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 23Na40K\mathrm{^{23}Na^{40}K} molecules

In this work we model and realize stimulated Raman adiabatic passage (STIRAP) in the diatomic $\mathrm{^{23}Na^{40}K}$ molecule from weakly bound Feshbach molecules to the rovibronic ground state via the $\left|v_d=5,J=\Omega=1\right\rangle$ excited state in the $d^3\Pi$ 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