cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.abjmkf
title: Occurrence Download

description: A dataset containing 6394 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heterotheca villosa (Pursh) Shinners",
 "Geometry POLYGON((-141 24, -53 24, -53 83, -141 83, -141 24))"
 ]
}

 The dataset includes 6394 records from 144 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012338-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/xwfs6xbk47.1
title: Human Bone Fractures Multi-modal Image Dataset (HBFMID)

description: Human Bone Fractures Multi-modal Image Dataset (HBFMID) consists of X-ray and MRI images including the elbow, ﬁnger, forearm, humerus, shoulder, femur, shinbone, knee, hipbone, wrist, spinal cord, and some of the healthy bones. The dataset primarily used 641 raw images, including 510 for X-ray and 131 for MRI images. After that, we divided our dataset into three sections: training (449 images), validation(128 images), and testing (64 images). We used annotation techniques to label the image data. The auto-orient, resized the image size to 640 X 640, and made contrast adjustments have been applied for image pre-processing. For augmentation, several techniques like scaling, shifting, shearing, cropping, random rotation, and brightness change are applied. In the augmentation technique, the following parameters were used: Flip (Horizontal, Vertical), Rotation (Between -5◦and +5◦), Shear (±2◦Horizontal, ±2◦Vertical), Saturation (Between -5 % and +5 %), Brightness: (Between -10 % and +10 %), and zooming (2%). After augmentation, we received an augmented image comprising about 1347 (449x3) images. In HBFMID dataset a total of 1539 ((1347:training)+(128: validation)+(64: testing)) images are used.
DOI: 10.25810/k55w-7a04.79
title: World Radio Coverage of Climate Change or Global Warming, 2000-2024 - October 2024

description: The Media and Climate Change Observatory Data monitors 130 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.17632/ftvf398znk
title: Surface environmental changes of ZSLB

description: The data contains the figures of the research about the surface environmental changes in Zonag-Salt Lake Basin( ZSLB)
DOI: 10.17632/ftvf398znk.1
title: Surface environmental changes of ZSLB

description: The data contains the figures of the research about the surface environmental changes in Zonag-Salt Lake Basin( ZSLB)
DOI: 10.17632/p6tr7665t5.1
title: OMV PETROM-behind the numbers 2024

description: This business case aims to assess the understanding and ability to apply the main concepts, principles, and techniques of financial reporting and demonstrate how the integrated financial information contribute to an effective professional judgment for strategic business decisions.
DOI: 10.17632/txcwh85rj7.1
title: CSR Beliefs of bank customers

description: The database conttains anonymized survThe database contains anonymized survey data collected for the research study titled "The Effect of CSR Beliefs among Bank Customers during Difficult Economic Times." The dataset includes responses from banking customers in five countries: the United States, Spain, Mexico, Brazil, and Argentina, who reported being aware of CSR initiatives by their banks. The survey was conducted to analyze the relationships between perceived CSR motives, CSR beliefs, and customer outcomes such as trust, commitment, attitudes, and skepticism during the economic crisis caused by the COVID-19 pandemic.ey data collected for the research study titled "The Effect of CSR Beliefs among Bank Customers during Difficult Economic Times." The dataset includes responses from banking customers in five countries: the United States, Spain, Mexico, Brazil, and Argentina, who reported being aware of CSR initiatives by their banks. The survey was conducted to analyze the relationships between perceived CSR motives, CSR beliefs, and customer outcomes such as trust, commitment, attitudes, and skepticism during the economic crisis caused by the COVID-19 pandemic.
DOI: 10.17632/rb8hgnp586.1
title: MegaLTR and EDTA derived long terminal repeat retrotransposons libraries in assorted cultivated plants

description: This dataset contains libraries of long terminal repeat retrotransposons generated by MegaLTR and EDTA tools in assorted cultivated plants: Ananas comosus, Camellia sinensis, Capsicum annuum, C. baccatum, C. chinense, Carica papaya, Citrus sinensis, Cocos nucifera, Coffea arabica, Cucumis melo, Cucurbita pepo, Eucalyptus grandis, Glycine max, Mangifera indica, Manihot esculenta, Musa acuminata, Prunus persica, Solanum lycopersicum, Theobroma cacao and Zingiber officinale. The resultant repetitive libraries are valuable and now available to the interested community for downstream analysis.
DOI: 10.17632/m79hnngy8s.1
title: TorchAmi: Generalized CPU/GPU implementation of algorithmic Matsubara integration

description: We present torchami, an advanced implementation of algorithmic Matsubara integration (AMI) that utilizes pytorch as a backend to provide easy parallelization and GPU support. AMI is a tool for analytically resolving the sequence of nested Matsubara integrals that arise in virtually all Feynman perturbative expansions. In this implementation we present a new AMI algorithm that creates a more natural symbolic representation of the Feynman integrands. In addition, we include peripheral tools that allow for import and labeling of simple graph structures and conversion to torchami input. The code is written in c++ with python bindings provided.
DOI: 10.5281/zenodo.13899643
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.13901040
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.13924327
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

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.13931771
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

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.14012953
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

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.14036329
title: Innovations in Large Language Models: Story Energy, Universal Harmony Energy, SA-UUH-UPP, and Quantum-Inspired Approaches

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.13896292
title: Groundbreaking 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.13898492
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.13899241
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.13899380
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.13899400
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.