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Fundamentals of Neuroscience, Part 1: The Electrical Properties of the Neuron
Organic Chemistry 1
Mountains 101
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Explore advanced mathematical concepts in homological mirror symmetry, Hodge theory, and algebraic geometry through expert lectures from leading researchers in the field.
Explore algebraic techniques solving major combinatorial problems like Kakeya, distinct distances, and cap-sets through expert talks from Harvard's 2017 workshop.
Explore advanced mathematical theories connecting Donaldson-Thomas and Gromov-Witten invariants through K3 fibrations, stable pairs, and S-duality conjectures in algebraic geometry.
Explore advanced connections between four-manifold topology and supersymmetric quantum field theory, covering Donaldson invariants, Seiberg-Witten theory, and higher-dimensional extensions.
Explore the connection between optimal transport and complete Calabi-Yau metrics on log varieties, bridging differential geometry and boundary regularity theory.
Explore Gromov-Hausdorff limits of collapsing Calabi-Yau fibrations and their metric behavior, featuring recent progress on identifying limits with fibration bases using RCD properties.
Explore how protected data of 4d N=2 SCFTs can be characterized through algebro-geometric methods, focusing on Macdonald index reconstruction in Argyres-Douglas theories.
Explore exotic E7+1/2 gauge theory in 6d F-theory through anomaly cancellation, M-theory geometry, and elliptic genus analysis of single-string worldsheet CFT.
Explore cutting-edge intersections of mathematics and AI through expert talks on theorem proving, neural networks, and automated mathematical discovery at Harvard CMSA.
Explore the mathematical foundations of Yang-Mills theories and quantum field theory, covering the Clay Millennium Prize problem and recent progress in constructive field theory.
Explore RCD structures on 3D projective varieties with klt singularities, connecting algebraic geometry with Ricci curvature bounds and Nash entropy.
Explore discretization techniques and distribution learning in diffusion models, covering randomized midpoints, score matching, and applications to parameter estimation and density learning.
Discover how side information in unlabeled data improves machine learning models through iterative pseudo-labeling and error decorrelation analysis.
Discover how negative stepsizes enable Gradient-Descent-Ascent convergence on min-max problems through innovative time-varying, asymmetric stepsize schedules and slingshot dynamics.
Explore self-play reinforcement learning for theorem proving where LLMs act as both conjecturers and provers, achieving state-of-the-art results on mathematical benchmarks.
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