top- items. Despite the memory
efficiency concern, the users could suffer from privacy loss if participating
in the task without proper protection, since their contributed local data
streams may continually leak sensitive individual information. However, most
existing works solely focus on addressing either the memory-efficiency problem
or the privacy concerns but seldom jointly, which cannot achieve a satisfactory
tradeoff between memory efficiency, privacy protection, and detection accuracy.
In this paper, we present a novel framework HG-LDP to achieve accurate
Top- item detection at bounded memory expense, while providing rigorous
local differential privacy (LDP) protection. Specifically, we identify two key
challenges naturally arising in the task, which reveal that directly applying
existing LDP techniques will lead to an inferior ``accuracy-privacy-memory
efficiency'' tradeoff. Therefore, we instantiate three advanced schemes under
the framework by designing novel LDP randomization methods, which address the
hurdles caused by the large size of the item domain and by the limited space of
the memory. We conduct comprehensive experiments on both synthetic and
real-world datasets to show that the proposed advanced schemes achieve a
superior ``accuracy-privacy-memory efficiency'' tradeoff, saving
memory over baseline methods when the item domain size is . Our code is
open-sourced via the link
OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization
Vertical Federated Learning (FL) is a new paradigm that enables users with
non-overlapping attributes of the same data samples to jointly train a model
without directly sharing the raw data. Nevertheless, recent works show that
it's still not sufficient to prevent privacy leakage from the training process
or the trained model. This paper focuses on studying the privacy-preserving
tree boosting algorithms under the vertical FL. The existing solutions based on
cryptography involve heavy computation and communication overhead and are
vulnerable to inference attacks. Although the solution based on Local
Differential Privacy (LDP) addresses the above problems, it leads to the low
accuracy of the trained model.
This paper explores to improve the accuracy of the widely deployed tree
boosting algorithms satisfying differential privacy under vertical FL.
Specifically, we introduce a framework called OpBoost. Three order-preserving
desensitization algorithms satisfying a variant of LDP called distance-based
LDP (dLDP) are designed to desensitize the training data. In particular, we
optimize the dLDP definition and study efficient sampling distributions to
further improve the accuracy and efficiency of the proposed algorithms. The
proposed algorithms provide a trade-off between the privacy of pairs with large
distance and the utility of desensitized values. Comprehensive evaluations show
that OpBoost has a better performance on prediction accuracy of trained models
compared with existing LDP approaches on reasonable settings. Our code is open
source
The Long-term Monitoring Results of Insight-HXMT in the First 4 Yr Galactic Plane Scanning Survey
The first X-ray source catalog of Insight-HXMT Galactic Plane (|b|<10deg)
Scanning Survey (GPSS) is presented based on the data accumulated from June
2017 to August 2021. The 4 yr limit sensitivities at main energy bands can
reach 8.2x10^(-12) erg/s/cm^2} (2-6 keV), 4.21x10^(-11) erg/s/cm^2 (7-40 keV)
and 2.78x10^(-11) erg/s/cm^2 (25-100 keV). More than 1300 sources have been
monitored at a wide band (1100\,keV), of which 223 sources have a
signal-to-noise ratio greater than 5. We combined the GPSS data of Insight-HXMT
and MAXI and found it is feasible to obtain more complete long-term light
curves from their scanning results. The flux variabilities at different energy
bands of the 223 bright sources are analyzed based on the excess variances. It
is found that the fluxes of X-ray binaries are more active than those of
supernova remnants and isolated pulsars. Different types of binaries, e.g.,
low-mass X-ray binaries (LMXBs), high-mass X-ray binaries (HMXBs), neutron star
binaries, and black hole binaries, also distinctively show different
regularities. In addition, the relations between the hardness ratio (HR) and
excess variances, and HR and source types are analyzed. It is obvious that the
HRs of HMXBs tend to be harder than those of LMXBs and HMXBs tend to be more
active than those of LMXBs.Comment: 43 pages, 26 figures, accepted for publication in ApJ
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