ethicalabs/Research-Intent-Collab huggingface dataset:
The Impact of Accreditation on Patient Safety and Quality of Care as Perceived by Nursing Staff in a Cardiac Care Centre in the Eastern Province, Kingdome of Saudi Arabia

Accreditation is one of the approaches to evaluate the quality of health care services, and it’s an essential requirement for the continuity of services. Nursing staff play a major role in the accreditation preparation, process and continuity. The study aimed to assess the nursing perception of the impact of accreditation on the quality of care and safety in a tertiary cardiac care center in the Eastern Province, Kingdome of Saudi Arabia. A cross-sectional descriptive research design was used. A convenient sample of 56 nursing staff was included. The study tool was a self-administered online questionnaire. It consisted of four major sections with 20 subscales rated from 1 to 5 on a Likert scale. Nursing documentation, patient medication information, healthcare-associated infection, leadership and support sections are all included. The study findings indicated that nurses have a 94.6% positive perception of the impact of accreditation on the patient’s safety. This perception was measured in accordance with the items related to international patients’ safety goals. Higher agreement was found on the items of nursing clinical documentation and medication safety process. The study findings support the overall positive impact of accreditation. The study further recommends exploring the nursing perception of accreditation impact in different settings.
GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery

13 pages, 9 figures
Early warning system on extreme weather events for disaster risk reduction

The objective of this research was to propose a strategy based on the design and implementation of an early warning system (EWS) for extreme weather events. This project had the following phases: training for municipal and regional actors, preliminary technical diagnosis of the study areas, monitoring network, and the weather forecasts using numerical models WRF and GFS. This EWS is the result of the Macro-project EWS for Climate Events in the basins of the Pamplonita River and Zulia in the North of Santander (SATC), executed by the University of Pamplona and financed by the National Risk Management Unit (UNGRD) and the German Cooperation Agency (GIZ). The research concluded that the application of a disaster risk reduction strategy through an EWS for extreme weather events is an important tool and instrument for the planning of higher risk management because it helps anticipate disasters and consequently preserve lives.
Climate Uncertainty, Real Possibilities and the Precautionary Principle

AbstractA challenge faced by defenders of the precautionary principle is to clarify when the evidence that a harmful event might occur suffices to regard this prospect as a real possibility. Plausible versions of the principle must articulate some epistemic threshold, or de minimis requirement, which specifies when precautionary measures are justified. Critics have argued that formulating such a threshold is problematic in the context of the precautionary principle. First, this is because the precautionary principle appears to be ambiguous about the distinction between risk and uncertainty: should the principle merely be invoked when evidential probabilities are absent, or also when probabilities have low epistemic credentials? Secondly, defenders of the precautionary principle face an aggregation puzzle: in judging whether or not the de minimis requirement has been met, how should first-order evidential probabilities and their second-order epistemic standing be aggregated? This article argues that the ambiguity can be resolved, and the epistemological puzzle can be solved. Focusing on decisions in the context of climate uncertainty, I advance a version of the precautionary principle that serves as a plausible decision rule, to be adopted in situations where its main alternative—cost–benefit analysis—does not deliver.
The Geometry of Thermodynamic Uncertainty Relations in Chemical Reaction Networks

3 figures, 6 pages
Key areas for conserving United States' biodiversity likely threatened by future land use change

A major challenge for biodiversity conservation is to mitigate the effects of future environmental change, such as land use, in important areas for biodiversity conservation. In the United States, recent conservation efforts by The Nature Conservancy and partners have identified and mapped the nation's Areas of Biodiversity Significance (ABS), representing the best remaining habitats for the full diversity of native species and ecosystems, and thus the most important and suitable areas for the conservation of native biodiversity. Our goal was to understand the potential consequences of future land use changes on the nation's ABS, and identify regions where ABS are likely to be threatened due to future land use expansion. For this, we used an econometric‐based model to forecast land use changes between 2001 and 2051 across the conterminous U.S. under alternative scenarios of future land use change. Our model predicted a total of ∼100,000 to 160,000 km2of natural habitats within ABS replaced by urban, crop and pasture expansion depending on the scenario (5% to 8% habitat loss across the conterminous U.S.), with some regions experiencing up to 30% habitat loss. The majority of the most threatened ABS were located in the Eastern half of the country. Results for our different scenarios were generally fairly consistent, but some regions exhibited notable difference from the baseline under specific policies and changes in commodity prices. Overall, our study suggests that key areas for conserving United States' biodiversity are likely threatened by future land use change, and efforts trying to preserve the ecological and conservation values of ABS will need to address the potential intensification of human land uses.
SORS/womeninBSC: alphaFold predicted structures expand our understanding of functional ivergence in protein families

The recent development of the AlphaFold2 method by DeepMind, has led to a massive expansion in high quality predicted protein structure data. Our group have developed computational protocols (Chainsaw, CATH-AlphaFlow) to classify these structural data into evolutionary families. In collaboration with the group of David Jones, also at UCL, we have classified >200 million predicted structures in the AlphaFold database into evolutionary families. This information is available in a new resource – TED – The Encyclopaedia of Domains. My group have also developed methods for subclassifying proteins in evolutionary families into functional families. In the talk I will present some insights from TED and describe how we are using the TED data and our functional families for drug repurposing and to analyse the impacts of genetic variations in lung cancer.
Executive Summary on the 2024 UAP Workshop: "Unidentified Anomalous Phenomena (UAP): A Dialogue on Science, Public Engagement and Communication"

To address growing interest in Unidentified Anomalous Phenomena (UAP) research, a workshop titled "Unidentified Anomalous Phenomena (UAP): A Dialogue on Science, Public Engagement, and Communication" was held from May 15-17, 2024, in the Washington, D.C. area. Funded by the National Science Foundation (NSF) as part of the Global Network for Accelerating Synergies Through Research On Astronomy Culture, Communication, Education and Learning (ASTRO ACCEL) project, the workshop gathered academics, researchers, and professionals from various fields to foster interdisciplinary discussions, develop strategies for effective communication and education, and examine the societal impacts of UAP research. The outcomes of the workshop will include a comprehensive report with recommendations for future research and broader applications. This workshop responds to current interest in UAP studies, driven by recent declassifications of government reports and the formation of dedicated investigative agencies, organizations, technologies, and methodologies. The scientific community increasingly recognizes UAP research as an interdisciplinary field with implications for understanding our earth and space environments and addressing national security concerns. Integrating UAP studies within the broader ASTRO ACCEL network aligns with the project's goals of advancing scientific understanding and public engagement in astronomy and space sciences, leveraging public interest to enhance scientific discourse and education, and promoting science and cultural literacy.
Monitoring the sustainable development goals through human rights accountability reviews

The Universal Periodic Review is a comprehensive, state-to-state peer-review mechanism of the United Nations (UN) Human Rights Council. Created in 2006, the mechanism scrutinizes the human rights record of all UN Member States, including their efforts to realize the right to health. However, the mechanism is relatively under-used in global health governance compared to treaty-based procedures, such as those overseen by the Committee on the Rights of Persons with Disabilities or the Committee on the Elimination of Discrimination against Women. We suggest that the Universal Periodic Review could be used to support the monitoring and review processes of the sustainable development goals (SDGs). The review could offer a unique perspective for other actors on how to ensure accountability for the complex and intertwined SDGs, including their commitments for health. This article provides an overview of how health-related rights have been addressed in the Universal Periodic Review process and how the review can contribute to advancing global commitments to health, including those embodied in the SDGs. We present some of the current limitations in the way health is addressed in the Universal Periodic Review. We also consider what role specialized UN agencies, such as the World Health Organization, might play during the Universal Periodic Review process and how this involvement can contribute towards the comprehensive realization of health and wellbeing for all.
Promoting anti-corruption, transparency and accountability to achieve universal health coverage

Anti-corruption, transparency and accountability measures are often missing from efforts to promote universal health coverage. Yet, if unchecked, corruption represents a significant drain on domest...
Rights-Based Approach to Food and Nutrition Security in Nigeria

This chapter focuses on the need for a rights-based approach to food and nutrition security in Nigeria. The topic is introduced with the definition of basic terms used throughout the chapter. The objectives of this chapter are to create awareness of the need to adopt a rights-based approach to food and nutrition security and help define the context of the right to food in Nigeria. The rest of the chapter sets out the roles of right holders, duty bearers, and accountable agents in food and nutrition security. It highlights their rights, obligations, and responsibilities, as well as voluntary guidelines and implications for a rights-based approach to food. The chapter also reviews policies that have been developed toward ensuring a right to food in Nigeria.
Mix, MinHash, and Match: Cross-Source Agreement for Multilingual Pretraining Datasets

Multilingual data from the web is essential for LLM pretraining. Yet, scraping it is expensive, and research groups repeatedly crawl the same content. For example, we found that over 40\% of tokens across major Arabic web corpora are duplicated between sources. In this work, we propose to use this wasteful redundancy as a quality signal to create high-quality pretraining datasets. Our key insight is that cross-source agreement functions as a free, model-free quality filter: content retained by multiple independent pipelines is more likely to represent high-quality text. Crucially, this signal requires no additional computation beyond standard deduplication, which is already performed at scale when pretraining language models. So, we propose MixMinMatch, a method that combines multiple existing web corpora, performs cross-dataset MinHash deduplication, and identifies documents independently recovered by multiple sources. We apply MixMinMatch to Arabic, Turkish, and Hindi, producing corpora that match or exceed the quality of the best single-source baselines, while providing up to 4$\times$ more unique tokens. On Arabic, our matched subset achieves a 4.5\% relative improvement over ArabicWeb24, while on Turkish, we improve over FineWeb-2 by 5.5\%. We release the datasets at: https://huggingface.co/collections/AdaMLLab/mixminmatch
Forks Over Knives: Predictive Inconsistency in Criminal Justice Algorithmic Risk Assessment Tools

AbstractBig data and algorithmic risk prediction tools promise to improve criminal justice systems by reducing human biases and inconsistencies in decision-making. Yet different, equally justifiable choices when developing, testing and deploying these socio-technical tools can lead to disparate predicted risk scores for the same individual. Synthesising diverse perspectives from machine learning, statistics, sociology, criminology, law, philosophy and economics, we conceptualise this phenomenon as predictive inconsistency. We describe sources of predictive inconsistency at different stages of algorithmic risk assessment tool development and deployment and consider how future technological developments may amplify predictive inconsistency. We argue, however, that in a diverse and pluralistic society we should not expect to completely eliminate predictive inconsistency. Instead, to bolster the legal, political and scientific legitimacy of algorithmic risk prediction tools, we propose identifying and documenting relevant and reasonable ‘forking paths’ to enable quantifiable, reproducible multiverse and specification curve analyses of predictive inconsistency at the individual level.
A MEASUREMENT OF THE COSMIC MICROWAVE BACKGROUND<i>B</i>-MODE POLARIZATION POWER SPECTRUM AT SUB-DEGREE SCALES WITH POLARBEAR

22 pages, 12 figures. v3 is updated to reflect the erratum published in ApJ 2017 848:73, which changed the rejection of the hypothesis of no B-mode polarization power from gravitational lensing from 97.2% to 97.1%
The Concept of Trials in Absentia in International Criminal Law and the Special Tribunal for Lebanon: An Overview

Trials in absentia have always been quite a contentious source of controversies in international criminal law, especially given the nature of crimes that result into these trials and given the question regarding what can be an acceptable standard of legal due process that ought to be adhered to in course of such trials. Throughout this paper, the author has sought to present an overview of the concept as it is prevalent in the current domestic and international legal scenarios. An effort has also been made to portray the range and acceptability of the arguments that are advanced by the proponents and detractors of the notion of accepting this category of trials as a matter of course to combat international and domestic crimes. The varying approaches of common law and civil law&#x0D; jurisdictions vis-à-vis this subject-matter has also been examined, together with the practices prevalent in renowned international tribunals such as International Criminal Tribunal for the former Yugoslavia [hereinafter referred to as “ICTY”], the International Criminal Tribunal for Rwanda [hereinafter referred to as “ICTR”] and the International Criminal Court [hereinafter referred to as “ICC”]. Finally, the author has looked into the manner in which the question of the validity of such trials has once again come to the forefront owing to its acceptance by the Special Tribunal for Lebanon.
Prediction of climate change impacts on cotton yields in Greece under eight climatic models using the AquaCrop crop simulation model and discriminant function analysis

Abstract   The impact of climate change on cotton yields in seven main arable crop sites in Greece (Agrinio, Alexandroupoli, Arta, Karditsa, Mikra, Pyrgos, Yliki) was investigated. The FAO AquaCrop (v.4) water driven model was used as a crop growth simulation tool under eight climatic models (HadRM3, C4I, REMO-MPI, ETHZ, CNRM, DMI-HIRHAM, KNMI, SMHI) based on IPPC's A1B emission scenario. The mean values of the models ensemble for temperature were +1.8 °C until 2050 and +4 °C until the end of the century. The respective values for precipitation were −11% and −24%. The research was applied over three periods, 1961–1990, 2021–2050 and 2071–2100. AquaCrop was calibrated for 2006 and validated for 2005 and 2007 using the field data from the experiments carried out in Karditsa (Central Greece). Root Mean Square Error for yield and biomass was 0.17 and 0.49 t/ha, respectively, while Index of Agreement was 0.93 and 0.94. AquaCrop was run using the Growing Degree Day mode in order to account better for the temperature variations. However, it gave erratic results for some specific climatic models (SMHI, KNMI, CNRM) in some years within the period 1961–1990. A tendency towards increasing yields by the end of the century was detected for the majority of the climate models, especially in Western Greece (Arta, Agrinio, Pyrgos) and Northern Greece (Mikra, Alexandroupoli). The efficiency of the eight models for yield predictions in the seven sites was assessed by means of a discriminant function analysis. On the account of their function coefficients over the seven sites, it was found that the models DMI and C4I explained consistently a great proportion of variation among the three time periods whereas the models ETHZ, SMHI and KNMI were more efficient only in the periods 1961–1990, 2021–2050 and 2071–2099, respectively. By running the models DMI and C4I the relative impacts of climate change on seedcotton yield in the different areas were predicted and the results were discussed on the account of the corresponding changes in precipitation, temperature and crop evapotranspiration. These results will be useful for future irrigation planning in the study areas.
Spin Environment of a Superconducting Qubit in High Magnetic Fields

Superconducting qubits equipped with quantum non-demolition readout and active feedback can be used as information engines to probe and manipulate microscopic degrees of freedom, whether intentionally designed or naturally occurring in their environment. In the case of spin systems, the required magnetic field bias presents a challenge for superconductors and Josephson junctions. Here we demonstrate a granular aluminum nanojunction fluxonium qubit (gralmonium) with spectrum and coherence resilient to fields beyond one Tesla. Sweeping the field reveals a paramagnetic spin-1/2 ensemble, which is the dominant gralmonium loss mechanism when the electron spin resonance matches the qubit. We also observe a suppression of MHz range fast flux noise in magnetic field, suggesting the freezing of surface spins. Using an active state stabilization sequence, the qubit hyperpolarizes long-lived two-level systems (TLSs) in its environment, previously speculated to be spins. Surprisingly, the coupling to these TLSs is unaffected by magnetic fields, leaving the question of their origin open. The robust operation of gralmoniums in Tesla fields offers new opportunities to explore unresolved questions in spin environment dynamics and facilitates hybrid architectures linking superconducting qubits with spin systems.
Federated Transfer Learning with Differential Privacy

Federated learning has emerged as a powerful framework for analysing distributed data, yet two challenges remain pivotal: heterogeneity across sites and privacy of local data. In this paper, we address both challenges within a federated transfer learning framework, aiming to enhance learning on a target data set by leveraging information from multiple heterogeneous source data sets while adhering to privacy constraints. We rigorously formulate the notion of federated differential privacy, which offers privacy guarantees for each data set without assuming a trusted central server. Under this privacy model, we study four statistical problems: univariate mean estimation, low-dimensional linear regression, high-dimensional linear regression, and M-estimation. By investigating the minimax rates and quantifying the cost of privacy, we show that federated differential privacy is an intermediate privacy model between the well-established local and central models of differential privacy. Our analyses account for data heterogeneity and privacy, highlighting the fundamental costs associated with each factor and the benefits of knowledge transfer in federated learning.
The association between wearable device metrics and clinical outcomes in oncology: A systematic review with evidence synthesis and meta-analysis

The emerging study of wearable devices (WDs) in patients with cancer provides opportunities to harness real-time patient data for predicting clinical outcomes. We conducted a systematic review with best evidence synthesis to examine the association between WD metrics and clinical outcomes in patients with cancer.MEDLINE and Embase were searched from inception until June 2022. Risk of bias assessment and best evidence synthesis were performed and, If possible, meta-analysis was conducted.A total of 34 studies was included. We found moderate-to-strong evidence for associations between circadian rest-activity metrics and OS. Disrupted I<O was associated with increased hazard for death (HR 2.08; 95 %CI: 1.50-2.88). For most associations there was insufficient evidence due to lack of studies (n = 32) or inconsistent results (n = 14).Meta-analysis was greatly hampered due to heterogeneity and different methodology used between studies. Studies primarily designed to investigate the association between WD metrics and clinical outcomes are warranted.
Scalable tensor-network error mitigation for near-term quantum computing

Until fault-tolerance becomes implementable at scale, quantum computing will heavily rely on noise mitigation techniques. While methods such as zero noise extrapolation with probabilistic error amplification (ZNE-PEA) and probabilistic error cancellation (PEC) have been successfully tested on hardware recently, their scalability to larger circuits may be limited. Here, we introduce the tensor-network error mitigation (TEM) algorithm, which acts in post-processing to correct the noise-induced errors in estimations of physical observables. The method consists of the construction of a tensor network representing the inverse of the global noise channel affecting the state of the quantum processor, and the consequent application of the map to informationally complete measurement outcomes obtained from the noisy state. TEM does therefore not require additional quantum operations other than the implementation of informationally complete POVMs, which can be achieved through randomised local measurements. The key advantage of TEM is that the measurement overhead is quadratically smaller than in PEC. We test TEM extensively in numerical simulations in different regimes. We find that TEM can be applied to circuits of twice the depth compared to what is achievable with PEC under realistic conditions with sparse Pauli-Lindblad noise, such as those in [E. van den Berg et al., Nat. Phys. (2023)]. By using Clifford circuits, we explore the capabilities of the method in wider and deeper circuits with lower noise levels. We find that in the case of 100 qubits and depth 100, both PEC and ZNE fail to produce accurate results by using $\sim 10^5$ shots, while TEM succeeds.