Prompt Tuning on Graph-augmented Low-resource Text Classification

Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with no or few labeled samples, presents a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) to address low-resource text classification in a two-pronged approach. During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore handcrafted discrete prompts and continuous prompt tuning for the jointly pre-trained model to achieve zero- and few-shot classification, respectively. Besides, for generalizing continuous prompts to unseen classes, we propose conditional prompt tuning on graphs (G2P2$^*$). Extensive experiments on four real-world datasets demonstrate the strength of G2P2 in zero- and few-shot low-resource text classification tasks, and illustrate the advantage of G2P2$^*$ in dealing with unseen classes.Comment: 14 pages, journal under review. arXiv admin note: substantial text overlap with arXiv:2305.0332

Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.Comment: 7 pages, Accepted by CIKM23 Applied Research Trac

First-principles Methodology for studying magnetotransport in magnetic materials

Unusual magnetotransport behaviors such as temperature dependent negative magnetoresistance(MR) and bowtie-shaped MR have puzzled us for a long time. Although several mechanisms have been proposed to explain them, the absence of comprehensive quantitative calculations has made these explanations less convincing. In our work, we introduce a methodology to study the magnetotransport behaviors in magnetic materials. This approach integrates anomalous Hall conductivity induced by Berry curvature, with a multi-band ordinary conductivity tensor, employing a combination of first-principles calculations and semi-classical Boltzmann transport theory. Our method incorporates both the temperature dependency of relaxation time and anomalous Hall conductivity, as well as the field dependency of anomalous Hall conductivity. We initially test this approach on two-band models and then apply it to a Weyl semimetal \CSS. The results, which align well with experimental observations in terms of magnetic field and temperature dependencies, demonstrate the efficacy of our approach. Additionally, we have investigated the distinct behaviors of magnetoresistance (MR) and Hall resistivities across various types of magnetic materials. This methodology provides a comprehensive and efficient means to understand the underlying mechanisms of the unusual behaviors observed in magneto-transport measurements in magnetic materials.Comment: 6 pages, 4 figure

Rethinking Image Editing Detection in the Era of Generative AI Revolution

The accelerated advancement of generative AI significantly enhance the viability and effectiveness of generative regional editing methods. This evolution render the image manipulation more accessible, thereby intensifying the risk of altering the conveyed information within original images and even propagating misinformation. Consequently, there exists a critical demand for robust capable of detecting the edited images. However, the lack of comprehensive dataset containing images edited with abundant and advanced generative regional editing methods poses a substantial obstacle to the advancement of corresponding detection methods. We endeavor to fill the vacancy by constructing the GRE dataset, a large-scale generative regional editing dataset with the following advantages: 1) Collection of real-world original images, focusing on two frequently edited scenarios. 2) Integration of a logical and simulated editing pipeline, leveraging multiple large models in various modalities. 3) Inclusion of various editing approaches with distinct architectures. 4) Provision of comprehensive analysis tasks. We perform comprehensive experiments with proposed three tasks: edited image classification, edited method attribution and edited region localization, providing analysis of distinct editing methods and evaluation of detection methods in related fields. We expect that the GRE dataset can promote further research and exploration in the field of generative region editing detection

Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow

The advancement of generative AI has extended to the realm of Human Dance Generation, demonstrating superior generative capacities. However, current methods still exhibit deficiencies in achieving spatiotemporal consistency, resulting in artifacts like ghosting, flickering, and incoherent motions. In this paper, we present Dance-Your-Latents, a framework that makes latents dance coherently following motion flow to generate consistent dance videos. Firstly, considering that each constituent element moves within a confined space, we introduce spatial-temporal subspace-attention blocks that decompose the global space into a combination of regular subspaces and efficiently model the spatiotemporal consistency within these subspaces. This module enables each patch pay attention to adjacent areas, mitigating the excessive dispersion of long-range attention. Furthermore, observing that body part's movement is guided by pose control, we design motion flow guided subspace align & restore. This method enables the attention to be computed on the irregular subspace along the motion flow. Experimental results in TikTok dataset demonstrate that our approach significantly enhances spatiotemporal consistency of the generated videos.Comment: 10 pages, 5 figure

Ordovician successions in southern-central Xizang (Tibet), China—Refining the stratigraphy of the Himalayan and Lhasa terranes

The Ordovician stratigraphy of southern-central Xizang (Tibet) has been revised based on new conodont data recovered from 43 samples in four stratigraphic units and their integration with existing nautiloid and graptolite data. The Histiodella holodentata and Pygodus serra biozones have been identified respectively in the Alai and Jiaqu formations of the Chiatsun Group exposed near Alai village in Nyalam County within the Himalayan terrane, and the Yangtzeplacognathus foliaceus Subbiozone (lower part of the Pygodus serra Biozone) in the Sangqu Formation exposed at the Guyu section within Zayu County in the Lhasa terrane. Recognition of these biozones has increased the precision of correlation of the middle-upper Darriwilian strata in the region. Regional reassessment of the Ordovician stratigraphy permitted by new biostratigraphic data has allowed revised definitions for the Chiatsun and Keerduo groups and the Sangqu and Xainza formations. The Chiatsun Group is defined herein to include three lithologically distinctive formations in descending order, the Jiaqu, the Alai and the Adang formations. The stratigraphic age for the Jiaqu and Alai formations in the type area ranges from the middle Darriwilian (Histiodella holodentata Biozone) to middle Katian (Hamarodus brevirameus Biozone), but the age of the Adang Formation remains less certain

Application of the (fr)AGILE scale in the evaluation of multidimensional frailty in elderly inpatients from internal medicine wards: a cross-sectional observational study

BackgroundWith the rapid growth of an aging global population and proportion, the prevalence of frailty is constantly increasing. Therefore, finding a frailty assessment tool suitable for clinical application by physicians has become the primary link in the comprehensive management of frailty in elderly patients. This study used the (fr)AGILE scale to investigate the frailty status of elderly patients from internal medicine wards and identified relevant factors that affect the severity of frailty.MethodIn this study, 408 elderly inpatients in internal medicine departments of Qilu Hospital of Shandong University from May 2021 to August 2022 were enrolled as research subjects, and a cross-sectional observational study was conducted. Researchers evaluated the frailty based on the (fr)AGILE scale score. The general condition, past medical history, physical examination, laboratory examination, nutrition control score, intervention and treatment measures and other elderly patient information was collected. Logistic regression analysis was used to analyze the relevant factors that affect the severity of frailty and hospitalization costs.ResultsAccording to the (fr)AGILE scale score, the elderly patients were divided into groups to determine whether they were frail and the severity of the frailty. Among them, 164 patients were in the prefrailty stage, which accounted for 40.2%. There were 188 cases of mild frailty that accounted for 46.1%, and 56 cases of moderate to severe frailty that accounted for 13.7%. Decreased grip strength, elevated white blood cell levels, and low sodium and potassium are independent risk factors affecting the severity of frailty. As the severity of frailty increases, the proportion of sodium, potassium, albumin supplementation as well as anti-infection gradually increases.ConclusionFrailty is a common elderly syndrome with a high incidence among elderly patients in internal medicine departments. The main manifestations of frailty vary with different severity levels. Inflammation, anemia, and poor nutritional status can lead to an increase in the severity of frailty as well as blood hypercoagulability, myocardial damage, and additional supportive interventions. This ultimately leads to prolonged hospitalization and increased hospitalization costs

Multiple sclerosis and breast cancer risk: a meta-analysis of observational and Mendelian randomization studies

BackgroundSeveral observational studies have explored the relationships between multiple sclerosis (MS) and breast cancer; however, whether an association exists remains unknown.MethodsWe conducted a meta-analysis of observational studies and Mendelian randomization (MR) based on genetic variants to identify the relationship between MS and breast cancer. The observational studies were searched from PubMed, Embase, Web of Science, and Scopus to assess the relationship between MS and breast cancer from inception to 07 Nov 2022. Moreover, we explored the association between genetically pre-disposed MS and breast cancer risk based on an MR study. The summary analysis for MS from two separate databases [International Multiple Sclerosis Genetics Consortium (IMSGC), FinnGen] and the summary analysis for breast cancer from Breast Cancer Association Consortium.ResultsFifteen cohort studies involving 173,565 female MS patients were included in this meta-analysis. The correlation between MS and breast cancer was not statistically significant [relative ratio (RR) = 1.08, 95% confidence interval (CI) = 0.99–1.17]. In the MR analysis, we did not observe causal associations of genetically determined MS with breast cancer and its subtypes from both the IMSGC and FinnGen datasets.ConclusionThe meta-analysis of observational and MR based on genetic variants does not support the correlation between MS and breast cancer