2,655 research outputs found
Distributed entanglement induced by dissipative bosonic media
We describe a scheme with analytic result that allows to generate
steady-state entanglement for two atoms over a dissipative bosonic medium. The
resonant coupling between the mediating bosonic mode and cavity modes produces
three collective atomic decay channels. This dissipative dynamics, together
with the unitary process induced by classical microwave fields, drives the two
atoms to the symmetric or asymmetric entangled steady state conditional upon
the choice of the phases of the microwave fields. The effects on the
steady-state entanglement of off-resonance mediating bosonic modes are
analyzed. The entanglement can be obtained with high fidelity regardless of the
initial state and there is a linear relation in the scaling of the fidelity
with the cooperativity parameter. The fidelity is insensitive to the
fluctuation of the Rabi frequencies of the classical driving fields.Comment: to appear in Europhysics Letter
Quantitative Planar Laser-Induced Fluorescence Technology
Planar laser-induced fluorescence (PLIF) is a highly sensitive and space-time-resolved laser diagnostic technique. It is widely used in the diagnosis of combustion and flow fields to obtain the thermodynamic information of active components and interested molecules in flames. Nowadays, the PLIF technology is developing in two directions: high speed and quantification. In view of the high spatial and temporal resolution characteristics of PLIF technology that other laser diagnostics do not possess, this chapter will focus on the basic principle of laser-induced fluorescence and the current research status of quantitative PLIF technology. In addition, the advantages and disadvantages of various quantitative technologies of component concentration in flames based on laser-induced fluorescence technology are analyzed. At last, the latest works on the quantification of species concentration using planar laser-induced fluorescence in combustion are introduced
Modeling the adiabatic creation of ultracold, polar molecules
In this work we model and realize stimulated Raman adiabatic passage (STIRAP)
in the diatomic molecule from weakly bound Feshbach
molecules to the rovibronic ground state via the
excited state in the electronic
potential. We demonstrate how to set up a quantitative model for polar molecule
production by taking into account the rich internal structure of the molecules
and the coupling laser phase noise. We find excellent agreement between the
model predictions and the experiment, demonstrating the applicability of the
model in the search of an ideal STIRAP transfer path. In total we produce 5000
fermionic groundstate molecules. The typical phase-space density of the sample
is 0.03 and induced dipole moments of up to 0.54 Debye could be observed.Comment: 7 pages, 5 figures Version 2: Fixed a few typos, elaborated more on
the differences between different choices of intermediate state, clarified
H\"onl-London factor, added a intuitive explanation of the benefits of
detuned STIRAP, elaborated on realized dipole moments in diatomics, compared
phase-space density reducing processes in the whole molecule creation
process, added two more reference
Energy Spectrum Theory of Incommensurate Systems
Due to the lack of the translational symmetry, calculating the energy
spectrum of an incommensurate system has always been a theoretical challenge.
Here, we propose a natural approach to generalize the energy band theory to the
incommensurate systems without reliance on the commensurate approximation, thus
providing a comprehensive energy spectrum theory of the incommensurate systems.
Except for a truncation dependent weighting factor, the formulae of this theory
are formally almost identical to that of the Bloch electrons, making it
particularly suitable for complex incommensurate structures. To illustrate the
application of this theory, we give three typical examples: one-dimensional
bichromatic and trichromatic incommensurate potential model, as well as a
moir\'{e} quasicrystal. Our theory establishes a fundamental framework for
understanding the incommensurate systems.Comment: 7 pages, 3 figure
MFM-Net: Unpaired Shape Completion Network with Multi-stage Feature Matching
Unpaired 3D object completion aims to predict a complete 3D shape from an
incomplete input without knowing the correspondence between the complete and
incomplete shapes during training. To build the correspondence between two data
modalities, previous methods usually apply adversarial training to match the
global shape features extracted by the encoder. However, this ignores the
correspondence between multi-scaled geometric information embedded in the
pyramidal hierarchy of the decoder, which makes previous methods struggle to
generate high-quality complete shapes. To address this problem, we propose a
novel unpaired shape completion network, named MFM-Net, using multi-stage
feature matching, which decomposes the learning of geometric correspondence
into multi-stages throughout the hierarchical generation process in the point
cloud decoder. Specifically, MFM-Net adopts a dual path architecture to
establish multiple feature matching channels in different layers of the
decoder, which is then combined with the adversarial learning to merge the
distribution of features from complete and incomplete modalities. In addition,
a refinement is applied to enhance the details. As a result, MFM-Net makes use
of a more comprehensive understanding to establish the geometric correspondence
between complete and incomplete shapes in a local-to-global perspective, which
enables more detailed geometric inference for generating high-quality complete
shapes. We conduct comprehensive experiments on several datasets, and the
results show that our method outperforms previous methods of unpaired point
cloud completion with a large margin
Federated Class-Incremental Learning with Prompting
As Web technology continues to develop, it has become increasingly common to
use data stored on different clients. At the same time, federated learning has
received widespread attention due to its ability to protect data privacy when
let models learn from data which is distributed across various clients.
However, most existing works assume that the client's data are fixed. In
real-world scenarios, such an assumption is most likely not true as data may be
continuously generated and new classes may also appear. To this end, we focus
on the practical and challenging federated class-incremental learning (FCIL)
problem. For FCIL, the local and global models may suffer from catastrophic
forgetting on old classes caused by the arrival of new classes and the data
distributions of clients are non-independent and identically distributed
(non-iid).
In this paper, we propose a novel method called Federated Class-Incremental
Learning with PrompTing (FCILPT). Given the privacy and limited memory, FCILPT
does not use a rehearsal-based buffer to keep exemplars of old data. We choose
to use prompts to ease the catastrophic forgetting of the old classes.
Specifically, we encode the task-relevant and task-irrelevant knowledge into
prompts, preserving the old and new knowledge of the local clients and solving
the problem of catastrophic forgetting. We first sort the task information in
the prompt pool in the local clients to align the task information on different
clients before global aggregation. It ensures that the same task's knowledge
are fully integrated, solving the problem of non-iid caused by the lack of
classes among different clients in the same incremental task. Experiments on
CIFAR-100, Mini-ImageNet, and Tiny-ImageNet demonstrate that FCILPT achieves
significant accuracy improvements over the state-of-the-art methods
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