ethicalabs/Research-Intent-Collab huggingface dataset:
Acoustic Peaks and Dips in the Cosmic Microwave Background Power Spectrum: Observational Data and Cosmological Constraints

The locations and amplitudes of three acoustic peaks and two dips in the BOOMERANG, MAXIMA, and DASI measurements of the cosmic microwave background (CMB) anisotropy power spectra as well as their statistical confidence levels are determined in a model-independent way. It is shown that the BOOMERANG 2001 data from Netterfield and coworkers fix the location and amplitude of the first acoustic peak at more than 3 ? confidence. The next two peaks and dips are determined at a confidence level above 1 ? but below 2 ?. The locations and amplitudes of the first three peaks and two dips are l = 212 ? 17, A = 5426 ? 1218 ?K2; l = 413 ? 50, A = 1960 ? 503 ?K2; l = 544 ? 56, A = 2266 ? 607 ?K2; l = 746 ? 89, A = 1605 ? 650 ?K2; and l = 843 ? 35, A = 2077 ? 876 ?K2, respectively (1 ? errors include statistical and systematic errors). The MAXIMA and DASI experiments give similar values for the extrema that they determine. For MAXIMA these are the first and third peaks, for DASI the first and second peaks and the first dip. Moreover, the locations and amplitudes of the extrema determined from the combined data of all experiments are quite close to the corresponding values extracted from the BOOMERANG data alone. In order to use these data in a fast search for cosmological parameters, an accurate analytic approximation to calculate CMB peak and dip positions and amplitudes in mixed dark matter models with cosmological constant and curvature is derived and tested. The determined cosmological parameters from the CMB acoustic extrema data show good agreement with other determinations, especially with the baryon content as deduced from standard nucleosynthesis constraints, as shown by Burles and coworkers. These data supplemented by constraints from direct measurements of some cosmological parameters and data on large-scale structure lead to a best-fit model that agrees with practically all the used experimental data within 1 ?. The best-fit parameters are ?? = 0.64, ?m = 0.36, ?b = 0.047, ns = 1.0, h = 0.65, and ?c = 0.15. The best-fit values of ?? and T/S are close to zero, and their 1 ? upper limits are 0.17 and 1.7, respectively.
When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation

Diffusion MRI (dMRI) is an advanced imaging technique characterizing tissue microstructure and white matter structural connectivity of the human brain. The demand for high-quality dMRI data is growing, driven by the need for better resolution and improved tissue contrast. However, acquiring high-quality dMRI data is expensive and time-consuming. In this context, deep generative modeling emerges as a promising solution to enhance image quality while minimizing acquisition costs and scanning time. In this study, we propose a novel generative approach to perform dMRI generation using deep diffusion models. It can generate high dimension (4D) and high resolution data preserving the gradients information and brain structure. We demonstrated our method through an image mapping task aimed at enhancing the quality of dMRI images from 3T to 7T. Our approach demonstrates highly enhanced performance in generating dMRI images when compared to the current state-of-the-art (SOTA) methods. This achievement underscores a substantial progression in enhancing dMRI quality, highlighting the potential of our novel generative approach to revolutionize dMRI imaging standards.
A study on factors influencing music enthusiast’s behavioral intention to use generative AI for music composition based on an expanded technology acceptance model

This study aims to understand the intentions of music enthusiasts in using generative artificial intelligence (GenAI) for music composition and to elucidate the factors influencing those intentions. A covariance-based structural equation modeling approach is applied to construct a technology acceptance model (TAM). A total of 429 valid questionnaire responses were analyzed to explore music enthusiasts' acceptance mechanisms regarding GenAI tools. The sample encompasses diverse age groups, educational backgrounds, and experience levels, thus ensuring the broad applicability of the findings. The results show that relationships within the original TAM structure remain robust. Specifically, perceived AI risk negatively impacts intention to use (IU) and perceived ease of use (PEOU); however, it does not correlate significantly with perceived usefulness (PU). Perceived trust positively influences PEOU and PU but does not directly affect IU. Content quality is positively correlated with PEOU, PU, and IU. Social influence is correlated only with PU and does not affect PEOU or IU. Perceived enjoyment enhances both PEOU and PU but has no effect on IU. Demographic analysis reveals no differences in GenAI tool evaluations among music enthusiasts in terms of gender, age, or education level. Overall, this report provides essential theoretical and practical insights for applying GenAI in music creation by integrating traditional and novel factors.
Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer

Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to focus on tumor regions and potentially unable to fully leverage the prognostic information within the tumor regions. In this study, we propose a radiomics-enhanced deep multi-task framework for outcome prediction from PET/CT images, in the context of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR 2022). In our framework, our novelty is to incorporate radiomics as an enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS). The DeepMTS jointly learns to predict the survival risk scores of patients and the segmentation masks of tumor regions. Radiomics features are extracted from the predicted tumor regions and combined with the predicted survival risk scores for final outcome prediction, through which the prognostic information in tumor regions can be further leveraged. Our method achieved a C-index of 0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068 lower in C-index than the 1st place.
Research on Human-Computer Cooperative Teaching Supported by Artificial Intelligence Robot Assistant

The artificial intelligence robot assistant is the physical embodiment of the educational application of artificial intelligence. It is different from the online virtual robot assistant experience to the robot psychological and habit experience, and the robot application of educational situation relies on the design and implementation of human-computer cooperation mechanism. Human-computer collaborative gives full parts to the advantages of teachers and artificial intelligence and promotes the personalized educational development of learners. How to promote the collaborative teaching of teachers and artificial intelligence robot assistant in the smart learning environment is a topic worth to study. Therefore, based on the application scenario of the artificial intelligence robot assistant, this chapter analyzes key technologies of artificial intelligence supported by the artificial intelligence robot assistant, constructs the collaborative teaching environment supported by the artificial intelligence robot assistant under the framework of online platform foundation and fog computing environment, analyzes the collaborative teaching process and application supported by the artificial intelligence robot assistant, and designs the collaborative teaching environment. At last, we summarize the application effect and related discussion of the application of artificial intelligence robot assistant in the classroom. Artificial intelligence robot assistant is an integrated and concrete classroom embodiment of artificial intelligence technology. The artificial intelligence robot assistant can be used as a teacher assistant in the classroom, which can form a new human-computer cooperative teaching mode.
Fedssl: privacy-preserving federated self-supervised learning with differential privacy guarantees for heterogeneous edge environments

In this study, we delve into the significant impact of AI, investigating its multifaceted consequences on society. In a rapidly evolving digital landscape, the integration of artificial intelligence has emerged as a transformative force in reshaping human progress and well-being. Drawing from historical perspectives, we trace the evolution of AI and its transformative journey. Our research aims to comprehensively analyze approaches for fostering responsible AI growth while mitigating potential hazards. By illuminating both the promise and perils of AI, this study contributes to informed decision-making in the unfolding AL era. This research explores the innovative utilization of AI technologies to optimize individual and societal gains while concurrently influencing the reconstruction of prevailing social norms. By harnessing the power of AI, we embark on a journey toward a new era, characterized by more efficient problem-solving, enhanced decision-making, and the redefinition of traditional social paradigms. This study investigates the multifaceted impacts of AI on human development, offering insights into its potential to revolutionize our world and foster a future marked by unprecedented advancements in progress, wellness, and social transformation.
Methods for simulation of dark matter dynamics in cosmology

While dark matter accounts for approximately 85% of the matter content of the observable universe, its fundamental nature is one of the most important mysteries of modern physics. Many different theories of dark matter have been proposed. We are studying ultra-light dark matter, a scenario in which the dark matter particle is far lighter than any particle currently known to have a non-zero mass. The behaviour of ultra-light dark matter is governed by the “Schroedinger-Poisson equations”. We will introduce PyUltraLight, the code we have developed at the University of Auckland to solve this system of coupled partial differential equations. PyUltraLight is written in Python and can run inside a Jupyter notebook; it is currently the only freely shared, open-source Schroedinger-Poisson solver available to astrophysicists. We are now using PyUltraLight to study galaxy formation and galaxy-galaxy interactions in a universe with ultra-light dark matter. One of the main challenges we are now facing is the need to simultaneously simulate large physical volumes while resolving dynamics on small scales, e.g. in the central regions of the galaxies or the detailed dynamics of galactic mergers. Increasing spatial resolution across the whole simulation is computationally expensive and we are developing a simulation tool that uses adaptive mesh refinement (AMR), a standard technique used to tackle problems where a significant proportion of the simulation volume is empty. AMR codes allow the resolution to be increased only in the locations of greatest interest, on the basis of specified refinement criteria. We will describe the numerical methods we are now developing to simulate dark matter dynamics and discuss some recent results and future research directions. ABOUT THE AUTHOR(S) Emily Kendall is a PhD student under Richard Easther at the University of Auckland. Mateja Gosenca is a Postdoctoral researcher in the cosmology group at the University of Auckland. Both are working on the theory and computational modelling of ultra-light dark matter.
Tribological Properties of Fullerene‐Enhanced Fluororubber: A Reactive Force Field Molecular Dynamics Simulation

ABSTRACT                                        The friction process of fluororubber (FKM) seals was found to involve intricate mechanical–chemical coupling effects. To investigate the underlying mechanisms, the tribological behavior of FKM was examined using ReaxFF molecular dynamics simulations at various temperatures (300, 500, 700, and 1000 K). Molecular models of the FKM and FKM/fullerene (C                     60                     ) were established to assess the tribological properties of FKM under the influence of temperature and fillers. Notable improvements in shear resistance and thermal stability of the FKM matrix were observed with C                     60                     incorporation, which was accompanied by reduced abrasion loss. The C                     60                     enhanced intermolecular interactions with its structure, thereby improving material stability and indirectly boosting matrix wear resistance. The suppression of CC bond cleavage and reduction in side‐chain hydrogen/fluorine atoms generation were demonstrated by C                     60                     across the temperature range of 300–700 K. This action indirectly boosted FKM's high‐temperature tribological performance. Severe degradation of the mechanical properties in FKM was observed at 1000 K. This work provides a theoretical method for studying the tribological performance of FKM at different temperatures.
Calculation of internal overvoltages using a wide band transformer model based on limited information about transformer design

Specific insulation coordination studies might require an accurate modeling of power transformers. This is the case for instance when transfers of fast front overvoltages from the high voltage side to the low voltage side of a transformer are involved. Modeling accurately the transformer’s electromagnetic behavior for the frequency range of fast front overvoltages requires special care. There are numerous existing transformer models which are intended to model the wide band transformer’s behavior. However, many of them are too complex or require confidential information about the transformer design. This makes them not suitable for power engineering applications. In this paper a wide band transformer model based on limited information about the transformer design and compatible with an electromagnetic transients program is presented. Then, its application on calculating internal overvoltages distribution along transformer windings, taking into account different conditions on the outer transformer terminals, is shown for lightning impulse.
Medicine, Patriarchy and the Ongoing Impact on Sexual and Reproductive Health and Rights

Modern medicine is considered to be scientific and evidence based. However, it was born within deeply patriarchal, col0onial and exclusionary sy6stems and founded on the violent suppression of women healers during the European witch hunts. This origin laid the groundwork for a male-dominated medical establishment that deliberately erased generations of feminine knowledge and healing traditions. Women were ousted from caregiving roles through a calculated mix of violence, professionalization, and ridicule. Instructions like Harvard medical school refused entry to women up until 1945 citing laughable reasons – from fears of distraction to the supposed drying of ovaries from too much thinking.  With colonization, these ideologies and systems were imposed on the countries in Asia, Africa and Latin America, suppressing and dismissing centuries of indigenous medical knowledge and also criminalizing sexual and reproductive norms. Homosexuality, gender fluidity, sexual expressions and abortion were all systematically pathologized, policed, and punished through newly imposed medical and legal systems. The repercussions of this are still being felt today through the Penal Codes set up in the 1800s.  Women’s pain is disbelieved, their sexual health curated for ensuring male pleasure and their reproductive choices controlled. While women patients face medical gaslighting, women doctors themselves face gender bias and systemic barriers to leadership.
Subtropical lake under projected climate change: 3D model assessment and insights

&amp;lt;p&amp;gt;Located in a highly sensitive climate area, according to the IPCC reports, Lake Kinneret is exposed to extreme changes in the next few decades. Lake Kinneret is a warm monomictic lake, located between subtropical and arid climatic belts. It is thermally stratified throughout most of the year and mixes thoroughly each winter when the epilimnion water temperature reaches equilibrium with the hypolimnion water temperature by surface cooling and turbulence. Using high-resolution atmospheric projections obtained from COSMO-CLM simulation to force a 3D hydrodynamical model, we show significant changes in the stratification and circulation of the lake over the next five decades. As air temperature is expected to rise by up to 2.5&amp;lt;sup&amp;gt;o&amp;lt;/sup&amp;gt;C in winter and autumn by 2070, the water column full mixing is expected to be suppressed. Unmixed years are expected to appear more often and full mixing will be re-activated just when cold enough winter conditions allow a full water column turnover. The lack of mixing between the epilimnion and the hypolimnion may create an environment that is oxygen depleted in which very few organisms can live, hence, might strongly affect the lake&amp;#039;s present ecological system, its sustainability, and its water quality. Stratification Index analysis results show that Lake Kinneret will remain stratified throughout the year 20%-30% of the time in 2030-2070 under the moderate climate change scenario RCP4.5. However, abrupt cooling of the lake surface due to enhanced latent heat loss is identified, around the year 2065, in the model results, and is expected to restrain the dramatic change in the lake stratification.&amp;lt;/p&amp;gt;
Concepts and Methods of Conflict Resolution and Peace-Building: Imperatives for Religious Leaders in Nigeria

The need for conflict resolution in our society is crucial. It is imperative because no two people perceive a particular thing exactly in the same way. Efforts to curb conflict and crisis-situations have often times been unsuccessful; occasioned not only by the complex nature of humanity, but also by the methods and means employed by those on mediation. It is the position of this paper that a factor that has not received sufficient attention in this regard is the culture, tradition and biblical frameworks or paradigms determining our understanding of conflicts, their causes and appropriate tools for  reconciliation. This paper will locate and explore these  necessary basic tools and frameworks on peace initiatives and conflict resolutions as the symbolical rites of procedure. It will argue that all of them have to be taken seriously if we really have to move from rhetorics and good intentions to sustainable behaviour change that leads to solid reconciliation and peace that will finally lead to decreased rates of conflict situations and socio-political rascalities in Nigeria today.
Balancing surveillance and privacy rights: A constitutional study of the digital era in Pakistan

Rapid technological advancements in surveillance in the digital age have primarily transformed the collection, analysis and use of data by governments, corporations and individuals. Whereas these advancements enrich security and efficiency, they also pose substantial risks to privacy, sovereignty, and civil liberties. The introduction of digital technology over the constitutional system of Pakistan generated the necessary changes in the justice practice by developing new benefits and, at the same time, requiring the essential moral solutions to the complex problems. This research investigates Pakistan’s digital constitutional era through an ethical and legal technological framework that investigates constitutional protections and judicial supervision of developing balancing surveillance of legislation.
Validation of Natural Language Processing for Surgical Complication Surveillance: Detecting Eleven Postoperative Complications from Electronic Health Records

ABSTRACT                                    Background                   Postoperative complications (PCs) rates are crucial quality metrics in surgery, as they reflect both patient outcomes, perioperative care effectiveness and healthcare resource strain. Despite their importance, efficient, accurate, and affordable methods for tracking PCs are lacking. This study aimed to evaluate whether natural language processing (NLP) models could detect eleven PCs from surgical electronic health records (EHRs) at a level comparable to human curation.                                                     Methods                   17 486 surgical cases from 18 hospitals across two regions in Denmark, spanning six years, were included. The dataset was divided into training, validation, and test sets for NLP-model development and evaluation (50.2%/33.6%/16.2%). Model performance was compared against the current method of PC monitoring (ICD-10 codes) and manual curation, the latter serving as the gold standard.                                                     Results                   The NLP-models had a ROC AUC between 0.901 to 0.999 for the test set. Sensitivity of the models when compared to manual curation ranged from 0.701 to 1.00, except for myocardial infarction (0.500). Positive Predictive Value (PPV) ranged from 0.0165 to 0.947, and Negative Predictive Value from 0.995 to 1.00. The NLP-models significantly outperformed ICD-10 coding in detecting PC, resulting in 16.3% of cases would require manual curation to reach a PPV of 1.00                                                     Conclusion                   The NLP models alone were able to detect PCs at an acceptable level and performed superior to ICD-10 codes. Combining NLP based and manual curation was required to reach a PPV of 1.00. Therefore, NLP algorithms present a potential solution for comprehensive and real-time monitoring of PCs across the surgical field.
The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review

Artificial intelligence (AI) and image processing are revolutionising the diagnosis and management of liver cancer. Recent advancements showcase AI's ability to analyse medical imaging data, like computed tomography scans and magnetic resonance imaging, accurately detecting and classifying liver cancer lesions for early intervention. Predictive models aid prognosis estimation and recurrence pattern identification, facilitating personalised treatment planning. Image processing techniques enhance data analysis by precise segmentation of liver structures, fusion of information from multiple modalities, and feature extraction for informed decision-making. Despite progress, challenges persist, including the need for standardised datasets and regulatory considerations.