7,103 research outputs found
Cryptanalysis of a multi-party quantum key agreement protocol with single particles
Recently, Sun et al. [Quant Inf Proc DOI: 10.1007/s11128-013-0569-x]
presented an efficient multi-party quantum key agreement (QKA) protocol by
employing single particles and unitary operations. The aim of this protocol is
to fairly and securely negotiate a secret session key among parties with a
high qubit efficiency. In addition, the authors claimed that no participant can
learn anything more than his/her prescribed output in this protocol, i.e., the
sub-secret keys of the participants can be kept secret during the protocol.
However, here we points out that the sub-secret of a participant in Sun et
al.'s protocol can be eavesdropped by the two participants next to him/her. In
addition, a certain number of dishonest participants can fully determine the
final shared key in this protocol. Finally, we discuss the factors that should
be considered when designing a really fair and secure QKA protocol.Comment: 7 page
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
Information-Preserved Blending Method for Forward-Looking Sonar Mosaicing in Non-Ideal System Configuration
Forward-Looking Sonar (FLS) has started to gain attention in the field of
near-bottom close-range underwater inspection because of its high resolution
and high framerate features. Although Automatic Target Recognition (ATR)
algorithms have been applied tentatively for object-searching tasks, human
supervision is still indispensable, especially when involving critical areas. A
clear FLS mosaic containing all suspicious information is in demand to help
experts deal with tremendous perception data. However, previous work only
considered that FLS is working in an ideal system configuration, which assumes
an appropriate sonar imaging setup and the availability of accurate positioning
data. Without those promises, the intra-frame and inter-frame artifacts will
appear and degrade the quality of the final mosaic by making the information of
interest invisible. In this paper, we propose a novel blending method for FLS
mosaicing which can preserve interested information. A Long-Short Time Sliding
Window (LST-SW) is designed to rectify the local statistics of raw sonar
images. The statistics are then utilized to construct a Global Variance Map
(GVM). The GVM helps to emphasize the useful information contained in images in
the blending phase by classifying the informative and featureless pixels,
thereby enhancing the quality of final mosaic. The method is verified using
data collected in the real environment. The results show that our method can
preserve more details in FLS mosaics for human inspection purposes in practice
Kosterlitz-Thouless phase transition and reentrance in an anisotropic 3-state Potts model on the generalized Kagome lattice
The unusual reentrant phenomenon is observed in the anisotropic 3-state Potts
model on a gen- eralized Kagome lattice. By employing the linearized tensor
renormalization group method, we find that the reentrance can appear in the
region not only under a partial ordered phase as commonly known but also a
phase without a local order parameter, which is uncovered to fall into the uni-
versality of the Kosterlitz-Thouless (KT) type. The region of the reentrance
depends strongly on the ratios of the next nearest couplings {\alpha} = J2 /|J1
| and {\beta} = J3 /|J1 |. The phase diagrams in the plane of temperature
versus {\beta} for different {\alpha} are obtained. Through massive
calculations, it is also revealed that the quasi-entanglement entropy can be
used to accurately detect the KT transition temperature
BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation
Generating human action proposals in untrimmed videos is an important yet
challenging task with wide applications. Current methods often suffer from the
noisy boundary locations and the inferior quality of confidence scores used for
proposal retrieving. In this paper, we present BSN++, a new framework which
exploits complementary boundary regressor and relation modeling for temporal
proposal generation. First, we propose a novel boundary regressor based on the
complementary characteristics of both starting and ending boundary classifiers.
Specifically, we utilize the U-shaped architecture with nested skip connections
to capture rich contexts and introduce bi-directional boundary matching
mechanism to improve boundary precision. Second, to account for the
proposal-proposal relations ignored in previous methods, we devise a proposal
relation block to which includes two self-attention modules from the aspects of
position and channel. Furthermore, we find that there inevitably exists data
imbalanced problems in the positive/negative proposals and temporal durations,
which harm the model performance on tail distributions. To relieve this issue,
we introduce the scale-balanced re-sampling strategy. Extensive experiments are
conducted on two popular benchmarks: ActivityNet-1.3 and THUMOS14, which
demonstrate that BSN++ achieves the state-of-the-art performance.Comment: Submitted to AAAI21. arXiv admin note: substantial text overlap with
arXiv:2007.0988
Numerical Research on The Nozzle Damping Effect by A Wave Attenuation Method
AbstractNozzle damping is one of the most important factors in the suppression of combustion instability in solid rocket motors. For an engineering solid rocket motor that experiences combustion instability at the end of burning, a wave attenuation method is proposed to assess the nozzle damping characteristics numerically. In this method, a periodic pressure oscillation signal which frequency equals to the first acoustic mode is superimposed on a steady flow at the head end of the chamber. When the pressure oscillation is turned off, the decay rate of the pressure can be used to determine the nozzle attenuation constant. The damping characteristics of three other nozzle geometries are numerically studied with this method under the same operating condition. The results show that the convex nozzle provides more damping than the conical nozzle which in turn provides more damping than the concave nozzle. All the three nozzles have better damping effect than that of basic nozzle geometry. At last, the phase difference in the chamber is analyzed, and the numerical pressure distribution satisfies well with theoretical distribution
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