We show that the quadratic measure need not be postulated, but follows from the compatibility of two structural features of physical processes: linear reversible evolution prior to the formation of persistent records, and multiplicative composition of outcome weights once such records are established. Reversible evolution combines configurations additively at the level of a compatibility parameter, while the formation of persistent records induces a multiplicative structure on the weights assigned to physically realized outcomes. Requiring consistency between these two regimes constrains the admissible weight assignment to be quadratic in the associated amplitude. The Born rule therefore emerges as the unique measure compatible with reversible linear evolution and irreversible record formation, without assuming a probabilistic interpretation or a specific quantum formalism.
Large Language Models (LLMs) are increasingly used to generate and edit scientific abstracts, yet their integration into academic writing raises questions about trust, quality, and disclosure. Despite growing adoption, little is known about how readers perceive LLM-generated summaries and how these perceptions influence evaluations of scientific work. This paper presents a mixed-methods survey experiment investigating whether readers with ML expertise can distinguish between human- and LLM-generated abstracts, how actual and perceived LLM involvement affects judgments of quality and trustworthiness, and what orientations readers adopt toward AI-assisted writing. Our findings show that participants struggle to reliably identify LLM-generated content, yet their beliefs about LLM involvement significantly shape their evaluations. Notably, abstracts edited by LLMs are rated more favorably than those written solely by humans or LLMs. We also identify three distinct reader orientations toward LLM-assisted writing, offering insights into evolving norms and informing policy around disclosure and acceptable use in scientific communication.
Real-time reflection plays a vital role in synchronous communication. It enables users to adjust their communication strategies dynamically, thereby improving the effectiveness of their communication. Generative AI holds significant potential to enhance real-time reflection due to its ability to comprehensively understand the current context and generate personalized and nuanced content. However, it is challenging to design the way of interaction and information presentation to support the real-time workflow rather than disrupt it. In this position paper, we present a review of existing research on systems designed for reflection in different synchronous communication scenarios. Based on that, we discuss design implications on how to design human-AI interaction to support reflection in real time.
Usability evaluations are essential for ensuring that modern interfaces meet user needs, yet traditional heuristic evaluations by human experts can be time-consuming and subjective, especially early in development. This paper investigates whether large language models (LLMs) can provide reliable and consistent heuristic assessments at the development stage. By applying Jakob Nielsen's ten usability heuristics to thirty open-source websites, we generated over 850 heuristic evaluations in three independent evaluations per site using a pipeline of OpenAI's GPT-4o. For issue detection, the model demonstrated moderate consistency, with an average pairwise Cohen's Kappa of 0.50 and an exact agreement of 84%. Severity judgments showed more variability: weighted Cohen's Kappa averaged 0.63, but exact agreement was just 56%, and Krippendorff's Alpha was near zero. These results suggest that while GPT-4o can produce internally consistent evaluations, especially for identifying the presence of usability issues, its ability to judge severity varies and requires human oversight in practice. Our findings highlight the feasibility and limitations of using LLMs for early-stage, automated usability testing, and offer a foundation for improving consistency in automated User Experience (UX) evaluation. To the best of our knowledge, our work provides one of the first quantitative inter-rater reliability analyses of automated heuristic evaluation and highlights methods for improving model consistency.
The shift in research focus from Industry 4.0 to Industry 5.0 (I5.0) promises a human-centric workplace, with social and well-being values at the centre of technological implementation. Human-Robot Collaboration (HRC) is a core aspect of I5.0 development, with an increase in adaptive and personalised interactions and behaviours. This review investigates recent advancements towards personalised HRC, where user-centric adaption is key. There is a growing trend for adaptable HRC research, however there lacks a consistent and unified approach. The review highlights key research trends on which personal factors are considered, workcell and interaction design, and adaptive task completion. This raises various key considerations for future developments, particularly around the ethical and regulatory development of personalised systems, which are discussed in detail.
Jens V. Rüppel, Andrey Rudenko, Tim Schreiter
et al.
The rapid development of Large Language Models (LLMs) creates an exciting potential for flexible, general knowledge-driven Human-Robot Interaction (HRI) systems for assistive robots. Existing HRI systems demonstrate great progress in interpreting and following user instructions, action generation, and robot task solving. On the other hand, bi-directional, multi-modal, and context-aware support of the user in collaborative tasks still remains an open challenge. In this paper, we present a gaze- and speech-informed interface to the assistive robot, which is able to perceive the working environment from multiple vision inputs and support the dynamic user in their tasks. Our system is designed to be modular and transferable to adapt to diverse tasks and robots, and it is capable of real-time use of language-based interaction state representation and fast on board perception modules. Its development was supported by multiple public dissemination events, contributing important considerations for improved robustness and user experience. Furthermore, in two lab studies, we compare the performance and user ratings of our system with those of a traditional scripted HRI pipeline. Our findings indicate that an LLM-based approach enhances adaptability and marginally improves user engagement and task execution metrics but may produce redundant output, while a scripted pipeline is well suited for more straightforward tasks.
Cristian Vizitiu, Kevin Dominey, Alexandru Nistorescu
et al.
Designing IoT-based cognitive assessment and monitoring devices for older adults poses critical challenges in managing trade-offs between accessibility and functionality. With the global aging population to exceed 2 billion by 2050, an increasing number of older adults will require Active Assisted Living (AAL) technologies to support independent living. Cognitive impairments make standard interfaces difficult to use, necessitating user-centered design approaches. Effective solutions must address the transition from document-centric to model-based design, incorporating co-design with users and caregivers, iterative modeling cycles, and continuous model evolution. This study highlights key factors that call for a hybrid approach, blending the flexibility of rapid prototyping with the accuracy and robustness of precision engineering. This study demonstrates a successful implementation of a cognitive detection device within an AAL European project by presenting Design SynMeth, a novel blended Model-Based Systems Engineering methodology that maps Object-Process Methodology (OPM) to Systems Modeling Language (SysML) using a modified MagicGrid framework. This approach bridges early and late design phases, integrating OPM’s strength in conceptual modeling and SysML’s rigor in technical specifications. Design SynMeth enhances system design efficiency and adaptability to IoT challenges. The case study reveals how Design SynMeth methodology models the architecture of a mobile health well-being device for detecting cognitive issues, supporting seniors’ autonomy. It highlights the dynamic interplay between problem and solution domains, leveraging OPM diagrams for problem domain and SysML diagrams for requirements and solution domain. This work advances the state of the art in IoT-based cognitive monitoring and promotes innovative, human-centered engineering for aging societies.
Mohamed E. El Zowalaty, Louis J. Taylor, Yongwoo Son
et al.
ABSTRACT The COVID-19 pandemic caused by SARS-CoV-2 has intensified efforts to identify emerging coronaviruses (CoVs) across diverse hosts. Spillover events, where CoVs transition from wildlife reservoirs to other species, can lead to infections with varying clinical outcomes, emphasizing the need for continued surveillance. Understanding the diversity and distribution of both known and novel CoVs in wildlife reservoirs is crucial for predicting and preventing future spillover events. In this study, we employed Illumina’s Pan-CoV library panel and next-generation sequencing (NGS) to identify five novel avian-associated gammacoronavirus genomes from cloacal and oropharyngeal swabs of clinically healthy feral pigeons captured in Durban, KwaZulu-Natal Province, South Africa, in 2018. The genome sequences, ranging from 27,582 to 27,611 nucleotides, clustered with gammacoronaviruses in phylogenetic analyses but formed a distinct clade. Comparative analysis of the five conserved domains in the ORF1ab coding sequence with other gammacoronaviruses revealed Pairwise Patristic Distances (PPD) that exceeded both species and subgenus demarcation cutoffs. These findings highlight the critical need for ongoing surveillance to enhance understanding of CoV diversity, host range, and potential for cross-species transmission, aligning with One Health principles. Based on these results, we propose Gammacoronavirus columbae as a new species for these pigeon gammacoronaviruses that clusters with the recently reported duck gammacoronaviruses to form a novel subgenus within Gammacoronavirus.IMPORTANCEAlthough coronaviruses are significant pathogens affecting a wide range of hosts, surveillance efforts have predominantly focused on wild mammals, leaving the diversity of avian coronaviruses largely underexplored. Here, we report the detection of novel gammacoronaviruses from feral pigeons in South Africa and propose revisions to the current taxonomic classification of Gammacoronavirus based on genetic distance analyses. These findings highlight the potential role of wild birds in the dissemination of novel coronaviruses, analogous to their established involvement in the transmission of avian influenza viruses. Our study also highlights the utility of next-generation sequencing (NGS) technologies in uncovering the hidden diversity of viruses in wildlife populations. Finally, this study reinforces the need for ongoing surveillance, continued vigilance, and further research into avian coronaviruses. The ongoing highly pathogenic avian influenza (HPAI) outbreaks in the USA have demonstrated the devastating impact of emerging avian viruses on wildlife, agriculture, and public health. Given the unpredictability of coronavirus evolution, failing to monitor their diversity and potential for cross-species transmission risks leaving us unprepared for future outbreaks. This study reinforces the urgent need for proactive, large-scale genomic surveillance of wildlife reservoirs to identify emerging CoVs before they become significant threats to animal and human populations.
ABSTRACT In recent years, compound dry‐hot events have significantly impacted human society, particularly affecting the source and load sides of the power system. With the increasing penetration of renewable energy, these events pose growing challenges to power supply‐demand balances. Therefore, this paper proposes the concept of ‘power drought’ for the first time to quantify the severity of supply‐demand imbalances and identify their spatial‐temporal evolution under compound dry‐hot events. The analysis begins by examining the coupling between meteorological parameters, renewable energy output and load demand under compound dry‐hot events. Specifically, the concept of power drought is defined, followed by the formulation of relevant evaluation metrics. Then, a spatial‐temporal clustering algorithm and a centroid migration model are applied to analyse the evolution characteristics of power drought events. Finally, the validity and practicality of the proposed method are demonstrated using practical data from a certain region to analyse the evolution of power drought over the past decade. Case studies reveal a south‐westward migration of power drought centroids, with 66.49% of grids showing positive correlation between the standardised compound event index and the power drought index.
With the advent of AI technologies, humans and robots are increasingly teaming up to perform collaborative tasks. To enable smooth and effective collaboration, the topic of value alignment (operationalized herein as the degree of dynamic goal alignment within a task) between the robot and the human is gaining increasing research attention. Prior literature on value alignment makes an inherent assumption that aligning the values of the robot with that of the human benefits the team. This assumption, however, has not been empirically verified. Moreover, prior literature does not account for human's trust in the robot when analyzing human-robot value alignment. Thus, a research gap needs to be bridged by answering two questions: How does alignment of values affect trust? Is it always beneficial to align the robot's values with that of the human? We present a simulation study and a human-subject study to answer these questions. Results from the simulation study show that alignment of values is important for trust when the overall risk level of the task is high. We also present an adaptive strategy for the robot that uses Inverse Reinforcement Learning (IRL) to match the values of the robot with those of the human during interaction. Our simulations suggest that such an adaptive strategy is able to maintain trust across the full spectrum of human values. We also present results from an empirical study that validate these findings from simulation. Results indicate that real-time personalized value alignment is beneficial to trust and perceived performance by the human when the robot does not have a good prior on the human's values.
Human image animation involves generating videos from a character photo, allowing user control and unlocking the potential for video and movie production. While recent approaches yield impressive results using high-quality training data, the inaccessibility of these datasets hampers fair and transparent benchmarking. Moreover, these approaches prioritize 2D human motion and overlook the significance of camera motions in videos, leading to limited control and unstable video generation. To demystify the training data, we present HumanVid, the first large-scale high-quality dataset tailored for human image animation, which combines crafted real-world and synthetic data. For the real-world data, we compile a vast collection of real-world videos from the internet. We developed and applied careful filtering rules to ensure video quality, resulting in a curated collection of 20K high-resolution (1080P) human-centric videos. Human and camera motion annotation is accomplished using a 2D pose estimator and a SLAM-based method. To expand our synthetic dataset, we collected 10K 3D avatar assets and leveraged existing assets of body shapes, skin textures and clothings. Notably, we introduce a rule-based camera trajectory generation method, enabling the synthetic pipeline to incorporate diverse and precise camera motion annotation, which can rarely be found in real-world data. To verify the effectiveness of HumanVid, we establish a baseline model named CamAnimate, short for Camera-controllable Human Animation, that considers both human and camera motions as conditions. Through extensive experimentation, we demonstrate that such simple baseline training on our HumanVid achieves state-of-the-art performance in controlling both human pose and camera motions, setting a new benchmark. Demo, data and code could be found in the project website: https://humanvid.github.io/.
Disturbance from human activities has intensified the evolution of ecosystem structure in the Jianghan Plain of China, leading to intensified conflicts between ecosystem services. It is essential to clarify the trade-off synergies between ecosystem services in the Jianghan Plain of China to better coordinate the economic and social development and ecological protection of the region. Based on historical data and scenario predictions using the GeoSOS-FLUS model, the InVEST model was applied to five key ecosystem services: Carbon storage, crop production, habitat quality, soil conservation and water yield from 2000 to 2020. Spearman correlation analysis was used to explore the trade-off synergies between different ecosystem services in space and time. The results showed that arable land and water land areas are the most important land types in the Jianghan Plain of China. From 2000 to 2020, the increase in build-up land and water land areas was accompanied by a decrease in arable land, forest land and unused land, and an increase in forest land. The natural development scenario in 2035 continues this trend except forest land reduction, while the ecological protection scenario reverses this trend. From 2000 to 2020, crop production, water yield, and soil conservation increased in the Jianghan Plain of China, while carbon storage and habitat quality declined significantly, showing a spatial distribution pattern of higher in the northwest and lower in the southeast. The comprehensive ecosystem services simulated in 2035 showed a downward trend compared with 2020, and the ecological protection scenario has the smallest decrease. There is an overall synergistic relationship between the five ecosystem services in the Jianghan Plain of China, and the strongest synergistic relationship is between soil conservation and water yield. The spatiotemporal relationship between the ecosystems in the Jianghan Plain of China is dynamic and requires sustainable management. Thus, it is necessary to rationally utilize land resources and enhance the ecological functions of the area to minimize trade-offs based on scientific land and spatial planning to maximize synergy.
ABSTRACT Fungi were some of the earliest organismal systems used to explore mutational processes and its phenotypic consequences on members of a species. Yeasts that cause significant human disease were quickly incorporated into these investigations to define the genetic and phenotypic drivers of virulence. Among Candida species, Candida albicans has emerged as a model for studying genomic processes of evolution because of its clinical relevance, relatively small genome, and ability to tolerate complex chromosomal changes. Here, we describe major recent findings that used evolution of strains from defined genetic backgrounds to delineate mutational and adaptative processes and include how nascent exploration into naturally occurring variation is contributing to these conceptual frameworks. Ultimately, efforts to discern adaptive mechanisms used by C. albicans will continue to divulge new biology and can better inform treatment regimens for the increasing prevalence of fungal disease.
Sofia Yfantidou, Marios Constantinides, Dimitris Spathis
et al.
This paper explores the intersection of Artificial Intelligence and Machine Learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive analysis of MobileHCI proceedings published between 2017 and 2022, we first aim to understand the current state of algorithmic fairness in the community. By manually analyzing 90 papers, we found that only a small portion (5%) thereof adheres to modern fairness reporting, such as analyses conditioned on demographic breakdowns. At the same time, the overwhelming majority draws its findings from highly-educated, employed, and Western populations. We situate these findings within recent efforts to capture the current state of algorithmic fairness in mobile and wearable computing, and envision that our results will serve as an open invitation to the design and development of fairer ubiquitous technologies.
ObjectiveInfantile hemangioma (IH), the most common infantile vascular neoplasm, is uniquely characterized by rapid proliferation followed by slow spontaneous involution lasting for years. In IH lesions, perivascular cells are the most dynamic cell subset during the transition from the proliferation phase to the involution phase, and we aimed to systematically study this kind of cell.Methods and resultsCD146-selective microbeads were used to isolate IH-derived mural-like cells (HemMCs). Mesenchymal markers of HemMCs were detected by flow cytometry, and the multilineage differentiation potential of HemMCs was detected by specific staining after conditioned culture. CD146-selected nonendothelial cells from IH samples showed characteristics of mesenchymal stem cells with distinct angiogenesis-promoting effects detected by transcriptome sequencing. HemMCs spontaneously differentiated into adipocytes 2 weeks after implantation into immunodeficient mice, and almost all HemMCs had differentiated into adipocytes within 4 weeks. HemMCs could not be induced to differentiate into endothelial cells in vitro. However, 2 weeks after implantation in vivo, HemMCs in combination with human umbilical vein endothelial cells (HUVECs) formed GLUT1+ IH-like blood vessels, which spontaneously involuted into adipose tissue 4 weeks after implantation.ConclusionsIn conclusion, we identified a specific cell subset that not only showed behavior consistent with the evolution of IH but also recapitulated the unique course of IH. Thus, we speculate that proangiogenic HemMCs may be a potential target for the construction of hemangioma animal models and the study of IH pathogenesis.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Assistive multi-armed bandit problems can be used to model team situations between a human and an autonomous system like a domestic service robot. To account for human biases such as the risk-aversion described in the Cumulative Prospect Theory, the setting is expanded to using observable rewards. When robots leverage knowledge about the risk-averse human model they eliminate the bias and make more rational choices. We present an algorithm that increases the utility value of such human-robot teams. A brief evaluation indicates that arbitrary reward functions can be handled.
Asanka G. Perera, Yee Wei Law, Titilayo T. Ogunwa
et al.
Advancements in deep neural networks have contributed to near perfect results for many computer vision problems such as object recognition, face recognition and pose estimation. However, human action recognition is still far from human-level performance. Owing to the articulated nature of the human body, it is challenging to detect an action from multiple viewpoints, particularly from an aerial viewpoint. This is further compounded by a scarcity of datasets that cover multiple viewpoints of actions. To fill this gap and enable research in wider application areas, we present a multi-viewpoint outdoor action recognition dataset collected from YouTube and our own drone. The dataset consists of 20 dynamic human action classes, 2324 video clips and 503086 frames. All videos are cropped and resized to 720x720 without distorting the original aspect ratio of the human subjects in videos. This dataset should be useful to many research areas including action recognition, surveillance and situational awareness. We evaluated the dataset with a two-stream CNN architecture coupled with a recently proposed temporal pooling scheme called kernelized rank pooling that produces nonlinear feature subspace representations. The overall baseline action recognition accuracy is 74.0%.