Optimal entanglement generation in cavity QED with dissipation

We investigate a two-level atom coupled to a cavity with a strong classical driving field in a dissipative environment and find an analytical expression of the time evolution density matrix for the system. The analytical density operator is then used to study the entanglement between the atom and cavity by considering the competing process between the atom-field interactions and the field-environment interactions. It is shown that there is an optimal interaction time for generating atom-cavity entanglement.Comment: 9 pages, 7 figure

A new potential radiosensitizer: ammonium persulfate modified WCNTs

Radiotherapy plays a very important role in cancer treatment. Radiosensitizers have been widely used to enhance the radiosensitivity of cancer cells at given radiations. Here we fabricate multi-walled carbon nanotubes with ammonium persulfate, and get very short samples with 30-50 nanometer length. Cell viability assay show that f-WCNTs induce cell death significantly. We hypothesize that free radicals originated from hydroxyl and carbonyl groups on the surface of f-WCNTs lead cell damage

Lattice study on ηc2\eta_{c2} and X(3872)

Properties of $2^{-+}$ charmonium $\eta_{c2}$ are investigated in quenched lattice QCD. The mass of $\eta_{c2}$ is determined to be 3.80(3) GeV, which is close to the mass of $D$-wave charmonium $\psi(3770)$ and in agreement with quark model predictions. The transition width of $\eta_{c2}\to \gamma J/\psi$ is also obtained with a value $\Gamma=3.8(9)$ keV. Since the possible $2^{-+}$ assignment to X(3872) has not been ruled out by experiments, our results help to clarify the nature of X(3872).Comment: 15 pages, 8 figures. typos, grammatical errors and some references corrected, redundant discussions deleted, conclusion does not change. published versio

A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversations

Emotion recognition in conversations (ERC), the task of recognizing the emotion of each utterance in a conversation, is crucial for building empathetic machines. Existing studies focus mainly on capturing context- and speaker-sensitive dependencies on the textual modality but ignore the significance of multimodal information. Different from emotion recognition in textual conversations, capturing intra- and inter-modal interactions between utterances, learning weights between different modalities, and enhancing modal representations play important roles in multimodal ERC. In this paper, we propose a transformer-based model with self-distillation (SDT) for the task. The transformer-based model captures intra- and inter-modal interactions by utilizing intra- and inter-modal transformers, and learns weights between modalities dynamically by designing a hierarchical gated fusion strategy. Furthermore, to learn more expressive modal representations, we treat soft labels of the proposed model as extra training supervision. Specifically, we introduce self-distillation to transfer knowledge of hard and soft labels from the proposed model to each modality. Experiments on IEMOCAP and MELD datasets demonstrate that SDT outperforms previous state-of-the-art baselines.Comment: 13 pages, 10 figures. Accepted by IEEE Transactions on Multimedia (TMM