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BOOST: Out-of-distribution-informed adaptive sampling for bias mitigation in stylistic convolutional neural networks
Get PDFThe pervasive issue of bias in AI presents a significant challenge to painting classification, and is getting more serious as these systems become increasingly integrated into tasks like art curation and restoration. Biases, often arising from imbalanced datasets where certain artistic styles dominate, compromise the fairness and accuracy of model predictions, i.e., classifiers are less accurate on rarely seen paintings. While prior research has made strides in improving classification performance, it has largely overlooked the critical need to address these underlying biases, that is, when dealing with out-of-distribution (OOD) data. Our insight highlights the necessity of a more robust approach to bias mitigation in AI models for art classification on biased training data. We propose a novel OOD-informed model bias adaptive sampling method called BOOST (Bias-Oriented OOD Sampling and Tuning). It addresses these challenges by dynamically adjusting temperature scaling and sampling probabilities, thereby promoting a more equitable representation of all classes. We evaluate our proposed approach to the KaoKore and PACS datasets, focusing on the model's ability to reduce class-wise bias. We further propose a new metric, Same-Dataset OOD Detection Score (SODC), designed to assess class-wise separation and per-class bias reduction. Our method demonstrates the ability to balance high performance with fairness, making it a robust solution for unbiasing AI models in the art domain
Earthquakes and the intergenerational delegation of responsibility
Get PDFDo earthquakes affect parents’ preferences for raising responsible children? By merging data on random variations in the frequency and timing of earthquakes with five waves of the World Values Survey from 1995 to 2022 at the district level across 90 countries, our event-specific difference-in-differences estimates reveal that parents affected by moderate earthquakes increase their preference for responsible children by 5.9 percentage points due to perceived risks. We argue that moderate shocks heighten risk perceptions without depleting parental capacity, whereas strong earthquakes dampen this effect by reducing the capacity required to instill responsibility. Our empirical evidence suggests that governments should embed child-centred disaster risk reduction frameworks within post-disaster recovery strategies to enhance long-term disaster resilience
nuSTORM as a Precision Probe of the Standard Model and New Physics
Get PDFThe Neutrinos from Stored Muons (nuSTORM) facility will generate neutrino beams from both muon and meson decays in a storage ring, providing a neutrino flux known to the percent level. This unprecedented precision enables a rich physics programme, including high-precision tests of the Standard Model and searches for new phenomena. In this paper we demonstrate nuSTORM’s sensitivity to key Standard Model processes such as, measurements of the weak mixing angle at low Q2 and the rare process of neutrino trident production. We also show its powerful reach for a diverse range of beyond-the-Standard-Model scenarios, including eV-scale sterile neutrinos, Kaluza-Klein excitations from large extra dimensions and lepton flavour violation. Furthermore, nuSTORM can place significant constraints on heavy QCD axions and other axion-like particles produced in rare kaon decays. These capabilities establish nuSTORM as a powerful and complementary probe to long baseline experiments and collider searches
Deep Learning-Based Diagnostic Classification of Multiple Sclerosis Using Multicenter Optical Coherence Tomography Data
Get PDFBackground Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system, where timely and accurate diagnosis is essential for effective management. Optical coherence tomography (OCT) enables non-invasive evaluation of retinal changes that may serve as biomarkers for MS. Unlike other ophthalmologic diseases, raw cross-sectional OCT images in MS show subtle alterations often indistinguishable from healthy controls (HCs). Consequently, retinal layer thickness and boundary-derived surface features offer greater discriminatory power. Methods We investigated three categories of artificial intelligence (AI) models: (1) feature extraction with auto-encoder (AE) and shallow networks, (2) custom-designed deep networks, and (3) fine-tuned pre-trained networks. Retinal layer thickness and surface maps derived from OCT were analyzed to determine the most informative features, with channel-wise combination and mosaicing applied for feature integration. Model interpretability was assessed using occlusion sensitivity and Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations. The dataset included 38 HC and 78 MS eyes obtained from independent public and local sources. Patient-wise partitioning was implemented to prevent data leakage. Results The proposed deep network using channel-wise combined thickness maps of retinal nerve fiber layer (RNFL), ganglion cell and inner plexiform layer (GCIPL), and inner nuclear layer (INL) layers achieved balanced accuracy of 97.3% (SD = 4.16; 95% CI: 92.3–100%), specificity of 97.3% (SD = 5.59; 95% CI: 92.6–100%), sensitivity of 97.4% (SD = 3.54; 95% CI: 92.6–100%), g-mean of 97.3% (SD = 4.18; 95% CI: 92.24-100%), F1-score of 98.0% (SD = 3.86; 95% CI: 92.6–100%), and an AUC of 0.96 (SD = 0.08; 95% CI: 0.95–1.00). Notably, the high performance observed in internal cross-validation was achieved when public and local datasets were combined. However, performance decreased substantially in cross-dataset evaluations, where models were trained on one dataset and tested on the other, indicating limited external generalizability, particularly when trained on public data and applied to local clinical data. Conclusions AI-based analysis of OCT-derived retinal layer features enables accurate and interpretable classification of MS, supporting its potential as a valuable clinical biomarker
Feedback on the discussion of “assessment of an amended soil as a climate adaptive barrier: Element testing and physical modelling”
No full textGoldstone-Mediated Polar Instability in Hexagonal Barium Titanate
No full textWe discover a rare structural manifestation of the Goldstone paradigm in a hexagonal polytype of the prototypical ferroelectric BaTiO3. First-principles calculations confirm the Goldstone character of the order parameter, while our high-resolution diffraction measurements unveil an unusual re-entrant Goldstone regime manifesting as a quasi-continuous domain texture in the vicinity of the ferroelectric transition. We develop a minimal Landau model that encapsulates these observations, illustrating how U(1) symmetry can be restored at the ferroelectric transition. Our findings demonstrate how exotic Goldstone physics can be unlocked in systems dominated by highly anharmonic interactions, presenting a promising pathway to stabilize emergent polar topologies in bulk materials
Review article: Social media for managing disasters triggered by natural hazards: a critical review of data collection strategies and actionable insights
Get PDFCorporate Decarbonization via Technology and Management
Get PDFThis study provides a comprehensive overview of key findings on decarbonization, advanced technologies, and management strategies, highlighting emerging themes shaping the field. Advanced technologies enhance carbon reduction through efficiency, real- time monitoring, and optimizing resource optimization. However, their integration remains challenging due to technological and organizational complexity, necessitating a shift in management strategies. Growing climate regulations and environmental responsibility make decarbonization a strategic business priority. This study synthesizes research to identify key factors influencing decarbonization management and presents a conceptual framework with research propositions for future study. We argue that successful corporate decarbonization requires a holistic approach integrating technology, revised management strategies, and systemic transformation to accelerate the transition towards a net- zero economy
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