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We survey some lesser-known facts concerning properties of embeddings and projections between forcing notions. We will also state some generalizations of Easton’s lemma. To our knowledge, many of these facts have not b...

With the consolidation of deep learning in drug discovery, several novel algorithms for learning molecular representations have been proposed. Despite the interest of the community in developing new methods for learning ...

Protein aggregation plays a central role in the pathogenesis of many neurodegenerative diseases and poses major challenges in protein engineering. A key driver of this process is the presence of aggregation-prone regions...

We study complex-valued word embeddings where each word is represented by a magnitude and phase. Using a skip-gram objective, the real component captures symmetric similarity while the imaginary component induces directi...

Abstract Cross-lingual information retrieval limits global exchange of data because of the high diversity in the methods to classify, document and encode medical procedures. Traditional keyword-based or single-language s...

Suppose M and N are transitive models of set theory, P is a forcing notion in M and G is P-generic over M. An elementary embedding j : (M, ∈) → (N, ∈) lifts to M[G] if there is j+ : (M[G], G, ∈) → (N[j+(G)], j+...

Subjects / keywords: AGREGACIÓN DE PROTEÍNAS, REGIONES APR, EMBEDDINGS, PROT-T5, MODELOS DE LENGUAJE DE PROTEÍNAS, APRENDIZAJE AUTOMÁTICO PROFUNDO, REDES NEURONALES, BIOINFORMÁTICA, PROTEÍNAS AMILOIDES, MUTACIONES PUNTUALES, PROTEÍNAS...

"En el presente trabajo se estudia la aplicación de la técnica de análisis de sentimiento sobre un conjunto de datos extraído de la red social Twitter, donde los usuarios realizan publicaciones de textos cortos, gene...

Nils Reimers, Iryna Gurevych. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Abstract A prompt and precise diagnosis is necessary to lessen the worldwide burden of asthma, a common chronic respiratory condition. Spirometry is one of the resource-intensive conventional methods that frequently misc...

Column type annotation (CTA) is a critical task in semantic table interpretation (STI), where meaningful semantic labels are automatically assigned to table columns. This process enriches tabular data with essential sema...

Organizations have become highly reliant on a range of data sources that span structured, semi-structured, and unstructured data types. These repositories allow large-scale storage for faster ingestion and analytics but ...

Opinion mining, a critical subfield of natural language processing, seeks to extract and analyze user-generated expressions. This paper proposes a novel Multi-Task Opinion Mining framework that integrates psychological i...

The rapid proliferation of Learning Management Systems (LMS) in K–12 education has generated a substantial body of research, yet how its core themes emerge, converge, and transform over time remains insufficiently unde...

Abstract Graph Neural Networks have emerged as a powerful paradigm for artificial intelligence driven drug discovery, offering molecular representation learning that surpasses many conventional approaches. Traditional ex...

Abstract Advances in artificial intelligence (AI) and multi-omics integration are reshaping precision oncology by enabling deeper mechanistic understanding, improved characterization of tumor heterogeneity, and the accel...

The rapid expansion of digital communication has made social media an integral part of daily life, yet it has simultaneously amplified the reach of harmful behaviors. Early detection of cyberbullying is recognized as a c...

This study aims to identify the best predictive model for analysing online product reviews (OPRs) in the electronics industry, with a secondary focus on leveraging unstructured customer feedback to support product improv...

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the...

Understanding how consumers behave in e-commerce is essential for businesses, especially in today’s digital world where people rely heavily on online shopping platforms. A key part of this understanding comes from sent...

Information campaigns increasingly use LLMs to generate persuasive competing narratives around federal research agency press releases. Prior work largely centers on post hoc assessment, emphasizing detectability, charact...

Using a semiclassical approach to Gravitoelectromagnetic Inflation (GEMI), we study the origin and evolution of seminal inflaton and electromagnetic fields in the early inflationary universe from a 5D vacuum state. We us...

This paper introduces a novel hybrid machine learning model that combines Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug localization across multiple software platforms. Th...