ethicalabs/Research-Intent-Collab huggingface dataset:
Media Enabled by Artificial Intelligence Generated Content as Public Pedagogy for Heritage Education

In the era of algorithmic ubiquity and New Quality Productive Forces (NQPF), county-level media in China are undergoing radical ontological transformation. Breslin and Ren (2024) define NQPF as China's theoretical framework for shifting from resource-intensive growth to technology-driven high-quality development, yet the translation of productive efficiency into educational value requires specific mechanisms. This study argues that AIGC-enabled media addresses this gap by generating "Cognitive Surplus" liberating human creators from repetitive tasks (e.g., historical photo restoration) to focus on higher-order "meaning construction" and "curriculum design". This study argued that JMCC has constructed a 1+N+1 multimodal learning ecosystem that operationalizes United Nations Sustainable Development Goal (SDG) 11 by bridging the urban-rural digital divide. Through detailed analysis of the Jinsha Relics and Wonderful Series campaigns, it demonstrates how AIGC facilitates the transmission of Object Authenticity and the creation of Synthetic Authenticity, thereby fostering a dynamic, inter-generational cultural curriculum. However, this technological acceleration introduces critical pedagogical risks, including algorithmic homogeneity and digital suspension. This paper introduces a human-machine collaborative framework designed to ensure the digitalization of heritage functions as a robust mechanism for public education and rural revitalization.
Template-Driven AI Platforms: Using Infrastructure as Code and GitOps to Standardize and Scale AI/ML Environments

Abstract  Enterprise AI and ML initiatives are often slowed down not by algorithms, but by fragmented, snowflake environments that are hard to reproduce, scale, and govern. Each new project tends to reinvent its own cloud setup, Kubernetes cluster, data access pattern, and ML toolchain, creating a long tail of operational toil and “hidden technical debt” in production systems [3]. In this paper, I present a template-driven approach for building AI platforms using Infrastructure as Code (IaC) and GitOps, with Terraform, Helm, and Kubernetes as the core building blocks. Drawing on cloud-native principles and lessons from real-world transformations, I describe how standardized templates, modular Terraform and Helm packages, and Git-centric workflows can deliver repeatable, compliant, and scalable AI/ML environments across teams, environments, and clouds. I outline the architecture of a template-driven AI platform, walk through a concrete multi-cloud implementation, and evaluate the approach along dimensions such as time-to-environment, deployment frequency, drift reduction, and compliance. I close with a discussion of trade-offs, organizational challenges, and practical recommendations for platform teams that want to industrialize AI without sacrificing flexibility for data scientists and ML engineers.  Index Terms  Infrastructure as Code, GitOps, Terraform, Helm, Kubernetes, MLOps, AI Platforms, Multi-Cloud, DevOps, Continuous Delivery.
Forces between electric charges in motion: Rutherford scattering, circular Keplerian orbits, action-at-a-distance and Newton's third law in relativistic classical electrodynamics

Standard formulae of classical electromagnetism for the forces between electric charges in motion derived from retarded potentials are compared with those obtained from a recently developed relativistic classical electrodynamic theory with an instantaneous inter-charge force. Problems discussed include small angle Rutherford scattering, Jackson's recent `torque paradox' and circular Keplerian orbits. Results consistent with special relativity are obtained only with an instantaneous interaction. The impossiblity of stable circular motion with retarded fields in either classical electromagnetism or Newtonian gravitation is demonstrated.
Adaptive federated learning with differential privacy for multi-institutional healthcare diagnosis: the DP-FedAvg+ framework

The sensitivity of patient data in the context of federated healthcare systems and the statistical variability in the various institutions constitute a major challenge for privacy-preserving machine learning. This paper introduces a novel adaptive federated learning framework (DP-FedAvg+) which combines two key techniques, namely, moment accountant based differential privacy (DP) and adaptive gradient clipping, with a heterogeneity-aware client weighting scheme. DP-FedAvg+ dynamically scales the privacy budget ε per communication round according to the local sensitivity and divergence of data at each client, which is estimated, unlike FedAvg. We prove the convergence of DP-FedAvg+ rigorously under non-i.i.d. conditions at an O(1/√T) convergence rate with (ε, δ)-DP privacy guarantee with δ = 10−5. After conducting extensive experiments on three benchmark healthcare datasets, namely MIMIC-III, CheXpert and Alzheimer MRI, it is shown that DP-FedAvg+ has an average diagnostic accuracy of 91.3% (±0.4%), beating FedAvg (85.1%), DP-SGD (82.7%) and SCAFFOLD (88.1%) with a much smaller privacy budget. The ablation study verifies the contribution of each component and the fairness analysis indicates that there is no demographic bias between sub-groups. The suggested framework improves the state of the art in the domain of federated medical AI, by providing an advancement in the privacy-utility trade-off.
Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering

Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and is available on CRAN.
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis

Monitoring changes in the Earth's surface is crucial for understanding natural processes and human impacts, necessitating precise and comprehensive interpretation methodologies. Remote sensing satellite imagery offers a unique perspective for monitoring these changes, leading to the emergence of remote sensing image change interpretation (RSICI) as a significant research focus. Current RSICI technology encompasses change detection and change captioning, each with its limitations in providing comprehensive interpretation. To address this, we propose an interactive Change-Agent, which can follow user instructions to achieve comprehensive change interpretation and insightful analysis, such as change detection and change captioning, change object counting, change cause analysis, etc. The Change-Agent integrates a multi-level change interpretation (MCI) model as the eyes and a large language model (LLM) as the brain. The MCI model contains two branches of pixel-level change detection and semantic-level change captioning, in which the BI-temporal Iterative Interaction (BI3) layer is proposed to enhance the model's discriminative feature representation capabilities. To support the training of the MCI model, we build the LEVIR-MCI dataset with a large number of change masks and captions of changes. Experiments demonstrate the SOTA performance of the MCI model in achieving both change detection and change description simultaneously, and highlight the promising application value of our Change-Agent in facilitating comprehensive interpretation of surface changes, which opens up a new avenue for intelligent remote sensing applications. To facilitate future research, we will make our dataset and codebase of the MCI model and Change-Agent publicly available at https://github.com/Chen-Yang-Liu/Change-Agent
5G Network Slice Isolation

This article reveals an adequate comprehension of basic defense, security challenges, 2 and attack vectors in deploying multi-network slicing. Network slicing is a revolutionary concept 3 of providing mobile network on-demand and expanding mobile networking business and services 4 to a new era. The new business paradigm and service opportunities are encouraging vertical 5 industries to join and develop their own mobile network capabilities for enhanced performances 6 that are coherent with their applications. However, a number of security concerns are also raised 7 in this new era. In this article, we focus on the deployment of multi-network slicing with multi8 tenancy. We identify the security concerns, and discuss about the defense approaches such as 9 network slice isolation and insulation in a multi-layer network slicing security model. Also, we 10 identify the importance to appropriately select the network slice isolation points, and propose 11 a generic framework to optimize the isolation policy regarding the implementation cost while 12 guaranteeing the security and performance requirements.
Observability and Monitoring for Front-End: A Holistic Approach to Logging, Tracing, and Metrics with the Advent of AI

Abstract—This paper explores the concept of observability in the context of front-end applications, examining traditional methods like logging, tracing, and metrics, and how AI is revolutionizing this field. It discusses the challenges and limitations of using AI for front-end observability and monitoring, and the future trends and potential of AI in this domain. It also includes an architecture workflow for an Observability and Monitoring system. Index Terms—Observability, monitoring, frontend, logging, tracing, metrics, AI
Safety, Effectiveness and Cost-effectiveness of Intravascular Ultrasound in Comparison with Invasive Angiography: A Rapid Health Technology Assessment

Background: Intravascular imaging is a tool to detect coronary artery atherosclerosis which plays the major role in vessel stenosis degree determination and plaque pathology as a supplement of invasive angiography. This study aimed at comparing Intravascular imaging with invasive angiography. Materials and Methods: For retrieving second type studies, PubMed, Cochrane Library and Centre for Review and Dissemination (CRD) were searched up to September 2013.  Searching was carried out by two persons using angiography and intravascular imaging keywords. Studies which compared intravascular ultrasound technology with invasive angiography were analyzed. Results: Based on studies which reported the rate of target lesion revascularization (TLR) after one year of follow-up, there was a statistically significant difference between intravascular ultrasound and invasive angiography alone. It seems that TLR rate was lower with intravascular ultrasound (P = 0.02).In terms of cost effectiveness, approximately 54, 000 USD spend per each QALY using IVUS method. Conclusion: Although, intravascular ultrasound could be able to detect coronary arteries damages and help out physician in selecting the most appropriate treatment, but due to the high cost of an intravascular ultrasound catheter, the invasive angiography is choice of physicians and patients in coronary intervention.
The SMART Framework: Integration of Citizen Science, Community-Based Participatory Research, and Systems Science for Population Health Science in the Digital Age (Preprint)

UNSTRUCTURED                         Citizen science enables citizens to actively contribute to all aspects of the research process, from conceptualization and data collection, to knowledge translation and evaluation. Citizen science is gradually emerging as a pertinent approach in population health research. Given that citizen science has intrinsic links with community-based research, where participatory action drives the research agenda, these two approaches could be integrated to address complex population health issues. Community-based participatory research has a strong record of application across multiple disciplines and sectors to address health inequities. Citizen science can use the structure of community-based participatory research to take local approaches of problem solving to a global scale, because citizen science emerged through individual environmental activism that is not limited by geography. This synergy has significant implications for population health research if combined with systems science, which can offer theoretical and methodological strength to citizen science and community-based participatory research. Systems science applies a holistic perspective to understand the complex mechanisms underlying causal relationships within and between systems, as it goes beyond linear relationships by utilizing big data–driven advanced computational models. However, to truly integrate citizen science, community-based participatory research, and systems science, it is time to realize the power of ubiquitous digital tools, such as smartphones, for connecting us all and providing big data. Smartphones have the potential to not only create equity by providing a voice to disenfranchised citizens but smartphone-based apps also have the reach and power to source big data to inform policies. An imminent challenge in legitimizing citizen science is minimizing bias, which can be achieved by standardizing methods and enhancing data quality—a rigorous process that requires researchers to collaborate with citizen scientists utilizing the principles of community-based participatory research action. This study advances SMART, an evidence-based framework that integrates citizen science, community-based participatory research, and systems science through ubiquitous tools by addressing core challenges such as citizen engagement, data management, and internet inequity to legitimize this integration.
Supplementary document for Optical Model Compression Learning for Galaxy Morphology Classification with Diffractive Deep Neural Networks - 7842522.pdf

Supplementary Material 1: Simulation details, generalization experiments on MNIST dataset, and phase characteristic analysis of two-layer D2NNs for the OMCL framework
“A Cross-Comparative Study of Adolescent Mental Health and Social Media Use in South Korea and Canada”

In an increasingly digital world, the mental health of adolescents is reflected in their interactions on social networking sites (SNS). As adolescence represents a pivotal transitional period characterized by an increase in social relationships, the impact of the COVID-19 pandemic and its transferal of in-person interactions to online spheres is a crucial component of adolescent mental health in 2022. Where excessive social media using during the lockdowns led to an increase in cyberbullying (highlighted in Canada’s updated criminal code) and eating disorders (explored through mukbang, or ‘escapist eating’ in South Korea), the type of SNS interactions adolescents engage in—comparative social media use and more passive, autonomous enjoyment—are critical to distinguishing between the positive and negative effects of social media on adolescent health. To identify the link between pre, during, and post-pandemic adolescent mental health, this study utilizes a comparative analysis of surveillance and harm within social media consumption in South Korea and Canada. For South Korean and Canadian adolescents, the distinction between active and passive social media use and the degree of online autonomy can define the level of harm to mental stability, emphasizing the importance of autonomous social media consumption in ‘safe digital spaces.’&#x0D;
Convergence Rate of Projected Subgradient Method with Time-varying Step-sizes

We establish the optimal ergodic convergence rate for the classical projected subgradient method with a time-varying step-size. This convergence rate remains the same even if we slightly increase the weight of the most recent points, thereby relaxing the ergodic sense.
Role of Managers’ Transformational Leadership in Developing Government Employees’ Organizational Citizenship Behaviors

One of the major factors in directing staff behavior is leadership style applied in the organization. More and more organic organizational structure and transformational leadership style will increase the possibility for employees to develop and show organizational citizenship behaviors. As economic margrave of a country, customs has an especially important role in implementing the country economic policies. One of the factors which can have profound effects on the service quality in this part is organizational citizenship behavior. Accordingly, this study was aimed at investigating the role of leadership in the development and promotion of organizational citizenship behaviors among customs staff of Zahedan. The research method in terms of purpose is applied and in terms of data collection method is descriptive-correlational. The research population was all of Zahedan customs employees. Surveying sample was determined 110 people by Cochran method. Questionnaires were used for collecting data. T test, confirmatory factor analysis test, and Smart PLS software were utilized to analyze the data. Questions reliability was ideally analyzed using professors’ comments and the variance average. Cronbach's alpha of whole the variables is greater than 0.6 therefore, all the variables are approved in terms of reliability. Variance average (AVE) extracted is always greater than 0.5 thus, convergent reliability was proven as well. Also, compound reliability is greater than AVE. Results showed that there is a positive and significant correlation between transformational leadership coefficient and organizational citizenship behavior with effect coefficient of 0.712
Topological order: from long-range entangled quantum matter to an unification of light and electrons

In primary school, we were told that there are four states of matter: solid, liquid, gas, and plasma. In college, we learned that there are much more than four states of matter. For example, there are ferromagnetic states as revealed by the phenomenon of magnetization and superfluid states as defined by the phenomenon of zero-viscosity. The various phases in our colorful world are so rich that it is amazing that they can be understood systematically by the symmetry breaking theory of Landau. In this paper, we will review the progress in last 20 -- 30 years, during which we discovered that there are even more interesting phases that are beyond Landau symmetry breaking theory. We discuss new topological phenomena, such as topological degeneracy, that reveal the existence of those new phases - topologically ordered phases. Just like zero-viscosity defines the superfluid order, the new topological phenomena define the topological order at macroscopic level. As a new type of order, topological order requires a new mathematical frame work, such as fusion category and group cohomology, to describe it. More recently, we find that, at microscopical level, topological order is due to long-range quantum entanglements, just like fermion superfluid is due to fermion-pair condensation. Long-range quantum entanglements lead to many amazing emergent phenomena, such as fractional quantum numbers, fractional/non-Abelian statistics, and perfect conducting boundary channels. Long-range quantum entanglements can even provide a unified origin of light and electrons (or more generally, gauge interactions and Fermi statistics): light waves (gauge fields) are fluctuations of long-range entanglements, and electrons (fermions) are defects of long-range entanglements.
Orbital and superorbital variability and their coupling in X-ray binaries

We review X-ray flux modulation from X-ray binaries on time scales corresponding to the orbital period and those at longer time scales (so called superorbital). Those modulations provide a powerful tool to constrain geometry of the accretion flow. The most common cause of the superorbital variability appears to be precession. We then discuss two specific examples of discoveries of a coupling between the two types of variability and their physical interpretation. One is Cyg X-1, a black-hole binary with a high-mass companion, in which case we find the presence of an accretion bulge formed by collision of the stellar wind with the outer edge of the precessing accretion disc. The other is 4U 1820-303, a neutron star accreting from a low-mass white dwarf, in which case we interpret the superorbital variability as accretion rate modulation induced by interactions in a triple stellar system. Then, the varying accretion rate leads to changes of the size of the accretion bulge in that system, obscuring the centrally-emitted X-rays.
Ongoing Vaccine and Monoclonal Antibody HIV Prevention Efficacy Trials and Considerations for Sequel Efficacy Trial Designs

Four randomized placebo-controlled efficacy trials of a candidate vaccine or passively infused monoclonal antibody for prevention of HIV-1 infection are underway (HVTN 702 in South African men and women; HVTN 705 in sub-Saharan African women; HVTN 703/HPTN 081 in sub-Saharan African women; HVTN 704/HPTN 085 in U.S., Peruvian, Brazilian, and Swiss men or transgender persons who have sex with men). Several challenges are posed to the optimal design of the sequel efficacy trials, including: (1) how to account for the evolving mosaic of effective prevention interventions that may be part of the trial design or standard of prevention; (2) how to define viable and optimal sequel trial designs depending on the primary efficacy results and secondary 'correlates of protection' results of each of the ongoing trials; and (3) how to define the primary objective of sequel efficacy trials if HIV-1 incidence is expected to be very low in all study arms such that a standard trial design has a steep opportunity cost. After summarizing the ongoing trials, I discuss statistical science considerations for sequel efficacy trial designs, both generally and specifically to each trial listed above. One conclusion is that the results of 'correlates of protection' analyses, which ascertain how different host immunological markers and HIV-1 viral features impact HIV-1 risk and prevention efficacy, have an important influence on sequel trial design. This influence is especially relevant for the monoclonal antibody trials because of the focused pre-trial hypothesis that potency and coverage of serum neutralization constitutes a surrogate endpoint for HIV-1 infection... (see manuscript for the full abstract)
Convergence Analyses of Davis-Yin Splitting via Scaled Relative Graphs II: Convex Optimization Problems

The prior work of [SIAM J. Optim., 2025] used scaled relative graphs (SRG) to analyze the convergence of Davis--Yin splitting (DYS) iterations on monotone inclusion problems. In this work, we use this machinery to analyze DYS iterations on convex optimization problems and obtain state-of-the-art linear convergence rates.