Loss of decisional capacity, coupled with the increasing absence of reliable human proxies, raises urgent questions about how individuals' values can be represented in Advance Care Planning (ACP). To probe this fraught design space of high-risk, high
In democracies, major policy decisions typically require some form of majority or consensus, so elites must secure mass support to govern. Historically, elites could shape support only through limited instruments like schooling and mass media; advanc
Artificial intelligence has advanced rapidly across perception, language, reasoning, and multimodal domains. Yet despite these achievements, modern AI systems remain fundamentally limited in their ability to self-monitor, self-correct, and regulate t
As computational demands continue to rise, assessing the environmental footprint of artificial intelligence (AI) requires moving beyond energy and water consumption to include the material demands of specialized hardware. This study quantifies the ma
Tracking body fat percentage is essential for effective weight management, yet gold-standard methods such as DEXA scans remain expensive and inaccessible for most people. This study evaluates the feasibility of artificial intelligence (AI) models as
Since human randomness production has been studied and widely used to assess executive functions (especially inhibition), many measures have been suggested to assess the degree to which a sequence is random-like. However, each of them focuses on one
The redshift distribution of gamma-ray bursts (GRBs) is strongly biased by selection effects. We investigate, via Monte Carlo simulations, one possible selection effect that may be modifying the Swift GRB redshift distribution. We show how telescope
Magnetic Resonance Fingerprinting (MRF) leverages transient-state signal dynamics generated by the tunable acquisition parameters, making the design of an optimal, robust sequence a complex, high-dimensional sequential decision problem, such as optim
Second-order optimization methods, which leverage curvature information, offer faster and more stable convergence than first-order methods such as stochastic gradient descent (SGD) and Adam. However, their practical adoption is hindered by the prohib
The rapid integration of generative artificial intelligence into education has driven digital transformation in e-teaching, yet user perceptions of AI educational apps remain underexplored. This study performs a sentiment-driven evaluation of user re
We propose a Hybrid Spatio-Temporal Quantum Graph Convolutional Network (H-STQGCN) algorithm by combining the strengths of quantum computing and classical deep learning to predict the taxi destination within urban road networks. Our algorithm consist
In regions of the Middle East and North Africa (MENA), there is a high demand for wastewater treatment plants (WWTPs), crucial for sustainable water management. Precise identification of WWTPs from satellite images enables environmental monitoring. T
Vehicle Dispatching Systems (VDSs) are critical to the operational efficiency of Automated Container Terminals (ACTs). However, their widespread commercialization is hindered due to their low transferability across diverse terminals. This transferabi
Machine learning (ML) is increasingly used in structural engineering and design, yet its broader adoption is hampered by the lack of openly accessible datasets of structural systems. We introduce BridgeNet, a publicly available graph-based dataset of
Large Language Models (LLMs) deliver exceptional performance across natural language tasks but demand substantial computational resources, limiting their deployment on resource-constrained edge devices. Existing compression techniques, such as quanti
This chapter demonstrates how computational social science (CSS) tools are extending and expanding research on aging. The depth and context from traditionally qualitative methods such as participant observation, in-depth interviews, and historical do
Fossil gas is sometimes presented as an enabler of variable solar and wind generation beyond 2050, despite being a primary source of greenhouse gas emissions from methane leakage and combustion. We find that balancing solar and wind generation with p
Multimodal cardiovascular magnetic resonance (CMR) imaging provides comprehensive and non-invasive insights into cardiovascular disease (CVD) diagnosis and underlying mechanisms. Despite decades of advancements, its widespread clinical adoption remai
Low-dose PET imaging is crucial for reducing patient radiation exposure but faces challenges like noise interference, reduced contrast, and difficulty in preserving physiological details. Existing methods often neglect both projection-domain physics
Since its inception, quantum theory has been the subject of fierce interpretive controversy, which persists to this day. Disputed topics include the basic ontology and dynamics of the theory, the role (if any) of measurement, the meaning of probabili
This study proposes a group deliberation multi-agent dialogue model to optimize the limitations of single-language models for complex reasoning tasks. The model constructs a threelevel role division architecture of 'generation -verificationintegratio
Benign laryngeal voice disorders affect nearly one in five individuals and often manifest as dysphonia, while also serving as non-invasive indicators of broader physiological dysfunction. We introduce a clinically inspired hierarchical machine learni
Pharmaceutical three-dimensional (3D) printing is an advanced fabrication technology with the potential to enable truly personalised dosage forms. Recent studies have integrated artificial intelligence (AI) to accelerate formulation and process devel
Semi-supervised learning (SSL) has demonstrated high performance in image classification tasks by effectively utilizing both labeled and unlabeled data. However, existing SSL methods often suffer from poor calibration, with models yielding overconfid
In computational pathology, weak supervision has become the standard for deep learning due to the gigapixel scale of WSIs and the scarcity of pixel-level annotations, with Multiple Instance Learning (MIL) established as the principal framework for sl
The velocity of the inner ejecta of stripped-envelope core-collapse supernovae (CC-SNe) is studied by means of an analysis of their nebular spectra. Stripped-envelope CC-SNe are the result of the explosion of bare cores of massive stars ( geq 8 M _{
I analyse peculiar properties of light curve and continua of enigmatic Ibn supernovae, including SN 2006jc, and argue in favour of the early strong circumstellar interaction. This interaction explains the high luminosity and fast flux rise of SN 1999
Analytical expressions for spectrum, eigenfunctions and dipole matrix elements of a square double quantum well (DQW) are presented for a general case when the potential in different regions of the DQW has different heights and effective masses are di
Existing image forgery detection (IFD) methods either exploit low-level, semantics-agnostic artifacts or rely on multimodal large language models (MLLMs) with high-level semantic knowledge. Although naturally complementary, these two information stre
Up to 90% of the chemical reactions during star formation occurs on ice surfaces, probably including the formation of complex organics. Only the most abundant ice species are however observed directly by infrared spectroscopy. This study aims to deve
Using archival Hubble Space Telescope (HST) imaging data, we report the multiband photometric properties of 13 ultraluminous X-ray sources (ULXs) that have a unique compact optical counterpart. Both magnitude and color variation are detected at time
One of the experimental tests of Lorentz invariance violation is to measure the helicity dependence of the propagation velocity of photons originating in distant cosmological obejcts. Using a recent determination of the distance of the Gamma-Ray Burs
This study introduces an innovative multilingual bias evaluation framework for assessing bias in Large Language Models, combining explicit bias assessment through the BBQ benchmark with implicit bias measurement using a prompt-based Implicit Associat
Conventional heterogeneous computing systems built on PCIe interconnects suffer from inefficient fine-grained host-device interactions and complex programming models. In recent years, many proprietary and open cache-coherent interconnect standards ha
The increasing availability of data and advancements in computational intelligence have accelerated the adoption of data-driven methods (DDMs) in product development. However, their integration into product development remains fragmented. This fragme
Measuring gases for air quality monitoring is a challenging task that claims a lot of time of observation and large numbers of sensors. The aim of this project is to develop a partially autonomous unmanned aerial vehicle (UAV) equipped with sensors,
Not all nations on earth have previously been surveyed accurately enough to know for certain which peak is the national highpoint, the highest peak in the country. Knowledge of these peaks is important for understanding the physical geography of thes
Pt deposited on a Ge(001) surface spontaneously forms nanowire arrays. These nanowires are thermodynamically stable and can be hundreds of atoms long. The nanowires only occur on a reconstructed Pt-Ge-surface where they fill the troughs between the d
Mixture-of-Experts (MoE) has become a practical architecture for scaling LLM capacity while keeping per-token compute modest, but deploying MoE models on a single, memory-limited GPU remains difficult because expert weights dominate the HBM footprint
We study dynamics of a two-component Bose-Einstein condensate where the two components are coupled via an optical lattice. In particular, we focus on the dynamics as one drives the system through a critical point of a first order phase transition cha
We calculate the location of the magnetically-inactive dead zone in the minimum-mass protosolar disk, under ionization scenarios including stellar X-rays, long- or short-lived radionuclide decay, and energetic protons arriving from the general inters
Environmental, Social, and Governance (ESG) metric knowledge is inherently structured, connecting industries, reporting frameworks, metric categories, metrics, and calculation models through compositional dependencies, yet in practice this structure
We point out that EXP3 can be implemented in constant time per round, propose more practical algorithms, and analyze the trade-offs between the regret bounds and time complexities of these algorithms.
Recognizing whether outputs from large language models (LLMs) contain faithfulness hallucination is crucial for real-world applications, e.g., retrieval-augmented generation and summarization. In this paper, we introduce Faith-Lens, a cost-efficient
The deep learning (DL) has been penetrating daily life in many domains, how to keep the DL model inference secure and sample privacy in an encrypted environment has become an urgent and increasingly important issue for various security-critical appli
We study the design of robust subexponential algorithms for classical connectivity problems on intersection graphs of similarly sized fat objects in R d . In this setting, each vertex corresponds to a geometric object, and two vertices are adjacent i
Widespread Pb (lead) contamination of urban soil significantly impacts food safety and public health and hinders city greening efforts. However, most existing technologies for measuring Pb are labor-intensive and costly. In this study, we propose Soi
We consider issues associated with the Lagrangian characterisation of flow structures arising in aperiodically time-dependent vector fields that are only known on a finite time interval. A major motivation for the consideration of this problem arises
The many similarities between the prompt emission pulses in gamma ray bursts (GRBs) and X-ray flares during the fast decay and afterglow phases of GRBs suggest a common origin. In the cannonball (CB) model of GRBs, this common origin is mass accretio
Galles and Pearl claimed that 'for recursive models, the causal model framework does not add any restrictions to counterfactuals, beyond those imposed by Lewis's [possible-worlds] framework.' This claim is examined carefully, with the goal of clarify
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