213476 research outputs found
Sort by
The Future Is Fluid: Revolutionizing DOA Estimation With Sparse Fluid Antennas
No full textThis paper investigates a design framework for sparse fluid antenna systems (FAS) enabling high-performance direction-of-arrival (DOA) estimation, particularly in challenging millimeter-wave (mmWave) environments. By ingeniously harnessing the mobility of fluid antenna (FA) elements, the proposed architectures achieve an extended range of spatial degrees of freedom (DoFs) compared to conventional fixed-position antenna (FPA) arrays. This innovation not only facilitates the seamless application of super-resolution DOA estimators but also enables robust DOA estimation, accurately localizing more sources than the number of physical antenna elements. We introduce two bespoke FA array structures and mobility strategies tailored to scenarios with aligned and misaligned received signals, respectively, demonstrating a hardware-driven approach to overcoming complexities typically addressed by intricate algorithms. A key contribution is a light-of-sight (LoS)-centric, closed-form DOA estimator, which first employs an eigenvalue-ratio test for precise LoS path number detection, followed by a polynomial root-finding procedure. This method distinctly showcases the unique advantages of FAS by simplifying the estimation process while enhancing accuracy. Numerical results compellingly verify that the proposed FA array designs and estimation techniques yield an extended DoFs range, deliver superior DOA accuracy, and maintain robustness across diverse signal conditions
Importance of individual differences in cone spectral sensitivities and color matching functions and how to correct for them: tutorial
No full textThis tutorial covers human cone spectral sensitivities and their linear transformations, the color matching functions. We focus on the mean or standard functions and the individual differences that occur between observers and how we can correct for those differences. The differences arise mainly because of genetically determined spectral shifts of the L- and M-cone photopigment curves, variability in the optical densities of the lens and macular pigments, and variability in the optical densities of the three photopigments. These can lead to people seeing colors on displays and on printed or dyed material differently even though, according to color standards, they should all appear the same. Such discrepancies have become more apparent and their correction more urgent with the emergence of narrow-band light sources and primaries that expand the color gamut. The discrepancies can be reduced by using better color standards, but to eliminate them completely requires corrections be made for individual differences
Mastitis and Mammary Abscess Management Audit (MAMMA): A Survey of Patients' Perspective on the Management of Mammary Abscesses in the UK
No full textBackground: The recent Mastitis and Mammary Abscess Management Audit demonstrated widespread variation in the management of breast abscesses across the United Kingdom (UK), with up to one-fifth undergoing surgical drainage rather than image-guided needle aspiration. The impact of these practices on patient’s perspective and quality of life is unclear. This study aimed to assess patients’ experiences following breast abscess treatment, focusing on treatment modality, cosmesis, breastfeeding and quality of life. /
Methods: A cross-sectional online survey was conducted between February and August 2024, aimed at UK-wide adult women with a history of breast abscess. Descriptive and thematic analyses were performed using SPSS and NVivo software, with multiple imputation for missing data. /
Results: Of 172 participants, half underwent needle aspiration (50.58%), while 23.84% received surgical incision and drainage. Among those undergoing surgery, 68.29% reported prolonged wound healing, 85.37% experienced permanent scarring and a significant negative impact on their breast appearance (p = 0.029). Breastfeeding was disrupted in 58.12%, and 40.17% were unable to resume breastfeeding following treatment. Amongst participants who underwent surgery, 36.5% reported negative impacts on sexual well-being, 31.7% on mental health, and 24.4% on self-confidence. Thematic analysis revealed two major themes: repercussions of the treatment and issues with provision of care, highlighting delays in diagnosis, inadequate breastfeeding support, and negative cosmetic outcomes. /
Discussion: This study is the first to investigate patients’ experiences of breast abscess management, highlighting significant variability in practice and the association of worse cosmetic and breastfeeding outcomes with surgical treatment. Standardisation of care and improved patient counselling may improve patient experience and outcomes
Dynamic obstacle avoidance of unmanned surface vehicles in maritime environments using Gaussian processes based motion planning
No full textDuring recent years, unmanned surface vehicles are extensively utilised in maritime missions. To successfully accomplish these missions, motion planning algorithms that can generate smooth and collision-free trajectories to avoid both static and dynamic obstacles in an efficient manner are essential. In this article, we propose a novel motion planning algorithm, which successfully extends the application scope of the Gaussian process motion planner 2 into complex and dynamic environments with both static and dynamic obstacles. First, we introduce an approach to generate safe areas for dynamic obstacles using modified multivariate Gaussian distributions. Second, we introduce an approach to integrate real-time status information of dynamic obstacles into the modified multivariate Gaussian distributions. The multivariate Gaussian distributions with real-time statuses of dynamic obstacles can be innovatively added into the optimisation process of factor graph to generate an optimised trajectory. We also develop a variant of the proposed algorithm that integrates the international regulations for preventing collisions at sea, enhancing its operational effectiveness in maritime environments. The proposed algorithms have been validated in a series of benchmark simulations and two dynamic obstacle avoidance missions in a high-fidelity maritime environment in the Robotic operating system to demonstrate the functionality and practicability
Using facial expression to predict indoor thermal-acoustic comfort
No full textNon-intrusive facial expression measurement has been proven effective in predicting human perceptions in single-domain environments. However, the mechanisms by which this technology influences and predicts comfort under the interactive effects of thermal-acoustic environments have not yet been thoroughly explored. Investigating this area is not only crucial for enhancing prediction accuracy but also for uncovering the patterns of facial expression changes in complex thermal-acoustic environments and their underlying mechanisms affecting comfort. To address this gap, this study conducted experiments in an environmental chamber, involving 6 common indoor temperature levels (18, 20, 22, 24, 26, and 28 °C) and 9 sound pressure levels (35, 40, 45, 50, 55, 60, 65, 70, and 75 dBA). The results showed that facial action units (AUs) can effectively reflect changes in comfort under thermal-acoustic environments, with AU04 and AU14 being core predictive indicators across models. Using a random forest classifier, the proposed model performs well in predicting seven levels of comfort based on facial and thermal–acoustic features. Additionally, the relationship between facial expressions and comfort varied across different thermal-acoustic environments: in comfortable thermal-acoustic environments, more active facial expressions correlated with higher comfort, whereas the opposite was true in harsh environments. This study deepens understanding of how environmental interactions influence facial expressions and comfort, offering insights for multi-domain prediction models, while acknowledging that the use of a controlled laboratory setting and limited demographic scope may constrain generalizability
Tracing carbon flow to unravel carbon lock-in in China through a supernetwork-based perspective for targeted decarbonization
No full textThe pathway to carbon neutrality requires not only reducing emissions but also addressing the structural complexity of how emissions are generated, transmitted, and embedded across regions and sectors. Conventional mitigation strategies target high-emission locations, yet they overlook who emits, who enables, and who intermediates in the carbon system. This study develops a carbon flow supernetwork by integrating multi-regional input-output analysis with supernetwork theory, enabling tracing where emissions occur, how they move, and who sustains them from 2007 to 2017. Results reveal a three-layered structure of carbon lock-in in China. Upstream emitters like Inner Mongolia, Shanxi, and Hebei concentrate emissions through coal-based electricity and heavy industries. Downstream distributors, notably coastal regions such as Guangdong and Jiangsu, account for over 60 % of carbon inflows via embedded trade and final demand. Structural intermediaries, including Shandong and Henan via logistics and information services, exhibit high network centrality and govern carbon circulation despite moderate emission levels. Furthermore, the Jing-Jin-Ji and Yangtze River Delta function as systemic carbon anchors, where dense industrial networks and embedded supply chains lock China's economy into high-emission trajectories. As the system matured from 2007 to 2015, connectivity and internal carbon cycling increased, but signs of topological reconfiguration emerged post-2015, coinciding with China's green transition efforts. Carbon governance should shift from targeting emission volume to incorporating network-sensitive, system-level interventions. Prioritizing central intermediaries and redesigning flow pathways offers a more effective and equitable route toward carbon neutrality in structurally complex economies like China
A brief history of Asian summer monsoon evolution in the Cenozoic era
No full textThe evolution of the Asian Summer Monsoon (ASM) over geological timescale remains uncertain1,2,3 despite its fundamental role in shaping regional climate4,5, ecosystems6,7, and civilizations8. Using a series of time-slice simulations with a paleo-climate model, we assess how India–Eurasia collision tectonics9,10, Tibetan Plateau (TP) uplift11,12, and atmospheric CO2 variability13 influenced ASM evolution through the Cenozoic. Our simulation-based results suggest that ASM intensification was contingent on the TP exceeding an areal mean elevation of ~ 3.5 km in the late Eocene–Oligocene (27–38 million years ago, Ma), which strengthened the upper-tropospheric temperature gradient, promoted the seasonal northward shift of the Intertropical Convergence Zone (ITCZ), and restructured atmospheric circulation. Initially confined to East Asia14, monsoonal rainfall expanded across South Asia by the Oligocene, coinciding with enhanced circulation and reversing the meridional relative sea surface temperature gradient in the Neotethys. While TP uplift played the primary role in early ASM evolution, declining atmospheric CO2 levels became increasingly influential after the late Miocene. These findings, supported by sedimentary records of weathering and erosion15,16, underscore the dominant role of TP in climate–tectonic interactions and ASM evolution over geological timescales
Beyond the TESSERACT: Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers
No full textThe reliability of machine learning critically depends on dataset quality. While machine learning applied to
computer vision and natural language processing benefits from
high-quality benchmark datasets, cyber security often falls behind, as quality ties to the ability of accessing hard-to-obtain
realistic data that may evolve over time. Android is, however,
positioned uniquely in this ecosystem due to AndroZoo and other
sources, which provide large-scale, continuously updated, and
timestamped repositories of benign and malicious apps.
Since their release, such data sources provided access to
populations of Android apps that researchers can sample from
to evaluate learning-based methods in realistic settings, i.e.,
over temporal frames to account for apps evolution (natural
distribution shift) and test datasets that reflect in-the-wild class
ratios. Surprisingly, we observe that despite this abundance
of data, performance discrepancies of learning-based Android
malware classifiers still persist even after satisfying such realistic
requirements, which challenges our ability to understand what
the state-of-the-art in this field is. In this work, we identify five
novel factors that influence such discrepancies: we show how such
factors have been largely overlooked and the impact they have on
providing sound evaluations. Our findings and recommendations
help define a methodology for creating trustworthy datasets
towards sound evaluations of Android malware classifiers
Moving Beyond Clicks: Rethinking Consent and User Control in the Age of AI
No full textCurrent privacy consent mechanisms often let users down: cookie banners violate informed consent requirements, privacy policies are still difficult to understand, and transparency alone does not guarantee the protection of personal data. In other words, privacy controls are often not user-friendly, let alone felt as mechanisms for empowerment. As AI processes more and more personal data and plays an increasingly important role in society, these challenges are becoming more acute. Emerging systems based on large-scale data and machine learning complicate the boundaries of user control and consent; invisible inferences, decisions delegated to AI agents, and opaque personalisation create new challenges. While prior HCI research has examined the usability of consent and explored ways to improve it, the community still lacks a systematic exploration of consent in the age of AI. Therefore, this workshop brings together experts from AI, HCI, privacy, social sciences, policy, and law fields, to imagine how consent and control must evolve beyond ``scroll-and-click'' towards richer, contextual, and adaptive mechanisms reflecting human capabilities and values. It re-imagines consent and user control in the AI era, distinguishing between explicit decisions and the broader ways in which people can influence how their data is used. Using the Futures Design Toolkit, participants will develop future personas and create design provocations through prototyping. We are seeking position papers that address: novel consent mechanisms, the privacy impact of AI, privacy decision delegation models, and new interaction modalities for user consent and control. We will produce design artefacts and research directions for privacy control tools that are more effective, usable, and accessible than existing mechanisms