ethicalabs/Research-Intent-Collab huggingface dataset:
The Rise of Artificial Intelligence in Intellectual Property Law: Patentability and Copyright Issues

The rise of Artificial Intelligence (AI) is transforming and posing important challenges in the field of Intellectual Property (IP) law regarding patentability and copyright. As AI systems increasingly produce inventions and creative works without human involvement, doubts arise on how suitable the present framework of intellectual property rights-invented to protect human-made innovations-is for such a confused state of AI. This paper develops its line of analysis around the AI implications for patent and copyright laws, checking out if current legal structures are such that they can address outputs from AI systems. While current laws in the patent regime recognize only a human inventor, an AI system can produce novel and non- obvious inventions. Should AI be viewed as an inventor, or would patent laws be transformed to recognize that AI is merely a tool in the invention process? The paper discusses various in-applicable case law and decisions such as the 2019 ruling of the USPTO that in certain inventions, the inventor could only be a human being, which manifests the fact that patentability frameworks remain unfit for AI-generated inventions. Moreover, in relation to copyright, it can be noted that the contingent works executed by AI challenge and suspend the necessity for human authorship. When AI generates such works on its way, the alternative arises and must be critically analysed who is to be considered the actual owner of copyright over the work? Current copyright frameworks in various jurisdictions, such as the U.S., do not extend protection to AI-generated works without incorporation of some human involvement in their creation. Possible suggestions of reform like placing authorship over X work under the AIs developer or user, or a New IP category addressing the input of AI in creation, formulations were discussed in the paper. Ultimately, innovations in both patent and copyright laws ought to grow in a balanced way that, while producing adequate protective layers over human creators, prepares itself to come into terms with rising Aid creativity and invention. For maintaining justice and fairness, reform in legal constructs for tracking with AI-generated creations is inevitable.
Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange (Preprint)

BACKGROUND                         Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange.                                                                       OBJECTIVE                         This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability.                                                                       METHODS                         Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the &lt;i&gt;ICD-9-CM&lt;/i&gt; (&lt;i&gt;International Classification of Diseases, Ninth Revision, Clinical Modification&lt;/i&gt;), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes).                                                                       RESULTS                         The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names.                                                                       CONCLUSIONS                         This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.                                                                       CLINICALTRIAL
Climate model fidelity and projections of climate change

Relative entropy, which is a measure of the difference between two probability distributions, has been calculated for the simulations of the climate of the 20th century from 13 climate models and the observed surface air temperature during the past 100 years. This quantity is used as a measure of model fidelity: a small value of relative entropy indicates that a given model's distribution is close to the observed. It is found that there is an inverse relationship between relative entropy and the sensitivity of the model to doubling of the concentration of CO2. The models that have lower values of relative entropy, hence have higher fidelity in simulating the present climate, produce higher values of global warming for a doubling of CO2. This suggests that the projected global warming due to increasing CO2 is likely to be closer to the highest projected estimates among the current generation of climate models.
Uncertainty and Precaution 2: The Precautionary Principle and its relevance to science

After Second World War public and political decision makers wished to correct the apparent negative consequences of science and technology. This resulted in a call for ethical responsibility in science and technology and a new generation of environmental regimes aimed at controlling or managing the consequences of human interaction with the environment. The claim of this paper is that the Precautionary Principle can be understood as a combination of these trends. The Precautionary Principle is presented and discussed in detail. In particular it is specified a) when the Principle is to be used, b) what measures follow from the decision to use it, and c) who is to be consulted in democratic decisions of the use of the Principle.
Supplementary Figures 1-13 from Genome-wide Profiling of Chromatin Signatures Reveals Epigenetic Regulation of MicroRNA Genes in Colorectal Cancer

Supplementary Figures 1-13 from Genome-wide Profiling of Chromatin Signatures Reveals Epigenetic Regulation of MicroRNA Genes in Colorectal Cancer
PathoGen-X: A Cross-Modal Genomic Feature Trans-Align Network for Enhanced Survival Prediction from Histopathology Images

Accurate survival prediction is essential for personalized cancer treatment. However, genomic data - often a more powerful predictor than pathology data - is costly and inaccessible. We present the cross-modal genomic feature translation and alignment network for enhanced survival prediction from histopathology images (PathoGen-X). It is a deep learning framework that leverages both genomic and imaging data during training, relying solely on imaging data at testing. PathoGen-X employs transformer-based networks to align and translate image features into the genomic feature space, enhancing weaker imaging signals with stronger genomic signals. Unlike other methods, PathoGen-X translates and aligns features without projecting them to a shared latent space and requires fewer paired samples. Evaluated on TCGA-BRCA, TCGA-LUAD, and TCGA-GBM datasets, PathoGen-X demonstrates strong survival prediction performance, emphasizing the potential of enriched imaging models for accessible cancer prognosis.
UNVEILING TRANSPARENCY: THE TREASURY SINGLE ACCOUNT'S CRUSADE AGAINST CORRUPTION IN NIGERIA'S PUBLIC SPHERE

The Treasury Single Account (TSA) stands as a pivotal initiative in President Muhammadu Buhari's strategic arsenal against public sector corruption, aimed at enhancing accountability, financial discipline, and overall financial management in Nigeria. Initiated on August 9, 2015, the TSA directive mandated all Ministries, Departments, and Agencies (MDAs) to consolidate their accounts from deposit money banking institutions into a designated federation government account. This move was orchestrated to bring transparency to financial transactions, ensuring a seamless link among all payment systems (Osagioduwa, 2019).  This study delves into the profound impact of the TSA on the Nigerian government's financial landscape. The policy's core objective is to streamline the collection and disbursement of government funds, eliminating the need for multiple financial systems. The implementation of the TSA has significantly heightened the transparency of government finances, curbing corruption within the government sector and reinforcing cost control mechanisms in various government parastatals (Ganyam, 2018). The research builds upon the hypothesis posited by Ivungu et al. (2020), suggesting that the TSA policy facilitates the efficient collection and timely deposit of all government earnings into the public treasury, bypassing the complexities of diverse financial systems.  The findings of this study underscore the transformative potential of the TSA policy in reshaping financial practices within the Nigerian government. By examining the impact on transparency, corruption mitigation, and cost management, the research contributes valuable insights to the ongoing discourse on public sector reforms. The implications of the TSA extend beyond financial management, providing a blueprint for other nations grappling with similar challenges in enhancing accountability and curbing corruption within the public sector
Estimating the power spectrum of the cosmic microwave background

23 Pages, 16 Figures. Final published version
Digitalization, retail payments and Central Bank Digital Currency

Artículo de revista
DesignMinds: Enhancing Video-Based Design Ideation with a Vision-Language Model and a Context-Injected Large Language Model

Ideation is a critical component of video-based design (VBD), where videos serve as the primary medium for design exploration and inspiration. The emergence of generative AI offers considerable potential to enhance this process by streamlining video analysis and facilitating idea generation. In this paper, we present DesignMinds, a prototype that integrates a state-of-the-art Vision-Language Model (VLM) with a context-enhanced Large Language Model (LLM) to support ideation in VBD. To evaluate DesignMinds, we conducted a between-subject study with 35 design practitioners, comparing its performance to a baseline condition. Our results demonstrate that DesignMinds significantly enhances the flexibility and originality of ideation, while also increasing task engagement. Importantly, the introduction of this technology did not negatively impact user experience, technology acceptance, or usability.
Re-imagining health and well-being in low resource African settings using an augmented AI system and a 3D digital twin

This paper discusses and explores the potential and relevance of recent developments in artificial intelligence (AI) and digital twins for health and well-being in low-resource African countries. We use the case of public health emergency response to disease outbreaks and epidemic control. There is potential to take advantage of the increasing availability of data and digitization to develop advanced AI methods for analysis and prediction. Using an AI systems perspective, we review emerging trends in AI systems and digital twins and propose an initial augmented AI system architecture to illustrate how an AI system can work with a 3D digital twin to address public health goals. We highlight scientific knowledge discovery, continual learning, pragmatic interoperability, and interactive explanation and decision-making as essential research challenges for AI systems and digital twins.
A evidenciação da prática corporativa de ações de responsabilidade social com o uso do balanço social = Evidence of the corporate practice of socially responsible actions with the use of the social report

A responsabilidade social corporativa é uma estratégia recente para potencializar o desenvolvimento das organizações no engajamento socioambiental. Essa tendência decorre da maior conscientização do consumidor, que procura por produtos e práticas que gerem melhorias para o meio ambiente e para a comunidade, valorizando os aspectos éticos ligados com a cidadania empresarial. A contabilidade possui instrumentos capazes de evidenciar a prática de ações de responsabilidade social, resumidos no balanço social, um demonstrativo que evidencia informações de caráter econômico, financeiro, social e ambiental, podendo ser direcionados para um grupo diversificado de usuários. O objetivo central da pesquisa é estudar as práticas de ações de responsabilidade social em uma empresa que industrializa estofados. Os procedimentos metodológicos adotados caracterizam a pesquisa como exploratória, quanto aos objetivos; estudo de caso, quanto aos procedimentos; e, qualitativa e quantitativa, quanto à abordagem do problema. O demonstrativo sugerido contempla um conjunto de indicadores que possibilitam avaliar a postura da cidadania empresarial no período analisado. As conclusões remetem ao entendimento de que o balanço social é um instrumento que complementa o conjunto de relatórios aos usuários da informação contábil.The corporate social responsibility is a recent strategy used to improve the development of the organizations on social and environmental engagement. This tendency arises from the increased awareness of consumers who demand products and practices that generate improvements to the environment and the community, highlighting the ethical aspects related to corporate citizenship. The Accountancy owns tools that are capable of demonstrating the practice of socially responsible actions, which are summarized in the social report, and also a demonstrative that shows economic, financial, social and environmental information that can be directed to a diversified group of users. The main objective of this research is to study the practical of socially responsible actions in a company that industrializes upholstered furniture. The methodological procedures adopted characterized the research as exploratory for its objectives; a case study for the procedures; and qualitative and quantitative as to the problem approach. The suggested demonstrative includes a group of indicators which allows evaluating the corporate citizenship of the analyzed period. The conclusions suggested that the social report is a tool that complements the set of reports for the users of the accounting information.
ChemInform Abstract: Nanocrystalline Iron Oxide Based Electroactive Materials in Lithium Ion Batteries: The Critical Role of Crystallite Size, Morphology, and Electrode Heterostructure on Battery Relevant Electrochemistry

AbstractReview: 96 refs.
Direct dark matter detection: the next decade

Direct dark matter searches are promising techniques to identify the nature of dark matter particles. I describe the future of this field of research, focussing on the question of what can be achieved in the next decade. I will present the main techniques and R&D projects that will allow to build so-called ultimate WIMP detectors, capable of probing spin-independent interactions down to the unimaginably low cross section of 1e-48 cm2, before the irreducible neutrino background takes over. If a discovery is within the reach of a near-future dark matter experiment, these detectors will be able to constrain WIMP properties such as its mass, scattering cross section and possibly spin. With input from the LHC and from indirect searches, direct detection experiments will hopefully allow to determine the local density and to constrain the local phase-space structure of our dark matter halo.
Roman Galactic Plane Survey Definition Committee Report

The Roman Galactic Plane Survey (RGPS) is a 700-hour program approved for early definition as a community-designed General Astrophysics Survey. It was selected following a proposal call for science programs that would benefit from an early community-based definition (Sanderson et al 2024). The community was invited to submit white papers and science pitches with a deadline of May 20, 2024; the Roman Galactic Plane Survey Definition Committee (RGPS-DC) first met on Sep 11, 2024. Based on the input provided, the RGPS-DC recommends a survey consisting of three elements: (1) a wide-field science element (691 sq deg, 541 hrs) covering the Galactic plane, Galactic latitude |b|<2 deg and Galactic longitude l=+50.1 deg to -79 deg (281 deg), in four filters (F129, F159, F184, and F213) with higher latitude extensions for the bulge, the Serpens South/W40 star formation region, and Carina, (2) a time-domain science element (19 sq deg , 130 hrs) of six fields, including the full Nuclear Stellar Disk (NSD) and Central Molecular Zone (CMZ), with coverage in seven filters and repeat observations in one or more filters with cadences from 11 minutes to weeks, and (3) a deep-field/spectroscopic science element (4 sq deg , 30 hrs) consisting of fifteen Roman pointings (with a wide range of extinction, diffuse emission, stellar density and population) using longer exposure times in seven filters in addition to grism and prism observations. This document summarizes the science that can be done with this survey, the process of survey definition, and details on all of the program elements.
Assessing patient-reported quality of life outcomes in vulva cancer patients: a systematic literature review

Objectives Vulva cancer (VC) treatment carries a high risk of severe late effects that may have a negative impact on quality of life (QoL). Patient- reported outcome measures (PROMs) are increasingly used when evaluating disease- and treatment- specific effects. However, the adequacy of measures used to assess sequelae and QoL in VC remains unclear. The aims of the present study were to evaluate disease- and treatment-related effects as measured by PROMs in VC patients and to identify available VC-specific PROMs. Methods/Materials A systematic literature search from 1990 to 2016 was performed. The inclusion criterion was report of disease- and treatment- related effects in VC patients using PROMs in the assessment. Methodological and reporting quality was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. This systematic review was performed as part of phase 1 of the development of a European Organisation for Research and Treatment of Cancer QoL questionnaire for VC patients. Results The search revealed 2299 relevant hits, with 11 articles extracted including a total of 535 women with VC ; no randomized controlled trials were identified. The selected studies exhibited great heterogeneity in terms of PROMs use. Twenty-one different instruments assessed QoL. Most of the questionnaires were generic. Different issues (sexuality, lymphedema, body image, urinary and bowel function, vulva-specific symptoms) were reported as potentially important, but the results were not systematically collected. Only one VC- specific questionnaire was identified but did not allow for assessment and reporting on a scale level. Conclusions Vulva cancer treatment is associated with considerable morbidity deteriorating QoL. To date, there is no validated PROM available that provides adequate coverage of VC-related issues. The study confirms the need for a VC-specific QoL instrument with sensitive scales that allows for broad cross-cultural application for use in clinical trials
Medi Molecule: An AI-Powered Platform for Accelerating Drug Discovery through Molecule Generation and Real-Time Collaboration

Introduction: Medi Molecule is changing drug discovery with its AI platform that tackles common challenges in pharmaceutical research. Traditional drug discovery can take a lot of time and effort, often slowed down by manual data analysis. Using AI models like Nvidia MolMIM, Medi Molecule improves the search, creation, and analysis of molecules. Its strong connections with databases like PubChem and RDKit provide researchers with a wealth of chemical data and solid molecular modeling tools. The platform also features real-time collaboration, allowing researchers to interact and share ideas easily. Interactive data visualizations give useful insights into molecular properties, helping scientists make informed decisions. Medi Molecule is designed for scalability, security, and ease of use, making it an essential tool for scientists, academia, and pharmaceutical companies. This innovative platform shows how AI can help in drug discovery, cutting down time and costs while making it easier to develop life-saving drugs.  Objectives: Molecular Generation, Molecular Scoring and Interpretation, and Molecule Representation and Input Handling,  Methods: In the Medi Molecule project, RDKit plays a crucial role in the cheminformatics pipeline. It supports several stages of the machine learning-based drug discovery process. When users enter a SMILES representation of a molecule, RDKit converts it into a molecular graph, where atoms are nodes and bonds are edges. This molecular graph structure is vital for many computational treatments and manipulations, which helps deepen the understanding of molecular structures. Additionally, RDKit allows for the conversion between SMILES and other molecular representations, providing flexibility in how molecules can be represented and how molecular information is input into the system.  Results: Medi Molecule’s main features, driven by artificial intelligence tools and real-time collaboration, have greatly improved the drug discovery process. These innovations have brought revolutionary changes that help scientists work faster, one of the platform's most valuable features is its AI-based search function, which has drastically reduced the time needed to access useful molecular information. Combining molecule generation with toxicity prediction models has given researchers initial estimates of the safety profiles for newly identified chemicals.  Conclusions: The frontend connects to the backend through API endpoints, facilitating the smooth transfer of user inputs like SMILES strings and molecular parameters. For molecule generation, Nvidia MolMIM assists in the backend, while RDKit is responsible for tasks such as molecular visualization, descriptor calculation, and optimization. Furthermore, PubChem APIs confirm that the generated compounds are original.