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Understanding the impossibility of machine learning fairness with data examples

Pillutla et al. | Sep 04, 2026

Understanding the impossibility of machine learning fairness with data examples

Machine learning systems are often expected to make fair decisions, yet the most widely used fairness criteria—independence, separation, and sufficiency—cannot generally be satisfied at the same time. In this study, students tested these criteria using a logistic regression model on a real-world student performance dataset and found that each criterion was met only at different prediction thresholds, with no threshold satisfying all three simultaneously. These results illustrate the inherent trade-offs in algorithmic fairness and highlight why achieving perfectly fair machine learning models is often impossible in practice.

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Study of PINN sensor layout in evaluating WSS with application to patient-specific carotid flow

Nie et al. | Sep 04, 2026

Study of PINN sensor layout in evaluating WSS with application to patient-specific carotid flow

Physics‑Informed Neural Networks (PINNs) offer a promising way to estimate blood‑flow behavior in arteries, especially near the vessel wall where traditional methods struggle. In this study, students tested how sensor density and placement affect PINN accuracy in modeling carotid artery flow and found that accuracy improves up to a moderate sensor density and depends strongly on how close sensors are placed to the arterial wall. Applying the optimized configuration to a patient‑specific carotid model produced velocity predictions closely matching computational fluid dynamics results, highlighting PINNs’ potential for future personalized cardiovascular assessment.

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Retinal biomarkers for the detection of neurological disease using deep learning

Kim et al. | Sep 04, 2026

Retinal biomarkers for the detection of neurological disease using deep learning

The authors looked at whether retinal features, specifically lens clarity, optic nerve cupping, and hyperreflective foci, can serve as biomarkers for neuro-ophthalmological diseases, which share pathological mechanisms with neurodegeneration and may indicate broader neurological risk. Using multimodal clinical data and machine learning, they investigated the potential of retinal imaging as a complementary diagnostic tool for more accurate and accessible detection of neurological disorders.

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A cross-cultural analysis of consumer behavior and payment preferences: Insights from India and the United Arab Emirates

Bansal et al. | Sep 01, 2026

A cross-cultural analysis of consumer behavior and payment preferences: Insights from India and the United Arab Emirates

Consumers in India and the United Arab Emirates live in very different economic and cultural environments, and this study set out to measure how that shapes the way they shop and pay. Surveying 50 people in each country, they found that Indian consumers were more price-sensitive and favored cash, while UAE consumers prioritized convenience, gravitating toward credit cards, mobile wallets, and online shopping. The results offer practical insight for retailers, marketers, and policymakers working across these two fast-growing but culturally distinct markets.

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