Back to the Continuous Attractor.

Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals. Unfortunately, continuous attractors suffer from severe structural instability in general--they are destroyed by most infinitesimal changes of the dynamical law that defines them. This fragility limits their utility especially in biological systems as their recurrent dynamics are subject to constant perturbations.

Dataset of Windows operating system forensics artefacts.

The dataset consists of records from the NTFS file system and event logs. In this study, we used images of devices from capture the flags competitions focused on the digital forensic of Windows operating systems and user activities. We created timelines of the security incident from the disk images using the Plaso tool, which we then processed and transformed the attributes of the timelines into binary values to simplify the application of data analysis and machine learning methods.

Developing and validating a prediction model of live birth following single vitrified-warmed blastocyst transfer.

Research Question: Can the developed clinical prediction model offer an accurate estimate of the likelihood of live birth, involving blastocyst morphology and vitrification day after single vitrified-warmed blastocyst transfer (SVBT), and therefore assist clinicians and patients?

Study Design: Retrospective cohort study conducted at a Spanish university-based reproductive medicine unit (2017-2021) including consecutive vitrified-warmed blastocysts from IVF cycles. A multivariable logistic regression incorporated key live birth predictors: vitrification day, embryo score, embryo ploidy status and clinically relevant variables, i.e.

Persistent learning signals and working memory without continuous attractors.

Neural dynamical systems with stable attractor structures, such as point attractors and continuous attractors, are hypothesized to underlie meaningful temporal behavior that requires working memory. However, working memory may not support useful learning signals necessary to adapt to changes in the temporal structure of the environment. We show that in addition to the continuous attractors that are widely implicated, periodic and quasi-periodic attractors can also support learning arbitrarily long temporal relationships.

Parameter-free differential evolution algorithm for the analytic continuation of imaginary time correlation functions.

We report on differential evolution for analytic continuation: a parameter-free evolutionary algorithm to generate the dynamic structure factor from imaginary time correlation functions. Our approach to this long-standing problem in quantum many-body physics achieves enhanced spectral fidelity while using fewer compute (CPU) hours. The need for fine-tuning of algorithmic control parameters is eliminated by embedding them within the genome to be optimized for this evolutionary computation-based algorithm.