Quantum-beat Auger spectroscopy

The concept of nonlinear quantum-beat pump-probe Auger spectroscopy is introduced by discussing a relatively simple four-level model system. We consider a coherent wave packet involving two low-lying states that was prepared by an appropriate pump pulse. This wave packet is subsequently probed by a weak, time-delayed probe pulse with nearly resonant coupling to a core-excited state of the atomic or molecular system. The resonant Auger spectra are then studied as a function of the duration of the probe pulse and the time delay. With a bandwidth of the probe pulse approaching the energy spread of the wave packet, the Auger yields and spectra show quantum beats as a function of pump-probe delay. An analytic theory for the quantum-beat Auger spectroscopy will be presented, which allows for the reconstruction of the wave packet by analyzing the delaydependent Auger spectra. The possibility of extending this method to a more complex manifold of electronic and vibrational energy levels is also discussed.Comment: 13 papees,6 figure

Holographic R\'enyi entropy in AdS3_3/LCFT2_2 correspondence

The recent study in AdS$_3$/CFT$_2$ correspondence shows that the tree level contribution and 1-loop correction of holographic R\'enyi entanglement entropy (HRE) exactly match the direct CFT computation in the large central charge limit. This allows the R\'enyi entanglement entropy to be a new window to study the AdS/CFT correspondence. In this paper we generalize the study of R\'enyi entanglement entropy in pure AdS$_3$ gravity to the massive gravity theories at the critical points. For the cosmological topological massive gravity (CTMG), the dual conformal field theory (CFT) could be a chiral conformal field theory or a logarithmic conformal field theory (LCFT), depending on the asymptotic boundary conditions imposed. In both cases, by studying the short interval expansion of the R\'enyi entanglement entropy of two disjoint intervals with small cross ratio $x$, we find that the classical and 1-loop HRE are in exact match with the CFT results, up to order $x^6$. To this order, the difference between the massless graviton and logarithmic mode can be seen clearly. Moreover, for the cosmological new massive gravity (CNMG) at critical point, which could be dual to a logarithmic CFT as well, we find the similar agreement in the CNMG/LCFT correspondence. Furthermore we read the 2-loop correction of graviton and logarithmic mode to HRE from CFT computation. It has distinct feature from the one in pure AdS$_3$ gravity.Comment: 28 pages. Typos corrected, published versio

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal

The Oblique Corrections from Heavy Scalars in Irreducible Representations

The contributions to $S$, $T$, and $U$ from heavy scalars in any irreducible representation of the electroweak gauge group $SU(2)_L\times U(1)_Y$ are obtained. We find that in the case of a heavy scalar doublet there is a slight difference between the $S$ parameter we have obtained and that in previous works.Comment: 6 pages, 2 axodraw figures; minor changes, references update

Dynamic Path-Controllable Deep Unfolding Network for Compressive Sensing

Deep unfolding network (DUN) that unfolds the optimization algorithm into a deep neural network has achieved great success in compressive sensing (CS) due to its good interpretability and high performance. Each stage in DUN corresponds to one iteration in optimization. At the test time, all the sampling images generally need to be processed by all stages, which comes at a price of computation burden and is also unnecessary for the images whose contents are easier to restore. In this paper, we focus on CS reconstruction and propose a novel Dynamic Path-Controllable Deep Unfolding Network (DPC-DUN). DPC-DUN with our designed path-controllable selector can dynamically select a rapid and appropriate route for each image and is slimmable by regulating different performance-complexity tradeoffs. Extensive experiments show that our DPC-DUN is highly flexible and can provide excellent performance and dynamic adjustment to get a suitable tradeoff, thus addressing the main requirements to become appealing in practice. Codes are available at https://github.com/songjiechong/DPC-DUN.Comment: TIP, 202