A Study on the Characterization of Hagar Shipley

The Stone Angel, the first novel of the Manawaka Cycle, is generally regarded as Laurence’s representative work. This novel narrates the story of Hagar Shipley, who struggles to search for her self-identity and freedom all through her life. Hagar’s life reflects Canadian ideology and ideological trends during that specific period. Hagar’s pride leads to her rebellious life. She seems like the sightless stone angel in the Manawaka cemetery. She cannot realize her pride and prejudice. She cannot understand people around her. People cannot understand her either. Hagar doesn’t achieve her self-identity and spiritual freedom until the very end of her life. This thesis intends to analyze the characterization of Hagar and her inner journey towards self-identity and freedom, and further to evaluate Laurence’s contribution to Canadian Literature

Does High Knowledge Contribution Mean Low Knowledge Withholding? Distinguishing Their Underlying Mechanisms by Integrating the Motivation and Neutralization Perspectives

Prior studies have failed to compare the different mechanisms of knowledge contribution and withholding in a same, simultaneous model. Based on the prevailing pro-sharing norms in online communities, this study incorporates intrinsic motivation and extrinsic motivation from a norm-advocated contributing perspective and neutralization techniques from a norm-deviant withholding perspective to investigate their distinct impacts on knowledge contribution and withholding in online communities in a simultaneous model. Results of an online survey of 448 respondents demonstrate that the effects of intrinsic motivation, extrinsic motivation, and neutralization techniques on KC and KW are strikingly distinct. In addition, differences between the moderating effect of prosocial motivation on the effect of each of these factors on KC and KW are also examined. This research summarizes with a discussion of the theoretical contribution and the practical implication

ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model

We present ShapeCrafter, a neural network for recursive text-conditioned 3D shape generation. Existing methods to generate text-conditioned 3D shapes consume an entire text prompt to generate a 3D shape in a single step. However, humans tend to describe shapes recursively-we may start with an initial description and progressively add details based on intermediate results. To capture this recursive process, we introduce a method to generate a 3D shape distribution, conditioned on an initial phrase, that gradually evolves as more phrases are added. Since existing datasets are insufficient for training this approach, we present Text2Shape++, a large dataset of 369K shape-text pairs that supports recursive shape generation. To capture local details that are often used to refine shape descriptions, we build on top of vector-quantized deep implicit functions that generate a distribution of high-quality shapes. Results show that our method can generate shapes consistent with text descriptions, and shapes evolve gradually as more phrases are added. Our method supports shape editing, extrapolation, and can enable new applications in human-machine collaboration for creative design

ProMix: Combating Label Noise via Maximizing Clean Sample Utility

The ability to train deep neural networks under label noise is appealing, as imperfectly annotated data are relatively cheaper to obtain. State-of-the-art approaches are based on semi-supervised learning(SSL), which selects small loss examples as clean and then applies SSL techniques for boosted performance. However, the selection step mostly provides a medium-sized and decent-enough clean subset, which overlooks a rich set of clean samples. In this work, we propose a novel noisy label learning framework ProMix that attempts to maximize the utility of clean samples for boosted performance. Key to our method, we propose a matched high-confidence selection technique that selects those examples having high confidence and matched prediction with its given labels. Combining with the small-loss selection, our method is able to achieve a precision of 99.27 and a recall of 98.22 in detecting clean samples on the CIFAR-10N dataset. Based on such a large set of clean data, ProMix improves the best baseline method by +2.67% on CIFAR-10N and +1.61% on CIFAR-100N datasets. The code and data are available at https://github.com/Justherozen/ProMixComment: Winner of the 1st Learning and Mining with Noisy Labels Challenge in IJCAI-ECAI 2022 (an informal technical report

Nanoscale pore and crack evolution in shear thin layers of shales and the shale gas reservoir effect

Studies on matrix-related pores from the nanometer to the micrometer scale in shales have made considerable progress in recent decades. However, nanoscale pores and cracks developed in the shear thin layers have not been systematically discussed. In this work, interlayer shear slip occurring in shales are observed through practical examples. The results show that the shear thin layer constructed by nanograin coating is widely distributed on superimposed shear slip planes. Usually, the development of the shear thin layer undergoes viscoelastic-rheological-embrittling deformation stages, and the nanograin texture assembled in the shear thin layer can demonstrate three pore and crack structure types. Based on the mechanical analysis concerning nanoscale cohesion force, it is identified that, as long as force remains a state, the shear thin layer must bear a nanoscale pore and crack character. Furthermore, the shale gas reservoir effect of the nanoscale pore and crack is simply discussed. Obviously, the adsorbed gas effect of the nanograin itself has a larger nanoscale size and surface functionality than those of kerogen and clay particles in the shales; three structure types of the nanoscale pore and crack can act as given controlling factors of storage and permeability for the free gas. Both the matrix-related pores and the three pore and crack structures have an intimate connection with respect to each other in the genetic mechanism and temporal-spatial evolution. This work has important theoretical implications for supplementing the pore and crack classification of shale. Moreover, it makes a significant contribution to shale gas exploration and development.Cited as: Sun, Y., Ju, Y., Zhou, W., Qiao, P., Tao, L., Xiao, L. Nanoscale pore and crack evolution in shear thin layers of shales and the shale gas reservoir effect. Advances in Geo-Energy Research, 2022, 6(3): 221-229. https://doi.org/10.46690/ager.2022.03.0

Propyl 4-hydroxy­benzoate

There are two mol­ecules in the asymmetric unit of the title compound, C10H12O3. In the crystal, mol­ecules are linked by O—H⋯O hydrogen bonds into chains running along [010]. Adjacent chains are joined together by weak π–π inter­actions between benzene rings [centroid–centroid distance = 4.040 (2) Å]

FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models

Collecting high-quality labeled data for model training is notoriously time-consuming and labor-intensive for various NLP tasks. While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention. It is still underexplored how to reduce the annotation cost in the LLMs era. To bridge this, we revolutionize traditional active learning and propose an innovative collaborative learning framework FreeAL to interactively distill and filter the task-specific knowledge from LLMs. During collaborative training, an LLM serves as an active annotator inculcating its coarse-grained knowledge, while a downstream SLM is incurred as a student to filter out high-quality in-context samples to feedback LLM for the subsequent label refinery. Extensive experiments on eight benchmark datasets demonstrate that FreeAL largely enhances the zero-shot performances for both SLM and LLM without any human supervision. The code is available at https://github.com/Justherozen/FreeAL .Comment: Accepted to EMNLP 2023 (Main conference

Unilateral Microinjection of Acrolein into Thoracic Spinal Cord Produces Acute and Chronic Injury and Functional Deficits

Although lipid peroxidation has long been associated with spinal cord injury (SCI), the specific role of lipid peroxidation-derived byproducts such as acrolein in mediating damage remains to be fully understood. Acrolein, an α-β unsaturated aldehyde, is highly reactive with proteins, DNA, and phospholipids and is considered as a second toxic messenger that disseminates and augments initial free radical events. Previously, we showed that acrolein increased following traumatic SCI and injection of acrolein induced tissue damage. Here, we demonstrate that microinjection of acrolein into the thoracic spinal cord of adult rats resulted in dose-dependent tissue damage and functional deficits. At 24 h (acute) after the microinjection, tissue damage, motoneuron loss, and spinal cord swelling were observed on sections stained with Cresyl Violet. Luxol fast blue staining further showed that acrolein injection resulted in dose-dependent demyelination. At 8 weeks (chronic) after the microinjection, cord shrinkage, astrocyte activation, and macrophage infiltration were observed along with tissue damage, neuron loss, and demyelination. These pathological changes resulted in behavioral impairments as measured by both the Basso, Beattie, and Bresnahan (BBB) locomotor rating scale and grid walking analysis. Electron microscopy further demonstrated that acrolein induced axonal degeneration, demyelination, and macrophage infiltration. These results, combined with our previous reports, strongly suggest that acrolein may play a critical causal role in the pathogenesis of SCI and that targeting acrolein could be an attractive strategy for repair after SCI