Abstract
The large gap between the generalization level of state-of-the-art machine learning and human learning systems calls for the development of artificial intelligence (AI) models that are truly inspired by human cognition. In tasks related to image analysis, searching for pixel-level regularities has reached a power of information extraction still far from what humans capture with image-based observations. This leads to poor generalization when even small shifts occur at the level of the observations. We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations, using models capable of combining symbolic manipulation, probabilistic reasoning, and pattern recognition abilities. We briefly review and explore connections of research from machine learning, cognitive science, and related fields of human behavior to support our perspective for the direction to more robust and human-like artificial learning systems.

Abstract

Abstract
The Vision Transformer (ViT) architecture has emerged as a potential game-changer in computer vision, offering scalability and global attention that have generated considerable interest in recent years. Its adaptability has fueled enthusiasm for its application. This work investigates the boundaries of the architecture, focusing on developing new techniques targeting explicitly complex tasks, such as medical imaging datasets, which often exhibit high variability, class imbalance, and limited sample sizes. We propose a set of mixed regularisation and augmentation techniques to enhance the performance of models. These include a novel loss function and a smoothly differentiable activation function, leading to more stable training and model performance. The results show that incorporating these techniques improves model performance and training convergence.

Abstract

Abstract
Modulation instability (MI) of the continuous wave (cw) has been associated with the onset of stable solitons in conservative and dissipative systems. The cubic complex Ginzburg-Landau equation (CGLE) is a prototype of a damped, driven, nonlinear, and dispersive system. The inclusion of nonlinear gradients is essential to stabilize pulses whether stationary or oscillatory. The soliton solutions of this model have been reasonably studied; however, its cw solution characteristics and stability have not been reported yet. Here, we obtain the cw solutions of the cubic CGLE with nonlinear gradient terms and study its short- and long-term evolution under the effect of small perturbations. We have found that, for each admissible amplitude, there are two branches of cw solutions, and all of them are unstable. Then, through direct integration of the evolution equation, we study the evolution of those cw solutions, observing the emergence of plain and oscillatory solitons. Depending on whether the cw and/or its perturbation are sinusoidal, we can obtain a train of a finite number of pulses or bound states.

Abstract
Measuring the density of tartrazine (TZ) powder allowed to develop a protocol for fast preparation of aqueous solutions with a desired concentration. The stability time of these solutions decreases exponentially with the increase of TZ concentration: solutions with TZ concentrations below 25% remain stable for more than 24 h, while the solution with 60% TZ remains stable only for 35 min. To validate the developed protocol, muscle samples were immersed in the 40% TZ solution and, as expected, the tissue transparency increased smoothly and exponentially during the whole treatment of 30 min. The diffusion time of TZ in ex vivo skeletal muscle was quantitatively determined with high accuracy as tau TZ = 5.39 +/- 0.49 min for sample thickness of 0.5 mm. By measuring the refractive index of TZ solutions during preparation, it will be easier to prepare such solutions in a fast manner for future research on tissue optical clearing.