Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.Comment: Submitted to ROBIO 201

Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.Comment: IEEE BHI 2019 accepte

Protein Interaction Prediction Method Based on Feature Engineering and XGBoost

Human protein interaction prediction studies occupy an important place in systems biology. The understanding of human protein interaction networks and interactome will provide important insights into the regulation of developmental, physiological and pathological processes. In this study, we propose a method based on feature engineering and integrated learning algorithms to construct protein interaction prediction models. Principal Component Analysis (PCA) and Locally Linear Embedding (LLE) dimensionality reduction methods were used to extract sequence features from the 174-dimensional human protein sequence vector after Normalized Difference Sequence Feature (NDSF) encoding, respectively. The classification performance of three integrated learning methods (AdaBoost, Extratrees, XGBoost) applied to PCA and LLE features was compared, and the best combination of parameters was found using cross-validation and grid search methods. The results show that the classification accuracy is significantly higher when using the linear dimensionality reduction method PCA than the nonlinear dimensionality reduction method LLE. the classification with XGBoost achieves a model accuracy of 99.2%, which is the best performance among all models. This study suggests that NDSF combined with PCA and XGBoost may be an effective strategy for classifying different human protein interactions

Bioproduction of polyesters

Book of Abstracts of CEB Annual Meeting 2017[Excerpt] Polyesters are polymers comprising of repeating ester groups as chain structure backbone, being the most popular biodegradable polymers. Many studies related with the synthesis of aliphatic and aromatic polyesters using chemical processes have been carried out [1]. Poly (ethylene glutarate) (PEG), Poly(ethylene malonate) (PEM), Poly(ethylene phthalate) (PEP) are attractive polyesters by virtue of their easiness in synthesis and widely diversity of applications such as textile manufacturing, microelectronics, bioprocessing, food packaging as well as in bio medical like surgical threads, contact lenses, supporting material in bone repairing, treat air leaks in lung injury. [...]info:eu-repo/semantics/publishedVersio

Thrust: Adaptively Propels Large Language Models with External Knowledge

Although large-scale pre-trained language models (PTLMs) are shown to encode rich knowledge in their model parameters, the inherent knowledge in PTLMs can be opaque or static, making external knowledge necessary. However, the existing information retrieval techniques could be costly and may even introduce noisy and sometimes misleading knowledge. To address these challenges, we propose the instance-level adaptive propulsion of external knowledge (IAPEK), where we only conduct the retrieval when necessary. To achieve this goal, we propose measuring whether a PTLM contains enough knowledge to solve an instance with a novel metric, Thrust, which leverages the representation distribution of a small number of seen instances. Extensive experiments demonstrate that thrust is a good measurement of PTLM models' instance-level knowledgeability. Moreover, we can achieve significantly higher cost-efficiency with the Thrust score as the retrieval indicator than the naive usage of external knowledge on 88% of the evaluated tasks with 26% average performance improvement. Such findings shed light on the real-world practice of knowledge-enhanced LMs with a limited knowledge-seeking budget due to computation latency or costs.Comment: 13 pages, 6 figure

Chinglish: Unraveling the Cultural and Cognitive Pattern Differences in Cross-Linguistic Communication

English, as a foreign language in China, is often influenced by the first language (L1), which is Chinese, giving rise to the phenomenon of Chinglish. Chinglish, a distinct English variety, significantly differs from standard English in various aspects. The prominent reason for these differences lies in the variance of culture and thinking patterns. Unfortunately, Chinglish impedes effective cross-cultural communication and hinders the advancement of English learners’ proficiency. This study aims to elucidate the current state and specific manifestations of Chinglish concerning vocabulary and syntax from the perspective of cultural and cognitive disparities. The research employs questionnaire surveys and material analysis methods to gather data. Preliminary analysis reveals the widespread prevalence of Chinglish during the English learning process, with the primary vocabulary errors being redundancy, improper word usage, and incorrect collocation. In terms of syntax, word order, tense, and voice are particularly discussed as problematic areas. This paper concludes with suggestions for avoiding Chinglish, aiming to enhance cross-cultural communication and elevate English proficiency levels among Chinese learners

New chinglish in social media: Chinese college students’ language usage patterns

The large number of English learners in China establishes a strong foundation for English users in the country. The rapid development of Chinese social media platforms offers a broader platform for English usage in China. College students, with their bilingual or multilingual backgrounds, play a vital role in using English. Unlike the traditional English taught in educational settings, English used on social media differs and is referred to as New Chinglish. This study aims to explore the role of New Chinglish in the lives of college students on Chinese social media platforms, focusing on their behaviours and habits related to its usage. The qualitative analysis findings highlight the commonality of using New Chinglish among college students, often evolving into an unconscious habit. Moreover, New Chinglish has transcended the confines of the social media platform, extending into offline life. The study also reveals contradictory emotional trends underlying their user behaviour and habits. It provides a detailed description of the role of New Chinglish in the lives of Chinese youth, emphasizing the integration of English as a part of their daily routines. Additionally, this study contributes significantly to the existing literature by depicting the English usage situation in China

PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training

Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions. While fact-checking is a hard task in general, in many cases, false statements can be easily debunked based on analytics over tables with reliable information. Hence, table-based fact verification has recently emerged as an important and growing research area. Yet, progress has been limited due to the lack of datasets that can be used to pre-train language models (LMs) to be aware of common table operations, such as aggregating a column or comparing tuples. To bridge this gap, in this paper we introduce PASTA, a novel state-of-the-art framework for table-based fact verification via pre-training with synthesized sentence-table cloze questions. In particular, we design six types of common sentence-table cloze tasks, including Filter, Aggregation, Superlative, Comparative, Ordinal, and Unique, based on which we synthesize a large corpus consisting of 1.2 million sentence-table pairs from WikiTables. PASTA uses a recent pre-trained LM, DeBERTaV3, and further pretrains it on our corpus. Our experimental results show that PASTA achieves new state-of-the-art performance on two table-based fact verification benchmarks: TabFact and SEM-TAB-FACTS. In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4.7 points (85.6% vs. 80.9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1.5 points (90.6% vs. 92.1%).Comment: EMNLP 202