A Study on the Subtitle Translation of A Long Cherished Dream From the Perspective of Multimodal Discourse Analysis

The documentary is an important medium for cross-cultural communication, and its subtitle translation has gradually become increasingly important. The emergence of multimodal discourse analysis theory extends the study of single discourse to multimodal discourse, and at the same time broadens the depth and breadth of subtitle translation research. This paper selects the subtitle translation of the documentary A Long Cherished Dream as a case study for analysis. Based on the multimodal discourse analysis framework proposed by Zhang Delu, this paper explores how translators translate the subtitles according to the coordination of various modalities to convey the overall meaning of the documentary at the cultural level, the contextual level, the content level and the expression level respectively. The main research results are as follows. Firstly, translators should pay attention to cultural phenomena at the cultural level and the literal translation method is usually adopted. Secondly, the translator should consider the contextual background to adopt an addition or omission translation method at the contextual level. Thirdly, the translator can adopt the omission translation method to maximize the complementary effect of the English subtitles and multiple modalities at the content level. This study will be expected to be useful for the subtitle translation study of documentaries

Kinematic topologies of black holes

We investigate the kinematic topologies of light rings (LRs) and massive particle rings (PRs) encircling spherical and axisymmetric black holes. Our results demonstrate that the global topology number of LRs is consistently -1 for asymptotically flat and (Anti-)de Sitter spacetime. Additionally, we show that the global topology of PRs varies, with a value of 0 in asymptotically flat and Anti-de Sitter spacetime but -1 in asymptotic de Sitter spacetime.Comment: 6 pages, version accepted in PR

Protein-Protein Affinity Determination by Quantitative FRET Quenching.

The molecular dissociation constant, Kd, is a well-established parameter to quantitate the affinity of protein-protein or other molecular interactions. Recently, we reported the theoretical basis and experimental procedure for Kd determination using a quantitative FRET method. Here we report a new development of Kd determination by measuring the reduction in donor fluorescence due to acceptor quenching in FRET. A new method of Kd determination was developed from the quantitative measurement of donor fluorescence quenching. The estimated Kd values of SUMO1-Ubc9 interaction based on this method are in good agreement with those determined by other technologies, including FRET acceptor emission. Thus, the acceptor-quenched approach can be used as a complement to the previously developed acceptor excitation method. The new methodology has more general applications regardless whether the acceptor is an excitable fluorophore or a quencher. Thus, these developments provide a complete methodology for protein or other molecule interaction affinity determinations in solution

An integrated tool for performance based engineering of structures in fire

Performance based engineering (PBE) is increasingly recognised as the gold standard for ensuring structural safety under extreme loading conditions such as a post-flashover fire. While no universally agreed methodology exists for implementing PBE for various kinds of extreme loadings in general, there are three clearly defined stages for doing so in order to design or assess structural resistance under fire loading. The fire loading is characterised in the first stage, which may range from simple prescribed time-temperature relationships if standard fires are adopted, which is against the spirit of PBE, to an expensive computational fluid dynamics simulation, which in most cases would constitute overkill. A number of options are available and gradually being developed that lie between these two extremes. A realistic characterisation of the load should in general allow the possibility of non-uniform heat fluxes to structural surfaces, which makes the second stage of determining structural temperatures very tedious. Furthermore, the computational models used in the third stage of determining nonlinear structural response are usually very different from the models used in the second stage thereby requiring significant manual intervention by the analyst. In the author’s view this, bar the need for further research on realistic fire scenarios, is the greatest obstacle in carrying out PBE for structural fire resistance design. This paper presents a simulation tool developed within the open source software framework OpenSees with the aim of integrating all the stages of the analysis discussed earlier in order to make PBE feasible even for design offices with modest resources in terms of trained analysts and computing hardware

MLIC: Multi-Reference Entropy Model for Learned Image Compression

Recently, learned image compression has achieved remarkable performance. The entropy model, which estimates the distribution of the latent representation, plays a crucial role in boosting rate-distortion performance. However, most entropy models only capture correlations in one dimension, while the latent representation contain channel-wise, local spatial, and global spatial correlations. To tackle this issue, we propose the Multi-Reference Entropy Model (MEM) and the advanced version, MEM$^+$. These models capture the different types of correlations present in latent representation. Specifically, We first divide the latent representation into slices. When decoding the current slice, we use previously decoded slices as context and employ the attention map of the previously decoded slice to predict global correlations in the current slice. To capture local contexts, we introduce two enhanced checkerboard context capturing techniques that avoids performance degradation. Based on MEM and MEM$^+$, we propose image compression models MLIC and MLIC$^+$. Extensive experimental evaluations demonstrate that our MLIC and MLIC+ models achieve state-of-the-art performance, reducing BD-rate by $8.05\%$ and $11.39\%$ on the Kodak dataset compared to VTM-17.0 when measured in PSNR.Comment: Fixed some typos and re-organized the pape

Memory Load Influences Taste Sensitivities

Previous literature reports have demonstrated that taste perception would be influenced by different internal brain status or external environment stimulation. Although there are different hypotheses about the cross-modal interactive process, it still remains unclear as of how the brain modulates and processes taste perception, particularly with different memory load. Here in this study we address this question. To do so we assign the participants different memory loads in the form of varying lengths of alphanumerical items, before tasting different concentrations of sweet or bitter tastants. After tasting they were asked to recall the alphanumerical items they were assigned. Our results show that the memory load reduces sweet and bitter taste sensitivities, from sub-threshold level to high concentration. Higher the memory load, less is the taste sensitivity. The study has extended our previous results and supports our previous hypothesis that the cognitive status, such as the general stress of memory load, influences sensory perception

Biomechanical stability of oblique lateral interbody fusion combined with four types of internal fixations: finite element analysis

Objective: Using finite element analysis to identify the optimal internal fixation method for oblique lateral lumbar interbody fusion (OLIF), providing guidance for clinical practice.Methods: A finite element model of the L4 – L5 segment was created. Five types of internal fixations were simulated in the generated L4-L5 finite element (FE) model. Then, six loading scenarios, i.e., flexion, extension, left-leaning, right-leaning, rotate left, and rotate right, were simulated in the FE models with different types of fixations. The biomechanical stability of the spinal segment after different fixations was investigated.Results: Regarding the range of motion (ROM) of the fused segment, OLIF + Bilateral Pedicle Screws (BPS) has a maximum ROM of 1.82° during backward bending and the smallest ROM in all directions of motion compared with other models. In terms of the von Mises stress distribution on the cage, the average stress on every motion direction of OLIF + BPS is about 17.08MPa, and of OLIF + Unilateral Vertebral Screw - Pedicle Screw (UVS-PS) is about 19.29 MPa. As for the von Mises stress distribution on the internal fixation, OLIF + BPS has the maximum internal fixator stress in left rotation (31.85 MPa) and OLIF + Unilateral Pedicle Screw (UPS) has the maximum internal fixator stress in posterior extension (76.59 MPa). The data of these two models were smaller than those of other models.Conclusion: OLIF + BPS provides the greatest biomechanical stability, OLIF + UPS has adequate biomechanical stability, OLIF + UVS-PS is inferior to OLIF + UPS synthetically, and OLIF + Double row vertical screw (DRVS) and Individual OLIF (IO) do not present significant obvious advantages

Data-Driven Modeling of Landau Damping by Physics-Informed Neural Networks

Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale or multiscale systems. One of the long-standing problems in plasma physics is the integration of kinetic physics into fluid models, which is often achieved through sophisticated analytical closure terms. In this study, we successfully construct a multi-moment fluid model with an implicit fluid closure included in the neural network using machine learning. The multi-moment fluid model is trained with a small fraction of sparsely sampled data from kinetic simulations of Landau damping, using the physics-informed neural network (PINN) and the gradient-enhanced physics-informed neural network (gPINN). The multi-moment fluid model constructed using either PINN or gPINN reproduces the time evolution of the electric field energy, including its damping rate, and the plasma dynamics from the kinetic simulations. For the first time, we introduce a new variant of the gPINN architecture, namely, gPINN$p$ to capture the Landau damping process. Instead of including the gradients of all the equation residuals, gPINN$p$ only adds the gradient of the pressure equation residual as one additional constraint. Among the three approaches, the gPINN$p$-constructed multi-moment fluid model offers the most accurate results. This work sheds new light on the accurate and efficient modeling of large-scale systems, which can be extended to complex multiscale laboratory, space, and astrophysical plasma physics problems.Comment: 11 pages, 7 figure