cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13902782
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

description: This series of experiments explores four novel approaches to improving large language models (LLMs): Story Energy, Universal Harmony Energy, SA-UUH-UPP framework, and Quantum-Inspired Mechanisms. Conducted on Google Cloud TPU infrastructure, the experiments revealed key improvements, including a 16% increase in narrative coherence, a 28% reduction in energy consumption, an 18% boost in output coherence, and a 21% improvement in word sense disambiguation. These advancements can significantly reduce infrastructure costs while improving performance in AI-driven applications like content generation, machine translation, and adaptive learning systems.

 

The 28% reduction in energy consumption is estimated to deliver up to 25% in cloud infrastructure cost savings, with global AI training and inference deployments potentially saving over $1 billion annually. For individual companies operating large-scale AI models—such as Google, Amazon, Meta, OpenAI, and Microsoft—annual savings could reach $100 million or more, depending on the scale of their operations and infrastructure requirements. These results highlight the potential for both cost reductions and improved system performance in AI development.
DOI: 10.5281/zenodo.13903017
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

description: This series of experiments explores four novel approaches to improving large language models (LLMs): Story Energy, Universal Harmony Energy, SA-UUH-UPP framework, and Quantum-Inspired Mechanisms. Conducted on Google Cloud TPU infrastructure, the experiments revealed key improvements, including a 16% increase in narrative coherence, a 28% reduction in energy consumption, an 18% boost in output coherence, and a 21% improvement in word sense disambiguation. These advancements can significantly reduce infrastructure costs while improving performance in AI-driven applications like content generation, machine translation, and adaptive learning systems.

 

The 28% reduction in energy consumption is estimated to deliver up to 25% in cloud infrastructure cost savings, with global AI training and inference deployments potentially saving over $1 billion annually. For individual companies operating large-scale AI models—such as Google, Amazon, Meta, OpenAI, and Microsoft—annual savings could reach $100 million or more, depending on the scale of their operations and infrastructure requirements. These results highlight the potential for both cost reductions and improved system performance in AI development.
DOI: 10.5281/zenodo.13923667
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

description: This series of experiments investigates seven groundbreaking approaches to enhancing large language models (LLMs): Story Energy, Universal Harmony Energy, the SA-UUH-UPP framework, Quantum-Inspired Mechanisms, Active Inference, Fractal Leaping, and Master Fractal Templates. Conducted on Google Cloud TPU infrastructure, the experiments demonstrated impressive results, including a 16% increase in narrative coherence, a 28% reduction in energy consumption, an 18% boost in output coherence, a 21% improvement in word sense disambiguation, and significant security improvements through advanced pattern recognition and adaptive learning.

By incorporating Active Inference, Fractal Leaping, and Master Fractal Templates, we achieved an AGI-like performance improvement from 58% to 92%, moving closer to true Artificial General Intelligence (AGI). These techniques also provided enhanced security through more robust anomaly detection, predictive adaptability, and protection against adversarial attacks. The advancements can significantly reduce infrastructure costs while improving the performance and security of AI-driven applications like content generation, machine translation, and adaptive learning systems.

The 28% reduction in energy consumption is estimated to deliver up to 25% in cloud infrastructure cost savings, with global AI training and inference potentially saving over $1 billion annually. For individual companies operating large-scale AI models—such as Google, Amazon, Meta, OpenAI, and Microsoft—annual savings could reach $100 million or more. These findings highlight the dual benefits of improved performance, enhanced security, and significant cost savings in AI development.
DOI: 10.5281/zenodo.14036679
title: SAUUHUPP Based Innovations in AI

description: This series of experiments investigates eight innovative approaches to enhancing large language models (LLMs): Story Energy, Universal Harmony Energy, the SA-UUH-UPP framework, Quantum-Inspired Mechanisms, Active Inference, Fractal Leaping, Master Fractal Templates, and the newly integrated Core Finding framework. Conducted on Google Cloud TPU infrastructure, these experiments achieved significant results, including a 16% increase in narrative coherence, a 28% reduction in energy consumption, a 21% improvement in word sense disambiguation, and enhanced security through advanced pattern recognition.

With the integration of Core Finding, which optimizes task prioritization and efficiency through fractal-based patterns, we observed a 30% improvement in task prioritization, a 24% reduction in computational resource usage, and a 25% boost in response accuracy, further enhancing performance and cross-domain adaptability. Incorporating Active Inference, Fractal Leaping, and Master Fractal Templates led to an AGI-like performance increase from 58% to 92%, significantly advancing the path to true Artificial General Intelligence (AGI). The combined techniques also provided enhanced security through improved anomaly detection and predictive adaptability.

The 28% reduction in energy consumption is estimated to deliver up to 25% in cloud infrastructure cost savings, potentially saving over $1 billion annually in global AI training and inference. Major AI companies—such as Google, Amazon, Meta, OpenAI, and Microsoft—could see annual savings of $100 million or more, underscoring the dual benefits of improved performance, enhanced security, and significant cost reductions in AI development.
DOI: 10.5281/zenodo.13898887
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

description: This series of research papers, presented by Prudencio Mendez with the support of ChatGPT 4, introduces cutting-edge advancements in large language models (LLMs) through four key concepts: Story Energy, Universal Harmony Energy, the SA-UUH-UPP framework, and quantum-inspired algorithms. These interconnected works propose novel methods for improving LLMs by enhancing coherence, adaptability, and efficiency, and by drawing inspiration from fractal patterns, energy optimization, recursive self-awareness, and quantum principles.

 

The research findings reveal significant improvements in key areas such as long-form text generation, cross-domain generalization, computational efficiency, and model introspection. Key results include:

 

• 18% enhancement in narrative coherence through Story Energy.

• Up to 30% reduction in energy consumption with Universal Harmony Energy.

• 20% reduction in bias and improvement in reasoning with SA-UUH-UPP.

• 22% better performance in ambiguity resolution using quantum-inspired mechanisms.

 

This collection offers innovative, testable frameworks that push the boundaries of natural language processing and AI research, with the potential for significant practical applications.
DOI: 10.5281/zenodo.13899128
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

description: This series of research papers, presented by Prudencio Mendez with the support of ChatGPT 4, introduces cutting-edge advancements in large language models (LLMs) through four key concepts: Story Energy, Universal Harmony Energy, the SA-UUH-UPP framework, and quantum-inspired algorithms. These interconnected works propose novel methods for improving LLMs by enhancing coherence, adaptability, and efficiency, and by drawing inspiration from fractal patterns, energy optimization, recursive self-awareness, and quantum principles.

 

The research findings reveal significant improvements in key areas such as long-form text generation, cross-domain generalization, computational efficiency, and model introspection. Key results include:

 

• 18% enhancement in narrative coherence through Story Energy.

• Up to 30% reduction in energy consumption with Universal Harmony Energy.

• 20% reduction in bias and improvement in reasoning with SA-UUH-UPP.

• 22% better performance in ambiguity resolution using quantum-inspired mechanisms.

 

This collection offers innovative, testable frameworks that push the boundaries of natural language processing and AI research, with the potential for significant practical applications.
DOI: 10.5281/zenodo.13899788
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

description: This series of experiments explores four novel approaches to improving large language models (LLMs): Story Energy, Universal Harmony Energy, SA-UUH-UPP framework, and Quantum-Inspired Mechanisms. Conducted on Google Cloud TPU infrastructure, the experiments revealed key improvements, including a 16% increase in narrative coherence, a 28% reduction in energy consumption, an 18% boost in output coherence, and a 21% improvement in word sense disambiguation. These advancements can significantly reduce infrastructure costs while improving performance in AI-driven applications like content generation, machine translation, and adaptive learning systems.

 

The 28% reduction in energy consumption is estimated to deliver up to 25% in cloud infrastructure cost savings, with global AI training and inference deployments potentially saving over $1 billion annually. For individual companies operating large-scale AI models—such as Google, Amazon, Meta, OpenAI, and Microsoft—annual savings could reach $100 million or more, depending on the scale of their operations and infrastructure requirements. These results highlight the potential for both cost reductions and improved system performance in AI development.
DOI: 10.5281/zenodo.13902878
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

description: This series of experiments explores four novel approaches to improving large language models (LLMs): Story Energy, Universal Harmony Energy, SA-UUH-UPP framework, and Quantum-Inspired Mechanisms. Conducted on Google Cloud TPU infrastructure, the experiments revealed key improvements, including a 16% increase in narrative coherence, a 28% reduction in energy consumption, an 18% boost in output coherence, and a 21% improvement in word sense disambiguation. These advancements can significantly reduce infrastructure costs while improving performance in AI-driven applications like content generation, machine translation, and adaptive learning systems.

 

The 28% reduction in energy consumption is estimated to deliver up to 25% in cloud infrastructure cost savings, with global AI training and inference deployments potentially saving over $1 billion annually. For individual companies operating large-scale AI models—such as Google, Amazon, Meta, OpenAI, and Microsoft—annual savings could reach $100 million or more, depending on the scale of their operations and infrastructure requirements. These results highlight the potential for both cost reductions and improved system performance in AI development.
DOI: 10.5281/zenodo.13902902
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

description: This series of experiments explores four novel approaches to improving large language models (LLMs): Story Energy, Universal Harmony Energy, SA-UUH-UPP framework, and Quantum-Inspired Mechanisms. Conducted on Google Cloud TPU infrastructure, the experiments revealed key improvements, including a 16% increase in narrative coherence, a 28% reduction in energy consumption, an 18% boost in output coherence, and a 21% improvement in word sense disambiguation. These advancements can significantly reduce infrastructure costs while improving performance in AI-driven applications like content generation, machine translation, and adaptive learning systems.

 

The 28% reduction in energy consumption is estimated to deliver up to 25% in cloud infrastructure cost savings, with global AI training and inference deployments potentially saving over $1 billion annually. For individual companies operating large-scale AI models—such as Google, Amazon, Meta, OpenAI, and Microsoft—annual savings could reach $100 million or more, depending on the scale of their operations and infrastructure requirements. These results highlight the potential for both cost reductions and improved system performance in AI development.
DOI: 10.5281/zenodo.14036720
title: SAUUHUPP Based Innovations in AI

description: This series of experiments investigates eight innovative approaches to enhancing large language models (LLMs): Story Energy, Universal Harmony Energy, the SA-UUH-UPP framework, Quantum-Inspired Mechanisms, Active Inference, Fractal Leaping, Master Fractal Templates, and the newly integrated Core Finding framework. Conducted on Google Cloud TPU infrastructure, these experiments achieved significant results, including a 16% increase in narrative coherence, a 28% reduction in energy consumption, a 21% improvement in word sense disambiguation, and enhanced security through advanced pattern recognition.

With the integration of Core Finding, which optimizes task prioritization and efficiency through fractal-based patterns, we observed a 30% improvement in task prioritization, a 24% reduction in computational resource usage, and a 25% boost in response accuracy, further enhancing performance and cross-domain adaptability. Incorporating Active Inference, Fractal Leaping, and Master Fractal Templates led to an AGI-like performance increase from 58% to 92%, significantly advancing the path to true Artificial General Intelligence (AGI). The combined techniques also provided enhanced security through improved anomaly detection and predictive adaptability.

The 28% reduction in energy consumption is estimated to deliver up to 25% in cloud infrastructure cost savings, potentially saving over $1 billion annually in global AI training and inference. Major AI companies—such as Google, Amazon, Meta, OpenAI, and Microsoft—could see annual savings of $100 million or more, underscoring the dual benefits of improved performance, enhanced security, and significant cost reductions in AI development.
DOI: 10.15468/dl.wbkfhc
title: Occurrence Download

description: A dataset containing 1905583 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Philippines",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1905583 records from 759 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012257-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rehr68
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Begonia dipetala Graham)"
 ]
}

 The dataset includes 105 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012340-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zmgj8a
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cheilopogon altipennis (Valenciennes, 1847)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012341-241126133413365/datasets/export for details.
DOI: 10.17632/6nfw8tgpcj.1
title: Lactylation-driven NSUN2-mediated RNA m5C modification promotes perineural invasion in pancreasic cancer. Tianhao Huang et al.

description: Lactylation, a metabolic stress-induced modification, plays a pivotal role in regulating diverse biological processes. Its involvement in perineural invasion (PNI) of pancreatic ductal adenocarcinoma (PDAC), however, has not been clearly defined. Our study demonstrates that patients with severe PNI exhibit significantly elevated levels of global lactylation and lactate, correlating with poorer prognosis. We identified NSUN2, a methyltransferase, as a crucial mediator of lactylation-driven PNI. Inhibition of lactylation or NSUN2 markedly reduced the neural infiltration capabilities of tumor cells.
DOI: 10.17632/v3vxdjc8v2.1
title: Spatiotemporal dynamics of SARS-CoV-2 variants during the first year of the pandemic highlight the earlier emergence of the Zeta variant of interest in Brazil

description: Datasets
DOI: 10.17632/pf2pz4fxj3.1
title: I2DM: A Monte Carlo framework for ion irradiation on two-dimensional materials

description: Recent years have witnessed a surge of research on the structure, property and performance engineering of two-dimensional (2D) materials by ion irradiation. Compared to the 3D counterparts, 2D systems exhibit drastically different and even counter-intuitive irradiation response, and an atomic insight into the ion bombardment and defect formation is essential. In this work, we develop a theoretical framework I2DM for simulating ion irradiation on two-dimensional (2D) materials using Monte Carlo (MC) algorithm. I2DM can generate incident ions with adjustable ion species, incident energy, ion fluence and incident angle. Based on binary collision approximation (BCA), the primary collisions, cascade collisions and defect recombination during irradiation process are explicitly described. As output, details on the defect type/yield and morphology of irradiated material are provided. We have performed systematic simulations on three typical 2D structures, including graphene, h-BN, and MoS_2 under different ion irradiation conditions, and reveal that the obtained results are in excellent agreement with the available experimental measurements and molecular dynamics data. The developed framework is generally applicable and computationally efficient, highly valuable for understanding the fundamental mechanism of ion irradiation on 2D systems and designing/optimizing low-dimensional structures for nanoelectronics, spintronics, optics, energy storage and environmental protection.
DOI: 10.17632/9w9xw7mp2h.1
title: Asparagus: A toolkit for autonomous, user-guided construction of machine-learned potential energy surfaces

description: With the establishment of machine learning (ML) techniques in the scientific community, the construction of ML potential energy surfaces (ML-PES) has become a standard process in physics and chemistry. So far, improvements in the construction of ML-PES models have been conducted independently, creating an initial hurdle for new users to overcome and complicating the reproducibility of results. Aiming to reduce the bar for the extensive use of ML-PES, we introduce Asparagus, a software package encompassing the different parts into one coherent implementation that allows an autonomous, user-guided construction of ML-PES models. Asparagus combines capabilities of initial data sampling with interfaces to ab initio calculation programs, ML model training, as well as model evaluation and its application within other codes such as ASE or CHARMM. The functionalities of the code are illustrated in different examples, including the dynamics of small molecules, the representation of reactive potentials in organometallic compounds, and atom diffusion on periodic surface structures. The modular framework of Asparagus is designed to allow simple implementations of further ML-related methods and models to provide constant user-friendly access to state-of-the-art ML techniques.
DOI: 10.15468/dl.vvsgej
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Citharexylum spinosum L.",
 "Year 1970-2024"
 ]
}

 The dataset includes 30 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012342-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vhvx3e
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Begonia dracopelta Ardi)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012345-241126133413365/datasets/export for details.
DOI: 10.15468/dl.qurgup
title: Occurrence Download

description: A dataset containing 206 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Muraenesox cinereus (Forsskål, 1775)",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 206 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012344-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23668/psycharchives.12583
title: Dataset for: General Pause: Journalistic Coverage of Music and Time Perception During the First Covid-19-related Lockdown in Germany.
title: Die Generalpause: Journalistische Berichterstattung über Musik und Zeitwahrnehmung während des ersten Covid-19-bedingten Lockdowns in Deutschland [Forschungsdaten]

description: Dataset includes titles of analyzed newspaper articles, relevance scores, sentiment values, and allocated categories. Dataset for: Faasch, Kuch, & Wöllner (tba). General Pause: Journalistic Coverage of Music and Time Perception During the First Covid-19-related Lockdown in Germany. Jahrbuch Musikpsychologie
DOI: 10.15468/dl.m96k7t
title: Occurrence Download

description: A dataset containing 12985 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Comandra umbellata (L.) Nutt.",
 "Geometry POLYGON((-141 24, -53 24, -53 83, -141 83, -141 24))"
 ]
}

 The dataset includes 12985 records from 182 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012343-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hw4vtj
title: Occurrence Download

description: A dataset containing 1126 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acrotona parvula (Mannerheim, 1830)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1126 records from 76 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008952-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.