Related papers: The MAID Legacy and Future
Saliency modeling has been an active research area in computer vision for about two decades. Existing state of the art models perform very well in predicting where people look in natural scenes. There is, however, the risk that these models…
Breast cancer as a medical condition and mammograms as images exhibit many dimensions of variability across the population. Similarly, the way diagnostic systems are used and maintained by clinicians varies between imaging centres and…
Electromagnetic meson production formalisms are reviewed, with emphasise placed on their ability in search for new baryon resonances via gamma p -> K+ Lambda and gamma p -> eta p processes. The relevant studies, aiming to deepen our…
In computational imaging, hardware for signal sampling and software for object reconstruction are designed in tandem for improved capability. Examples of such systems include computed tomography (CT), magnetic resonance imaging (MRI), and…
Masked Image Modeling (MIM) is a self-supervised learning technique that involves masking portions of an image, such as pixels, patches, or latent representations, and training models to predict the missing information using the visible…
Foundation models are at the forefront of AI research, appealing for their ability to learn from vast datasets and cater to diverse tasks. Yet, their significant computational demands raise issues of environmental impact and the risk of…
Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances,…
The last decade has witnessed great experimental progress that has led to measurements of cross sections--polarized as well as unpolarized--of of high accuracy in several $NN \to NNx$ reaction channels. These data, naturally, pose…
This conference covers an extremely broad range of topics and in just a few pages it is impossible to even touch on all the areas which will be discussed. We have chosen to summarise just one area where there has recently been impressive…
Masked Image Modeling (MIM) has emerged as a promising method for deriving visual representations from unlabeled image data by predicting missing pixels from masked portions of images. It excels in region-aware learning and provides strong…
Partial Information Decomposition (PID) is a principled and flexible method to unveil complex high-order interactions in multi-unit network systems. Though being defined exclusively for random variables, PID is ubiquitously applied to…
Although reproducibility is a core tenet of the scientific method, it remains challenging to reproduce many results. Surprisingly, this also holds true for computational results in domains such as systems biology where there have been…
A theory of tau mesonic decays is proposed. The general formula of the axial-vector currents of mesons is presented. The meson vertices are obtained from an effective chiral theory of mesons. The vertices depend on momentum. Many decay…
The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when shared on social media, can mislead extensive audiences and erode…
Energy-dependent and single-energy fits to the existing pion photoproduction database have been updated to cover the region from threshold to 2.7 GeV in the laboratory photon energy. Revised resonance photo-decay couplings have been…
A diverse experimental program for the study of the photoproduction of mesons off nuclei has been carried out - and is still running - at the Mainz MAMI and Bonn ELSA electron accelerators with the TAPS, Crystal Barrel, and Crystal Ball…
The WAGASCI experiment being built at the J-PARC neutrino beam line will measure the difference in cross sections from neutrinos interacting with a water and scintillator targets, in order to constrain neutrino cross sections, essential for…
A new class of models for dynamic networks is proposed, called mutually exciting point process graphs (MEG). MEG is a scalable network-wide statistical model for point processes with dyadic marks, which can be used for anomaly detection…
A WEB-page containing materials of comparing experimental data and UrQMD model calculations has been designed. The page provides its user with a variety of tasks solved with the help of the model, accuracy and/or quality of experimental…
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models, are…