ethicalabs/Research-Intent-Collab huggingface dataset:
How Hospice and Palliative Care Are Portrayed in Artificial Intelligence-Generated Images: A Content Analysis

Background:                     AI-generated images can support or impede health communication efforts and influence perceptions of health-related topics, making it important to evaluate how these images portray topics like palliative care (PC) and hospice care (HC), which are characterized by limited public understanding (e.g., regarding where these services are delivered) and misperceptions (e.g., widespread conflation of these services).                                                           Objectives:                     To examine characteristics of AI-generated PC and HC images.                                                           Design:                                            Three generative AI tools (ChatGPT, DreamStudio, and Midjourney) were used to generate 40 images each for the prompts “a photograph of palliative care” and “a photograph of hospice care” (                       N                       = 240). Images were coded for features such as the setting and the characteristics of people in the image (patients, providers, and caregivers). Code frequencies and percentages were calculated using STATA. Differences were assessed across prompts and AI tools.                                                                                Results:                     PC images more often depicted medical settings (PC = 37.5%, HC = 27.5%) and medical equipment (PC = 67.5%, HC = 50.0%). DreamStudio images were the most medicalized. When included, patients were generally presented as White (PC = 85.0%, HC = 91.0%), female (PC = 81.0%, HC = 75.3%), and older (PC = 88.0%, HC = 94.4%) and rarely had negative affect (PC = 5.0%, HC = 2.2%) or looked ill (PC = 4.0%, HC = 2.2%). Providers were more racially diverse (PC = 41.1%, HC = 48.4% non-White), but nearly all were women (PC = 95.9%, HC = 96.9% female). Providers displayed supportive touch frequently (PC = 72.6%, HC = 71.9%) but were rarely shown engaging in medical tasks (PC = 12.3%, HC = 6.3%). Caregivers were infrequently included in either PC or HC images.                                                           Conclusions:                     The few substantial differences identified between PC and HC images could reinforce conflation between these services. Some image features could also create additional misperceptions (e.g., regarding the role of caregivers).
Visual Communication and Generative Processes: Production and Design of Digital Environments Using Diffusion Models

[Resumen] La integración de herramientas de inteligencia artificial xerativa en los procesos de diseño es un fenómeno emergente que transforma tanto los métodos como los resultados de la práctica creativa. No ámbito específico del diseño de entornos audiovisuales, estas herramientas de aprendizaje profundo, como las redes neurais convolucionais (CNN), las redes xerativas antagónicas (GAN) y, sobre todo, los modelos de difusión, presentan un potencial significativo para ampliar las posibilidades expresivas y comunicativas del diseño visual. Al mismo tiempo, suscitan nuevas preguntas alrededor del control de autoría, la coherencia formal y conceptual de los resultados, y el papel del diseñador en los flujos de trabajo mediados por algoritmos. En este marco, la presente Tesis Doctoral persigue dos objetivos principales, interconectados entre sí. En primer lugar, la investigación se centra en el análisis del impacto de estas herramientas en los flujos de diseño audiovisual, valorando sus implicaciones sobre la comunicación visual, la creatividad y la toma de decisiones durante el proceso. Para ello, se estudian diversos aspectos como la consistencia en las generaciones sucesivas, el comportamiento de los parámetros de control, y la influencia de los modelos empleados en la configuración de la imagen. En segundo lugar, el estudio pretende establecer un sistema de evaluación que permita comprender la percepción del potencial creativo de estas herramientas por parte de profesionales del diseño, así como definir criterios para el análisis de la coherencia y adecuación de los resultados generados al contexto comunicativo. Esta segunda línea se desarrolla paralelamente a la primera, pues la comprensión crítica de las capacidades y limitaciones de los modelos de difusión contribuye a reformular el rol de la inteligencia artificial dentro del pensamiento y la práctica del diseño audiovisual
The Nigerian Tragedy, Environmental Regulation of Transnational Corporations, and the Human Right to a Healthy Environment

This chapter examines the environmental damage caused by transnational corporations (TNCs) in the course of oil exploitation activities in Ogoniland and other parts of the Niger Delta region of Nigeria. It analyses the creation of effective mechanisms for the adjudication and enforcement of the right to a healthy environment against both states and TNCs. The chapter summarizes the tragic events which occurred in Nigeria, and details past and current efforts, both international and national, to regulate the environmental practices of TNCs. It demonstrates why existing environmental controls failed to prevent the tragedy in Nigeria, and surveys recent interactions between international human rights law and the protection of the environment. The chapter suggests that the time has come for an international treaty formally recognizing the human right to a healthy environment. It discusses mechanisms for international adjudication and enforcement of international environmental law and the right to a healthy environment against TNCs and states.
Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy

Federated learning provides a novel solution to collaborative learning among untrusted entities. Through a local-trai-ning-and-central-aggregation pattern,the federated learning algorithm trains a global model while protects local data privacy of each entity. However,recent studies show that local models uploaded by clients and global models produced by the server may still leak users' private information. Secure multi-party computation and differential privacy are two mainstream privacy-preserving techniques,which are used to protect the privacy of computation process and computation outputs respectively. There are few works that exploit the benefits of these two techniques at the same time. This paper proposes a privacy-preserving federated learning scheme for deep learning by combining secure multi-party computation and differential privacy. Clients add noise to local models,and secret share them to multiple servers. Servers aggregate these model shares by secure multi-party computation to obtain a private global model. The proposed scheme not only protects the privacy of local model updates uploaded by clients,but also prevents adversaries from inferring sensitive information from globally shared data such as aggregated models. The scheme also allows dropout of unstable clients and is compatible with complex aggregation functions. In addition,it can be naturally extended to the decentralized setting for real-world applications where no trusted centers exist. We implement our system in Python and Pytorch. Experiments validate that the proposed scheme achieves the same level of efficiency and accuracy as plaintext fede-rated learning.
International Human Rights Law in International and Hybrid Criminal Courts and Tribunals

Abstract                Several international and hybrid criminal courts and tribunals (ICTs) have been established in the last decades. Generally speaking, international crimes constitute serious violations of international human rights law (IHRL). In the establishment of ICTs, diverse actors have sought that the procedural and institutional frameworks of ICTs include and respect the rights of the accused in the light of IHRL standards. Against such background, the present paper addresses this main research question: How is IHRL present in the law and especially the practice of ICTs? It is argued and shown herein that IHRL is, to a larger or lesser extent, embedded in the law and, particularly, the jurisprudence of ICTs, but subject and adapted to the nature and mandate of ICTs. Importantly, it is also sustained herein that IHRL has been useful for ICTs to interpret and/or apply norms on crimes against humanity (substantive international criminal law) and defence rights (international criminal procedure).
Structure Function Studies for Turbulent Interstellar Medium

We study structure functions of rotation measures in the Canadian Galactic Plane Survey (CGPS) region and the North Galactic Pole (NGP) to extract the interstellar medium (ISM) fluctuation information. The CGPS data are divided into three longitude intervals: $82^\circ<l<96^\circ$ (CGPS1), $115^\circ<l<130^\circ$ (CGPS2) and $130^\circ<l<146^\circ$ (CGPS3). The structure functions of all three regions have large uncertainties when the angular separation is smaller than $δθ\approx 1^\circ$. A power law can fit the structure function well for $δθ>1^\circ$. The power law indices get smaller from CGPS1 to CGPS3 and the amplitudes decrease. The variations of the large-scale field and the electron density have only negligible effects on the structure function and thus cannot account for the changes, indicating that the turbulent properties of the Galactic ISM are intrinsically longitude-dependent. The Kolmogorov-like fluctuation spectrum of the electron density or the magnetic field should produce a power law structure function with an index of 5/3 or 2/3, neither of which is consistent with our results of small indices in the three sub-CGPS regions. For the NGP region, the structure function is flat, showing that the rotation measures are mostly intrinsic to the extragalactic sources, and the ISM is very random in that part of our Galaxy. It is obvious that the ISM fluctuation is latitude-dependent when comparing the results in the NGP region and the CGPS regions.
Aligning Large Language Models for Controllable Recommendations

Inspired by the exceptional general intelligence of Large Language Models (LLMs), researchers have begun to explore their application in pioneering the next generation of recommender systems - systems that are conversational, explainable, and controllable. However, existing literature primarily concentrates on integrating domain-specific knowledge into LLMs to enhance accuracy, often neglecting the ability to follow instructions. To address this gap, we initially introduce a collection of supervised learning tasks, augmented with labels derived from a conventional recommender model, aimed at explicitly improving LLMs' proficiency in adhering to recommendation-specific instructions. Subsequently, we develop a reinforcement learning-based alignment procedure to further strengthen LLMs' aptitude in responding to users' intentions and mitigating formatting errors. Through extensive experiments on two real-world datasets, our method markedly advances the capability of LLMs to comply with instructions within recommender systems, while sustaining a high level of accuracy performance.
Generation of a high confidence set of domain–domain interface types to guide protein complex structure predictions by AlphaFold

Abstract                                   Motivation                   While the release of AlphaFold (AF) represented a breakthrough for the prediction of protein complex structures, its sensitivity, especially when using full length protein sequences, still remains limited. Modeling success rates might increase if AF predictions were guided by likely interacting protein fragments. This approach requires available sets of highly confident protein–protein interface types. Computational resources, such as 3did, infer interacting globular domain types from observed contacts in protein structures. Assessing the accuracy of these predicted interface types is difficult because we lack hand-curated reference sets of verified domain–domain interface (DDI) types.                                                   Results                   To improve protein complex modeling of DDIs by AF, we manually inspected 80 randomly selected DDI types from the 3did resource to generate a first reference set of DDI types. Identified cases of DDI type nonapproval (40%) primarily resulted from inaccurate Pfam domain matches, crystal contacts, and synthetic protein constructs. Using logistic regression, we predicted a subset of 2411 out of 5724 considered DDI types in 3did to be of high confidence, which we subsequently applied to 53 000 human–protein interactions to predict DDIs followed by AF modeling. We obtained highly confident AF models for 604 out of 1129 predicted DDIs. Of note, for 47% of them no confident AF structural model could be obtained using full length protein sequences.                                                   Availability and implementation                   Code is available at https://github.com/KatjaLuckLab/DDI_manuscript.
Topological Mixed States: Phases of Matter from Axiomatic Approaches

For closed quantum systems, topological orders are understood through the equivalence classes of ground states of gapped local Hamiltonians. The generalization of this conceptual paradigm to open quantum systems, however, remains elusive, often relying on operational definitions without fundamental principles. Here, we fill this gap by proposing an approach based on three axioms: ($i$) local recoverability, ($ii$) absence of long-range correlations, and ($iii$) spatial uniformity. States that satisfy these axioms are fixed points; requiring the axioms only after coarse-graining promotes each fixed point to an equivalence class, i.e., a phase, presenting the first step towards the axiomatic classification of mixed-state phases of matter: mixed-state bootstrap program.   From these axioms, a rich set of topological data naturally emerges; importantly, these data are robust under relaxation of axioms. For example, each topological mixed state supports locally indistinguishable classical and/or quantum logical memories with distinct responses to topological operations. These data label distinct mixed-state phases, allowing one to distinguish them. We further uncover a hierarchy of secret-sharing constraints: in non-Abelian phases, reliable recovery-even of information that looks purely classical-demands a specific coordination among spatial subregions, a requirement different across non-Abelian classes. This originates from non-Abelian fusion rules that can stay robust under decoherence. Finally, we performed large-scale numerical simulations to corroborate stability: weakly decohered fixed points respect the axioms once coarse-grained. These results lay the foundation for a systematic classification of topological states in open quantum systems.
Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health

Linking clinical narratives to standardized vocabularies and coding systems is a key component of unlocking the information in medical text for analysis. However, many domains of medical concepts lack well-developed terminologies that can support effective coding of medical text. We present a framework for developing natural language processing (NLP) technologies for automated coding of under-studied types of medical information, and demonstrate its applicability via a case study on physical mobility function. Mobility is a component of many health measures, from post-acute care and surgical outcomes to chronic frailty and disability, and is coded in the International Classification of Functioning, Disability, and Health (ICF). However, mobility and other types of functional activity remain under-studied in medical informatics, and neither the ICF nor commonly-used medical terminologies capture functional status terminology in practice. We investigated two data-driven paradigms, classification and candidate selection, to link narrative observations of mobility to standardized ICF codes, using a dataset of clinical narratives from physical therapy encounters. Recent advances in language modeling and word embedding were used as features for established machine learning models and a novel deep learning approach, achieving a macro F-1 score of 84% on linking mobility activity reports to ICF codes. Both classification and candidate selection approaches present distinct strengths for automated coding in under-studied domains, and we highlight that the combination of (i) a small annotated data set; (ii) expert definitions of codes of interest; and (iii) a representative text corpus is sufficient to produce high-performing automated coding systems. This study has implications for the ongoing growth of NLP tools for a variety of specialized applications in clinical care and research.
Internet of Things (IoT): Data Security and Privacy Concerns under the General Data Protection Regulation (GDPR)

Internet of Things (IoT) refers to the seamless communication and interconnectivity of multiple devices within a certain network enabled by sensors and other technologies facilitating unusual processing of personal data for the performance of a certain goal. This article examines the various definitions of the IoT from technical and socio-technical perspectives and goes ahead to describe some practical examples of IoT by demonstrating their functionalities vis a vis the anticipated privacy and information security implications. Predominantly, the article discusses the information security and privacy risks posed by the operationality of IoT as envisaged under the EU GDPR and makes a few recommendations on how to address the risks.
Employer and Employee Attitudes toward Genetic Testing in the Workplace

The present study aimed to examine both employer and employee attitudes concerning the disclosure of genetic testing results. This endeavor was inspired in light of current standards that limit the individual’s right to privacy with regard to the results of genetic tests and previous studies, which demonstrate that this may cause both physical and psychological harm to affected individuals. This study surveyed employers and employees from workplaces subject to and exempt from the Genetic Information Nondiscrimination Act (GINA). One hundred employers and five hundred employees completed surveys meant to assess their feelings about genetic testing and the effects of the employer-employee relationship and prescribed government standards. It was found that employees reported increased willingness to undergo genetic testing if they were not required to disclose results to their employers. In addition, the results showed that GINA employers were more willing to hire those testing positive for genetic conditions than were non-GINA employers. Finally, it was found that GINA employers were less likely to support genetic testing mandates for new employees than non-GINA employers. These findings highlight the importance of the individual’s right to privacy with respect to genetic testing. In order to minimize harm, in this case discrimination in the workplace, and maximize welfare, encouraging proactive health precautions, individuals must reserve the sole right to disclose genetic testing results at their own discretion.
RESPONSIBILITY TO PROTECT AND MILITARIZED HUMANITARIAN INTERVENTION: When and Why the Churches Failed to Discern Moral Hazard

ABSTRACTThis essay addresses moral hazards associated with the emerging doctrine of the Responsibility to Protect (R2P). It reviews the broad acceptance by the Vatican and the World Council of Churches of the doctrine between September 2003 and September 2008, and attempts to identify grounds for more adequate investigation of the moral issues arising. Three themes are pursued: how a changing political context is affecting notions of sovereignty; the authority that can approve or refuse the use of force; and plural foundations for human rights in a religiously and otherwise plural world such that the human rights protection does not become tyrannical.
Jarvis Health: An AI-Based Voice-Enabled Symptom Checker Chatbot for Preliminary Health Assessment

In the evolving digital healthcare landscape, Artificial Intelligence (AI) plays a crucial role in early diagnosis and patient engagement. This paper presents "Jarvis Health", a voice-enabled AI chatbot designed to assist users in conducting preliminary health assessments through natural conversation. Using Natural Language Processing (NLP), speech-to-text, and a Flask-based back-end, Jarvis Health provides a seamless interface for users to report symptoms and receive preliminary advice. The system integrates literature-backed chatbot design principles and ensures inclusive and ethical AI considerations. The goal is to ease the burden on medical professionals by acting as a first-level screen while promoting accessibility for remote and under-served areas. The prototype is developed using open-source tools and is evaluated for conversational performance and user-friendliness.
Multi-objective Software Architecture Refactoring driven by Quality Attributes

Architecture optimization is the process of automatically generating design options, typically to enhance software's quantifiable quality attributes, such as performance and reliability. Multi-objective optimization approaches have been used in this situation to assist the designer in selecting appropriate trade-offs between a number of non-functional features. Through automated refactoring, design alternatives can be produced in this process, and assessed using non-functional models.   This type of optimization tasks are hard and time- and resource-intensive, which frequently hampers their use in software engineering procedures.   In this paper, we present our optimization framework where we examined the performance of various genetic algorithms. We also exercised our framework with two case studies with various levels of size, complexity, and domain served as our test subjects.
Accounting for hidden common causes when inferring cause and effect from observational data

Identifying causal relationships from observation data is difficult, in large part, due to the presence of hidden common causes. In some cases, where just the right patterns of conditional independence and dependence lie in the data---for example, Y-structures---it is possible to identify cause and effect. In other cases, the analyst deliberately makes an uncertain assumption that hidden common causes are absent, and infers putative causal relationships to be tested in a randomized trial. Here, we consider a third approach, where there are sufficient clues in the data such that hidden common causes can be inferred.