Adapting Deep Learning for Sentiment Classification of Code-Switched Informal Short Text

Nowadays, an abundance of short text is being generated that uses nonstandard writing styles influenced by regional languages. Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification. In this work, we (1) present a labeled dataset called MultiSenti for sentiment classification of code-switched informal short text, (2) explore the feasibility of adapting resources from a resource-rich language for an informal one, and (3) propose a deep learning-based model for sentiment classification of code-switched informal short text. We aim to achieve this without any lexical normalization, language translation, or code-switching indication. The performance of the proposed models is compared with three existing multilingual sentiment classification models. The results show that the proposed model performs better in general and adapting character-based embeddings yield equivalent performance while being computationally more efficient than training word-based domain-specific embeddings

Anomalous thermal properties and spin crossover of ferromagnesite (Mg,Fe)CO3

Ferromagnesite (Mg,Fe)CO3, also referred to as magnesiosiderite at high iron concentration, is a solid solution of magnesite (MgCO3) and siderite (FeCO3). Ferromagnesite is believed to enter the Earth's lower mantle via subduction and is considered a major carbon carrier in the Earth's lower mantle, playing a key role in the Earth's deep carbon cycle. Experiments have shown that ferromagnesite undergoes a pressure-induced spin crossover, accompanied by volume and elastic anomalies, in the lower-mantle pressure range. In this work, we investigate thermal properties of (Mg,Fe)CO3 using first-principles calculations. We show that nearly all thermal properties of ferromagnesite are drastically altered by iron spin crossover, including anomalous reduction of volume, anomalous softening of bulk modulus, and anomalous increases of thermal expansion, heat capacity, and Guneisen parameter. Remarkably, the anomaly of heat capacity remains prominent (up to 40%) at high temperature without smearing out, which suggests that iron spin crossover may significantly affect the thermal properties of subducting slabs and the Earth's deep carbon cycle.Comment: Main text: 24 pages and 8 figures; Supplemental Material: 8 pages, 3 tables, and 1 figur

Constituency Parsing using LLMs

Constituency parsing is a fundamental yet unsolved natural language processing task. In this paper, we explore the potential of recent large language models (LLMs) that have exhibited remarkable performance across various domains and tasks to tackle this task. We employ three linearization strategies to transform output trees into symbol sequences, such that LLMs can solve constituency parsing by generating linearized trees. We conduct experiments using a diverse range of LLMs, including ChatGPT, GPT-4, OPT, LLaMA, and Alpaca, comparing their performance against the state-of-the-art constituency parsers. Our experiments encompass zero-shot, few-shot, and full-training learning settings, and we evaluate the models on one in-domain and five out-of-domain test datasets. Our findings reveal insights into LLMs' performance, generalization abilities, and challenges in constituency parsing

Local Field effects on the radiative lifetime of emitters in surrounding media: virtual- or real-cavity model?

For emitters embedded in media of various refractive indices, different macroscopic or microscopic theoretical models predict different dependencies of the spontaneous emission lifetime on refractive index. Among those models are the two most promising models: the virtual-cavity model and the real-cavity model. It is a priori not clear which model is more relevant for a given situation. By close analysis of the available experimental results and examining the assumptions underlying the two models, we reach a consistent interpretation of the experimental results and give the criteria which model should apply for a given situation.Comment: 12 pages with 4 figure

The effect of continuous venovenous hemofiltration on neutrophil gelatinase-associated lipocalin plasma levels in patients with septic acute kidney injury

International audienceCe texte d’introduction au dossier de Flux 2017/2 (N° 108) questionne l’émergence de la thématique de la circularité des matières dans les politiques publiques urbaines contemporaines. Les articles ont en commun de porter une attention minutieuse à la matérialité des flux qui traversent et constituent la ville et aux objets sociaux qui la composent. Ils analysent les modalités et les conséquences de leur mise en circulation, ainsi que les régulations et les conflits qui l’accompagnent. Que l’ensemble des articles traite de pratiques et de politiques ancrées dans l’espace de la région de Lyon résulte moins d’une volonté monographique que d’une rencontre en partie fortuite. Mais cela souligne en tout cas l’importance d’une approche toujours attentive aux faits géographiques et aux effets de lieu dans la diversité de leurs échelles. Trois thématiques transversales sont présentes : d’abord, en identifiant de nouvelles ressources, les articles permettent de réfléchir à l’invention et à la construction de nouveaux circuits pour les matières. Ensuite, la régulation de ces circuits implique l’identification de nouveaux acteurs et la mise en place de nouvelles formes de relations avec les producteurs et gestionnaires des matières, formant donc l’espace d’une gouvernance renouvelée. Enfin, si ces circuits se structurent dans un espace qui est celui de la proximité géographique, ils s’inscrivent néanmoins dans une logique relationnelle qui ne cesse de questionner les normes et les échelles. Ce numéro permet ainsi de nuancer et de re-matérialiser les injonctions à faire advenir l’économie circulaire dans les villes

Theme Trends and Knowledge Structure on Mobile Health Apps: Bibliometric Analysis

BACKGROUND: Due to the widespread and unprecedented popularity of mobile phones, the use of digital medicine and mobile health apps has seen significant growth. Mobile health apps have tremendous potential for monitoring and treating diseases, improving patient care, and promoting health. OBJECTIVE: This paper aims to explore research trends, coauthorship networks, and the research hot spots of mobile health app research. METHODS: Publications related to mobile health apps were retrieved and extracted from the Web of Science database with no language restrictions. Bibliographic Item Co-Occurrence Matrix Builder was employed to extract bibliographic information (publication year and journal source) and perform a descriptive analysis. We then used the VOSviewer (Leiden University) tool to construct and visualize the co-occurrence networks of researchers, research institutions, countries/regions, citations, and keywords. RESULTS: We retrieved 2802 research papers on mobile health apps published from 2000 to 2019. The number of annual publications increased over the past 19 years. JMIR mHealth and uHealth (323/2802, 11.53%), Journal of Medical Internet Research (106/2802, 3.78%), and JMIR Research Protocols (82/2802, 2.93%) were the most common journals for these publications. The United States (1186/2802, 42.33%), England (235/2802, 8.39%), Australia (215/2802, 7.67%), and Canada (112/2802, 4.00%) were the most productive countries of origin. The University of California San Francisco, the University of Washington, and the University of Toronto were the most productive institutions. As for the authors\u27 contributions, Schnall R, Kuhn E, Lopez-Coronado M, and Kim J were the most active researchers. The co-occurrence cluster analysis of the top 100 keywords forms 5 clusters: (1) the technology and system development of mobile health apps; (2) mobile health apps for mental health; (3) mobile health apps in telemedicine, chronic disease, and medication adherence management; (4) mobile health apps in health behavior and health promotion; and (5) mobile health apps in disease prevention via the internet. CONCLUSIONS: We summarize the recent advances in mobile health app research and shed light on their research frontier, trends, and hot topics through bibliometric analysis and network visualization. These findings may provide valuable guidance on future research directions and perspectives in this rapidly developing field

Chinese Synesthesia Detection: New Dataset and Models

In this paper, we introduce a new task called synesthesia detection, which aims to extract the sensory word of a sentence, and to predict the original and synesthetic sensory modalities of the corresponding sensory word. Synesthesia refers to the description of perceptions in one sensory modality through concepts from other modalities. It involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought and action, which makes it become a bridge between figurative linguistic phenomenon and abstract cognition, and thus be helpful to understand the deep semantics. To address this, we construct a large-scale human-annotated Chinese synesthesia dataset, which contains 7,217 annotated sentences accompanied by 187 sensory words. Based on this dataset, we propose a family of strong and representative baseline models. Upon these baselines, we further propose a radical-based neural network model to identify the boundary of the sensory word, and to jointly detect the original and synesthetic sensory modalities for the word. Through extensive experiments, we observe that the importance of the proposed task and dataset can be verified by the statistics and progressive performances. In addition, our proposed model achieves state-of-the-art results on the synesthesia dataset