Disclosure and Cross-listing: Evidence from Asia-Pacific Firms

Purpose – The purpose of this paper is to examine whether both country disclosure environment and firm-level disclosures are associated with cross-listing in the USA or London or otherwise. Design/methodology/approach – The authors test the association using a sample of Asia-Pacific firms covered in the Standard and Poor\u27s, 2001/2002 disclosure survey, capturing the country-level disclosure using the Center for International Financial Analysis and Research (CIFAR) score. The firm-level disclosure is measured using the S&P disclosure score. The authors conduct a logistic regression analysis and a two-stage least squares analysis to examine whether the outcome, cross-listing or not, is associated with the country disclosure environment and firm-level disclosures. Findings – The authors find that Asia-Pacific firms from weak disclosure environments and having higher firm-level disclosure scores are more likely to seek listing in the USA. Further, the paper provides initial evidence that these Asia-Pacific firms are as likely to seek listing in London as in the USA. No significant difference was found in S&P scores between US and London cross-listings after controlling for the effects of other variables. This suggests that firms that cross-list in London present similar disclosure levels to firms that cross-list in the USA. Originality/value – The paper\u27s findings contribute to the cross-listing literature on disclosure by showing that the interaction between firm-level disclosure and country-level disclosure has an impact on whether a firm cross-lists in the USA/London or not. The authors\u27 comparison of US cross-listings versus London cross-listings provides the first evidence that disclosures of US and London cross-listings are not significantly different

Experiments and Models of Thermo-Induced Shape Memory Polymers

Recent advances in experiments and models of thermo-induced shape memory polymers (TSMPs) were reviewed. Some important visco-elastic and visco-plastic features, such as rate-dependent and temperature-dependent stress-strain curves and nonuniform temperature distribution were experimentally investigated, and the interaction between the mechanical deformation and the internal heat generation was discussed. The influences of loading rate and peak strain on the shape memory effect (SME) and shape memory degeneration of TSMPs were revealed under monotonic and cyclic thermo-mechanical loadings, respectively. Based on experimental observations, the capability of recent developed visco-elastic and visco-plastic models for predicting the SME was evaluated, and the thermo-mechanically coupled models were used to reasonably predict the thermo-mechanical responses of TSMPs

Few-shot Class-incremental Audio Classification Using Stochastic Classifier

It is generally assumed that number of classes is fixed in current audio classification methods, and the model can recognize pregiven classes only. When new classes emerge, the model needs to be retrained with adequate samples of all classes. If new classes continually emerge, these methods will not work well and even infeasible. In this study, we propose a method for fewshot class-incremental audio classification, which continually recognizes new classes and remember old ones. The proposed model consists of an embedding extractor and a stochastic classifier. The former is trained in base session and frozen in incremental sessions, while the latter is incrementally expanded in all sessions. Two datasets (NS-100 and LS-100) are built by choosing samples from audio corpora of NSynth and LibriSpeech, respectively. Results show that our method exceeds four baseline ones in average accuracy and performance dropping rate. Code is at https://github.com/vinceasvp/meta-sc.Comment: 5 pages, 3 figures, 4 tables. Accepted for publication in INTERSPEECH 202

Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration

Recent efforts have been made on acoustic scene classification in the audio signal processing community. In contrast, few studies have been conducted on acoustic scene clustering, which is a newly emerging problem. Acoustic scene clustering aims at merging the audio recordings of the same class of acoustic scene into a single cluster without using prior information and training classifiers. In this study, we propose a method for acoustic scene clustering that jointly optimizes the procedures of feature learning and clustering iteration. In the proposed method, the learned feature is a deep embedding that is extracted from a deep convolutional neural network (CNN), while the clustering algorithm is the agglomerative hierarchical clustering (AHC). We formulate a unified loss function for integrating and optimizing these two procedures. Various features and methods are compared. The experimental results demonstrate that the proposed method outperforms other unsupervised methods in terms of the normalized mutual information and the clustering accuracy. In addition, the deep embedding outperforms many state-of-the-art features.Comment: 9 pages, 6 figures, 11 tables. Accepted for publication in IEEE TM

Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes

New classes of sounds constantly emerge with a few samples, making it challenging for models to adapt to dynamic acoustic environments. This challenge motivates us to address the new problem of few-shot class-incremental audio classification. This study aims to enable a model to continuously recognize new classes of sounds with a few training samples of new classes while remembering the learned ones. To this end, we propose a method to generate discriminative prototypes and use them to expand the model's classifier for recognizing sounds of new and learned classes. The model is first trained with a random episodic training strategy, and then its backbone is used to generate the prototypes. A dynamic relation projection module refines the prototypes to enhance their discriminability. Results on two datasets (derived from the corpora of Nsynth and FSD-MIX-CLIPS) show that the proposed method exceeds three state-of-the-art methods in average accuracy and performance dropping rate.Comment: 5 pages,2 figures, Accepted by Interspeech 202

Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet

We present a work on low-complexity acoustic scene classification (ASC) with multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge. This subtask focuses on classifying audio samples of multiple devices with a low-complexity model, where two main difficulties need to be overcome. First, the audio samples are recorded by different devices, and there is mismatch of recording devices in audio samples. We reduce the negative impact of the mismatch of recording devices by using some effective strategies, including data augmentation (e.g., mix-up, spectrum correction, pitch shift), usages of multi-patch network structure and channel attention. Second, the model size should be smaller than a threshold (e.g., 128 KB required by the DCASE2021 challenge). To meet this condition, we adopt a ResNet with both depthwise separable convolution and channel attention as the backbone network, and perform model compression. In summary, we propose a low-complexity ASC method using data augmentation and a lightweight ResNet. Evaluated on the official development and evaluation datasets, our method obtains classification accuracy scores of 71.6% and 66.7%, respectively; and obtains Log-loss scores of 1.038 and 1.136, respectively. Our final model size is 110.3 KB which is smaller than the maximum of 128 KB.Comment: 5 pages, 5 figures, 4 tables. Accepted for publication in the 16th IEEE International Conference on Signal Processing (IEEE ICSP

The role of tumor-associated macrophages in the progression, prognosis and treatment of endometrial cancer

Tumor-associated macrophages (TAMs) are the main immune cells in the tumor microenvironment (TME) of endometrial cancer (EC). TAMs recruitment and polarization in EC is regulated by the TME of EC, culminating in a predominantly M2-like macrophage infiltration. TAMs promote lymphatic angiogenesis through cytokine secretion, aid immune escape of EC cells by synergizing with other immune cells, and contribute to the development of EC through secretion of exosomes so as to promoting EC development. EC is a hormone- and metabolism-dependent cancer, and TAMs promote EC through interactions on estrogen receptor (ER) and metabolic factors such as the metabolism of glucose, lipids, and amino acids. In addition, we have explored the predictive significance of some TAM-related indicators for EC prognosis, and TAMs show remarkable promise as a target for EC immunotherapy

Exploration of Macro-Micro Biomarkers for Dampness-Heat Syndrome Differentiation in Different Diseases

Increased attention is being paid to traditional Chinese medicine (TCM) as a complementary and alternative medicine to provide an effective approach for personalized diagnosis and clinical treatment. TMC performs treatment based on differentiation of TCM syndrome (ZHENG), which may identify special phenotypes by symptoms and signs of patients even if they are in different diseases. There has, however, been skepticism and criticism because syndrome classification only depends on observation, knowledge, and clinical experience of TCM practitioners, which lacks objectivity and repeatability. In order to transform syndrome classification into mainstream medicine, we introduce a macro-micro approach that combines symptoms, clinical indicators, and metabolites. The present paper explores the macro-micro biomarkers of dampness-heat syndrome in chronic hepatitis B and nonalcoholic fatty liver patients, which could provide the basis for developing a possible population-screening tool for selecting target individuals and creating an evaluation index for personalized treatment

Molecular analysis of phosphomannomutase (PMM) genes reveals a unique PMM duplication event in diverse Triticeae species and the main PMM isozymes in bread wheat tissues

BACKGROUND: Phosphomannomutase (PMM) is an essential enzyme in eukaryotes. However, little is known about PMM gene and function in crop plants. Here, we report molecular evolutionary and biochemical analysis of PMM genes in bread wheat and related Triticeae species. RESULTS: Two sets of homoeologous PMM genes (TaPMM-1 and 2) were found in bread wheat, and two corresponding PMM genes were identified in the diploid progenitors of bread wheat and many other diploid Triticeae species. The duplication event yielding PMM-1 and 2 occurred before the radiation of diploid Triticeae genomes. The PMM gene family in wheat and relatives may evolve largely under purifying selection. Among the six TaPMM genes, the transcript levels of PMM-1 members were comparatively high and their recombinant proteins were all enzymatically active. However, PMM-2 homoeologs exhibited lower transcript levels, two of which were also inactive. TaPMM-A1, B1 and D1 were probably the main active isozymes in bread wheat tissues. The three isozymes differed from their counterparts in barley and Brachypodium distachyon in being more tolerant to elevated test temperatures. CONCLUSION: Our work identified the genes encoding PMM isozymes in bread wheat and relatives, uncovered a unique PMM duplication event in diverse Triticeae species, and revealed the main active PMM isozymes in bread wheat tissues. The knowledge obtained here improves the understanding of PMM evolution in eukaryotic organisms, and may facilitate further investigations of PMM function in the temperature adaptability of bread wheat

Multi-channel convolutional neural network for targeted sentiment classification

In recent years, targeted sentiment analysis has received great attention as a fine-grained sentiment analysis. Determining the sentiment polarity of a specific target in a sentence is the main task. This paper proposes a multi-channel convolutional neural network (MCL-CNN) for targeted sentiment classification. Our approach can not only parallelize over the words of a sentence, but also extract local features effectively. Contexts and targets can be more comprehensively utilized by using part-of-speech information, semantic information and interactive information, so that diverse features can be obtained. Finally, experimental results on the SemEval 2014 dataset demonstrate the effectiveness of this method