Exploring Intersemiotic Translation Models -- A Case Study of Ang Lee’s Films

Roman Jakobson’s notion of intersemiotic translation provides an opportunity for translation studies scholars to respond to the broad move from the dominance of writing to the dominance of the medium of the image. Due to the linguistic bias of translation studies, however, intersemiotic translation has yet to receive systematic attention. The present research is thus designed to respond to this under-discussed and yet growing phenomenon in the age of digitalization and aims to contribute an understanding of intersemiotic translation by focusing on the case of film as one of the most notable instances of intersemiotic translation. Though intersemiotic translation enables film to be discussed through the prism of translation studies, past research in this area, which perceives film as a transmission from verbal signs to non-verbal signs, oversimplifies the mechanism of film-making. This comes at a price, however, since the researchers neglect the fact that other parameters of film language, such as cinematography, performance, setting and sound are governed by audio-visual patterns that are included in film’s other prior materials. To remedy this deficiency, a rigorous investigation of these audio-visual patterns has been carried out, and answers are provided for the research question: How do intersemiotic translators translate? In this dissertation, these quality-determining audio-visual patterns are considered as the film-maker’s intersemiotic translation models, which provide translation solutions for verbal text segments in the screenplay. Using elements from Even-Zohar’s polysystem theory and Rey Chow’s theory of cultural translation, a multi-levelled system of intersemiotic translation is proposed, comprised of a hierarchy of two levels: cultural and semiotic. In this system, each intersemiotic translation model is considered to be the result of a cross-level combination that relates to a specific type of semiotic system within a specific cultural system, employed in one or several parameters of film ‘language’. These intersemiotic translation models and their functions are explored through case studies of three of Ang Lee’s films, namely, Crouching Tiger, Hidden Dragon, Lust, Caution, and Life of Pi

PAH degradation in wetland soils as influenced by redox potential

Polycyclic aromatic hydrocarbons (PAHs) are a common contaminant in wetland soils. They are a group of compounds widely distributed in the environment and tend to accumulate in soils. Major contribution to removal of PAHs is biological degradation. For investigating the biodegradation potential of PAHs influenced by tidal actions, equipment was built for simulating the tidal actions, and concentrations of phenanthrene, pyrene, and benzo[e]pyrene were added to the soils samples which were collected from wetland. Experiments were then conducted over 120 days. Redox potentials and PAHs concentrations were measured and analyzed. Results are concluded: 1) influenced by tidal action, phenanthrene, pyrene, benzo[e]pyrene were rapidly biodegraded during the first 40 days followed by slow but continuous biodegradation in the next 80 days, 2) tidal action enhanced approximately 15.2%, 13.9%, 12.2% of the removal efficiencies of phenanthrene, pyrene, and benzo[e]pyrene in first 37 days, 3) redox potential can change rapidly and significantly in coastal wetland soils in response of flooding and draining, 4) redox potentials in submerged soils and periodically emerged soils were significantly different, which is 70 mV higher in the periodically emerged one

Judgments of mathematical beauty are resistant to revision through external opinion

We here address the question of the extent to which judgments of mathematical beauty (which we categorize as biological beauty) are resistant to revision through external opinion. A total of 100 mathematicians of different national and ethnic origins were asked to rate 60 mathematical equations for their beauty; after being presented a fictitious “expert rating,” they were asked to re-rate the same equations. Results showed that the judgments of mathematical beauty had a high level of resistance to external opinion. This is in line with the resistance to revision of a judgments for other categories of biological beauty

Structural, Electronic and Optical Properties of CsMI3(M=Ge,Sn,Pb) Perovskite from First Principles

The all-inorganic lead halide perovskites has received wide attention in optoelectronic applications such as solar cells and light-emitting diodes due to its high photoabsorption, suitable bandgap and good stability. Based on the first principles, the electronic structure and optical properties of the structure are studied by substituting all the lead elements in CsPbI3 with Ge and Sn.We found that the structural stability of all the substituted materials was enhanced. The tolerance factors of CsGeI3 and CsSnI3 were 0.934 and 0.874, respectively. The most important point is to replace the toxic Pb element, which not only reduces environmental pollution but also can be more suitable for commercial production. By analyzing the imaginary part of the dielectric function and absorption coefficient, it is found that the blue shift occurs in all the materials which replace Pb element, and the absorption ability of sun light is stronger in the visible light range, which proves the foundation for lead free perovskite solar cells

Hypergraph Neural Networks

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.Comment: Accepted in AAAI'201

MQENet: A Mesh Quality Evaluation Neural Network Based on Dynamic Graph Attention

With the development of computational fluid dynamics, the requirements for the fluid simulation accuracy in industrial applications have also increased. The quality of the generated mesh directly affects the simulation accuracy. However, previous mesh quality metrics and models cannot evaluate meshes comprehensively and objectively. To this end, we propose MQENet, a structured mesh quality evaluation neural network based on dynamic graph attention. MQENet treats the mesh evaluation task as a graph classification task for classifying the quality of the input structured mesh. To make graphs generated from structured meshes more informative, MQENet introduces two novel structured mesh preprocessing algorithms. These two algorithms can also improve the conversion efficiency of structured mesh data. Experimental results on the benchmark structured mesh dataset NACA-Market show the effectiveness of MQENet in the mesh quality evaluation task

High genetic abundance of Rpi-blb2/Mi-1.2/Cami gene family in Solanaceae

Relative genomic positions of genes among potato (upper), pepper (middle) and tomato (lower) along chromosome 6. (DOCX 282 kb

Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series

Learning directed acyclic graphs (DAGs) to identify causal relations underlying observational data is crucial but also poses significant challenges. Recently, topology-based methods have emerged as a two-step approach to discovering DAGs by first learning the topological ordering of variables and then eliminating redundant edges, while ensuring that the graph remains acyclic. However, one limitation is that these methods would generate numerous spurious edges that require subsequent pruning. To overcome this limitation, in this paper, we propose an improvement to topology-based methods by introducing limited time series data, consisting of only two cross-sectional records that need not be adjacent in time and are subject to flexible timing. By incorporating conditional instrumental variables as exogenous interventions, we aim to identify descendant nodes for each variable. Following this line, we propose a hierarchical topological ordering algorithm with conditional independence test (HT-CIT), which enables the efficient learning of sparse DAGs with a smaller search space compared to other popular approaches. The HT-CIT algorithm greatly reduces the number of edges that need to be pruned. Empirical results from synthetic and real-world datasets demonstrate the superiority of the proposed HT-CIT algorithm

Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors

Data-driven cooperative control of connected and automated vehicles (CAVs) has gained extensive research interest as it can utilize collected data to generate control actions without relying on parametric system models that are generally challenging to obtain. Existing methods mainly focused on improving traffic safety and stability, while less emphasis has been placed on energy efficiency in the presence of uncertainties and diversities of human-driven vehicles (HDVs). In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers. Specifically, by incorporating the physical relationship of the studied system and the Hankel matrix update from the generalized behavior representation to a particular one, we develop a new Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle human driver diversities. In particular, a power consumption term is added to the DeePC cost function to reduce the holistic energy consumption of both CAVs and HDVs. Simulation results demonstrate the effectiveness of our approach in accurately capturing random human driver behaviors and addressing the complex dynamics of mixed traffic flows, while ensuring driving safety and traffic efficiency. Furthermore, the proposed optimization framework achieves substantial reductions in energy consumption, i.e., average reductions of 4.83% and 9.16% when compared to the benchmark algorithms