Hasil untuk "Military Science"

XIAO Haixia, CUI Shuangyue, LI Dawei, SUN Mingming, LIU Xianzhong, YANG Zhenxin

The enhancement effect of the classic Nonnegative Matrix Factorization (NMF) applied to underwater acoustic target signal is unsatisfactory for the feature overlap of underwater acoustic target signal and the variability of ocean underwater acoustic field. And so, an improved NMF underwater acoustic signal enhancement algorithm based on feature preservation is proposed by adaptively improving the classic NMF algorithm using the characteristics of underwater acoustic target signals. The β-divergence constraints and similarity detection is first applied to the NMF feature basis matrix to eliminate redundancy, optimized NMF features with size invariant characteristics, while avoiding the loss of basis vectors due to coefficient dispersion caused by similar basis vectors. And then, the real-time environmental noise received by the sonar is used to improve the adaptability of the NMF noise basis vector, achieving noise reduction and enhancement of the underwater acoustic target signal. The experimental results show that, compared to the classical NMF, the manifold constrained NMF that is used to the underwater acoustic signal enhancement, the proposed method achieved the better signal enhancement effect.

Zhuoqi Lyu, Qing Ke

Understanding the changing structure of science over time is essential to elucidating how science evolves. We develop diachronic embeddings of scholarly periodicals to quantify "semantic changes" of periodicals across decades, allowing us to track the evolution of research topics and identify rapidly developing fields. By mapping periodicals within a physical-life-health triangle, we reveal an evolving interdisciplinary science landscape, finding an overall trend toward specialization for most periodicals but increasing interdisciplinarity for bioscience periodicals. Analyzing a periodical's trajectory within this triangle over time allows us to visualize how its research focus shifts. Furthermore, by monitoring the formation of local clusters of periodicals, we can identify emerging research topics such as AIDS research and nanotechnology in the 1980s. Our work offers novel quantification in the science of science and provides a quantitative lens to examine the evolution of science, which may facilitate future investigations into the emergence and development of research fields.

Muhan Gao, Jash Shah, Weiqi Wang et al.

Scientific knowledge is growing rapidly, making it difficult to track progress and high-level conceptual links across broad disciplines. While tools like citation networks and search engines help retrieve related papers, they lack the abstraction needed to capture the needed to represent the density and structure of activity across subfields. We motivate SCIENCE HIERARCHOGRAPHY, the goal of organizing scientific literature into a high-quality hierarchical structure that spans multiple levels of abstraction -- from broad domains to specific studies. Such a representation can provide insights into which fields are well-explored and which are under-explored. To achieve this goal, we develop a hybrid approach that combines efficient embedding-based clustering with LLM-based prompting, striking a balance between scalability and semantic precision. Compared to LLM-heavy methods like iterative tree construction, our approach achieves superior quality-speed trade-offs. Our hierarchies capture different dimensions of research contributions, reflecting the interdisciplinary and multifaceted nature of modern science. We evaluate its utility by measuring how effectively an LLM-based agent can navigate the hierarchy to locate target papers. Results show that our method improves interpretability and offers an alternative pathway for exploring scientific literature beyond traditional search methods. Code, data and demo are available: https://github.com/JHU-CLSP/science-hierarchography

Hongbo Fang, James Evans

Scientific progress has long been understood as recombinant, with breakthroughs arising when existing ideas are joined in new ways. Empirical work in this tradition has focused on the inputs to discovery, asking whether a paper draws together atypical or distant prior knowledge. Far less is known about how knowledge is supplied for downstream recombination, or how individual contributions are forged to play distinct and distant roles in the broader system of science. Using citation networks from tens of millions of publications in OpenAlex and the Web of Science, here we show that scientific contributions stably decompose into three functional types, foundations, extensions, and generalizations, distinguishable by the local structure of their forward citations. This decomposition of the 'functional role' of scientific work presents an unseen pattern of scientific production: foundational and extensional work, which respectively build and elaborate within disciplines, dominated the post-war decades but has declined steadily since the early 1990s, while generalizations, meaning compressed and modular contributions reused in distant fields, have risen sharply. Stacked difference-in-differences analyses that exploit venues' transitions to online access and authors' adoption of large language models provide causal evidence that digital knowledge infrastructure is driving this shift. The locus of innovation has thus migrated from within what Simon might characterize as nearly decomposable disciplinary modules to the interfaces between them, recasting the much-discussed decline of disruption as a structural reorganization of science rather than a slowdown, and revealing a growing misalignment between how science now advances and how it is recognized and rewarded.

Eva Seidlmayer, Lukas Galke, Konrad U. Förstner

Disseminators of disinformation often seek to attract attention or evoke emotions - typically to gain influence or generate revenue - resulting in distinctive rhetorical patterns that can be exploited by machine learning models. In this study, we explore linguistic and rhetorical features as proxies for distinguishing disinformative texts from other health and life-science text genres, applying both large language models and classical machine learning classifiers. Given the limitations of existing datasets, which mainly focus on fact checking misinformation, we introduce Four Shades of Life Sciences (FSoLS): a novel, labeled corpus of 2,603 texts on 14 life-science topics, retrieved from 17 diverse sources and classified into four categories of life science publications. The source code for replicating, and updating the dataset is available on GitHub: https://github.com/EvaSeidlmayer/FourShadesofLifeSciences

Jing-Yi Wang, Rui Xie, Yun Feng et al.

Giovanni Brizzi

Powers in the Western Mediterranean. A Strategic Assessment in Roma History.

Kevin G. Yager

Artificial intelligence (AI) methods are poised to revolutionize intellectual work, with generative AI enabling automation of text analysis, text generation, and simple decision making or reasoning. The impact to science is only just beginning, but the opportunity is significant since scientific research relies fundamentally on extended chains of cognitive work. Here, we review the state of the art in agentic AI systems, and discuss how these methods could be extended to have even greater impact on science. We propose the development of an exocortex, a synthetic extension of a person's cognition. A science exocortex could be designed as a swarm of AI agents, with each agent individually streamlining specific researcher tasks, and whose inter-communication leads to emergent behavior that greatly extend the researcher's cognition and volition.

Sirui Hong, Yizhang Lin, Bang Liu et al.

Large Language Model (LLM)-based agents have shown effectiveness across many applications. However, their use in data science scenarios requiring solving long-term interconnected tasks, dynamic data adjustments and domain expertise remains challenging. Previous approaches primarily focus on individual tasks, making it difficult to assess the complete data science workflow. Moreover, they struggle to handle real-time changes in intermediate data and fail to adapt dynamically to evolving task dependencies inherent to data science problems. In this paper, we present Data Interpreter, an LLM-based agent designed to automatically solve various data science problems end-to-end. Our Data Interpreter incorporates two key modules: 1) Hierarchical Graph Modeling, which breaks down complex problems into manageable subproblems, enabling dynamic node generation and graph optimization; and 2) Programmable Node Generation, a technique that refines and verifies each subproblem to iteratively improve code generation results and robustness. Extensive experiments consistently demonstrate the superiority of Data Interpreter. On InfiAgent-DABench, it achieves a 25% performance boost, raising accuracy from 75.9% to 94.9%. For machine learning and open-ended tasks, it improves performance from 88% to 95%, and from 60% to 97%, respectively. Moreover, on the MATH dataset, Data Interpreter achieves remarkable performance with a 26% improvement compared to state-of-the-art baselines. The code is available at https://github.com/geekan/MetaGPT.

Игорь Анатольевич Кириллов, Станислав Вениаминович Петров, Василий Васильевич Батырев

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Michał J. Gajda

Philosophy of science attempts to describe all parts of the scientific process in a general way in order to facilitate the description, execution and improvements of this process. So far, all proposed philosophies have only covered existing processes and disciplines partially and imperfectly. In particular logical approaches have always received a lot of attention due to attempts to fundamentally address issues with the definition of science as a discipline with reductionist theories. We propose a new way to approach the problem from the perspective of computational complexity and argue why this approach may be better than previous propositions based on pure logic and mathematics.

Stefan Hajkowicz, Conrad Sanderson, Sarvnaz Karimi et al.

Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across various fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960-2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically: the AI "springs" and "winters". We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.

José Clemente Jacinto Ferreira, Ana Paula Matias Gama, Luiz Paulo Fávero et al.

The interest in studies aimed at understanding the integration of the stock market with the economic performance of countries has been growing in recent years, perhaps driven by the recent economic crises faced by the world. Although several studies on the topic have been carried out, the results are still far from a meaningful conclusion. In this sense, this paper considered the dual objective of investigating whether there is significant variance in the economic performance of developed and emerging markets’ countries and whether the global risk factors are statistically significant in explaining the variations in their future economic performance over time. From a sample of (i) gross domestic products from BRICS and G7 countries (total of twelve countries), and (ii) returns of the risk factors of developed and emerging stock markets for the period 1993 to 2019, we applied longitudinal regression modeling for five distinct percentiles, and random coefficients modeling (RCM) with repeated measures. We found that risk factors explain the future economic performance, there is significant variation in economic performance over time among countries, and the temporal variation in the random effects of intercepts can be explained by RCM. The results of this study confirm that stock markets follow an integration process and that moderately integrated markets may have the same risk factors. Furthermore, considering that risk factors are related to future GDP growth, they act as proxies for unidentified state variables.

Ayu Sabrina, Hermini Susiatiningsih, Fendy Eko Wahyudi

<div><p class="Els-history-head">In the last two decades, the Australian Government has intensified the practice of securitizing migration issues. The difference is that the pre-Scott Morrison migration securitization program was more focused on handling cases of Illegal Maritime Arrivals, while the core of Scott Morrison's migration program was to reduce the pressure on the immigrant population. Through the discourse of Planning for Australia's Future Population, Scott Morrison cut the quota of permanent immigrants from 190,000 to 160,000 people. Scott Morrison also implemented immigration transfer policies and migration reforms. Therefore, this study focuses on analyzing the process of framing the issue of Australian immigrants under the leadership of Scott Morrison. This research uses securitization theory and qualitative methods, particularly process-tracing. As a result, this research found that Scott Morrison, as the securitization actor, intentionally created a speech act and convinced the public that the referent object, namely Australia's national security, was in a threatening situation due to the surge in the immigrant population. Functional actors, including parliament, media, and epistemic groups, reinforced Scott Morrison's speech acts. </p></div>

Gecia Bravo-Hermsdorff, Valkyrie Felso, Emily Ray et al.

One can point to a variety of historical milestones for gender equality in STEM (science, technology, engineering, and mathematics), however, practical effects are incremental and ongoing. It is important to quantify gender differences in subdomains of scientific work in order to detect potential biases and monitor progress. In this work, we study the relevance of gender in scientific collaboration patterns in the Institute for Operations Research and the Management Sciences (INFORMS), a professional society with sixteen peer-reviewed journals. Using their publication data from 1952 to 2016, we constructed a large temporal bipartite network between authors and publications, and augmented the author nodes with gender labels. We characterized differences in several basic statistics of this network over time, highlighting how they have changed with respect to relevant historical events. We find a steady increase in participation by women (e.g., fraction of authorships by women and of new women authors) starting around 1980. However, women still comprise less than 25% of the INFORMS society and an even smaller fraction of authors with many publications. Moreover, we describe a methodology for quantifying the structural role of an authorship with respect to the overall connectivity of the network, using it to measure subtle differences between authorships by women and by men. Specifically, as measures of structural importance of an authorship, we use effective resistance and contraction importance, two measures related to diffusion throughout a network. As a null model, we propose a degree-preserving temporal and geometric network model with emergent communities. Our results suggest the presence of systematic differences between the collaboration patterns of men and women that cannot be explained by only local statistics.

Micah J. Smith, Jürgen Cito, Kelvin Lu et al.

While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams. We describe challenges to scaling data science collaborations and present a conceptual framework and ML programming model to address them. We instantiate these ideas in Ballet, a lightweight framework for collaborative, open-source data science through a focus on feature engineering, and an accompanying cloud-based development environment. Using our framework, collaborators incrementally propose feature definitions to a repository which are each subjected to an ML performance evaluation and can be automatically merged into an executable feature engineering pipeline. We leverage Ballet to conduct a case study analysis of an income prediction problem with 27 collaborators, and discuss implications for future designers of collaborative projects.

Mahdi Bohlouli, Patrick Uhr, Fabian Merges et al.

Knowledge organization, infrastructure, and knowledge-based activities are all subjects that help in the creation of business strategies for the new enterprise. In this paper, the first basics of knowledge-based systems are studied. Practical issues and challenges of Knowledge Management (KM) implementations are then illustrated. Finally, a comparison of different knowledge-based projects is presented along with abstracted information on their implementation, techniques, and results. Most of these projects are in the field of medical science. Based on our study and evaluation of different KM projects, we conclude that KM is being used in every science, industry, and business. But its importance in medical science and assisted living projects are highlighted nowadays with the most of research institutes. Most medical centers are interested in using knowledge-based services like portals and learning techniques of knowledge for their future innovations and supports.

Daimer Ospina Contreras, Luis Carlos Roldán

This article presents the conceptual design of the structure of a three-stage rocket capable of carrying 200 kg to low Earth orbits, developed in the project of grade of two students of aeronautical engineering from the Fundación Universitaria Los Libertadores. The design starts from the decision of how they are going to form the structure to protect three rocket engines inside, considering the force generated by the first one since this is the largest, followed by the design of the profiles that make up the structure and selection of a lightweight material such as aluminum (Al) to support efforts up to 280 N/mm2, hence the calculation of total area, the required areas and moments of inertia, begins with the calculation of the stringer profile, followed by the bulkhead profile and even the rivet diameter obtaining dimensioning, structural distribution and the final configuration of the structure.

Piotr Bilko, Szymon Sawczynski

The article presents the possibilities of using girders made of plastics in hall covering structures in comparison with girders made of traditional materials, such as steel and wood, commonly used in civil engineering. Profiles made of polymers reinforced with glass fibre with the pultrusion method show enormous potential in the construction business. Until today polymers have been used as construction materials only occasionally despite the numerous benefits they offer, such as improved durability in aggressive environments and smaller weight in comparison with traditional materials, to mention but a few of their flag advantages. Polymer composites have a relatively low resistance to high temperatures, especially fire has a very negative influence on them.