A Corpus-Based Study on the Chinese Translation of Run Away from the Perspective of Androgyny

Run Away, the collection of eight short stories written by Nobel laureate Alice Munro examines the lives of Canadian women throughout the last century. As a female writer’s novels, it inevitably flashes valuable female consciousness. From the perspective of androgyny, this thesis will explore the gender awareness in Li Wenjun’s Chinese version on the lexical level by investigating corpus data and analyzing specific cases. This study holds that male translators with androgynous perspectives could cross the gender border and translate from other gender’s perspective to make feminine elements visible

Object-Centric Unsupervised Image Captioning

Image captioning is a longstanding problem in the field of computer vision and natural language processing. To date, researchers have produced impressive state-of-the-art performance in the age of deep learning. Most of these state-of-the-art, however, requires large volume of annotated image-caption pairs in order to train their models. When given an image dataset of interests, practitioner needs to annotate the caption for each image in the training set and this process needs to happen for each newly collected image dataset. In this paper, we explore the task of unsupervised image captioning which utilizes unpaired images and texts to train the model so that the texts can come from different sources than the images. A main school of research on this topic that has been shown to be effective is to construct pairs from the images and texts in the training set according to their overlap of objects. Unlike in the supervised setting, these constructed pairings are however not guaranteed to have fully overlapping set of objects. Our work in this paper overcomes this by harvesting objects corresponding to a given sentence from the training set, even if they don't belong to the same image. When used as input to a transformer, such mixture of objects enables larger if not full object coverage, and when supervised by the corresponding sentence, produced results that outperform current state of the art unsupervised methods by a significant margin. Building upon this finding, we further show that (1) additional information on relationship between objects and attributes of objects also helps in boosting performance; and (2) our method also extends well to non-English image captioning, which usually suffers from a scarcer level of annotations. Our findings are supported by strong empirical results. Our code is available at https://github.com/zihangm/obj-centric-unsup-caption.Comment: ECCV 202

A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI

Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, complex differential equation modeling methods (e.g., Dynamic Causal Modeling) are usually employed to infer the neuronal processes and to fit the resulting fMRI signals. However, this modeling strategy is computationally expensive and remains to be mostly a confirmatory or hypothesis-driven approach. One major statistical challenge here is to infer, in a data-driven fashion, the underlying differential equation models from fMRI data. In this paper, we propose a causal dynamic network (CDN) method to estimate brain activations and connections simultaneously. Our method links the observed fMRI data with the latent neuronal states modeled by an ordinary differential equation (ODE) model. Using the basis function expansion approach in functional data analysis, we develop an optimization-based criterion that combines data-fitting errors and ODE fitting errors. We also develop and implement a block coordinate-descent algorithm to compute the ODE parameters efficiently. We illustrate the numerical advantages of our approach using data from realistic simulations and two task-related fMRI experiments. Compared with various effective connectivity methods, our method achieves higher estimation accuracy while improving the computational speed by from tens to thousands of times. Though our method is developed for task-related fMRI, we also demonstrate the potential applicability of our method (with a simple modification) to resting-state fMRI, by analyzing both simulated and real data from medium-sized networks

An Attack on a Certificateless Signature Scheme

This paper demonstrates that a certificateless signature scheme recently proposed by Gorantla and Saxena is insecure. It is shown that an adversary who replaces the public key of a signer can then forge valid signatures for that signer without knowledge of the signer\u27s private key

Investigation on the Concept of Limit Involving E by Exploring the Secret of Mr. Buffet

This article uses problem-driven teaching methods to explore a mathematical class of important limit in course design. We first start with the story of Buffett’s wealth to gain students’ interest in the concept. We establish mathematical models based on the financial management issues and guide students to explore them. Then, through the analysis of the compound interest problem, solution with limit expression of the problem and its relationship with the natural constant e are obtained. Finally, in response to Buffett’s sentiment, we use this limit to analyze and explain the mathematical principles of investment issues and to lead students to think about their views of life and values, resulting in a positive influence on their life planning