An Offensive Pragmatic Analysis of “不然呢” in Spoken Chinese

This article takes real cases as the starting point to explore the reasons why “不然呢” leads to impolite feelings. Firstly, the pragmatic function and historical origin of the relationship between “不然呢” and “不然” are discovered. Then, by comparing “你说呢” and “不然呢” horizontally in the same context, it is concluded that in addition to the language structure of “不然呢” itself, offensive speech acts in conversation are influenced by factors such as the social identity of both parties, kinship, and language sensitivity of the recipient. Finally, it was found that “不然呢” has a trend of being widely used in spoken language, with its meaning becoming blurred and its function in expressing emotions and tones greatly strengthened

Analysis of Tourism Copywriting for Chinese International Teachers from a Multimodal Perspective

International Chinese language volunteers often need to introduce Chinese tourism culture when teaching abroad. Due to the inability of learners to personally experience China in foreign environments, it can have a significant impact on teaching effectiveness. The multimodal form of combining images and text can more intuitively help overseas Chinese language learners understand and understand China, enrich their Chinese language learning after class, and satisfy their curiosity and longing for China. This article uses Halliday’s multimodal theory to organize and analyze 43 tourism texts edited by international Chinese language volunteers in teaching from four aspects: cultural level, contextual level, meaning level, and formal level. It also summarizes the precautions that Chinese language international teachers should pay attention to when writing tourism texts

Learning and Prediction Theory of Distributed Least Squares

With the fast development of the sensor and network technology, distributed estimation has attracted more and more attention, due to its capability in securing communication, in sustaining scalability, and in enhancing safety and privacy. In this paper, we consider a least-squares (LS)-based distributed algorithm build on a sensor network to estimate an unknown parameter vector of a dynamical system, where each sensor in the network has partial information only but is allowed to communicate with its neighbors. Our main task is to generalize the well-known theoretical results on the traditional LS to the current distributed case by establishing both the upper bound of the accumulated regrets of the adaptive predictor and the convergence of the distributed LS estimator, with the following key features compared with the existing literature on distributed estimation: Firstly, our theory does not need the previously imposed independence, stationarity or Gaussian property on the system signals, and hence is applicable to stochastic systems with feedback control. Secondly, the cooperative excitation condition introduced and used in this paper for the convergence of the distributed LS estimate is the weakest possible one, which shows that even if any individual sensor cannot estimate the unknown parameter by the traditional LS, the whole network can still fulfill the estimation task by the distributed LS. Moreover, our theoretical analysis is also different from the existing ones for distributed LS, because it is an integration of several powerful techniques including stochastic Lyapunov functions, martingale convergence theorems, and some inequalities on convex combination of nonnegative definite matrices.Comment: 14 pages, submitted to IEEE Transactions on Automatic Contro

The Future of Urban Resilience

A collaborative knowledge exchange between MA Narrative Environments at CSM and Arup Foresight. Each multi-disciplinary group create an animated storyboard, from the viewpoint of a different persona, showing how communities in and around Kings Cross may fragment, cohere or transform as they encounter extreme weather events in 2025

Leave No Stone Unturned: Mine Extra Knowledge for Imbalanced Facial Expression Recognition

Facial expression data is characterized by a significant imbalance, with most collected data showing happy or neutral expressions and fewer instances of fear or disgust. This imbalance poses challenges to facial expression recognition (FER) models, hindering their ability to fully understand various human emotional states. Existing FER methods typically report overall accuracy on highly imbalanced test sets but exhibit low performance in terms of the mean accuracy across all expression classes. In this paper, our aim is to address the imbalanced FER problem. Existing methods primarily focus on learning knowledge of minor classes solely from minor-class samples. However, we propose a novel approach to extract extra knowledge related to the minor classes from both major and minor class samples. Our motivation stems from the belief that FER resembles a distribution learning task, wherein a sample may contain information about multiple classes. For instance, a sample from the major class surprise might also contain useful features of the minor class fear. Inspired by that, we propose a novel method that leverages re-balanced attention maps to regularize the model, enabling it to extract transformation invariant information about the minor classes from all training samples. Additionally, we introduce re-balanced smooth labels to regulate the cross-entropy loss, guiding the model to pay more attention to the minor classes by utilizing the extra information regarding the label distribution of the imbalanced training data. Extensive experiments on different datasets and backbones show that the two proposed modules work together to regularize the model and achieve state-of-the-art performance under the imbalanced FER task. Code is available at https://github.com/zyh-uaiaaaa.Comment: Accepted by NeurIPS202

Design and High-Throughput Screening of High Entropy Alloys

A balanced parameter was proposed to design the high entropy alloys (HEAs), which defined by average melting temperature Tm times entropy of mixing ΔSm over enthalpy of mixing ΔHm, Ω=TmΔSm/ΔHm, if Ω is larger than 1.1, we can predict that the entropy is high enough to overcome the enthalpy, and solid solution is likely to form rather than the intermetallic ordered phases. The composition can be further refined by using high-throughput screening by preparing the compositional gradient films. Multiple targets co-sputtering is usually used to prepare the films, and physical masking can separate the samples independently, chemical masking can also applied if possible. One example is the self-sharpening screening by using nanoindentations, the serration behaviors may related to the self-sharpening compositions

Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings

The classification of histopathological images is of great value in both cancer diagnosis and pathological studies. However, multiple reasons, such as variations caused by magnification factors and class imbalance, make it a challenging task where conventional methods that learn from image-label datasets perform unsatisfactorily in many cases. We observe that tumours of the same class often share common morphological patterns. To exploit this fact, we propose an approach that learns similarity-based multi-scale embeddings (SMSE) for magnification-independent histopathological image classification. In particular, a pair loss and a triplet loss are leveraged to learn similarity-based embeddings from image pairs or image triplets. The learned embeddings provide accurate measurements of similarities between images, which are regarded as a more effective form of representation for histopathological morphology than normal image features. Furthermore, in order to ensure the generated models are magnification-independent, images acquired at different magnification factors are simultaneously fed to networks during training for learning multi-scale embeddings. In addition to the SMSE, to eliminate the impact of class imbalance, instead of using the hard sample mining strategy that intuitively discards some easy samples, we introduce a new reinforced focal loss to simultaneously punish hard misclassified samples while suppressing easy well-classified samples. Experimental results show that the SMSE improves the performance for histopathological image classification tasks for both breast and liver cancers by a large margin compared to previous methods. In particular, the SMSE achieves the best performance on the BreakHis benchmark with an improvement ranging from 5% to 18% compared to previous methods using traditional features

TBFormer: Two-Branch Transformer for Image Forgery Localization

Image forgery localization aims to identify forged regions by capturing subtle traces from high-quality discriminative features. In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer). Firstly, two feature extraction branches are elaborately designed, taking advantage of the discriminative stacked Transformer layers, for both RGB and noise domain features. Secondly, an Attention-aware Hierarchical-feature Fusion Module (AHFM) is proposed to effectively fuse hierarchical features from two different domains. Although the two feature extraction branches have the same architecture, their features have significant differences since they are extracted from different domains. We adopt position attention to embed them into a unified feature domain for hierarchical feature investigation. Finally, a Transformer decoder is constructed for feature reconstruction to generate the predicted mask. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed model.Comment: 5 pages, 3 figure