THE EFFECTS OF SELF-EFFICACY ON LEARNERS’ PERCEPTIONS OF COGNITIVE PRESENCE IN ONLINE COLLABORATIVE LEARNING ACTIVITIES

The applications of web2.0 platforms provide online learning opportunities to focus more on community collaborations as well as the knowledge construction. Cognitive presence (CP) is one of the most critical elements of community of inquiry, and ideal learning outcomes would require deeper stages of cognitive presence (integration and resolution stages), that usually difficult to achieve. Past research on CP felt short in investigating the influences of individual differences, including the effects of learners’ internal motivation on higher-order thinking. We consider Self-efficacy is one of such as it emphasizes a combination of learners’ motivation and cognition. This study intends to explore the influence of learners’ online learning self-efficacy on CP, as well as to explore the relationship between learners’ CP and learning achievements.An experiment was conducted to verify the above issues. Participants were 8th graders from a vocational school. They were required to complete their learning tasks through online collaboration by Facebook and Google Cloud. Questionnaires were applied to measure learners’ CP and self-efficacy after study. Results show that phase’s distribution of learners’ CP in this study is satisfying, and there are significant correlations between CP and self-efficacy as well as CP and learning achievements. Therefore, this study suggests that instructors should take different strategies for students with different self-efficacy and take some strategies which can enhance self-efficacy.&nbsp

Relationship between Corporate Governance and Voluntary Disclosure in Annual Reports: Evidence from Listed Companies in China and UK

Corporate voluntary disclosure has been extensively documented across markets, such as in the U.S., UK and Asian region. This current dissertation investigates the different voluntary disclosure level of listed companies between China and UK as well as the interplay between corporate governance and voluntary disclosure. I regressed voluntary disclosure index of three different information types (strategic information, financial information and non-financial information) on five governance variables (board size, presence of audit committee, ownership concentration, board composition and CEO duality) and three control variables (firm size, leverage and profitability) with data collected from corporate annual reports of 100 listed companies in Food & Beverage industry. The findings show that listed companies in China have lower overall voluntary disclosure than listed companies in UK and also suggest the presence of a complementary relationship between governance and voluntary disclosure. With regard to listed companies in China, CEO duality is the most important influencing factor on voluntary disclosure of Chinese listed companies and firm size, profitability, leverage and board composition only has influence on part of voluntary disclosure in the predicted direction. This finding is supported by Hossain (1994) and Ross (1979). As for listed companies in UK, firm size is the most significant influencing factor on voluntary disclosure and ownership concentration, profitability, board composition; presence of audit committee, CEO duality only has influence on part of voluntary disclosure. This result is consistent with Chow (1987), Meek (1995), Lim (2007) and Eng (2003). The dissertation can contribute to the understanding of the relationship between governance and voluntary disclosure of different information types by a particular comparison between two countries. This may be of interest of to practitioners and regulators

GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts

Geometric deep learning (GDL) has gained significant attention in various scientific fields, chiefly for its proficiency in modeling data with intricate geometric structures. Yet, very few works have delved into its capability of tackling the distribution shift problem, a prevalent challenge in many relevant applications. To bridge this gap, we propose GDL-DS, a comprehensive benchmark designed for evaluating the performance of GDL models in scenarios with distribution shifts. Our evaluation datasets cover diverse scientific domains from particle physics and materials science to biochemistry, and encapsulate a broad spectrum of distribution shifts including conditional, covariate, and concept shifts. Furthermore, we study three levels of information access from the out-of-distribution (OOD) testing data, including no OOD information, only OOD features without labels, and OOD features with a few labels. Overall, our benchmark results in 30 different experiment settings, and evaluates 3 GDL backbones and 11 learning algorithms in each setting. A thorough analysis of the evaluation results is provided, poised to illuminate insights for DGL researchers and domain practitioners who are to use DGL in their applications.Comment: Code and data are available at https://github.com/Graph-COM/GDL_D

Parameter-Saving Adversarial Training: Reinforcing Multi-Perturbation Robustness via Hypernetworks

Adversarial training serves as one of the most popular and effective methods to defend against adversarial perturbations. However, most defense mechanisms only consider a single type of perturbation while various attack methods might be adopted to perform stronger adversarial attacks against the deployed model in real-world scenarios, e.g., $\ell_2$ or $\ell_\infty$. Defending against various attacks can be a challenging problem since multi-perturbation adversarial training and its variants only achieve suboptimal robustness trade-offs, due to the theoretical limit to multi-perturbation robustness for a single model. Besides, it is impractical to deploy large models in some storage-efficient scenarios. To settle down these drawbacks, in this paper we propose a novel multi-perturbation adversarial training framework, parameter-saving adversarial training (PSAT), to reinforce multi-perturbation robustness with an advantageous side effect of saving parameters, which leverages hypernetworks to train specialized models against a single perturbation and aggregate these specialized models to defend against multiple perturbations. Eventually, we extensively evaluate and compare our proposed method with state-of-the-art single/multi-perturbation robust methods against various latest attack methods on different datasets, showing the robustness superiority and parameter efficiency of our proposed method, e.g., for the CIFAR-10 dataset with ResNet-50 as the backbone, PSAT saves approximately 80\% of parameters with achieving the state-of-the-art robustness trade-off accuracy.Comment: 9 pages, 2 figure

Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation

In this paper, we present a novel method for achieving dexterous manipulation of complex objects, while simultaneously securing the object without the use of passive support surfaces. We posit that a key difficulty for training such policies in a Reinforcement Learning framework is the difficulty of exploring the problem state space, as the accessible regions of this space form a complex structure along manifolds of a high-dimensional space. To address this challenge, we use two versions of the non-holonomic Rapidly-Exploring Random Trees algorithm; one version is more general, but requires explicit use of the environment's transition function, while the second version uses manipulation-specific kinematic constraints to attain better sample efficiency. In both cases, we use states found via sampling-based exploration to generate reset distributions that enable training control policies under full dynamic constraints via model-free Reinforcement Learning. We show that these policies are effective at manipulation problems of higher difficulty than previously shown, and also transfer effectively to real robots. Videos of the real-hand demonstrations can be found on the project website: https://sbrl.cs.columbia.edu/Comment: 10 pages, 6 figures, submitted to Robotics Science & Systems 202

Cloning, Heterologous Expression, and Characterization of a βκ-Carrageenase From Marine Bacterium Wenyingzhuangia funcanilytica: A Specific Enzyme for the Hybrid Carrageenan–Furcellaran

Carrageenan is a group of important food polysaccharides with high structural heterogeneity. Furcellaran is a typical hybrid carrageenan, which contains the structure consisted of alternative beta-carrageenan and kappa-carrageenan motifs. Although several furcellaran-hydrolyzing enzymes have been characterized, their specificity for the glycosidic linkage was still unclear. In this study, we cloned, expressed, and characterized a novel GH16_13 furcellaran-hydrolyzing enzyme Cgbk16A_Wf from the marine bacterium Wenyingzhuangia fucanilytica CZ1127. Cgbk16A_Wf exhibited its maximum activity at 50 degrees C and pH 6.0 and showed high thermal stability. The oligosaccharides in enzymatic products were identified by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) and nuclear magnetic resonance (NMR) spectroscopy. It was confirmed that Cgbk16A_Wf specifically cleaves the beta-1,4 linkages between beta-carrageenan and kappa-carrageenan motifs from non-reducing end to reducing end. Considering the structural heterogeneity of carrageenan and for the unambiguous indication of the specificity, we recommended to name the furcellaran-hydrolyzing activity represented by Cgbk16A as beta kappa-carrageenase instead of furcellaranase

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing

De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the problem to a translation task, potentially overlooking critical nuances between spectra and peptides. In our research, we present ContraNovo, a pioneering algorithm that leverages contrastive learning to extract the relationship between spectra and peptides and incorporates the mass information into peptide decoding, aiming to address these intricacies more efficiently. Through rigorous evaluations on two benchmark datasets, ContraNovo consistently outshines contemporary state-of-the-art solutions, underscoring its promising potential in enhancing de novo peptide sequencing. The source code is available at https://github.com/BEAM-Labs/ContraNovo.Comment: This paper has been accepted by AAAI 202

A synthetic lethal screen identifies HDAC4 as a potential target in MELK overexpressing cancers

Maternal embryonic leucine zipper kinase (MELK) is frequently overexpressed in cancer, but the role of MELK in cancer is still poorly understood. MELK was shown to have roles in many cancer-associated processes including tumor growth, chemotherapy resistance, and tumor recurrence. To determine whether the frequent overexpression of MELK can be exploited in therapy, we performed a high-throughput screen using a library of Saccharomyces cerevisiae mutants to identify genes whose functions become essential when MELK is overexpressed. We identified two such genes: LAG2 and HDA3. LAG2 encodes an inhibitor of the SCF ubiquitin-ligase complex, while HDA3 encodes a subunit of the HDA1 histone deacetylase complex. We find that one of these synthetic lethal interactions is conserved in mammalian cells, as inhibition of a human homolog of HDA3 (HDAC4) is synthetically toxic in MELK overexpression cells. Altogether, our work identified a novel potential drug target for tumors that overexpress MELK