Global Theory Research Articles

MaskDGNets: Masked-attention guided dynamic graph aggregation network for event extraction.

Considering that the traditional deep learning event extraction method ignores the correlation between word features and sequence information, it cannot fully explore the hidden associations between events and events and between events and primary attributes. To solve these problems, we developed a new framework for event extraction called the masked attention-guided dynamic graph aggregation network. On the one hand, to obtain effective word representation and sequence representation, an interaction and complementary relationship are established between word vectors and character vectors. At the same time, a squeeze layer is introduced in the bidirectional independent recurrent unit to model the sentence sequence from both positive and negative directions while retaining the local spatial details to the maximum extent and establishing practical long-term dependencies and rich global context representations. On the other hand, the designed masked attention mechanism can effectively balance the word vector features and sequence semantics and refine these features. The designed dynamic graph aggregation module establishes effective connections between events and events, and between events and essential attributes, strengthens the interactivity and association between them, and realizes feature transfer and aggregation on graph nodes in the neighborhood through dynamic strategies to improve the performance of event extraction. We designed a reconstructed weighted loss function to supervise and adjust each module individually to ensure the optimal feature representation. Finally, the proposed MaskDGNets framework is evaluated on two baseline datasets, DuEE and CCKS2020. It demonstrates its robustness and event extraction performance, with F1 of 81.443% and 87.382%, respectively.

Global bifurcation for monotone fronts of elliptic equations

We present two results on global continuation of monotone front-type solutions to elliptic PDEs posed on infinite cylinders. This is done under quite general assumptions, and in particular applies even to fully nonlinear equations as well as quasilinear problems with transmission boundary conditions. Our approach is rooted in the analytic global bifurcation theory of Dancer (1973) and Buffoni–Toland (2003), but extending it to unbounded domains requires contending with new potential limiting behavior relating to loss of compactness. We obtain an exhaustive set of alternatives for the global behavior of the solution curve that is sharp, with each possibility having a direct analogue in the bifurcation theory of second-order ODEs.As a major application of the general theory, we construct global families of internal hydrodynamic bores. These are traveling front solutions of the full two-phase Euler equation in two dimensions. The fluids are confined to a channel that is bounded above and below by rigid walls, with incompressible and irrotational flow in each layer. Small-amplitude fronts for this system have been obtained by several authors. We give the first large-amplitude result in the form of continuous curves of elevation and depression bores. Following the elevation curve to its extreme, we find waves whose interfaces either overturn (develop a vertical tangent) or become exceptionally singular in that the flow in both layers degenerates at a single point on the boundary. For the curve of depression waves, we prove that either the interface overturns or it comes into contact with the upper wall.

Multi-head attention-based variational autoencoders ensemble for remaining useful life prediction of aero-engines

Abstract Accurate remaining useful life (RUL) prediction of aero-engines through condition monitoring (CM) data is of great significance for flight reliability and safety. Although deep learning (DL)-based approaches have been widely considered, individual DL models suffer from significant stochasticity and limited generalizability when predicting the RUL. To solve this issue, a novel multi-head attention-based variational autoencoders (MHAT-VAEs) ensemble model is proposed. Two distinct MHAT-VAEs are designed, employing linear and convolutional operations to capture global and temporal compressed representations of the CM data. Additionally, a dual-level ensemble strategy is introduced to adaptively fuse the outputs of the two base learners. A hyperparameter optimization method is also implemented to further enhance the efficiency and performance of the base learners. The effectiveness of the proposed method is validated using the C-MAPSS and N-CMAPSS datasets, with experimental results showing that it outperforms state-of-the-art approaches.

Multi-scale Information Aggregation for Spoofing Detection

Synthesis artifacts that span scales from small to large are important cues for spoofing detection. However, few spoofing detection models leverage artifacts across different scales together. In this paper, we propose a spoofing detection system built on SincNet and Deep Layer Aggregation (DLA), which leverages speech representations at different levels to distinguish synthetic speech. DLA is totally convolutional with an iterative tree-like structure. The unique topology of DLA makes possible compounding of speech features from convolution layers at different depths, and therefore the local and the global speech representations can be incorporated simultaneously. Moreover, SincNet is employed as the frontend feature extractor to circumvent manual feature extraction and selection. SincNet can learn fine-grained features directly from the input speech waveform, thus making the proposed spoofing detection system end-to-end. The proposed system outperforms the baselines when tested on ASVspoof LA and DF datasets. Notably, our single model surpasses all competing systems in ASVspoof DF competition with an equal error rate (EER) of 13.99%, which demonstrates the importance of multi-scale information aggregation for synthetic speech detection.

The evolution of cyberconflict studies

Abstract In our increasingly digital world, cyberconflict poses a significant challenge to global security, prompting the emergence of an academic field dedicated to its study. Understanding the trajectory, evolution and dominant characteristics of cyberconflict studies is vital for scholars and policy-makers alike. By means of a thorough analysis of articles from the 125 top political science and International Relations journals, we illuminate the field's conceptual and methodological landscape. Our findings uncover a notable Europeanization of the field and a shift towards more rigorous methodologies, particularly experimental designs. While early research focused primarily on conceptual development and strategic topics, the contemporary field of studies exhibits a broader scope and greater diversity of scholarship. While the field has progressed beyond its nascent stages, it has yet to reach its full potential. We address several potential issues facing its growth and relevance. Firstly, there is a lack of global representation among researchers and in the incidents studied. Secondly, embracing interdisciplinary approaches is crucial for addressing newly emerging issues effectively. Thirdly, the diversification of research identities within the field of cyberconflict studies in the US and Europe must be bridged to foster collaboration and exchange of ideas. To overcome these challenges, adopting a pragmatic middle-ground approach is essential.

Seismic random noise suppression via mining multi-scale local and global information

Suppressing random noise is critical for revealing real subsurface structures. Convolutional neural networks (CNNs), the leading seismic data denoising methods, excel at extracting local features but struggle to capture global representations. Unet can extract and reuse multi-scale features, aiding in the precise detection of details and semantic information; however, being based on convolutional operations, it struggles to capture global information. To capture global representations, researchers normally employ Transformers in high-level visual tasks, owing to their self-attention mechanisms. This paper introduces a method for mining multi-scale local and global information based on hybrid-gated Unet (HGUnet), which integrates Transformer, CNN, and Unet architectures to enhance the feature representation capability for seismic random noise suppression tasks. HGUnet comprises hybrid-gated blocks (HGB) embedded within a U-shaped architecture, employing a concurrent structure of Octave convolution and lightweight multi-head self-attention mechanism to efficiently extract multi-scale local and global features simultaneously. Moreover, at the conclusion of the HGB, to precisely leverage information and reduce computing costs, a gated feedforward network is designed to retain valuable information and prune redundancies for feature fusion. Synthetic and field experimental results demonstrate that HGUnet improves denoising quality over traditional and CNN methods without adding significant computing costs.

Global gender representation among presidents of cardiothoracic surgery societies

ObjectiveFemales continue to be underrepresented in academia. An analysis of gender representation among presidents of cardiothoracic surgery societies worldwide was performed. MethodsA comprehensive search was performed to identify cardiothoracic surgical societies present worldwide and divided by regions. Respective Society's official webpage was searched to extract data on past and present presidents. Gender was determined and verified via publicly available online profiles. Proportions and respective 95 ​% confidence interval(CI) were calculated using Binomial exact calculation. ResultsA total of 34 cardiothoracic surgery societies were identified globally, of which only 16 provided information on past presidents in the public domain. A total of 563 past and current society presidents were identified. Women constituted only 16 [2.84 ​%; 95 ​% confidence interval: 1.63 ​%; 4.57 ​%] presidents. The first-ever women president was appointed in the year 2007 by the STSA during the 54th Annual meeting. ConclusionStark lag persists in gender representation of presidential roles among the cardiothoracic societies globally.

Hierarchical Fusion of Infrared and Visible Images Based on Channel Attention Mechanism and Generative Adversarial Networks.

In order to solve the problem that existing visible and infrared image fusion methods rely only on the original local or global information representation, which has the problem of edge blurring and non-protrusion of salient targets, this paper proposes a layered fusion method based on channel attention mechanism and improved Generative Adversarial Network (HFCA_GAN). Firstly, the infrared image and visible image are decomposed into a base layer and fine layer, respectively, by a guiding filter. Secondly, the visible light base layer is fused with the infrared image base layer by histogram mapping enhancement to improve the contour effect. Thirdly, the improved GAN algorithm is used to fuse the infrared and visible image refinement layer, and the depth transferable module and guided fusion network are added to enrich the detailed information of the fused image. Finally, the multilayer convolutional fusion network with channel attention mechanism is used to correlate the local information of the layered fusion image, and the final fusion image containing contour gradient information and useful details is obtained. TNO and RoadSence datasets are selected for training and testing. The results show that the proposed algorithm retains the global structure features of multilayer images and has obvious advantages in fusion performance, model generalization and computational efficiency.

Neural Correlates of Psychedelic, Sleep, and Sedated States Support Global Theories of Consciousness.

Understanding neural mechanisms of consciousness remains a challenging question in neuroscience. A central debate in the field concerns whether consciousness arises from global interactions that involve multiple brain regions or focal neural activity, such as in sensory cortex. Additionally, global theories diverge between the Global Neuronal Workspace (GNW) hypothesis, which emphasizes frontal and parietal areas, and the Integrated Information Theory (IIT), which focuses on information integration within posterior cortical regions. To disentangle the global vs. local and frontoparietal vs. posterior dilemmas, we measured global functional connectivity and local neural synchrony with functional magnetic resonance imaging (fMRI) data across a spectrum of conscious states in humans induced by psychedelics, sleep, and deep sedation. We found that psychedelic states are associated with increased global functional connectivity and decreased local neural synchrony. In contrast, non-REM sleep and deep sedation displayed the opposite pattern, suggesting that consciousness arises from global brain network interactions rather than localized activity. This mirror-image pattern between enhanced and diminished states was observed in both anterior-posterior (A-P) and posterior-posterior (P-P) brain regions but not within the anterior part of the brain alone. Moreover, anterior transmodal regions played a key role in A-P connectivity, while both posterior transmodal and posterior unimodal regions were critical for P-P connectivity. Overall, these findings provide empirical evidence supporting global theories of consciousness in relation to varying states of consciousness. They also bridge the gap between two prominent theories, GNW and IIT, by demonstrating how different theories can converge on shared neuronal mechanisms.

An optimal global biochar application strategy based on matching biochar and soil properties to reduce global cropland greenhouse gas emissions: findings from a global meta-analysis and density functional theory calculation

Biochar has been extensively utilized to amend soil and mitigate greenhouse gas (GHG) emissions from croplands. However, the effectiveness of biochar application in reducing cropland GHG emissions remains uncertain due to variations in soil properties and environmental conditions across regions. In this study, the impact of biochar surface functional groups on soil GHG emissions was investigated using molecular model calculation. Machine learning (ML) technology was applied to predict the responses of soil GHG emissions and crop yields under different biochar feedstocks and application rates, aiming to determine the optimum biochar application strategies based on specific soil properties and environmental conditions on a global scale. The findings suggest that the functional groups play an essential role in determining biochar surface activity and the soil’s capacity for adsorbing GHGs. ML was an effective method in predicting the changes in soil GHG emissions and crop yield following biochar application. Moreover, poor-fertility soils exhibited greater changes in GHG emissions compared to fertile soil. Implementing an optimized global strategy for biochar application may result in a substantial reduction of 684.25 Tg year−1 CO2 equivalent (equivalent to 7.87% of global cropland GHG emissions) while simultaneously improving crop yields. This study improves our understanding of the interaction between biochar surface properties and soil GHG, confirming the potential of global biochar application strategies in mitigating cropland GHG emissions and addressing global climate degradation. Further research efforts are required to optimize such strategies.Graphical

“Leaving No One Behind” As a New Motto for the UN System

Educational governance has become increasingly complex due to the emergence of the post-welfare state together with specialised networks acting across nations, such as the recently formed United Nations (UN) Sustainable Development Group. In this context, the global circulation of policies, priorities and lifelong learning narratives impacts national agendas through frameworks and diverse multilevel policy instruments. Adopting elements of global governance theory, this article examines the 2017 Shared Framework on Leaving No One Behind and argues that it has served as a milestone in supporting the implementation of the 2030 Agenda on the national level, while contributing to mainstreaming communication and written discourses in all sectors of the educational arena since its publication. The article reflects on aspects of Portugal’s alignment to “Leaving No One Behind” (LNOB) through critical discourse analysis (CDA) of textual evidence in two interrelated national policy documents. The findings show the impact of this global policy-speak on recent Portuguese official documents.

Research Trends in Educational Administration Studies in Japan under New Public Management

With the continuous development of global public management theories, New Public Management (NPM), as an important public management reform model, has profoundly influenced governments and their management practices worldwide. In Japan, the practice of educational administration has also been significantly influenced by the theories of New Public Management, driving the modernization and efficiency improvement of educational management. This paper aims to systematically explore the research trends in educational administration studies in Japan under New Public Management, analyzing its theoretical foundations, implementation effects, and its profound impact on educational management.

Editorial diversity correlates with journal impact factor and author diversity in cardiothoracic surgery.

While there are no widely accepted diversity, equity, and inclusion (DEI) metrics for journals, geographic and sex diversity across a journal's editorial board may provide a surrogate measure of its commitment to DEI. We explored the association between journal quality and DEI metrics for cardiothoracic surgery (CTS) journals and investigated whether editorial diversity correlates with diversity across published articles. We collected the following data for 30 CTS journals: country of publication (categorized by income level), journal quality metrics (citation-based metrics, e.g., impact factor (IF) or H-index), and sex and geographic representation across editorial boards and published articles. Bivariate correlations between numeric variables were assessed using Spearman's correlation. Female representation across editorial boards was 12.1%. Most editorial board members belonged to the United States (35.2%), with only 7.4% from the lower-middle-income countries and 0% from low-income countries. IF showed a strong positive correlation with female editorial representation (r=0.70) but an inverse correlation with low- and middle-income countries (LMICs) editorial representation (r=-0.45). Female editorial representation demonstrated a significant positive correlation with female first authorship (r=0.45), whereas LMIC editorial representation correlated strongly with LMIC corresponding authorship (r=0.85). Women and researchers from LMICs are in the minority across editorial boards of CTS journals. However, a strong correlation between journal H-index and female editorial representation indicates that top-ranked journals are spearheading efforts to improve equitable sex-based and gender representations. Similar efforts are required to ensure more global geographic representation across editorial boards and top-ranked CTS journals are the best placed to lead by example.

Global language geography and language history: challenges and opportunities

As the several thousand languages spoken by people all around the world became more and more systematically assessed and catalogued in the 20th century, it became clear that linguistic diversity is unevently distributed across the globe. Up to the present day, the reasons for that are poorly understood. Linguists are thus in the embarassing situation that they do not understand significant regularities in the way the objects of their study –languages– pattern; human sciences at large are faced with the fact that the way humans produce that key cultural product which is often seen as defining the essence of what makes them humans –language–remains in the dark. In this essay, I explore three interrelated strands of thought associated with the problem of explaining patterns in global language diversity to create a perspective that is different from those explored so far. First, I suggest that instead of looking at present-day levels of diversity and find parameters of variation between the regions in which they are spoken, we should take a process-based approach that looks into how these distributions were generated. Related to this point and in contradistinction to extant work, second, I advocate an inductive approach that departs from qualitative case studies which inform theory-building. Third, I ponder that, in contrast to the traditional focus of historical linguistics on language diversification and expansion, understanding how the ranges of languages are reduced might be the key missing piece of evidence in a global theory of language diversity and its genesis.

The goat pan-genome reveals patterns of gene loss during domestication

BackgroundUnveiling genetic diversity features and understanding the genetic mechanisms of diverse goat phenotypes are pivotal in facilitating the preservation and utilization of these genetic resources. However, the total genetic diversity within a species can’t be captured by the reference genome of a single individual. The pan-genome is a collection of all the DNA sequences that occur in a species, and it is expected to capture the total genomic diversity of the specific species.ResultsWe constructed a goat pan-genome using map-to-pan assemble based on 813 individuals, including 723 domestic goats and 90 samples from their wild relatives, which presented a broad regional and global representation. In total, 146 Mb sequences and 974 genes were identified as absent from the reference genome (ARS1.2; GCF_001704415.2). We identified 3,190 novel single nucleotide polymorphisms (SNPs) using the pan-genome analysis. These novel SNPs could properly reveal the population structure of domestic goats and their wild relatives. Presence/absence variation (PAV) analysis revealed gene loss and intense negative selection during domestication and improvement.ConclusionsOur research highlights the importance of the goat pan-genome in capturing the missing genetic variations. It reveals the changes in genomic architecture during goat domestication and improvement, such as gene loss. This improves our understanding of the evolutionary and breeding history of goats.

Multi-granularity self-attention mechanisms for few-shot Learning

Few-shot learning aims to classify novel data categories with limited labeled samples. Although metric-based meta-learning has shown better generalization ability as a few-shot classification method, it still faces challenges in handling data noise and maintaining inter-sample distance stability. To address these issues, our study proposes an innovative few-shot learning approach to enhance image features' global and local semantic representation. Initially, our method employs a multiscale residual module to facilitate extracting multi-granularity features within images. Subsequently, it optimizes the fusion of local and global features using the self- attention mechanism inherent in the Transformer module. Additionally, a weighted metric module is integrated to improve the model's resilience against noise interference. Empirical evaluations on CIFAR-FS and MiniImageNet few-shot datasets using 5-way 1-shot and 5-way 5-shot scenarios demonstrate the effectiveness of our approach in capturing multi-level and multi-granularity image representations. Compared to other methods, our method improves accuracy by 2.63% and 1.27% for 5-shot scenes on these two datasets. The experimental results validate the efficacy of our model in significantly enhancing few-shot image classification performance.

The Landscape of Cutaneous T-Cell Lymphoma (CTCL) in the Middle East and North Africa (MENA) and the Establishment of the MENA CTCL Working Group

The high cancer burden in the Middle East and North Africa (MENA region) is coupled with an increasing cancer incidence. While the MENA region constitutes 6% of the world’s population, it remains underrepresented in clinical trials. Cutaneous T-cell lymphomas (CTCLs) represent a heterogeneous group of rare extranodal non-Hodgkin lymphomas with variable clinical presentation. In the MENA region, where darker skin colors are more common than in the West, CTCL generally presents at a younger age and with distinct clinical features that necessitate special expertise and management across disciplines: rare forms of CTCL are more common (hypo- and hyperpigmented MF) and a higher prevalence of pediatric MF is noticed. The multidisciplinary approach to cancer management is growing worldwide and is necessary for the comprehensive management of CTCL. The MENA CTCL group was established with the aim of creating a collaborative environment for the diagnosis and treatment of CTCL in the region. Its first meeting was held in May 2023. The group plans to increase the global representation of the MENA region and establish CTCL registries and patient advocacy groups in the region.

HeGCL: Advance Self-Supervised Learning in Heterogeneous Graph-Level Representation.

Representation learning in heterogeneous graphs with massive unlabeled data has aroused great interest. The heterogeneity of graphs not only contains rich information, but also raises difficult barriers to designing unsupervised or self-supervised learning (SSL) strategies. Existing methods such as random walk-based approaches are mainly dependent on the proximity information of neighbors and lack the ability to integrate node features into a higher-level representation. Furthermore, previous self-supervised or unsupervised frameworks are usually designed for node-level tasks, which are commonly short of capturing global graph properties and may not perform well in graph-level tasks. Therefore, a label-free framework that can better capture the global properties of heterogeneous graphs is urgently required. In this article, we propose a self-supervised heterogeneous graph neural network (GNN) based on cross-view contrastive learning (HeGCL). The HeGCL presents two views for encoding heterogeneous graphs: the meta-path view and the outline view. Compared with the meta-path view that provides semantic information, the outline view encodes the complex edge relations and captures graph-level properties by using a nonlocal block. Thus, the HeGCL learns node embeddings through maximizing mutual information (MI) between global and semantic representations coming from the outline and meta-path view, respectively. Experiments on both node-level and graph-level tasks show the superiority of the proposed model over other methods, and further exploration studies also show that the introduction of nonlocal block brings a significant contribution to graph-level tasks.

The Feasibility of Asynchronous Rotation via Thermal Tides for Diverse Atmospheric Compositions

The equilibrium rotation rate of a planet is determined by the sum of torques acting on its solid body. For planets with atmospheres, the dominant torques are usually the gravitational tide, which acts to slow the planet’s rotation rate, and the atmospheric thermal tide, which acts to spin up the planet. Previous work demonstrated that rocky planets with thick atmospheres may produce strong enough thermal tides to avoid tidal locking, but a study of how the strength of the thermal tide depends on atmospheric properties has not been done. In this work, we use a combination of simulations from a global climate model and analytic theory to explore how the thermal tide depends on the shortwave and longwave optical depth of the atmosphere, the surface pressure, and the absorbed stellar radiation. We find that for planets in the habitable zones of M stars only high-pressure but low-opacity atmospheres permit asynchronous rotation owing to the weakening of the thermal tide at high longwave and shortwave optical depths. We conclude that asynchronous rotation may be very unlikely around low-mass stars, which may limit the potential habitability of planets around M stars.

Illusionism Big and Small: Some Options for Explaining Consciousness.

Illusionism is a general philosophical framework in which specific theories of consciousness can be constructed without having to invoke a magical mind essence. The advantages of illusionism are not widely recognized, perhaps because scholars tend to think only of the most extreme forms and miss the range of possibilities. The brain's internal models are never fully accurate, nothing is exactly as the brain represents it, and therefore some element of illusionism is almost certainly necessary for any working theory of consciousness or of any other property that is accessed through introspection. Here I describe the illusionist framework and propose six specific theories. One purpose of this article is to demonstrate the range of possibilities in a domain that is not yet sufficiently explored. The second purpose is to argue that even existing, popular theories, such as the integrated information theory or the global workspace theory, can be transformed and greatly strengthened by adding an illusionist layer. The third purpose is to argue that when illusionist logic is used, even very disparate theories of consciousness that emerge from unrelated conceptual origins begin to converge onto a deeper, unified understanding.