Trauma Recovery of Tara Westover in Educated: A Memoir

Educated: A Memoir is an autobiographical novel of Tara Westover. This paper analyzes the protagonist Tara Westover’s trauma experiences and recovery process based on trauma theory. Combining trauma theory with this book can help readers better understand Tara’s experiences and deeply reveal the themes of this novel

Implementing Controlled Digital Lending with Google Drive and Apps Script: A Case Study at the NYU Shanghai Library

The unexpected outbreak of COVID-19 near the beginning of 2020 has significantly interrupted the daily operation of a wide range of academic institutions worldwide. As a result, libraries faced a challenge of providing their patrons access to physical collections while the campuses may remain closed. Discussions on the implementation of Controlled Digital Lending (CDL) among libraries have been trending ever since. In theory, CDL enables libraries to digitize a physical item from their collections and loan the access-restricted file to one user at a time based on the “owned to loaned” ratio in the library’s collections, for a limited time. Despite all the discussions and enthusiasm about CDL, there is, however, still a lack of technical infrastructure to support individual libraries to manage their self-hosted collections. With COVID-19 still very much a presence in our lives, many libraries are more than eager to figure out the best approach to circulating materials that only exist in print form to their users in a secure and legitimate way. This article describes the author's temporary but creative implementation of CDL amid the COVID-19 pandemic. We managed to work out a technical solution in a very short time, to lend out digital versions of library materials to users when the library is physically inaccessible to them. By collaborating with our campus IT, a Google Spreadsheet with Google Apps Scripts was developed to allow a team of Access Services Staff to do hourly loans, which is a desired function for our reserve collection. Further, when the access to a file expires, staff will be notified via email. We hope our experience can be useful for those libraries that are interested in lending their physical materials using CDL and are in urgent need for an applicable solution without a cost

PoKE: Prior Knowledge Enhanced Emotional Support Conversation with Latent Variable

Emotional support conversation (ESC) task can utilize various support strategies to help people relieve emotional distress and overcome the problem they face, which has attracted much attention in these years. However, most state-of-the-art works rely heavily on external commonsense knowledge to infer the mental state of the user in every dialogue round. Although effective, they may suffer from significant human effort, knowledge update and domain change in a long run. Therefore, in this article, we focus on exploring the task itself without using any external knowledge. We find all existing works ignore two significant characteristics of ESC. (a) Abundant prior knowledge exists in historical conversations, such as the responses to similar cases and the general order of support strategies, which has a great reference value for current conversation. (b) There is a one-to-many mapping relationship between context and support strategy, i.e.multiple strategies are reasonable for a single context. It lays a better foundation for the diversity of generations. Taking into account these two key factors, we propose Prior Knowledge Enhanced emotional support model with latent variable, PoKE. The proposed model fully taps the potential of prior knowledge in terms of exemplars and strategy sequence and then utilizes a latent variable to model the one-to-many relationship of strategy. Furthermore, we introduce a memory schema to incorporate the encoded knowledge into decoder. Experiment results on benchmark dataset show that our PoKE outperforms existing baselines on both automatic evaluation and human evaluation. Compared with the model using external knowledge, PoKE still can make a slight improvement in some metrics. Further experiments prove that abundant prior knowledge is conducive to high-quality emotional support, and a well-learned latent variable is critical to the diversity of generations

Aldehyd Dehydrogenase Familie 3 Mitglied A2 reguliert die TGFβ-abhängige Fibroblastenaktivierung in der systemischen Sklerose

Abstract Systemic sclerosis (SSc), also known as scleroderma, is a chronic autoimmune disease, which is characterized by extensive fibrosis, vascular alterations, inflammation and autoantibodies against various cellular antigens. Its etiology is highly complex and still incompletely understood. A major hallmark of Systemic Sclerosis (SSc) is an uncontrolled and persistent transforming growth factor-β (TGFβ)-induced fibroblasts activation, which release excessive amounts of extracellular matrix (ECM), and disrupts the tissue architecture leading to organ dysfunction and causing morbidity and mortality of SSc. Thus, the precise molecular mechanisms and the intracellular signaling that mediate the TGFβ-induced activation of fibroblasts and production of ECM are essential for developing effective therapeutic options for SSc. Aldehyde dehydrogenase 3A2 (ALDH3A2) is found to be involved in the oxidation of fatty aldehyde and fatty acid generation. Some research has found that ALDH3A2 is involved the process of fibrotic disease. However, physiological roles of FALDH in fibrosis are unclear. The purpose of the study was to characterize whether ALDH3A2 contributes to the pathologic activation of dermal fibroblasts in patients with SSc and to evaluate the anti-fibrotic potential of ALDH3A2 reduction. We found that ALDH3A2 is the predominant Aldehyde dehydrogenase in skin with pronounced downregulated in patients with SSc as compared to non-fibrotic controls. In addition, ALDH3A2 was down regulated by TGFβ both in vivo and in vitro. We also demonstrated that decrease of ALDH3A2 plays an antifibrotic role in fibrobast activation and collagen release in vitro. And the knockdown of ALDH3A2 (ALDH3A2-KD) ameliorated TBR-, bleomycin-induced dermal fibrosis, as well as in experimental sclerodermatous cGvHD induced dermal fibrosis. Finally, the RNAseq analysis of ALDH3A2-KD fibroblast showed that inactivation of ALDH3A2 interferes with the activation of fibroblast and metabolism of collagen. Knockdown of ALDH3A2 interfered with the TGFβ-dependent regulation of WNT signaling, NOTCH signaling, NODAL signaling and Hedgehog signaling

A mixed lubrication analysis of a thrust bearing with fractal rough surfaces

Fractal descriptions of rough surfaces are widely used in tribology. The fractal dimension, D, is an important parameter which has been regarded as instrument and scale independent, although recent findings bring this into question. A thrust bearing is analyzed in the mixed lubrication regime while considering the fractal nature. Surface data obtained from a thrust bearing surface are characterized and used to calculate the fractal dimension value by the roughness-length method. Then these parameters are used to generate different rough surfaces via a filtering algorithm. By comparing the predicted performance between the measured surface and generated fractal surfaces, it is found that the fractal dimension must be used carefully when characterizing the tribological performance of rough surfaces, and other parameters need to be found

Supreme People\u27s Court Annual Report on Intellectual Property Cases (2013) (China)

The Supreme People’s Court of China began publishing its Annual Report on Intellectual Property Cases in 2008. The annual reports, published in April of each year, summarize and review new intellectual property cases. This translation includes all 30 cases and 39 legal issues of the 2013 Annual Report. It addresses patent law, trademark law, copyright law, unfair competition, contractual intellectual property rights, liability of intellectual property infringement, and intellectual property litigation procedure and evidence. While China is not a common law country, these cases and guidelines provide lower courts with meaningful insight and direction

How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?

Scaling language models have revolutionized widespread NLP tasks, yet little comprehensively explored few-shot relation extraction with large language models. In this paper, we investigate principal methodologies, in-context learning and data generation, for few-shot relation extraction via GPT-3.5 through exhaustive experiments. To enhance few-shot performance, we further propose task-related instructions and schema-constrained data generation. We observe that in-context learning can achieve performance on par with previous prompt learning approaches, and data generation with the large language model can boost previous solutions to obtain new state-of-the-art few-shot results on four widely-studied relation extraction datasets. We hope our work can inspire future research for the capabilities of large language models in few-shot relation extraction. Code is available in https://github.com/zjunlp/DeepKE/tree/main/example/llm.Comment: SustaiNLP Workshop@ACL 202

Relieving Triplet Ambiguity: Consensus Network for Language-Guided Image Retrieval

Language-guided image retrieval enables users to search for images and interact with the retrieval system more naturally and expressively by using a reference image and a relative caption as a query. Most existing studies mainly focus on designing image-text composition architecture to extract discriminative visual-linguistic relations. Despite great success, we identify an inherent problem that obstructs the extraction of discriminative features and considerably compromises model training: \textbf{triplet ambiguity}. This problem stems from the annotation process wherein annotators view only one triplet at a time. As a result, they often describe simple attributes, such as color, while neglecting fine-grained details like location and style. This leads to multiple false-negative candidates matching the same modification text. We propose a novel Consensus Network (Css-Net) that self-adaptively learns from noisy triplets to minimize the negative effects of triplet ambiguity. Inspired by the psychological finding that groups perform better than individuals, Css-Net comprises 1) a consensus module featuring four distinct compositors that generate diverse fused image-text embeddings and 2) a Kullback-Leibler divergence loss, which fosters learning among the compositors, enabling them to reduce biases learned from noisy triplets and reach a consensus. The decisions from four compositors are weighted during evaluation to further achieve consensus. Comprehensive experiments on three datasets demonstrate that Css-Net can alleviate triplet ambiguity, achieving competitive performance on benchmarks, such as $+2.77\%$ R@10 and $+6.67\%$ R@50 on FashionIQ.Comment: 11 page