stylegan-xl,generative models
Large-scale StyleGAN.
1,106 technical terms and definitions
Large-scale StyleGAN.
High-quality GAN with style control.
StyleGAN3 improves alias-free generation through rotation and translation equivariance.
Mechanical surface roughness measurement.
Break into multiple queries.
Subgoals are intermediate objectives required to achieve overall goals.
Subgradient methods optimize structured prediction objectives by computing subgradients of non-smooth loss functions.
Determine if one graph contains another.
Subgroup frequency determines sampling rate for detecting process shifts.
Subgroup size affects control chart sensitivity and capability estimation accuracy.
Subject-driven generation creates images featuring specific subjects from reference images.
Subsampling randomly selects training examples reducing privacy cost.
Align domain subspaces.
Atom replacing lattice atom.
Unwanted signals through substrate.
Operate below threshold voltage.
Subtractive etch patterns metal by depositing then etching rather than damascene filling.
Statistical inference from zero failures.
Demonstrate reliability goal.
Successive inspection verifies output before next operation begins.
Successor features decouple environment dynamics from rewards enabling transfer across tasks with shared state space.
Successor representation separates environment dynamics from rewards enabling fast policy evaluation under new rewards.
Suggestion systems encourage employee ideas for improvement.
Summarize old messages to save space.
Summarization compresses long text to key points. Use for fitting long docs into context or creating memory.
Summarize long documents. Key points extraction.
Generate summaries during pre-training.
Stepwise refinement for text generation.
# Sunnyvale, California ## Overview **Sunnyvale** is a city located in **Santa Clara County, California**, positioned in the heart of **Silicon Valley**. The city derives its name from its characteristic sunny climate. ## Geographic & Demographic Data ### Location Coordinates $$ \text{Latitude: } 37.3688° \, N \quad \text{Longitude: } 122.0363° \, W $$ ### Population Statistics - **Estimated Population**: $\approx 155,000 - 160,000$ residents - **Population Density**: $\approx 6,500 \, \text{people/mi}^2$ - **Land Area**: $\approx 22.0 \, \text{mi}^2$ or $57.0 \, \text{km}^2$ $$ \text{Population Density} = \frac{\text{Population}}{\text{Area}} = \frac{155,000}{22.0} \approx 7,045 \, \text{people/mi}^2 $$ ### Elevation $$ \text{Elevation} \approx 40 \, \text{ft} \approx 12 \, \text{m} \quad \text{(above sea level)} $$ ## Climate Data Sunnyvale features a **Mediterranean climate** (Köppen classification: $Csb$). ### Average Temperature | Season | Temperature (°F) | Temperature (°C) | |--------|------------------|------------------| | Summer High | $82°F$ | $\frac{5}{9}(82-32) \approx 28°C$ | | Winter Low | $42°F$ | $\frac{5}{9}(42-32) \approx 6°C$ | $$ T_{°C} = \frac{5}{9}(T_{°F} - 32) $$ ### Annual Rainfall $$ \text{Average Annual Precipitation} \approx 15.5 \, \text{inches} \approx 394 \, \text{mm} $$ ### Sunny Days $$ \text{Average Sunny Days per Year} \approx 257 \, \text{days} $$ $$ \text{Sunshine Percentage} = \frac{257}{365} \times 100 \approx 70.4\% $$ ## Economic Profile ### Major Industries - **Technology** - Software development - Hardware manufacturing - Cloud computing - Artificial intelligence - **Aerospace & Defense** - Lockheed Martin Space - Historical defense contractors - **Semiconductors** - AMD (Advanced Micro Devices) - Various chip design firms ### Notable Companies Headquartered in Sunnyvale - **LinkedIn** (Microsoft subsidiary) - **Yahoo** (historical HQ) - **AMD** (Advanced Micro Devices) - **Juniper Networks** - **NetApp** - **Fortinet** - **Trimble Inc.** ### Economic Formulas #### Median Household Income $$ \text{Median Household Income} \approx 140{,}000 - 160{,}000 \, \text{USD} $$ #### Cost of Living Index $$ \text{Cost of Living Index} \approx 180 - 200 \quad (\text{US Average} = 100) $$ $$ \text{Relative Cost} = \frac{\text{Sunnyvale COL}}{\text{US Average COL}} = \frac{190}{100} = 1.9x $$ ## Real Estate Metrics ### Housing Prices $$ \text{Median Home Price} \approx 1{,}800{,}000 - 2{,}200{,}000 \, \text{USD} $$ ### Price Per Square Foot $$ \text{Price per ft}^2 \approx 1{,}000 - 1{,}400 \, \text{USD} $$ ### Mortgage Calculation Example For a $2{,}000{,}000 USD home with $20\%$ down payment at $7\%$ interest over $30$ years: $$ \text{Principal} = 2{,}000{,}000 \times 0.80 = 1{,}600{,}000 \, \text{USD} $$ Monthly payment formula: $$ M = P \times \frac{r(1+r)^n}{(1+r)^n - 1} $$ Where: - $P = 1{,}600{,}000 \, \text{USD}$ (principal) - $r = \frac{0.07}{12} \approx 0.00583$ (monthly interest rate) - $n = 360$ (total months) $$ M = 1{,}600{,}000 \times \frac{0.00583(1.00583)^{360}}{(1.00583)^{360} - 1} \approx 10{,}644 \, \text{USD/month} $$ ## Transportation ### Distance to Major Cities | Destination | Distance (miles) | Distance (km) | |-------------|------------------|---------------| | San Jose | $\approx 8$ | $\approx 13$ | | San Francisco | $\approx 40$ | $\approx 64$ | | Oakland | $\approx 35$ | $\approx 56$ | $$ \text{Distance}_{km} = \text{Distance}_{mi} \times 1.60934 $$ ### Commute Statistics - **Average Commute Time**: $\approx 28 - 32 \, \text{minutes}$ - **Public Transit Options**: - Caltrain (commuter rail) - VTA (Valley Transportation Authority) buses and light rail - ACE Train access nearby $$ \text{Annual Commute Hours} = \text{Daily Commute} \times \text{Work Days} = 1 \, \text{hr} \times 250 = 250 \, \text{hours/year} $$ ## Historical Timeline ### Key Dates - **1850s**: Agricultural settlement (orchards) - **1901**: Incorporated as a city - **1912**: Population $\approx 1,700$ - **1956**: Lockheed Missiles & Space Division established - **1990s-2000s**: Tech boom transformation - **Present**: Major Silicon Valley hub ### Population Growth Model Exponential growth approximation: $$ P(t) = P_0 \times e^{rt} $$ Where: - $P_0$ = initial population - $r$ = growth rate - $t$ = time in years ## Education ### School District Statistics - **Sunnyvale School District** (K-8) - **Fremont Union High School District** (9-12) #### Student-to-Teacher Ratio $$ \text{Ratio} = \frac{\text{Students}}{\text{Teachers}} \approx \frac{22}{1} = 22:1 $$ ### Nearby Universities - **Stanford University**: $\approx 10 \, \text{miles}$ - **Santa Clara University**: $\approx 5 \, \text{miles}$ - **San Jose State University**: $\approx 10 \, \text{miles}$ ## Points of Interest ### Parks & Recreation - **Baylands Park** - Area: $\approx 50 \, \text{acres}$ - Features: wetlands, trails, wildlife - **Las Palmas Park** - Tennis courts - Playground facilities - **Columbia Park** - Swimming pool - Sports fields ### Downtown Murphy Avenue - **Length**: $\approx 0.5 \, \text{miles}$ of shops/restaurants - **Establishments**: $\approx 50+$ businesses ## Technology Metrics ### Tech Employment $$ \text{Tech Workforce} \approx 35\% - 40\% \text{ of total employment} $$ ### Patent Activity $$ \text{Patents Filed Annually (Silicon Valley)} \approx 15,000 - 20,000 $$ ### Startup Density $$ \text{Startups per } 10,000 \text{ residents} \approx 15 - 25 $$ ## Summary Statistics | Metric | Value | |--------|-------| | Population | $\sim 155,000$ | | Area | $22.0 \, \text{mi}^2$ | | Elevation | $40 \, \text{ft}$ | | Median Income | $\sim 150{,}000 \, \text{USD}$ | | Median Home Price | $\sim 2{,}000{,}000 \, \text{USD}$ | | Sunny Days | $257 \, \text{days/year}$ | | Major Industry | Technology | ## Mathematical ### Useful Conversions $$ \begin{aligned} 1 \, \text{mile} &= 1.60934 \, \text{km} \\ 1 \, \text{ft}^2 &= 0.0929 \, \text{m}^2 \\ 1 \, \text{acre} &= 43,560 \, \text{ft}^2 = 4,047 \, \text{m}^2 \\ °C &= \frac{5}{9}(°F - 32) \end{aligned} $$ ### Statistical Formulas Used #### Mean $$ \bar{x} = \frac{1}{n} \sum_{i=1}^{n} x_i $$ #### Standard Deviation $$ \sigma = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (x_i - \bar{x})^2} $$ #### Compound Growth Rate $$ \text{CAGR} = \left( \frac{V_f}{V_i} \right)^{\frac{1}{t}} - 1 $$
Supabase is open source Firebase alternative. PostgreSQL.
Highest priority lot.
Very abrupt doping profile.
Large instruction dataset.
Upscale images to higher resolution using deep learning.
Super-steep retrograde profiles combine abrupt junctions with retrograde wells for advanced scaling.
Predict Tc for superconductors.
Use supercritical fluid.
Suite of difficult language understanding tasks.
SuperGLUE includes harder language understanding tasks than original GLUE.
More challenging version of GLUE.
Supermarkets are controlled inventory locations supplying downstream processes on pull signals.
Binary masks that work without training.
Train network containing all candidate architectures.
Supernet training creates a weight-sharing over-parameterized network encompassing all candidate architectures for efficient performance estimation.
Large-scale DGX cluster.
Networks pack more features than dimensions.
Superposition: models encode more features than neurons. Polysemantic neurons respond to multiple concepts.
Use labels to improve contrastive learning.
Supervised Fine-Tuning (SFT) uses input to output pairs (chat transcripts, QA, code fixes) to teach a model specific behaviors before RLHF/DPO.
Supervisely focuses on computer vision annotation. Segmentation, video.