It is demonstrated that lemmatization does not produce uniform gains across architectures: while linear models and croBERT display small but measurable improvements from morphological normalization, non-linear models such as RBF SVM and neural networks experience substantial declines in performance.
I. Ljubi, M. Horvat, G. Gledec et al.· Electronics· 0 citations
Results showed that long-context LLMs tended to achieve higher accuracy than smaller models, suggesting that LLMs are currently better suited to support human-in-the-loop root cause analysis than to fully automate it, and motivating further work to improve prediction accuracy for log-based RCA.
Rahmanu Hermawan, Alessio Bucaioni, Eduard Paul Enoiu et al.· Innovations in Systems and S...· 0 citations
This work introduces CytoGate-Bench, a benchmark that reformulates this per-step procedure as a zero-shot, panel-agnostic task for large language models, and contributes a public benchmark that tests precisely that ability across 11 human cohorts.
Jaesik Kim, Byounghan Lee, Namhyuk Ahn et al.· bioRxiv· 0 citations
This work demonstrates that MGTP-Seg not only provides an accurate, interpretable, and clinically relevant solution for automatic GTV delineation, but also offers a novel methodological framework to fuse spatial priors with semantic knowledge in medical image analysis.
Chengwei Chen, Hongfei Sun, Yuxuan Yao et al.· Physics in Medicine and Biol...· 0 citations
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BACKGROUND
Underreporting is a defining problem in the registration of workplace violence across public services. This is reinforced by inconsistent, often poor incident descriptions and the absence of a clear, shared nomenclature. Without an ontology-backed structure, similar events are recorded differently across settings or remain uncodable and effectively invisible. Moreover, WPV is typically registered and studied within sector- and even organization-specific categories and local reporting logics rather than through a shared semantic framework, limiting comparability and cumulative understanding across sectors. To address this, we propose creating and publicizing a workplace violence ontology (WPV-ONTO) to unify the representation of WPV events across public services.
OBJECTIVE
The objective of this protocol is to describe and justify a research methodology for developing a cross-sector ontology and reference nomenclature for WPV in public services (WPV-ONTO), including the scoping review, expert-consensus, and evaluation procedures used to build it. Promoting openness and high standards during its creation and encouraging its uptake once available are broader project goals that this protocol is designed to support, rather than measurable objectives that this protocol itself tests.
METHODS
Using the Protégé ontology editor and the METHONTOLOGY ontology development life-cycle guidelines, we will create an ontology that captures the cross-sector WPV domain in Web Ontology Language (OWL). In order to find common WPV concepts, definitions, and synonyms, the modeling process will employ a methodologically defined, iterative workflow that combines (1) focused scoping searches (Web of Science/PubMed/APA PsycInfo/ERIC/Sociological Abstracts); (2) structured extraction from industry-standard incident reporting tools and code lists; and (3) expert consensus. Based on iterative rounds of scientific literature reviews and industry-standard code lists, a team of domain experts will use a hybrid top-down and bottom-up approach to define and identify key ideas and relationships. A small pilot with 6-8 frontline workers from two sectors will test the prototype reporting form against usability criteria before version 1.0 release.
RESULTS
The primary output will be a comprehensive, versioned WPV-ONTO accommodating key WPV concepts relevant to public services, augmented with synonyms, definitions, and references. WPV-ONTO will include an explicit hierarchical structure and relations supporting inheritance and compositional incident encoding. WPV-ONTO seeks to integrate the needs and conceptualizations of frontline workers, safety and aftercare professionals, sector policymakers, WPV researchers, and health/information systems experts. Recruitment of the expert panel is planned to begin in March 2027, and WPV-ONTO version 1.0 is expected at the end of the 24-month development period.
CONCLUSIONS
WPV-ONTO is expected to enable reasoning, inference, and consistent representation of relationships among WPV concepts for application in multiple contexts, including cross-sector reporting harmonization, software and API development, research data integration, and evaluation of prevention and aftercare initiatives. By providing a shared vocabulary and explicit structure, WPV-ONTO may also facilitate linkage with other relevant information systems, such as electronic medical records, and justice and police information systems, reducing methodological fragmentation and supporting coordinated cross-sector learning while retaining the contextual specificity necessary for public service environments.
CLINICALTRIAL
Not applicable.
I. Steenhout, Ronald Buyl· JMIR Research Protocols· 0 citations
NavAI is an extensible navigation framework that leverages large language models (LLMs) to support both basic action commands and multi-step goal-oriented navigation through an application-agnostic screenshot-and-control interface and explores optimization strategies for virtual scene understanding and navigation goal decision making.
Jiajie Wang, Sumesh Surendran Letha, M. DiGiovanni et al.· International Conference on...· 0 citations
Experiments show that FiUni can effectively infer latent batch-level task affiliations and achieve competitive performance against advanced task-aware CL methods with fewer trainable parameters.
Dezheng Han, Anlan Zhang, Zhiwu Zhu et al.· 0 citations
Experiments across video understanding and reasoning benchmarks show that the Evidence-Grounded Self-Teacher framework consistently improves upon Standard OPSD across multiple backbones and achieves performance comparable to GRPO while requiring substantially less training time, establishing an effective and efficient post-training approach for Video-LLMs.
Zi-Yue Wang, Shiqi Huang, Weiwen Xu et al.· 0 citations
This work proposes a composite metric that combines two orthogonal criteria: information retention and throughput gains and finds that it allocates more resources to the most expressive layers compared to evolutionary search, specialized accelerators, or Shapley-value-based approaches that require expensive approximate inference.
How do we define an occupation? By its job title? An accountant at a small trading company keeps the books; at a listed firm the same title demands a certified-accountant licence, and the week goes to the reports that regulators and the board read. Same title, different bar, different work. What defines an occupation is who it lets in and what it asks them to do. In a rapidly changing labor market, tracking those requirements and tasks is how to take the market's pulse. Yet no instrument reads both at the speed they change. Official occupational directories like O*NET report one national average per occupation, updated every few years. Job postings are timely but unstructured. Research built on them works from job titles plus proprietary skill keywords, which blur what is asked of a candidate into what a candidate is asked to do. The blur matters, because rising requirements and changing tasks are different events with different causes. We separate them. From 752.6 million job ads posted on China's five leading recruitment platforms between 2022 and 2026, we extract the phrases employers write, unify those that name the same thing, and validate the mapping from text back to entry. By doing so we construct two catalogs, 20,721 requirements a candidate must meet and 44,479 tasks the hire will do. With the entries standardized, we annotate them further. Each task, for example, carries a score for how far a language model could absorb it. Matched back onto every ad, the catalogs read the market month by month. Two examples show what the layer beneath the job title buys. First, the occupational registry records one accountant where the ads record a staircase, the junior certificate at the bottom of the wage range and the intermediate one at the top. Second, counting occupations says the work most exposed to language models is disappearing, and counting tasks says far less of it is.
Qingqing Chen, Ying Fang, Xiang-Yu Wang et al.· 0 citations
It is demonstrated that finite-difference time-domain dynamics can be reproduced with field variables restricted to the ternary alphabet, and its extension to acoustics, Virieux-type elastodynamics and Schrodinger-equation solvers points to a broader class of quantised physics solvers for resource-constrained and specialised hardware.
This work empirically study the potential of outer prefixes, revealing the mechanism of the impact of distributional discrepancy to the exploration dynamics in RLVR training and efficiently mitigates entropy collapse without requiring additional SFT, intricate reward designs, or complex prompting.
Xin Shen, Huishuai Zhang, Peng Li et al.· 0 citations
