Failure mechanisms and dynamic process control measures of deep buried tunnels in tectonic fracture zones under high in-situ stresses—a case study in Southwestern China

Squeezing deformation in tectonic fracture zones under high in-situ stresses has created great difficulties to deep tunnel construction in Southwestern China. This study reports an investigation on large deformation and failure mechanisms of the Wanhe tunnel on the China-Laos Railway through several field tests including the in-situ stress, loosened zone, deformation monitoring, and internal stresses of steel arches. The dynamic process control method is proposed following the combination principle of stress releasing and support resistance. Further, the dynamic process control measures including the advanced and primary supports, the deep-shallow coupled delayed grouting method, and the double steel arches method were applied on site to resist the deformation development. The results of this study indicate that the rapid growth of the tunnel deformation in the early stage was caused by the squeezing effect, and later the loosening effect led to another growing trend of the vault settlement. The dynamic process control method allows to release the deformation of the surrounding rock in the rapid growth stage. Then, it requires to control the deformation within the reserved range by reinforcing the surrounding rock and increasing the stiffness of supports in the later stage. From the feedback of monitoring results, large deformation of Wanhe tunnel was well released and effectively controlled within the deformation allowance. Thus these countermeasures based on the dynamic process control method can guarantee the construction safety of deep buried tunnels in tectonic fracture zones under high in-situ stresses

Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification

Data imbalance between common and rare diseases during model training often causes intelligent diagnosis systems to have biased predictions towards common diseases. The state-of-the-art approaches apply a two-stage learning framework to alleviate the class-imbalance issue, where the first stage focuses on training of a general feature extractor and the second stage focuses on fine-tuning the classifier head for class rebalancing. However, existing two-stage approaches do not consider the fine-grained property between different diseases, often causing the first stage less effective for medical image classification than for natural image classification tasks. In this study, we propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Extensive experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches, suggesting that metric learning can be used as an effective plug-in component in the two-stage framework for fine-grained class-imbalanced image classification tasks

HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models

Fairness has become a trending topic in natural language processing (NLP), which addresses biases targeting certain social groups such as genders and religions. However, regional bias in language models (LMs), a long-standing global discrimination problem, still remains unexplored. This paper bridges the gap by analysing the regional bias learned by the pre-trained language models that are broadly used in NLP tasks. In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups. We accordingly propose a HiErarchical Regional Bias evaluation method (HERB) utilising the information from the sub-region clusters to quantify the bias in pre-trained LMs. Experiments show that our hierarchical metric can effectively evaluate the regional bias with respect to comprehensive topics and measure the potential regional bias that can be propagated to downstream tasks. Our codes are available at https://github.com/Bernard-Yang/HERB.Comment: Accepted at AACL 2022 as Long Finding

Chinese Open Instruction Generalist: A Preliminary Release

Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat.openai.com/}}. Despite impressive progress in English-oriented large-scale language models (LLMs), it is still under-explored whether English-based foundation LLMs can perform similarly on multilingual tasks compared to English tasks with well-designed instruction tuning and how we can construct the corpora needed for the tuning. To remedy this gap, we propose the project as an attempt to create a Chinese instruction dataset by various methods adapted to the intrinsic characteristics of 4 sub-tasks. We collect around 200k Chinese instruction tuning samples, which have been manually checked to guarantee high quality. We also summarize the existing English and Chinese instruction corpora and briefly describe some potential applications of the newly constructed Chinese instruction corpora. The resulting \textbf{C}hinese \textbf{O}pen \textbf{I}nstruction \textbf{G}eneralist (\textbf{COIG}) corpora are available in Huggingface\footnote{\url{https://huggingface.co/datasets/BAAI/COIG}} and Github\footnote{\url{https://github.com/FlagOpen/FlagInstruct}}, and will be continuously updated

MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training

Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is primarily due to the distinctive challenges associated with modelling musical knowledge, particularly its tonal and pitched characteristics of music. To address this research gap, we propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training. In our exploration, we identified a superior combination of teacher models, which outperforms conventional speech and audio approaches in terms of performance. This combination includes an acoustic teacher based on Residual Vector Quantization - Variational AutoEncoder (RVQ-VAE) and a musical teacher based on the Constant-Q Transform (CQT). These teachers effectively guide our student model, a BERT-style transformer encoder, to better model music audio. In addition, we introduce an in-batch noise mixture augmentation to enhance the representation robustness. Furthermore, we explore a wide range of settings to overcome the instability in acoustic language model pre-training, which allows our designed paradigm to scale from 95M to 330M parameters. Experimental results indicate that our model can generalise and perform well on 14 music understanding tasks and attains state-of-the-art (SOTA) overall scores. The code and models are online: https://github.com/yizhilll/MERT

A4. En tekst om å ville â og ikke ville være vanlig

People living outside conventional families have to grapple with the concept of ordinariness. If their lives are not seen as ordinary intimate lives, what life choices and narrative choices do they have in claiming and responding to this extraordinariness? The article explores ordinariness as a theoretical and cultural concept, and shows how both theoretical approaches and self-narratives can have very different as well as ambivalent attitudes towards ordinariness

Confinement of carbon dots localizing to the ultrathin layered double hydroxides toward simultaneous triple-mode bioimaging and photothermal therapy

It is a great challenge to develop multifunctional nanocarriers for cancer diagnosis and therapy. Herein, versatile CDs/ICG-uLDHs nanovehicles for triple-modal fluorescence/photoacoustic/two-photon bioimaging and effective photothermal therapy were prepared via a facile self-assembly of red emission carbon dots (CDs), indocyanine green (ICG) with the ultrathin layered double hydroxides (uLDHs). Due to the J-aggregates of ICG constructed in the self-assembly process, CDs/ICG-uLDHs was able to stabilize the photothermal agent ICG and enhanced its photothermal efficiency. Furthermore, the unique confinement effect of uLDHs has extended the fluorescence lifetime of CDs in favor of bioimaging. Considering the excellent in vitro and in vivo phototherapeutics and multimodal imaging effects, this work provides a promising platform for the construction of multifunctional theranostic nanocarrier system for the cancer treatment

Towards automated 10?30 m resolution land cover mapping in insular South-East Asia

10.1080/10106049.2017.1408700Geocarto International3404443-45