X-ray scattering measurements of in situ human brain tissue encode structural signatures of pathological cross-$β$ inclusions, yet systematic exploitation of these data for automated detection remains challenging due to substrate contamination, strong inter-feature correlations, and limited sample sizes. This work develops a three-stage classification framework for identifying cross-$β$ structural inclusions-a hallmark of Alzheimer's disease-in X-ray scattering profiles of post-mortem human brain. Stage 1 employs a Bayes-optimal classifier to separate mica substrate from tissue regions on the basis of their distinct scattering signatures. Stage 2 introduces a multicollinearityaware, class-conditional correlation pruning scheme with formal guarantees on the induced Bayes risk and approximation error, thereby reducing redundancy while retaining class-discriminative information. Stage 3 trains a compact neural network on the pruned feature set to detect the presence or absence of cross-$β$ fibrillar ordering. The top-performing model, optimized with a composite loss combining Focal and Dice objectives, attains a test F1-score of 84.30% using 11 of 211 candidate features and 174 trainable parameters. The overall framework yields an interpretable, theory-grounded strategy for data-limited classification problems involving correlated, high-dimensional experimental measurements, exemplified here by X-ray scattering profiles of neurodegenerative tissue.

In this paper, we investigate continual learning performance metrics used in class incremental learning strategies for continual learning (CL) using some high performing methods. We investigate especially mean task accuracy. First, we show that it lacks of expressiveness through some simple experiments to capture performance. We show that monitoring average tasks performance is over optimistic and can lead to misleading conclusions for future real life industrial uses. Then, we propose first a simple metric, Minimal Incremental Class Accuracy (MICA) which gives a fair and more useful evaluation of different continual learning methods. Moreover, in order to provide a simple way to easily compare different methods performance in continual learning, we derive another single scalar metric that take into account the learning performance variation as well as our newly introduced metric.

This systematic review focuses on analyzing the use of machine learning techniques for identifying and quantifying analytes in various electrochemical applications, presenting the available applications in the literature. Machine learning is a tool that can facilitate the analysis and enhance the understanding of processes involving various analytes. In electrochemical biosensors, it increases the precision of medical diagnostics, improving the identification of biomarkers and pathogens with high reliability. It can be effectively used for the classification of complex chemical products; in environmental monitoring, using low-cost sensors; in portable devices and wearable systems; among others. Currently, the analysis of some analytes is still performed manually, requiring the expertise of a specialist in the field and thus hindering the generalization of results. In light of the advancements in artificial intelligence today, this work proposes to carry out a systematic review of the literature on the applications of artificial intelligence techniques. A set of articles has been identified that address electrochemical problems using machine learning techniques, more specifically, supervised learning.

Black-box deep learning approaches have showcased significant potential in the realm of medical image analysis. However, the stringent trustworthiness requirements intrinsic to the medical field have catalyzed research into the utilization of Explainable Artificial Intelligence (XAI), with a particular focus on concept-based methods. Existing concept-based methods predominantly apply concept annotations from a single perspective (e.g., global level), neglecting the nuanced semantic relationships between sub-regions and concepts embedded within medical images. This leads to underutilization of the valuable medical information and may cause models to fall short in harmoniously balancing interpretability and performance when employing inherently interpretable architectures such as Concept Bottlenecks. To mitigate these shortcomings, we propose a multi-modal explainable disease diagnosis framework that meticulously aligns medical images and clinical-related concepts semantically at multiple strata, encompassing the image level, token level, and concept level. Moreover, our method allows for model intervention and offers both textual and visual explanations in terms of human-interpretable concepts. Experimental results on three skin image datasets demonstrate that our method, while preserving model interpretability, attains high performance and label efficiency for concept detection and disease diagnosis.

This is part 5 of a Guide in 6 parts about Artificial Intelligence. The guide covers some of its basic concepts, history and present applications, possible developments in the future, and also its challenges as opportunities. Reviewing some case studies helps to bring artificial intelligence to life, and to understand how it is used. Here we will review the field of entertainment, where the company Magic Leap has made great strides with the use of artificial intelligence. Magic Leap is a start up company located in the USA.

This is part 5 of a Guide in 6 parts about Artificial Intelligence. The guide covers some of its basic concepts, history and present applications, possible developments in the future, and also its challenges as opportunities. Reviewing some case studies helps to bring artificial intelligence to life, and to understand how it is used. Here we will review the field of entertainment, where the company Magic Leap has made great strides with the use of artificial intelligence. Magic Leap is a start up company located in the USA.

But Mica, the company's artificial intelligence, really deserves attention. With "Mica", the augmented reality company Magic Leap has introduced the next evolution of virtual assistants. In contrast to Cortana, Alexa or Siri with their disembodied voices, Mica has an incredibly real avatar. She yawns, makes eye contact and interacts with the wearer using Magic Leap's augmented reality glasses. At the time of Alexa, Siri or Cortana we are used to having ubiquitous but disembodied vocal assistants.

Unlike VR, when you're talking about augmented reality, describing what an experience is like can be incredibly difficult -- primarily because the experiences are even more contextual than relatively static virtual worlds that don't involve real-world settings. In AR, everything is about how "you" see things interacting with your real environment. Such is the case with what I'm calling the most important demonstration of Magic Leap technology to date in the form of an AI assistant called Mica. The experience was previewed on stage during Wednesday's keynote event at the L.E.A.P. conference in Los Angeles by Magic Leap's John Monos, vice president of human-centered AR and dDNA, and Andrew Rabinovich, the company's head of AI. Together, the team described a world in which a Magic Leap user will be able to interact with intelligent assistants in the form of fully realized augmented reality humans that can recognize your position in a room, as well as items in that room.

Magic Leap introduced a concept called Mica and called it "her" during a section of its 3-hour keynote this week about how an artificial intelligence could operate as an assistant to humans. I feel like I met in person what Magic Leap showed in its video. After the keynote, I sought out Mica and found her sitting downstairs. I was able to see her because Magic Leap equipped me with a Magic Leap One and told me to enter the room. When I saw her it was clear "she" wanted me to sit down with a wave of her arm, and I did.

Magic Leap showed off a demo of Mica, a humanlike artificial intelligence that can be viewed in the company's augmented reality glasses, the Magic Leap One Creator Edition. I saw a demo of Mica, a short-haired woman who doesn't speak but still communicates in warm ways with the viewer. I put the AR glasses on my head and looked through prescription inserts to see the virtual overlays on the real world. I thought it was the best thing Magic Leap showed off. I walked into a physical room and sat in a chair.