ethicalabs/Research-Intent-Collab huggingface dataset:
Artificial creativity and agency negotiation: Understanding AI-generated visual art from artistic practitioners’ perceptions

Creativity is widely understood as a distinct human characteristic, yet the advent of generative artificial intelligence (AI) is reshaping creative ecosystems, presenting both opportunities and challenges. Despite these advancements, comprehensive insights into how social groups, especially stakeholders, perceive and experience AI-generated art remain inadequate. Through semi-structured interviews with 23 artistic practitioners in China across visual art domains such as painting, design, and filmmaking, this study identifies themes related to practitioners’ perceptions of the creative process (mechanism and time), created works (aesthetic and quality), and creating actors (role and ownership) from a human-AI comparative view. Contextualized perceptions shaped by variations across art domains are also revealed. As human-AI collaboration in artistic creation gains prominence, this study also illustrates how artistic practitioners negotiate their agency with machine agency through dual routes: the affective route and the action route. The findings contribute to the ongoing discourse of artificial creativity, enrich the socio-technical understandings of AI-generated visual art and shed light on the agency dynamics between human and AI within the context of artistic creation.
Internet Users’ Beliefs about a Novice-user of Child Sexual Abuse Material (CSAM): What Can They Tell Us about Introducing Offender-focused Prevention Initiatives?

Interest in preventing child sexual abuse material (CSAM) offending is growing. A variety of initiatives have been proposed, including the use of warning messages triggered by keyword searches. An under-explored question is how the public might perceive such initiatives. Existing research reveals that the public holds very negative views about individuals labeled ‘sex offenders’ and ‘pedophiles’. Yet, little is known about how the public views individuals who commit CSAM offenses. This study explores the beliefs of 491 young adult Internet users toward a hypothetical novice-user of CSAM. Using thematic analysis, five key themes are identified. Informed by this analysis, implications for introducing prevention initiatives are discussed.
The Spin-7 Lattice Model: Phase Transitions and Statistical Mechanics of the GTE Polynomial

The polynomial p(L,C,R) = C + R - CR - LCR over F_7, derived as the unique MDL-minimal substrate encoding the Standard Model from first principles, defines a natural statistical mechanics system: a 7-state nearest-neighbor spin model with three-body interactions. We analyze this spin-7 model in one and two spatial dimensions. In 1D, the exact transfer matrix acts on pair states (s_i-1, s_i); its Perron eigenvalue at = 1 is _1 = 1.4846, the correlation length grows from 2.9 sites at = 1 to 15.0 at = 2 and diverges as , and the local zero-energy windows number 43 = _6(7) (consistent with the Cyclotomic Point Count Theorem). The global zero-energy rings are exactly the three uniform configurations \0^n, 1^n, 5^n\ at every ring length n 3 - an exact threefold ground-space degeneracy, machine-certified in Lean 4 - so the ground space realizes the ternary repetition code of length n and distance n. In 2D, Metropolis Monte Carlo simulations on L L tori (L = 4-16) reveal a phase transition at _c 0.35 (T_c 2.86\,J) with Z_3-like symmetry breaking among three degenerate global ground states: the uniform configurations \0, 1, 5\, algebraically characterized as the roots of p(x,x,x) = -x(x-1)(x-5) 7. A secondary feature at _c,2 1.7 exhibits strong hysteresis consistent with domain-wall locking. The zero-temperature support of the 1D transfer matrix is the deterministic pair-state digraph of the CMCA vacuum sector, and the thermodynamic entropy rate S_CMCA() = _1() interpolates from 7 = 1.946 nats at infinite temperature through 0.395 nats per site per update at = 1 to 0 at zero temperature, where only the three rigid ground states survive. The three ground-state sectors \0, 1, 5\ map to the color charge sectors of the Standard Model, and the NL identity NL(p) = 1 - c_H/7^2 connects the polynomial's cryptographic nonlinearity to the Higgs branch capacity c_H = 13. A minimal experimental realization using 7-level Rydberg atoms in optical tweezer arrays is proposed.
Explainable retinal deep learning for cardiovascular risk stratification: a multiple modality analysis framework with vascular-centric interpretability and robustness

Abstract                   Early non-invasive prediction of cardiovascular risk is an essential prerequisite for preventive medicine, especially in resource-limited settings. Retinal imaging provides unique insights into systemic vascular health; however, most existing deep learning models lack pathophysiological explainability and generalizability, which limits their clinical adoption. Furthermore, existing approaches fail to effectively integrate specific retinal biomarkers with multimodal indicators of cardiovascular conditions and are not adaptable to variations in demographics or imaging conditions. This limits their applicability in real-time environments, particularly within SDN-enabled 5G healthcare infrastructures, where explainability and reliability are essential. In this work, a comprehensive and explainable deep learning architecture is developed for retinal-based cardiovascular risk assessment. The proposed framework integrates five analytical components: (1) MARGE-Net, which incorporates clinical biomarkers with vascular topology; (2) SCCIM, which visualizes causally important retinal features; (3) HVAAT, a hierarchical transformer for modelling vessel attributes; (4) PaVSCM, which ensures anatomical plausibility across populations; and (5) ReCVD-LDM, which separates imaging artifacts from true anatomical predictors using contrastive learning. These modules collectively improve accuracy, interpretability, and robustness, while enabling compatibility with real-time deployment in Software-Defined Networking (SDN)-enabled 5G healthcare infrastructures. This is further supported by a conceptual edge–cloud architecture that enables efficient data transmission and low-latency inference. The model is validated on publicly available datasets, including MESSIDOR, AV-WIDE, and the STARE VAS subset, achieving an AUC of 0.94, with a 31% improvement in interpretability and 26% reduction in prediction errors caused by imaging artifacts. Overall, this work advances trustworthy and interpretable AI for cardiovascular risk prediction from retinal images in next-generation healthcare systems.
Controlling the degree of caution in statistical inference with the Bayesian and frequentist approaches as opposite extremes

In statistical practice, whether a Bayesian or frequentist approach is used in inference depends not only on the availability of prior information but also on the attitude taken toward partial prior information, with frequentists tending to be more cautious than Bayesians. The proposed framework defines that attitude in terms of a specified amount of caution, thereby enabling data analysis at the level of caution desired and on the basis of any prior information. The caution parameter represents the attitude toward partial prior information in much the same way as a loss function represents the attitude toward risk. When there is very little prior information and nonzero caution, the resulting inferences correspond to those of the candidate confidence intervals and p-values that are most similar to the credible intervals and hypothesis probabilities of the specified Bayesian posterior. On the other hand, in the presence of a known physical distribution of the parameter, inferences are based only on the corresponding physical posterior. In those extremes of either negligible prior information or complete prior information, inferences do not depend on the degree of caution. Partial prior information between those two extremes leads to intermediate inferences that are more frequentistic to the extent that the caution is high and more Bayesian to the extent that the caution is low.
Experimental Optimization of Air Jets Impinging on a Continuously Moving Flat Plate

A single air jet or an array of air jets, impinging normally on a surface, may be used to achieve enhanced heat and mass transfer coefficients for convective heating, cooling and drying. To provide data for designers of industrial equipment, a programme of research has been implemented to study the heat and mass transfer processes. An original optimization of nozzle arrays under impinging air jets has been given for both academic and industrial users. Also optimum parameters presented here are simple and have a wide applicability.
Cosmic ray escape from supernova remnants

Galactic cosmic rays are believed to be accelerated at supernova remnants via diffusive shock acceleration. Though this mechanism gives fairly robust predictions for the spectrum of particles accelerated at the shock, the spectrum of the cosmic rays which are eventually injected in the interstellar medium is more uncertain and depends on the details of the process of particle escape from the shock. Knowing the spectral shape of these escaping particles is of crucial importance in order to assess the validity of the supernova remnant paradigm for cosmic ray origin. Moreover, after escaping from a supernova remnant, cosmic rays interact with the surrounding ambient gas and produce gamma rays in the vicinity of the remnant itself. The detection of this radiation can be used as an indirect proof of the fact that the supernova remnant was indeed accelerating cosmic rays in the past.
The use of persistent homology in data science for topological data analysis

Topological data analysis (TDA) is a young field that has been rapidly growing over the last years and which blends algebraic topology, statistics and computer science. It arises motivated by the fact that the topology of a space gives useful information about it. In particular, when working with scientific data, information about their internal structure may provide important properties about the phenomena that it represents or its behaviour. The goal of topological data analysis is to provide well-founded mathematical, statistical and algorithmic methods to infer the topological structure underlying the data. The aims of this thesis are two. On the one hand, to introduce this field and its mathematical tools, focusing on persistent homology and its representation by persistence diagrams. On the other hand, the study of the latter using techniques from optimal transport, proposing a vectorization based on transport maps.
Matter Field, Dark Matter and Dark Energy

A model concerning particle theory and cosmology is proposed. Matter field, dark matter and dark energy are created by an energy flow from space to primordial matter fields at the phase transition in the early universe.
Natural Language Processing Identification of Non-prescribed Fentanyl Use in Electronic Health Records (Preprint)

BACKGROUND                   Overdose and suicide deaths due to non-prescribed fentanyl have increased significantly. Fortunately, there are treatments available that could reduce the risk of death. However, for healthcare systems to implement programs addressing the needs of patients who use non-prescribed fentanyl, they must first be able to identify them. International Classification of Diseases (ICD) codes are not a good option for identifying patients who use non-prescribed fentanyl for two reasons. First, opioid use disorder (OUD) diagnoses are not always coded. Second, when they are coded, ICD codes for OUD do not specify whether fentanyl is being used rather than other, less lethal opioids.                                                     OBJECTIVE                   To develop natural language processing (NLP) approaches to identifying current non-prescribed fentanyl use in electronic health record (EHR) documentation.                                                     METHODS                   This retrospective cross-sectional study included Veterans Health Administration (VHA) patients seen in the VHA between 4/5/23 and 12/23/24. A term list was developed to identify fentanyl-related mentions in clinical text, and 250-character snippets surrounding identified mentions were extracted. Veterans (n=3,878) were randomly sampled from five predefined groups based on the presence of one of four terms (fentanyl, fent, blues, tranq) in their EHR documentation. Physician annotators classified snippets into “non-prescribed fentanyl use,” “prescribed fentanyl use,” or “other.” Labeled data was used to train, validate, and compare the ability of penalized logistic regression, Bio-Clinical BERT, LLaMA 3 8B, and Mistral 7B to classify snippets. Model performance was evaluated using weighted average precision, recall, and F1 scores.                                                     RESULTS                   Results: Among 7,389 snippets, 9.6% were “non-prescribed fentanyl use,” 40.3% were “prescribed fentanyl use,” and 50% were “other.” LLaMA 3 8B achieved the highest weighted average F1 score (0.954) outperformed penalized logistic regression with a small statistically significant difference, while differences between Mistral 7B and LLaMA 3 8B were not statistically significant. Top discriminative terms included “consumption,” “purchased,” and “streets” for non prescribed fentanyl use; “dressing,” “patch(es),” and “1500” for prescribed; and “laced,” “testing,” and “fears” for other fentanyl use.                                                     CONCLUSIONS                   NLP can accurately identify non-prescribed fentanyl use in EHR documentation. This approach may support risk prediction and targeting of interventions to patients exposed to non-prescribed fentanyl.
PERANCANGAN HUMAN RESOURCE INFORMATION SYSTEM (HRIS) BERBASIS WEBSITE MENGGUNAKAN METODE WATERFALL PADA PT. XYZ

Pada era digital saat ini, pemanfaatan teknologi informasi dalam pengelolaan sumber daya manusia menjadi kebutuhan mendesak bagi perusahaan, termasuk PT. XYZ. Pengelolaan data karyawan yang masih dilakukan secara manual dengan Microsoft Excel menyebabkan berbagai permasalahan seperti tingginya risiko kesalahan input, kehilangan data, dan keterlambatan proses pelaporan. Selain itu, tingginya turn over karyawan menuntut adanya sistem yang mampu mengelola informasi karyawan secara lebih efektif dan efisien. Human Resource Information System (HRIS) merupakan solusi teknologi yang dapat digunakan untuk mengotomatisasi berbagai fungsi manajemen SDM seperti absensi, penggajian, dan pengelolaan data karyawan. Penelitian ini bertujuan untuk merancang sistem HRIS berbasis website pada PT. XYZ guna meningkatkan efisiensi pengelolaan SDM dan mengurangi risiko kesalahan pengolahan data. Metode yang digunakan dalam penelitian ini adalah model waterfall yang mencakup tahapan perencanaan, perancangan, implementasi, pengujian, dan pemeliharaan sistem. Hasil dari penelitian ini diharapkan mampu memberikan solusi yang tepat dalam mengelola data SDM secara terintegrasi serta mendukung proses pengambilan keputusan manajemen di PT. XYZ.
Neutral interstellar helium parameters based on IBEX-Lo observations and test particle calculations

Neutral Interstellar Helium (NISHe) is almost unaffected at the heliospheric interface with the interstellar medium and freely enters the solar system. It provides some of the best information on the characteristics of the interstellar gas in the Local Interstellar Cloud. The Interstellar Boundary Explorer (IBEX) is the second mission to directly detect NISHe. We present a comparison between recent IBEX NISHe observations and simulations carried out using a well-tested quantitative simulation code. Simulation and observation results compare well for times when measured fluxes are dominated by NISHe (and contributions from other species are small). Differences between simulations and observations indicate a previously undetected secondary population of neutral helium, likely produced by interaction of interstellar helium with plasma in the outer heliosheath. Interstellar neutral parameters are statistically different from previous in situ results obtained mostly from the GAS/Ulysses experiment, but they do agree with the local interstellar flow vector obtained from studies of interstellar absorption: the newly-established flow direction is ecliptic longitude 79.2 deg, latitude -5.1 deg, the velocity is \sim 22.8 km/s, and the temperature is 6200 K. These new results imply a markedly lower absolute velocity of the gas and thus significantly lower dynamic pressure on the boundaries of the heliosphere and different orientation of the Hydrogen Deflection Plane compared to prior results from Ulysses. A different orientation of this plane also suggests a new geometry of the interstellar magnetic field and the lower dynamic pressure calls for a compensation by other components of the pressure balance, most likely a higher density of interstellar plasma and strength of interstellar magnetic field.
Technology Solutions to Support Central Bank Digital Currency with Limited Connectivity

This note explores the implementation of central bank digital currencies in environments with limited connectivity, emphasizing the need for offline solutions to enhance financial inclusion. It discusses various scenarios, such as stored-value card systems and device-to-device payment methods, which allow transactions without constant internet access. Key considerations include the form factor of devices, operational risks, and privacy issues associated with offline transactions. The note highlights the importance of security measures, diverse technological solutions, and user experience in the design of these systems. It explains that no single architecture fits all use cases, underscoring the need for continuous stakeholder engagement and adaptation to evolving technologies. Ultimately, the report advocates for robust offline payment systems that can effectively meet the challenges of limited connectivity while fostering resilience and inclusivity in financial systems.
The Association Between Home Palliative Care Services and Quality of End-of-Life Care Indicators in the Province of Québec

In Canada, governments have increased spending on home care to promote better end-of-life care. In the province of Québec, Canada, home palliative care (PC) services (HPCS) are provided by Public Local Community-Based Health Care Service providers (Centres Locaux de Services Communautaires [CLSC]) with universal coverage. Accordingly, there should be no regional variations of these services and their effect on quality of end-of-life PC (QEoLPC) indicators.To test if all the CLSCs provided the same level of HPCS to cancer patients in the province of Québec, Canada, and the association between level of HPCS and QEoLPC indicators.Characteristics of 52,316 decedents with cancer were extracted from administrative databases between 2003 and 2006. Two gender-specific "adjusted performance of CLSCs in delivering HPCS" models were created using gender-specific hierarchical regression adjusted for patient and CLSC neighborhood characteristics. Using the same approach, the strength of the association between the adjusted performance of CLSCs in delivering HPCS and the QEoLPC indicators was estimated.Overall, 27,255 (52.1%) decedents had at least one HPCS. Significant variations in the adjusted performance of CLSC in delivering HPCS were found. Higher performance led to a lower proportion of men having more than one emergency room visit during the last month of life (risk ratio [RR] 0.924; 95% CI 0.867-0.985), and for women, a higher proportion dying at home (RR 2.255; 95% CI 1.703-2.984) and spending less time in hospital (RR 0.765; 95% CI 0.692-0.845).Provision of HPCS remained limited in Québec, but when present, they were associated with improved QEoLPC indicators.
Changes in Pain, Quality of Life, Sleep, and Mental Health after Spinal Neurosurgery in Palestine: A Prospective Study of Patient-Reported Outcomes

Abstract                 Background The use of patient-reported outcomes measures (PROMs) is an application of the volume-to-value-based healthcare services, and was quantitatively used in the field of neurosurgery. Therefore, the current study aimed to investigate the preoperative and postoperative, as well as changes and factors of changes, in specific PROMs among spinal neurosurgery patients in a tertiary hospital in Palestine. Methods The study was conducted using a prospective longitudinal design on a convenience sample of 99 lumbar and 35 cervical spine neurosurgery patients, who were interviewed to fill in a preoperative and one-month postoperative questionnaire that measures pain, quality of life (QoL), sleep quality, and mental health PROMs. Valid versions of Arabic translated tools were used, including Neck Disability Index (NDI), Oswestry Disability Index (ODI), EuroQoL (EQ-5D-5L), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), and Patient Health Questionnaire (PHQ-9). Data were analyzed using SPSS with full commitment to ethical considerations of anonymity and confidentiality. Results The patients had a mean age of 49.16 years, and were 50.7% females, 74.6% married, 59.7% underwent discectomy, a mean diagnosis-to-operation period of 7.15 weeks, and used preoperative paracetamol (69.4%), cortisones (76.9%), and NSAIDs (59.7%). All Proms showed significant postoperative overall improvements (p-value &lt; 0.001), where better NDI improvements are found among urban residents and congenital disease-related operations, better ODI improvement among tumor resection patients, without hormonal disorders or use of preoperative cortisones, while better EQ-VAS improvements found among patients who are younger, and did not use preoperative paracetamol or muscle relaxants, and better ESS improvement are shown among older patients (p-value &lt; 0.05). Conclusions The current study found an overall significant improvement among spinal neurosurgery patients in PROMs of pain, QoL, sleep quality, and mental health. Several studies agree with the findings of the current study, with differences in the affecting factors related to sampling and population characteristics differences. Patient’s engagement in preoperative education, resource allocation, and conduct of multicenter studies and RCTs is recommended.
Parallel high-performance computing algorithm to generate FEM-compliant volumetric mesh representations of biomolecules at atomic scale

Abstract                 The computational study of biomolecules has been undermined by the lack of models that accurately represent the structure of big complexes at the atomic level. In this work, we report the development of an algorithm to generate a volumetric mesh of a biomolecule, of any size and shape, based on its atomic structure. Our mesh generation tool leverages the octree algorithm properties with parallel high-performance computing techniques to produce a discretized hexahedral model faster than previous methods. The reported algorithm is memory efficient and generates volumetric meshes suitable to be used directly in Finite Element Analysis. We tested the algorithm by producing mesh models of different biomolecule types and complex size, and also performed numerical simulations for the largest case. The Finite Element results show that our mesh models reproduce experimental data.
Secondary use of clinical trials data in health research: A Practical Guide

This document presents a theoretical framework for the use of clinical trials and other health data for secondary research purposes, which was derived from research papers, consultation with stakeholders and the research community.  Four overall scenarios for data reuse were identified; scenario 1: evidence synthesis, scenario 2: secondary analyses, scenario 3: reproducibility, replication and validation, and scenario 4: education and methods development.