Search

With the rapid development of artificial intelligence (AI), AI-assisted medical imaging analysis demonstrates remarkable performance in early lung cancer screening. However, the costly annotation process and privacy concerns limit the construction of large-scale medical datasets, hampering the further application of AI in healthcare. To address the data scarcity in lung cancer screening, we propose Lung-DDPM, a thoracic CT image synthesis approach that effectively generates high-fidelity 3D synthetic CT images, which prove helpful in downstream lung nodule segmentation tasks. Our method is based on semantic layout-guided denoising diffusion probabilistic models (DDPM), enabling anatomically reasonable, seamless, and consistent sample generation even from incomplete semantic layouts. Our results suggest that the proposed method outperforms other state-of-the-art (SOTA) generative models in image quality evaluation and downstream lung nodule segmentation tasks. Specifically, Lung-DDPM achieved superior performance on our large validation cohort, with a Fr\'echet inception distance (FID) of 0.0047, maximum mean discrepancy (MMD) of 0.0070, and mean squared error (MSE) of 0.0024. These results were 7.4$\times$, 3.1$\times$, and 29.5$\times$ better than the second-best competitors, respectively. Furthermore, the lung nodule segmentation model, trained on a dataset combining real and Lung-DDPM-generated synthetic samples, attained a dice coefficient (Dice) of 0.3914 and sensitivity of 0.4393. This represents 8.8\% and 18.6\% improvements in DICE and sensitivity compared to the model trained solely on real samples. The experimental results highlight Lung-DDPM's potential for a broader range of medical imaging applications, such as general tumor segmentation, cancer survival estimation, and risk prediction., Comment: The code and pretrained models are available at https://github.com/Manem-Lab/Lung-DDPM

We present a quantum computing formulation to address a challenging problem in the development of probabilistic learning on manifolds (PLoM). It involves solving the spectral problem of the high-dimensional Fokker-Planck (FKP) operator, which remains beyond the reach of classical computing. Our ultimate goal is to develop an efficient approach for practical computations on quantum computers. For now, we focus on an adapted formulation tailored to quantum computing. The methodological aspects covered in this work include the construction of the FKP equation, where the invariant probability measure is derived from a training dataset, and the formulation of the eigenvalue problem for the FKP operator. The eigen equation is transformed into a Schr\"odinger equation with a potential V, a non-algebraic function that is neither simple nor a polynomial representation. To address this, we propose a methodology for constructing a multivariate polynomial approximation of V, leveraging polynomial chaos expansion within the Gaussian Sobolev space. This approach preserves the algebraic properties of the potential and adapts it for quantum algorithms. The quantum computing formulation employs a finite basis representation, incorporating second quantization with creation and annihilation operators. Explicit formulas for the Laplacian and potential are derived and mapped onto qubits using Pauli matrix expressions. Additionally, we outline the design of quantum circuits and the implementation of measurements to construct and observe specific quantum states. Information is extracted through quantum measurements, with eigenstates constructed and overlap measurements evaluated using universal quantum gates., Comment: 27 pages, 6 Figures

A semiring generalises the notion of a ring, replacing the additive abelian group structure with that of a commutative monoid. In this paper, we study a notion positioned between a ring and a semiring -- a semiring whose additive monoid is a commutative inverse semigroup. These inverse semirings include some important classes of semirings, as well as some new motivating examples. We devote particular attention to the inverse semiring of bounded polynomials and argue for their computational significance. We then prove a number of fundamental results about inverse semirings, their modules and their ideals. Parts of the theory show strong similarities with rings, while other parts are akin to the theory of idempotent semirings or distributive lattices. We note in particular that downward-closed submodules are precisely kernels. We end by exploring a connection to the theory of E-unitary inverse semigroups., Comment: 16 pages

Cayley's theorem tells us that all groups $\mathbf{G}$ occur as subgroups of the group of automorphisms over some set $X$. In this paper we consider a `sort-of' converse to this question: given a set $X$ and some transformation group $\mathbf{S}$ over $X$, what are the possible group structures on $X$ that result in groups represented by $\mathbf{S}$? We solve this problem in the more general setting of faithful semigroups and observe that the solutions to this problem, which we term unrepresentations, have the structure of a heap. We study this phenomenon in depth and then move onto looking at particular classes of semigroups namely monoids, groups, inverse semigroups and Clifford semigroups., Comment: 12 pages

This article concerns the mathematical justification of an averaged system of partial differential equations governing the evolution of a two-phase mixture of heat non-conductive, compressible fluids, in space dimension 1 with periodic boundary conditions. The first step is to write a system of partial differential equations governing the physics at a mesoscopical level where the two phases are separated by sharp interfaces. In a second step we study the behavior of a sequence of solutions corresponding to highly oscillating initial densities and temperatures. The limit of such a sequence is described byan averaged system of partial differential equations that has a different algebraic structure than the mesoscopic model and that reflects the macroscopical properties of the mixture. The originality of the paper consists in the fact that both the density and temperature are allowed to oscillate, so that the limiting model is a six-equation, two-pressures, two-temperatures model. The key point is to show the strong convergence of the stress tensor in $L^2((0, T) \times (0, 1))$. In order to obtain uniform estimates in spite of the presence of oscillating coefficients in the energy equation, we look at solutions with low regularity for the density and the temperature.For the readers convenience, we provide in a separated section the modelization of the mesoscopic system and the formal derivation of the averaged two-phase system. We also provide, at the end of the paper, numerical illustrations comparing the unknowns solving the mesoscopic system and the one satisfying the averaged two-phase system.

The enthalpy of mixing in the liquid phase is a thermodynamic property reflecting interactions between elements that is key to predict phase transformations. Widely used models exist to predict it, but they have never been systematically evaluated. To address this, we collect a large amount of enthalpy of mixing data in binary liquids from a review of about 1000 thermodynamic evaluations. This allows us to clarify the prediction accuracy of Miedema's model which is state-of-the-art. We show that more accurate predictions can be obtained from a machine learning model based on LightGBM, and we provide them in 2415 binary systems. The data we collect also allows us to evaluate another empirical model to predict the excess heat capacity that we apply to 2211 binary liquids. We then extend the data collection to ternary metallic liquids and find that, when mixing is exothermic, extrapolations from the binary systems by Muggianu's model systematically lead to slight overestimations of roughly 10% close to the equimolar composition. Therefore, our LightGBM model can provide reasonable estimates for ternary alloys and, by extension, for multicomponent alloys. Our findings extracted from rich datasets can be used to feed thermodynamic, empirical and machine learning models for material development., Comment: Manuscript: 27 pages, 5 figures ; Supplementary notes: 3 pages, 5 figures

Conservation measures are implemented to support biodiversity in areas that are degraded or under anthropogenic pressure. Over the past decade, numerous projects aimed at rehabilitating a fish nursery function in ports, through the installation of artificial structures, have emerged. While studies conducted on these solutions seem promising on a very local scale (e.g., higher densities of juvenile fish on artificial fish nurseries compared to bare port infrastructures), no evaluation has been undertaken yet to establish their contribution to the renewal of coastal fish populations or their performance compared to other conservation measures such as fishing regulation. Here, we used a coupled model of fish population dynamics and fisheries management, ISIS-fish, to describe the coastal commercial fish population, the white seabream (Diplodus sargus) in the highly artificialized Bay of Toulon. Using ISIS-Fish, we simulated rehabilitation and fisheries management scenarios. We provided the first quantitative assessment of the implementation of artificial structures in ports covering 10% and 100% of the available port area and compared, at population level and fishing fleets level, the quantitative consequences of these rehabilitation measures with fishing control measures leading to strict compliance with minimum catch sizes. The rehabilitation of the nursery function in ports demonstrated a potential to enhance the renewal of fish populations and catches. When the size of projects is small the outcomes they provide remain relatively modest in contrast to the impact of regulatory fishing measures. However, we have demonstrated that combining fishing reduction measures and rehabilitation projects has a synergistic effect on fish populations, resulting in increased populations and catches. This study is the first quantitative assessment of fish nursery rehabilitation projects in port areas, by evaluating their effectiveness in renewing coastal fish populations and fisheries and comparing their outcomes with fishing control measures. Small-scale port-area nursery rehabilitation projects can support fish populations, but are less effective than controlling fisheries.

We present two alternative methods for optimizing minimum energy conical intersection (MECI) molecular geometries without knowledge of the derivative coupling (DC). These methods are based on the utilization of Lagrange multipliers: i) one method uses an approximate calculation of the DC, while the other ii) do not require the DC. Both methods use the fact that information of the DC is contained in the Hessian of the squared energy difference. Tests done on a set of small molecular systems, in comparison with other methods, show the ability of the proposed methods to optimize MECIs. Finally, we apply the methods to the furimamide molecule, to optimize and characterize its S$_1$ /S$_2$ MECI, and to optimizing the S$_0$ /S$_1$ MECI of the silver trimer.

A critical challenge for tumour segmentation models is the ability to adapt to diverse clinical settings, particularly when applied to poor-quality neuroimaging data. The uncertainty surrounding this adaptation stems from the lack of representative datasets, leaving top-performing models without exposure to common artifacts found in MRI data throughout Sub-Saharan Africa (SSA). We replicated a framework that secured the 2nd position in the 2022 BraTS competition to investigate the impact of dataset composition on model performance and pursued four distinct approaches through training a model with: 1) BraTS-Africa data only (train_SSA, N=60), 2) BraTS-Adult Glioma data only (train_GLI, N=1251), 3) both datasets together (train_ALL, N=1311), and 4) through further training the train_GLI model with BraTS-Africa data (train_ftSSA). Notably, training on a smaller low-quality dataset alone (train_SSA) yielded subpar results, and training on a larger high-quality dataset alone (train_GLI) struggled to delineate oedematous tissue in the low-quality validation set. The most promising approach (train_ftSSA) involved pre-training a model on high-quality neuroimages and then fine-tuning it on the smaller, low-quality dataset. This approach outperformed the others, ranking second in the MICCAI BraTS Africa global challenge external testing phase. These findings underscore the significance of larger sample sizes and broad exposure to data in improving segmentation performance. Furthermore, we demonstrated that there is potential for improving such models by fine-tuning them with a wider range of data locally., Comment: 14 pages, 5 figures, 3 tables

We present the SARAO MeerKAT Galactic Plane Survey (SMGPS), a 1.3 GHz continuum survey of almost half of the Galactic Plane (251\deg $\le l \le$ 358\deg and 2\deg $\le l \le$ 61\deg at $|b| \le 1.5\deg $). SMGPS is the largest, most sensitive and highest angular resolution 1 GHz survey of the Plane yet carried out, with an angular resolution of 8" and a broadband RMS sensitivity of $\sim$10--20 $\mu$ Jy/beam. Here we describe the first publicly available data release from SMGPS which comprises data cubes of frequency-resolved images over 908--1656 MHz, power law fits to the images, and broadband zeroth moment integrated intensity images. A thorough assessment of the data quality and guidance for future usage of the data products are given. Finally, we discuss the tremendous potential of SMGPS by showcasing highlights of the Galactic and extragalactic science that it permits. These highlights include the discovery of a new population of non-thermal radio filaments; identification of new candidate supernova remnants, pulsar wind nebulae and planetary nebulae; improved radio/mid-IR classification of rare Luminous Blue Variables and discovery of associated extended radio nebulae; new radio stars identified by Bayesian cross-matching techniques; the realisation that many of the largest radio-quiet WISE HII region candidates are not true HII regions; and a large sample of previously undiscovered background HI galaxies in the Zone of Avoidance., Comment: Accepted for publication in MNRAS. The data release is live and links can be found in the Data Availability Statement in the paper

The dataset focuses on Wikipedia users and contains information about demographic and socioeconomic characteristics of the respondents and their activity on Wikipedia. The data was collected using a questionnaire available online between June and July 2023. The link to the questionnaire was distributed via a banner published in 8 languages on the Wikipedia page. Filling out the questionnaire was voluntary and not incentivised in any way. The survey includes 200 questions about: what people were doing on Wikipedia before clicking the link to the questionnaire; how they use Wikipedia as readers (``professional'' and ``personal'' uses); their opinion on the quality, the thematic coverage, the importance of the encyclopaedia; the making of Wikipedia (how they think it is made, if they have ever contributed and how); their social, sport, artistic and cultural activities, both online and offline; their socio-economic characteristics including political beliefs, and trust propensities. More than 200 000 people opened the questionnaire, 100 332 started to answer, and constitute our dataset, and 10 576 finished it. Among other themes identified by future researchers, the dataset can be useful for advancing the research regarding the features of readers vs contributors of online commons, the relationship between trust, information, sources, and the use made of this information.

The advent of exascale computing invites an assessment of existing best practices for developing application readiness on the world's largest supercomputers. This work details observations from the last four years in preparing scientific applications to run on the Oak Ridge Leadership Computing Facility's (OLCF) Frontier system. This paper addresses a range of topics in software including programmability, tuning, and portability considerations that are key to moving applications from existing systems to future installations. A set of representative workloads provides case studies for general system and software testing. We evaluate the use of early access systems for development across several generations of hardware. Finally, we discuss how best practices were identified and disseminated to the community through a wide range of activities including user-guides and trainings. We conclude with recommendations for ensuring application readiness on future leadership computing systems., Comment: Accepted at SC23