ethicalabs/Research-Intent-Collab huggingface dataset:
The role of empowering leadership, safety culture and safety climate in the prediction of mindful organizing in an air traffic management company

Mindful organizing is the collective capability of teams to anticipate, detect, and contain early signs of emerging problems, act proactively, and recover quickly if unexpected events and errors occur. The present study aimed to add to our currently limited understanding of antecedents of mindful organizing: empowering leadership, safety culture, and team safety climate. To do so, we tested a moderated mediation model using a sample of 73 Air Traffic Management company employees. The model evaluated whether safety climate mediates the relationship between empowering leadership and mindful organizing, and whether safety culture understanding as enacted value of safety moderates the effect of empowering leadership on team safety climate. The results showed that a strong safety climate was a significant predictor of mindful organizing. Moreover, empowering leadership influenced mindful organizing indirectly through its positive effect on the safety climate in a work unit. However, the moderating role of safety culture was not confirmed.
Effect of charge on the dynamics of an acoustically forced bubble

The effect of charge on the dynamics of a gas bubble undergoing forced oscillations in a liquid due to incidence of an ultrasonic wave is theoretically investigated. The limiting values of the possible charge a bubble may physically carry are obtained. The presence of charge influences the regime in which the bubble's radial oscillations fall. The extremal compressive and expansive dimensions of the bubble are also studied as a function of the amplitude of the driving pressure. It is shown that the limiting value of the bubble charge is dictated both by the minimal value reachable of the bubble radius as well as the amplitude of the driving ultrasound pressure wave. A non-dimensional ratio zeta is defined that is a comparative measure of the extremal values the bubble can expand or contract to and find the existence of an unstable regime for zeta as a function of the driving pressure amplitude, Ps. This unstable regime is gradually suppressed with increasing bubble size. The Blake and the upper transient pressure thresholds for the system are then discussed.
Quantum Continual Learning Overcoming Catastrophic Forgetting

Catastrophic forgetting describes the fact that machine learning models will likely forget the knowledge of previously learned tasks after the learning process of a new one. It is a vital problem in the continual learning scenario and recently has attracted tremendous concern across different communities. In this paper, we explore the catastrophic forgetting phenomena in the context of quantum machine learning. We find that, similar to those classical learning models based on neural networks, quantum learning systems likewise suffer from such forgetting problem in classification tasks emerging from various application scenes. We show that based on the local geometrical information in the loss function landscape of the trained model, a uniform strategy can be adapted to overcome the forgetting problem in the incremental learning setting. Our results uncover the catastrophic forgetting phenomena in quantum machine learning and offer a practical method to overcome this problem, which opens a new avenue for exploring potential quantum advantages towards continual learning.
An Introduction to Error-Correcting Codes: From Classical to Quantum

This report surveys quantum error-correcting codes. As Preskill claimed, 21st century would be the golden age of quantum error correction. Quantum channels behave differently from classical channels, so researchers face difficulties in developing robust quantum codes. Fortunately, the classical error control methods have been well developed. If we can learn many lessons from classical coding theory, we can expedite the development of quantum codes. Scientists have discovered that quantum error correction shares many concepts with classical counterpart. Both quantum and classical coding schemes add redundancy to information to protect against noises. They also have similar conditions for error detectability and correctability.
Short Glass Fiber-Reinforced Recycled Polyethylene Terephthalate Composites for Additive Manufacturing: Modification Strategies, Processing, Characterization and 3D Printing

In response to the growing demand for sustainable manufacturing, 3D printing using recycled polyethylene terephthalate (rPET) offers a novel waste-to-value conversion method. Although the application of rPET in additive manufacturing has attracted significant attention from both the academic and industrial sectors, substantial challenges impede its further development, notably the high processing shrinkage and poor mechanical properties of the final product. This study focuses on developing recycled PET-based composites with favorable processing, thermal, and mechanical properties. Regranulates were produced via twin-screw extrusion using PET flakes, multifunctional chain extenders, and short glass fibers (GFs). The rPET-GF composites were characterized in terms of their processing, thermal, thermomechanical, and mechanical properties. Epoxy-functional chain extender modification effectively increased the molecular weight and improved the processability, whereas GF reinforcement enhanced the tensile properties of both injection-molded and FDM-manufactured parts. A primary advantage of the rPET systems developed in this study is their delayed crystallization kinetics. These findings highlight the significant potential of the composites developed herein for extrusion-based additive manufacturing (MEX-AM), as delayed crystallization facilitates enhanced interfacial adhesion, lower volumetric shrinkage, and superior dimensional stability.
Process Discovery using Inductive Miner and Decomposition

This report presents a submission to the Process Discovery Contest. The contest is dedicated to the assessment of tools and techniques that discover business process models from event logs. The objective is to compare the efficiency of techniques to discover process models that provide a proper balance between overfitting and underfitting. In the context of the Process Discovery Contest, process discovery is turned into a classification task with a training set and a test set; where a process model needs to decide whether traces are fitting or not. In this report, we first show how we use two discovery techniques, namely: Inductive Miner and Decomposition, to discover process models from the training set using ProM tool. Second, we show how we use replay results to 1) check the rediscoverability of models, and to 2) classify unseen traces (in test logs) as fitting or not. Then, we discuss the classification results of validation logs, the complexity of discovered models, and their impact on the selection of models for submission. The report ends with the pictures of the submitted process models.
Call to Protect the Dark and Quiet Sky from Harmful Interference by Satellite Constellations

The growing number of satellite constellations in low Earth orbit (LEO) enhances global communications and Earth observation, and support of space commerce is a high priority of many governments. At the same time, the proliferation of satellites in LEO has negative effects on astronomical observations and research, and the preservation of the dark and quiet sky. These satellite constellations reflect sunlight onto optical telescopes, and their radio emission impacts radio observatories, jeopardising our access to essential scientific discoveries through astronomy. The changing visual appearance of the sky also impacts our cultural heritage and environment. Both ground-based observatories and space-based telescopes in LEO are affected, and there are no places on Earth that can escape the effects of satellite constellations given their global nature. The minimally disturbed dark and radio-quiet sky is crucial for conducting fundamental research in astronomy and important public services such as planetary defence, technology development, and high-precision geolocation. Some aspects of satellite deployment and operation are regulated by States and intergovernmental organisations. While regulatory agencies in some States have started to require operators to coordinate with their national astronomy agencies over impacts, mitigation of the impact of space objects on astronomical activities is not sufficiently regulated. To address this issue, the CPS urges States and the international community to take steps to protect the dark and quiet sky as specified in this paper.
INTERPRETABILITY, EXPLAINABILITY AND CAUSALITY IN DEEP LEARNING

Despite revolutionizing sectors like medicine, finance, natural language processing, and autonomous technologies, the intricate and non-transparent designs of deep learning architectures restrict confidence and clarity. Existing post-hoc explanation methods (e.g., saliency maps, SHAP, and counterfactuals) yield insights that are often incomplete and inconsistent, frequently uncovering statistical correlations instead of definitive causal factors. Consequently, establishing interpretability and causality is now crucial for successfully deploying AI in critical applications. Causal inference provides a more solid framework by identifying authentic cause-and-effect relationships and boosting model reliability when data distributions change. Nevertheless, incorporating robust causal structures into neural networks is challenging due to data availability and high computational demands. This paper examines prominent interpretability and causal methodologies, discusses their drawbacks, and proposes future research avenues aimed at engineering deep learning models that are simultaneously precise, transparent, and trustworthy
Self-Supervised Ranking for Representation Learning

We present a new framework for self-supervised representation learning by formulating it as a ranking problem in an image retrieval context on a large number of random views (augmentations) obtained from images. Our work is based on two intuitions: first, a good representation of images must yield a high-quality image ranking in a retrieval task; second, we would expect random views of an image to be ranked closer to a reference view of that image than random views of other images. Hence, we model representation learning as a learning to rank problem for image retrieval. We train a representation encoder by maximizing average precision (AP) for ranking, where random views of an image are considered positively related, and that of the other images considered negatives. The new framework, dubbed S2R2, enables computing a global objective on multiple views, compared to the local objective in the popular contrastive learning framework, which is calculated on pairs of views. In principle, by using a ranking criterion, we eliminate reliance on object-centric curated datasets. When trained on STL10 and MS-COCO, S2R2 outperforms SimCLR and the clustering-based contrastive learning model, SwAV, while being much simpler both conceptually and at implementation. On MS-COCO, S2R2 outperforms both SwAV and SimCLR with a larger margin than on STl10. This indicates that S2R2 is more effective on diverse scenes and could eliminate the need for an object-centric large training dataset for self-supervised representation learning.
Synergy between adaptations and resilience of livelihood from climate change vulnerability: A group-wise comparison of adapters and non-adapters

The similarities, differences, and contradictions regarding climate change adaptation and resilience by academics and practitioners have already been documented. It is the need of time to set new precedence by observing the adaptations and resilience as tools to respond to the climate variations. This study analyzed the influence of climate change adaptations and synergy between resilience from livelihood vulnerability and adaptations. A field survey of 489 farming households is conducted with the help of a well-structured questionnaire from four districts of the south part of Punjab province of Pakistan. This study uses the Endogenous Switching Regression model for the sake of analysis. The outcomes of the study reveal that age, education, family size, total land, and seed price have significant linkage with the adoption of adaptations. The synergistic effects of adaptation and resilience are also visible here as the adaptations factors are significantly contributing towards yield, per capita income, poverty, and poverty gap of the respondents. This study suggests the provision of proper education and smart technology to help in enhancing the adaptive capacity of farmers. More imperatively, adaptations to climate variations can be concluded as a remedial tool for resilient livelihood. It is believed that the present study can be considered as a guide for future research on other regions of Pakistan and neighboring countries.
Adaptive all-optical sigmoid activation functions for Photonic Neural Networks using Fabry-Perot laser diodes under optical injection

We experimentally validate the all-optical activation functions in Fabry-Perot lasers under optical injection for random and non-random inputs. Sigmoid-like activations for 100 ps pulses are reconfigured using injection parameters, consuming 1.4 pJ per nonlinear operation.
Statistical Mechanics of Phase Transitions with a Hierarchy of Structures

AbstractRescaling (renormalization-group) [1] and restructuring [2] transformations are used in the statistical mechanics of systems with different complexities at different length scales, starting with the microscopic scale. These transformations are effected by a partial trace of the partition function, yielding effective coupling constants that incorporate the free energy contribution of the structures and length scales that are summed out of the problem. An example of this approach is a quantitative treatment [3] of the phase diagram of krypton adsorbed onto graphite. At the smallest length scales, adatom adsorption (first and second layer) and vibrations at adsorption sites are considered. At the next scale, sublattice formation and vacancy occurrence on the hexagonal graphite surface are considered. At a larger scale, heavy and superheavy domain wall formation, wall crossings and annihilations (dislocations) [4] are considered. This problem is treated by a sequence of 5 different transformations. Starting with the microscopic potentials, phase diagrams are obtained in temperature versus pressure or coverage, featuring disordered, commensurate and incommensurate solid phases, in good agreement with experiments [5]. The mechanism for reentrant phase transitions is divulged. Such solutions can be considered approximate treatments of physical systems or, alternatively, exact solutions of “hierarchical models” [6]. This “realizability” on hierarchical lattices insures the robustness of the approximation.
Genetic discrimination, insurance, and solidarity: an analysis of the argumentation for fair risk classification

This article analyzes the argumentation used in European governmental and expert discussion on genetic discrimination in life insurance. The article focuses on competing justifications for fair risk classification in private insurance, and explores the ambiguity of the principle of solidarity in this context. The analysis starts by examining common justifications for the necessity of discrimination in life and health insurance. After that, the article explores the arguments used, in particular, for problematizing genetic discrimination, and shows what kind of implications this may have for the relationship between private and social insurance. In the final section, the discussion on this issue in Finland is contrasted with the diverse debates found elsewhere in Europe and internationally to show how the discussion varies locally, depending much on what is considered to be the social task of life insurance in a given societal context.
Scalable Non-Equivariant 3D Molecule Generation via Rotational Alignment

Equivariant diffusion models have achieved impressive performance in 3D molecule generation. These models incorporate Euclidean symmetries of 3D molecules by utilizing an SE(3)-equivariant denoising network. However, specialized equivariant architectures limit the scalability and efficiency of diffusion models. In this paper, we propose an approach that relaxes such equivariance constraints. Specifically, our approach learns a sample-dependent SO(3) transformation for each molecule to construct an aligned latent space. A non-equivariant diffusion model is then trained over the aligned representations. Experimental results demonstrate that our approach performs significantly better than previously reported non-equivariant models. It yields sample quality comparable to state-of-the-art equivariant diffusion models and offers improved training and sampling efficiency. Our code is available at https://github.com/skeletondyh/RADM
The role of neurologists in the era of cancer immunotherapy: Focus on CAR T-cell therapy and immune checkpoint inhibitors

Cancer immunotherapy represents a novel anticancer strategy that acts directly on the immune system, promoting its activation toward cancer cells to enhance its natural ability to fight cancer. Among various treatments currently used or investigated, chimeric antigen receptors (CAR) T-cell therapy and immune checkpoint inhibitors (ICIs) have consistently proven their efficacy. These innovations are progressively improving the standard of care in cancer treatment, yet they are hampered by novel neurological adverse events, attributing to neurologists a key role in the multidisciplinary oncological team. Indeed, neurotoxicity may develop in up to 77% of patients who received CAR T-cell therapy and usually presents with encephalopathy characterized by a predominant frontal lobe dysfunction. This neurotoxicity is related to cytokine release syndrome, a systemic hyperinflammatory condition triggered by CAR T-cells. On the other hand, following treatment with ICIs, unrestrained T-cells may lead to central and peripheral neurological disorders by antigen-directed autoimmunity. Notably, biological and clinical similarities have been underlined between neurotoxicity related to CAR T-cell therapy and neurological manifestations of cytokine storms (e.g. COVID-19-related encephalopathy), as well as between a subgroup of ICI-related neurological adverse events and paraneoplastic neurological syndromes. Therefore, these cancer immunotherapy-related neurological syndromes may provide an unprecedented, perhaps transitory, opportunity to shed light on the underlying pathogenic mechanisms of a wide spectrum of neurological syndromes and to push forward our knowledge in neuroimmunology.
Early Dynamical Architecture of the WISPIT 2 System: A Primary-Centric Framework Analysis of Giant Planet Formation in a Protoplanetary Disk

We present a dynamical and structural investigation of the newly identifiedprotoplanetary system WISPIT 2, one of the very few systems in which multipleforming giant planets have been directly detected within a circumstellar disk.The system hosts a wide-orbit gas giant, WISPIT 2b, at a projected separationof $\sim$57 AU, together with a more massive inner companion, WISPIT 2c, andexhibits additional disk substructure suggestive of a possible third, lower-mass planet. This configuration provides a rare opportunity to probe the early stages of giant planet formation over a wide range of orbital scales.We analyze the system within the framework of the Primary-Centric Framework (PCF), in which orbital architecture is described in terms of the dimensionless angular momentum ratio $\Lambda(a)=\mathrm{LOM}/\mathrm{LOD}$. By mapping the observed planetary locations onto the $\Lambda(a)$ topology, we identify the relative dynamical states of WISPIT 2b and WISPIT 2c with respect to the proto-Clarke orbits, and examine whether signatures of long-term equilibrium configurations are already present during the disk-embedded phase. The analysis suggests that the system may already encode a multi-equilibrium structure consistent with ongoing angular momentum redistribution within the disk.We further compare the WISPIT 2 system with the well-studied PDS 70 system,highlighting the transition from compact to wide-orbit architectures andsuggesting that WISPIT 2 probes a distinct regime of planet formation, possibly involving a hybrid pathway combining core accretion, disk instability, and migration. The presence of an additional gap in the outer disk points to the possible existence of a third, lower-mass planet, providing a testable prediction for future observations with next-generation facilities such as the ExtremelyLarge Telescope.These results demonstrate that the Primary-Centric Framework can be extended to forming planetary systems, offering a unified approach to linking disk structure, planet formation processes, and emergent orbital architecture.
sj-docx-1-dps-10.1177_10442073221118124 – Supplemental material for The Effects of Medicaid Expansion Under the Affordable Care Act on Health Insurance Coverage, Health Care Access, and Health Care Use for People With Disabilities: A Scoping Review

Supplemental material, sj-docx-1-dps-10.1177_10442073221118124 for The Effects of Medicaid Expansion Under the Affordable Care Act on Health Insurance Coverage, Health Care Access, and Health Care Use for People With Disabilities: A Scoping Review by Xiaobei Dong and Nancy A. Miller in Journal of Disability Policy Studies
Deep learning generative model for crystal structure prediction

Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of viable structures. Coupling such capabilities with physically significant data to construct trained models for materials discovery is crucial to moving this emerging field forward. Here, we present a universal GM for crystal structure prediction (CSP) via a conditional crystal diffusion variational autoencoder (Cond-CDVAE) approach, which is tailored to allow user-defined material and physical parameters such as composition and pressure. This model is trained on an expansive dataset containing over 670,000 local minimum structures, including a rich spectrum of high-pressure structures, along with ambient-pressure structures in Materials Project database. We demonstrate that the Cond-CDVAE model can generate physically plausible structures with high fidelity under diverse pressure conditions without necessitating local optimization, accurately predicting 59.3% of the 3,547 unseen ambient-pressure experimental structures within 800 structure samplings, with the accuracy rate climbing to 83.2% for structures comprising fewer than 20 atoms per unit cell. These results meet or exceed those achieved via conventional CSP methods based on global optimization. The present findings showcase substantial potential of GMs in the realm of CSP.
Modelling the Epidemiology of Infectious Diseases for Decision Analysis

The number of economic evaluations related to infectious disease topics has increased over the last 2 decades. However, many such evaluations rely on models that do not take into account unique features of infectious diseases that can affect the estimated value of interventions against them. These include their transmissibility from infected to susceptible individuals, the possibility of acquiring natural immunity following recovery from infection and the uncertainties that arise as a result of their complex natural history and epidemiology. Modellers conducting economic evaluations of infectious disease interventions need to know the main features of different types of infectious disease models, the situations in which they should be applied and the effects of model choices on the cost effectiveness of interventions.