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

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

Efficient dynamic service maintenance for edge services

The emergence of many new computing applications, such as Internet of Vehicles (IoV) and smart homes, has been made possible by the large pool of cloud resources and services. However, the cloud computing paradigm is unable to meet the requirements of delay-sensitive business applications, such as low latency, mobility support, and location awareness. In this context, Mobile Edge Computing (MEC) is introduced to improve the quality of experience (QoE) by bringing cloud resources and services closer to the user by leveraging available resources in the edge networks. However, the performance of MEC is dynamic in nature due to its location awareness, mobility and proximity. As a result, an effective mechanism is needed for providing efficient dynamic service maintenance for edge services. In this paper, we propose applying the Skyline Graph Model and employing the Directed Acyclic Graph theory to store and update mobile edge services. Specifically, the Skyline Graph (SG) algorithm is designed to solve the insertion, deletion, updating and searching of mobile edge services to achieve efficient maintenance for edge services. Comprehensive experiments are conducted on both real-world web services and simulated datasets to evaluate the effectiveness and efficiency of our approaches. The results show that our algorithms can achieve significantly better performance and robustness than the baseline algorithm

Macrophages—bone marrow mesenchymal stem cells crosstalk in bone healing

Bone healing is associated with many orthopedic conditions, including fractures and osteonecrosis, arthritis, metabolic bone disease, tumors and periprosthetic particle-associated osteolysis. How to effectively promote bone healing has become a keen topic for researchers. The role of macrophages and bone marrow mesenchymal stem cells (BMSCs) in bone healing has gradually come to light with the development of the concept of osteoimmunity. Their interaction regulates the balance between inflammation and regeneration, and when the inflammatory response is over-excited, attenuated, or disturbed, it results in the failure of bone healing. Therefore, an in-depth understanding of the function of macrophages and bone marrow mesenchymal stem cells in bone regeneration and the relationship between the two could provide new directions to promote bone healing. This paper reviews the role of macrophages and bone marrow mesenchymal stem cells in bone healing and the mechanism and significance of their interaction. Several new therapeutic ideas for regulating the inflammatory response in bone healing by targeting macrophages and bone marrow mesenchymal stem cells crosstalk are also discussed

Granulocyte colony-stimulating factor affects the distribution and clonality of TRGV and TRDV repertoire of T cells and graft-versus-host disease

<p>Abstract</p> <p>Background</p> <p>The immune modulatory effect of granulocyte colony-stimulating factor (G-CSF) on T cells resulted in an unexpected low incidence of graft-versus-host disease (GVHD) in allogeneic peripheral blood stem cell transplantation (allo-PBSCT). Recent data indicated that gamma delta⁺T cells might participate in mediating graft-versus-host disease (GVHD) and graft-versus-leukemia (GVL) effect after allogeneic hematopoietic stem cell transplantation. However, whether G-CSF could influence the T cell receptors (TCR) of gamma delta⁺T cells (<it>TRGV </it>and <it>TRDV </it>repertoire) remains unclear. To further characterize this feature, we compared the distribution and clonality of <it>TRGV </it>and <it>TRDV </it>repertoire of T cells before and after G-CSF mobilization and investigated the association between the changes of TCR repertoire and GVHD in patients undergoing G-CSF mobilized allo-PBSCT.</p> <p>Methods</p> <p>The complementarity-determining region 3 (CDR3) sizes of three <it>TRGV </it>and eight <it>TRDV </it>subfamily genes were analyzed in peripheral blood mononuclear cells (PBMCs) from 20 donors before and after G-CSF mobilization, using RT-PCR and genescan technique. To determine the expression levels of <it>TRGV </it>subfamily genes, we performed quantitative analysis of <it>TRGV</it>I~III subfamilies by real-time PCR.</p> <p>Results</p> <p>The expression levels of three <it>TRGV </it>subfamilies were significantly decreased after G-CSF mobilization (<it>P </it>= 0.015, 0.009 and 0.006, respectively). The pattern of <it>TRGV </it>subfamily expression levels was <it>TRGV</it>II ><it>TRGV </it>I ><it>TRGV </it>III before mobilization, and changed to <it>TRGV </it>I ><it>TRGV </it>II ><it>TRGV </it>III after G-CSF mobilization. The expression frequencies of <it>TRGV </it>and <it>TRDV </it>subfamilies changed at different levels after G-CSF mobilization. Most <it>TRGV </it>and <it>TRDV </it>subfamilies revealed polyclonality from pre-G-CSF-mobilized and G-CSF-mobilized samples. Oligoclonality was detected in <it>TRGV </it>and <it>TRDV </it>subfamilies in 3 donors before mobilization and in another 4 donors after G-CSF mobilization, distributed in <it>TRGV</it>II, <it>TRDV</it>1, <it>TRDV</it>3 and <it>TRDV</it>6, respectively. Significant positive association was observed between the invariable clonality of <it>TRDV</it>1 gene repertoire after G-CSF mobilization and low incidence of GVHD in recipients (<it>P </it>= 0.015, <it>OR </it>= 0.047).</p> <p>Conclusions</p> <p>G-CSF mobilization not only influences the distribution and expression levels of <it>TRGV </it>and <it>TRDV </it>repertoire, but also changes the clonality of gamma delta⁺T cells. This alteration of <it>TRGV </it>and <it>TRDV </it>repertoire might play a role in mediating GVHD in G-CSF mobilized allo-PBSCT.</p