278 research outputs found
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
and 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 to capture the Landau
damping process. Instead of including the gradients of all the equation
residuals, gPINN only adds the gradient of the pressure equation residual as
one additional constraint. Among the three approaches, the gPINN-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
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