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Asymmetric autocatalytic reactions and their stationary distribution

Author(s): Gallinger C; Popovic L;

We consider a general class of autocatalytic reactions, which has been shown to display stochastic switching behaviour (discreteness-induced transitions (DITs)) in some parameter regimes. This behaviour was shown to occur either when the overall species count is low or when the rate of inflow and outflow of species is relatively much smaller than the rate ...

Article GUID: 39679357


MuscleMap: An Open-Source, Community-Supported Consortium for Whole-Body Quantitative MRI of Muscle

Author(s): McKay MJ; Weber KA; Wesselink EO; Smith ZA; Abbott R; Anderson DB; Ashton-James CE; Atyeo J; Beach AJ; Burns J; Clarke S; Collins NJ; Coppieters MW; Cornwall J; Crawford RJ; De Martino E; Dunn AG; Eyles JP; Feng HJ; Fortin M; Franettovic ...

Disorders affecting the neurological and musculoskeletal systems represent international health priorities. A significant impediment to progress in trials of new therapies is the absence of responsive, objective, and valid outcome measures sensitive to early disease changes. A key finding in indi ...

Article GUID: 39590726


A protocol for trustworthy EEG decoding with neural networks

Author(s): Borra D; Magosso E; Ravanelli M;

Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of many hyperparameters, defining data pre-processing, network architecture, network training, and data augment ...

Article GUID: 39549492


Near-optimal learning of Banach-valued, high-dimensional functions via deep neural networks

Author(s): Adcock B; Brugiapaglia S; Dexter N; Moraga S;

The past decade has seen increasing interest in applying Deep Learning (DL) to Computational Science and Engineering (CSE). Driven by impressive results in applications such as computer vision, Uncertainty Quantification (UQ), genetics, simulations and image processing, DL is increasingly supplanting classical algorithms, and seems poised to revolutionize ...

Article GUID: 39454372


Deep neural network-based robotic visual servoing for satellite target tracking

Author(s): Ghiasvand S; Xie WF; Mohebbi A;

In response to the costly and error-prone manual satellite tracking on the International Space Station (ISS), this paper presents a deep neural network (DNN)-based robotic visual servoing solution to the automated tracking operation. This innovative approach directly addresses the critical issue of motion decoupling, which poses a significant challenge in ...

Article GUID: 39440297


Generalization limits of Graph Neural Networks in identity effects learning

Author(s): D' Inverno GA; Brugiapaglia S; Ravanelli M;

Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is closely linked to the Weisfeiler-Lehman (WL) test for graph isomorphism to which they have been proven equivale ...

Article GUID: 39426036


Cell Fate Dynamics Reconstruction Identifies TPT1 and PTPRZ1 Feedback Loops as Master Regulators of Differentiation in Pediatric Glioblastoma-Immune Cell Networks

Author(s): Abicumaran Uthamacumaran

Pediatric glioblastoma is a complex dynamical disease that is difficult to treat due to its multiple adaptive behaviors driven largely by phenotypic plasticity. Integrated data science and network theory pipelines offer novel approaches to studying glioblastoma cell fate dynamics, particularly phenotypic transitions over time. Here we used various single- ...

Article GUID: 39420135


A Survey on Error Exponents in Distributed Hypothesis Testing: Connections with Information Theory, Interpretations, and Applications

Author(s): Espinosa S; Silva JF; Céspedes S;

A central challenge in hypothesis testing (HT) lies in determining the optimal balance between Type I (false positive) and Type II (non-detection or false negative) error probabilities. Analyzing these errors' exponential rate of convergence, known as error exponents, provides crucial insights into system performance. Error exponents offer a lens thro ...

Article GUID: 39056958


Social network dynamics, infant loss, and gut microbiota composition in female Colobus vellerosus during time periods with alpha male challenges

Author(s): Samartino S; Christie D; Penna A; Sicotte P; Ting N; Wikberg E;

The gut microbiota of group-living animals is strongly influenced by their social interactions, but it is unclear how it responds to social instability. We investigated whether social instability associated with the arrival of new males and challenges to the alpha male position could explain differences in the gut microbiota in adult female Colobus veller ...

Article GUID: 38735025


The immunomodulatory effect of oral NaHCO3 is mediated by the splenic nerve: multivariate impact revealed by artificial neural networks

Author(s): Alvarez MR; Alkaissi H; Rieger AM; Esber GR; Acosta ME; Stephenson SI; Maurice AV; Valencia LMR; Roman CA; Alarcon JM;

Stimulation of the inflammatory reflex (IR) is a promising strategy for treating systemic inflammatory disorders. Recent studies suggest oral sodium bicarbonate (NaHCO3) as a potential activator of the IR, offering a safe and cost-effective treatment approach. However, the mechanisms underlying N ...

Article GUID: 38549144


CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets

Author(s): Islam M; Zunair H; Mohammed N;

Crafting effective deep learning models for medical image analysis is a complex task, particularly in cases where the medical image dataset lacks significant inter-class variation. This challenge is further aggravated when employing such datasets to generate synthetic images using generative adversarial networks (GANs), as the output of GANs heavily relie ...

Article GUID: 38492455


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