Medication errors pose a significant threat to patient safety, making pharmacist verification (PV) a critical, yet heavily burdened, final safeguard. The direct application of Large Language Models (LLMs) to this zero-tolerance domain is untenable due to their inherent factual unreliability, lack of traceability, and weakness in complex reasoning. To address these challenges, we introduce PharmGraph-Auditor, a novel system designed for safe and evidence-grounded prescription auditing. The core of our system is a trustworthy Hybrid Pharmaceutical Knowledge Base (HPKB), implemented under the Virtual Knowledge Graph (VKG) paradigm. This architecture strategically unifies a relational component for set constraint satisfaction and a graph component for topological reasoning via a rigorous mapping layer. To construct this HPKB, we propose the Iterative Schema Refinement (ISR) algorithm, a framework that enables the co-evolution of both graph and relational schemas from medical texts. For auditing, we introduce the KB-grounded Chain of Verification (CoV), a new reasoning paradigm that transforms the LLM from an unreliable generator into a transparent reasoning engine. CoV decomposes the audit task into a sequence of verifiable queries against the HPKB, generating hybrid query plans to retrieve evidence from the most appropriate data store. Experimental results demonstrate robust knowledge extraction capabilities and show promises of using PharmGraph-Auditor to enable pharmacists to achieve safer and faster prescription verification.
Recent Mixture-of-Experts (MoE)-based large language models (LLMs) such as Qwen-MoE and DeepSeek-MoE are transforming generative AI in natural language processing. However, these models require vast and diverse training data. Federated learning (FL) addresses this challenge by leveraging private data from heterogeneous edge devices for privacy-preserving MoE training. Nonetheless, traditional FL approaches require devices to host local MoE models, which is impractical for resource-constrained devices due to large model sizes. To address this, we propose DeepFusion, the first scalable federated MoE training framework that enables the fusion of heterogeneous on-device LLM knowledge via federated knowledge distillation, yielding a knowledge-abundant global MoE model. Specifically, DeepFusion features each device to independently configure and train an on-device LLM tailored to its own needs and hardware limitations. Furthermore, we propose a novel View-Aligned Attention (VAA) module that integrates multi-stage feature representations from the global MoE model to construct a predictive perspective aligned with on-device LLMs, thereby enabling effective cross-architecture knowledge distillation. By explicitly aligning predictive perspectives, VAA resolves the view-mismatch problem in traditional federated knowledge distillation, which arises from heterogeneity in model architectures and prediction behaviors between on-device LLMs and the global MoE model. Experiments with industry-level MoE models (Qwen-MoE and DeepSeek-MoE) and real-world datasets (medical and finance) demonstrate that DeepFusion achieves performance close to centralized MoE training. Compared with key federated MoE baselines, DeepFusion reduces communication costs by up to 71% and improves token perplexity by up to 5.28%.
I. V. Feldblum, R. V. Polibin, V. V. Semerikov
et al.
Relevance. The National vaccination schedule (NVS) of the Russian Federation includes 12 infections against 17–18 indicated in the Vaccination Schedules of most EU countries, and requires improvement in terms of expanding the number of infections and contingents subject to immunization, which is reflected in the «Strategy for the development of vaccine prevention of infectious diseases for the period up to 2035» and the roadmap for its implementation. Since the process of entry of new infections and new vaccines into NVS is quite «difficult» and economically costly (development of domestic vaccines, modernization of domestic enterprises, localization of production of foreign vaccines at Russian enterprises, formation of commitment of medical workers and public confidence in new immunobiological medicines), part of the tasks is to ensure public accessibility to vaccine prevention of infections not indicated in the The NVS can be successfully solved within the framework of regional vaccination schedules (RVS).Aims. To propose and interpret the concept of rational regional vaccination schedules, and also to outline the principles of its formation and assessment of the immediate and long-term prospects for the development of regional foundations of vaccination prevention.Results and discussion. RVS is an expanded version of NVS by including additional vaccine-controlled infections, taking into account the epidemic situation and the presence of high-risk groups of infection in the region. In modern conditions, RVS have been developed and are effectively used in a number of subjects of the Russian Federation, such as Moscow, Perm Krai, Sverdlovsk, Chelyabinsk, Tyumen Regions, Yamalo-Nenets Autonomous District, etc. The implementation of vaccine prophylaxis in the regions using this tool provides conditions for equal access of children and adults to modern highly effective vaccines, increases public awareness of the benefits and risks of vaccination and the level of population immunity, contributes to improving mechanisms for providing citizens with immunobiological drugs and the formation of epidemic well-being in the region based on a risk-based approach.Conclusions. In accordance with this paradigm, the authors introduce and interpret the concept of an «Rational regional vaccination schedules», define the basic principles of its formation and assess the short- and long-term prospects for the development of the regional foundations of vaccination.
In this paper, we present KG20C and KG20C-QA, two curated datasets for advancing question answering (QA) research on scholarly data. KG20C is a high-quality scholarly knowledge graph constructed from the Microsoft Academic Graph through targeted selection of venues, quality-based filtering, and schema definition. Although KG20C has been available online in non-peer-reviewed sources such as GitHub repository, this paper provides the first formal, peer-reviewed description of the dataset, including clear documentation of its construction and specifications. KG20C-QA is built upon KG20C to support QA tasks on scholarly data. We define a set of QA templates that convert graph triples into natural language question--answer pairs, producing a benchmark that can be used both with graph-based models such as knowledge graph embeddings and with text-based models such as large language models. We benchmark standard knowledge graph embedding methods on KG20C-QA, analyze performance across relation types, and provide reproducible evaluation protocols. By officially releasing these datasets with thorough documentation, we aim to contribute a reusable, extensible resource for the research community, enabling future work in QA, reasoning, and knowledge-driven applications in the scholarly domain. The full datasets will be released at https://github.com/tranhungnghiep/KG20C/ upon paper publication.
Michael Oakeshott’s epistemology revolves around the concept of Modes of the understanding of the meanings of the world. These meanings had been mediated by our forefathers and had been descended to us through generation to generations. The whole of these meanings is the common capital for all human beings irrespective of terrestrial affiliation or adherence to any theology or association to any race. Without learning the meanings, we are just a bipod specie or Homo Erectus and Homo Sapiens but not ‘Beings Human’. Thus he founded his epistemology on concept of ‘learning to be human’ and he made a difference between a ‘human being’ and ‘being human’. Islamic teachings also support Oakeshott’s ideas regarding, Epistemology, Theory of Knowledge and educational Philosophy.
The article deals with the subject of poetic language, its qualities, conditions and purpose. It is the language of creativity and sets out the metaphysics in art. The metaphysics considered is the intersection of being and time in thing and thing as a special place where thoughts appear in the artistic act. The first plan of the stated metaphysics is considered in the fourfold geometric intersection of the essential-artistic space – the square (Gefirt) of the world, earth, mortals and immortals. The result of the semantic field of landscape from our memory of native lands is a history of the meanings of thinking, and things are pre-thought states. A thing is the way the world is given - a narrative of the structure of thinking, life and human activity. The second plan of the stated metaphysics is considered, on the one hand, from the position of poets and oracles, as those who express themselves historically, and, on the other hand, from the inner basis of thinking. This foundation is intuitive, affective-intellectual thought. It emerges in the circle of unknown, unexplored, impossible things – in the topos, where the world of life is formed by co-existence with what we enter into, creating meaning for ourselves. Co-existence means examining the thing for the interruption of being and time and what information about the world can be extracted. But it turns out that there is no being at all and no time at all, for there are no two things of one thing: that which belongs to being and that which belongs to time. It turns out that the intersection is thinking. It builds meaningful forms, lives them and carries them in language. To a certain extent, the thinking we possess is seen as a device of time. Martin Heidegger, through the existential Care, arranges thinking by the relation of being and time in a thing. He draws our attention to the fact that not only man owes his truth to the co-existence of life, but also the thing with its nature. The purpose of this article is to investigate the foundations of poetic language of creation as a way of presentation of metaphysics in art.
High-level automation is increasingly critical in AI, driven by rapid advances in large language models (LLMs) and AI agents. However, LLMs, despite their general reasoning power, struggle significantly in specialized, data-sensitive tasks such as designing Graph Neural Networks (GNNs). This difficulty arises from (1) the inherent knowledge gaps in modeling the intricate, varying relationships between graph properties and suitable architectures and (2) the external noise from misleading descriptive inputs, often resulting in generic or even misleading model suggestions. Achieving proficiency in designing data-aware models -- defined as the meta-level capability to systematically accumulate, interpret, and apply data-specific design knowledge -- remains challenging for existing automated approaches, due to their inefficient construction and application of meta-knowledge. To achieve meta-level proficiency, we propose DesiGNN, a knowledge-centered framework that systematically converts past model design experience into structured, fine-grained knowledge priors well-suited for meta-learning with LLMs. To account for the inherent variability and external noise, DesiGNN aligns empirical property filtering from extensive benchmarks with adaptive elicitation of literature insights via LLMs. By constructing a solid meta-knowledge between unseen graph understanding and known effective architecture patterns, DesiGNN can deliver top-5.77% initial model proposals for unseen datasets within seconds and achieve consistently superior performance with minimal search cost compared to baselines.
Sagar Srinivas Sakhinana, Krishna Sai Sudhir Aripirala, Shivam Gupta
et al.
Accurately predicting the behavior of complex dynamical systems, characterized by high-dimensional multivariate time series(MTS) in interconnected sensor networks, is crucial for informed decision-making in various applications to minimize risk. While graph forecasting networks(GFNs) are ideal for forecasting MTS data that exhibit spatio-temporal dependencies, prior works rely solely on the domain-specific knowledge of time-series variables inter-relationships to model the nonlinear dynamics, neglecting inherent relational structural dependencies among the variables within the MTS data. In contrast, contemporary works infer relational structures from MTS data but neglect domain-specific knowledge. The proposed hybrid architecture addresses these limitations by combining both domain-specific knowledge and implicit knowledge of the relational structure underlying the MTS data using Knowledge-Based Compositional Generalization. The hybrid architecture shows promising results on multiple benchmark datasets, outperforming state-of-the-art forecasting methods. Additionally, the architecture models the time varying uncertainty of multi-horizon forecasts.
We give an elementary description of $2$-categories $\mathbf{Cat}\left(\mathcal{E}\right)$ of internal categories, functors and natural transformations, where $\mathcal{E}$ is a category modelling Lawvere's elementary theory of the category of sets (ETCS). This extends Bourke's characterisation of $2$-categories $\mathbf{Cat}\left(\mathcal{E}\right)$ where $\mathcal{E}$ has pullbacks to take account for the extra properties in ETCS, and Lawvere's characterisation of the (one dimensional) category of small categories to take account of the two-dimensional structure. Important two-dimensional concepts which we introduce include $2$-well-pointedness, full-subobject classifiers, and the categorified axiom of choice. Along the way, we show how generating families (resp. orthogonal factorisation systems) on $\mathcal{E}$ give rise to generating families (resp. orthogonal factorisation systems) on $\mathbf{Cat}\left(\mathcal{E}\right)_{1}$, results which we believe are of independent interest.
N. M. Kolyasnikova, L. G. Chistyakova, A. V. Ponomareva
et al.
Relevance. The Sverdlovsk district is a tense natural focus for Lyme borreliosis (LB) with a constantly recorded incidence that exceeds these indicators in the Russian Federation and the Ural Federal region several times. LB is registered on 56 of 59 administrative territories of the district. Currently, the count of LB in the Russian Federation includes both infections caused by Borrelia burgdorferi sensu lato group Borrelia and infections caused by Borrelia miyamotoi sensu lato group Borrelia.Aim. To characterize the epidemic process of LB in the territory of the Sverdlovsk district over a 20-year period (2002–2021).Materials and methods. The paper uses data from federal and regional statistical observations, reporting materials of the Department of Rospotrebnadzor in the Sverdlovsk district for 2002–2021, methods of statistical analysis.Results and discussion. During the follow–up period (2002–2021), the incidence rate of BL in the Sverdlovsk district exceeded the average long-term level in the Russian Federation by almost 3 times, and in the Ural Federal region – by 1.8 times. Among the mechanisms and ways of transmission of LB pathogens, the vector-borne mechanism dominated (84.5%), the share of the unidentified pathway was 15.4%, alimentary (when using raw goat's milk) – 0.1%. The duration of the epidemic season for LB in the district was 10 months (from March to December) with a peak incidence in June. Cases of LB were registered among people of different ages, with a predominance in the group of 60 years and older (39.1%). An analysis of the distribution of people with LB by social composition showed that pensioners prevailed among them – 36.3%, officials made up 27.8%, unemployed – 14.1%. Among the cases, a high proportion of schoolchildren and children under 7 years old were noted. It was found that erythematous and non-erythematous forms of LB are registered in the district, with a predominance of erythematous (64.5%). The main etiological agent of the erythematous form is Borrelia garinii, while the non-erythematous form is Borrelia miyamotoi and B. garinii. In the general structure of morbidity, the number of cases of LB is largely determined by the city inhabitants (92.9%). There were no fatal outcomes in LB in the territory of the district during the studied period.Conclusions. Due to the lack of vaccination of LB worldwide, the only strategy to reduce the incidence of LB is non-specific prevention and timely diagnosis of the disease, including data from epidemiological history, clinical and laboratory diagnostics. The long-term clinical and epidemiological and laboratory studies conducted by us in the Sverdlovsk district allowed us to characterize the main manifestations of the epidemic process of LB and to improve the diagnosis of the disease.
Este artigo discute a complexa relação entre propriedade e violência no âmbito do romance Grande sertão: veredas (1956), de João Guimarães Rosa. Entende-se que, para além da rica matéria social, histórica e política do sertão, uma das principais dimensões dos conflitos narrados se vincula intrinsecamente à concentração de terras por parte dos grandes potentados locais, os coronéis, e pela prática quase institucionalizada do banditismo armado, o jaguncismo. Longe de ser mera fabulação, a análise dialética entre literatura e sociedade almeja sublinhar que o romance de Guimarães Rosa é uma possibilidade de pesquisa e interpretação de um passado não muito distante e que ainda reflete, em maior ou menor grau, determinada lógica de coerções, roubos e assassinatos em áreas características do território brasileiro.
Epistemology. Theory of knowledge, History (General)
Este artigo analisa um fenômeno intelectual – a psicologia positiva – a partir das relações que este estabelece entre as hierarquias disciplinares universitárias e outras plataformas de consagração. Através de uma análise do estatuto da psicologia positiva e de sua história institucional, o artigo almeja contextualizar os desafios que tal projeto impõe não apenas aos debates teóricos do campo da psicologia como, também, às fronteiras entre universidade e mercado. Para tanto, foram levados em consideração os processos de institucionalização da psicologia positiva nos Estados Unidos e no Brasil, bem como as críticas elaboradas em ambos os contextos.
L. S. Karpova, K. A. Stolyarov, N. M. Popovtseva
et al.
Relevance. The ongoing COVID-19 pandemic in the world, which is characterized by a long undulating course, requires an in-depth study of the features of the epidemic process, including the influence of natural, climatic and social factors on it. Aim. Compare the intensity of three waves of the COVID-19 pandemic in Russia. To identify the features of the parameters of the COVID-19 pandemic in Russia in the age groups of the population and in the federal districts. Materials and methods. Data from the computer database of the Influenza Research Institute and the Stop-coronavirus website were used. Results. The construction of the weekly dynamics of COVID-19 made it possible to clarify the start, peak and end dates of each wave in megacities, federal districts and among the population of the Russia. Conclusion. In the dynamics of the incidence of COVID-19 in the population of the Russian Federation from March 2020 to September 2021, three waves were detected: I spring-summer wave, II autumn-winter, III spring -summer. All three waves started in megacities, first in Moscow, and spread across federal districts. The rise of morbidity in Russia as a whole began and peaked in the autumn-winter wave later than in the spring-summer waves (immediately after the megacities). The total duration of the epidemic and the period of its development in the autumn-winter wave were longer than in the spring-summer waves. Morbidity, hospitalization and mortality depended on age, and in all three waves were higher among people over 65 years of age. The intensity of COVID-19 in the first spring-summer wave was the lowest. The II autumn-winter wave was the most intense in terms of morbidity, hospitalization rate and mortality in all age groups. The III spring-summer wave in terms of morbidity and hospitalization was less intensive, than the II autumn-winter wave, but there were no significant differences between the mortality rates in the II and III wave hospitalization and fatal outcomes were revealed.
João Barroca, Abhishek Shivkumar, Beatriz Quintino Ferreira
et al.
Knowledge Graphs offer a very useful and powerful structure for representing information, consequently, they have been adopted as the backbone for many applications in e-commerce scenarios. In this paper, we describe an application of existing techniques for enriching thelarge-scale Fashion Knowledge Graph (FKG) that we build at Farfetch. In particular, we apply techniques for named entity recognition (NER) and entity linking (EL) in order to extract and link rich metadata from product textual descriptions to entities in the FKG. Having a complete and enriched FKG as an e-commerce backbone can have a highly valuable impact on downstream applications such as search and recommendations. However, enriching a Knowledge Graph in the fashion domain has its own challenges. Data representation is different from a more generic KG, like Wikidata and Yago, as entities (e.g. product attributes) are too specific to the domain, and long textual descriptions are not readily available. Data itself is also scarce, as labelling datasets to train supervised models is a very laborious task. Even more, fashion products display a high variability and require an intricate ontology of attributes to link to. We use a transfer learning based approach to train an NER module on a small amount of manually labeled data, followed by an EL module that links the previously identified named entities to the appropriate entities within the FKG. Experiments using a pre-trained model show that it is possible to achieve 89.75% accuracy in NER even with a small manually labeled dataset. Moreover, the EL module, despite relying on simple rule-based or ML models (due to lack of training data), is able to link relevant attributes to products, thus automatically enriching the FKG.
Over the years, reasoning over knowledge graphs (KGs), which aims to infer new conclusions from known facts, has mostly focused on static KGs. The unceasing growth of knowledge in real life raises the necessity to enable the inductive reasoning ability on expanding KGs. Existing inductive work assumes that new entities all emerge once in a batch, which oversimplifies the real scenario that new entities continually appear. This study dives into a more realistic and challenging setting where new entities emerge in multiple batches. We propose a walk-based inductive reasoning model to tackle the new setting. Specifically, a graph convolutional network with adaptive relation aggregation is designed to encode and update entities using their neighboring relations. To capture the varying neighbor importance, we employ a query-aware feedback attention mechanism during the aggregation. Furthermore, to alleviate the sparse link problem of new entities, we propose a link augmentation strategy to add trustworthy facts into KGs. We construct three new datasets for simulating this multi-batch emergence scenario. The experimental results show that our proposed model outperforms state-of-the-art embedding-based, walk-based and rule-based models on inductive KG reasoning.
Abstract This contribution brings to the fore the lesbian silences veiled by dominant theorizations of queer studies in academia, nowadays more concerned with analyzing social affections such as queer diasporas, terrorism, human rights and necropolitics and positing intersectionality as the key configuration of queer epistemology. Yet, I am interested in eliciting how such existing approaches can help chart queer horizons in more inclusive ways without ignoring lesbian voices. Concomitantly, I will posit such lesbian positions as critical epistemologies we cannot do without, since only by unfolding past accretive knowledge on gender and sexuality will queer discourses become inclusive and relational. Hence, this article traces the evolution and theoretical shifts that queer theory has undergone in the last decades and further explores why “the lesbian” continues being dismissed as a marginal site of knowledge and material production, enacting a closeted identity, muted by other legitimate discourses in academia. Such a move toward new queer and affective frameworks, while convincingly essential, should not overshadow lesbian criticism. By drawing on relational and affective modes of being, I suggest recasting “the lesbian” as both a textual and ontological possibility capable of embracing the variety of lesbian-identified persons traditionally silenced by queer theory’s canonical institutionalization.
У статті розглянуто феномен «інтерпретації» в контексті герменевтичного аналізу. Проаналізовано погляди дослідників герменевтики: Й. Хладного, Ф. Маєра, Й. Ернесті, Ф.Шлейєрмахера, Й.Дройзена, В.Дільтея, П.Рікера, А.Вайтгеда, М. Гайдеґґера, Г.‑Ґ.Ґадамера, В.Швирьова, С.Квіта та ін. на природу й сутність інтерпретації, її методи, структуру й значення в життєдіяльності людини.