4,723 research outputs found
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 and X(3872)
Properties of charmonium are investigated in quenched
lattice QCD. The mass of is determined to be 3.80(3) GeV, which is
close to the mass of -wave charmonium and in agreement with
quark model predictions. The transition width of
is also obtained with a value keV. Since the possible
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
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