edge pooling, graph neural networks
Edge pooling contracts edges to coarsen graphs learning which edges to collapse for hierarchical representations.
422 technical terms and definitions
Edge pooling contracts edges to coarsen graphs learning which edges to collapse for hierarchical representations.
Pool edges to coarsen graph.
Discover subnetworks via masking.
Edge probing evaluates whether representations capture syntactic relationships between word spans.
Smooth wafer edge.
Remove wafer edge.
Split computation between edge and cloud.
Edge inference runs models on device (phone, IoT). Lower latency, offline capable, privacy. Requires smaller models.
Electronic Data Interchange automates business document exchange between supply chain partners.
Generate by editing initial sequence.
Modify capabilities using task directions.
Modify real images via inversion.
Embedded Deterministic Test generates pseudo-random patterns with controlled bits.
Analyze brain wave patterns.
End-to-End Neural Diarization performs joint speech activity and speaker detection using single model.
Mini-cleanroom at tool front with robot for wafer handling.
Equipment Front End Module provides automated wafer handling interface.
Effect sizes quantify practical significance of differences or relationships.
Determine carrier effective masses.
Another quantum correction approach.
Various efficient attention variants.
Attention mechanisms faster than O(n²).
EfficientNet family balances accuracy and efficiency. Compound scaling. Good for mobile/edge.
Share weights across architectures during search.
EfficientNet architecture discovered through NAS balances depth width and resolution using compound scaling.
EfficientNet scaling jointly optimizes depth width and resolution using compound coefficient.
Scale networks systematically.
Improved training and scaling.
E(n) Equivariant Graph Neural Networks maintain equivariance to Euclidean transformations while learning node and edge features in geometric graphs.
Use principal components for CAM.
Excessive variation.
Relation between mobility and diffusion.
# Einstein's Mass-Energy Equivalence ## E = mc² Einstein's mass–energy equivalence states that mass and energy are interconvertible according to: $$ E = mc^2 $$ Where: - $E$ = energy (joules, J) - $m$ = mass (kilograms, kg) - $c$ = speed of light in vacuum ≈ $3.00 \times 10^8$ m/s ## What It Means Mass and energy are two manifestations of the same fundamental quantity. A body with mass $m$ possesses an intrinsic "rest energy" even in its stationary state: $$ E_0 = mc^2 $$ Since $c^2$ is enormous (~$9 \times 10^{16}$ m²/s²), a minuscule amount of mass corresponds to an enormous quantity of energy. ## Common Misconception The equation does **not** state that "mass converts to energy only at high velocities." The formula describes the rest mass energy inherent to all matter. When objects move, the total energy becomes larger; in relativistic mechanics: $$ E^2 = (pc)^2 + (mc^2)^2 $$ where $p$ is relativistic momentum. ## Practical Examples - **1 kg of mass** = $9 \times 10^{16}$ joules (equivalent to ~21 megatons of TNT) - **Nuclear fission**: Converts ~0.1% of mass to energy - **Nuclear fusion**: Converts ~0.7% of mass to energy - **Matter-antimatter annihilation**: Converts 100% of mass to energy (complete conversion) ## Semiconductor Physics Context Mass-energy equivalence appears in semiconductor physics and device engineering: - **Electron rest mass energy**: $m_0 c^2 \approx 0.511$ MeV - **Relativistic band structure**: Heavy elements require relativistic corrections for accurate electronic structure - **Pair production thresholds**: Radiation damage and high-energy particle interactions - **Positron emission tomography (PET)**: Advanced defect imaging and characterization techniques - **Ion implantation**: Energetic particle transport relies on relativistic dynamics ## Historical Significance Einstein's equation fundamentally transformed our understanding of matter, energy, and the universe. It enabled breakthrough technologies including: - Nuclear power generation - Particle accelerators (linear accelerators for ion implantation) - Medical imaging (PET, radiation therapy) - Astrophysics and stellar processes
Eisner algorithm efficiently finds the highest-scoring projective dependency tree using dynamic programming over chart structures.
Predict mechanical properties.
L1+L2 regularized attack.
Measure hydrogen and light elements.
Regularization technique to prevent catastrophic forgetting by protecting important weights.
Protect important weights.
Elbow method identifies optimal cluster count by plotting within-cluster variance.
Train discriminator to detect replaced tokens.
Efficient pre-training using discriminative task instead of masking.
On-wafer structures for measuring parameters.
Test dies electrically on wafer.
Electrical width is measured linewidth from resistance or capacitance differing from physical dimension.
Metal transport under bias.
Deposit copper by electroplating.
Electrodeionization combines ion exchange and electrodialysis for continuous ultra-pure water production.
Chemical plating without external current.
Light emission from electrical injection.