Baroque Optics and the Disappearance of the Observer: From Kepler’s Optics to Descartes’ Doubt

In the seventeenth century the human observer gradually disappeared from optical treatises. It was a paradoxical process: the naturalization of the eye estranged the mind from its objects. Turned into a material optical instrument, the eye no longer furnished the observer with genuine representations of visible objects. It became a mere screen, on which rested a blurry array of light stains, accidental effects of a purely causal process. It thus befell the intellect to decipher one natural object—a flat image of no inherent epistemic value—as the vague, reversed reflection of another, wholly independent object. In reflecting on and trespassing the boundaries between natural and artificial, orderly and disorderly, this optical paradox was a Baroque intellectual phenomenon; and it was the origin of Descartes’ celebrated doubt— whether we know anything at all

The effect of alpha-linolenic acid supplementation on ADHD symptoms in children: a randomized controlled double-blind study

Background: Attention deficit-hyperactivity disorder (ADHD) is the most common neuro-developmental disorder in childhood. Its pharmacologic treatment mostly includes methylphenidate, yet many parents seek alternative, “natural,” therapeutic options, commonly omega-3 fatty acids. Previous studies of supplementation with fish oil or long-chain omega-3 fatty acids to children with ADHD yielded mixed results. The use of alpha-linolenic acid (ALA), a medium-chained, plant-based omega-3 fatty acid (18:3 n-3), has not been sufficiently examined in this population. Methods: Forty untreated children with ADHD, aged 6–16 years, were randomized to receive either 2 g/day of oil containing 1 g ALA or placebo, for 8 weeks. Before and after supplementation, the children underwent a physician assessment of ADHD symptoms and a computerized continuous performance functions test. The children’s parents and teachers filled out Conners’ and DSM questionnaires. Results: Seventeen (42.5%) children completed the study, eight in the supplementation group, nine in the placebo group. Main drop-out reasons were capsule size, poor compliance, and a sense of lack of effect. No significant difference was found in any of the measured variables tested before and after supplementation, in both study groups. No between-group difference was found in the changes of the various measures of ADHD symptoms throughout the study period. Conclusion: Supplementation of 2 g/day of oil containing 1 g ALA did not significantly reduce symptoms in children with ADHD. Future studies in this field should consider an alternative method to deliver the oil, a higher dose, and a larger sample size

A generalized light-driven model of community transitions along coral reef depth gradients

Aim: Coral reefs shift between distinct communities with depth throughout the world. Yet, despite over half a century of research on coral reef depth gradients, researchers have not addressed the driving force of these patterns. We present a theoretical, process-based model of light’s influence on the shallow to mesophotic reef transition as a single quantitative framework. We also share an interactive web application. Moving beyond depth as an ecological proxy will enhance research conducted on deeper coral reefs. Location: Global; subtropical and tropical coral reefs, oligotrophic and turbid coastal waters. Time period: Present day (2020). Major taxa: Scleractinia. Methods: We constructed ordinary differential equations representing the preferred light environments of shallow and mesophotic Scleractinia. We projected these as depth bands using light attenuation coefficients from around the world, and performed a sensitivity analysis. Results: We found light relationships alone are sufficient to capture major ecological features across coral reef depth gradients. Our model supports the depth limits currently used in coral reef ecology, predicting a global range for the shallow-upper mesophotic boundary at 36.1 m ± 5.6 and the upper-lower mesophotic boundary at 61.9 m ± 9.6. However, our model allows researchers to move past these fixed depth limits, and quantitatively predict the depths of reef zones in locations around the world. Main conclusions: The use of depth as a proxy for changes in coral reef communities offers no guidance for environmental variation between sites. We have shown it is possible to use light to predict the depth boundaries of reef zones as a continuous variable, and to accommodate this variability. Predicting the depths of reef zones in unusual light environments suggests that shallow-water turbid reefs should be considered as mesophotic coral ecosystems. Nonetheless, the current depth-based heuristics are relatively accurate at a global level

ENRICHING APPLICATION PROGRAMMING INTERFACE (API) DOCUMENTATION USING A LARGE LANGUAGE MODEL (LLM) ARCHITECTURE TO IMPROVE LLM API INTERPRETABILITY

Proposed herein is an innovative system designed to address the critical challenge of maintaining accurate and detailed Application Programming Interface (API) documentation in the fast-past environment of software development. Broadly, the system streamlines the creation and updating an of API documentation by leveraging a Large Language Model (LLM) that intelligently analyzes code changes and generates documentation patches/updates based on any detected code changes

Explainable Multi-View Deep Networks Methodology for Experimental Physics

Physical experiments often involve multiple imaging representations, such as X-ray scans and microscopic images. Deep learning models have been widely used for supervised analysis in these experiments. Combining different image representations is frequently required to analyze and make a decision properly. Consequently, multi-view data has emerged - datasets where each sample is described by views from different angles, sources, or modalities. These problems are addressed with the concept of multi-view learning. Understanding the decision-making process of deep learning models is essential for reliable and credible analysis. Hence, many explainability methods have been devised recently. Nonetheless, there is a lack of proper explainability in multi-view models, which are challenging to explain due to their architectures. In this paper, we suggest different multi-view architectures for the vision domain, each suited to another problem, and we also present a methodology for explaining these models. To demonstrate the effectiveness of our methodology, we focus on the domain of High Energy Density Physics (HEDP) experiments, where multiple imaging representations are used to assess the quality of foam samples. We apply our methodology to classify the foam samples quality using the suggested multi-view architectures. Through experimental results, we showcase the improvement of accurate architecture choice on both accuracy - 78% to 84% and AUC - 83% to 93% and present a trade-off between performance and explainability. Specifically, we demonstrate that our approach enables the explanation of individual one-view models, providing insights into the decision-making process of each view. This understanding enhances the interpretability of the overall multi-view model. The sources of this work are available at: https://github.com/Scientific-Computing-Lab-NRCN/Multi-View-Explainability

Depth electrode neurofeedback with a virtual reality interface

Invasive brain–computer interfaces (BCI) provide better signal quality in terms of spatial localization, frequencies and signal/noise ratio, in addition to giving access to deep brain regions that play important roles in cognitive or affective processes. Despite some anecdotal attempts, little work has explored the possibility of integrating such BCI input into more sophisticated interactive systems like those which can be developed with game engines. In this article, we integrated an amygdala depth electrode recorder with a virtual environment controlling a virtual crowd. Subjects were asked to down regulate their amygdala using the level of unrest in the virtual room as feedback on how successful they were. We report early results which suggest that users adapt very easily to this paradigm and that the timing and fluctuations of amygdala activity during self-regulation can be matched by crowd animation in the virtual room. This suggests that depth electrodes could also serve as high-performance affective interfaces, notwithstanding their strictly limited availability, justified on medical grounds only

Transfer learning of deep neural network representations for fMRI decoding

Background: Deep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g., fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractical given the scarcity of available data. New method: In this work we propose a robust method to transfer information from deep learning (DL) features to brain fMRI data with the goal of decoding. By adopting Reduced Rank Regression with Ridge Regularisation we establish a multivariate link between imaging data and the fully connected layer (fc7) of a CNN. We exploit the reconstructed fc7 features by performing an object image classification task on two datasets: one of the largest fMRI databases, taken from different scanners from more than two hundred subjects watching different movie clips, and another with fMRI data taken while watching static images. Results: The fc7 features could be significantly reconstructed from the imaging data, and led to significant decoding performance. Comparison with existing methods: The decoding based on reconstructed fc7 outperformed the decoding based on imaging data alone. Conclusion: In this work we show how to improve fMRI-based decoding benefiting from the mapping between functional data and CNN features. The potential advantage of the proposed method is twofold: the extraction of stimuli representations by means of an automatic procedure (unsupervised) and the embedding of high-dimensional neuroimaging data onto a space designed for visual object discrimination, leading to a more manageable space from dimensionality point of view

Towards empathic neurofeedback for interactive storytelling

Interactive Narrative is a form of digital entertainment based on AI techniques which support narrative generation and user interaction. Despite recent progress in the field, there is still a lack of unified models integrating narrative generation, user response and interaction. This paper addresses this issue by revisiting existing Interactive Narrative paradigms, granting explicit status to users’ disposition towards story characters. We introduce a novel Brain-Computer Interface (BCI) design, which attempts to capture empathy for the main character in a way that is compatible with filmic theories of emotion. Results from two experimental studies with a fully-implemented system demonstrate the effectiveness of a neurofeedback-based approach, showing that subjects can successfully modulate their emotional support for a character who is confronted with challenging situations. A preliminary fMRI analysis also shows activation during user interaction, in regions of the brain associated with emotional control

A novel socially assistive robotic platform for cognitive-motor exercises for individuals with Parkinson's Disease: a participatory-design study from conception to feasibility testing with end users

The potential of socially assistive robots (SAR) to assist in rehabilitation has been demonstrated in contexts such as stroke and cardiac rehabilitation. Our objective was to design and test a platform that addresses specific cognitive-motor training needs of individuals with Parkinson’s disease (IwPD). We used the participatory design approach, and collected input from a total of 62 stakeholders (IwPD, their family members and clinicians) in interviews, brainstorming sessions and in-lab feasibility testing of the resulting prototypes. The platform we developed includes two custom-made mobile desktop robots, which engage users in concurrent cognitive and motor tasks. IwPD (n = 16) reported high levels of enjoyment when using the platform (median = 5/5) and willingness to use the platform in the long term (median = 4.5/5). We report the specifics of the hardware and software design as well as the detailed input from the stakeholders