Analysis on Howard Goldblatt’s Translation of Rice From the Perspective of Translator’ Subjectivity

With the emergence of culture turn in the 1980s, translator’s invisible status has been changed. Translation process is no longer considered as a simple conversion process from the original language to the target language, but a process full of creativity. Rice is the Chinese novelist Su Tong’s second novel which deals with China in the 1930s. He vividly portrays Depression-era China and the characters that populate this novel. Howard Goldblatt devotes himself to the translation of modern and contemporary Chinese novels into English and Rice is one of his numerous works. His impeccable translation does much justice to the flow of the tale. From the study on Goldblatt’s case, inspirations can be drawn on the exercise of translator’s subjectivity in the process of introducing and translating Chinese literature to the world.Research methods such as exemplification and induction were adopted in this article. Different interpretations of translator’s subjectivity were reviewed at the beginning of the article according to Professor Lü Jun’s division of three paradigms in Chinese translation study. The influential factors on translator’s subjectivity were analyzed on the theoretical basis of manipulation school and functionalist school. The manifestation of Howard Goldblatt’s subjectivity in the translation of Rice was analyzed, followed by reflections on the exercise of translator’s subjectivity

DNA sequences classification and computation scheme based on the symmetry principle

The DNA sequences containing multifarious novel symmetrical structure frequently play crucial role in how genomes work. Here we present a new scheme for understanding the structural features and potential mathematical rules of symmetrical DNA sequences using a method containing stepwise classification and recursive computation. By defining the symmetry of DNA sequences, we classify all sequences and conclude a series of recursive equations for computing the quantity of all classes of sequences existing theoretically; moreover, the symmetries of the typical sequences at different levels are analyzed. The classification and quantitative relation demonstrate that DNA sequences have recursive and nested properties. The scheme may help us better discuss the formation and the growth mechanism of DNA sequences because it has a capability of educing the information about structure and quantity of longer sequences according to that of shorter sequences by some recursive rules. Our scheme may provide a new stepping stone to the theoretical characterization, as well as structural analysis, of DNA sequences

Towards Automatic SAR-Optical Stereogrammetry over Urban Areas using Very High Resolution Imagery

In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established handcrafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging. Keywords: Synthetic Aperture Radar (SAR), optical images, remote sensing, data fusion, stereogrammetr

Stochastic Approximate Gradient Descent via the Langevin Algorithm

We introduce a novel and efficient algorithm called the stochastic approximate gradient descent (SAGD), as an alternative to the stochastic gradient descent for cases where unbiased stochastic gradients cannot be trivially obtained. Traditional methods for such problems rely on general-purpose sampling techniques such as Markov chain Monte Carlo, which typically requires manual intervention for tuning parameters and does not work efficiently in practice. Instead, SAGD makes use of the Langevin algorithm to construct stochastic gradients that are biased in finite steps but accurate asymptotically, enabling us to theoretically establish the convergence guarantee for SAGD. Inspired by our theoretical analysis, we also provide useful guidelines for its practical implementation. Finally, we show that SAGD performs well experimentally in popular statistical and machine learning problems such as the expectation-maximization algorithm and the variational autoencoders