MobileMold provides 4941 smartphone microscopy images and shows deep learning models reach 99.5% accuracy on mold detection and food classification tasks.
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Exploiting visual artifacts to expose deepfakes and face manipulations
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Direction maps and pinwheel structures in MT emerge spontaneously when a spatiotemporal deep network is trained on videos with contrastive self-supervised learning and spatial regularization.
CutMix augmentation during training induces spatial locality in early layers of Vision Transformers trained from scratch, as measured by reduced Mean Attention Distance.
Small-scale programs exhibit notable compile-time and run-time configurability that grows over time and correlates with size, supporting the value of reducing variability for simpler software.
Video forgeries are detectable via binary classification on multimedia stream descriptors without pixel analysis.
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
Derivation of the incompressible XMHD dispersion relation yields ion cyclotron and whistler branches that saturate at the ion and electron gyrofrequencies, providing a smoother description than Hall MHD at high wave numbers.
This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.
The QBF Gallery 2023 report consolidates submitted solvers and formulas into a public benchmark set and compares solver performance on it while outlining future directions for the community.
citing papers explorer
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MobileMold: A Smartphone-Based Microscopy Dataset for Food Mold Detection
MobileMold provides 4941 smartphone microscopy images and shows deep learning models reach 99.5% accuracy on mold detection and food classification tasks.
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Inducing Spatial Locality in Vision Transformers through the Training Protocol
CutMix augmentation during training induces spatial locality in early layers of Vision Transformers trained from scratch, as measured by reduced Mean Attention Distance.