367 research outputs found
Torus knots in Lens spaces, open Gromov-Witten invariants, and topological recursion
Starting from a torus knot in the lens space , we
construct a Lagrangian sub-manifold in
under the conifold
transition. We prove a mirror theorem which relates the all genus open-closed
Gromov-Witten invariants of to the topological
recursion on the B-model spectral curve. This verifies a conjecture in
\cite{Bor-Bri} in the case of lens space.Comment: 43 pages, 6 figure
Numerical simulation of the optimal two-mode attacks for two-way continuous-variable quantum cryptography in reverse reconciliation
We analyze the security of the two-way continuous-variable quantum key
distribution protocol in reverse reconciliation against general two-mode
attacks, which represent all accessible attacks at fixed channel parameters.
Rather than against one specific attack model, the expression of secret key
rates of the two-way protocol are derived against all accessible attack models.
It is found that there is an optimal two-mode attack to minimize the
performance of the protocol in terms of both secret key rates and maximal
transmission distances. We identify the optimal two-mode attack, give the
specific attack model of the optimal two-mode attack and show the performance
of the two-way protocol against the optimal two-mode attack. Even under the
optimal two-mode attack, the performances of two-way protocol are still better
than the corresponding one-way protocol, which shows the advantage of making a
double use of the quantum channel and the potential of long-distance secure
communication using two-way protocol.Comment: 14 pages, 8 figure
Improvement of two-way continuous-variable quantum key distribution with virtual photon subtraction
We propose a method to improve the performance of two-way continuous-variable
quantum key distribution protocol by virtual photon subtraction. The Virtual
photon subtraction implemented via non-Gaussian post-selection not only
enhances the entanglement of two-mode squeezed vacuum state but also has
advantages in simplifying physical operation and promoting efficiency. In
two-way protocol, virtual photon subtraction could be applied on two sources
independently. Numerical simulations show that the optimal performance of
renovated two-way protocol is obtained with photon subtraction only used by
Alice. The transmission distance and tolerable excess noise are improved by
using the virtual photon subtraction with appropriate parameters. Moreover, the
tolerable excess noise maintains a high value with the increase of distance so
that the robustness of two-way continuous-variable quantum key distribution
system is significantly improved, especially at long transmission distance.Comment: 15 pages, 6 figure
Deep Landscape Forecasting for Real-time Bidding Advertising
The emergence of real-time auction in online advertising has drawn huge
attention of modeling the market competition, i.e., bid landscape forecasting.
The problem is formulated as to forecast the probability distribution of market
price for each ad auction. With the consideration of the censorship issue which
is caused by the second-price auction mechanism, many researchers have devoted
their efforts on bid landscape forecasting by incorporating survival analysis
from medical research field. However, most existing solutions mainly focus on
either counting-based statistics of the segmented sample clusters, or learning
a parameterized model based on some heuristic assumptions of distribution
forms. Moreover, they neither consider the sequential patterns of the feature
over the price space. In order to capture more sophisticated yet flexible
patterns at fine-grained level of the data, we propose a Deep Landscape
Forecasting (DLF) model which combines deep learning for probability
distribution forecasting and survival analysis for censorship handling.
Specifically, we utilize a recurrent neural network to flexibly model the
conditional winning probability w.r.t. each bid price. Then we conduct the bid
landscape forecasting through probability chain rule with strict mathematical
derivations. And, in an end-to-end manner, we optimize the model by minimizing
two negative likelihood losses with comprehensive motivations. Without any
specific assumption for the distribution form of bid landscape, our model shows
great advantages over previous works on fitting various sophisticated market
price distributions. In the experiments over two large-scale real-world
datasets, our model significantly outperforms the state-of-the-art solutions
under various metrics.Comment: KDD 2019. The reproducible code and dataset link is
https://github.com/rk2900/DL
Deep Recurrent Survival Analysis
Survival analysis is a hotspot in statistical research for modeling
time-to-event information with data censorship handling, which has been widely
used in many applications such as clinical research, information system and
other fields with survivorship bias. Many works have been proposed for survival
analysis ranging from traditional statistic methods to machine learning models.
However, the existing methodologies either utilize counting-based statistics on
the segmented data, or have a pre-assumption on the event probability
distribution w.r.t. time. Moreover, few works consider sequential patterns
within the feature space. In this paper, we propose a Deep Recurrent Survival
Analysis model which combines deep learning for conditional probability
prediction at fine-grained level of the data, and survival analysis for
tackling the censorship. By capturing the time dependency through modeling the
conditional probability of the event for each sample, our method predicts the
likelihood of the true event occurrence and estimates the survival rate over
time, i.e., the probability of the non-occurrence of the event, for the
censored data. Meanwhile, without assuming any specific form of the event
probability distribution, our model shows great advantages over the previous
works on fitting various sophisticated data distributions. In the experiments
on the three real-world tasks from different fields, our model significantly
outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code:
https://github.com/rk2900/drs
CdS Quantum Dots as Fluorescent Probes for Detection of Naphthalene
A novel sensing system has been designed for
naphthalene detection based on the quenched fluorescence signal of
CdS quantum dots. The fluorescence intensity of the system reduced
significantly after adding CdS quantum dots to the water pollution
model because of the fluorescent static quenching f mechanism.
Herein, we have demonstrated the facile methodology can offer a
convenient and low analysis cost with the recovery rate as
97.43%-103.2%, which has potential application prospect
Noiseless Linear Amplifiers in Entanglement-Based Continuous-Variable Quantum Key Distribution
We propose a method to improve the performance of two entanglement-based
continuous-variable quantum key distribution protocols using noiseless linear
amplifiers. The two entanglement-based schemes consist of an entanglement
distribution protocol with an untrusted source and an entanglement swapping
protocol with an untrusted relay. Simulation results show that the noiseless
linear amplifiers can improve the performance of these two protocols, in terms
of maximal transmission distances, when we consider small amounts of
entanglement, as typical in realistic setups.Comment: Special issue on Quantum Cryptograph
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