Machine Learning Workflow to Explain Black-box Models for Early Alzheimer's Disease Classification Evaluated for Multiple Datasets

Purpose: Hard-to-interpret Black-box Machine Learning (ML) were often used for early Alzheimer's Disease (AD) detection. Methods: To interpret eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) black-box models a workflow based on Shapley values was developed. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated for an independent ADNI test set, as well as the external Australian Imaging and Lifestyle flagship study of Ageing (AIBL), and Open Access Series of Imaging Studies (OASIS) datasets. Shapley values were compared to intuitively interpretable Decision Trees (DTs), and Logistic Regression (LR), as well as natural and permutation feature importances. To avoid the reduction of the explanation validity caused by correlated features, forward selection and aspect consolidation were implemented. Results: Some black-box models outperformed DTs and LR. The forward-selected features correspond to brain areas previously associated with AD. Shapley values identified biologically plausible associations with moderate to strong correlations with feature importances. The most important RF features to predict AD conversion were the volume of the amygdalae, and a cognitive test score. Good cognitive test performances and large brain volumes decreased the AD risk. The models trained using cognitive test scores significantly outperformed brain volumetric models ($p<0.05$). Cognitive Normal (CN) vs. AD models were successfully transferred to external datasets. Conclusion: In comparison to previous work, improved performances for ADNI and AIBL were achieved for CN vs. Mild Cognitive Impairment (MCI) classification using brain volumes. The Shapley values and the feature importances showed moderate to strong correlations

Photophoretic Strength on Chondrules. 2. Experiment

Photophoretic motion can transport illuminated particles in protoplanetary disks. In a previous paper we focused on the modeling of steady state photophoretic forces based on the compositions derived from tomography and heat transfer. Here, we present microgravity experiments which deviate significantly from the steady state calculations of the first paper. The experiments on average show a significantly smaller force than predicted with a large variation in absolute photophoretic force and in the direction of motion with respect to the illumination. Time-dependent modeling of photophoretic forces for heat-up and rotation show that the variations in strength and direction observed can be well explained by the particle reorientation in the limited experiment time of a drop tower experiment. In protoplanetary disks, random rotation subsides due to gas friction on short timescales and the results of our earlier paper hold. Rotation has a significant influence in short duration laboratory studies. Observing particle motion and rotation under the influence of photophoresis can be considered as a basic laboratory analog experiment to Yarkovsky and YORP effects

PreprintResolver: Improving Citation Quality by Resolving Published Versions of ArXiv Preprints using Literature Databases

The growing impact of preprint servers enables the rapid sharing of time-sensitive research. Likewise, it is becoming increasingly difficult to distinguish high-quality, peer-reviewed research from preprints. Although preprints are often later published in peer-reviewed journals, this information is often missing from preprint servers. To overcome this problem, the PreprintResolver was developed, which uses four literature databases (DBLP, SemanticScholar, OpenAlex, and CrossRef / CrossCite) to identify preprint-publication pairs for the arXiv preprint server. The target audience focuses on, but is not limited to inexperienced researchers and students, especially from the field of computer science. The tool is based on a fuzzy matching of author surnames, titles, and DOIs. Experiments were performed on a sample of 1,000 arXiv-preprints from the research field of computer science and without any publication information. With 77.94 %, computer science is highly affected by missing publication information in arXiv. The results show that the PreprintResolver was able to resolve 603 out of 1,000 (60.3 %) arXiv-preprints from the research field of computer science and without any publication information. All four literature databases contributed to the final result. In a manual validation, a random sample of 100 resolved preprints was checked. For all preprints, at least one result is plausible. For nine preprints, more than one result was identified, three of which are partially invalid. In conclusion the PreprintResolver is suitable for individual, manually reviewed requests, but less suitable for bulk requests. The PreprintResolver tool (https://preprintresolver.eu, Available from 2023-08-01) and source code (https://gitlab.com/ippolis_wp3/preprint-resolver, Accessed: 2023-07-19) is available online.Comment: Accepted for International Conference on Theory and Practice of Digital Libraries (TPDL 2023

Interplay of nematic and magnetic orders in FeSe under pressure

We offer an explanation for the recently observed pressure-induced magnetic state in the iron-chalcogenide FeSe based on \textit{ab initio} estimates for the pressure evolution of the most important Coulomb interaction parameters. We find that an increase of pressure leads to an overall decrease mostly in the nearest-neighbor Coulomb repulsion, which in turn leads to a reduction of the nematic order and the generation of magnetic stripe order. We treat the concomitant effects of band renormalization and the induced interplay of nematic and magnetic order in a self-consistent way and determine the generic topology of the temperature-pressure phase diagram, and find qualitative agreement with the experimentally determined phase diagram.Comment: 13 pages, 6 fig

How space-number associations may be created in preliterate children : six distinct mechanisms

The directionality of space-number association (SNA) is shaped by cultural experiences. It usually follows the culturally dominant reading direction. Smaller numbers are generally associated with the starting side for reading (left side in Western cultures), while larger numbers are associated with the right endpoint side. However, SNAs consistent with cultural reading directions are present before children can actually read and write. Therefore, these SNAs cannot only be shaped by the direction of children's own reading/writing behavior. We propose six distinct processes - one biological and five cultural/educational - underlying directional SNAs before formal reading acquisition: (i) Brain lateralization, (ii) Monitoring adult reading behavior, (iii) Pretend reading and writing, and rudimentary reading and writing skills, (iv) Dominant attentional directional preferences in a society, not directly related to reading direction, (v) Direct spatial-numerical learning, (vi) Other spatial-directional processes independent of reading direction. In this mini-review, we will differentiate between these processes, elaborate when in development they might emerge, discuss how they may create the SNAs observed in preliterate children and propose how they can be studied in the future

Onset of phase diffusion in high kinetic inductance granular aluminum micro-SQUIDs

Superconducting granular aluminum is attracting increasing interest due to its high kinetic inductance and low dissipation, favoring its use in kinetic inductance particle detectors, superconducting resonators or quantum bits. We perform switching current measurements on DC-SQUIDs, obtained by introducing two identical geometric constrictions in granular aluminum rings of various normal-state resistivities in the range from ρ n = 250–5550 μΩ cm. The relative high kinetic inductance of the SQUID loop, in the range of tens of nH, leads to a suppression of the modulation in the measured switching current versus magnetic flux, accompanied by a distortion towards a triangular shape. We observe a change in the temperature dependence of the switching current histograms with increasing normal-state film resistivity. This behavior suggests the onset of a diffusive motion of the superconducting phase across the constrictions in the two-dimensional washboard potential of the SQUIDs, which could be caused by a change of the local electromagnetic environment of films with increasing normal-state resistivities

Explainable AI in medical imaging:An overview for clinical practitioners - Saliency-based XAI approaches

Since recent achievements of Artificial Intelligence (AI) have proven significant success and promising results throughout many fields of application during the last decade, AI has also become an essential part of medical research. The improving data availability, coupled with advances in high-performance computing and innovative algorithms, has increased AI's potential in various aspects. Because AI rapidly reshapes research and promotes the development of personalized clinical care, alongside its implementation arises an urgent need for a deep understanding of its inner workings, especially in high-stake domains. However, such systems can be highly complex and opaque, limiting the possibility of an immediate understanding of the system's decisions. Regarding the medical field, a high impact is attributed to these decisions as physicians and patients can only fully trust AI systems when reasonably communicating the origin of their results, simultaneously enabling the identification of errors and biases. Explainable AI (XAI), becoming an increasingly important field of research in recent years, promotes the formulation of explainability methods and provides a rationale allowing users to comprehend the results generated by AI systems. In this paper, we investigate the application of XAI in medical imaging, addressing a broad audience, especially healthcare professionals. The content focuses on definitions and taxonomies, standard methods and approaches, advantages, limitations, and examples representing the current state of research regarding XAI in medical imaging. This paper focuses on saliency-based XAI methods, where the explanation can be provided directly on the input data (image) and which naturally are of special importance in medical imaging.</p