143174 research outputs found
Sort by
Explainable prediction of the mechanical properties of composites with CNNs
No full textComposites are amongst the most important materials manufactured today, as evidenced by their use in countless applications. In order to establish the suitability of composites in specific applications, finite element (FE) modelling, a numerical method based on partial differential equations, is the industry standard for assessing their mechanical properties. However, FE modelling is exceptionally costly from a computational viewpoint, a limitation which has led to efforts towards applying AI models to this task. However, in these approaches: the chosen model architectures were rudimentary, feed-forward neural networks giving limited accuracy; the studies focus on predicting elastic mechanical properties, without considering material strength limits; and the models lacked transparency, hindering trustworthiness by users. In this paper, we show that convolutional neural networks (CNNs) equipped with methods from explainable AI (XAI) can be successfully deployed to solve this problem. Our approach uses customised CNNs trained on a dataset we generate using transverse tension tests in FE modelling to predict composites' mechanical properties, i.e., Young's modulus and yield strength. We show empirically that our approach achieves high accuracy, outperforming a baseline, ResNet-34, in estimating the mechanical properties. We then use SHAP and Integrated Gradients, two post-hoc XAI methods, to explain the predictions, showing that the CNNs use the critical geometrical features that influence the composites' behaviour, thus allowing engineers to verify that the models are trustworthy by representing the science of composites
Perceived value of video games, but not hours played, predicts mental well-being in casual adult Nintendo players
No full textStudies on video games and well-being often rely on self-report measures or data from a single game. Here, we study how 703 casually engaged US adults’ time spent playing for over 140 000 h across 150 Nintendo Switch games relates to their life satisfaction, affect, depressive symptoms and general mental well-being. We replicate previous findings that playtime over the past two weeks does not predict well-being, and extend these findings to a wider range of timescales (1 h to 1 year). Equivalence tests were inconclusive, and thus we do not find evidence of absence, but results suggest that practically meaningful effects lasting more than 2 h after gameplay are unlikely. Our non-causal findings suggest substantial confounding would be needed to shift a meaningful true effect to the observed null. Although playtime was not related to well-being, players’ assessments of the value of game time—so-called gaming life fit—were. Results emphasize the importance of defining the gaming population of interest, collecting data from more than one game, and focusing on how players integrate gaming into their lives rather than the amount of time spent
Artifacts in photoacoustic imaging: origins and mitigations
No full textPhotoacoustic imaging (PAI) is rapidly moving from the laboratory to the clinic, increasing the need to understand confounders which might adversely affect patient care. Over the past five years, landmark studies have shown the clinical utility of PAI, leading to regulatory approval of several devices. In this article, we describe the various causes of artifacts in PAI, providing schematic overviews and practical examples, simulated as well as experimental. This work serves two purposes: (1) educating clinical users to identify artifacts, understand their causes, and assess their impact, and (2) providing a reference of the limitations of current systems for those working to improve them. We explain how two aspects of PAI systems lead to artifacts: their inability to measure complete data sets, and embedded assumptions during reconstruction. We describe the physics underlying PAI, and propose a classification of the artifacts. The paper concludes by discussing possible advanced mitigation strategies
Integration of poliovirus and enteropathogen sewage surveillance in Dhaka Bangladesh: a longitudinal surveillance study June 2019 – June 2020
No full textEnvironmental surveillance (ES) for poliovirus is a surveillance method used by the Global Polio Eradication Initiative (GPEI). ES will continue following certification of poliovirus eradication, potentially through its integration into other infectious disease surveillance programs.
We evaluated TaqMan array cards (TAC) to detect poliovirus in sewage, whilst simultaneously testing for 11 other enteric pathogens and 34 markers of antimicrobial resistance (AMR) across 12 sites in Dhaka, Bangladesh. Sites were selected following mapping of the informal sewage network and a demographic survey of the population. Samples were collected before and after a bivalent oral polio vaccine (bOPV) campaign. 372 samples were collected over 379 days. A water-quality probe measured physicochemical properties of the sewage. A Multivariable mixed-effects Gamma Hurdle regression model was used to measure the association between enterovirus detection and concentration with site properties.
The highest concentration of Sabin-1 and -3 poliovirus was detected two weeks after the bOPV campaign (mean (SD) Sabin- 1 and -3 viral copies per L of sewage: 0·83 (2·13) and 0·84 (1·96)) , (vs baseline 0·05 (0·21) and 0·11 (0·50) respectively) [p=0·004: SL1, p=0·005: SL3] . Detection of enteroviruses was more likely with increasing levels of Total Dissolved Solids (mg/L) (aOR per absolute increase of 100 units 1·39 95%CI: 1·17 – 1·61). The median ES viral load of rotavirus was 0·567 (IQR 0·202-0·839), and this pathogen had the strongest correlation with respective concurrent clinical case incidence (cor = 0·828, p = 0·0017). Thirty-one AMR genes of clinical significance were detected.
When GPEI dissolves, poliovirus surveillance needs to be integrated into other surveillance programs. TAC may provide a method to screen suitable pathogens to survey in sewage alongside poliovirus. Further validation is now required across different geographies and poliovirus prevalence, and interpretation of data requires an understanding of site sensitivity.
Funding: Gates Foundation (INV-007652
Bounding elastic photon-photon scattering at s≈1 MeV using a laser-plasma platform
No full textWe report on a direct search for elastic photon-photon scattering using x-ray and photons from a laser-plasma
based experiment. A photon beam produced by a laser wakeeld accelerator provided a broadband spectrum
extending to above = 200 MeV. These were collided with a dense x-ray eld produced by the emission from
a laser heated germanium foil at ≈ 1.4 keV, corresponding to an invariant mass of √ = 1.22 ± 0.22 MeV. In
these asymmetric collisions elastic scattering removes one x-ray and one high-energy photon and outputs two
lower energy photons. No changes in the photon spectrum were observed as a result of the collisions allowing
us to place a 95% upper bound on the cross section of 1.5 × 1015 μb. Although far from the QED prediction, this
represents the lowest upper limit obtained so far for √ ≲ 1 MeV
Bias correction of quadratic spectral estimators
No full textThe three cardinal, statistically consistent, families of non-parametric estimators to the power spectral density of a time series are lag-window, multitaper and Welch estimators. However, when estimating power spectral densities from a finite sample each can be subject to non-ignorable bias. Astfalck et al. (2024) developed a method that offers significant bias reduction for finite samples for Welch’s estimator, which this article extends to the larger family of quadratic estimators, thus offering similar theory for bias correction of lag-window and multitaper estimators as well as combinations thereof. Importantly, this theory may be used in conjunction with any and all tapers and lag-sequences designed for bias reduction, and so should be seen as an extension to valuable work in these fields, rather than a supplanting methodology. The order of computation is larger than O(n log n) typical in spectral analyses, but not insurmountable in practice. Simulation studies support the theory with comparisons across variations of quadratic estimators
Density estimation with LLMs: a geometric investigation of in-context learning trajectories
No full textLarge language models (LLMs) demonstrate remarkable emergent abilities to perform in-context learning across various tasks, including time series forecasting. This work investigates LLMs’ ability to estimate probability density functions (PDFs) from data observed in-context; such density estimation (DE) is a fundamental task underlying many probabilistic modeling problems. We leverage the
Intensive Principal Component Analysis (InPCA) to visualize and analyze the in-context learning dynamics of LLaMA-2 models. Our main finding is that these LLMs all follow similar learning trajectories in a low-dimensional InPCA space,
which are distinct from those of traditional density estimation methods like histograms and Gaussian kernel density estimation (KDE). We interpret the LLaMA
in-context DE process as a KDE with an adaptive kernel width and shape. This custom kernel model captures a significant portion of LLaMA’s behavior despite having only two parameters. We further speculate on why LLaMA’s kernel width and shape differs from classical algorithms, providing insights into the mechanism of in-context probabilistic reasoning in LLMs
Foliation adjunction
No full textWe present an adjunction formula for foliations on varieties and we consider applications of the adjunction formula to the cone theorem for rank one foliations and the study of foliation singularities
Li⁺ concentration and morphological changes at the anode and cathode interphases inside solid-state lithium metal batteries
No full textIrregular Li heterostructure growth at the interphase between the solid electrolyte and anode
reduces solid-state Li metal battery (SSLMB) performance, but the fundamental cause is still
elusive. Measuring and imaging Li+ ion diffusion in operando inside an SSLMB using a
commercially standard cell configuration are extremely challenging because the ultra-light Li
element exhibits a minute signal-to-noise ratio using most x-ray-related characterization methods,
and the weak x-ray signals of Li+ are buried by strong signals of other heavy transition metal
elements in the cathode and battery enclosure. Here, we pioneer novel operando correlative
imaging of coupling x-ray Compton scattering with computed tomography (XCS-CT), which is
able to quantify the interplay between spatially resolved Li+ ion diffusion kinetics and Li0 metal
structure growth at the interphases of both the anode and cathode sides inside a full-cell SSLMB
using a solid polymer electrolyte (SPE) and commercially standard cell configuration during
(dis)charging. We show a 61% increase in the efficiency of extracting Li+ ions from the cathode
LiNi0.6Mn0.2Co0.2O2 to the anode during charging at 0.1 C compared with at 1 C due to restricted
Li+ ion diffusion at the higher rate inside SSLMB. However, this led to the formation of a more
irregular interfacial morphology, consisting not only of Li0 dendrites, but also sub-surface pore
formation at the anode/SPE interphase. We find that surprisingly, the irregular Li0
structure
initiation and growth are accelerated during the first Li stripping step, not the Li plating step, and
the root cause is the onset imbalance of Li+ ion diffusion and redox reactions between the anode
and cathode. These insights highlight the benefits of asymmetric charging and discharging rates as
a promising solution to improving SSLMB performance with SPEs. The operando correlative
XCS-CT imaging technique has the potential to study the relationship between active ion
concentrations and buried morphological changes for a variety of battery chemistries
ROV teleoperation in the presence of cross-currents using soft haptics
No full textThe remote operation of underwater vehicles at depth is complicated by the presence of invisible and unpredictable environmental disturbances such as cross-currents. Communicating the presence of these disturbances to an operator on the surface is made more difficult by the nature of the disturbance and the lack of visible features to highlight in the visual display presented to the operator. Here we explore the use of a novel interactive soft haptic touchpad that utilizes vibration and particle jamming to provide information about the presence and direction of cross-currents to the operator of an ROV (remotely operated vehicle). An in water experiment using a thruster-based ROV and artificially generated cross-current was performed with non-expert ROV operators to evaluate the effectiveness of multimodal haptic feedback to communicate complex environmental information during high-risk operations. Advanced haptic displays can signal both the presence of external factors as well as their direction, information that can enhance operational performance as well as reduce operator cognitive load. Using haptic feedback resulted in a statistically significant reduction in cognitive load of 24.3% and increase in positioning accuracy of 28.3% for novice operators. Deviation from an ideal path was also reduced by 29.5% for experienced operators when using haptic feedback compared to without. While this experiment took place in controlled conditions with a fixed direction cross-current and haptic interface, this approach could be extended to communicate real-time environmental information in real-world unstructured environments